Visual Analytics for Law Enforcement: Deploying a Service-Oriented Analytic Framework for Web-based Visualization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dowson, Scott T.; Bruce, Joseph R.; Best, Daniel M.

2009-04-14

This paper presents key components of the Law Enforcement Information Framework (LEIF) that provides communications, situational awareness, and visual analytics tools in a service-oriented architecture supporting web-based desktop and handheld device users. LEIF simplifies interfaces and visualizations of well-established visual analytical techniques to improve usability. Advanced analytics capability is maintained by enhancing the underlying processing to support the new interface. LEIF development is driven by real-world user feedback gathered through deployments at three operational law enforcement organizations in the US. LEIF incorporates a robust information ingest pipeline supporting a wide variety of information formats. LEIF also insulates interface and analyticalmore » components from information sources making it easier to adapt the framework for many different data repositories.« less

The challenge of big data in public health: an opportunity for visual analytics.

PubMed

Ola, Oluwakemi; Sedig, Kamran

2014-01-01

Public health (PH) data can generally be characterized as big data. The efficient and effective use of this data determines the extent to which PH stakeholders can sufficiently address societal health concerns as they engage in a variety of work activities. As stakeholders interact with data, they engage in various cognitive activities such as analytical reasoning, decision-making, interpreting, and problem solving. Performing these activities with big data is a challenge for the unaided mind as stakeholders encounter obstacles relating to the data's volume, variety, velocity, and veracity. Such being the case, computer-based information tools are needed to support PH stakeholders. Unfortunately, while existing computational tools are beneficial in addressing certain work activities, they fall short in supporting cognitive activities that involve working with large, heterogeneous, and complex bodies of data. This paper presents visual analytics (VA) tools, a nascent category of computational tools that integrate data analytics with interactive visualizations, to facilitate the performance of cognitive activities involving big data. Historically, PH has lagged behind other sectors in embracing new computational technology. In this paper, we discuss the role that VA tools can play in addressing the challenges presented by big data. In doing so, we demonstrate the potential benefit of incorporating VA tools into PH practice, in addition to highlighting the need for further systematic and focused research.

The Challenge of Big Data in Public Health: An Opportunity for Visual Analytics

PubMed Central

Ola, Oluwakemi; Sedig, Kamran

2014-01-01

Public health (PH) data can generally be characterized as big data. The efficient and effective use of this data determines the extent to which PH stakeholders can sufficiently address societal health concerns as they engage in a variety of work activities. As stakeholders interact with data, they engage in various cognitive activities such as analytical reasoning, decision-making, interpreting, and problem solving. Performing these activities with big data is a challenge for the unaided mind as stakeholders encounter obstacles relating to the data’s volume, variety, velocity, and veracity. Such being the case, computer-based information tools are needed to support PH stakeholders. Unfortunately, while existing computational tools are beneficial in addressing certain work activities, they fall short in supporting cognitive activities that involve working with large, heterogeneous, and complex bodies of data. This paper presents visual analytics (VA) tools, a nascent category of computational tools that integrate data analytics with interactive visualizations, to facilitate the performance of cognitive activities involving big data. Historically, PH has lagged behind other sectors in embracing new computational technology. In this paper, we discuss the role that VA tools can play in addressing the challenges presented by big data. In doing so, we demonstrate the potential benefit of incorporating VA tools into PH practice, in addition to highlighting the need for further systematic and focused research. PMID:24678376

Toward a Visualization-Supported Workflow for Cyber Alert Management using Threat Models and Human-Centered Design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Franklin, Lyndsey; Pirrung, Megan A.; Blaha, Leslie M.

Cyber network analysts follow complex processes in their investigations of potential threats to their network. Much research is dedicated to providing automated tool support in the effort to make their tasks more efficient, accurate, and timely. This tool support comes in a variety of implementations from machine learning algorithms that monitor streams of data to visual analytic environments for exploring rich and noisy data sets. Cyber analysts, however, often speak of a need for tools which help them merge the data they already have and help them establish appropriate baselines against which to compare potential anomalies. Furthermore, existing threat modelsmore » that cyber analysts regularly use to structure their investigation are not often leveraged in support tools. We report on our work with cyber analysts to understand they analytic process and how one such model, the MITRE ATT&CK Matrix [32], is used to structure their analytic thinking. We present our efforts to map specific data needed by analysts into the threat model to inform our eventual visualization designs. We examine data mapping for gaps where the threat model is under-supported by either data or tools. We discuss these gaps as potential design spaces for future research efforts. We also discuss the design of a prototype tool that combines machine-learning and visualization components to support cyber analysts working with this threat model.« less

Visual business ecosystem intelligence: lessons from the field.

PubMed

Basole, Rahul C

2014-01-01

Macroscopic insight into business ecosystems is becoming increasingly important. With the emergence of new digital business data, opportunities exist to develop rich, interactive visual-analytics tools. Georgia Institute of Technology researchers have been developing and implementing visual business ecosystem intelligence tools in corporate settings. This article discusses the challenges they faced, the lessons learned, and opportunities for future research.

Web-based Visual Analytics for Extreme Scale Climate Science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A; Evans, Katherine J; Harney, John F

In this paper, we introduce a Web-based visual analytics framework for democratizing advanced visualization and analysis capabilities pertinent to large-scale earth system simulations. We address significant limitations of present climate data analysis tools such as tightly coupled dependencies, ineffi- cient data movements, complex user interfaces, and static visualizations. Our Web-based visual analytics framework removes critical barriers to the widespread accessibility and adoption of advanced scientific techniques. Using distributed connections to back-end diagnostics, we minimize data movements and leverage HPC platforms. We also mitigate system dependency issues by employing a RESTful interface. Our framework embraces the visual analytics paradigm via newmore » visual navigation techniques for hierarchical parameter spaces, multi-scale representations, and interactive spatio-temporal data mining methods that retain details. Although generalizable to other science domains, the current work focuses on improving exploratory analysis of large-scale Community Land Model (CLM) and Community Atmosphere Model (CAM) simulations.« less

Visual analytics in healthcare education: exploring novel ways to analyze and represent big data in undergraduate medical education

PubMed Central

Nilsson, Gunnar; Zary, Nabil

2014-01-01

Introduction. The big data present in the medical curriculum that informs undergraduate medical education is beyond human abilities to perceive and analyze. The medical curriculum is the main tool used by teachers and directors to plan, design, and deliver teaching and assessment activities and student evaluations in medical education in a continuous effort to improve it. Big data remains largely unexploited for medical education improvement purposes. The emerging research field of visual analytics has the advantage of combining data analysis and manipulation techniques, information and knowledge representation, and human cognitive strength to perceive and recognize visual patterns. Nevertheless, there is a lack of research on the use and benefits of visual analytics in medical education. Methods. The present study is based on analyzing the data in the medical curriculum of an undergraduate medical program as it concerns teaching activities, assessment methods and learning outcomes in order to explore visual analytics as a tool for finding ways of representing big data from undergraduate medical education for improvement purposes. Cytoscape software was employed to build networks of the identified aspects and visualize them. Results. After the analysis of the curriculum data, eleven aspects were identified. Further analysis and visualization of the identified aspects with Cytoscape resulted in building an abstract model of the examined data that presented three different approaches; (i) learning outcomes and teaching methods, (ii) examination and learning outcomes, and (iii) teaching methods, learning outcomes, examination results, and gap analysis. Discussion. This study identified aspects of medical curriculum that play an important role in how medical education is conducted. The implementation of visual analytics revealed three novel ways of representing big data in the undergraduate medical education context. It appears to be a useful tool to explore such data with possible future implications on healthcare education. It also opens a new direction in medical education informatics research. PMID:25469323

Visual analytics in healthcare education: exploring novel ways to analyze and represent big data in undergraduate medical education.

PubMed

Vaitsis, Christos; Nilsson, Gunnar; Zary, Nabil

2014-01-01

Introduction. The big data present in the medical curriculum that informs undergraduate medical education is beyond human abilities to perceive and analyze. The medical curriculum is the main tool used by teachers and directors to plan, design, and deliver teaching and assessment activities and student evaluations in medical education in a continuous effort to improve it. Big data remains largely unexploited for medical education improvement purposes. The emerging research field of visual analytics has the advantage of combining data analysis and manipulation techniques, information and knowledge representation, and human cognitive strength to perceive and recognize visual patterns. Nevertheless, there is a lack of research on the use and benefits of visual analytics in medical education. Methods. The present study is based on analyzing the data in the medical curriculum of an undergraduate medical program as it concerns teaching activities, assessment methods and learning outcomes in order to explore visual analytics as a tool for finding ways of representing big data from undergraduate medical education for improvement purposes. Cytoscape software was employed to build networks of the identified aspects and visualize them. Results. After the analysis of the curriculum data, eleven aspects were identified. Further analysis and visualization of the identified aspects with Cytoscape resulted in building an abstract model of the examined data that presented three different approaches; (i) learning outcomes and teaching methods, (ii) examination and learning outcomes, and (iii) teaching methods, learning outcomes, examination results, and gap analysis. Discussion. This study identified aspects of medical curriculum that play an important role in how medical education is conducted. The implementation of visual analytics revealed three novel ways of representing big data in the undergraduate medical education context. It appears to be a useful tool to explore such data with possible future implications on healthcare education. It also opens a new direction in medical education informatics research.

Data Visualization: An Exploratory Study into the Software Tools Used by Businesses

ERIC Educational Resources Information Center

Diamond, Michael; Mattia, Angela

2017-01-01

Data visualization is a key component to business and data analytics, allowing analysts in businesses to create tools such as dashboards for business executives. Various software packages allow businesses to create these tools in order to manipulate data for making informed business decisions. The focus is to examine what skills employers are…

Data Visualization: An Exploratory Study into the Software Tools Used by Businesses

ERIC Educational Resources Information Center

Diamond, Michael; Mattia, Angela

2015-01-01

Data visualization is a key component to business and data analytics, allowing analysts in businesses to create tools such as dashboards for business executives. Various software packages allow businesses to create these tools in order to manipulate data for making informed business decisions. The focus is to examine what skills employers are…

Real-Time Visualization of Network Behaviors for Situational Awareness

DOE Office of Scientific and Technical Information (OSTI.GOV)

Best, Daniel M.; Bohn, Shawn J.; Love, Douglas V.

Plentiful, complex, and dynamic data make understanding the state of an enterprise network difficult. Although visualization can help analysts understand baseline behaviors in network traffic and identify off-normal events, visual analysis systems often do not scale well to operational data volumes (in the hundreds of millions to billions of transactions per day) nor to analysis of emergent trends in real-time data. We present a system that combines multiple, complementary visualization techniques coupled with in-stream analytics, behavioral modeling of network actors, and a high-throughput processing platform called MeDICi. This system provides situational understanding of real-time network activity to help analysts takemore » proactive response steps. We have developed these techniques using requirements gathered from the government users for which the tools are being developed. By linking multiple visualization tools to a streaming analytic pipeline, and designing each tool to support a particular kind of analysis (from high-level awareness to detailed investigation), analysts can understand the behavior of a network across multiple levels of abstraction.« less

Reducing the Analytical Bottleneck for Domain Scientists: Lessons from a Climate Data Visualization Case Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dasgupta, Aritra; Poco, Jorge; Bertini, Enrico

2016-01-01

The gap between large-scale data production rate and the rate of generation of data-driven scientific insights has led to an analytical bottleneck in scientific domains like climate, biology, etc. This is primarily due to the lack of innovative analytical tools that can help scientists efficiently analyze and explore alternative hypotheses about the data, and communicate their findings effectively to a broad audience. In this paper, by reflecting on a set of successful collaborative research efforts between with a group of climate scientists and visualization researchers, we introspect how interactive visualization can help reduce the analytical bottleneck for domain scientists.

A results-based process for evaluation of diverse visual analytics tools

NASA Astrophysics Data System (ADS)

Rubin, Gary; Berger, David H.

2013-05-01

With the pervasiveness of still and full-motion imagery in commercial and military applications, the need to ingest and analyze these media has grown rapidly in recent years. Additionally, video hosting and live camera websites provide a near real-time view of our changing world with unprecedented spatial coverage. To take advantage of these controlled and crowd-sourced opportunities, sophisticated visual analytics (VA) tools are required to accurately and efficiently convert raw imagery into usable information. Whether investing in VA products or evaluating algorithms for potential development, it is important for stakeholders to understand the capabilities and limitations of visual analytics tools. Visual analytics algorithms are being applied to problems related to Intelligence, Surveillance, and Reconnaissance (ISR), facility security, and public safety monitoring, to name a few. The diversity of requirements means that a onesize- fits-all approach to performance assessment will not work. We present a process for evaluating the efficacy of algorithms in real-world conditions, thereby allowing users and developers of video analytics software to understand software capabilities and identify potential shortcomings. The results-based approach described in this paper uses an analysis of end-user requirements and Concept of Operations (CONOPS) to define Measures of Effectiveness (MOEs), test data requirements, and evaluation strategies. We define metrics that individually do not fully characterize a system, but when used together, are a powerful way to reveal both strengths and weaknesses. We provide examples of data products, such as heatmaps, performance maps, detection timelines, and rank-based probability-of-detection curves.

XCluSim: a visual analytics tool for interactively comparing multiple clustering results of bioinformatics data

PubMed Central

2015-01-01

Background Though cluster analysis has become a routine analytic task for bioinformatics research, it is still arduous for researchers to assess the quality of a clustering result. To select the best clustering method and its parameters for a dataset, researchers have to run multiple clustering algorithms and compare them. However, such a comparison task with multiple clustering results is cognitively demanding and laborious. Results In this paper, we present XCluSim, a visual analytics tool that enables users to interactively compare multiple clustering results based on the Visual Information Seeking Mantra. We build a taxonomy for categorizing existing techniques of clustering results visualization in terms of the Gestalt principles of grouping. Using the taxonomy, we choose the most appropriate interactive visualizations for presenting individual clustering results from different types of clustering algorithms. The efficacy of XCluSim is shown through case studies with a bioinformatician. Conclusions Compared to other relevant tools, XCluSim enables users to compare multiple clustering results in a more scalable manner. Moreover, XCluSim supports diverse clustering algorithms and dedicated visualizations and interactions for different types of clustering results, allowing more effective exploration of details on demand. Through case studies with a bioinformatics researcher, we received positive feedback on the functionalities of XCluSim, including its ability to help identify stably clustered items across multiple clustering results. PMID:26328893

Visualization techniques for computer network defense

NASA Astrophysics Data System (ADS)

Beaver, Justin M.; Steed, Chad A.; Patton, Robert M.; Cui, Xiaohui; Schultz, Matthew

2011-06-01

Effective visual analysis of computer network defense (CND) information is challenging due to the volume and complexity of both the raw and analyzed network data. A typical CND is comprised of multiple niche intrusion detection tools, each of which performs network data analysis and produces a unique alerting output. The state-of-the-practice in the situational awareness of CND data is the prevalent use of custom-developed scripts by Information Technology (IT) professionals to retrieve, organize, and understand potential threat events. We propose a new visual analytics framework, called the Oak Ridge Cyber Analytics (ORCA) system, for CND data that allows an operator to interact with all detection tool outputs simultaneously. Aggregated alert events are presented in multiple coordinated views with timeline, cluster, and swarm model analysis displays. These displays are complemented with both supervised and semi-supervised machine learning classifiers. The intent of the visual analytics framework is to improve CND situational awareness, to enable an analyst to quickly navigate and analyze thousands of detected events, and to combine sophisticated data analysis techniques with interactive visualization such that patterns of anomalous activities may be more easily identified and investigated.

Interaction Junk: User Interaction-Based Evaluation of Visual Analytic Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Endert, Alexander; North, Chris

2012-10-14

With the growing need for visualization to aid users in understanding large, complex datasets, the ability for users to interact and explore these datasets is critical. As visual analytic systems have advanced to leverage powerful computational models and data analytics capabilities, the modes by which users engage and interact with the information are limited. Often, users are taxed with directly manipulating parameters of these models through traditional GUIs (e.g., using sliders to directly manipulate the value of a parameter). However, the purpose of user interaction in visual analytic systems is to enable visual data exploration – where users can focusmore » on their task, as opposed to the tool or system. As a result, users can engage freely in data exploration and decision-making, for the purpose of gaining insight. In this position paper, we discuss how evaluating visual analytic systems can be approached through user interaction analysis, where the goal is to minimize the cognitive translation between the visual metaphor and the mode of interaction (i.e., reducing the “Interactionjunk”). We motivate this concept through a discussion of traditional GUIs used in visual analytics for direct manipulation of model parameters, and the importance of designing interactions the support visual data exploration.« less

ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining.

PubMed

Huan, Tianxiao; Sivachenko, Andrey Y; Harrison, Scott H; Chen, Jake Y

2008-08-12

New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed. We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges according to associated data values. We demonstrated the advantages of these new capabilities through three biological network visualization case studies: human disease association network, drug-target interaction network and protein-peptide mapping network. The architectural design of ProteoLens makes it suitable for bioinformatics expert data analysts who are experienced with relational database management to perform large-scale integrated network visual explorations. ProteoLens is a promising visual analytic platform that will facilitate knowledge discoveries in future network and systems biology studies.

Coastal On-line Assessment and Synthesis Tool 2.0

NASA Technical Reports Server (NTRS)

Brown, Richard; Navard, Andrew; Nguyen, Beth

2011-01-01

COAST (Coastal On-line Assessment and Synthesis Tool) is a 3D, open-source Earth data browser developed by leveraging and enhancing previous NASA open-source tools. These tools use satellite imagery and elevation data in a way that allows any user to zoom from orbit view down into any place on Earth, and enables the user to experience Earth terrain in a visually rich 3D view. The benefits associated with taking advantage of an open-source geo-browser are that it is free, extensible, and offers a worldwide developer community that is available to provide additional development and improvement potential. What makes COAST unique is that it simplifies the process of locating and accessing data sources, and allows a user to combine them into a multi-layered and/or multi-temporal visual analytical look into possible data interrelationships and coeffectors for coastal environment phenomenology. COAST provides users with new data visual analytic capabilities. COAST has been upgraded to maximize use of open-source data access, viewing, and data manipulation software tools. The COAST 2.0 toolset has been developed to increase access to a larger realm of the most commonly implemented data formats used by the coastal science community. New and enhanced functionalities that upgrade COAST to COAST 2.0 include the development of the Temporal Visualization Tool (TVT) plug-in, the Recursive Online Remote Data-Data Mapper (RECORD-DM) utility, the Import Data Tool (IDT), and the Add Points Tool (APT). With these improvements, users can integrate their own data with other data sources, and visualize the resulting layers of different data types (such as spatial and spectral, for simultaneous visual analysis), and visualize temporal changes in areas of interest.

MemAxes: Visualization and Analytics for Characterizing Complex Memory Performance Behaviors.

PubMed

Gimenez, Alfredo; Gamblin, Todd; Jusufi, Ilir; Bhatele, Abhinav; Schulz, Martin; Bremer, Peer-Timo; Hamann, Bernd

2018-07-01

Memory performance is often a major bottleneck for high-performance computing (HPC) applications. Deepening memory hierarchies, complex memory management, and non-uniform access times have made memory performance behavior difficult to characterize, and users require novel, sophisticated tools to analyze and optimize this aspect of their codes. Existing tools target only specific factors of memory performance, such as hardware layout, allocations, or access instructions. However, today's tools do not suffice to characterize the complex relationships between these factors. Further, they require advanced expertise to be used effectively. We present MemAxes, a tool based on a novel approach for analytic-driven visualization of memory performance data. MemAxes uniquely allows users to analyze the different aspects related to memory performance by providing multiple visual contexts for a centralized dataset. We define mappings of sampled memory access data to new and existing visual metaphors, each of which enabling a user to perform different analysis tasks. We present methods to guide user interaction by scoring subsets of the data based on known performance problems. This scoring is used to provide visual cues and automatically extract clusters of interest. We designed MemAxes in collaboration with experts in HPC and demonstrate its effectiveness in case studies.

Visual analytics in medical education: impacting analytical reasoning and decision making for quality improvement.

PubMed

Vaitsis, Christos; Nilsson, Gunnar; Zary, Nabil

2015-01-01

The medical curriculum is the main tool representing the entire undergraduate medical education. Due to its complexity and multilayered structure it is of limited use to teachers in medical education for quality improvement purposes. In this study we evaluated three visualizations of curriculum data from a pilot course, using teachers from an undergraduate medical program and applying visual analytics methods. We found that visual analytics can be used to positively impacting analytical reasoning and decision making in medical education through the realization of variables capable to enhance human perception and cognition on complex curriculum data. The positive results derived from our evaluation of a medical curriculum and in a small scale, signify the need to expand this method to an entire medical curriculum. As our approach sustains low levels of complexity it opens a new promising direction in medical education informatics research.

Planetary Surface Visualization and Analytics

NASA Astrophysics Data System (ADS)

Law, E. S.; Solar System Treks Team

2018-04-01

An introduction and update of the Solar System Treks Project which provides a suite of interactive visualization and analysis tools to enable users (engineers, scientists, public) to access large amounts of mapped planetary data products.

Large High Resolution Displays for Co-Located Collaborative Sensemaking: Display Usage and Territoriality

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bradel, Lauren; Endert, Alexander; Koch, Kristen

2013-08-01

Large, high-resolution vertical displays carry the potential to increase the accuracy of collaborative sensemaking, given correctly designed visual analytics tools. From an exploratory user study using a fictional textual intelligence analysis task, we investigated how users interact with the display to construct spatial schemas and externalize information, as well as how they establish shared and private territories. We investigated the space management strategies of users partitioned by type of tool philosophy followed (visualization- or text-centric). We classified the types of territorial behavior exhibited in terms of how the users interacted with information on the display (integrated or independent workspaces). Next,more » we examined how territorial behavior impacted the common ground between the pairs of users. Finally, we offer design suggestions for building future co-located collaborative visual analytics tools specifically for use on large, high-resolution vertical displays.« less

Visualization Techniques for Computer Network Defense

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beaver, Justin M; Steed, Chad A; Patton, Robert M

2011-01-01

Effective visual analysis of computer network defense (CND) information is challenging due to the volume and complexity of both the raw and analyzed network data. A typical CND is comprised of multiple niche intrusion detection tools, each of which performs network data analysis and produces a unique alerting output. The state-of-the-practice in the situational awareness of CND data is the prevalent use of custom-developed scripts by Information Technology (IT) professionals to retrieve, organize, and understand potential threat events. We propose a new visual analytics framework, called the Oak Ridge Cyber Analytics (ORCA) system, for CND data that allows an operatormore » to interact with all detection tool outputs simultaneously. Aggregated alert events are presented in multiple coordinated views with timeline, cluster, and swarm model analysis displays. These displays are complemented with both supervised and semi-supervised machine learning classifiers. The intent of the visual analytics framework is to improve CND situational awareness, to enable an analyst to quickly navigate and analyze thousands of detected events, and to combine sophisticated data analysis techniques with interactive visualization such that patterns of anomalous activities may be more easily identified and investigated.« less

moocRP: Enabling Open Learning Analytics with an Open Source Platform for Data Distribution, Analysis, and Visualization

ERIC Educational Resources Information Center

Pardos, Zachary A.; Whyte, Anthony; Kao, Kevin

2016-01-01

In this paper, we address issues of transparency, modularity, and privacy with the introduction of an open source, web-based data repository and analysis tool tailored to the Massive Open Online Course community. The tool integrates data request/authorization and distribution workflow features as well as provides a simple analytics module upload…

Prototyping Visual Learning Analytics Guided by an Educational Theory Informed Goal

ERIC Educational Resources Information Center

Hillaire, Garron; Rappolt-Schlichtmann, Gabrielle; Ducharme, Kim

2016-01-01

Prototype work can support the creation of data visualizations throughout the research and development process through paper prototypes with sketching, designed prototypes with graphic design tools, and functional prototypes to explore how the implementation will work. One challenging aspect of data visualization work is coordinating the expertise…

T.Rex Visual Analytics for Transactional Exploration

ScienceCinema

None

2018-01-16

T.Rex is PNNL's visual analytics tool that specializes in tabular structured data, like you might open with Excel. It's a client-server application, allowing the server to do a lot of the heavy lifting and the client to open spreadsheets with millions of rows. With datasets of that size, especially if you're unfamiliar with the contents, it's very hard to get a good grasp of what's in it using traditional tools. With T.Rex, the multiple views allow you to see categorical, temporal, numerical, relational, and summary data. The interactivity lets you look across your data and see how things relate to each other.

T.Rex Visual Analytics for Transactional Exploration

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

2014-07-01

T.Rex is PNNL's visual analytics tool that specializes in tabular structured data, like you might open with Excel. It's a client-server application, allowing the server to do a lot of the heavy lifting and the client to open spreadsheets with millions of rows. With datasets of that size, especially if you're unfamiliar with the contents, it's very hard to get a good grasp of what's in it using traditional tools. With T.Rex, the multiple views allow you to see categorical, temporal, numerical, relational, and summary data. The interactivity lets you look across your data and see how things relate tomore » each other.« less

Data visualisation in surveillance for injury prevention and control: conceptual bases and case studies

PubMed Central

Martinez, Ramon; Ordunez, Pedro; Soliz, Patricia N; Ballesteros, Michael F

2016-01-01

Background The complexity of current injury-related health issues demands the usage of diverse and massive data sets for comprehensive analyses, and application of novel methods to communicate data effectively to the public health community, decision-makers and the public. Recent advances in information visualisation, availability of new visual analytic methods and tools, and progress on information technology provide an opportunity for shaping the next generation of injury surveillance. Objective To introduce data visualisation conceptual bases, and propose a visual analytic and visualisation platform in public health surveillance for injury prevention and control. Methods The paper introduces data visualisation conceptual bases, describes a visual analytic and visualisation platform, and presents two real-world case studies illustrating their application in public health surveillance for injury prevention and control. Results Application of visual analytic and visualisation platform is presented as solution for improved access to heterogeneous data sources, enhance data exploration and analysis, communicate data effectively, and support decision-making. Conclusions Applications of data visualisation concepts and visual analytic platform could play a key role to shape the next generation of injury surveillance. Visual analytic and visualisation platform could improve data use, the analytic capacity, and ability to effectively communicate findings and key messages. The public health surveillance community is encouraged to identify opportunities to develop and expand its use in injury prevention and control. PMID:26728006

Process monitoring and visualization solutions for hot-melt extrusion: a review.

PubMed

Saerens, Lien; Vervaet, Chris; Remon, Jean Paul; De Beer, Thomas

2014-02-01

Hot-melt extrusion (HME) is applied as a continuous pharmaceutical manufacturing process for the production of a variety of dosage forms and formulations. To ensure the continuity of this process, the quality of the extrudates must be assessed continuously during manufacturing. The objective of this review is to provide an overview and evaluation of the available process analytical techniques which can be applied in hot-melt extrusion. Pharmaceutical extruders are equipped with traditional (univariate) process monitoring tools, observing barrel and die temperatures, throughput, screw speed, torque, drive amperage, melt pressure and melt temperature. The relevance of several spectroscopic process analytical techniques for monitoring and control of pharmaceutical HME has been explored recently. Nevertheless, many other sensors visualizing HME and measuring diverse critical product and process parameters with potential use in pharmaceutical extrusion are available, and were thoroughly studied in polymer extrusion. The implementation of process analytical tools in HME serves two purposes: (1) improving process understanding by monitoring and visualizing the material behaviour and (2) monitoring and analysing critical product and process parameters for process control, allowing to maintain a desired process state and guaranteeing the quality of the end product. This review is the first to provide an evaluation of the process analytical tools applied for pharmaceutical HME monitoring and control, and discusses techniques that have been used in polymer extrusion having potential for monitoring and control of pharmaceutical HME. © 2013 Royal Pharmaceutical Society.

Explorative visual analytics on interval-based genomic data and their metadata.

PubMed

Jalili, Vahid; Matteucci, Matteo; Masseroli, Marco; Ceri, Stefano

2017-12-04

With the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless "sense-making" of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines. This paper presents abstractions for supporting the early analysis of NGS processed data and their implementation in an associated tool, named GenoMetric Space Explorer (GeMSE). This tool serves the needs of the GenoMetric Query Language, an innovative cloud-based system for computing complex queries over heterogeneous processed data. It can also be used starting from any text files in standard BED, BroadPeak, NarrowPeak, GTF, or general tab-delimited format, containing numerical features of genomic regions; metadata can be provided as text files in tab-delimited attribute-value format. GeMSE allows interactive analytics, consisting of on-the-fly cycling among steps of data exploration, analysis and visualization that help biologists and bioinformaticians in making sense of heterogeneous genomic datasets. By means of an explorative interaction support, users can trace past activities and quickly recover their results, seamlessly going backward and forward in the analysis steps and comparative visualizations of heatmaps. GeMSE effective application and practical usefulness is demonstrated through significant use cases of biological interest. GeMSE is available at http://www.bioinformatics.deib.polimi.it/GeMSE/ , and its source code is available at https://github.com/Genometric/GeMSE under GPLv3 open-source license.

VisualUrText: A Text Analytics Tool for Unstructured Textual Data

NASA Astrophysics Data System (ADS)

Zainol, Zuraini; Jaymes, Mohd T. H.; Nohuddin, Puteri N. E.

2018-05-01

The growing amount of unstructured text over Internet is tremendous. Text repositories come from Web 2.0, business intelligence and social networking applications. It is also believed that 80-90% of future growth data is available in the form of unstructured text databases that may potentially contain interesting patterns and trends. Text Mining is well known technique for discovering interesting patterns and trends which are non-trivial knowledge from massive unstructured text data. Text Mining covers multidisciplinary fields involving information retrieval (IR), text analysis, natural language processing (NLP), data mining, machine learning statistics and computational linguistics. This paper discusses the development of text analytics tool that is proficient in extracting, processing, analyzing the unstructured text data and visualizing cleaned text data into multiple forms such as Document Term Matrix (DTM), Frequency Graph, Network Analysis Graph, Word Cloud and Dendogram. This tool, VisualUrText, is developed to assist students and researchers for extracting interesting patterns and trends in document analyses.

Using Maps in Web Analytics to Evaluate the Impact of Web-Based Extension Programs

ERIC Educational Resources Information Center

Veregin, Howard

2015-01-01

Maps can be a valuable addition to the Web analytics toolbox for Extension programs that use the Web to disseminate information. Extension professionals use Web analytics tools to evaluate program impacts. Maps add a unique perspective through visualization and analysis of geographic patterns and their relationships to other variables. Maps can…

Rethinking Visual Analytics for Streaming Data Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crouser, R. Jordan; Franklin, Lyndsey; Cook, Kris

In the age of data science, the use of interactive information visualization techniques has become increasingly ubiquitous. From online scientific journals to the New York Times graphics desk, the utility of interactive visualization for both storytelling and analysis has become ever more apparent. As these techniques have become more readily accessible, the appeal of combining interactive visualization with computational analysis continues to grow. Arising out of a need for scalable, human-driven analysis, primary objective of visual analytics systems is to capitalize on the complementary strengths of human and machine analysis, using interactive visualization as a medium for communication between themore » two. These systems leverage developments from the fields of information visualization, computer graphics, machine learning, and human-computer interaction to support insight generation in areas where purely computational analyses fall short. Over the past decade, visual analytics systems have generated remarkable advances in many historically challenging analytical contexts. These include areas such as modeling political systems [Crouser et al. 2012], detecting financial fraud [Chang et al. 2008], and cybersecurity [Harrison et al. 2012]. In each of these contexts, domain expertise and human intuition is a necessary component of the analysis. This intuition is essential to building trust in the analytical products, as well as supporting the translation of evidence into actionable insight. In addition, each of these examples also highlights the need for scalable analysis. In each case, it is infeasible for a human analyst to manually assess the raw information unaided, and the communication overhead to divide the task between a large number of analysts makes simple parallelism intractable. Regardless of the domain, visual analytics tools strive to optimize the allocation of human analytical resources, and to streamline the sensemaking process on data that is massive, complex, incomplete, and uncertain in scenarios requiring human judgment.« less

Scientists' sense making when hypothesizing about disease mechanisms from expression data and their needs for visualization support.

PubMed

Mirel, Barbara; Görg, Carsten

2014-04-26

A common class of biomedical analysis is to explore expression data from high throughput experiments for the purpose of uncovering functional relationships that can lead to a hypothesis about mechanisms of a disease. We call this analysis expression driven, -omics hypothesizing. In it, scientists use interactive data visualizations and read deeply in the research literature. Little is known, however, about the actual flow of reasoning and behaviors (sense making) that scientists enact in this analysis, end-to-end. Understanding this flow is important because if bioinformatics tools are to be truly useful they must support it. Sense making models of visual analytics in other domains have been developed and used to inform the design of useful and usable tools. We believe they would be helpful in bioinformatics. To characterize the sense making involved in expression-driven, -omics hypothesizing, we conducted an in-depth observational study of one scientist as she engaged in this analysis over six months. From findings, we abstracted a preliminary sense making model. Here we describe its stages and suggest guidelines for developing visualization tools that we derived from this case. A single case cannot be generalized. But we offer our findings, sense making model and case-based tool guidelines as a first step toward increasing interest and further research in the bioinformatics field on scientists' analytical workflows and their implications for tool design.

Scientists’ sense making when hypothesizing about disease mechanisms from expression data and their needs for visualization support

PubMed Central

2014-01-01

A common class of biomedical analysis is to explore expression data from high throughput experiments for the purpose of uncovering functional relationships that can lead to a hypothesis about mechanisms of a disease. We call this analysis expression driven, -omics hypothesizing. In it, scientists use interactive data visualizations and read deeply in the research literature. Little is known, however, about the actual flow of reasoning and behaviors (sense making) that scientists enact in this analysis, end-to-end. Understanding this flow is important because if bioinformatics tools are to be truly useful they must support it. Sense making models of visual analytics in other domains have been developed and used to inform the design of useful and usable tools. We believe they would be helpful in bioinformatics. To characterize the sense making involved in expression-driven, -omics hypothesizing, we conducted an in-depth observational study of one scientist as she engaged in this analysis over six months. From findings, we abstracted a preliminary sense making model. Here we describe its stages and suggest guidelines for developing visualization tools that we derived from this case. A single case cannot be generalized. But we offer our findings, sense making model and case-based tool guidelines as a first step toward increasing interest and further research in the bioinformatics field on scientists’ analytical workflows and their implications for tool design. PMID:24766796

User-Driven Sampling Strategies in Image Exploitation

DOE PAGES

Harvey, Neal R.; Porter, Reid B.

2013-12-23

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-drivenmore » sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. We discovered that in user-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. Furthermore, in preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.« less

User-driven sampling strategies in image exploitation

NASA Astrophysics Data System (ADS)

Harvey, Neal; Porter, Reid

2013-12-01

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. User-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. In preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.

Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus.

PubMed

Fan Du; Shneiderman, Ben; Plaisant, Catherine; Malik, Sana; Perer, Adam

2017-06-01

The growing volume and variety of data presents both opportunities and challenges for visual analytics. Addressing these challenges is needed for big data to provide valuable insights and novel solutions for business, security, social media, and healthcare. In the case of temporal event sequence analytics it is the number of events in the data and variety of temporal sequence patterns that challenges users of visual analytic tools. This paper describes 15 strategies for sharpening analytic focus that analysts can use to reduce the data volume and pattern variety. Four groups of strategies are proposed: (1) extraction strategies, (2) temporal folding, (3) pattern simplification strategies, and (4) iterative strategies. For each strategy, we provide examples of the use and impact of this strategy on volume and/or variety. Examples are selected from 20 case studies gathered from either our own work, the literature, or based on email interviews with individuals who conducted the analyses and developers who observed analysts using the tools. Finally, we discuss how these strategies might be combined and report on the feedback from 10 senior event sequence analysts.

Data visualisation in surveillance for injury prevention and control: conceptual bases and case studies.

PubMed

Martinez, Ramon; Ordunez, Pedro; Soliz, Patricia N; Ballesteros, Michael F

2016-04-01

The complexity of current injury-related health issues demands the usage of diverse and massive data sets for comprehensive analyses, and application of novel methods to communicate data effectively to the public health community, decision-makers and the public. Recent advances in information visualisation, availability of new visual analytic methods and tools, and progress on information technology provide an opportunity for shaping the next generation of injury surveillance. To introduce data visualisation conceptual bases, and propose a visual analytic and visualisation platform in public health surveillance for injury prevention and control. The paper introduces data visualisation conceptual bases, describes a visual analytic and visualisation platform, and presents two real-world case studies illustrating their application in public health surveillance for injury prevention and control. Application of visual analytic and visualisation platform is presented as solution for improved access to heterogeneous data sources, enhance data exploration and analysis, communicate data effectively, and support decision-making. Applications of data visualisation concepts and visual analytic platform could play a key role to shape the next generation of injury surveillance. Visual analytic and visualisation platform could improve data use, the analytic capacity, and ability to effectively communicate findings and key messages. The public health surveillance community is encouraged to identify opportunities to develop and expand its use in injury prevention and control. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

A Virtual World of Visualization

NASA Technical Reports Server (NTRS)

1998-01-01

In 1990, Sterling Software, Inc., developed the Flow Analysis Software Toolkit (FAST) for NASA Ames on contract. FAST is a workstation based modular analysis and visualization tool. It is used to visualize and animate grids and grid oriented data, typically generated by finite difference, finite element and other analytical methods. FAST is now available through COSMIC, NASA's software storehouse.

Software Tools on the Peregrine System | High-Performance Computing | NREL

Science.gov Websites

Debugger or performance analysis Tool for understanding the behavior of MPI applications. Intel VTune environment for statistical computing and graphics. VirtualGL/TurboVNC Visualization and analytics Remote Tools on the Peregrine System Software Tools on the Peregrine System NREL has a variety of

Application of Data Provenance in Healthcare Analytics Software: Information Visualisation of User Activities

PubMed Central

Xu, Shen; Rogers, Toby; Fairweather, Elliot; Glenn, Anthony; Curran, James; Curcin, Vasa

2018-01-01

Data provenance is a technique that describes the history of digital objects. In health data settings, it can be used to deliver auditability and transparency, and to achieve trust in a software system. However, implementing data provenance in analytics software at an enterprise level presents a different set of challenges from the research environments where data provenance was originally devised. In this paper, the challenges of reporting provenance information to the user is presented. Provenance captured from analytics software can be large and complex and visualizing a series of tasks over a long period can be overwhelming even for a domain expert, requiring visual aggregation mechanisms that fit with complex human cognitive activities involved in the process. This research studied how provenance-based reporting can be integrated into a health data analytics software, using the example of Atmolytics visual reporting tool. PMID:29888084

Developing Visual Thinking in the Electronic Health Record.

PubMed

Boyd, Andrew D; Young, Christine D; Amatayakul, Margret; Dieter, Michael G; Pawola, Lawrence M

2017-01-01

The purpose of this vision paper is to identify how data visualization could transform healthcare. Electronic Health Records (EHRs) are maturing with new technology and tools being applied. Researchers are reaping the benefits of data visualization to better access compilations of EHR data for enhanced clinical research. Data visualization, while still primarily the domain of clinical researchers, is beginning to show promise for other stakeholders. A non-exhaustive review of the literature indicates that respective to the growth and development of the EHR, the maturity of data visualization in healthcare is in its infancy. Visual analytics has been only cursorily applied to healthcare. A fundamental issue contributing to fragmentation and poor coordination of healthcare delivery is that each member of the healthcare team, including patients, has a different view. Summarizing all of this care comprehensively for any member of the healthcare team is a "wickedly hard" visual analytics and data visualization problem to solve.

A visual analytics approach for pattern-recognition in patient-generated data.

PubMed

Feller, Daniel J; Burgermaster, Marissa; Levine, Matthew E; Smaldone, Arlene; Davidson, Patricia G; Albers, David J; Mamykina, Lena

2018-06-13

To develop and test a visual analytics tool to help clinicians identify systematic and clinically meaningful patterns in patient-generated data (PGD) while decreasing perceived information overload. Participatory design was used to develop Glucolyzer, an interactive tool featuring hierarchical clustering and a heatmap visualization to help registered dietitians (RDs) identify associative patterns between blood glucose levels and per-meal macronutrient composition for individuals with type 2 diabetes (T2DM). Ten RDs participated in a within-subjects experiment to compare Glucolyzer to a static logbook format. For each representation, participants had 25 minutes to examine 1 month of diabetes self-monitoring data captured by an individual with T2DM and identify clinically meaningful patterns. We compared the quality and accuracy of the observations generated using each representation. Participants generated 50% more observations when using Glucolyzer (98) than when using the logbook format (64) without any loss in accuracy (69% accuracy vs 62%, respectively, p = .17). Participants identified more observations that included ingredients other than carbohydrates using Glucolyzer (36% vs 16%, p = .027). Fewer RDs reported feelings of information overload using Glucolyzer compared to the logbook format. Study participants displayed variable acceptance of hierarchical clustering. Visual analytics have the potential to mitigate provider concerns about the volume of self-monitoring data. Glucolyzer helped dietitians identify meaningful patterns in self-monitoring data without incurring perceived information overload. Future studies should assess whether similar tools can support clinicians in personalizing behavioral interventions that improve patient outcomes.

Teaching Science through Pictorial Models during Read-Alouds

ERIC Educational Resources Information Center

Oliveira, Alandeom W.; Rivera, Seema; Glass, Rory; Mastroianni, Michael; Wizner, Francine; Amodeo, Vincent

2013-01-01

This study examines how three elementary teachers refer to pictorial models (photographs, drawings, and cartoons) during science read-alouds. While one teacher used realistic photographs for the purpose of visually verifying facts about crystals, another employed analytical diagrams as heuristic tools to help students visualize complex target…

Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering.

PubMed

Endert, A; Fiaux, P; North, C

2012-12-01

Visual analytic tools aim to support the cognitively demanding task of sensemaking. Their success often depends on the ability to leverage capabilities of mathematical models, visualization, and human intuition through flexible, usable, and expressive interactions. Spatially clustering data is one effective metaphor for users to explore similarity and relationships between information, adjusting the weighting of dimensions or characteristics of the dataset to observe the change in the spatial layout. Semantic interaction is an approach to user interaction in such spatializations that couples these parametric modifications of the clustering model with users' analytic operations on the data (e.g., direct document movement in the spatialization, highlighting text, search, etc.). In this paper, we present results of a user study exploring the ability of semantic interaction in a visual analytic prototype, ForceSPIRE, to support sensemaking. We found that semantic interaction captures the analytical reasoning of the user through keyword weighting, and aids the user in co-creating a spatialization based on the user's reasoning and intuition.

Integration of bus stop counts data with census data for improving bus service.

DOT National Transportation Integrated Search

2016-04-01

This research project produced an open source transit market data visualization and analysis tool suite, : The Bus Transit Market Analyst (BTMA), which contains user-friendly GIS mapping and data : analytics tools, and state-of-the-art transit demand...

LATUX: An Iterative Workflow for Designing, Validating, and Deploying Learning Analytics Visualizations

ERIC Educational Resources Information Center

Martinez-Maldonado, Roberto; Pardo, Abelardo; Mirriahi, Negin; Yacef, Kalina; Kay, Judy; Clayphan, Andrew

2015-01-01

Designing, validating, and deploying learning analytics tools for instructors or students is a challenge that requires techniques and methods from different disciplines, such as software engineering, human-computer interaction, computer graphics, educational design, and psychology. Whilst each has established its own design methodologies, we now…

Modeling and Visualizing Flow of Chemical Agents Across Complex Terrain

NASA Technical Reports Server (NTRS)

Kao, David; Kramer, Marc; Chaderjian, Neal

2005-01-01

Release of chemical agents across complex terrain presents a real threat to homeland security. Modeling and visualization tools are being developed that capture flow fluid terrain interaction as well as point dispersal downstream flow paths. These analytic tools when coupled with UAV atmospheric observations provide predictive capabilities to allow for rapid emergency response as well as developing a comprehensive preemptive counter-threat evacuation plan. The visualization tools involve high-end computing and massive parallel processing combined with texture mapping. We demonstrate our approach across a mountainous portion of North California under two contrasting meteorological conditions. Animations depicting flow over this geographical location provide immediate assistance in decision support and crisis management.

Single Cell Proteomics in Biomedicine: High-dimensional Data Acquisition, Visualization and Analysis

PubMed Central

Su, Yapeng; Shi, Qihui; Wei, Wei

2017-01-01

New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions. PMID:28128880

Visualizing the Geography of the Diseases of China: Western Disease Maps from Analytical Tools to Tools of Empire, Sovereignty, and Public Health Propaganda, 1878-1929.

PubMed

Hanson, Marta

2017-09-01

Argument This article analyzes for the first time the earliest western maps of diseases in China spanning fifty years from the late 1870s to the end of the 1920s. The 24 featured disease maps present a visual history of the major transformations in modern medicine from medical geography to laboratory medicine wrought on Chinese soil. These medical transformations occurred within new political formations from the Qing dynasty (1644-1911) to colonialism in East Asia (Hong Kong, Taiwan, Manchuria, Korea) and hypercolonialism within China (Tianjin, Shanghai, Amoy) as well as the new Republican Chinese nation state (1912-49). As a subgenre of persuasive graphics, physicians marshaled disease maps for various rhetorical functions within these different political contexts. Disease maps in China changed from being mostly analytical tools to functioning as tools of empire, national sovereignty, and public health propaganda legitimating new medical concepts, public health interventions, and political structures governing over human and non-human populations.

SOCRAT Platform Design: A Web Architecture for Interactive Visual Analytics Applications

PubMed Central

Kalinin, Alexandr A.; Palanimalai, Selvam; Dinov, Ivo D.

2018-01-01

The modern web is a successful platform for large scale interactive web applications, including visualizations. However, there are no established design principles for building complex visual analytics (VA) web applications that could efficiently integrate visualizations with data management, computational transformation, hypothesis testing, and knowledge discovery. This imposes a time-consuming design and development process on many researchers and developers. To address these challenges, we consider the design requirements for the development of a module-based VA system architecture, adopting existing practices of large scale web application development. We present the preliminary design and implementation of an open-source platform for Statistics Online Computational Resource Analytical Toolbox (SOCRAT). This platform defines: (1) a specification for an architecture for building VA applications with multi-level modularity, and (2) methods for optimizing module interaction, re-usage, and extension. To demonstrate how this platform can be used to integrate a number of data management, interactive visualization, and analysis tools, we implement an example application for simple VA tasks including raw data input and representation, interactive visualization and analysis. PMID:29630069

SOCRAT Platform Design: A Web Architecture for Interactive Visual Analytics Applications.

PubMed

Kalinin, Alexandr A; Palanimalai, Selvam; Dinov, Ivo D

2017-04-01

The modern web is a successful platform for large scale interactive web applications, including visualizations. However, there are no established design principles for building complex visual analytics (VA) web applications that could efficiently integrate visualizations with data management, computational transformation, hypothesis testing, and knowledge discovery. This imposes a time-consuming design and development process on many researchers and developers. To address these challenges, we consider the design requirements for the development of a module-based VA system architecture, adopting existing practices of large scale web application development. We present the preliminary design and implementation of an open-source platform for Statistics Online Computational Resource Analytical Toolbox (SOCRAT). This platform defines: (1) a specification for an architecture for building VA applications with multi-level modularity, and (2) methods for optimizing module interaction, re-usage, and extension. To demonstrate how this platform can be used to integrate a number of data management, interactive visualization, and analysis tools, we implement an example application for simple VA tasks including raw data input and representation, interactive visualization and analysis.

Jupiter Environment Tool

NASA Technical Reports Server (NTRS)

Sturm, Erick J.; Monahue, Kenneth M.; Biehl, James P.; Kokorowski, Michael; Ngalande, Cedrick,; Boedeker, Jordan

2012-01-01

The Jupiter Environment Tool (JET) is a custom UI plug-in for STK that provides an interface to Jupiter environment models for visualization and analysis. Users can visualize the different magnetic field models of Jupiter through various rendering methods, which are fully integrated within STK s 3D Window. This allows users to take snapshots and make animations of their scenarios with magnetic field visualizations. Analytical data can be accessed in the form of custom vectors. Given these custom vectors, users have access to magnetic field data in custom reports, graphs, access constraints, coverage analysis, and anywhere else vectors are used within STK.

DIVE: A Graph-based Visual Analytics Framework for Big Data

PubMed Central

Rysavy, Steven J.; Bromley, Dennis; Daggett, Valerie

2014-01-01

The need for data-centric scientific tools is growing; domains like biology, chemistry, and physics are increasingly adopting computational approaches. As a result, scientists must now deal with the challenges of big data. To address these challenges, we built a visual analytics platform named DIVE: Data Intensive Visualization Engine. DIVE is a data-agnostic, ontologically-expressive software framework capable of streaming large datasets at interactive speeds. Here we present the technical details of the DIVE platform, multiple usage examples, and a case study from the Dynameomics molecular dynamics project. We specifically highlight our novel contributions to structured data model manipulation and high-throughput streaming of large, structured datasets. PMID:24808197

Social Media Visual Analytics for Events

NASA Astrophysics Data System (ADS)

Diakopoulos, Nicholas; Naaman, Mor; Yazdani, Tayebeh; Kivran-Swaine, Funda

For large-scale multimedia events such as televised debates and speeches, the amount of content on social media channels such as Facebook or Twitter can easily become overwhelming, yet still contain information that may aid and augment understanding of the multimedia content via individual social media items, or aggregate information from the crowd's response. In this work we discuss this opportunity in the context of a social media visual analytic tool, Vox Civitas, designed to help journalists, media professionals, or other researchers make sense of large-scale aggregations of social media content around multimedia broadcast events. We discuss the design of the tool, present and evaluate the text analysis techniques used to enable the presentation, and detail the visual and interaction design. We provide an exploratory evaluation based on a user study in which journalists interacted with the system to analyze and report on a dataset of over one 100 000 Twitter messages collected during the broadcast of the U.S. State of the Union presidential address in 2010.

Effects of Learning Analytics Dashboard: Analyzing the Relations among Dashboard Utilization, Satisfaction, and Learning Achievement

ERIC Educational Resources Information Center

Kim, Jeonghyun; Jo, Il-Hyun; Park, Yeonjeong

2016-01-01

The learning analytics dashboard (LAD) is a newly developed learning support tool for virtual classrooms that is believed to allow students to review their online learning behavior patterns intuitively through the provision of visual information. The purpose of this study was to empirically validate the effects of LAD. An experimental study was…

Evaluation methodology for comparing memory and communication of analytic processes in visual analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ragan, Eric D; Goodall, John R

2014-01-01

Provenance tools can help capture and represent the history of analytic processes. In addition to supporting analytic performance, provenance tools can be used to support memory of the process and communication of the steps to others. Objective evaluation methods are needed to evaluate how well provenance tools support analyst s memory and communication of analytic processes. In this paper, we present several methods for the evaluation of process memory, and we discuss the advantages and limitations of each. We discuss methods for determining a baseline process for comparison, and we describe various methods that can be used to elicit processmore » recall, step ordering, and time estimations. Additionally, we discuss methods for conducting quantitative and qualitative analyses of process memory. By organizing possible memory evaluation methods and providing a meta-analysis of the potential benefits and drawbacks of different approaches, this paper can inform study design and encourage objective evaluation of process memory and communication.« less

SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations.

PubMed

Liu, Dongyu; Weng, Di; Li, Yuhong; Bao, Jie; Zheng, Yu; Qu, Huamin; Wu, Yingcai

2017-01-01

The problem of formulating solutions immediately and comparing them rapidly for billboard placements has plagued advertising planners for a long time, owing to the lack of efficient tools for in-depth analyses to make informed decisions. In this study, we attempt to employ visual analytics that combines the state-of-the-art mining and visualization techniques to tackle this problem using large-scale GPS trajectory data. In particular, we present SmartAdP, an interactive visual analytics system that deals with the two major challenges including finding good solutions in a huge solution space and comparing the solutions in a visual and intuitive manner. An interactive framework that integrates a novel visualization-driven data mining model enables advertising planners to effectively and efficiently formulate good candidate solutions. In addition, we propose a set of coupled visualizations: a solution view with metaphor-based glyphs to visualize the correlation between different solutions; a location view to display billboard locations in a compact manner; and a ranking view to present multi-typed rankings of the solutions. This system has been demonstrated using case studies with a real-world dataset and domain-expert interviews. Our approach can be adapted for other location selection problems such as selecting locations of retail stores or restaurants using trajectory data.

Rhodobase, a meta-analytical tool for reconstructing gene regulatory networks in a model photosynthetic bacterium.

PubMed

Moskvin, Oleg V; Bolotin, Dmitry; Wang, Andrew; Ivanov, Pavel S; Gomelsky, Mark

2011-02-01

We present Rhodobase, a web-based meta-analytical tool for analysis of transcriptional regulation in a model anoxygenic photosynthetic bacterium, Rhodobacter sphaeroides. The gene association meta-analysis is based on the pooled data from 100 of R. sphaeroides whole-genome DNA microarrays. Gene-centric regulatory networks were visualized using the StarNet approach (Jupiter, D.C., VanBuren, V., 2008. A visual data mining tool that facilitates reconstruction of transcription regulatory networks. PLoS ONE 3, e1717) with several modifications. We developed a means to identify and visualize operons and superoperons. We designed a framework for the cross-genome search for transcription factor binding sites that takes into account high GC-content and oligonucleotide usage profile characteristic of the R. sphaeroides genome. To facilitate reconstruction of directional relationships between co-regulated genes, we screened upstream sequences (-400 to +20bp from start codons) of all genes for putative binding sites of bacterial transcription factors using a self-optimizing search method developed here. To test performance of the meta-analysis tools and transcription factor site predictions, we reconstructed selected nodes of the R. sphaeroides transcription factor-centric regulatory matrix. The test revealed regulatory relationships that correlate well with the experimentally derived data. The database of transcriptional profile correlations, the network visualization engine and the optimized search engine for transcription factor binding sites analysis are available at http://rhodobase.org. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

T.Rex is used to explore tabular data sets containing up to ten million records to help rapidly understand a previously unknown data set. Analysis can quickly identify patterns of interest and the records and fields that capture those patterns. T.Rex contains a growing set of deep analytical tools and supports robust export capabilities that selected data can be incorporated into to other specialized tools for further analysis. T.Rex is flexible in ingesting different types and formats of data, allowing the user to interactively experiment and perform trial and error guesses on the structure of the data; and also has amore » variety of linked visual analytic tools that enable exploration of the data to find relevant content, relationships among content, trends within the content, and capture knowledge about the content. Finally, T.Rex has a rich export capability, to extract relevant subsets of a larger data source, to further analyze their data in other analytic tools.« less

T.Rex

DOE Office of Scientific and Technical Information (OSTI.GOV)

2016-06-08

T.Rex is used to explore tabular data sets containing up to ten million records to help rapidly understand a previously unknown data set. Analysis can quickly identify patterns of interest and the records and fields that capture those patterns. T.Rex contains a growing set of deep analytical tools and supports robust export capabilities that selected data can be incorporated into to other specialized tools for further analysis. T.Rex is flexible in ingesting different types and formats of data, allowing the user to interactively experiment and perform trial and error guesses on the structure of the data; and also has amore » variety of linked visual analytic tools that enable exploration of the data to find relevant content, relationships among content, trends within the content, and capture knowledge about the content. Finally, T.Rex has a rich export capability, to extract relevant subsets of a larger data source, to further analyze their data in other analytic tools.« less

VESPA: software to facilitate genomic annotation of prokaryotic organisms through integration of proteomic and transcriptomic data.

PubMed

Peterson, Elena S; McCue, Lee Ann; Schrimpe-Rutledge, Alexandra C; Jensen, Jeffrey L; Walker, Hyunjoo; Kobold, Markus A; Webb, Samantha R; Payne, Samuel H; Ansong, Charles; Adkins, Joshua N; Cannon, William R; Webb-Robertson, Bobbie-Jo M

2012-04-05

The procedural aspects of genome sequencing and assembly have become relatively inexpensive, yet the full, accurate structural annotation of these genomes remains a challenge. Next-generation sequencing transcriptomics (RNA-Seq), global microarrays, and tandem mass spectrometry (MS/MS)-based proteomics have demonstrated immense value to genome curators as individual sources of information, however, integrating these data types to validate and improve structural annotation remains a major challenge. Current visual and statistical analytic tools are focused on a single data type, or existing software tools are retrofitted to analyze new data forms. We present Visual Exploration and Statistics to Promote Annotation (VESPA) is a new interactive visual analysis software tool focused on assisting scientists with the annotation of prokaryotic genomes though the integration of proteomics and transcriptomics data with current genome location coordinates. VESPA is a desktop Java™ application that integrates high-throughput proteomics data (peptide-centric) and transcriptomics (probe or RNA-Seq) data into a genomic context, all of which can be visualized at three levels of genomic resolution. Data is interrogated via searches linked to the genome visualizations to find regions with high likelihood of mis-annotation. Search results are linked to exports for further validation outside of VESPA or potential coding-regions can be analyzed concurrently with the software through interaction with BLAST. VESPA is demonstrated on two use cases (Yersinia pestis Pestoides F and Synechococcus sp. PCC 7002) to demonstrate the rapid manner in which mis-annotations can be found and explored in VESPA using either proteomics data alone, or in combination with transcriptomic data. VESPA is an interactive visual analytics tool that integrates high-throughput data into a genomic context to facilitate the discovery of structural mis-annotations in prokaryotic genomes. Data is evaluated via visual analysis across multiple levels of genomic resolution, linked searches and interaction with existing bioinformatics tools. We highlight the novel functionality of VESPA and core programming requirements for visualization of these large heterogeneous datasets for a client-side application. The software is freely available at https://www.biopilot.org/docs/Software/Vespa.php.

VESPA: software to facilitate genomic annotation of prokaryotic organisms through integration of proteomic and transcriptomic data

PubMed Central

2012-01-01

Background The procedural aspects of genome sequencing and assembly have become relatively inexpensive, yet the full, accurate structural annotation of these genomes remains a challenge. Next-generation sequencing transcriptomics (RNA-Seq), global microarrays, and tandem mass spectrometry (MS/MS)-based proteomics have demonstrated immense value to genome curators as individual sources of information, however, integrating these data types to validate and improve structural annotation remains a major challenge. Current visual and statistical analytic tools are focused on a single data type, or existing software tools are retrofitted to analyze new data forms. We present Visual Exploration and Statistics to Promote Annotation (VESPA) is a new interactive visual analysis software tool focused on assisting scientists with the annotation of prokaryotic genomes though the integration of proteomics and transcriptomics data with current genome location coordinates. Results VESPA is a desktop Java™ application that integrates high-throughput proteomics data (peptide-centric) and transcriptomics (probe or RNA-Seq) data into a genomic context, all of which can be visualized at three levels of genomic resolution. Data is interrogated via searches linked to the genome visualizations to find regions with high likelihood of mis-annotation. Search results are linked to exports for further validation outside of VESPA or potential coding-regions can be analyzed concurrently with the software through interaction with BLAST. VESPA is demonstrated on two use cases (Yersinia pestis Pestoides F and Synechococcus sp. PCC 7002) to demonstrate the rapid manner in which mis-annotations can be found and explored in VESPA using either proteomics data alone, or in combination with transcriptomic data. Conclusions VESPA is an interactive visual analytics tool that integrates high-throughput data into a genomic context to facilitate the discovery of structural mis-annotations in prokaryotic genomes. Data is evaluated via visual analysis across multiple levels of genomic resolution, linked searches and interaction with existing bioinformatics tools. We highlight the novel functionality of VESPA and core programming requirements for visualization of these large heterogeneous datasets for a client-side application. The software is freely available at https://www.biopilot.org/docs/Software/Vespa.php. PMID:22480257

Visual Images and Imagination in Pursuit of Mimesis and French Society.

ERIC Educational Resources Information Center

Santorini, George

1990-01-01

Focuses on activities that took place in courses on contemporary French society. In these courses, students and instructor attempted to develop a series of analytical tools from a systematic body of visual, oral, and textual materials in order to increase cultural understanding and speech styles of French-speaking communities or social groups.…

A Visual Analytics Approach for Station-Based Air Quality Data

PubMed Central

Du, Yi; Ma, Cuixia; Wu, Chao; Xu, Xiaowei; Guo, Yike; Zhou, Yuanchun; Li, Jianhui

2016-01-01

With the deployment of multi-modality and large-scale sensor networks for monitoring air quality, we are now able to collect large and multi-dimensional spatio-temporal datasets. For these sensed data, we present a comprehensive visual analysis approach for air quality analysis. This approach integrates several visual methods, such as map-based views, calendar views, and trends views, to assist the analysis. Among those visual methods, map-based visual methods are used to display the locations of interest, and the calendar and the trends views are used to discover the linear and periodical patterns. The system also provides various interaction tools to combine the map-based visualization, trends view, calendar view and multi-dimensional view. In addition, we propose a self-adaptive calendar-based controller that can flexibly adapt the changes of data size and granularity in trends view. Such a visual analytics system would facilitate big-data analysis in real applications, especially for decision making support. PMID:28029117

A Visual Analytics Approach for Station-Based Air Quality Data.

PubMed

Du, Yi; Ma, Cuixia; Wu, Chao; Xu, Xiaowei; Guo, Yike; Zhou, Yuanchun; Li, Jianhui

2016-12-24

With the deployment of multi-modality and large-scale sensor networks for monitoring air quality, we are now able to collect large and multi-dimensional spatio-temporal datasets. For these sensed data, we present a comprehensive visual analysis approach for air quality analysis. This approach integrates several visual methods, such as map-based views, calendar views, and trends views, to assist the analysis. Among those visual methods, map-based visual methods are used to display the locations of interest, and the calendar and the trends views are used to discover the linear and periodical patterns. The system also provides various interaction tools to combine the map-based visualization, trends view, calendar view and multi-dimensional view. In addition, we propose a self-adaptive calendar-based controller that can flexibly adapt the changes of data size and granularity in trends view. Such a visual analytics system would facilitate big-data analysis in real applications, especially for decision making support.

Novel tools for in situ detection of biodiversity and function of dechlorinating and uranium-reducing bacteria in contaminated environments

USDA-ARS?s Scientific Manuscript database

Toxic heavy metals and radionuclides pose a growing, global threat to the environment. For an intelligent remediation design, reliable analytical tools for detection of relevant species are needed, such as PCR. However, PCR cannot visualize its targets and thus provide information about the morpholo...

Using geovisual analytics in Google Earth to understand disease distribution: a case study of campylobacteriosis in the Czech Republic (2008-2012).

PubMed

Marek, Lukáš; Tuček, Pavel; Pászto, Vít

2015-01-28

Visual analytics aims to connect the processing power of information technologies and the user's ability of logical thinking and reasoning through the complex visual interaction. Moreover, the most of the data contain the spatial component. Therefore, the need for geovisual tools and methods arises. Either one can develop own system but the dissemination of findings and its usability might be problematic or the widespread and well-known platform can be utilized. The aim of this paper is to prove the applicability of Google Earth™ software as a tool for geovisual analytics that helps to understand the spatio-temporal patterns of the disease distribution. We combined the complex joint spatio-temporal analysis with comprehensive visualisation. We analysed the spatio-temporal distribution of the campylobacteriosis in the Czech Republic between 2008 and 2012. We applied three main approaches in the study: (1) the geovisual analytics of the surveillance data that were visualised in the form of bubble chart; (2) the geovisual analytics of the disease's weekly incidence surfaces computed by spatio-temporal kriging and (3) the spatio-temporal scan statistics that was employed in order to identify high or low rates clusters of affected municipalities. The final data are stored in Keyhole Markup Language files and visualised in Google Earth™ in order to apply geovisual analytics. Using geovisual analytics we were able to display and retrieve information from complex dataset efficiently. Instead of searching for patterns in a series of static maps or using numerical statistics, we created the set of interactive visualisations in order to explore and communicate results of analyses to the wider audience. The results of the geovisual analytics identified periodical patterns in the behaviour of the disease as well as fourteen spatio-temporal clusters of increased relative risk. We prove that Google Earth™ software is a usable tool for the geovisual analysis of the disease distribution. Google Earth™ has many indisputable advantages (widespread, freely available, intuitive interface, space-time visualisation capabilities and animations, communication of results), nevertheless it is still needed to combine it with pre-processing tools that prepare the data into a form suitable for the geovisual analytics itself.

Fusion Analytics: A Data Integration System for Public Health and Medical Disaster Response Decision Support

PubMed Central

Passman, Dina B.

2013-01-01

Objective The objective of this demonstration is to show conference attendees how they can integrate, analyze, and visualize diverse data type data from across a variety of systems by leveraging an off-the-shelf enterprise business intelligence (EBI) solution to support decision-making in disasters. Introduction Fusion Analytics is the data integration system developed by the Fusion Cell at the U.S. Department of Health and Human Services (HHS), Office of the Assistant Secretary for Preparedness and Response (ASPR). Fusion Analytics meaningfully augments traditional public and population health surveillance reporting by providing web-based data analysis and visualization tools. Methods Fusion Analytics serves as a one-stop-shop for the web-based data visualizations of multiple real-time data sources within ASPR. The 24-7 web availability makes it an ideal analytic tool for situational awareness and response allowing stakeholders to access the portal from any internet-enabled device without installing any software. The Fusion Analytics data integration system was built using off-the-shelf EBI software. Fusion Analytics leverages the full power of statistical analysis software and delivers reports to users in a secure web-based environment. Fusion Analytics provides an example of how public health staff can develop and deploy a robust public health informatics solution using an off-the shelf product and with limited development funding. It also provides the unique example of a public health information system that combines patient data for traditional disease surveillance with manpower and resource data to provide overall decision support for federal public health and medical disaster response operations. Conclusions We are currently in a unique position within public health. One the one hand, we have been gaining greater and greater access to electronic data of all kinds over the last few years. On the other, we are working in a time of reduced government spending to support leveraging this data for decision support with robust analytics and visualizations. Fusion Analytics provides an opportunity for attendees to see how various types of data are integrated into a single application for population health decision support. It also can provide them with ideas of how they can use their own staff to create analyses and reports that support their public health activities.

Single cell proteomics in biomedicine: High-dimensional data acquisition, visualization, and analysis.

PubMed

Su, Yapeng; Shi, Qihui; Wei, Wei

2017-02-01

New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

SEURAT: visual analytics for the integrated analysis of microarray data.

PubMed

Gribov, Alexander; Sill, Martin; Lück, Sonja; Rücker, Frank; Döhner, Konstanze; Bullinger, Lars; Benner, Axel; Unwin, Antony

2010-06-03

In translational cancer research, gene expression data is collected together with clinical data and genomic data arising from other chip based high throughput technologies. Software tools for the joint analysis of such high dimensional data sets together with clinical data are required. We have developed an open source software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data together with associated clinical data, array CGH data and SNP array data. The different data types are organized by a comprehensive data manager. Interactive tools are provided for all graphics: heatmaps, dendrograms, barcharts, histograms, eventcharts and a chromosome browser, which displays genetic variations along the genome. All graphics are dynamic and fully linked so that any object selected in a graphic will be highlighted in all other graphics. For exploratory data analysis the software provides unsupervised data analytics like clustering, seriation algorithms and biclustering algorithms. The SEURAT software meets the growing needs of researchers to perform joint analysis of gene expression, genomical and clinical data.

How Can Visual Analytics Assist Investigative Analysis? Design Implications from an Evaluation.

PubMed

Youn-Ah Kang; Görg, Carsten; Stasko, John

2011-05-01

Despite the growing number of systems providing visual analytic support for investigative analysis, few empirical studies of the potential benefits of such systems have been conducted, particularly controlled, comparative evaluations. Determining how such systems foster insight and sensemaking is important for their continued growth and study, however. Furthermore, studies that identify how people use such systems and why they benefit (or not) can help inform the design of new systems in this area. We conducted an evaluation of the visual analytics system Jigsaw employed in a small investigative sensemaking exercise, and compared its use to three other more traditional methods of analysis. Sixteen participants performed a simulated intelligence analysis task under one of the four conditions. Experimental results suggest that Jigsaw assisted participants to analyze the data and identify an embedded threat. We describe different analysis strategies used by study participants and how computational support (or the lack thereof) influenced the strategies. We then illustrate several characteristics of the sensemaking process identified in the study and provide design implications for investigative analysis tools based thereon. We conclude with recommendations on metrics and techniques for evaluating visual analytics systems for investigative analysis.

From Streaming Data to Streaming Insights: The Impact of Data Velocities on Mental Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Endert, Alexander; Pike, William A.; Cook, Kristin A.

The rise of Big Data has influenced the design and technical implementation of visual analytic tools required to handle the increased volumes, velocities, and varieties of data. This has required a set of data management and computational advancements to allow us to store and compute on such datasets. However, as the ultimate goal of visual analytic technology is to enable the discovery and creation of insights from the users, an under-explored area is understanding how these datasets impact their mental models. That is, how have the analytic processes and strategies of users changed? How have users changed their perception ofmore » how to leverage, and ask questions of, these datasets?« less

Harnessing scientific literature reports for pharmacovigilance. Prototype software analytical tool development and usability testing.

PubMed

Sorbello, Alfred; Ripple, Anna; Tonning, Joseph; Munoz, Monica; Hasan, Rashedul; Ly, Thomas; Francis, Henry; Bodenreider, Olivier

2017-03-22

We seek to develop a prototype software analytical tool to augment FDA regulatory reviewers' capacity to harness scientific literature reports in PubMed/MEDLINE for pharmacovigilance and adverse drug event (ADE) safety signal detection. We also aim to gather feedback through usability testing to assess design, performance, and user satisfaction with the tool. A prototype, open source, web-based, software analytical tool generated statistical disproportionality data mining signal scores and dynamic visual analytics for ADE safety signal detection and management. We leveraged Medical Subject Heading (MeSH) indexing terms assigned to published citations in PubMed/MEDLINE to generate candidate drug-adverse event pairs for quantitative data mining. Six FDA regulatory reviewers participated in usability testing by employing the tool as part of their ongoing real-life pharmacovigilance activities to provide subjective feedback on its practical impact, added value, and fitness for use. All usability test participants cited the tool's ease of learning, ease of use, and generation of quantitative ADE safety signals, some of which corresponded to known established adverse drug reactions. Potential concerns included the comparability of the tool's automated literature search relative to a manual 'all fields' PubMed search, missing drugs and adverse event terms, interpretation of signal scores, and integration with existing computer-based analytical tools. Usability testing demonstrated that this novel tool can automate the detection of ADE safety signals from published literature reports. Various mitigation strategies are described to foster improvements in design, productivity, and end user satisfaction.

Interactive entity resolution in relational data: a visual analytic tool and its evaluation.

PubMed

Kang, Hyunmo; Getoor, Lise; Shneiderman, Ben; Bilgic, Mustafa; Licamele, Louis

2008-01-01

Databases often contain uncertain and imprecise references to real-world entities. Entity resolution, the process of reconciling multiple references to underlying real-world entities, is an important data cleaning process required before accurate visualization or analysis of the data is possible. In many cases, in addition to noisy data describing entities, there is data describing the relationships among the entities. This relational data is important during the entity resolution process; it is useful both for the algorithms which determine likely database references to be resolved and for visual analytic tools which support the entity resolution process. In this paper, we introduce a novel user interface, D-Dupe, for interactive entity resolution in relational data. D-Dupe effectively combines relational entity resolution algorithms with a novel network visualization that enables users to make use of an entity's relational context for making resolution decisions. Since resolution decisions often are interdependent, D-Dupe facilitates understanding this complex process through animations which highlight combined inferences and a history mechanism which allows users to inspect chains of resolution decisions. An empirical study with 12 users confirmed the benefits of the relational context visualization on the performance of entity resolution tasks in relational data in terms of time as well as users' confidence and satisfaction.

IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics

PubMed Central

2016-01-01

Background We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes. Objective To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs. Methods The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets. Results Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix. Conclusions IBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise. PMID:27729304

IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics.

PubMed

Hoyt, Robert Eugene; Snider, Dallas; Thompson, Carla; Mantravadi, Sarita

2016-10-11

We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes. To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs. The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets. Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix. IBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise.

The Geoinformatica free and open source software stack

NASA Astrophysics Data System (ADS)

Jolma, A.

2012-04-01

The Geoinformatica free and open source software (FOSS) stack is based mainly on three established FOSS components, namely GDAL, GTK+, and Perl. GDAL provides access to a very large selection of geospatial data formats and data sources, a generic geospatial data model, and a large collection of geospatial analytical and processing functionality. GTK+ and the Cairo graphics library provide generic graphics and graphical user interface capabilities. Perl is a programming language, for which there is a very large set of FOSS modules for a wide range of purposes and which can be used as an integrative tool for building applications. In the Geoinformatica stack, data storages such as FOSS RDBMS PostgreSQL with its geospatial extension PostGIS can be used below the three above mentioned components. The top layer of Geoinformatica consists of a C library and several Perl modules. The C library comprises a general purpose raster algebra library, hydrological terrain analysis functions, and visualization code. The Perl modules define a generic visualized geospatial data layer and subclasses for raster and vector data and graphs. The hydrological terrain functions are already rather old and they suffer for example from the requirement of in-memory rasters. Newer research conducted using the platform include basic geospatial simulation modeling, visualization of ecological data, linking with a Bayesian network engine for spatial risk assessment in coastal areas, and developing standards-based distributed water resources information systems in Internet. The Geoinformatica stack constitutes a platform for geospatial research, which is targeted towards custom analytical tools, prototyping and linking with external libraries. Writing custom analytical tools is supported by the Perl language and the large collection of tools that are available especially in GDAL and Perl modules. Prototyping is supported by the GTK+ library, the GUI tools, and the support for object-oriented programming in Perl. New feature types, geospatial layer classes, and tools as extensions with specific features can be defined, used, and studied. Linking with external libraries is possible using the Perl foreign function interface tools or with generic tools such as Swig. We are interested in implementing and testing linking Geoinformatica with existing or new more specific hydrological FOSS.

Collaborative Web-Enabled GeoAnalytics Applied to OECD Regional Data

NASA Astrophysics Data System (ADS)

Jern, Mikael

Recent advances in web-enabled graphics technologies have the potential to make a dramatic impact on developing collaborative geovisual analytics (GeoAnalytics). In this paper, tools are introduced that help establish progress initiatives at international and sub-national levels aimed at measuring and collaborating, through statistical indicators, economic, social and environmental developments and to engage both statisticians and the public in such activities. Given this global dimension of such a task, the “dream” of building a repository of progress indicators, where experts and public users can use GeoAnalytics collaborative tools to compare situations for two or more countries, regions or local communities, could be accomplished. While the benefits of GeoAnalytics tools are many, it remains a challenge to adapt these dynamic visual tools to the Internet. For example, dynamic web-enabled animation that enables statisticians to explore temporal, spatial and multivariate demographics data from multiple perspectives, discover interesting relationships, share their incremental discoveries with colleagues and finally communicate selected relevant knowledge to the public. These discoveries often emerge through the diverse backgrounds and experiences of expert domains and are precious in a creative analytics reasoning process. In this context, we introduce a demonstrator “OECD eXplorer”, a customized tool for interactively analyzing, and collaborating gained insights and discoveries based on a novel story mechanism that capture, re-use and share task-related explorative events.

Early visual analysis tool using magnetoencephalography for treatment and recovery of neuronal dysfunction.

PubMed

Rasheed, Waqas; Neoh, Yee Yik; Bin Hamid, Nor Hisham; Reza, Faruque; Idris, Zamzuri; Tang, Tong Boon

2017-10-01

Functional neuroimaging modalities play an important role in deciding the diagnosis and course of treatment of neuronal dysfunction and degeneration. This article presents an analytical tool with visualization by exploiting the strengths of the MEG (magnetoencephalographic) neuroimaging technique. The tool automates MEG data import (in tSSS format), channel information extraction, time/frequency decomposition, and circular graph visualization (connectogram) for simple result inspection. For advanced users, the tool also provides magnitude squared coherence (MSC) values allowing personalized threshold levels, and the computation of default model from MEG data of control population. Default model obtained from healthy population data serves as a useful benchmark to diagnose and monitor neuronal recovery during treatment. The proposed tool further provides optional labels with international 10-10 system nomenclature in order to facilitate comparison studies with EEG (electroencephalography) sensor space. Potential applications in epilepsy and traumatic brain injury studies are also discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Seeing is believing: on the use of image databases for visually exploring plant organelle dynamics.

PubMed

Mano, Shoji; Miwa, Tomoki; Nishikawa, Shuh-ichi; Mimura, Tetsuro; Nishimura, Mikio

2009-12-01

Organelle dynamics vary dramatically depending on cell type, developmental stage and environmental stimuli, so that various parameters, such as size, number and behavior, are required for the description of the dynamics of each organelle. Imaging techniques are superior to other techniques for describing organelle dynamics because these parameters are visually exhibited. Therefore, as the results can be seen immediately, investigators can more easily grasp organelle dynamics. At present, imaging techniques are emerging as fundamental tools in plant organelle research, and the development of new methodologies to visualize organelles and the improvement of analytical tools and equipment have allowed the large-scale generation of image and movie data. Accordingly, image databases that accumulate information on organelle dynamics are an increasingly indispensable part of modern plant organelle research. In addition, image databases are potentially rich data sources for computational analyses, as image and movie data reposited in the databases contain valuable and significant information, such as size, number, length and velocity. Computational analytical tools support image-based data mining, such as segmentation, quantification and statistical analyses, to extract biologically meaningful information from each database and combine them to construct models. In this review, we outline the image databases that are dedicated to plant organelle research and present their potential as resources for image-based computational analyses.

A web-based data visualization tool for the MIMIC-II database.

PubMed

Lee, Joon; Ribey, Evan; Wallace, James R

2016-02-04

Although MIMIC-II, a public intensive care database, has been recognized as an invaluable resource for many medical researchers worldwide, becoming a proficient MIMIC-II researcher requires knowledge of SQL programming and an understanding of the MIMIC-II database schema. These are challenging requirements especially for health researchers and clinicians who may have limited computer proficiency. In order to overcome this challenge, our objective was to create an interactive, web-based MIMIC-II data visualization tool that first-time MIMIC-II users can easily use to explore the database. The tool offers two main features: Explore and Compare. The Explore feature enables the user to select a patient cohort within MIMIC-II and visualize the distributions of various administrative, demographic, and clinical variables within the selected cohort. The Compare feature enables the user to select two patient cohorts and visually compare them with respect to a variety of variables. The tool is also helpful to experienced MIMIC-II researchers who can use it to substantially accelerate the cumbersome and time-consuming steps of writing SQL queries and manually visualizing extracted data. Any interested researcher can use the MIMIC-II data visualization tool for free to quickly and conveniently conduct a preliminary investigation on MIMIC-II with a few mouse clicks. Researchers can also use the tool to learn the characteristics of the MIMIC-II patients. Since it is still impossible to conduct multivariable regression inside the tool, future work includes adding analytics capabilities. Also, the next version of the tool will aim to utilize MIMIC-III which contains more data.

AUVA - Augmented Reality Empowers Visual Analytics to explore Medical Curriculum Data.

PubMed

Nifakos, Sokratis; Vaitsis, Christos; Zary, Nabil

2015-01-01

Medical curriculum data play a key role in the structure and the organization of medical programs in Universities around the world. The effective processing and usage of these data may improve the educational environment of medical students. As a consequence, the new generation of health professionals would have improved skills from the previous ones. This study introduces the process of enhancing curriculum data by the use of augmented reality technology as a management and presentation tool. The final goal is to enrich the information presented from a visual analytics approach applied on medical curriculum data and to sustain low levels of complexity of understanding these data.

Development of a Suite of Analytical Tools for Energy and Water Infrastructure Knowledge Discovery

NASA Astrophysics Data System (ADS)

Morton, A.; Piburn, J.; Stewart, R.; Chandola, V.

2017-12-01

Energy and water generation and delivery systems are inherently interconnected. With demand for energy growing, the energy sector is experiencing increasing competition for water. With increasing population and changing environmental, socioeconomic, and demographic scenarios, new technology and investment decisions must be made for optimized and sustainable energy-water resource management. This also requires novel scientific insights into the complex interdependencies of energy-water infrastructures across multiple space and time scales. To address this need, we've developed a suite of analytical tools to support an integrated data driven modeling, analysis, and visualization capability for understanding, designing, and developing efficient local and regional practices related to the energy-water nexus. This work reviews the analytical capabilities available along with a series of case studies designed to demonstrate the potential of these tools for illuminating energy-water nexus solutions and supporting strategic (federal) policy decisions.

The World Spatiotemporal Analytics and Mapping Project (WSTAMP): Discovering, Exploring, and Mapping Spatiotemporal Patterns Across Heterogenous Space-Time Data

NASA Astrophysics Data System (ADS)

Morton, A.; Stewart, R.; Held, E.; Piburn, J.; Allen, M. R.; McManamay, R.; Sanyal, J.; Sorokine, A.; Bhaduri, B. L.

2017-12-01

Spatiotemporal (ST) analytics applied to major spatio-temporal data sources from major vendors such as USGS, NOAA, World Bank and World Health Organization have tremendous value in shedding light on the evolution of physical, cultural, and geopolitical landscapes on a local and global level. Especially powerful is the integration of these physical and cultural datasets across multiple and disparate formats, facilitating new interdisciplinary analytics and insights. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, changing attributes, and content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of this integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at the Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1) development of the WSTAMP database, a significant advance in ST data modeling that integrates 16000+ attributes covering 200+ countries for over 50 years from over 30 major sources and 2) a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We report on these advances, provide an illustrative case study, and inform how others may freely access the tool.

Decision exploration lab: a visual analytics solution for decision management.

PubMed

Broeksema, Bertjan; Baudel, Thomas; Telea, Arthur G; Crisafulli, Paolo

2013-12-01

We present a visual analytics solution designed to address prevalent issues in the area of Operational Decision Management (ODM). In ODM, which has its roots in Artificial Intelligence (Expert Systems) and Management Science, it is increasingly important to align business decisions with business goals. In our work, we consider decision models (executable models of the business domain) as ontologies that describe the business domain, and production rules that describe the business logic of decisions to be made over this ontology. Executing a decision model produces an accumulation of decisions made over time for individual cases. We are interested, first, to get insight in the decision logic and the accumulated facts by themselves. Secondly and more importantly, we want to see how the accumulated facts reveal potential divergences between the reality as captured by the decision model, and the reality as captured by the executed decisions. We illustrate the motivation, added value for visual analytics, and our proposed solution and tooling through a business case from the car insurance industry.

Implementing multiresolution models and families of models: from entity-level simulation to desktop stochastic models and "repro" models

NASA Astrophysics Data System (ADS)

McEver, Jimmie; Davis, Paul K.; Bigelow, James H.

2000-06-01

We have developed and used families of multiresolution and multiple-perspective models (MRM and MRMPM), both in our substantive analytic work for the Department of Defense and to learn more about how such models can be designed and implemented. This paper is a brief case history of our experience with a particular family of models addressing the use of precision fires in interdicting and halting an invading army. Our models were implemented as closed-form analytic solutions, in spreadsheets, and in the more sophisticated AnalyticaTM environment. We also drew on an entity-level simulation for data. The paper reviews the importance of certain key attributes of development environments (visual modeling, interactive languages, friendly use of array mathematics, facilities for experimental design and configuration control, statistical analysis tools, graphical visualization tools, interactive post-processing, and relational database tools). These can go a long way towards facilitating MRMPM work, but many of these attributes are not yet widely available (or available at all) in commercial model-development tools--especially for use with personal computers. We conclude with some lessons learned from our experience.

Visual Analytics Tools for Sustainable Lifecycle Design: Current Status, Challenges, and Future Opportunities.

PubMed

Ramanujan, Devarajan; Bernstein, William Z; Chandrasegaran, Senthil K; Ramani, Karthik

2017-01-01

The rapid rise in technologies for data collection has created an unmatched opportunity to advance the use of data-rich tools for lifecycle decision-making. However, the usefulness of these technologies is limited by the ability to translate lifecycle data into actionable insights for human decision-makers. This is especially true in the case of sustainable lifecycle design (SLD), as the assessment of environmental impacts, and the feasibility of making corresponding design changes, often relies on human expertise and intuition. Supporting human sense-making in SLD requires the use of both data-driven and user-driven methods while exploring lifecycle data. A promising approach for combining the two is through the use of visual analytics (VA) tools. Such tools can leverage the ability of computer-based tools to gather, process, and summarize data along with the ability of human-experts to guide analyses through domain knowledge or data-driven insight. In this paper, we review previous research that has created VA tools in SLD. We also highlight existing challenges and future opportunities for such tools in different lifecycle stages-design, manufacturing, distribution & supply chain, use-phase, end-of-life, as well as life cycle assessment. Our review shows that while the number of VA tools in SLD is relatively small, researchers are increasingly focusing on the subject matter. Our review also suggests that VA tools can address existing challenges in SLD and that significant future opportunities exist.

SEURAT: Visual analytics for the integrated analysis of microarray data

PubMed Central

2010-01-01

Background In translational cancer research, gene expression data is collected together with clinical data and genomic data arising from other chip based high throughput technologies. Software tools for the joint analysis of such high dimensional data sets together with clinical data are required. Results We have developed an open source software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data together with associated clinical data, array CGH data and SNP array data. The different data types are organized by a comprehensive data manager. Interactive tools are provided for all graphics: heatmaps, dendrograms, barcharts, histograms, eventcharts and a chromosome browser, which displays genetic variations along the genome. All graphics are dynamic and fully linked so that any object selected in a graphic will be highlighted in all other graphics. For exploratory data analysis the software provides unsupervised data analytics like clustering, seriation algorithms and biclustering algorithms. Conclusions The SEURAT software meets the growing needs of researchers to perform joint analysis of gene expression, genomical and clinical data. PMID:20525257

An Excel®-based visualization tool of 2-D soil gas concentration profiles in petroleum vapor intrusion

PubMed Central

Verginelli, Iason; Yao, Yijun; Suuberg, Eric M.

2017-01-01

In this study we present a petroleum vapor intrusion tool implemented in Microsoft® Excel® using Visual Basic for Applications (VBA) and integrated within a graphical interface. The latter helps users easily visualize two-dimensional soil gas concentration profiles and indoor concentrations as a function of site-specific conditions such as source strength and depth, biodegradation reaction rate constant, soil characteristics and building features. This tool is based on a two-dimensional explicit analytical model that combines steady-state diffusion-dominated vapor transport in a homogeneous soil with a piecewise first-order aerobic biodegradation model, in which rate is limited by oxygen availability. As recommended in the recently released United States Environmental Protection Agency's final Petroleum Vapor Intrusion guidance, a sensitivity analysis and a simplified Monte Carlo uncertainty analysis are also included in the spreadsheet. PMID:28163564

An Excel®-based visualization tool of 2-D soil gas concentration profiles in petroleum vapor intrusion.

PubMed

Verginelli, Iason; Yao, Yijun; Suuberg, Eric M

2016-01-01

In this study we present a petroleum vapor intrusion tool implemented in Microsoft ® Excel ® using Visual Basic for Applications (VBA) and integrated within a graphical interface. The latter helps users easily visualize two-dimensional soil gas concentration profiles and indoor concentrations as a function of site-specific conditions such as source strength and depth, biodegradation reaction rate constant, soil characteristics and building features. This tool is based on a two-dimensional explicit analytical model that combines steady-state diffusion-dominated vapor transport in a homogeneous soil with a piecewise first-order aerobic biodegradation model, in which rate is limited by oxygen availability. As recommended in the recently released United States Environmental Protection Agency's final Petroleum Vapor Intrusion guidance, a sensitivity analysis and a simplified Monte Carlo uncertainty analysis are also included in the spreadsheet.

Not Just a Game … When We Play Together, We Learn Together: Interactive Virtual Environments and Gaming Engines for Geospatial Visualization

NASA Astrophysics Data System (ADS)

Shipman, J. S.; Anderson, J. W.

2017-12-01

An ideal tool for ecologists and land managers to investigate the impacts of both projected environmental changes and policy alternatives is the creation of immersive, interactive, virtual landscapes. As a new frontier in visualizing and understanding geospatial data, virtual landscapes require a new toolbox for data visualization that includes traditional GIS tools and uncommon tools such as the Unity3d game engine. Game engines provide capabilities to not only explore data but to build and interact with dynamic models collaboratively. These virtual worlds can be used to display and illustrate data that is often more understandable and plausible to both stakeholders and policy makers than is achieved using traditional maps.Within this context we will present funded research that has been developed utilizing virtual landscapes for geographic visualization and decision support among varied stakeholders. We will highlight the challenges and lessons learned when developing interactive virtual environments that require large multidisciplinary team efforts with varied competences. The results will emphasize the importance of visualization and interactive virtual environments and the link with emerging research disciplines within Visual Analytics.

Modeling and evaluating user behavior in exploratory visual analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reda, Khairi; Johnson, Andrew E.; Papka, Michael E.

Empirical evaluation methods for visualizations have traditionally focused on assessing the outcome of the visual analytic process as opposed to characterizing how that process unfolds. There are only a handful of methods that can be used to systematically study how people use visualizations, making it difficult for researchers to capture and characterize the subtlety of cognitive and interaction behaviors users exhibit during visual analysis. To validate and improve visualization design, however, it is important for researchers to be able to assess and understand how users interact with visualization systems under realistic scenarios. This paper presents a methodology for modeling andmore » evaluating the behavior of users in exploratory visual analysis. We model visual exploration using a Markov chain process comprising transitions between mental, interaction, and computational states. These states and the transitions between them can be deduced from a variety of sources, including verbal transcripts, videos and audio recordings, and log files. This model enables the evaluator to characterize the cognitive and computational processes that are essential to insight acquisition in exploratory visual analysis, and reconstruct the dynamics of interaction between the user and the visualization system. We illustrate this model with two exemplar user studies, and demonstrate the qualitative and quantitative analytical tools it affords.« less

Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform

PubMed Central

Poucke, Sven Van; Zhang, Zhongheng; Schmitz, Martin; Vukicevic, Milan; Laenen, Margot Vander; Celi, Leo Anthony; Deyne, Cathy De

2016-01-01

With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner’s Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research. PMID:26731286

Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform.

PubMed

Van Poucke, Sven; Zhang, Zhongheng; Schmitz, Martin; Vukicevic, Milan; Laenen, Margot Vander; Celi, Leo Anthony; De Deyne, Cathy

2016-01-01

With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner's Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.

CollaborationViz: Interactive Visual Exploration of Biomedical Research Collaboration Networks

PubMed Central

Bian, Jiang; Xie, Mengjun; Hudson, Teresa J.; Eswaran, Hari; Brochhausen, Mathias; Hanna, Josh; Hogan, William R.

2014-01-01

Social network analysis (SNA) helps us understand patterns of interaction between social entities. A number of SNA studies have shed light on the characteristics of research collaboration networks (RCNs). Especially, in the Clinical Translational Science Award (CTSA) community, SNA provides us a set of effective tools to quantitatively assess research collaborations and the impact of CTSA. However, descriptive network statistics are difficult for non-experts to understand. In this article, we present our experiences of building meaningful network visualizations to facilitate a series of visual analysis tasks. The basis of our design is multidimensional, visual aggregation of network dynamics. The resulting visualizations can help uncover hidden structures in the networks, elicit new observations of the network dynamics, compare different investigators and investigator groups, determine critical factors to the network evolution, and help direct further analyses. We applied our visualization techniques to explore the biomedical RCNs at the University of Arkansas for Medical Sciences – a CTSA institution. And, we created CollaborationViz, an open-source visual analytical tool to help network researchers and administration apprehend the network dynamics of research collaborations through interactive visualization. PMID:25405477

KOLAM: a cross-platform architecture for scalable visualization and tracking in wide-area imagery

NASA Astrophysics Data System (ADS)

Fraser, Joshua; Haridas, Anoop; Seetharaman, Guna; Rao, Raghuveer M.; Palaniappan, Kannappan

2013-05-01

KOLAM is an open, cross-platform, interoperable, scalable and extensible framework supporting a novel multi- scale spatiotemporal dual-cache data structure for big data visualization and visual analytics. This paper focuses on the use of KOLAM for target tracking in high-resolution, high throughput wide format video also known as wide-area motion imagery (WAMI). It was originally developed for the interactive visualization of extremely large geospatial imagery of high spatial and spectral resolution. KOLAM is platform, operating system and (graphics) hardware independent, and supports embedded datasets scalable from hundreds of gigabytes to feasibly petabytes in size on clusters, workstations, desktops and mobile computers. In addition to rapid roam, zoom and hyper- jump spatial operations, a large number of simultaneously viewable embedded pyramid layers (also referred to as multiscale or sparse imagery), interactive colormap and histogram enhancement, spherical projection and terrain maps are supported. The KOLAM software architecture was extended to support airborne wide-area motion imagery by organizing spatiotemporal tiles in very large format video frames using a temporal cache of tiled pyramid cached data structures. The current version supports WAMI animation, fast intelligent inspection, trajectory visualization and target tracking (digital tagging); the latter by interfacing with external automatic tracking software. One of the critical needs for working with WAMI is a supervised tracking and visualization tool that allows analysts to digitally tag multiple targets, quickly review and correct tracking results and apply geospatial visual analytic tools on the generated trajectories. One-click manual tracking combined with multiple automated tracking algorithms are available to assist the analyst and increase human effectiveness.

Visualization and Analytics Tools for Infectious Disease Epidemiology: A Systematic Review

PubMed Central

Carroll, Lauren N.; Au, Alan P.; Detwiler, Landon Todd; Fu, Tsung-chieh; Painter, Ian S.; Abernethy, Neil F.

2014-01-01

Background A myriad of new tools and algorithms have been developed to help public health professionals analyze and visualize the complex data used in infectious disease control. To better understand approaches to meet these users' information needs, we conducted a systematic literature review focused on the landscape of infectious disease visualization tools for public health professionals, with a special emphasis on geographic information systems (GIS), molecular epidemiology, and social network analysis. The objectives of this review are to: (1) Identify public health user needs and preferences for infectious disease information visualization tools; (2) Identify existing infectious disease information visualization tools and characterize their architecture and features; (3) Identify commonalities among approaches applied to different data types; and (4) Describe tool usability evaluation efforts and barriers to the adoption of such tools. Methods We identified articles published in English from January 1, 1980 to June 30, 2013 from five bibliographic databases. Articles with a primary focus on infectious disease visualization tools, needs of public health users, or usability of information visualizations were included in the review. Results A total of 88 articles met our inclusion criteria. Users were found to have diverse needs, preferences and uses for infectious disease visualization tools, and the existing tools are correspondingly diverse. The architecture of the tools was inconsistently described, and few tools in the review discussed the incorporation of usability studies or plans for dissemination. Many studies identified concerns regarding data sharing, confidentiality and quality. Existing tools offer a range of features and functions that allow users to explore, analyze, and visualize their data, but the tools are often for siloed applications. Commonly cited barriers to widespread adoption included lack of organizational support, access issues, and misconceptions about tool use. Discussion and Conclusion As the volume and complexity of infectious disease data increases, public health professionals must synthesize highly disparate data to facilitate communication with the public and inform decisions regarding measures to protect the public's health. Our review identified several themes: consideration of users' needs, preferences, and computer literacy; integration of tools into routine workflow; complications associated with understanding and use of visualizations; and the role of user trust and organizational support in the adoption of these tools. Interoperability also emerged as a prominent theme, highlighting challenges associated with the increasingly collaborative and interdisciplinary nature of infectious disease control and prevention. Future work should address methods for representing uncertainty and missing data to avoid misleading users as well as strategies to minimize cognitive overload. PMID:24747356

Visualization and analytics tools for infectious disease epidemiology: a systematic review.

PubMed

Carroll, Lauren N; Au, Alan P; Detwiler, Landon Todd; Fu, Tsung-Chieh; Painter, Ian S; Abernethy, Neil F

2014-10-01

A myriad of new tools and algorithms have been developed to help public health professionals analyze and visualize the complex data used in infectious disease control. To better understand approaches to meet these users' information needs, we conducted a systematic literature review focused on the landscape of infectious disease visualization tools for public health professionals, with a special emphasis on geographic information systems (GIS), molecular epidemiology, and social network analysis. The objectives of this review are to: (1) identify public health user needs and preferences for infectious disease information visualization tools; (2) identify existing infectious disease information visualization tools and characterize their architecture and features; (3) identify commonalities among approaches applied to different data types; and (4) describe tool usability evaluation efforts and barriers to the adoption of such tools. We identified articles published in English from January 1, 1980 to June 30, 2013 from five bibliographic databases. Articles with a primary focus on infectious disease visualization tools, needs of public health users, or usability of information visualizations were included in the review. A total of 88 articles met our inclusion criteria. Users were found to have diverse needs, preferences and uses for infectious disease visualization tools, and the existing tools are correspondingly diverse. The architecture of the tools was inconsistently described, and few tools in the review discussed the incorporation of usability studies or plans for dissemination. Many studies identified concerns regarding data sharing, confidentiality and quality. Existing tools offer a range of features and functions that allow users to explore, analyze, and visualize their data, but the tools are often for siloed applications. Commonly cited barriers to widespread adoption included lack of organizational support, access issues, and misconceptions about tool use. As the volume and complexity of infectious disease data increases, public health professionals must synthesize highly disparate data to facilitate communication with the public and inform decisions regarding measures to protect the public's health. Our review identified several themes: consideration of users' needs, preferences, and computer literacy; integration of tools into routine workflow; complications associated with understanding and use of visualizations; and the role of user trust and organizational support in the adoption of these tools. Interoperability also emerged as a prominent theme, highlighting challenges associated with the increasingly collaborative and interdisciplinary nature of infectious disease control and prevention. Future work should address methods for representing uncertainty and missing data to avoid misleading users as well as strategies to minimize cognitive overload. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Oak Ridge Bio-surveillance Toolkit (ORBiT): Integrating Big-Data Analytics with Visual Analysis for Public Health Dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramanathan, Arvind; Pullum, Laura L; Steed, Chad A

In this position paper, we describe the design and implementation of the Oak Ridge Bio-surveillance Toolkit (ORBiT): a collection of novel statistical and machine learning tools implemented for (1) integrating heterogeneous traditional (e.g. emergency room visits, prescription sales data, etc.) and non-traditional (social media such as Twitter and Instagram) data sources, (2) analyzing large-scale datasets and (3) presenting the results from the analytics as a visual interface for the end-user to interact and provide feedback. We present examples of how ORBiT can be used to summarize ex- tremely large-scale datasets effectively and how user interactions can translate into the datamore » analytics process for bio-surveillance. We also present a strategy to estimate parameters relevant to dis- ease spread models from near real time data feeds and show how these estimates can be integrated with disease spread models for large-scale populations. We conclude with a perspective on how integrating data and visual analytics could lead to better forecasting and prediction of disease spread as well as improved awareness of disease susceptible regions.« less

An interactive visualization tool for mobile objects

NASA Astrophysics Data System (ADS)

Kobayashi, Tetsuo

Recent advancements in mobile devices---such as Global Positioning System (GPS), cellular phones, car navigation system, and radio-frequency identification (RFID)---have greatly influenced the nature and volume of data about individual-based movement in space and time. Due to the prevalence of mobile devices, vast amounts of mobile objects data are being produced and stored in databases, overwhelming the capacity of traditional spatial analytical methods. There is a growing need for discovering unexpected patterns, trends, and relationships that are hidden in the massive mobile objects data. Geographic visualization (GVis) and knowledge discovery in databases (KDD) are two major research fields that are associated with knowledge discovery and construction. Their major research challenges are the integration of GVis and KDD, enhancing the ability to handle large volume mobile objects data, and high interactivity between the computer and users of GVis and KDD tools. This dissertation proposes a visualization toolkit to enable highly interactive visual data exploration for mobile objects datasets. Vector algebraic representation and online analytical processing (OLAP) are utilized for managing and querying the mobile object data to accomplish high interactivity of the visualization tool. In addition, reconstructing trajectories at user-defined levels of temporal granularity with time aggregation methods allows exploration of the individual objects at different levels of movement generality. At a given level of generality, individual paths can be combined into synthetic summary paths based on three similarity measures, namely, locational similarity, directional similarity, and geometric similarity functions. A visualization toolkit based on the space-time cube concept exploits these functionalities to create a user-interactive environment for exploring mobile objects data. Furthermore, the characteristics of visualized trajectories are exported to be utilized for data mining, which leads to the integration of GVis and KDD. Case studies using three movement datasets (personal travel data survey in Lexington, Kentucky, wild chicken movement data in Thailand, and self-tracking data in Utah) demonstrate the potential of the system to extract meaningful patterns from the otherwise difficult to comprehend collections of space-time trajectories.

LitPathExplorer: a confidence-based visual text analytics tool for exploring literature-enriched pathway models.

PubMed

Soto, Axel J; Zerva, Chrysoula; Batista-Navarro, Riza; Ananiadou, Sophia

2018-04-15

Pathway models are valuable resources that help us understand the various mechanisms underpinning complex biological processes. Their curation is typically carried out through manual inspection of published scientific literature to find information relevant to a model, which is a laborious and knowledge-intensive task. Furthermore, models curated manually cannot be easily updated and maintained with new evidence extracted from the literature without automated support. We have developed LitPathExplorer, a visual text analytics tool that integrates advanced text mining, semi-supervised learning and interactive visualization, to facilitate the exploration and analysis of pathway models using statements (i.e. events) extracted automatically from the literature and organized according to levels of confidence. LitPathExplorer supports pathway modellers and curators alike by: (i) extracting events from the literature that corroborate existing models with evidence; (ii) discovering new events which can update models; and (iii) providing a confidence value for each event that is automatically computed based on linguistic features and article metadata. Our evaluation of event extraction showed a precision of 89% and a recall of 71%. Evaluation of our confidence measure, when used for ranking sampled events, showed an average precision ranging between 61 and 73%, which can be improved to 95% when the user is involved in the semi-supervised learning process. Qualitative evaluation using pair analytics based on the feedback of three domain experts confirmed the utility of our tool within the context of pathway model exploration. LitPathExplorer is available at http://nactem.ac.uk/LitPathExplorer_BI/. sophia.ananiadou@manchester.ac.uk. Supplementary data are available at Bioinformatics online.

ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology[OPEN

PubMed Central

Waese, Jamie; Fan, Jim; Yu, Hans; Fucile, Geoffrey; Shi, Ruian; Cumming, Matthew; Town, Chris; Stuerzlinger, Wolfgang

2017-01-01

A big challenge in current systems biology research arises when different types of data must be accessed from separate sources and visualized using separate tools. The high cognitive load required to navigate such a workflow is detrimental to hypothesis generation. Accordingly, there is a need for a robust research platform that incorporates all data and provides integrated search, analysis, and visualization features through a single portal. Here, we present ePlant (http://bar.utoronto.ca/eplant), a visual analytic tool for exploring multiple levels of Arabidopsis thaliana data through a zoomable user interface. ePlant connects to several publicly available web services to download genome, proteome, interactome, transcriptome, and 3D molecular structure data for one or more genes or gene products of interest. Data are displayed with a set of visualization tools that are presented using a conceptual hierarchy from big to small, and many of the tools combine information from more than one data type. We describe the development of ePlant in this article and present several examples illustrating its integrative features for hypothesis generation. We also describe the process of deploying ePlant as an “app” on Araport. Building on readily available web services, the code for ePlant is freely available for any other biological species research. PMID:28808136

EINVis: a visualization tool for analyzing and exploring genetic interactions in large-scale association studies.

PubMed

Wu, Yubao; Zhu, Xiaofeng; Chen, Jian; Zhang, Xiang

2013-11-01

Epistasis (gene-gene interaction) detection in large-scale genetic association studies has recently drawn extensive research interests as many complex traits are likely caused by the joint effect of multiple genetic factors. The large number of possible interactions poses both statistical and computational challenges. A variety of approaches have been developed to address the analytical challenges in epistatic interaction detection. These methods usually output the identified genetic interactions and store them in flat file formats. It is highly desirable to develop an effective visualization tool to further investigate the detected interactions and unravel hidden interaction patterns. We have developed EINVis, a novel visualization tool that is specifically designed to analyze and explore genetic interactions. EINVis displays interactions among genetic markers as a network. It utilizes a circular layout (specially, a tree ring view) to simultaneously visualize the hierarchical interactions between single nucleotide polymorphisms (SNPs), genes, and chromosomes, and the network structure formed by these interactions. Using EINVis, the user can distinguish marginal effects from interactions, track interactions involving more than two markers, visualize interactions at different levels, and detect proxy SNPs based on linkage disequilibrium. EINVis is an effective and user-friendly free visualization tool for analyzing and exploring genetic interactions. It is publicly available with detailed documentation and online tutorial on the web at http://filer.case.edu/yxw407/einvis/. © 2013 WILEY PERIODICALS, INC.

ENVIRONMENTAL SYSTEMS MANAGEMENT AS APPLIED TO WATERSHEDS, UTILIZING REMOTE SENSING, DECISION SUPPORT AND VISUALIZATION

EPA Science Inventory

Environmental Systems Management as a conceptual framework and as a set of interdisciplinary analytical approaches will be described within the context of sustainable watershed management, within devergent complex ecosystems. A specific subset of integrated tools are deployed to...

In situ visualization and data analysis for turbidity currents simulation

NASA Astrophysics Data System (ADS)

Camata, Jose J.; Silva, Vítor; Valduriez, Patrick; Mattoso, Marta; Coutinho, Alvaro L. G. A.

2018-01-01

Turbidity currents are underflows responsible for sediment deposits that generate geological formations of interest for the oil and gas industry. LibMesh-sedimentation is an application built upon the libMesh library to simulate turbidity currents. In this work, we present the integration of libMesh-sedimentation with in situ visualization and in transit data analysis tools. DfAnalyzer is a solution based on provenance data to extract and relate strategic simulation data in transit from multiple data for online queries. We integrate libMesh-sedimentation and ParaView Catalyst to perform in situ data analysis and visualization. We present a parallel performance analysis for two turbidity currents simulations showing that the overhead for both in situ visualization and in transit data analysis is negligible. We show that our tools enable monitoring the sediments appearance at runtime and steer the simulation based on the solver convergence and visual information on the sediment deposits, thus enhancing the analytical power of turbidity currents simulations.

Multi-focus and multi-level techniques for visualization and analysis of networks with thematic data

NASA Astrophysics Data System (ADS)

Cossalter, Michele; Mengshoel, Ole J.; Selker, Ted

2013-01-01

Information-rich data sets bring several challenges in the areas of visualization and analysis, even when associated with node-link network visualizations. This paper presents an integration of multi-focus and multi-level techniques that enable interactive, multi-step comparisons in node-link networks. We describe NetEx, a visualization tool that enables users to simultaneously explore different parts of a network and its thematic data, such as time series or conditional probability tables. NetEx, implemented as a Cytoscape plug-in, has been applied to the analysis of electrical power networks, Bayesian networks, and the Enron e-mail repository. In this paper we briefly discuss visualization and analysis of the Enron social network, but focus on data from an electrical power network. Specifically, we demonstrate how NetEx supports the analytical task of electrical power system fault diagnosis. Results from a user study with 25 subjects suggest that NetEx enables more accurate isolation of complex faults compared to an especially designed software tool.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A

Interactive data visualization leverages human visual perception and cognition to improve the accuracy and effectiveness of data analysis. When combined with automated data analytics, data visualization systems orchestrate the strengths of humans with the computational power of machines to solve problems neither approach can manage in isolation. In the intelligent transportation system domain, such systems are necessary to support decision making in large and complex data streams. In this chapter, we provide an introduction to several key topics related to the design of data visualization systems. In addition to an overview of key techniques and strategies, we will describe practicalmore » design principles. The chapter is concluded with a detailed case study involving the design of a multivariate visualization tool.« less

Open-source web-enabled data management, analyses, and visualization of very large data in geosciences using Jupyter, Apache Spark, and community tools

NASA Astrophysics Data System (ADS)

Chaudhary, A.

2017-12-01

Current simulation models and sensors are producing high-resolution, high-velocity data in geosciences domain. Knowledge discovery from these complex and large size datasets require tools that are capable of handling very large data and providing interactive data analytics features to researchers. To this end, Kitware and its collaborators are producing open-source tools GeoNotebook, GeoJS, Gaia, and Minerva for geosciences that are using hardware accelerated graphics and advancements in parallel and distributed processing (Celery and Apache Spark) and can be loosely coupled to solve real-world use-cases. GeoNotebook (https://github.com/OpenGeoscience/geonotebook) is co-developed by Kitware and NASA-Ames and is an extension to the Jupyter Notebook. It provides interactive visualization and python-based analysis of geospatial data and depending the backend (KTile or GeoPySpark) can handle data sizes of Hundreds of Gigabytes to Terabytes. GeoNotebook uses GeoJS (https://github.com/OpenGeoscience/geojs) to render very large geospatial data on the map using WebGL and Canvas2D API. GeoJS is more than just a GIS library as users can create scientific plots such as vector and contour and can embed InfoVis plots using D3.js. GeoJS aims for high-performance visualization and interactive data exploration of scientific and geospatial location aware datasets and supports features such as Point, Line, Polygon, and advanced features such as Pixelmap, Contour, Heatmap, and Choropleth. Our another open-source tool Minerva ((https://github.com/kitware/minerva) is a geospatial application that is built on top of open-source web-based data management system Girder (https://github.com/girder/girder) which provides an ability to access data from HDFS or Amazon S3 buckets and provides capabilities to perform visualization and analyses on geosciences data in a web environment using GDAL and GeoPandas wrapped in a unified API provided by Gaia (https://github.com/OpenDataAnalytics/gaia). In this presentation, we will discuss core features of each of these tools and will present lessons learned on handling large data in the context of data management, analyses and visualization.

Giving raw data a chance to talk: a demonstration of exploratory visual analytics with a pediatric research database using Microsoft Live Labs Pivot to promote cohort discovery, research, and quality assessment.

PubMed

Viangteeravat, Teeradache; Nagisetty, Naga Satya V Rao

2014-01-01

Secondary use of large and open data sets provides researchers with an opportunity to address high-impact questions that would otherwise be prohibitively expensive and time consuming to study. Despite the availability of data, generating hypotheses from huge data sets is often challenging, and the lack of complex analysis of data might lead to weak hypotheses. To overcome these issues and to assist researchers in building hypotheses from raw data, we are working on a visual and analytical platform called PRD Pivot. PRD Pivot is a de-identified pediatric research database designed to make secondary use of rich data sources, such as the electronic health record (EHR). The development of visual analytics using Microsoft Live Labs Pivot makes the process of data elaboration, information gathering, knowledge generation, and complex information exploration transparent to tool users and provides researchers with the ability to sort and filter by various criteria, which can lead to strong, novel hypotheses.

Giving Raw Data a Chance to Talk: A Demonstration of Exploratory Visual Analytics with a Pediatric Research Database Using Microsoft Live Labs Pivot to Promote Cohort Discovery, Research, and Quality Assessment

PubMed Central

Viangteeravat, Teeradache; Nagisetty, Naga Satya V. Rao

2014-01-01

Secondary use of large and open data sets provides researchers with an opportunity to address high-impact questions that would otherwise be prohibitively expensive and time consuming to study. Despite the availability of data, generating hypotheses from huge data sets is often challenging, and the lack of complex analysis of data might lead to weak hypotheses. To overcome these issues and to assist researchers in building hypotheses from raw data, we are working on a visual and analytical platform called PRD Pivot. PRD Pivot is a de-identified pediatric research database designed to make secondary use of rich data sources, such as the electronic health record (EHR). The development of visual analytics using Microsoft Live Labs Pivot makes the process of data elaboration, information gathering, knowledge generation, and complex information exploration transparent to tool users and provides researchers with the ability to sort and filter by various criteria, which can lead to strong, novel hypotheses. PMID:24808811

Holmes: a graphical tool for development, simulation and analysis of Petri net based models of complex biological systems.

PubMed

Radom, Marcin; Rybarczyk, Agnieszka; Szawulak, Bartlomiej; Andrzejewski, Hubert; Chabelski, Piotr; Kozak, Adam; Formanowicz, Piotr

2017-12-01

Model development and its analysis is a fundamental step in systems biology. The theory of Petri nets offers a tool for such a task. Since the rapid development of computer science, a variety of tools for Petri nets emerged, offering various analytical algorithms. From this follows a problem of using different programs to analyse a single model. Many file formats and different representations of results make the analysis much harder. Especially for larger nets the ability to visualize the results in a proper form provides a huge help in the understanding of their significance. We present a new tool for Petri nets development and analysis called Holmes. Our program contains algorithms for model analysis based on different types of Petri nets, e.g. invariant generator, Maximum Common Transitions (MCT) sets and cluster modules, simulation algorithms or knockout analysis tools. A very important feature is the ability to visualize the results of almost all analytical modules. The integration of such modules into one graphical environment allows a researcher to fully devote his or her time to the model building and analysis. Available at http://www.cs.put.poznan.pl/mradom/Holmes/holmes.html. piotr@cs.put.poznan.pl. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Visual analytics for semantic queries of TerraSAR-X image content

NASA Astrophysics Data System (ADS)

Espinoza-Molina, Daniela; Alonso, Kevin; Datcu, Mihai

2015-10-01

With the continuous image product acquisition of satellite missions, the size of the image archives is considerably increasing every day as well as the variety and complexity of their content, surpassing the end-user capacity to analyse and exploit them. Advances in the image retrieval field have contributed to the development of tools for interactive exploration and extraction of the images from huge archives using different parameters like metadata, key-words, and basic image descriptors. Even though we count on more powerful tools for automated image retrieval and data analysis, we still face the problem of understanding and analyzing the results. Thus, a systematic computational analysis of these results is required in order to provide to the end-user a summary of the archive content in comprehensible terms. In this context, visual analytics combines automated analysis with interactive visualizations analysis techniques for an effective understanding, reasoning and decision making on the basis of very large and complex datasets. Moreover, currently several researches are focused on associating the content of the images with semantic definitions for describing the data in a format to be easily understood by the end-user. In this paper, we present our approach for computing visual analytics and semantically querying the TerraSAR-X archive. Our approach is mainly composed of four steps: 1) the generation of a data model that explains the information contained in a TerraSAR-X product. The model is formed by primitive descriptors and metadata entries, 2) the storage of this model in a database system, 3) the semantic definition of the image content based on machine learning algorithms and relevance feedback, and 4) querying the image archive using semantic descriptors as query parameters and computing the statistical analysis of the query results. The experimental results shows that with the help of visual analytics and semantic definitions we are able to explain the image content using semantic terms and the relations between them answering questions such as what is the percentage of urban area in a region? or what is the distribution of water bodies in a city?

The geospatial modeling interface (GMI) framework for deploying and assessing environmental models

USDA-ARS?s Scientific Manuscript database

Geographical information systems (GIS) software packages have been used for close to three decades as analytical tools in environmental management for geospatial data assembly, processing, storage, and visualization of input data and model output. However, with increasing availability and use of ful...

Visualizing statistical significance of disease clusters using cartograms.

PubMed

Kronenfeld, Barry J; Wong, David W S

2017-05-15

Health officials and epidemiological researchers often use maps of disease rates to identify potential disease clusters. Because these maps exaggerate the prominence of low-density districts and hide potential clusters in urban (high-density) areas, many researchers have used density-equalizing maps (cartograms) as a basis for epidemiological mapping. However, we do not have existing guidelines for visual assessment of statistical uncertainty. To address this shortcoming, we develop techniques for visual determination of statistical significance of clusters spanning one or more districts on a cartogram. We developed the techniques within a geovisual analytics framework that does not rely on automated significance testing, and can therefore facilitate visual analysis to detect clusters that automated techniques might miss. On a cartogram of the at-risk population, the statistical significance of a disease cluster is determinate from the rate, area and shape of the cluster under standard hypothesis testing scenarios. We develop formulae to determine, for a given rate, the area required for statistical significance of a priori and a posteriori designated regions under certain test assumptions. Uniquely, our approach enables dynamic inference of aggregate regions formed by combining individual districts. The method is implemented in interactive tools that provide choropleth mapping, automated legend construction and dynamic search tools to facilitate cluster detection and assessment of the validity of tested assumptions. A case study of leukemia incidence analysis in California demonstrates the ability to visually distinguish between statistically significant and insignificant regions. The proposed geovisual analytics approach enables intuitive visual assessment of statistical significance of arbitrarily defined regions on a cartogram. Our research prompts a broader discussion of the role of geovisual exploratory analyses in disease mapping and the appropriate framework for visually assessing the statistical significance of spatial clusters.

LipidQC: Method Validation Tool for Visual Comparison to SRM 1950 Using NIST Interlaboratory Comparison Exercise Lipid Consensus Mean Estimate Values.

PubMed

Ulmer, Candice Z; Ragland, Jared M; Koelmel, Jeremy P; Heckert, Alan; Jones, Christina M; Garrett, Timothy J; Yost, Richard A; Bowden, John A

2017-12-19

As advances in analytical separation techniques, mass spectrometry instrumentation, and data processing platforms continue to spur growth in the lipidomics field, more structurally unique lipid species are detected and annotated. The lipidomics community is in need of benchmark reference values to assess the validity of various lipidomics workflows in providing accurate quantitative measurements across the diverse lipidome. LipidQC addresses the harmonization challenge in lipid quantitation by providing a semiautomated process, independent of analytical platform, for visual comparison of experimental results of National Institute of Standards and Technology Standard Reference Material (SRM) 1950, "Metabolites in Frozen Human Plasma", against benchmark consensus mean concentrations derived from the NIST Lipidomics Interlaboratory Comparison Exercise.

Visualizing Cloud Properties and Satellite Imagery: A Tool for Visualization and Information Integration

NASA Astrophysics Data System (ADS)

Chee, T.; Nguyen, L.; Smith, W. L., Jr.; Spangenberg, D.; Palikonda, R.; Bedka, K. M.; Minnis, P.; Thieman, M. M.; Nordeen, M.

2017-12-01

Providing public access to research products including cloud macro and microphysical properties and satellite imagery are a key concern for the NASA Langley Research Center Cloud and Radiation Group. This work describes a web based visualization tool and API that allows end users to easily create customized cloud product and satellite imagery, ground site data and satellite ground track information that is generated dynamically. The tool has two uses, one to visualize the dynamically created imagery and the other to provide access to the dynamically generated imagery directly at a later time. Internally, we leverage our practical experience with large, scalable application practices to develop a system that has the largest potential for scalability as well as the ability to be deployed on the cloud to accommodate scalability issues. We build upon NASA Langley Cloud and Radiation Group's experience with making real-time and historical satellite cloud product information, satellite imagery, ground site data and satellite track information accessible and easily searchable. This tool is the culmination of our prior experience with dynamic imagery generation and provides a way to build a "mash-up" of dynamically generated imagery and related kinds of information that are visualized together to add value to disparate but related information. In support of NASA strategic goals, our group aims to make as much scientific knowledge, observations and products available to the citizen science, research and interested communities as well as for automated systems to acquire the same information for data mining or other analytic purposes. This tool and the underlying API's provide a valuable research tool to a wide audience both as a standalone research tool and also as an easily accessed data source that can easily be mined or used with existing tools.

Query2Question: Translating Visualization Interaction into Natural Language.

PubMed

Nafari, Maryam; Weaver, Chris

2015-06-01

Richly interactive visualization tools are increasingly popular for data exploration and analysis in a wide variety of domains. Existing systems and techniques for recording provenance of interaction focus either on comprehensive automated recording of low-level interaction events or on idiosyncratic manual transcription of high-level analysis activities. In this paper, we present the architecture and translation design of a query-to-question (Q2Q) system that automatically records user interactions and presents them semantically using natural language (written English). Q2Q takes advantage of domain knowledge and uses natural language generation (NLG) techniques to translate and transcribe a progression of interactive visualization states into a visual log of styled text that complements and effectively extends the functionality of visualization tools. We present Q2Q as a means to support a cross-examination process in which questions rather than interactions are the focus of analytic reasoning and action. We describe the architecture and implementation of the Q2Q system, discuss key design factors and variations that effect question generation, and present several visualizations that incorporate Q2Q for analysis in a variety of knowledge domains.

The role of 3-D interactive visualization in blind surveys of H I in galaxies

NASA Astrophysics Data System (ADS)

Punzo, D.; van der Hulst, J. M.; Roerdink, J. B. T. M.; Oosterloo, T. A.; Ramatsoku, M.; Verheijen, M. A. W.

2015-09-01

Upcoming H I surveys will deliver large datasets, and automated processing using the full 3-D information (two positional dimensions and one spectral dimension) to find and characterize H I objects is imperative. In this context, visualization is an essential tool for enabling qualitative and quantitative human control on an automated source finding and analysis pipeline. We discuss how Visual Analytics, the combination of automated data processing and human reasoning, creativity and intuition, supported by interactive visualization, enables flexible and fast interaction with the 3-D data, helping the astronomer to deal with the analysis of complex sources. 3-D visualization, coupled to modeling, provides additional capabilities helping the discovery and analysis of subtle structures in the 3-D domain. The requirements for a fully interactive visualization tool are: coupled 1-D/2-D/3-D visualization, quantitative and comparative capabilities, combined with supervised semi-automated analysis. Moreover, the source code must have the following characteristics for enabling collaborative work: open, modular, well documented, and well maintained. We review four state of-the-art, 3-D visualization packages assessing their capabilities and feasibility for use in the case of 3-D astronomical data.

The Design and Analysis of Electrically Large Custom-Shaped Reflector Antennas

DTIC Science & Technology

2013-06-01

GEO) satellite data are imported into STK and plotted to visualize the regions of the sky that the spherical reflector must have line of sight for...Magnetic Conductor PO Physical Optics STK Systems Tool Kit TE Transverse Electric xvii Acronym Definition TLE Two Line Element TM Transverse Magnetic...study for the spherical reflector, Systems Tool Kit ( STK ) software from Analytical Graphics Inc. (AGI) is used. In completing the cross-shaped

Visualization of Twitter Data in the Classroom

ERIC Educational Resources Information Center

Sigman, Betsy Page; Garr, William; Pongsajapan, Robert; Selvanadin, Marie; McWilliams, Mindy; Bolling, Kristin

2016-01-01

The expression "big data" is ubiquitous in the business world today, but few undergraduate business students have the opportunity to gain practical experience with how new business analytics tools can be used in decision making. This article describes a set of hands-on labs that prepare students to incorporate streaming data analysis…

A robust and flexible Geospatial Modeling Interface (GMI) for deploying and evaluating natural resource models

USDA-ARS?s Scientific Manuscript database

Geographical information systems (GIS) software packages have been used for nearly three decades as analytical tools in natural resource management for geospatial data assembly, processing, storage, and visualization of input data and model output. However, with increasing availability and use of fu...

Microwave Workshop for Windows.

ERIC Educational Resources Information Center

White, Colin

1998-01-01

"Microwave Workshop for Windows" consists of three programs that act as teaching aid and provide a circuit design utility within the field of microwave engineering. The first program is a computer representation of a graphical design tool; the second is an accurate visual and analytical representation of a microwave test bench; the third…

Harnessing Scientific Literature Reports for Pharmacovigilance

PubMed Central

Ripple, Anna; Tonning, Joseph; Munoz, Monica; Hasan, Rashedul; Ly, Thomas; Francis, Henry; Bodenreider, Olivier

2017-01-01

Summary Objectives We seek to develop a prototype software analytical tool to augment FDA regulatory reviewers’ capacity to harness scientific literature reports in PubMed/MEDLINE for pharmacovigilance and adverse drug event (ADE) safety signal detection. We also aim to gather feedback through usability testing to assess design, performance, and user satisfaction with the tool. Methods A prototype, open source, web-based, software analytical tool generated statistical disproportionality data mining signal scores and dynamic visual analytics for ADE safety signal detection and management. We leveraged Medical Subject Heading (MeSH) indexing terms assigned to published citations in PubMed/MEDLINE to generate candidate drug-adverse event pairs for quantitative data mining. Six FDA regulatory reviewers participated in usability testing by employing the tool as part of their ongoing real-life pharmacovigilance activities to provide subjective feedback on its practical impact, added value, and fitness for use. Results All usability test participants cited the tool’s ease of learning, ease of use, and generation of quantitative ADE safety signals, some of which corresponded to known established adverse drug reactions. Potential concerns included the comparability of the tool’s automated literature search relative to a manual ‘all fields’ PubMed search, missing drugs and adverse event terms, interpretation of signal scores, and integration with existing computer-based analytical tools. Conclusions Usability testing demonstrated that this novel tool can automate the detection of ADE safety signals from published literature reports. Various mitigation strategies are described to foster improvements in design, productivity, and end user satisfaction. PMID:28326432

ANALYTiC: An Active Learning System for Trajectory Classification.

PubMed

Soares Junior, Amilcar; Renso, Chiara; Matwin, Stan

2017-01-01

The increasing availability and use of positioning devices has resulted in large volumes of trajectory data. However, semantic annotations for such data are typically added by domain experts, which is a time-consuming task. Machine-learning algorithms can help infer semantic annotations from trajectory data by learning from sets of labeled data. Specifically, active learning approaches can minimize the set of trajectories to be annotated while preserving good performance measures. The ANALYTiC web-based interactive tool visually guides users through this annotation process.

An Affordance-Based Framework for Human Computation and Human-Computer Collaboration.

PubMed

Crouser, R J; Chang, R

2012-12-01

Visual Analytics is "the science of analytical reasoning facilitated by visual interactive interfaces". The goal of this field is to develop tools and methodologies for approaching problems whose size and complexity render them intractable without the close coupling of both human and machine analysis. Researchers have explored this coupling in many venues: VAST, Vis, InfoVis, CHI, KDD, IUI, and more. While there have been myriad promising examples of human-computer collaboration, there exists no common language for comparing systems or describing the benefits afforded by designing for such collaboration. We argue that this area would benefit significantly from consensus about the design attributes that define and distinguish existing techniques. In this work, we have reviewed 1,271 papers from many of the top-ranking conferences in visual analytics, human-computer interaction, and visualization. From these, we have identified 49 papers that are representative of the study of human-computer collaborative problem-solving, and provide a thorough overview of the current state-of-the-art. Our analysis has uncovered key patterns of design hinging on human and machine-intelligence affordances, and also indicates unexplored avenues in the study of this area. The results of this analysis provide a common framework for understanding these seemingly disparate branches of inquiry, which we hope will motivate future work in the field.

A Data-Driven Approach to Interactive Visualization of Power Grids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Jun

Driven by emerging industry standards, electric utilities and grid coordination organizations are eager to seek advanced tools to assist grid operators to perform mission-critical tasks and enable them to make quick and accurate decisions. The emerging field of visual analytics holds tremendous promise for improving the business practices in today’s electric power industry. The conducted investigation, however, has revealed that the existing commercial power grid visualization tools heavily rely on human designers, hindering user’s ability to discover. Additionally, for a large grid, it is very labor-intensive and costly to build and maintain the pre-designed visual displays. This project proposes amore » data-driven approach to overcome the common challenges. The proposed approach relies on developing powerful data manipulation algorithms to create visualizations based on the characteristics of empirically or mathematically derived data. The resulting visual presentations emphasize what the data is rather than how the data should be presented, thus fostering comprehension and discovery. Furthermore, the data-driven approach formulates visualizations on-the-fly. It does not require a visualization design stage, completely eliminating or significantly reducing the cost for building and maintaining visual displays. The research and development (R&D) conducted in this project is mainly divided into two phases. The first phase (Phase I & II) focuses on developing data driven techniques for visualization of power grid and its operation. Various data-driven visualization techniques were investigated, including pattern recognition for auto-generation of one-line diagrams, fuzzy model based rich data visualization for situational awareness, etc. The R&D conducted during the second phase (Phase IIB) focuses on enhancing the prototyped data driven visualization tool based on the gathered requirements and use cases. The goal is to evolve the prototyped tool developed during the first phase into a commercial grade product. We will use one of the identified application areas as an example to demonstrate how research results achieved in this project are successfully utilized to address an emerging industry need. In summary, the data-driven visualization approach developed in this project has proven to be promising for building the next-generation power grid visualization tools. Application of this approach has resulted in a state-of-the-art commercial tool currently being leveraged by more than 60 utility organizations in North America and Europe .« less

Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics.

PubMed

Stolper, Charles D; Perer, Adam; Gotz, David

2014-12-01

As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics paradigm; design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics paradigm by clinical researchers analyzing electronic medical records.

ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology.

PubMed

Waese, Jamie; Fan, Jim; Pasha, Asher; Yu, Hans; Fucile, Geoffrey; Shi, Ruian; Cumming, Matthew; Kelley, Lawrence A; Sternberg, Michael J; Krishnakumar, Vivek; Ferlanti, Erik; Miller, Jason; Town, Chris; Stuerzlinger, Wolfgang; Provart, Nicholas J

2017-08-01

A big challenge in current systems biology research arises when different types of data must be accessed from separate sources and visualized using separate tools. The high cognitive load required to navigate such a workflow is detrimental to hypothesis generation. Accordingly, there is a need for a robust research platform that incorporates all data and provides integrated search, analysis, and visualization features through a single portal. Here, we present ePlant (http://bar.utoronto.ca/eplant), a visual analytic tool for exploring multiple levels of Arabidopsis thaliana data through a zoomable user interface. ePlant connects to several publicly available web services to download genome, proteome, interactome, transcriptome, and 3D molecular structure data for one or more genes or gene products of interest. Data are displayed with a set of visualization tools that are presented using a conceptual hierarchy from big to small, and many of the tools combine information from more than one data type. We describe the development of ePlant in this article and present several examples illustrating its integrative features for hypothesis generation. We also describe the process of deploying ePlant as an "app" on Araport. Building on readily available web services, the code for ePlant is freely available for any other biological species research. © 2017 American Society of Plant Biologists. All rights reserved.

Big data analytics in immunology: a knowledge-based approach.

PubMed

Zhang, Guang Lan; Sun, Jing; Chitkushev, Lou; Brusic, Vladimir

2014-01-01

With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow.

Dynamic Visualization of Co-expression in Systems Genetics Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

New, Joshua Ryan; Huang, Jian; Chesler, Elissa J

2008-01-01

Biologists hope to address grand scientific challenges by exploring the abundance of data made available through modern microarray technology and other high-throughput techniques. The impact of this data, however, is limited unless researchers can effectively assimilate such complex information and integrate it into their daily research; interactive visualization tools are called for to support the effort. Specifically, typical studies of gene co-expression require novel visualization tools that enable the dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters. These tools should allow biologists to develop an intuitive understanding of the structure of biologicalmore » networks and discover genes which reside in critical positions in networks and pathways. By using a graph as a universal data representation of correlation in gene expression data, our novel visualization tool employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool for interacting with gene co-expression data integrates techniques such as: graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by querying an optimized b-tree, dynamic level-of-detail graph abstraction, and template-based fuzzy classification using neural networks. We demonstrate our system using a real-world workflow from a large-scale, systems genetics study of mammalian gene co-expression.« less

BMDExpress Data Viewer: A Visualization Tool to Analyze ...

EPA Pesticide Factsheets

Regulatory agencies increasingly apply benchmark dose (BMD) modeling to determine points of departure in human risk assessments. BMDExpress applies BMD modeling to transcriptomics datasets and groups genes to biological processes and pathways for rapid assessment of doses at which biological perturbations occur. However, graphing and analytical capabilities within BMDExpress are limited, and the analysis of output files is challenging. We developed a web-based application, BMDExpress Data Viewer, for visualization and graphical analyses of BMDExpress output files. The software application consists of two main components: ‘Summary Visualization Tools’ and ‘Dataset Exploratory Tools’. We demonstrate through two case studies that the ‘Summary Visualization Tools’ can be used to examine and assess the distributions of probe and pathway BMD outputs, as well as derive a potential regulatory BMD through the modes or means of the distributions. The ‘Functional Enrichment Analysis’ tool presents biological processes in a two-dimensional bubble chart view. By applying filters of pathway enrichment p-value and minimum number of significant genes, we showed that the Functional Enrichment Analysis tool can be applied to select pathways that are potentially sensitive to chemical perturbations. The ‘Multiple Dataset Comparison’ tool enables comparison of BMDs across multiple experiments (e.g., across time points, tissues, or organisms, etc.). The ‘BMDL-BM

2D-Visualization of metabolic activity with planar optical chemical sensors (optodes)

NASA Astrophysics Data System (ADS)

Meier, R. J.; Liebsch, G.

2015-12-01

Microbia plays an outstandingly important role in many hydrologic compartments, such as e.g. the benthic community in sediments, or biologically active microorganisms in the capillary fringe, in ground water, or soil. Oxygen, pH, and CO2 are key factors and indicators for microbial activity. They can be measured using optical chemical sensors. These sensors record changing fluorescence properties of specific indicator dyes. The signals can be measured in a non-contact mode, even through transparent walls, which is important for many lab-experiments. They can measure in closed (transparent) systems, without sampling or intruding into the sample. They do not consume the analytes while measuring, are fully reversible and able to measure in non-stirred solutions. These sensors can be applied as high precision fiberoptic sensors (for profiling), robust sensor spots, or as planar sensors for 2D visualization (imaging). Imaging enables to detect thousands of measurement spots at the same time and generate 2D analyte maps over a region of interest. It allows for comparing different regions within one recorded image, visualizing spatial analyte gradients, or more important to identify hot spots of metabolic activity. We present ready-to-use portable imaging systems for the analytes oxygen, pH, and CO2. They consist of a detector unit, planar sensor foils and a software for easy data recording and evaluation. Sensors foils for various analytes and measurement ranges enable visualizing metabolic activity or analyte changes in the desired range. Dynamics of metabolic activity can be detected in one shot or over long time periods. We demonstrate the potential of this analytical technique by presenting experiments on benthic disturbance-recovery dynamics in sediments and microbial degradation of organic material in the capillary fringe. We think this technique is a new tool to further understand how microbial and geochemical processes are linked in (not solely) hydrologic systems.

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

PubMed Central

Satagopam, Venkata; Gu, Wei; Eifes, Serge; Gawron, Piotr; Ostaszewski, Marek; Gebel, Stephan; Barbosa-Silva, Adriano; Balling, Rudi; Schneider, Reinhard

2016-01-01

Abstract Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services—tranSMART, a Galaxy Server, and a MINERVA platform—are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data. PMID:27441714

The VAST Challenge: History, Scope, and Outcomes: An introduction to the Special Issue

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, Kristin A.; Grinstein, Georges; Whiting, Mark A.

2014-10-01

Visual analytics aims to facilitate human insight from complex data via a combination of visual representations, interaction techniques, and supporting algorithms. To create new tools and techniques that achieve this goal requires that researchers have an understanding of analytical questions to be addressed, data that illustrates the complexities and ambiguities found in realistic analytic settings, and methods for evaluating whether the plausible insights are gained through use of the new methods. However, researchers do not, generally speaking, have access to analysts who can articulate their problems or operational data that is used for analysis. To fill this gap, the Visualmore » Analytics Science and Technology (VAST) Challenge has been held annually since 2006. The VAST Challenge provides an opportunity for researchers to experiment with realistic but not real problems, using realistic synthetic data with known events embedded. Since its inception, the VAST Challenge has evolved along with the visual analytics research community to pose more complex challenges, ranging from text analysis to video analysis to large scale network log analysis. The seven years of the VAST Challenge have seen advancements in research and development, education, evaluation, and in the challenge process itself. This special issue of Information Visualization highlights some of the noteworthy advancements in each of these areas. Some of these papers focus on important research questions related to the challenge itself, and other papers focus on innovative research that has been shaped by participation in the challenge. This paper describes the VAST Challenge process and benefits in detail. It also provides an introduction to and context for the remaining papers in the issue.« less

Developing a Value of Information (VoI) Enabled System from Collection to Analysis

DTIC Science & Technology

2016-11-01

Information, Android, smartphone , information dissemination, visual analytic 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...List of Figures Fig. 1 Spot report main screen .........................................................................2 Fig. 2 Smartphone app...included the creation of 2 Android smartphone applications (apps) and the enhancement of an existing tool (Contour). Prior work with Android

Evaluation of Visual Analytics Environments: The Road to the Visual Analytics Science and Technology Challenge Evaluation Methodology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scholtz, Jean; Plaisant, Catherine; Whiting, Mark A.

The evaluation of visual analytics environments was a topic in Illuminating the Path [Thomas 2005] as a critical aspect of moving research into practice. For a thorough understanding of the utility of the systems available, evaluation not only involves assessing the visualizations, interactions or data processing algorithms themselves, but also the complex processes that a tool is meant to support (such as exploratory data analysis and reasoning, communication through visualization, or collaborative data analysis [Lam 2012; Carpendale 2007]). Researchers and practitioners in the field have long identified many of the challenges faced when planning, conducting, and executing an evaluation ofmore » a visualization tool or system [Plaisant 2004]. Evaluation is needed to verify that algorithms and software systems work correctly and that they represent improvements over the current infrastructure. Additionally to effectively transfer new software into a working environment, it is necessary to ensure that the software has utility for the end-users and that the software can be incorporated into the end-user’s infrastructure and work practices. Evaluation test beds require datasets, tasks, metrics and evaluation methodologies. As noted in [Thomas 2005] it is difficult and expensive for any one researcher to setup an evaluation test bed so in many cases evaluation is setup for communities of researchers or for various research projects or programs. Examples of successful community evaluations can be found [Chinchor 1993; Voorhees 2007; FRGC 2012]. As visual analytics environments are intended to facilitate the work of human analysts, one aspect of evaluation needs to focus on the utility of the software to the end-user. This requires representative users, representative tasks, and metrics that measure the utility to the end-user. This is even more difficult as now one aspect of the test methodology is access to representative end-users to participate in the evaluation. In many cases the sensitive nature of data and tasks and difficult access to busy analysts puts even more of a burden on researchers to complete this type of evaluation. User-centered design goes beyond evaluation and starts with the user [Beyer 1997, Shneiderman 2009]. Having some knowledge of the type of data, tasks, and work practices helps researchers and developers know the correct paths to pursue in their work. When access to the end-users is problematic at best and impossible at worst, user-centered design becomes difficult. Researchers are unlikely to go to work on the type of problems faced by inaccessible users. Commercial vendors have difficulties evaluating and improving their products when they cannot observe real users working with their products. In well-established fields such as web site design or office software design, user-interface guidelines have been developed based on the results of empirical studies or the experience of experts. Guidelines can speed up the design process and replace some of the need for observation of actual users [heuristics review references]. In 2006 when the visual analytics community was initially getting organized, no such guidelines existed. Therefore, we were faced with the problem of developing an evaluation framework for the field of visual analytics that would provide representative situations and datasets, representative tasks and utility metrics, and finally a test methodology which would include a surrogate for representative users, increase interest in conducting research in the field, and provide sufficient feedback to the researchers so that they could improve their systems.« less

Teaching Tectonics to Undergraduates with Web GIS

NASA Astrophysics Data System (ADS)

Anastasio, D. J.; Bodzin, A.; Sahagian, D. L.; Rutzmoser, S.

2013-12-01

Geospatial reasoning skills provide a means for manipulating, interpreting, and explaining structured information and are involved in higher-order cognitive processes that include problem solving and decision-making. Appropriately designed tools, technologies, and curriculum can support spatial learning. We present Web-based visualization and analysis tools developed with Javascript APIs to enhance tectonic curricula while promoting geospatial thinking and scientific inquiry. The Web GIS interface integrates graphics, multimedia, and animations that allow users to explore and discover geospatial patterns that are not easily recognized. Features include a swipe tool that enables users to see underneath layers, query tools useful in exploration of earthquake and volcano data sets, a subduction and elevation profile tool which facilitates visualization between map and cross-sectional views, drafting tools, a location function, and interactive image dragging functionality on the Web GIS. The Web GIS platform is independent and can be implemented on tablets or computers. The GIS tool set enables learners to view, manipulate, and analyze rich data sets from local to global scales, including such data as geology, population, heat flow, land cover, seismic hazards, fault zones, continental boundaries, and elevation using two- and three- dimensional visualization and analytical software. Coverages which allow users to explore plate boundaries and global heat flow processes aided learning in a Lehigh University Earth and environmental science Structural Geology and Tectonics class and are freely available on the Web.

mHealth Visual Discovery Dashboard.

PubMed

Fang, Dezhi; Hohman, Fred; Polack, Peter; Sarker, Hillol; Kahng, Minsuk; Sharmin, Moushumi; al'Absi, Mustafa; Chau, Duen Horng

2017-09-01

We present Discovery Dashboard, a visual analytics system for exploring large volumes of time series data from mobile medical field studies. Discovery Dashboard offers interactive exploration tools and a data mining motif discovery algorithm to help researchers formulate hypotheses, discover trends and patterns, and ultimately gain a deeper understanding of their data. Discovery Dashboard emphasizes user freedom and flexibility during the data exploration process and enables researchers to do things previously challenging or impossible to do - in the web-browser and in real time. We demonstrate our system visualizing data from a mobile sensor study conducted at the University of Minnesota that included 52 participants who were trying to quit smoking.

mHealth Visual Discovery Dashboard

PubMed Central

Fang, Dezhi; Hohman, Fred; Polack, Peter; Sarker, Hillol; Kahng, Minsuk; Sharmin, Moushumi; al'Absi, Mustafa; Chau, Duen Horng

2018-01-01

We present Discovery Dashboard, a visual analytics system for exploring large volumes of time series data from mobile medical field studies. Discovery Dashboard offers interactive exploration tools and a data mining motif discovery algorithm to help researchers formulate hypotheses, discover trends and patterns, and ultimately gain a deeper understanding of their data. Discovery Dashboard emphasizes user freedom and flexibility during the data exploration process and enables researchers to do things previously challenging or impossible to do — in the web-browser and in real time. We demonstrate our system visualizing data from a mobile sensor study conducted at the University of Minnesota that included 52 participants who were trying to quit smoking. PMID:29354812

An Integrated Multivariable Visualization Tool for Marine Sanctuary Climate Assessments

NASA Astrophysics Data System (ADS)

Shein, K. A.; Johnston, S.; Stachniewicz, J.; Duncan, B.; Cecil, D.; Ansari, S.; Urzen, M.

2012-12-01

The comprehensive development and use of ecological climate impact assessments by ecosystem managers can be limited by data access and visualization methods that require a priori knowledge about the various large and complex climate data products necessary to those impact assessments. In addition, it can be difficult to geographically and temporally integrate climate and ecological data to fully characterize climate-driven ecological impacts. To address these considerations, we have enhanced and extended the functionality of the NOAA National Climatic Data Center's Weather and Climate Toolkit (WCT). The WCT is a freely available Java-based tool designed to access and display NCDC's georeferenced climate data products (e.g., satellite, radar, and reanalysis gridded data). However, the WCT requires users already know how to obtain the data products, which products are preferred for a given variable, and which products are most relevant to their needs. Developed in cooperation with research and management customers at the Gulf of the Farallones National Marine Sanctuary, the Integrated Marine Protected Area Climate Tools (IMPACT) modification to the WCT simplifies or eliminates these requirements, while simultaneously adding core analytical functionality to the tool. Designed for use by marine ecosystem managers, WCT-IMPACT accesses a suite of data products that have been identified as relevant to marine ecosystem climate impact assessments, such as NOAA's Climate Data Records. WCT-IMPACT regularly crops these products to the geographic boundaries of each included marine protected area (MPA), and those clipped regions are processed to produce MPA-specific analytics. The tool retrieves the most appropriate data files based on the user selection of MPA, environmental variable(s), and time frame. Once the data are loaded, they may be visualized, explored, analyzed, and exported to other formats (e.g., Google KML). Multiple variables may be simultaneously visualized using a 4-panel display and compared via a variety of statistics such as difference, probability, or correlation maps.; NCDC's Weather and Climate Toolkit image of NARR-A non-convective cloud cover (%) over the Pacific Coast on June 17, 2012 at 09:00 GMT.

Hawkeye and AMOS: visualizing and assessing the quality of genome assemblies

PubMed Central

Schatz, Michael C.; Phillippy, Adam M.; Sommer, Daniel D.; Delcher, Arthur L.; Puiu, Daniela; Narzisi, Giuseppe; Salzberg, Steven L.; Pop, Mihai

2013-01-01

Since its launch in 2004, the open-source AMOS project has released several innovative DNA sequence analysis applications including: Hawkeye, a visual analytics tool for inspecting the structure of genome assemblies; the Assembly Forensics and FRCurve pipelines for systematically evaluating the quality of a genome assembly; and AMOScmp, the first comparative genome assembler. These applications have been used to assemble and analyze dozens of genomes ranging in complexity from simple microbial species through mammalian genomes. Recent efforts have been focused on enhancing support for new data characteristics brought on by second- and now third-generation sequencing. This review describes the major components of AMOS in light of these challenges, with an emphasis on methods for assessing assembly quality and the visual analytics capabilities of Hawkeye. These interactive graphical aspects are essential for navigating and understanding the complexities of a genome assembly, from the overall genome structure down to individual bases. Hawkeye and AMOS are available open source at http://amos.sourceforge.net. PMID:22199379

Cognitive Styles, Demographic Attributes, Task Performance and Affective Experiences: An Empirical Investigation into Astrophysics Data System (ADS) Core Users

NASA Astrophysics Data System (ADS)

Tong, Rong

As a primary digital library portal for astrophysics researchers, SAO/NASA ADS (Astrophysics Data System) 2.0 interface features several visualization tools such as Author Network and Metrics. This research study involves 20 ADS long term users who participated in a usability and eye tracking research session. Participants first completed a cognitive test, and then performed five tasks in ADS 2.0 where they explored its multiple visualization tools. Results show that over half of the participants were Imagers and half of the participants were Analytic. Cognitive styles were found to have significant impacts on several efficiency-based measures. Analytic-oriented participants were observed to spent shorter time on web pages and apps, made fewer web page changes than less-Analytic-driving participants in performing common tasks, whereas AI (Analytic-Imagery) participants also completed their five tasks faster than non-AI participants. Meanwhile, self-identified Imagery participants were found to be more efficient in their task completion through multiple measures including total time on task, number of mouse clicks, and number of query revisions made. Imagery scores were negatively associated with frequency of confusion and the observed counts of being surprised. Compared to those who did not claimed to be a visual person, self-identified Imagery participants were observed to have significantly less frequency in frustration and hesitation during their task performance. Both demographic variables and past user experiences were found to correlate with task performance; query revision also correlated with multiple time-based measurements. Considered as an indicator of efficiency, query revisions were found to correlate negatively with the rate of complete with ease, and positively with several time-based efficiency measures, rate of complete with some difficulty, and the frequency of frustration. These results provide rich insights into the cognitive styles of ADS' core users, the impact of such styles and demographic attributes on their task performance their affective and cognitive experiences, and their interaction behaviors while using the visualization component of ADS 2.0, and would subsequently contribute to the design of bibliographic retrieval systems for scientists.

iCOSSY: An Online Tool for Context-Specific Subnetwork Discovery from Gene Expression Data

PubMed Central

Saha, Ashis; Jeon, Minji; Tan, Aik Choon; Kang, Jaewoo

2015-01-01

Pathway analyses help reveal underlying molecular mechanisms of complex biological phenotypes. Biologists tend to perform multiple pathway analyses on the same dataset, as there is no single answer. It is often inefficient for them to implement and/or install all the algorithms by themselves. Online tools can help the community in this regard. Here we present an online gene expression analytical tool called iCOSSY which implements a novel pathway-based COntext-specific Subnetwork discoverY (COSSY) algorithm. iCOSSY also includes a few modifications of COSSY to increase its reliability and interpretability. Users can upload their gene expression datasets, and discover important subnetworks of closely interacting molecules to differentiate between two phenotypes (context). They can also interactively visualize the resulting subnetworks. iCOSSY is a web server that finds subnetworks that are differentially expressed in two phenotypes. Users can visualize the subnetworks to understand the biology of the difference. PMID:26147457

Visual analysis of online social media to open up the investigation of stance phenomena

PubMed Central

Kucher, Kostiantyn; Schamp-Bjerede, Teri; Kerren, Andreas; Paradis, Carita; Sahlgren, Magnus

2015-01-01

Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool. PMID:29249903

Visual analysis of online social media to open up the investigation of stance phenomena.

PubMed

Kucher, Kostiantyn; Schamp-Bjerede, Teri; Kerren, Andreas; Paradis, Carita; Sahlgren, Magnus

2016-04-01

Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool.

Visual programming for next-generation sequencing data analytics.

PubMed

Milicchio, Franco; Rose, Rebecca; Bian, Jiang; Min, Jae; Prosperi, Mattia

2016-01-01

High-throughput or next-generation sequencing (NGS) technologies have become an established and affordable experimental framework in biological and medical sciences for all basic and translational research. Processing and analyzing NGS data is challenging. NGS data are big, heterogeneous, sparse, and error prone. Although a plethora of tools for NGS data analysis has emerged in the past decade, (i) software development is still lagging behind data generation capabilities, and (ii) there is a 'cultural' gap between the end user and the developer. Generic software template libraries specifically developed for NGS can help in dealing with the former problem, whilst coupling template libraries with visual programming may help with the latter. Here we scrutinize the state-of-the-art low-level software libraries implemented specifically for NGS and graphical tools for NGS analytics. An ideal developing environment for NGS should be modular (with a native library interface), scalable in computational methods (i.e. serial, multithread, distributed), transparent (platform-independent), interoperable (with external software interface), and usable (via an intuitive graphical user interface). These characteristics should facilitate both the run of standardized NGS pipelines and the development of new workflows based on technological advancements or users' needs. We discuss in detail the potential of a computational framework blending generic template programming and visual programming that addresses all of the current limitations. In the long term, a proper, well-developed (although not necessarily unique) software framework will bridge the current gap between data generation and hypothesis testing. This will eventually facilitate the development of novel diagnostic tools embedded in routine healthcare.

GeneAnalytics: An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data.

PubMed

Ben-Ari Fuchs, Shani; Lieder, Iris; Stelzer, Gil; Mazor, Yaron; Buzhor, Ella; Kaplan, Sergey; Bogoch, Yoel; Plaschkes, Inbar; Shitrit, Alina; Rappaport, Noa; Kohn, Asher; Edgar, Ron; Shenhav, Liraz; Safran, Marilyn; Lancet, Doron; Guan-Golan, Yaron; Warshawsky, David; Shtrichman, Ronit

2016-03-01

Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from "data-to-knowledge-to-innovation," a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ ( geneanalytics.genecards.org ), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®--the human gene database; the MalaCards-the human diseases database; and the PathCards--the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®--the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene-tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell "cards" in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics, and others yet to emerge on the postgenomics horizon.

Updates in metabolomics tools and resources: 2014-2015.

PubMed

Misra, Biswapriya B; van der Hooft, Justin J J

2016-01-01

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources--in the form of tools, software, and databases--is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Human is the Loop: New Directions for Visual Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Endert, Alexander; Hossain, Shahriar H.; Ramakrishnan, Naren

2014-01-28

Visual analytics is the science of marrying interactive visualizations and analytic algorithms to support exploratory knowledge discovery in large datasets. We argue for a shift from a ‘human in the loop’ philosophy for visual analytics to a ‘human is the loop’ viewpoint, where the focus is on recognizing analysts’ work processes, and seamlessly fitting analytics into that existing interactive process. We survey a range of projects that provide visual analytic support contextually in the sensemaking loop, and outline a research agenda along with future challenges.

Code Pulse: Software Assurance (SWA) Visual Analytics for Dynamic Analysis of Code

DTIC Science & Technology

2014-09-01

31 4.5.1 Market Analysis...competitive market analysis to assess the tool potential. The final transition targets were selected and expressed along with our research on the topic...public release milestones. Details of our testing methodology is in our Software Test Plan deliv- erable, CP- STP -0001. A summary of this approach is

Finite-difference time-domain modelling of through-the-Earth radio signal propagation

NASA Astrophysics Data System (ADS)

Ralchenko, M.; Svilans, M.; Samson, C.; Roper, M.

2015-12-01

This research seeks to extend the knowledge of how a very low frequency (VLF) through-the-Earth (TTE) radio signal behaves as it propagates underground, by calculating and visualizing the strength of the electric and magnetic fields for an arbitrary geology through numeric modelling. To achieve this objective, a new software tool has been developed using the finite-difference time-domain method. This technique is particularly well suited to visualizing the distribution of electromagnetic fields in an arbitrary geology. The frequency range of TTE radio (400-9000 Hz) and geometrical scales involved (1 m resolution for domains a few hundred metres in size) involves processing a grid composed of millions of cells for thousands of time steps, which is computationally expensive. Graphics processing unit acceleration was used to reduce execution time from days and weeks, to minutes and hours. Results from the new modelling tool were compared to three cases for which an analytic solution is known. Two more case studies were done featuring complex geologic environments relevant to TTE communications that cannot be solved analytically. There was good agreement between numeric and analytic results. Deviations were likely caused by numeric artifacts from the model boundaries; however, in a TTE application in field conditions, the uncertainty in the conductivity of the various geologic formations will greatly outweigh these small numeric errors.

VAST Challenge 2016: Streaming Visual Analytics

DTIC Science & Technology

2016-10-25

understand rapidly evolving situations. To support such tasks, visual analytics solutions must move well beyond systems that simply provide real-time...received. Mini-Challenge 1: Design Challenge Mini-Challenge 1 focused on systems to support security and operational analytics at the Euybia...Challenge 1 was to solicit novel approaches for streaming visual analytics that push the boundaries for what constitutes a visual analytics system , and to

Modeling human comprehension of data visualizations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matzen, Laura E.; Haass, Michael Joseph; Divis, Kristin Marie

This project was inspired by two needs. The first is a need for tools to help scientists and engineers to design effective data visualizations for communicating information, whether to the user of a system, an analyst who must make decisions based on complex data, or in the context of a technical report or publication. Most scientists and engineers are not trained in visualization design, and they could benefit from simple metrics to assess how well their visualization's design conveys the intended message. In other words, will the most important information draw the viewer's attention? The second is the need formore » cognition-based metrics for evaluating new types of visualizations created by researchers in the information visualization and visual analytics communities. Evaluating visualizations is difficult even for experts. However, all visualization methods and techniques are intended to exploit the properties of the human visual system to convey information efficiently to a viewer. Thus, developing evaluation methods that are rooted in the scientific knowledge of the human visual system could be a useful approach. In this project, we conducted fundamental research on how humans make sense of abstract data visualizations, and how this process is influenced by their goals and prior experience. We then used that research to develop a new model, the Data Visualization Saliency Model, that can make accurate predictions about which features in an abstract visualization will draw a viewer's attention. The model is an evaluation tool that can address both of the needs described above, supporting both visualization research and Sandia mission needs.« less

Developing Guidelines for Assessing Visual Analytics Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scholtz, Jean

2011-07-01

In this paper, we develop guidelines for evaluating visual analytic environments based on a synthesis of reviews for the entries to the 2009 Visual Analytics Science and Technology (VAST) Symposium Challenge and from a user study with professional intelligence analysts. By analyzing the 2009 VAST Challenge reviews we gained a better understanding of what is important to our reviewers, both visualization researchers and professional analysts. We also report on a small user study with professional analysts to determine the important factors that they use in evaluating visual analysis systems. We then looked at guidelines developed by researchers in various domainsmore » and synthesized these into an initial set for use by others in the community. In a second part of the user study, we looked at guidelines for a new aspect of visual analytic systems – the generation of reports. Future visual analytic systems have been challenged to help analysts generate their reports. In our study we worked with analysts to understand the criteria they used to evaluate the quality of analytic reports. We propose that this knowledge will be useful as researchers look at systems to automate some of the report generation.1 Based on these efforts, we produced some initial guidelines for evaluating visual analytic environment and for evaluation of analytic reports. It is important to understand that these guidelines are initial drafts and are limited in scope because of the type of tasks for which the visual analytic systems used in the studies in this paper were designed. More research and refinement is needed by the Visual Analytics Community to provide additional evaluation guidelines for different types of visual analytic environments.« less

Demons registration for in vivo and deformable laser scanning confocal endomicroscopy.

PubMed

Chiew, Wei-Ming; Lin, Feng; Seah, Hock Soon

2017-09-01

A critical effect found in noninvasive in vivo endomicroscopic imaging modalities is image distortions due to sporadic movement exhibited by living organisms. In three-dimensional confocal imaging, this effect results in a dataset that is tilted across deeper slices. Apart from that, the sequential flow of the imaging-processing pipeline restricts real-time adjustments due to the unavailability of information obtainable only from subsequent stages. To solve these problems, we propose an approach to render Demons-registered datasets as they are being captured, focusing on the coupling between registration and visualization. To improve the acquisition process, we also propose a real-time visual analytics tool, which complements the imaging pipeline and the Demons registration pipeline with useful visual indicators to provide real-time feedback for immediate adjustments. We highlight the problem of deformation within the visualization pipeline for object-ordered and image-ordered rendering. Visualizations of critical information including registration forces and partial renderings of the captured data are also presented in the analytics system. We demonstrate the advantages of the algorithmic design through experimental results with both synthetically deformed datasets and actual in vivo, time-lapse tissue datasets expressing natural deformations. Remarkably, this algorithm design is for embedded implementation in intelligent biomedical imaging instrumentation with customizable circuitry. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

IN13B-1660: Analytics and Visualization Pipelines for Big Data on the NASA Earth Exchange (NEX) and OpenNEX

NASA Technical Reports Server (NTRS)

Chaudhary, Aashish; Votava, Petr; Nemani, Ramakrishna R.; Michaelis, Andrew; Kotfila, Chris

2016-01-01

We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.

Demons registration for in vivo and deformable laser scanning confocal endomicroscopy

NASA Astrophysics Data System (ADS)

Chiew, Wei Ming; Lin, Feng; Seah, Hock Soon

2017-09-01

A critical effect found in noninvasive in vivo endomicroscopic imaging modalities is image distortions due to sporadic movement exhibited by living organisms. In three-dimensional confocal imaging, this effect results in a dataset that is tilted across deeper slices. Apart from that, the sequential flow of the imaging-processing pipeline restricts real-time adjustments due to the unavailability of information obtainable only from subsequent stages. To solve these problems, we propose an approach to render Demons-registered datasets as they are being captured, focusing on the coupling between registration and visualization. To improve the acquisition process, we also propose a real-time visual analytics tool, which complements the imaging pipeline and the Demons registration pipeline with useful visual indicators to provide real-time feedback for immediate adjustments. We highlight the problem of deformation within the visualization pipeline for object-ordered and image-ordered rendering. Visualizations of critical information including registration forces and partial renderings of the captured data are also presented in the analytics system. We demonstrate the advantages of the algorithmic design through experimental results with both synthetically deformed datasets and actual in vivo, time-lapse tissue datasets expressing natural deformations. Remarkably, this algorithm design is for embedded implementation in intelligent biomedical imaging instrumentation with customizable circuitry.

Analytics and Visualization Pipelines for Big ­Data on the NASA Earth Exchange (NEX) and OpenNEX

NASA Astrophysics Data System (ADS)

Chaudhary, A.; Votava, P.; Nemani, R. R.; Michaelis, A.; Kotfila, C.

2016-12-01

We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.

Process Improvement Through Tool Integration in Aero-Mechanical Design

NASA Technical Reports Server (NTRS)

Briggs, Clark

2010-01-01

Emerging capabilities in commercial design tools promise to significantly improve the multi-disciplinary and inter-disciplinary design and analysis coverage for aerospace mechanical engineers. This paper explores the analysis process for two example problems of a wing and flap mechanical drive system and an aircraft landing gear door panel. The examples begin with the design solid models and include various analysis disciplines such as structural stress and aerodynamic loads. Analytical methods include CFD, multi-body dynamics with flexible bodies and structural analysis. Elements of analysis data management, data visualization and collaboration are also included.

Visual analysis of large heterogeneous social networks by semantic and structural abstraction.

PubMed

Shen, Zeqian; Ma, Kwan-Liu; Eliassi-Rad, Tina

2006-01-01

Social network analysis is an active area of study beyond sociology. It uncovers the invisible relationships between actors in a network and provides understanding of social processes and behaviors. It has become an important technique in a variety of application areas such as the Web, organizational studies, and homeland security. This paper presents a visual analytics tool, OntoVis, for understanding large, heterogeneous social networks, in which nodes and links could represent different concepts and relations, respectively. These concepts and relations are related through an ontology (also known as a schema). OntoVis is named such because it uses information in the ontology associated with a social network to semantically prune a large, heterogeneous network. In addition to semantic abstraction, OntoVis also allows users to do structural abstraction and importance filtering to make large networks manageable and to facilitate analytic reasoning. All these unique capabilities of OntoVis are illustrated with several case studies.

Hyperspectral imaging for non-contact analysis of forensic traces.

PubMed

Edelman, G J; Gaston, E; van Leeuwen, T G; Cullen, P J; Aalders, M C G

2012-11-30

Hyperspectral imaging (HSI) integrates conventional imaging and spectroscopy, to obtain both spatial and spectral information from a specimen. This technique enables investigators to analyze the chemical composition of traces and simultaneously visualize their spatial distribution. HSI offers significant potential for the detection, visualization, identification and age estimation of forensic traces. The rapid, non-destructive and non-contact features of HSI mark its suitability as an analytical tool for forensic science. This paper provides an overview of the principles, instrumentation and analytical techniques involved in hyperspectral imaging. We describe recent advances in HSI technology motivating forensic science applications, e.g. the development of portable and fast image acquisition systems. Reported forensic science applications are reviewed. Challenges are addressed, such as the analysis of traces on backgrounds encountered in casework, concluded by a summary of possible future applications. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scholtz, Jean

A new field of research, visual analytics, has recently been introduced. This has been defined as “the science of analytical reasoning facilitated by visual interfaces." Visual analytic environments, therefore, support analytical reasoning using visual representations and interactions, with data representations and transformation capabilities, to support production, presentation and dissemination. As researchers begin to develop visual analytic environments, it will be advantageous to develop metrics and methodologies to help researchers measure the progress of their work and understand the impact their work will have on the users who will work in such environments. This paper presents five areas or aspects ofmore » visual analytic environments that should be considered as metrics and methodologies for evaluation are developed. Evaluation aspects need to include usability, but it is necessary to go beyond basic usability. The areas of situation awareness, collaboration, interaction, creativity, and utility are proposed as areas for initial consideration. The steps that need to be undertaken to develop systematic evaluation methodologies and metrics for visual analytic environments are outlined.« less

Multi-Intelligence Analytics for Next Generation Analysts (MIAGA)

NASA Astrophysics Data System (ADS)

Blasch, Erik; Waltz, Ed

2016-05-01

Current analysts are inundated with large volumes of data from which extraction, exploitation, and indexing are required. A future need for next-generation analysts is an appropriate balance between machine analytics from raw data and the ability of the user to interact with information through automation. Many quantitative intelligence tools and techniques have been developed which are examined towards matching analyst opportunities with recent technical trends such as big data, access to information, and visualization. The concepts and techniques summarized are derived from discussions with real analysts, documented trends of technical developments, and methods to engage future analysts with multiintelligence services. For example, qualitative techniques should be matched against physical, cognitive, and contextual quantitative analytics for intelligence reporting. Future trends include enabling knowledge search, collaborative situational sharing, and agile support for empirical decision-making and analytical reasoning.

Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs

NASA Astrophysics Data System (ADS)

Pelorosso, Leandro; Diehl, Alexandra; Matković, Krešimir; Delrieux, Claudio; Ruiz, Juan; Gröeller, M. Eduard; Bruckner, Stefan

2016-04-01

Numerical weather forecasts are prone to uncertainty coming from inaccuracies in the initial and boundary conditions and lack of precision in numerical models. Ensemble of forecasts partially addresses these problems by considering several runs of the numerical model. Each forecast is generated with different initial and boundary conditions and different model configurations [GR05]. The ensembles can be expressed as probabilistic forecasts, which have proven to be very effective in the decision-making processes [DE06]. The ensemble of forecasts represents only some of the possible future atmospheric states, usually underestimating the degree of uncertainty in the predictions [KAL03, PH06]. Hamill and Whitaker [HW06] introduced the "Reforecast Analog Regression" (RAR) technique to overcome the limitations of ensemble forecasting. This technique produces probabilistic predictions based on the analysis of historical forecasts and observations. Visual analytics provides tools for processing, visualizing, and exploring data to get new insights and discover hidden information patterns in an interactive exchange between the user and the application [KMS08]. In this work, we introduce Albero, a visual analytics solution for probabilistic weather forecasting based on the RAR technique. Albero targets at least two different type of users: "forecasters", who are meteorologists working in operational weather forecasting and "researchers", who work in the construction of numerical prediction models. Albero is an efficient tool for analyzing precipitation forecasts, allowing forecasters to make and communicate quick decisions. Our solution facilitates the analysis of a set of probabilistic forecasts, associated statistical data, observations and uncertainty. A dashboard with small-multiples of probabilistic forecasts allows the forecasters to analyze at a glance the distribution of probabilities as a function of time, space, and magnitude. It provides the user with a more accurate measure of forecast uncertainty that could result in better decision-making. It offers different level of abstractions to help with the recalibration of the RAR method. It also has an inspection tool that displays the selected analogs, their observations and statistical data. It gives the users access to inner parts of the method, unveiling hidden information. References [GR05] GNEITING T., RAFTERY A. E.: Weather forecasting with ensemble methods. Science 310, 5746, 248-249, 2005. [KAL03] KALNAY E.: Atmospheric modeling, data assimilation and predictability. Cambridge University Press, 2003. [PH06] PALMER T., HAGEDORN R.: Predictability of weather and climate. Cambridge University Press, 2006. [HW06] HAMILL T. M., WHITAKER J. S.: Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application. Monthly Weather Review 134, 11, 3209-3229, 2006. [DE06] DEITRICK S., EDSALL R.: The influence of uncertainty visualization on decision making: An empirical evaluation. Springer, 2006. [KMS08] KEIM D. A., MANSMANN F., SCHNEIDEWIND J., THOMAS J., ZIEGLER H.: Visual analytics: Scope and challenges. Springer, 2008.

Search Analytics: Automated Learning, Analysis, and Search with Open Source

NASA Astrophysics Data System (ADS)

Hundman, K.; Mattmann, C. A.; Hyon, J.; Ramirez, P.

2016-12-01

The sheer volume of unstructured scientific data makes comprehensive human analysis impossible, resulting in missed opportunities to identify relationships, trends, gaps, and outliers. As the open source community continues to grow, tools like Apache Tika, Apache Solr, Stanford's DeepDive, and Data-Driven Documents (D3) can help address this challenge. With a focus on journal publications and conference abstracts often in the form of PDF and Microsoft Office documents, we've initiated an exploratory NASA Advanced Concepts project aiming to use the aforementioned open source text analytics tools to build a data-driven justification for the HyspIRI Decadal Survey mission. We call this capability Search Analytics, and it fuses and augments these open source tools to enable the automatic discovery and extraction of salient information. In the case of HyspIRI, a hyperspectral infrared imager mission, key findings resulted from the extractions and visualizations of relationships from thousands of unstructured scientific documents. The relationships include links between satellites (e.g. Landsat 8), domain-specific measurements (e.g. spectral coverage) and subjects (e.g. invasive species). Using the above open source tools, Search Analytics mined and characterized a corpus of information that would be infeasible for a human to process. More broadly, Search Analytics offers insights into various scientific and commercial applications enabled through missions and instrumentation with specific technical capabilities. For example, the following phrases were extracted in close proximity within a publication: "In this study, hyperspectral images…with high spatial resolution (1 m) were analyzed to detect cutleaf teasel in two areas. …Classification of cutleaf teasel reached a users accuracy of 82 to 84%." Without reading a single paper we can use Search Analytics to automatically identify that a 1 m spatial resolution provides a cutleaf teasel detection users accuracy of 82-84%, which could have tangible, direct downstream implications for crop protection. Automatically assimilating this information expedites and supplements human analysis, and, ultimately, Search Analytics and its foundation of open source tools will result in more efficient scientific investment and research.

Interactive Visual Analytics Approch for Exploration of Geochemical Model Simulations with Different Parameter Sets

NASA Astrophysics Data System (ADS)

Jatnieks, Janis; De Lucia, Marco; Sips, Mike; Dransch, Doris

2015-04-01

Many geoscience applications can benefit from testing many combinations of input parameters for geochemical simulation models. It is, however, a challenge to screen the input and output data from the model to identify the significant relationships between input parameters and output variables. For addressing this problem we propose a Visual Analytics approach that has been developed in an ongoing collaboration between computer science and geoscience researchers. Our Visual Analytics approach uses visualization methods of hierarchical horizontal axis, multi-factor stacked bar charts and interactive semi-automated filtering for input and output data together with automatic sensitivity analysis. This guides the users towards significant relationships. We implement our approach as an interactive data exploration tool. It is designed with flexibility in mind, so that a diverse set of tasks such as inverse modeling, sensitivity analysis and model parameter refinement can be supported. Here we demonstrate the capabilities of our approach by two examples for gas storage applications. For the first example our Visual Analytics approach enabled the analyst to observe how the element concentrations change around previously established baselines in response to thousands of different combinations of mineral phases. This supported combinatorial inverse modeling for interpreting observations about the chemical composition of the formation fluids at the Ketzin pilot site for CO2 storage. The results indicate that, within the experimental error range, the formation fluid cannot be considered at local thermodynamical equilibrium with the mineral assemblage of the reservoir rock. This is a valuable insight from the predictive geochemical modeling for the Ketzin site. For the second example our approach supports sensitivity analysis for a reaction involving the reductive dissolution of pyrite with formation of pyrrothite in presence of gaseous hydrogen. We determine that this reaction is thermodynamically favorable under a broad range of conditions. This includes low temperatures and absence of microbial catalysators. Our approach has potential for use in other applications that involve exploration of relationships in geochemical simulation model data.

Data Analytics and Visualization for Large Army Testing Data

DTIC Science & Technology

2013-09-01

and relationships in the data that would otherwise remain hidden. 7 Bibliography 1. Goodall , J. R.; Tesone, D. R. Visual Analytics for Network...Software Visualization, 2003, pp 143–149. 3. Goodall , J. R.; Sowul, M. VIAssist: Visual Analytics for Cyber Defense, IEEE Conference on Technologies

VISAGE Visualization for Integrated Satellite, Airborne and Ground-Based Data Exploration

NASA Technical Reports Server (NTRS)

Conover, Helen; Berendes, Todd; Naeger, Aaron; Maskey, Manil; Gatlin, Patrick; Wingo, Stephanie; Kulkarni, Ajinkya; Gupta, Shivangi; Nagaraj, Sriraksha; Wolff, David;

How can knowledge discovery methods uncover spatio-temporal patterns in environmental data?

NASA Astrophysics Data System (ADS)

Wachowicz, Monica

2000-04-01

This paper proposes the integration of KDD, GVis and STDB as a long-term strategy, which will allow users to apply knowledge discovery methods for uncovering spatio-temporal patterns in environmental data. The main goal is to combine innovative techniques and associated tools for exploring very large environmental data sets in order to arrive at valid, novel, potentially useful, and ultimately understandable spatio-temporal patterns. The GeoInsight approach is described using the principles and key developments in the research domains of KDD, GVis, and STDB. The GeoInsight approach aims at the integration of these research domains in order to provide tools for performing information retrieval, exploration, analysis, and visualization. The result is a knowledge-based design, which involves visual thinking (perceptual-cognitive process) and automated information processing (computer-analytical process).

The Genome Portal of the Department of Energy Joint Genome Institute

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nordberg, Henrik; Cantor, Michael; Dushekyo, Serge

2014-03-14

The JGI Genome Portal (http://genome.jgi.doe.gov) provides unified access to all JGI genomic databases and analytical tools. A user can search, download and explore multiple data sets available for all DOE JGI sequencing projects including their status, assemblies and annotations of sequenced genomes. Genome Portal in the past 2 years was significantly updated, with a specific emphasis on efficient handling of the rapidly growing amount of diverse genomic data accumulated in JGI. A critical aspect of handling big data in genomics is the development of visualization and analysis tools that allow scientists to derive meaning from what are otherwise terrabases ofmore » inert sequence. An interactive visualization tool developed in the group allows us to explore contigs resulting from a single metagenome assembly. Implemented with modern web technologies that take advantage of the power of the computer's graphical processing unit (gpu), the tool allows the user to easily navigate over a 100,000 data points in multiple dimensions, among many biologically meaningful parameters of a dataset such as relative abundance, contig length, and G+C content.« less

Familiarity Vs Trust: A Comparative Study of Domain Scientists' Trust in Visual Analytics and Conventional Analysis Methods.

PubMed

Dasgupta, Aritra; Lee, Joon-Yong; Wilson, Ryan; Lafrance, Robert A; Cramer, Nick; Cook, Kristin; Payne, Samuel

2017-01-01

Combining interactive visualization with automated analytical methods like statistics and data mining facilitates data-driven discovery. These visual analytic methods are beginning to be instantiated within mixed-initiative systems, where humans and machines collaboratively influence evidence-gathering and decision-making. But an open research question is that, when domain experts analyze their data, can they completely trust the outputs and operations on the machine-side? Visualization potentially leads to a transparent analysis process, but do domain experts always trust what they see? To address these questions, we present results from the design and evaluation of a mixed-initiative, visual analytics system for biologists, focusing on analyzing the relationships between familiarity of an analysis medium and domain experts' trust. We propose a trust-augmented design of the visual analytics system, that explicitly takes into account domain-specific tasks, conventions, and preferences. For evaluating the system, we present the results of a controlled user study with 34 biologists where we compare the variation of the level of trust across conventional and visual analytic mediums and explore the influence of familiarity and task complexity on trust. We find that despite being unfamiliar with a visual analytic medium, scientists seem to have an average level of trust that is comparable with the same in conventional analysis medium. In fact, for complex sense-making tasks, we find that the visual analytic system is able to inspire greater trust than other mediums. We summarize the implications of our findings with directions for future research on trustworthiness of visual analytic systems.

Characterization and measurement of polymer wear

NASA Technical Reports Server (NTRS)

Buckley, D. H.; Aron, P. R.

1984-01-01

Analytical tools which characterize the polymer wear process are discussed. The devices discussed include: visual observation of polymer wear with SEM, the quantification with surface profilometry and ellipsometry, to study the chemistry with AES, XPS and SIMS, to establish interfacial polymer orientation and accordingly bonding with QUARTIR, polymer state with Raman spectroscopy and stresses that develop in polymer films using a X-ray double crystal camera technique.

Pathways to Identity: Aiding Law Enforcement in Identification Tasks With Visual Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bruce, Joseph R.; Scholtz, Jean; Hodges, Duncan

The nature of identity has changed dramatically in recent years, and has grown in complexity. Identities are defined in multiple domains: biological and psychological elements strongly contribute, but also biographical and cyber elements are necessary to complete the picture. Law enforcement is beginning to adjust to these changes, recognizing its importance in criminal justice. The SuperIdentity project seeks to aid law enforcement officials in their identification tasks through research of techniques for discovering identity traits, generation of statistical models of identity and analysis of identity traits through visualization. We present use cases compiled through user interviews in multiple fields, includingmore » law enforcement, as well as the modeling and visualization tools design to aid in those use cases.« less

Bridging Theory with Practice: An Exploratory Study of Visualization Use and Design for Climate Model Comparison

DOE PAGES

Dasgupta, Aritra; Poco, Jorge; Wei, Yaxing; ...

2015-03-16

Evaluation methodologies in visualization have mostly focused on how well the tools and techniques cater to the analytical needs of the user. While this is important in determining the effectiveness of the tools and advancing the state-of-the-art in visualization research, a key area that has mostly been overlooked is how well established visualization theories and principles are instantiated in practice. This is especially relevant when domain experts, and not visualization researchers, design visualizations for analysis of their data or for broader dissemination of scientific knowledge. There is very little research on exploring the synergistic capabilities of cross-domain collaboration between domainmore » experts and visualization researchers. To fill this gap, in this paper we describe the results of an exploratory study of climate data visualizations conducted in tight collaboration with a pool of climate scientists. The study analyzes a large set of static climate data visualizations for identifying their shortcomings in terms of visualization design. The outcome of the study is a classification scheme that categorizes the design problems in the form of a descriptive taxonomy. The taxonomy is a first attempt for systematically categorizing the types, causes, and consequences of design problems in visualizations created by domain experts. We demonstrate the use of the taxonomy for a number of purposes, such as, improving the existing climate data visualizations, reflecting on the impact of the problems for enabling domain experts in designing better visualizations, and also learning about the gaps and opportunities for future visualization research. We demonstrate the applicability of our taxonomy through a number of examples and discuss the lessons learnt and implications of our findings.« less

Bridging Theory with Practice: An Exploratory Study of Visualization Use and Design for Climate Model Comparison

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dasgupta, Aritra; Poco, Jorge; Wei, Yaxing

Evaluation methodologies in visualization have mostly focused on how well the tools and techniques cater to the analytical needs of the user. While this is important in determining the effectiveness of the tools and advancing the state-of-the-art in visualization research, a key area that has mostly been overlooked is how well established visualization theories and principles are instantiated in practice. This is especially relevant when domain experts, and not visualization researchers, design visualizations for analysis of their data or for broader dissemination of scientific knowledge. There is very little research on exploring the synergistic capabilities of cross-domain collaboration between domainmore » experts and visualization researchers. To fill this gap, in this paper we describe the results of an exploratory study of climate data visualizations conducted in tight collaboration with a pool of climate scientists. The study analyzes a large set of static climate data visualizations for identifying their shortcomings in terms of visualization design. The outcome of the study is a classification scheme that categorizes the design problems in the form of a descriptive taxonomy. The taxonomy is a first attempt for systematically categorizing the types, causes, and consequences of design problems in visualizations created by domain experts. We demonstrate the use of the taxonomy for a number of purposes, such as, improving the existing climate data visualizations, reflecting on the impact of the problems for enabling domain experts in designing better visualizations, and also learning about the gaps and opportunities for future visualization research. We demonstrate the applicability of our taxonomy through a number of examples and discuss the lessons learnt and implications of our findings.« less

Spectrum simulation in DTSA-II.

PubMed

Ritchie, Nicholas W M

2009-10-01

Spectrum simulation is a useful practical and pedagogical tool. Particularly with complex samples or trace constituents, a simulation can help to understand the limits of the technique and the instrument parameters for the optimal measurement. DTSA-II, software for electron probe microanalysis, provides both easy to use and flexible tools for simulating common and less common sample geometries and materials. Analytical models based on (rhoz) curves provide quick simulations of simple samples. Monte Carlo models based on electron and X-ray transport provide more sophisticated models of arbitrarily complex samples. DTSA-II provides a broad range of simulation tools in a framework with many different interchangeable physical models. In addition, DTSA-II provides tools for visualizing, comparing, manipulating, and quantifying simulated and measured spectra.

Multimedia Analysis plus Visual Analytics = Multimedia Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chinchor, Nancy; Thomas, James J.; Wong, Pak C.

2010-10-01

Multimedia analysis has focused on images, video, and to some extent audio and has made progress in single channels excluding text. Visual analytics has focused on the user interaction with data during the analytic process plus the fundamental mathematics and has continued to treat text as did its precursor, information visualization. The general problem we address in this tutorial is the combining of multimedia analysis and visual analytics to deal with multimedia information gathered from different sources, with different goals or objectives, and containing all media types and combinations in common usage.

Potential of Near-Infrared Chemical Imaging as Process Analytical Technology Tool for Continuous Freeze-Drying.

PubMed

Brouckaert, Davinia; De Meyer, Laurens; Vanbillemont, Brecht; Van Bockstal, Pieter-Jan; Lammens, Joris; Mortier, Séverine; Corver, Jos; Vervaet, Chris; Nopens, Ingmar; De Beer, Thomas

2018-04-03

Near-infrared chemical imaging (NIR-CI) is an emerging tool for process monitoring because it combines the chemical selectivity of vibrational spectroscopy with spatial information. Whereas traditional near-infrared spectroscopy is an attractive technique for water content determination and solid-state investigation of lyophilized products, chemical imaging opens up possibilities for assessing the homogeneity of these critical quality attributes (CQAs) throughout the entire product. In this contribution, we aim to evaluate NIR-CI as a process analytical technology (PAT) tool for at-line inspection of continuously freeze-dried pharmaceutical unit doses based on spin freezing. The chemical images of freeze-dried mannitol samples were resolved via multivariate curve resolution, allowing us to visualize the distribution of mannitol solid forms throughout the entire cake. Second, a mannitol-sucrose formulation was lyophilized with variable drying times for inducing changes in water content. Analyzing the corresponding chemical images via principal component analysis, vial-to-vial variations as well as within-vial inhomogeneity in water content could be detected. Furthermore, a partial least-squares regression model was constructed for quantifying the water content in each pixel of the chemical images. It was hence concluded that NIR-CI is inherently a most promising PAT tool for continuously monitoring freeze-dried samples. Although some practicalities are still to be solved, this analytical technique could be applied in-line for CQA evaluation and for detecting the drying end point.

Discovering Tradeoffs, Vulnerabilities, and Dependencies within Water Resources Systems

NASA Astrophysics Data System (ADS)

Reed, P. M.

2015-12-01

There is a growing recognition and interest in using emerging computational tools for discovering the tradeoffs that emerge across complex combinations infrastructure options, adaptive operations, and sign posts. As a field concerned with "deep uncertainties", it is logically consistent to include a more direct acknowledgement that our choices for dealing with computationally demanding simulations, advanced search algorithms, and sensitivity analysis tools are themselves subject to failures that could adversely bias our understanding of how systems' vulnerabilities change with proposed actions. Balancing simplicity versus complexity in our computational frameworks is nontrivial given that we are often exploring high impact irreversible decisions. It is not always clear that accepted models even encompass important failure modes. Moreover as they become more complex and computationally demanding the benefits and consequences of simplifications are often untested. This presentation discusses our efforts to address these challenges through our "many-objective robust decision making" (MORDM) framework for the design and management water resources systems. The MORDM framework has four core components: (1) elicited problem conception and formulation, (2) parallel many-objective search, (3) interactive visual analytics, and (4) negotiated selection of robust alternatives. Problem conception and formulation is the process of abstracting a practical design problem into a mathematical representation. We build on the emerging work in visual analytics to exploit interactive visualization of both the design space and the objective space in multiple heterogeneous linked views that permit exploration and discovery. Many-objective search produces tradeoff solutions from potentially competing problem formulations that can each consider up to ten conflicting objectives based on current computational search capabilities. Negotiated design selection uses interactive visualization, reformulation, and optimization to discover desirable designs for implementation. Multi-city urban water supply portfolio planning will be used to illustrate the MORDM framework.

World Spatiotemporal Analytics and Mapping Project (wstamp): Discovering, Exploring, and Mapping Spatiotemporal Patterns across the World's Largest Open Soruce Data Sets

NASA Astrophysics Data System (ADS)

Stewart, R.; Piburn, J.; Sorokine, A.; Myers, A.; Moehl, J.; White, D.

2015-07-01

The application of spatiotemporal (ST) analytics to integrated data from major sources such as the World Bank, United Nations, and dozens of others holds tremendous potential for shedding new light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, and changing attributes, as well as content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of this integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1) development of the WSTAMP database, a significant advance in ST data modeling that integrates 10,000+ attributes covering over 200 nation states spanning over 50 years from over 30 major sources and 2) a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We discuss the status of this work and report on major findings.

The World Spatiotemporal Analytics and Mapping Project (WSTAMP): Further Progress in Discovering, Exploring, and Mapping Spatiotemporal Patterns Across the World's Largest Open Source Data Sets

NASA Astrophysics Data System (ADS)

Piburn, J.; Stewart, R.; Myers, A.; Sorokine, A.; Axley, E.; Anderson, D.; Burdette, J.; Biddle, C.; Hohl, A.; Eberle, R.; Kaufman, J.; Morton, A.

2017-10-01

Spatiotemporal (ST) analytics applied to major data sources such as the World Bank and World Health Organization has shown tremendous value in shedding light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. WSTAMP engages this opportunity by situating analysts, data, and analytics together within a visually rich and computationally rigorous online analysis environment. Since introducing WSTAMP at the First International Workshop on Spatiotemporal Computing, several transformative advances have occurred. Collaboration with human computer interaction experts led to a complete interface redesign that deeply immerses the analyst within a ST context, significantly increases visual and textual content, provides navigational crosswalks for attribute discovery, substantially reduce mouse and keyboard actions, and supports user data uploads. Secondly, the database has been expanded to include over 16,000 attributes, 50 years of time, and 200+ nation states and redesigned to support non-annual, non-national, city, and interaction data. Finally, two new analytics are implemented for analyzing large portfolios of multi-attribute data and measuring the behavioral stability of regions along different dimensions. These advances required substantial new approaches in design, algorithmic innovations, and increased computational efficiency. We report on these advances and inform how others may freely access the tool.

Implementing Operational Analytics using Big Data Technologies to Detect and Predict Sensor Anomalies

NASA Astrophysics Data System (ADS)

Coughlin, J.; Mital, R.; Nittur, S.; SanNicolas, B.; Wolf, C.; Jusufi, R.

2016-09-01

Operational analytics when combined with Big Data technologies and predictive techniques have been shown to be valuable in detecting mission critical sensor anomalies that might be missed by conventional analytical techniques. Our approach helps analysts and leaders make informed and rapid decisions by analyzing large volumes of complex data in near real-time and presenting it in a manner that facilitates decision making. It provides cost savings by being able to alert and predict when sensor degradations pass a critical threshold and impact mission operations. Operational analytics, which uses Big Data tools and technologies, can process very large data sets containing a variety of data types to uncover hidden patterns, unknown correlations, and other relevant information. When combined with predictive techniques, it provides a mechanism to monitor and visualize these data sets and provide insight into degradations encountered in large sensor systems such as the space surveillance network. In this study, data from a notional sensor is simulated and we use big data technologies, predictive algorithms and operational analytics to process the data and predict sensor degradations. This study uses data products that would commonly be analyzed at a site. This study builds on a big data architecture that has previously been proven valuable in detecting anomalies. This paper outlines our methodology of implementing an operational analytic solution through data discovery, learning and training of data modeling and predictive techniques, and deployment. Through this methodology, we implement a functional architecture focused on exploring available big data sets and determine practical analytic, visualization, and predictive technologies.

Comparison of Left Ventricular Hypertrophy by Electrocardiography and Echocardiography in Children Using Analytics Tool.

PubMed

Tague, Lauren; Wiggs, Justin; Li, Qianxi; McCarter, Robert; Sherwin, Elizabeth; Weinberg, Jacqueline; Sable, Craig

2018-05-17

Left ventricular hypertrophy (LVH) is a common finding on pediatric electrocardiography (ECG) leading to many referrals for echocardiography (echo). This study utilizes a novel analytics tool that combines ECG and echo databases to evaluate ECG as a screening tool for LVH. SQL Server 2012 data warehouse incorporated ECG and echo databases for all patients from a single institution from 2006 to 2016. Customized queries identified patients 0-18 years old with LVH on ECG and an echo performed within 24 h. Using data visualization (Tableau) and analytic (Stata 14) software, ECG and echo findings were compared. Of 437,699 encounters, 4637 met inclusion criteria. ECG had high sensitivity (≥ 90%) but poor specificity (43%), and low positive predictive value (< 20%) for echo abnormalities. ECG performed only 11-22% better than chance (AROC = 0.50). 83% of subjects with LVH on ECG had normal left ventricle (LV) structure and size on echo. African-Americans with LVH were least likely to have an abnormal echo. There was a low correlation between V 6 R on ECG and echo-derived Z score of left ventricle diastolic diameter (r = 0.14) and LV mass index (r = 0.24). The data analytics client was able to mine a database of ECG and echo reports, comparing LVH by ECG and LV measurements and qualitative findings by echo, identifying an abnormal LV by echo in only 17% of cases with LVH on ECG. This novel tool is useful for rapid data mining for both clinical and research endeavors.

Visual Analytics 101

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scholtz, Jean; Burtner, Edwin R.; Cook, Kristin A.

This course will introduce the field of Visual Analytics to HCI researchers and practitioners highlighting the contributions they can make to this field. Topics will include a definition of visual analytics along with examples of current systems, types of tasks and end users, issues in defining user requirements, design of visualizations and interactions, guidelines and heuristics, the current state of user-centered evaluations, and metrics for evaluation. We encourage designers, HCI researchers, and HCI practitioners to attend to learn how their skills can contribute to advancing the state of the art of visual analytics

Rational Design of Photonic Dust from Nanoporous Anodic Alumina Films: A Versatile Photonic Nanotool for Visual Sensing

PubMed Central

Chen, Yuting; Santos, Abel; Wang, Ye; Kumeria, Tushar; Ho, Daena; Li, Junsheng; Wang, Changhai; Losic, Dusan

2015-01-01

Herein, we present a systematic study on the development, optimisation and applicability of interferometrically coloured distributed Bragg reflectors based on nanoporous anodic alumina (NAA-DBRs) in the form of films and nanoporous microparticles as visual/colorimetric analytical tools. Firstly, we synthesise a complete palette of NAA-DBRs by galvanostatic pulse anodisation approach, in which the current density is altered in a periodic fashion in order to engineer the effective medium of the resulting photonic films in depth. NAA-DBR photonic films feature vivid colours that can be tuned across the UV-visible-NIR spectrum by structural engineering. Secondly, the effective medium of the resulting photonic films is assessed systematically by visual analysis and reflectometric interference spectroscopy (RIfS) in order to establish the most optimal nanoporous platforms to develop visual/colorimetric tools. Then, we demonstrate the applicability of NAA-DBR photonic films as a chemically selective sensing platform for visual detection of mercury(II) ions. Finally, we generate a new nanomaterial, so-called photonic dust, by breaking down NAA-DBRs films into nanoporous microparticles. The resulting microparticles (μP-NAA-DBRs) display vivid colours and are sensitive towards changes in their effective medium, opening new opportunities for developing advanced photonic nanotools for a broad range of applications. PMID:26245759

Rational Design of Photonic Dust from Nanoporous Anodic Alumina Films: A Versatile Photonic Nanotool for Visual Sensing

NASA Astrophysics Data System (ADS)

Chen, Yuting; Santos, Abel; Wang, Ye; Kumeria, Tushar; Ho, Daena; Li, Junsheng; Wang, Changhai; Losic, Dusan

2015-08-01

Herein, we present a systematic study on the development, optimisation and applicability of interferometrically coloured distributed Bragg reflectors based on nanoporous anodic alumina (NAA-DBRs) in the form of films and nanoporous microparticles as visual/colorimetric analytical tools. Firstly, we synthesise a complete palette of NAA-DBRs by galvanostatic pulse anodisation approach, in which the current density is altered in a periodic fashion in order to engineer the effective medium of the resulting photonic films in depth. NAA-DBR photonic films feature vivid colours that can be tuned across the UV-visible-NIR spectrum by structural engineering. Secondly, the effective medium of the resulting photonic films is assessed systematically by visual analysis and reflectometric interference spectroscopy (RIfS) in order to establish the most optimal nanoporous platforms to develop visual/colorimetric tools. Then, we demonstrate the applicability of NAA-DBR photonic films as a chemically selective sensing platform for visual detection of mercury(II) ions. Finally, we generate a new nanomaterial, so-called photonic dust, by breaking down NAA-DBRs films into nanoporous microparticles. The resulting microparticles (μP-NAA-DBRs) display vivid colours and are sensitive towards changes in their effective medium, opening new opportunities for developing advanced photonic nanotools for a broad range of applications.

Towards sustainable infrastructure management: knowledge-based service-oriented computing framework for visual analytics

NASA Astrophysics Data System (ADS)

Vatcha, Rashna; Lee, Seok-Won; Murty, Ajeet; Tolone, William; Wang, Xiaoyu; Dou, Wenwen; Chang, Remco; Ribarsky, William; Liu, Wanqiu; Chen, Shen-en; Hauser, Edd

2009-05-01

Infrastructure management (and its associated processes) is complex to understand, perform and thus, hard to make efficient and effective informed decisions. The management involves a multi-faceted operation that requires the most robust data fusion, visualization and decision making. In order to protect and build sustainable critical assets, we present our on-going multi-disciplinary large-scale project that establishes the Integrated Remote Sensing and Visualization (IRSV) system with a focus on supporting bridge structure inspection and management. This project involves specific expertise from civil engineers, computer scientists, geographers, and real-world practitioners from industry, local and federal government agencies. IRSV is being designed to accommodate the essential needs from the following aspects: 1) Better understanding and enforcement of complex inspection process that can bridge the gap between evidence gathering and decision making through the implementation of ontological knowledge engineering system; 2) Aggregation, representation and fusion of complex multi-layered heterogeneous data (i.e. infrared imaging, aerial photos and ground-mounted LIDAR etc.) with domain application knowledge to support machine understandable recommendation system; 3) Robust visualization techniques with large-scale analytical and interactive visualizations that support users' decision making; and 4) Integration of these needs through the flexible Service-oriented Architecture (SOA) framework to compose and provide services on-demand. IRSV is expected to serve as a management and data visualization tool for construction deliverable assurance and infrastructure monitoring both periodically (annually, monthly, even daily if needed) as well as after extreme events.

Interactive visual comparison of multimedia data through type-specific views

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burtner, Edwin R.; Bohn, Shawn J.; Payne, Deborah A.

2013-02-05

Analysts who work with collections of multimedia to perform information foraging understand how difficult it is to connect information across diverse sets of mixed media. The wealth of information from blogs, social media, and news sites often can provide actionable intelligence; however, many of the tools used on these sources of content are not capable of multimedia analysis because they only analyze a single media type. As such, analysts are taxed to keep a mental model of the relationships among each of the media types when generating the broader content picture. To address this need, we have developed Canopy, amore » novel visual analytic tool for analyzing multimedia. Canopy provides insight into the multimedia data relationships by exploiting the linkages found in text, images, and video co-occurring in the same document and across the collection. Canopy connects derived and explicit linkages and relationships through multiple connected visualizations to aid analysts in quickly summarizing, searching, and browsing collected information to explore relationships and align content. In this paper, we will discuss the features and capabilities of the Canopy system and walk through a scenario illustrating how this system might be used in an operational environment. Keywords: Multimedia (Image/Video/Music) Visualization.« less

Clustervision: Visual Supervision of Unsupervised Clustering.

PubMed

Kwon, Bum Chul; Eysenbach, Ben; Verma, Janu; Ng, Kenney; De Filippi, Christopher; Stewart, Walter F; Perer, Adam

2018-01-01

Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.

Changes in Visual/Spatial and Analytic Strategy Use in Organic Chemistry with the Development of Expertise

ERIC Educational Resources Information Center

Vlacholia, Maria; Vosniadou, Stella; Roussos, Petros; Salta, Katerina; Kazi, Smaragda; Sigalas, Michael; Tzougraki, Chryssa

2017-01-01

We present two studies that investigated the adoption of visual/spatial and analytic strategies by individuals at different levels of expertise in the area of organic chemistry, using the Visual Analytic Chemistry Task (VACT). The VACT allows the direct detection of analytic strategy use without drawing inferences about underlying mental…

User-Centered Evaluation of Visual Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scholtz, Jean C.

Visual analytics systems are becoming very popular. More domains now use interactive visualizations to analyze the ever-increasing amount and heterogeneity of data. More novel visualizations are being developed for more tasks and users. We need to ensure that these systems can be evaluated to determine that they are both useful and usable. A user-centered evaluation for visual analytics needs to be developed for these systems. While many of the typical human-computer interaction (HCI) evaluation methodologies can be applied as is, others will need modification. Additionally, new functionality in visual analytics systems needs new evaluation methodologies. There is a difference betweenmore » usability evaluations and user-centered evaluations. Usability looks at the efficiency, effectiveness, and user satisfaction of users carrying out tasks with software applications. User-centered evaluation looks more specifically at the utility provided to the users by the software. This is reflected in the evaluations done and in the metrics used. In the visual analytics domain this is very challenging as users are most likely experts in a particular domain, the tasks they do are often not well defined, the software they use needs to support large amounts of different kinds of data, and often the tasks last for months. These difficulties are discussed more in the section on User-centered Evaluation. Our goal is to provide a discussion of user-centered evaluation practices for visual analytics, including existing practices that can be carried out and new methodologies and metrics that need to be developed and agreed upon by the visual analytics community. The material provided here should be of use for both researchers and practitioners in the field of visual analytics. Researchers and practitioners in HCI and interested in visual analytics will find this information useful as well as a discussion on changes that need to be made to current HCI practices to make them more suitable to visual analytics. A history of analysis and analysis techniques and problems is provided as well as an introduction to user-centered evaluation and various evaluation techniques for readers from different disciplines. The understanding of these techniques is imperative if we wish to support analysis in the visual analytics software we develop. Currently the evaluations that are conducted and published for visual analytics software are very informal and consist mainly of comments from users or potential users. Our goal is to help researchers in visual analytics to conduct more formal user-centered evaluations. While these are time-consuming and expensive to carryout, the outcomes of these studies will have a defining impact on the field of visual analytics and help point the direction for future features and visualizations to incorporate. While many researchers view work in user-centered evaluation as a less-than-exciting area to work, the opposite is true. First of all, the goal is user-centered evaluation is to help visual analytics software developers, researchers, and designers improve their solutions and discover creative ways to better accommodate their users. Working with the users is extremely rewarding as well. While we use the term “users” in almost all situations there are a wide variety of users that all need to be accommodated. Moreover, the domains that use visual analytics are varied and expanding. Just understanding the complexities of a number of these domains is exciting. Researchers are trying out different visualizations and interactions as well. And of course, the size and variety of data are expanding rapidly. User-centered evaluation in this context is rapidly changing. There are no standard processes and metrics and thus those of us working on user-centered evaluation must be creative in our work with both the users and with the researchers and developers.« less

A cognitive task analysis of a visual analytic workflow: Exploring molecular interaction networks in systems biology.

PubMed

Mirel, Barbara; Eichinger, Felix; Keller, Benjamin J; Kretzler, Matthias

2011-03-21

Bioinformatics visualization tools are often not robust enough to support biomedical specialists’ complex exploratory analyses. Tools need to accommodate the workflows that scientists actually perform for specific translational research questions. To understand and model one of these workflows, we conducted a case-based, cognitive task analysis of a biomedical specialist’s exploratory workflow for the question: What functional interactions among gene products of high throughput expression data suggest previously unknown mechanisms of a disease? From our cognitive task analysis four complementary representations of the targeted workflow were developed. They include: usage scenarios, flow diagrams, a cognitive task taxonomy, and a mapping between cognitive tasks and user-centered visualization requirements. The representations capture the flows of cognitive tasks that led a biomedical specialist to inferences critical to hypothesizing. We created representations at levels of detail that could strategically guide visualization development, and we confirmed this by making a trial prototype based on user requirements for a small portion of the workflow. Our results imply that visualizations should make available to scientific users “bundles of features” consonant with the compositional cognitive tasks purposefully enacted at specific points in the workflow. We also highlight certain aspects of visualizations that: (a) need more built-in flexibility; (b) are critical for negotiating meaning; and (c) are necessary for essential metacognitive support.

The case for visual analytics of arsenic concentrations in foods.

PubMed

Johnson, Matilda O; Cohly, Hari H P; Isokpehi, Raphael D; Awofolu, Omotayo R

2010-05-01

Arsenic is a naturally occurring toxic metal and its presence in food could be a potential risk to the health of both humans and animals. Prolonged ingestion of arsenic contaminated water may result in manifestations of toxicity in all systems of the body. Visual Analytics is a multidisciplinary field that is defined as the science of analytical reasoning facilitated by interactive visual interfaces. The concentrations of arsenic vary in foods making it impractical and impossible to provide regulatory limit for each food. This review article presents a case for the use of visual analytics approaches to provide comparative assessment of arsenic in various foods. The topics covered include (i) metabolism of arsenic in the human body; (ii) arsenic concentrations in various foods; (ii) factors affecting arsenic uptake in plants; (ii) introduction to visual analytics; and (iv) benefits of visual analytics for comparative assessment of arsenic concentration in foods. Visual analytics can provide an information superstructure of arsenic in various foods to permit insightful comparative risk assessment of the diverse and continually expanding data on arsenic in food groups in the context of country of study or origin, year of study, method of analysis and arsenic species.

The Case for Visual Analytics of Arsenic Concentrations in Foods

PubMed Central

Johnson, Matilda O.; Cohly, Hari H.P.; Isokpehi, Raphael D.; Awofolu, Omotayo R.

2010-01-01

Arsenic is a naturally occurring toxic metal and its presence in food could be a potential risk to the health of both humans and animals. Prolonged ingestion of arsenic contaminated water may result in manifestations of toxicity in all systems of the body. Visual Analytics is a multidisciplinary field that is defined as the science of analytical reasoning facilitated by interactive visual interfaces. The concentrations of arsenic vary in foods making it impractical and impossible to provide regulatory limit for each food. This review article presents a case for the use of visual analytics approaches to provide comparative assessment of arsenic in various foods. The topics covered include (i) metabolism of arsenic in the human body; (ii) arsenic concentrations in various foods; (ii) factors affecting arsenic uptake in plants; (ii) introduction to visual analytics; and (iv) benefits of visual analytics for comparative assessment of arsenic concentration in foods. Visual analytics can provide an information superstructure of arsenic in various foods to permit insightful comparative risk assessment of the diverse and continually expanding data on arsenic in food groups in the context of country of study or origin, year of study, method of analysis and arsenic species. PMID:20623005

Analytical evaluation of two motion washout techniques

NASA Technical Reports Server (NTRS)

Young, L. R.

1977-01-01

Practical tools were developed which extend the state of the art of moving base flight simulation for research and training purposes. The use of visual and vestibular cues to minimize the actual motion of the simulator itself was a primary consideration. The investigation consisted of optimum programming of motion cues based on a physiological model of the vestibular system to yield 'ideal washout logic' for any given simulator constraints.

Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality

PubMed Central

Chen, Jin; Roth, Robert E; Naito, Adam T; Lengerich, Eugene J; MacEachren, Alan M

2008-01-01

Background Kulldorff's spatial scan statistic and its software implementation – SaTScan – are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S. Results We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results. Conclusion The geovisual analytics approach described in this manuscript facilitates the interpretation of spatial cluster detection methods by providing cartographic representation of SaTScan results and by providing visualization methods and tools that support selection of SaTScan parameters. Our methods distinguish between heterogeneous and homogeneous clusters and assess the stability of clusters across analytic scales. Method We analyzed the cervical cancer mortality data for the United States aggregated by county between 2000 and 2004. We ran SaTScan on the dataset fifty times with different parameter choices. Our geovisual analytics approach couples SaTScan with our visual analytic platform, allowing users to interactively explore and compare SaTScan results produced by different parameter choices. The Standardized Mortality Ratio and reliability scores are visualized for all the counties to identify stable, homogeneous clusters. We evaluated our analysis result by comparing it to that produced by other independent techniques including the Empirical Bayes Smoothing and Kafadar spatial smoother methods. The geovisual analytics approach introduced here is developed and implemented in our Java-based Visual Inquiry Toolkit. PMID:18992163

Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality.

PubMed

Chen, Jin; Roth, Robert E; Naito, Adam T; Lengerich, Eugene J; Maceachren, Alan M

2008-11-07

Kulldorff's spatial scan statistic and its software implementation - SaTScan - are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S. We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results. The geovisual analytics approach described in this manuscript facilitates the interpretation of spatial cluster detection methods by providing cartographic representation of SaTScan results and by providing visualization methods and tools that support selection of SaTScan parameters. Our methods distinguish between heterogeneous and homogeneous clusters and assess the stability of clusters across analytic scales. We analyzed the cervical cancer mortality data for the United States aggregated by county between 2000 and 2004. We ran SaTScan on the dataset fifty times with different parameter choices. Our geovisual analytics approach couples SaTScan with our visual analytic platform, allowing users to interactively explore and compare SaTScan results produced by different parameter choices. The Standardized Mortality Ratio and reliability scores are visualized for all the counties to identify stable, homogeneous clusters. We evaluated our analysis result by comparing it to that produced by other independent techniques including the Empirical Bayes Smoothing and Kafadar spatial smoother methods. The geovisual analytics approach introduced here is developed and implemented in our Java-based Visual Inquiry Toolkit.

GWAS in a Box: Statistical and Visual Analytics of Structured Associations via GenAMap

PubMed Central

Xing, Eric P.; Curtis, Ross E.; Schoenherr, Georg; Lee, Seunghak; Yin, Junming; Puniyani, Kriti; Wu, Wei; Kinnaird, Peter

2014-01-01

With the continuous improvement in genotyping and molecular phenotyping technology and the decreasing typing cost, it is expected that in a few years, more and more clinical studies of complex diseases will recruit thousands of individuals for pan-omic genetic association analyses. Hence, there is a great need for algorithms and software tools that could scale up to the whole omic level, integrate different omic data, leverage rich structure information, and be easily accessible to non-technical users. We present GenAMap, an interactive analytics software platform that 1) automates the execution of principled machine learning methods that detect genome- and phenome-wide associations among genotypes, gene expression data, and clinical or other macroscopic traits, and 2) provides new visualization tools specifically designed to aid in the exploration of association mapping results. Algorithmically, GenAMap is based on a new paradigm for GWAS and PheWAS analysis, termed structured association mapping, which leverages various structures in the omic data. We demonstrate the function of GenAMap via a case study of the Brem and Kruglyak yeast dataset, and then apply it on a comprehensive eQTL analysis of the NIH heterogeneous stock mice dataset and report some interesting findings. GenAMap is available from http://sailing.cs.cmu.edu/genamap. PMID:24905018

Interactive exploration of surveillance video through action shot summarization and trajectory visualization.

PubMed

Meghdadi, Amir H; Irani, Pourang

2013-12-01

We propose a novel video visual analytics system for interactive exploration of surveillance video data. Our approach consists of providing analysts with various views of information related to moving objects in a video. To do this we first extract each object's movement path. We visualize each movement by (a) creating a single action shot image (a still image that coalesces multiple frames), (b) plotting its trajectory in a space-time cube and (c) displaying an overall timeline view of all the movements. The action shots provide a still view of the moving object while the path view presents movement properties such as speed and location. We also provide tools for spatial and temporal filtering based on regions of interest. This allows analysts to filter out large amounts of movement activities while the action shot representation summarizes the content of each movement. We incorporated this multi-part visual representation of moving objects in sViSIT, a tool to facilitate browsing through the video content by interactive querying and retrieval of data. Based on our interaction with security personnel who routinely interact with surveillance video data, we identified some of the most common tasks performed. This resulted in designing a user study to measure time-to-completion of the various tasks. These generally required searching for specific events of interest (targets) in videos. Fourteen different tasks were designed and a total of 120 min of surveillance video were recorded (indoor and outdoor locations recording movements of people and vehicles). The time-to-completion of these tasks were compared against a manual fast forward video browsing guided with movement detection. We demonstrate how our system can facilitate lengthy video exploration and significantly reduce browsing time to find events of interest. Reports from expert users identify positive aspects of our approach which we summarize in our recommendations for future video visual analytics systems.

A digital future for the history of psychology?

PubMed

Green, Christopher D

2016-08-01

This article discusses the role that digital approaches to the history of psychology are likely to play in the near future. A tentative hierarchy of digital methods is proposed. A few examples are briefly described: a digital repository, a simple visualization using ready-made online database and tools, and more complex visualizations requiring the assembly of the database and, possibly, the analytic tools by the researcher. The relationship of digital history to the old "New Economic History" (Cliometrics) is considered. The question of whether digital history and traditional history need be at odds or, instead, might complement each other is woven throughout. The rapidly expanding territory of digital humanistic research outside of psychology is briefly discussed. Finally, the challenging current employment trends in history and the humanities more broadly are considered, along with the role that digital skills might play in mitigating those factors for prospective academic workers. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Beyond Control Panels: Direct Manipulation for Visual Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Endert, Alexander; Bradel, Lauren; North, Chris

2013-07-19

Information Visualization strives to provide visual representations through which users can think about and gain insight into information. By leveraging the visual and cognitive systems of humans, complex relationships and phenomena occurring within datasets can be uncovered by exploring information visually. Interaction metaphors for such visualizations are designed to enable users direct control over the filters, queries, and other parameters controlling how the data is visually represented. Through the evolution of information visualization, more complex mathematical and data analytic models are being used to visualize relationships and patterns in data – creating the field of Visual Analytics. However, the expectationsmore » for how users interact with these visualizations has remained largely unchanged – focused primarily on the direct manipulation of parameters of the underlying mathematical models. In this article we present an opportunity to evolve the methodology for user interaction from the direct manipulation of parameters through visual control panels, to interactions designed specifically for visual analytic systems. Instead of focusing on traditional direct manipulation of mathematical parameters, the evolution of the field can be realized through direct manipulation within the visual representation – where users can not only gain insight, but also interact. This article describes future directions and research challenges that fundamentally change the meaning of direct manipulation with regards to visual analytics, advancing the Science of Interaction.« less

Web-Based Geospatial Visualization of GPM Data with CesiumJS

NASA Technical Reports Server (NTRS)

Lammers, Matt

2018-01-01

Advancements in the capabilities of JavaScript frameworks and web browsing technology have made online visualization of large geospatial datasets such as those coming from precipitation satellites viable. These data benefit from being visualized on and above a three-dimensional surface. The open-source JavaScript framework CesiumJS (http://cesiumjs.org), developed by Analytical Graphics, Inc., leverages the WebGL protocol to do just that. This presentation will describe how CesiumJS has been used in three-dimensional visualization products developed as part of the NASA Precipitation Processing System (PPS) STORM data-order website. Existing methods of interacting with Global Precipitation Measurement (GPM) Mission data primarily focus on two-dimensional static images, whether displaying vertical slices or horizontal surface/height-level maps. These methods limit interactivity with the robust three-dimensional data coming from the GPM core satellite. Integrating the data with CesiumJS in a web-based user interface has allowed us to create the following products. We have linked with the data-order interface an on-the-fly visualization tool for any GPM/partner satellite orbit. A version of this tool also focuses on high-impact weather events. It enables viewing of combined radar and microwave-derived precipitation data on mobile devices and in a way that can be embedded into other websites. We also have used CesiumJS to visualize a method of integrating gridded precipitation data with modeled wind speeds that animates over time. Emphasis in the presentation will be placed on how a variety of technical methods were used to create these tools, and how the flexibility of the CesiumJS framework facilitates creative approaches to interact with the data.

Comparative Chemometric Analysis for Classification of Acids and Bases via a Colorimetric Sensor Array.

PubMed

Kangas, Michael J; Burks, Raychelle M; Atwater, Jordyn; Lukowicz, Rachel M; Garver, Billy; Holmes, Andrea E

2018-02-01

With the increasing availability of digital imaging devices, colorimetric sensor arrays are rapidly becoming a simple, yet effective tool for the identification and quantification of various analytes. Colorimetric arrays utilize colorimetric data from many colorimetric sensors, with the multidimensional nature of the resulting data necessitating the use of chemometric analysis. Herein, an 8 sensor colorimetric array was used to analyze select acid and basic samples (0.5 - 10 M) to determine which chemometric methods are best suited for classification quantification of analytes within clusters. PCA, HCA, and LDA were used to visualize the data set. All three methods showed well-separated clusters for each of the acid or base analytes and moderate separation between analyte concentrations, indicating that the sensor array can be used to identify and quantify samples. Furthermore, PCA could be used to determine which sensors showed the most effective analyte identification. LDA, KNN, and HQI were used for identification of analyte and concentration. HQI and KNN could be used to correctly identify the analytes in all cases, while LDA correctly identified 95 of 96 analytes correctly. Additional studies demonstrated that controlling for solvent and image effects was unnecessary for all chemometric methods utilized in this study.

Science-Driven Computing: NERSC's Plan for 2006-2010

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simon, Horst D.; Kramer, William T.C.; Bailey, David H.

NERSC has developed a five-year strategic plan focusing on three components: Science-Driven Systems, Science-Driven Services, and Science-Driven Analytics. (1) Science-Driven Systems: Balanced introduction of the best new technologies for complete computational systems--computing, storage, networking, visualization and analysis--coupled with the activities necessary to engage vendors in addressing the DOE computational science requirements in their future roadmaps. (2) Science-Driven Services: The entire range of support activities, from high-quality operations and user services to direct scientific support, that enable a broad range of scientists to effectively use NERSC systems in their research. NERSC will concentrate on resources needed to realize the promise ofmore » the new highly scalable architectures for scientific discovery in multidisciplinary computational science projects. (3) Science-Driven Analytics: The architectural and systems enhancements and services required to integrate NERSC's powerful computational and storage resources to provide scientists with new tools to effectively manipulate, visualize, and analyze the huge data sets derived from simulations and experiments.« less

Using business intelligence for efficient inter-facility patient transfer.

PubMed

Haque, Waqar; Derksen, Beth Ann; Calado, Devin; Foster, Lee

2015-01-01

In the context of inter-facility patient transfer, a transfer operator must be able to objectively identify a destination which meets the needs of a patient, while keeping in mind each facility's limitations. We propose a solution which uses Business Intelligence (BI) techniques to analyze data related to healthcare infrastructure and services, and provides a web based system to identify optimal destination(s). The proposed inter-facility transfer system uses a single data warehouse with an Online Analytical Processing (OLAP) cube built on top that supplies analytical data to multiple reports embedded in web pages. The data visualization tool includes map based navigation of the health authority as well as an interactive filtering mechanism which finds facilities meeting the selected criteria. The data visualization is backed by an intuitive data entry web form which safely constrains the data, ensuring consistency and a single version of truth. The overall time required to identify the destination for inter-facility transfers is reduced from hours to a few minutes with this interactive solution.

The Hico Image Processing System: A Web-Accessible Hyperspectral Remote Sensing Toolbox

NASA Astrophysics Data System (ADS)

Harris, A. T., III; Goodman, J.; Justice, B.

2014-12-01

As the quantity of Earth-observation data increases, the use-case for hosting analytical tools in geospatial data centers becomes increasingly attractive. To address this need, HySpeed Computing and Exelis VIS have developed the HICO Image Processing System, a prototype cloud computing system that provides online, on-demand, scalable remote sensing image processing capabilities. The system provides a mechanism for delivering sophisticated image processing analytics and data visualization tools into the hands of a global user community, who will only need a browser and internet connection to perform analysis. Functionality of the HICO Image Processing System is demonstrated using imagery from the Hyperspectral Imager for the Coastal Ocean (HICO), an imaging spectrometer located on the International Space Station (ISS) that is optimized for acquisition of aquatic targets. Example applications include a collection of coastal remote sensing algorithms that are directed at deriving critical information on water and habitat characteristics of our vulnerable coastal environment. The project leverages the ENVI Services Engine as the framework for all image processing tasks, and can readily accommodate the rapid integration of new algorithms, datasets and processing tools.

Examining the Use of a Visual Analytics System for Sensemaking Tasks: Case Studies with Domain Experts.

PubMed

Kang, Youn-Ah; Stasko, J

2012-12-01

While the formal evaluation of systems in visual analytics is still relatively uncommon, particularly rare are case studies of prolonged system use by domain analysts working with their own data. Conducting case studies can be challenging, but it can be a particularly effective way to examine whether visual analytics systems are truly helping expert users to accomplish their goals. We studied the use of a visual analytics system for sensemaking tasks on documents by six analysts from a variety of domains. We describe their application of the system along with the benefits, issues, and problems that we uncovered. Findings from the studies identify features that visual analytics systems should emphasize as well as missing capabilities that should be addressed. These findings inform design implications for future systems.

Dcode.org anthology of comparative genomic tools.

PubMed

Loots, Gabriela G; Ovcharenko, Ivan

2005-07-01

Comparative genomics provides the means to demarcate functional regions in anonymous DNA sequences. The successful application of this method to identifying novel genes is currently shifting to deciphering the non-coding encryption of gene regulation across genomes. To facilitate the practical application of comparative sequence analysis to genetics and genomics, we have developed several analytical and visualization tools for the analysis of arbitrary sequences and whole genomes. These tools include two alignment tools, zPicture and Mulan; a phylogenetic shadowing tool, eShadow for identifying lineage- and species-specific functional elements; two evolutionary conserved transcription factor analysis tools, rVista and multiTF; a tool for extracting cis-regulatory modules governing the expression of co-regulated genes, Creme 2.0; and a dynamic portal to multiple vertebrate and invertebrate genome alignments, the ECR Browser. Here, we briefly describe each one of these tools and provide specific examples on their practical applications. All the tools are publicly available at the http://www.dcode.org/ website.

Timely Reporting and Interactive Visualization of Animal Health and Slaughterhouse Surveillance Data in Switzerland.

PubMed

Muellner, Ulrich J; Vial, Flavie; Wohlfender, Franziska; Hadorn, Daniela; Reist, Martin; Muellner, Petra

2015-01-01

The reporting of outputs from health surveillance systems should be done in a near real-time and interactive manner in order to provide decision makers with powerful means to identify, assess, and manage health hazards as early and efficiently as possible. While this is currently rarely the case in veterinary public health surveillance, reporting tools do exist for the visual exploration and interactive interrogation of health data. In this work, we used tools freely available from the Google Maps and Charts library to develop a web application reporting health-related data derived from slaughterhouse surveillance and from a newly established web-based equine surveillance system in Switzerland. Both sets of tools allowed entry-level usage without or with minimal programing skills while being flexible enough to cater for more complex scenarios for users with greater programing skills. In particular, interfaces linking statistical softwares and Google tools provide additional analytical functionality (such as algorithms for the detection of unusually high case occurrences) for inclusion in the reporting process. We show that such powerful approaches could improve timely dissemination and communication of technical information to decision makers and other stakeholders and could foster the early-warning capacity of animal health surveillance systems.

The forensic validity of visual analytics

NASA Astrophysics Data System (ADS)

Erbacher, Robert F.

2008-01-01

The wider use of visualization and visual analytics in wide ranging fields has led to the need for visual analytics capabilities to be legally admissible, especially when applied to digital forensics. This brings the need to consider legal implications when performing visual analytics, an issue not traditionally examined in visualization and visual analytics techniques and research. While digital data is generally admissible under the Federal Rules of Evidence [10][21], a comprehensive validation of the digital evidence is considered prudent. A comprehensive validation requires validation of the digital data under rules for authentication, hearsay, best evidence rule, and privilege. Additional issues with digital data arise when exploring digital data related to admissibility and the validity of what information was examined, to what extent, and whether the analysis process was sufficiently covered by a search warrant. For instance, a search warrant generally covers very narrow requirements as to what law enforcement is allowed to examine and acquire during an investigation. When searching a hard drive for child pornography, how admissible is evidence of an unrelated crime, i.e. drug dealing. This is further complicated by the concept of "in plain view". When performing an analysis of a hard drive what would be considered "in plain view" when analyzing a hard drive. The purpose of this paper is to discuss the issues of digital forensics and the related issues as they apply to visual analytics and identify how visual analytics techniques fit into the digital forensics analysis process, how visual analytics techniques can improve the legal admissibility of digital data, and identify what research is needed to further improve this process. The goal of this paper is to open up consideration of legal ramifications among the visualization community; the author is not a lawyer and the discussions are not meant to be inclusive of all differences in laws between states and countries.

A Visual Analytic for High-Dimensional Data Exploitation: The Heterogeneous Data-Reduction Proximity Tool

DTIC Science & Technology

2013-07-01

structure of the data and Gower’s similarity coefficient as the algorithm for calculating the proximity matrices. The following section provides a...representative set of terrorist event data. Attribute Day Location Time Prim /Attack Sec/Attack Weight 1 1 1 1 1 Scale Nominal Nominal Interval Nominal...calculate the similarity it uses Gower’s similarity and multidimensional scaling algorithms contained in an R statistical computing environment

Human Subject Research Protocol: Computer-Aided Human Centric Cyber Situation Awareness: Understanding Cognitive Processes of Cyber Analysts

DTIC Science & Technology

2013-11-01

by existing cyber-attack detection tools far exceeds the analysts’ cognitive capabilities. Grounded in perceptual and cognitive theory , many visual...Processes Inspired by the sense-making theory discussed earlier, we model the analytical reasoning process of cyber analysts using three key...analyst are called “working hypotheses”); each hypothesis could trigger further actions to confirm or disconfirm it. New actions will lead to new

A workflow learning model to improve geovisual analytics utility

PubMed Central

Roth, Robert E; MacEachren, Alan M; McCabe, Craig A

2011-01-01

Introduction This paper describes the design and implementation of the G-EX Portal Learn Module, a web-based, geocollaborative application for organizing and distributing digital learning artifacts. G-EX falls into the broader context of geovisual analytics, a new research area with the goal of supporting visually-mediated reasoning about large, multivariate, spatiotemporal information. Because this information is unprecedented in amount and complexity, GIScientists are tasked with the development of new tools and techniques to make sense of it. Our research addresses the challenge of implementing these geovisual analytics tools and techniques in a useful manner. Objectives The objective of this paper is to develop and implement a method for improving the utility of geovisual analytics software. The success of software is measured by its usability (i.e., how easy the software is to use?) and utility (i.e., how useful the software is). The usability and utility of software can be improved by refining the software, increasing user knowledge about the software, or both. It is difficult to achieve transparent usability (i.e., software that is immediately usable without training) of geovisual analytics software because of the inherent complexity of the included tools and techniques. In these situations, improving user knowledge about the software through the provision of learning artifacts is as important, if not more so, than iterative refinement of the software itself. Therefore, our approach to improving utility is focused on educating the user. Methodology The research reported here was completed in two steps. First, we developed a model for learning about geovisual analytics software. Many existing digital learning models assist only with use of the software to complete a specific task and provide limited assistance with its actual application. To move beyond task-oriented learning about software use, we propose a process-oriented approach to learning based on the concept of scientific workflows. Second, we implemented an interface in the G-EX Portal Learn Module to demonstrate the workflow learning model. The workflow interface allows users to drag learning artifacts uploaded to the G-EX Portal onto a central whiteboard and then annotate the workflow using text and drawing tools. Once completed, users can visit the assembled workflow to get an idea of the kind, number, and scale of analysis steps, view individual learning artifacts associated with each node in the workflow, and ask questions about the overall workflow or individual learning artifacts through the associated forums. An example learning workflow in the domain of epidemiology is provided to demonstrate the effectiveness of the approach. Results/Conclusions In the context of geovisual analytics, GIScientists are not only responsible for developing software to facilitate visually-mediated reasoning about large and complex spatiotemporal information, but also for ensuring that this software works. The workflow learning model discussed in this paper and demonstrated in the G-EX Portal Learn Module is one approach to improving the utility of geovisual analytics software. While development of the G-EX Portal Learn Module is ongoing, we expect to release the G-EX Portal Learn Module by Summer 2009. PMID:21983545

A workflow learning model to improve geovisual analytics utility.

PubMed

Roth, Robert E; Maceachren, Alan M; McCabe, Craig A

2009-01-01

INTRODUCTION: This paper describes the design and implementation of the G-EX Portal Learn Module, a web-based, geocollaborative application for organizing and distributing digital learning artifacts. G-EX falls into the broader context of geovisual analytics, a new research area with the goal of supporting visually-mediated reasoning about large, multivariate, spatiotemporal information. Because this information is unprecedented in amount and complexity, GIScientists are tasked with the development of new tools and techniques to make sense of it. Our research addresses the challenge of implementing these geovisual analytics tools and techniques in a useful manner. OBJECTIVES: The objective of this paper is to develop and implement a method for improving the utility of geovisual analytics software. The success of software is measured by its usability (i.e., how easy the software is to use?) and utility (i.e., how useful the software is). The usability and utility of software can be improved by refining the software, increasing user knowledge about the software, or both. It is difficult to achieve transparent usability (i.e., software that is immediately usable without training) of geovisual analytics software because of the inherent complexity of the included tools and techniques. In these situations, improving user knowledge about the software through the provision of learning artifacts is as important, if not more so, than iterative refinement of the software itself. Therefore, our approach to improving utility is focused on educating the user. METHODOLOGY: The research reported here was completed in two steps. First, we developed a model for learning about geovisual analytics software. Many existing digital learning models assist only with use of the software to complete a specific task and provide limited assistance with its actual application. To move beyond task-oriented learning about software use, we propose a process-oriented approach to learning based on the concept of scientific workflows. Second, we implemented an interface in the G-EX Portal Learn Module to demonstrate the workflow learning model. The workflow interface allows users to drag learning artifacts uploaded to the G-EX Portal onto a central whiteboard and then annotate the workflow using text and drawing tools. Once completed, users can visit the assembled workflow to get an idea of the kind, number, and scale of analysis steps, view individual learning artifacts associated with each node in the workflow, and ask questions about the overall workflow or individual learning artifacts through the associated forums. An example learning workflow in the domain of epidemiology is provided to demonstrate the effectiveness of the approach. RESULTS/CONCLUSIONS: In the context of geovisual analytics, GIScientists are not only responsible for developing software to facilitate visually-mediated reasoning about large and complex spatiotemporal information, but also for ensuring that this software works. The workflow learning model discussed in this paper and demonstrated in the G-EX Portal Learn Module is one approach to improving the utility of geovisual analytics software. While development of the G-EX Portal Learn Module is ongoing, we expect to release the G-EX Portal Learn Module by Summer 2009.

Mining patterns in persistent surveillance systems with smart query and visual analytics

NASA Astrophysics Data System (ADS)

Habibi, Mohammad S.; Shirkhodaie, Amir

2013-05-01

In Persistent Surveillance Systems (PSS) the ability to detect and characterize events geospatially help take pre-emptive steps to counter adversary's actions. Interactive Visual Analytic (VA) model offers this platform for pattern investigation and reasoning to comprehend and/or predict such occurrences. The need for identifying and offsetting these threats requires collecting information from diverse sources, which brings with it increasingly abstract data. These abstract semantic data have a degree of inherent uncertainty and imprecision, and require a method for their filtration before being processed further. In this paper, we have introduced an approach based on Vector Space Modeling (VSM) technique for classification of spatiotemporal sequential patterns of group activities. The feature vectors consist of an array of attributes extracted from generated sensors semantic annotated messages. To facilitate proper similarity matching and detection of time-varying spatiotemporal patterns, a Temporal-Dynamic Time Warping (DTW) method with Gaussian Mixture Model (GMM) for Expectation Maximization (EM) is introduced. DTW is intended for detection of event patterns from neighborhood-proximity semantic frames derived from established ontology. GMM with EM, on the other hand, is employed as a Bayesian probabilistic model to estimated probability of events associated with a detected spatiotemporal pattern. In this paper, we present a new visual analytic tool for testing and evaluation group activities detected under this control scheme. Experimental results demonstrate the effectiveness of proposed approach for discovery and matching of subsequences within sequentially generated patterns space of our experiments.

DCODE.ORG Anthology of Comparative Genomic Tools

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loots, G G; Ovcharenko, I

2005-01-11

Comparative genomics provides the means to demarcate functional regions in anonymous DNA sequences. The successful application of this method to identifying novel genes is currently shifting to deciphering the noncoding encryption of gene regulation across genomes. To facilitate the use of comparative genomics to practical applications in genetics and genomics we have developed several analytical and visualization tools for the analysis of arbitrary sequences and whole genomes. These tools include two alignment tools: zPicture and Mulan; a phylogenetic shadowing tool: eShadow for identifying lineage- and species-specific functional elements; two evolutionary conserved transcription factor analysis tools: rVista and multiTF; a toolmore » for extracting cis-regulatory modules governing the expression of co-regulated genes, CREME; and a dynamic portal to multiple vertebrate and invertebrate genome alignments, the ECR Browser. Here we briefly describe each one of these tools and provide specific examples on their practical applications. All the tools are publicly available at the http://www.dcode.org/ web site.« less

Voice-enabled Knowledge Engine using Flood Ontology and Natural Language Processing

NASA Astrophysics Data System (ADS)

Sermet, M. Y.; Demir, I.; Krajewski, W. F.

2015-12-01

The Iowa Flood Information System (IFIS) is a web-based platform developed by the Iowa Flood Center (IFC) to provide access to flood inundation maps, real-time flood conditions, flood forecasts, flood-related data, information and interactive visualizations for communities in Iowa. The IFIS is designed for use by general public, often people with no domain knowledge and limited general science background. To improve effective communication with such audience, we have introduced a voice-enabled knowledge engine on flood related issues in IFIS. Instead of navigating within many features and interfaces of the information system and web-based sources, the system provides dynamic computations based on a collection of built-in data, analysis, and methods. The IFIS Knowledge Engine connects to real-time stream gauges, in-house data sources, analysis and visualization tools to answer natural language questions. Our goal is the systematization of data and modeling results on flood related issues in Iowa, and to provide an interface for definitive answers to factual queries. The goal of the knowledge engine is to make all flood related knowledge in Iowa easily accessible to everyone, and support voice-enabled natural language input. We aim to integrate and curate all flood related data, implement analytical and visualization tools, and make it possible to compute answers from questions. The IFIS explicitly implements analytical methods and models, as algorithms, and curates all flood related data and resources so that all these resources are computable. The IFIS Knowledge Engine computes the answer by deriving it from its computational knowledge base. The knowledge engine processes the statement, access data warehouse, run complex database queries on the server-side and return outputs in various formats. This presentation provides an overview of IFIS Knowledge Engine, its unique information interface and functionality as an educational tool, and discusses the future plans for providing knowledge on flood related issues and resources. IFIS Knowledge Engine provides an alternative access method to these comprehensive set of tools and data resources available in IFIS. Current implementation of the system accepts free-form input and voice recognition capabilities within browser and mobile applications.

An Analysis of Machine- and Human-Analytics in Classification.

PubMed

Tam, Gary K L; Kothari, Vivek; Chen, Min

2017-01-01

In this work, we present a study that traces the technical and cognitive processes in two visual analytics applications to a common theoretic model of soft knowledge that may be added into a visual analytics process for constructing a decision-tree model. Both case studies involved the development of classification models based on the "bag of features" approach. Both compared a visual analytics approach using parallel coordinates with a machine-learning approach using information theory. Both found that the visual analytics approach had some advantages over the machine learning approach, especially when sparse datasets were used as the ground truth. We examine various possible factors that may have contributed to such advantages, and collect empirical evidence for supporting the observation and reasoning of these factors. We propose an information-theoretic model as a common theoretic basis to explain the phenomena exhibited in these two case studies. Together we provide interconnected empirical and theoretical evidence to support the usefulness of visual analytics.

A reference web architecture and patterns for real-time visual analytics on large streaming data

NASA Astrophysics Data System (ADS)

Kandogan, Eser; Soroker, Danny; Rohall, Steven; Bak, Peter; van Ham, Frank; Lu, Jie; Ship, Harold-Jeffrey; Wang, Chun-Fu; Lai, Jennifer

2013-12-01

Monitoring and analysis of streaming data, such as social media, sensors, and news feeds, has become increasingly important for business and government. The volume and velocity of incoming data are key challenges. To effectively support monitoring and analysis, statistical and visual analytics techniques need to be seamlessly integrated; analytic techniques for a variety of data types (e.g., text, numerical) and scope (e.g., incremental, rolling-window, global) must be properly accommodated; interaction, collaboration, and coordination among several visualizations must be supported in an efficient manner; and the system should support the use of different analytics techniques in a pluggable manner. Especially in web-based environments, these requirements pose restrictions on the basic visual analytics architecture for streaming data. In this paper we report on our experience of building a reference web architecture for real-time visual analytics of streaming data, identify and discuss architectural patterns that address these challenges, and report on applying the reference architecture for real-time Twitter monitoring and analysis.

Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods.

PubMed

Martínez, María Jimena; Ponzoni, Ignacio; Díaz, Mónica F; Vazquez, Gustavo E; Soto, Axel J

2015-01-01

The design of QSAR/QSPR models is a challenging problem, where the selection of the most relevant descriptors constitutes a key step of the process. Several feature selection methods that address this step are concentrated on statistical associations among descriptors and target properties, whereas the chemical knowledge is left out of the analysis. For this reason, the interpretability and generality of the QSAR/QSPR models obtained by these feature selection methods are drastically affected. Therefore, an approach for integrating domain expert's knowledge in the selection process is needed for increase the confidence in the final set of descriptors. In this paper a software tool, which we named Visual and Interactive DEscriptor ANalysis (VIDEAN), that combines statistical methods with interactive visualizations for choosing a set of descriptors for predicting a target property is proposed. Domain expertise can be added to the feature selection process by means of an interactive visual exploration of data, and aided by statistical tools and metrics based on information theory. Coordinated visual representations are presented for capturing different relationships and interactions among descriptors, target properties and candidate subsets of descriptors. The competencies of the proposed software were assessed through different scenarios. These scenarios reveal how an expert can use this tool to choose one subset of descriptors from a group of candidate subsets or how to modify existing descriptor subsets and even incorporate new descriptors according to his or her own knowledge of the target property. The reported experiences showed the suitability of our software for selecting sets of descriptors with low cardinality, high interpretability, low redundancy and high statistical performance in a visual exploratory way. Therefore, it is possible to conclude that the resulting tool allows the integration of a chemist's expertise in the descriptor selection process with a low cognitive effort in contrast with the alternative of using an ad-hoc manual analysis of the selected descriptors. Graphical abstractVIDEAN allows the visual analysis of candidate subsets of descriptors for QSAR/QSPR. In the two panels on the top, users can interactively explore numerical correlations as well as co-occurrences in the candidate subsets through two interactive graphs.

Geo-Sandbox: An Interactive Geoscience Training Tool with Analytics to Better Understand Student Problem Solving Approaches

NASA Astrophysics Data System (ADS)

Butt, N.; Pidlisecky, A.; Ganshorn, H.; Cockett, R.

2015-12-01

The software company 3 Point Science has developed three interactive learning programs designed to teach, test and practice visualization skills and geoscience concepts. A study was conducted with 21 geoscience students at the University of Calgary who participated in 2 hour sessions of software interaction and written pre and post-tests. Computer and SMART touch table interfaces were used to analyze user interaction, problem solving methods and visualization skills. By understanding and pinpointing user problem solving methods it is possible to reconstruct viewpoints and thought processes. This could allow us to give personalized feedback in real time, informing the user of problem solving tips and possible misconceptions.

Performance Measurement, Visualization and Modeling of Parallel and Distributed Programs

NASA Technical Reports Server (NTRS)

Yan, Jerry C.; Sarukkai, Sekhar R.; Mehra, Pankaj; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

This paper presents a methodology for debugging the performance of message-passing programs on both tightly coupled and loosely coupled distributed-memory machines. The AIMS (Automated Instrumentation and Monitoring System) toolkit, a suite of software tools for measurement and analysis of performance, is introduced and its application illustrated using several benchmark programs drawn from the field of computational fluid dynamics. AIMS includes (i) Xinstrument, a powerful source-code instrumentor, which supports both Fortran77 and C as well as a number of different message-passing libraries including Intel's NX Thinking Machines' CMMD, and PVM; (ii) Monitor, a library of timestamping and trace -collection routines that run on supercomputers (such as Intel's iPSC/860, Delta, and Paragon and Thinking Machines' CM5) as well as on networks of workstations (including Convex Cluster and SparcStations connected by a LAN); (iii) Visualization Kernel, a trace-animation facility that supports source-code clickback, simultaneous visualization of computation and communication patterns, as well as analysis of data movements; (iv) Statistics Kernel, an advanced profiling facility, that associates a variety of performance data with various syntactic components of a parallel program; (v) Index Kernel, a diagnostic tool that helps pinpoint performance bottlenecks through the use of abstract indices; (vi) Modeling Kernel, a facility for automated modeling of message-passing programs that supports both simulation -based and analytical approaches to performance prediction and scalability analysis; (vii) Intrusion Compensator, a utility for recovering true performance from observed performance by removing the overheads of monitoring and their effects on the communication pattern of the program; and (viii) Compatibility Tools, that convert AIMS-generated traces into formats used by other performance-visualization tools, such as ParaGraph, Pablo, and certain AVS/Explorer modules.

Visual Analytics in Public Safety: Example Capabilities for Example Government Agencies

DTIC Science & Technology

2011-10-01

is not limited to: the Police Records Information Management Environment for British Columbia (PRIME-BC), the Police Reporting and Occurrence System...and filtering for rapid identification of relevant documents - Graphical environment for visual evidence marshaling - Interactive linking and...analytical reasoning facilitated by interactive visual interfaces and integration with computational analytics. Indeed, a wide variety of technologies

Analysis of Sea Level Rise in Action

NASA Astrophysics Data System (ADS)

Gill, K. M.; Huang, T.; Quach, N. T.; Boening, C.

2016-12-01

NASA's Sea Level Change Portal provides scientists and the general public with "one-stop" source for current sea level change information and data. Sea Level Rise research is a multidisciplinary research and in order to understand its causes, scientists must be able to access different measurements and to be able to compare them. The portal includes an interactive tool, called the Data Analysis Tool (DAT), for accessing, visualizing, and analyzing observations and models relevant to the study of Sea Level Rise. Using NEXUS, an open source, big data analytic technology developed at the Jet Propulsion Laboratory, the DAT is able provide user on-the-fly data analysis on all relevant parameters. DAT is composed of three major components: A dedicated instance of OnEarth (a WMTS service), NEXUS deep data analytic platform, and the JPL Common Mapping Client (CMC) for web browser based user interface (UI). Utilizing the global imagery, a user is capable of browsing the data in a visual manner and isolate areas of interest for further study. The interfaces "Analysis" tool provides tools for area or point selection, single and/or comparative dataset selection, and a range of options, algorithms, and plotting. This analysis component utilizes the Nexus cloud computing platform to provide on-demand processing of the data within the user-selected parameters and immediate display of the results. A RESTful web API is exposed for users comfortable with other interfaces and who may want to take advantage of the cloud computing capabilities. This talk discuss how DAT enables on-the-fly sea level research. The talk will introduce the DAT with an end-to-end tour of the tool with exploration and animating of available imagery, a demonstration of comparative analysis and plotting, and how to share and export data along with images for use in publications/presentations. The session will cover what kind of data is available, what kind of analysis is possible, and what are the outputs.

TimeBench: a data model and software library for visual analytics of time-oriented data.

PubMed

Rind, Alexander; Lammarsch, Tim; Aigner, Wolfgang; Alsallakh, Bilal; Miksch, Silvia

2013-12-01

Time-oriented data play an essential role in many Visual Analytics scenarios such as extracting medical insights from collections of electronic health records or identifying emerging problems and vulnerabilities in network traffic. However, many software libraries for Visual Analytics treat time as a flat numerical data type and insufficiently tackle the complexity of the time domain such as calendar granularities and intervals. Therefore, developers of advanced Visual Analytics designs need to implement temporal foundations in their application code over and over again. We present TimeBench, a software library that provides foundational data structures and algorithms for time-oriented data in Visual Analytics. Its expressiveness and developer accessibility have been evaluated through application examples demonstrating a variety of challenges with time-oriented data and long-term developer studies conducted in the scope of research and student projects.

Analytic and rule-based decision support tool for VDT workstation adjustment and computer accessories arrangement.

PubMed

Rurkhamet, Busagarin; Nanthavanij, Suebsak

2004-12-01

One important factor that leads to the development of musculoskeletal disorders (MSD) and cumulative trauma disorders (CTD) among visual display terminal (VDT) users is their work posture. While operating a VDT, a user's body posture is strongly influenced by the task, VDT workstation settings, and layout of computer accessories. This paper presents an analytic and rule-based decision support tool called EQ-DeX (an ergonomics and quantitative design expert system) that is developed to provide valid and practical recommendations regarding the adjustment of a VDT workstation and the arrangement of computer accessories. The paper explains the structure and components of EQ-DeX, input data, rules, and adjustment and arrangement algorithms. From input information such as gender, age, body height, task, etc., EQ-DeX uses analytic and rule-based algorithms to estimate quantitative settings of a computer table and a chair, as well as locations of computer accessories such as monitor, document holder, keyboard, and mouse. With the input and output screens that are designed using the concept of usability, the interactions between the user and EQ-DeX are convenient. Examples are also presented to demonstrate the recommendations generated by EQ-DeX.

Generating community-built tools for data sharing and analysis in environmental networks

USGS Publications Warehouse

Read, Jordan S.; Gries, Corinna; Read, Emily K.; Klug, Jennifer; Hanson, Paul C.; Hipsey, Matthew R.; Jennings, Eleanor; O'Reilley, Catherine; Winslow, Luke A.; Pierson, Don; McBride, Christopher G.; Hamilton, David

2016-01-01

Rapid data growth in many environmental sectors has necessitated tools to manage and analyze these data. The development of tools often lags behind the proliferation of data, however, which may slow exploratory opportunities and scientific progress. The Global Lake Ecological Observatory Network (GLEON) collaborative model supports an efficient and comprehensive data–analysis–insight life cycle, including implementations of data quality control checks, statistical calculations/derivations, models, and data visualizations. These tools are community-built and openly shared. We discuss the network structure that enables tool development and a culture of sharing, leading to optimized output from limited resources. Specifically, data sharing and a flat collaborative structure encourage the development of tools that enable scientific insights from these data. Here we provide a cross-section of scientific advances derived from global-scale analyses in GLEON. We document enhancements to science capabilities made possible by the development of analytical tools and highlight opportunities to expand this framework to benefit other environmental networks.

ANAlyte: A modular image analysis tool for ANA testing with indirect immunofluorescence.

PubMed

Di Cataldo, Santa; Tonti, Simone; Bottino, Andrea; Ficarra, Elisa

2016-05-01

The automated analysis of indirect immunofluorescence images for Anti-Nuclear Autoantibody (ANA) testing is a fairly recent field that is receiving ever-growing interest from the research community. ANA testing leverages on the categorization of intensity level and fluorescent pattern of IIF images of HEp-2 cells to perform a differential diagnosis of important autoimmune diseases. Nevertheless, it suffers from tremendous lack of repeatability due to subjectivity in the visual interpretation of the images. The automatization of the analysis is seen as the only valid solution to this problem. Several works in literature address individual steps of the work-flow, nonetheless integrating such steps and assessing their effectiveness as a whole is still an open challenge. We present a modular tool, ANAlyte, able to characterize a IIF image in terms of fluorescent intensity level and fluorescent pattern without any user-interactions. For this purpose, ANAlyte integrates the following: (i) Intensity Classifier module, that categorizes the intensity level of the input slide based on multi-scale contrast assessment; (ii) Cell Segmenter module, that splits the input slide into individual HEp-2 cells; (iii) Pattern Classifier module, that determines the fluorescent pattern of the slide based on the pattern of the individual cells. To demonstrate the accuracy and robustness of our tool, we experimentally validated ANAlyte on two different public benchmarks of IIF HEp-2 images with rigorous leave-one-out cross-validation strategy. We obtained overall accuracy of fluorescent intensity and pattern classification respectively around 85% and above 90%. We assessed all results by comparisons with some of the most representative state of the art works. Unlike most of the other works in the recent literature, ANAlyte aims at the automatization of all the major steps of ANA image analysis. Results on public benchmarks demonstrate that the tool can characterize HEp-2 slides in terms of intensity and fluorescent pattern with accuracy better or comparable with the state of the art techniques, even when such techniques are run on manually segmented cells. Hence, ANAlyte can be proposed as a valid solution to the problem of ANA testing automatization. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Instrumental resolution of the chopper spectrometer 4SEASONS evaluated by Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Kajimoto, Ryoichi; Sato, Kentaro; Inamura, Yasuhiro; Fujita, Masaki

2018-05-01

We performed simulations of the resolution function of the 4SEASONS spectrometer at J-PARC by using the Monte Carlo simulation package McStas. The simulations showed reasonably good agreement with analytical calculations of energy and momentum resolutions by using a simplified description. We implemented new functionalities in Utsusemi, the standard data analysis tool used in 4SEASONS, to enable visualization of the simulated resolution function and predict its shape for specific experimental configurations.

Visual and Analytic Strategies in Geometry

ERIC Educational Resources Information Center

Kospentaris, George; Vosniadou, Stella; Kazic, Smaragda; Thanou, Emilian

2016-01-01

We argue that there is an increasing reliance on analytic strategies compared to visuospatial strategies, which is related to geometry expertise and not on individual differences in cognitive style. A Visual/Analytic Strategy Test (VAST) was developed to investigate the use of visuo-spatial and analytic strategies in geometry in 30 mathematics…

Parameter Estimation of Computationally Expensive Watershed Models Through Efficient Multi-objective Optimization and Interactive Decision Analytics

NASA Astrophysics Data System (ADS)

Akhtar, Taimoor; Shoemaker, Christine

2016-04-01

Watershed model calibration is inherently a multi-criteria problem. Conflicting trade-offs exist between different quantifiable calibration criterions indicating the non-existence of a single optimal parameterization. Hence, many experts prefer a manual approach to calibration where the inherent multi-objective nature of the calibration problem is addressed through an interactive, subjective, time-intensive and complex decision making process. Multi-objective optimization can be used to efficiently identify multiple plausible calibration alternatives and assist calibration experts during the parameter estimation process. However, there are key challenges to the use of multi objective optimization in the parameter estimation process which include: 1) multi-objective optimization usually requires many model simulations, which is difficult for complex simulation models that are computationally expensive; and 2) selection of one from numerous calibration alternatives provided by multi-objective optimization is non-trivial. This study proposes a "Hybrid Automatic Manual Strategy" (HAMS) for watershed model calibration to specifically address the above-mentioned challenges. HAMS employs a 3-stage framework for parameter estimation. Stage 1 incorporates the use of an efficient surrogate multi-objective algorithm, GOMORS, for identification of numerous calibration alternatives within a limited simulation evaluation budget. The novelty of HAMS is embedded in Stages 2 and 3 where an interactive visual and metric based analytics framework is available as a decision support tool to choose a single calibration from the numerous alternatives identified in Stage 1. Stage 2 of HAMS provides a goodness-of-fit measure / metric based interactive framework for identification of a small subset (typically less than 10) of meaningful and diverse set of calibration alternatives from the numerous alternatives obtained in Stage 1. Stage 3 incorporates the use of an interactive visual analytics framework for decision support in selection of one parameter combination from the alternatives identified in Stage 2. HAMS is applied for calibration of flow parameters of a SWAT model, (Soil and Water Assessment Tool) designed to simulate flow in the Cannonsville watershed in upstate New York. Results from the application of HAMS to Cannonsville indicate that efficient multi-objective optimization and interactive visual and metric based analytics can bridge the gap between the effective use of both automatic and manual strategies for parameter estimation of computationally expensive watershed models.

Lack of habituation of evoked visual potentials in analytic information processing style: evidence in healthy subjects.

PubMed

Buonfiglio, Marzia; Toscano, M; Puledda, F; Avanzini, G; Di Clemente, L; Di Sabato, F; Di Piero, V

2015-03-01

Habituation is considered one of the most basic mechanisms of learning. Habituation deficit to several sensory stimulations has been defined as a trait of migraine brain and also observed in other disorders. On the other hand, analytic information processing style is characterized by the habit of continually evaluating stimuli and it has been associated with migraine. We investigated a possible correlation between lack of habituation of evoked visual potentials and analytic cognitive style in healthy subjects. According to Sternberg-Wagner self-assessment inventory, 15 healthy volunteers (HV) with high analytic score and 15 HV with high global score were recruited. Both groups underwent visual evoked potentials recordings after psychological evaluation. We observed significant lack of habituation in analytical individuals compared to global group. In conclusion, a reduced habituation of visual evoked potentials has been observed in analytic subjects. Our results suggest that further research should be undertaken regarding the relationship between analytic cognitive style and lack of habituation in both physiological and pathophysiological conditions.

Web-Based Geographic Information System Tool for Accessing Hanford Site Environmental Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Triplett, Mark B.; Seiple, Timothy E.; Watson, David J.

Data volume, complexity, and access issues pose severe challenges for analysts, regulators and stakeholders attempting to efficiently use legacy data to support decision making at the U.S. Department of Energy’s (DOE) Hanford Site. DOE has partnered with the Pacific Northwest National Laboratory (PNNL) on the PHOENIX (PNNL-Hanford Online Environmental Information System) project, which seeks to address data access, transparency, and integration challenges at Hanford to provide effective decision support. PHOENIX is a family of spatially-enabled web applications providing quick access to decades of valuable scientific data and insight through intuitive query, visualization, and analysis tools. PHOENIX realizes broad, public accessibilitymore » by relying only on ubiquitous web-browsers, eliminating the need for specialized software. It accommodates a wide range of users with intuitive user interfaces that require little or no training to quickly obtain and visualize data. Currently, PHOENIX is actively hosting three applications focused on groundwater monitoring, groundwater clean-up performance reporting, and in-tank monitoring. PHOENIX-based applications are being used to streamline investigative and analytical processes at Hanford, saving time and money. But more importantly, by integrating previously isolated datasets and developing relevant visualization and analysis tools, PHOENIX applications are enabling DOE to discover new correlations hidden in legacy data, allowing them to more effectively address complex issues at Hanford.« less

The NASA Reanalysis Ensemble Service - Advanced Capabilities for Integrated Reanalysis Access and Intercomparison

NASA Astrophysics Data System (ADS)

Tamkin, G.; Schnase, J. L.; Duffy, D.; Li, J.; Strong, S.; Thompson, J. H.

2017-12-01

NASA's efforts to advance climate analytics-as-a-service are making new capabilities available to the research community: (1) A full-featured Reanalysis Ensemble Service (RES) comprising monthly means data from multiple reanalysis data sets, accessible through an enhanced set of extraction, analytic, arithmetic, and intercomparison operations. The operations are made accessible through NASA's climate data analytics Web services and our client-side Climate Data Services Python library, CDSlib; (2) A cloud-based, high-performance Virtual Real-Time Analytics Testbed supporting a select set of climate variables. This near real-time capability enables advanced technologies like Spark and Hadoop-based MapReduce analytics over native NetCDF files; and (3) A WPS-compliant Web service interface to our climate data analytics service that will enable greater interoperability with next-generation systems such as ESGF. The Reanalysis Ensemble Service includes the following: - New API that supports full temporal, spatial, and grid-based resolution services with sample queries - A Docker-ready RES application to deploy across platforms - Extended capabilities that enable single- and multiple reanalysis area average, vertical average, re-gridding, standard deviation, and ensemble averages - Convenient, one-stop shopping for commonly used data products from multiple reanalyses including basic sub-setting and arithmetic operations (e.g., avg, sum, max, min, var, count, anomaly) - Full support for the MERRA-2 reanalysis dataset in addition to, ECMWF ERA-Interim, NCEP CFSR, JMA JRA-55 and NOAA/ESRL 20CR… - A Jupyter notebook-based distribution mechanism designed for client use cases that combines CDSlib documentation with interactive scenarios and personalized project management - Supporting analytic services for NASA GMAO Forward Processing datasets - Basic uncertainty quantification services that combine heterogeneous ensemble products with comparative observational products (e.g., reanalysis, observational, visualization) - The ability to compute and visualize multiple reanalysis for ease of inter-comparisons - Automated tools to retrieve and prepare data collections for analytic processing

In Vivo Tracking of Copper-64 Radiolabeled Nanoparticles in Lactuca sativa.

PubMed

Davis, Ryan A; Rippner, Devin A; Hausner, Sven H; Parikh, Sanjai J; McElrone, Andrew J; Sutcliffe, Julie L

2017-11-07

Engineered nanoparticles (NPs) are increasingly used in commercial products including automotive lubricants, clothing, deodorants, sunscreens, and cosmetics and can potentially accumulate in our food supply. Given their size it is difficult to detect and visualize the presence of NPs in environmental samples, including crop plants. New analytical tools are needed to fill the void for detection and visualization of NPs in complex biological and environmental matrices. We aimed to determine whether radiolabeled NPs could be used as a noninvasive, highly sensitive analytical tool to quantitatively track and visualize NP transport and accumulation in vivo in lettuce (Lactuca sativa) and to investigate the effect of NP size on transport and distribution over time using a combination of autoradiography, positron emission tomography (PET)/computed tomography (CT), scanning electron microscopy (SEM), and transition electron microscopy (TEM). Azide functionalized NPs were radiolabeled via a "click" reaction with copper-64 ( 64 Cu)-1,4,7-triazacyclononane triacetic acid (NOTA) azadibenzocyclooctyne (ADIBO) conjugate ([ 64 Cu]-ADIBO-NOTA) via copper-free Huisgen-1,3-dipolar cycloaddition reaction. This yielded radiolabeled [ 64 Cu]-NPs of uniform shape and size with a high radiochemical purity (>99%), specific activity of  2.2 mCi/mg of NP, and high stability (i.e., no detectable dissolution) over 24 h across a pH range of 5-9. Both PET/CT and autoradiography showed that [ 64 Cu]-NPs entered the lettuce seedling roots and were rapidly transported to the cotyledons with the majority of the accumulation inside the roots. Uptake and transport of intact NPs was size-dependent, and in combination with the accumulation within the roots suggests a filtering effect of the plant cell walls at various points along the water transport pathway.

Simulated color: a diagnostic tool for skin lesions like port-wine stain

NASA Astrophysics Data System (ADS)

Randeberg, Lise L.; Svaasand, Lars O.

2001-05-01

A device independent method for skin color visualization has been developed. Colors reconstructed from a reflectance spectrum are presented on a computer screen by sRGB (standard Red Green Blue) color coordinates. The colors are presented as adjacent patches surrounded by a medium grey border. CIELAB color coordinates and CIE (International Commission on Illumination) color difference (Delta) E are computed. The change in skin color due to a change in average blood content or scattering properties in dermis is investigated. This is done by analytical simulations based on the diffusion approximation. It is found that an 11% change in average blood content and a 15% change in scattering properties will give a visible color change. A supposed visibility limit for (Delta) E is given. This value is based on experimental testing and the known properties of the human visual system. This limit value can be used as a tool to determine when to terminate laser treatment of port- wine stain due to low treatment response, i.e. low (Delta) E between treatments. The visualization method presented seems promising for medical applications as port-wine stain diagnostics. The method gives good possibilities for electronic transfer of data between clinics because it is device independent.

Interactive visual comparison of multimedia data through type-specific views

NASA Astrophysics Data System (ADS)

Burtner, Russ; Bohn, Shawn; Payne, Debbie

2013-01-01

Analysts who work with collections of multimedia to perform information foraging understand how difficult it is to connect information across diverse sets of mixed media. The wealth of information from blogs, social media, and news sites often can provide actionable intelligence; however, many of the tools used on these sources of content are not capable of multimedia analysis because they only analyze a single media type. As such, analysts are taxed to keep a mental model of the relationships among each of the media types when generating the broader content picture. To address this need, we have developed Canopy, a novel visual analytic tool for analyzing multimedia. Canopy provides insight into the multimedia data relationships by exploiting the linkages found in text, images, and video co-occurring in the same document and across the collection. Canopy connects derived and explicit linkages and relationships through multiple connected visualizations to aid analysts in quickly summarizing, searching, and browsing collected information to explore relationships and align content. In this paper, we will discuss the features and capabilities of the Canopy system and walk through a scenario illustrating how this system might be used in an operational environment.

Cloud-Based Computational Tools for Earth Science Applications

NASA Astrophysics Data System (ADS)

Arendt, A. A.; Fatland, R.; Howe, B.

2015-12-01

Earth scientists are increasingly required to think across disciplines and utilize a wide range of datasets in order to solve complex environmental challenges. Although significant progress has been made in distributing data, researchers must still invest heavily in developing computational tools to accommodate their specific domain. Here we document our development of lightweight computational data systems aimed at enabling rapid data distribution, analytics and problem solving tools for Earth science applications. Our goal is for these systems to be easily deployable, scalable and flexible to accommodate new research directions. As an example we describe "Ice2Ocean", a software system aimed at predicting runoff from snow and ice in the Gulf of Alaska region. Our backend components include relational database software to handle tabular and vector datasets, Python tools (NumPy, pandas and xray) for rapid querying of gridded climate data, and an energy and mass balance hydrological simulation model (SnowModel). These components are hosted in a cloud environment for direct access across research teams, and can also be accessed via API web services using a REST interface. This API is a vital component of our system architecture, as it enables quick integration of our analytical tools across disciplines, and can be accessed by any existing data distribution centers. We will showcase several data integration and visualization examples to illustrate how our system has expanded our ability to conduct cross-disciplinary research.

Physics-based and human-derived information fusion for analysts

NASA Astrophysics Data System (ADS)

Blasch, Erik; Nagy, James; Scott, Steve; Okoth, Joshua; Hinman, Michael

2017-05-01

Recent trends in physics-based and human-derived information fusion (PHIF) have amplified the capabilities of analysts; however with the big data opportunities there is a need for open architecture designs, methods of distributed team collaboration, and visualizations. In this paper, we explore recent trends in the information fusion to support user interaction and machine analytics. Challenging scenarios requiring PHIF include combing physics-based video data with human-derived text data for enhanced simultaneous tracking and identification. A driving effort would be to provide analysts with applications, tools, and interfaces that afford effective and affordable solutions for timely decision making. Fusion at scale should be developed to allow analysts to access data, call analytics routines, enter solutions, update models, and store results for distributed decision making.

Numerical Study of Tip Vortex Flows

NASA Technical Reports Server (NTRS)

Dacles-Mariani, Jennifer; Hafez, Mohamed

1998-01-01

This paper presents an overview and summary of the many different research work related to tip vortex flows and wake/trailing vortices as applied to practical engineering problems. As a literature survey paper, it outlines relevant analytical, theoretical, experimental and computational study found in literature. It also discusses in brief some of the fundamental aspects of the physics and its complexities. An appendix is also included. The topics included in this paper are: 1) Analytical Vortices; 2) Experimental Studies; 3) Computational Studies; 4) Wake Vortex Control and Management; 5) Wake Modeling; 6) High-Lift Systems; 7) Issues in Numerical Studies; 8) Instabilities; 9) Related Topics; 10) Visualization Tools for Vertical Flows; 11) Further Work Needed; 12) Acknowledgements; 13) References; and 14) Appendix.

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase two, volume 4 : web-based bridge information database--visualization analytics and distributed sensing.

DOT National Transportation Integrated Search

2012-03-01

This report introduces the design and implementation of a Web-based bridge information visual analytics system. This : project integrates Internet, multiple databases, remote sensing, and other visualization technologies. The result : combines a GIS ...

Visualisation and Analytic Strategies for Anticipating the Folding of Nets

ERIC Educational Resources Information Center

Wright, Vince

2016-01-01

Visual and analytic strategies are features of students' schemes for spatial tasks. The strategies used by six students to anticipate the folding of nets were investigated. Evidence suggested that visual and analytic strategies were strongly connected in competent performance.

PACOM: A Versatile Tool for Integrating, Filtering, Visualizing, and Comparing Multiple Large Mass Spectrometry Proteomics Data Sets.

PubMed

Martínez-Bartolomé, Salvador; Medina-Aunon, J Alberto; López-García, Miguel Ángel; González-Tejedo, Carmen; Prieto, Gorka; Navajas, Rosana; Salazar-Donate, Emilio; Fernández-Costa, Carolina; Yates, John R; Albar, Juan Pablo

2018-04-06

Mass-spectrometry-based proteomics has evolved into a high-throughput technology in which numerous large-scale data sets are generated from diverse analytical platforms. Furthermore, several scientific journals and funding agencies have emphasized the storage of proteomics data in public repositories to facilitate its evaluation, inspection, and reanalysis. (1) As a consequence, public proteomics data repositories are growing rapidly. However, tools are needed to integrate multiple proteomics data sets to compare different experimental features or to perform quality control analysis. Here, we present a new Java stand-alone tool, Proteomics Assay COMparator (PACOM), that is able to import, combine, and simultaneously compare numerous proteomics experiments to check the integrity of the proteomic data as well as verify data quality. With PACOM, the user can detect source of errors that may have been introduced in any step of a proteomics workflow and that influence the final results. Data sets can be easily compared and integrated, and data quality and reproducibility can be visually assessed through a rich set of graphical representations of proteomics data features as well as a wide variety of data filters. Its flexibility and easy-to-use interface make PACOM a unique tool for daily use in a proteomics laboratory. PACOM is available at https://github.com/smdb21/pacom .

'Big Data' Collaboration: Exploring, Recording and Sharing Enterprise Knowledge

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sukumar, Sreenivas R; Ferrell, Regina Kay

2013-01-01

As data sources and data size proliferate, knowledge discovery from "Big Data" is starting to pose several challenges. In this paper, we address a specific challenge in the practice of enterprise knowledge management while extracting actionable nuggets from diverse data sources of seemingly-related information. In particular, we address the challenge of archiving knowledge gained through collaboration, dissemination and visualization as part of the data analysis, inference and decision-making lifecycle. We motivate the implementation of an enterprise data-discovery and knowledge recorder tool, called SEEKER based on real world case-study. We demonstrate SEEKER capturing schema and data-element relationships, tracking the data elementsmore » of value based on the queries and the analytical artifacts that are being created by analysts as they use the data. We show how the tool serves as digital record of institutional domain knowledge and a documentation for the evolution of data elements, queries and schemas over time. As a knowledge management service, a tool like SEEKER saves enterprise resources and time by avoiding analytic silos, expediting the process of multi-source data integration and intelligently documenting discoveries from fellow analysts.« less

Visual analytics of brain networks.

PubMed

Li, Kaiming; Guo, Lei; Faraco, Carlos; Zhu, Dajiang; Chen, Hanbo; Yuan, Yixuan; Lv, Jinglei; Deng, Fan; Jiang, Xi; Zhang, Tuo; Hu, Xintao; Zhang, Degang; Miller, L Stephen; Liu, Tianming

2012-05-15

Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging. Copyright © 2012 Elsevier Inc. All rights reserved.

Analysis procedures and subjective flight results of a simulator validation and cue fidelity experiment

NASA Technical Reports Server (NTRS)

Carr, Peter C.; Mckissick, Burnell T.

1988-01-01

A joint experiment to investigate simulator validation and cue fidelity was conducted by the Dryden Flight Research Facility of NASA Ames Research Center (Ames-Dryden) and NASA Langley Research Center. The primary objective was to validate the use of a closed-loop pilot-vehicle mathematical model as an analytical tool for optimizing the tradeoff between simulator fidelity requirements and simulator cost. The validation process includes comparing model predictions with simulation and flight test results to evaluate various hypotheses for differences in motion and visual cues and information transfer. A group of five pilots flew air-to-air tracking maneuvers in the Langley differential maneuvering simulator and visual motion simulator and in an F-14 aircraft at Ames-Dryden. The simulators used motion and visual cueing devices including a g-seat, a helmet loader, wide field-of-view horizon, and a motion base platform.

A survey of tools for variant analysis of next-generation genome sequencing data

PubMed Central

Pabinger, Stephan; Dander, Andreas; Fischer, Maria; Snajder, Rene; Sperk, Michael; Efremova, Mirjana; Krabichler, Birgit; Speicher, Michael R.; Zschocke, Johannes

2014-01-01

Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers. PMID:23341494

New solutions for climate network visualization

NASA Astrophysics Data System (ADS)

Nocke, Thomas; Buschmann, Stefan; Donges, Jonathan F.; Marwan, Norbert

2016-04-01

An increasing amount of climate and climate impact research methods deals with geo-referenced networks, including energy, trade, supply-chain, disease dissemination and climatic tele-connection networks. At the same time, the size and complexity of these networks increases, resulting in networks of more than hundred thousand or even millions of edges, which are often temporally evolving, have additional data at nodes and edges, and can consist of multiple layers even in real 3D. This gives challenges to both the static representation and the interactive exploration of these networks, first of all avoiding edge clutter ("edge spagetti") and allowing interactivity even for unfiltered networks. Within this presentation, we illustrate potential solutions to these challenges. Therefore, we give a glimpse on a questionnaire performed with climate and complex system scientists with respect to their network visualization requirements, and on a review of available state-of-the-art visualization techniques and tools for this purpose (see as well Nocke et al., 2015). In the main part, we present alternative visualization solutions for several use cases (global, regional, and multi-layered climate networks) including alternative geographic projections, edge bundling, and 3-D network support (based on CGV and GTX tools), and implementation details to reach interactive frame rates. References: Nocke, T., S. Buschmann, J. F. Donges, N. Marwan, H.-J. Schulz, and C. Tominski: Review: Visual analytics of climate networks, Nonlinear Processes in Geophysics, 22, 545-570, doi:10.5194/npg-22-545-2015, 2015

Graphical Descriptives: A Way to Improve Data Transparency and Methodological Rigor in Psychology.

PubMed

Tay, Louis; Parrigon, Scott; Huang, Qiming; LeBreton, James M

2016-09-01

Several calls have recently been issued to the social sciences for enhanced transparency of research processes and enhanced rigor in the methodological treatment of data and data analytics. We propose the use of graphical descriptives (GDs) as one mechanism for responding to both of these calls. GDs provide a way to visually examine data. They serve as quick and efficient tools for checking data distributions, variable relations, and the potential appropriateness of different statistical analyses (e.g., do data meet the minimum assumptions for a particular analytic method). Consequently, we believe that GDs can promote increased transparency in the journal review process, encourage best practices for data analysis, and promote a more inductive approach to understanding psychological data. We illustrate the value of potentially including GDs as a step in the peer-review process and provide a user-friendly online resource (www.graphicaldescriptives.org) for researchers interested in including data visualizations in their research. We conclude with suggestions on how GDs can be expanded and developed to enhance transparency. © The Author(s) 2016.

SU-F-BRB-16: A Spreadsheet Based Automatic Trajectory GEnerator (SAGE): An Open Source Tool for Automatic Creation of TrueBeam Developer Mode Robotic Trajectories

DOE Office of Scientific and Technical Information (OSTI.GOV)

Etmektzoglou, A; Mishra, P; Svatos, M

Purpose: To automate creation and delivery of robotic linac trajectories with TrueBeam Developer Mode, an open source spreadsheet-based trajectory generation tool has been developed, tested and made freely available. The computing power inherent in a spreadsheet environment plus additional functions programmed into the tool insulate users from the underlying schema tedium and allow easy calculation, parameterization, graphical visualization, validation and finally automatic generation of Developer Mode XML scripts which are directly loadable on a TrueBeam linac. Methods: The robotic control system platform that allows total coordination of potentially all linac moving axes with beam (continuous, step-and-shoot, or combination thereof) becomesmore » available in TrueBeam Developer Mode. Many complex trajectories are either geometric or can be described in analytical form, making the computational power, graphing and programmability available in a spreadsheet environment an easy and ideal vehicle for automatic trajectory generation. The spreadsheet environment allows also for parameterization of trajectories thus enabling the creation of entire families of trajectories using only a few variables. Standard spreadsheet functionality has been extended for powerful movie-like dynamic graphic visualization of the gantry, table, MLC, room, lasers, 3D observer placement and beam centerline all as a function of MU or time, for analysis of the motions before requiring actual linac time. Results: We used the tool to generate and deliver extended SAD “virtual isocenter” trajectories of various shapes such as parameterized circles and ellipses. We also demonstrated use of the tool in generating linac couch motions that simulate respiratory motion using analytical parameterized functions. Conclusion: The SAGE tool is a valuable resource to experiment with families of complex geometric trajectories for a TrueBeam Linac. It makes Developer Mode more accessible as a vehicle to quickly translate research ideas into machine readable scripts without programming knowledge. As an open source initiative, it also enables researcher collaboration on future developments. I am a full time employee at Varian Medical Systems, Palo Alto, California.« less

High Performance Visualization using Query-Driven Visualizationand Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bethel, E. Wes; Campbell, Scott; Dart, Eli

2006-06-15

Query-driven visualization and analytics is a unique approach for high-performance visualization that offers new capabilities for knowledge discovery and hypothesis testing. The new capabilities akin to finding needles in haystacks are the result of combining technologies from the fields of scientific visualization and scientific data management. This approach is crucial for rapid data analysis and visualization in the petascale regime. This article describes how query-driven visualization is applied to a hero-sized network traffic analysis problem.

On Establishing Big Data Wave Breakwaters with Analytics (Invited)

NASA Astrophysics Data System (ADS)

Riedel, M.

2013-12-01

The Research Data Alliance Big Data Analytics (RDA-BDA) Interest Group seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. RDA-BDA seeks to analyze different scientific domain applications and their potential use of various big data analytics techniques. A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. These combinations are complex since a wide variety of different data analysis algorithms exist (e.g. specific algorithms using GPUs of analyzing brain images) that need to work together with multiple analytical tools reaching from simple (iterative) map-reduce methods (e.g. with Apache Hadoop or Twister) to sophisticated higher level frameworks that leverage machine learning algorithms (e.g. Apache Mahout). These computational analysis techniques are often augmented with visual analytics techniques (e.g. computational steering on large-scale high performance computing platforms) to put the human judgement into the analysis loop or new approaches with databases that are designed to support new forms of unstructured or semi-structured data as opposed to the rather tradtional structural databases (e.g. relational databases). More recently, data analysis and underpinned analytics frameworks also have to consider energy footprints of underlying resources. To sum up, the aim of this talk is to provide pieces of information to understand big data analytics in the context of science and engineering using the aforementioned classification as the lighthouse and as the frame of reference for a systematic approach. This talk will provide insights about big data analytics methods in context of science within varios communities and offers different views of how approaches of correlation and causality offer complementary methods to advance in science and engineering today. The RDA Big Data Analytics Group seeks to understand what approaches are not only technically feasible, but also scientifically feasible. The lighthouse Goal of the RDA Big Data Analytics Group is a classification of clever combinations of various Technologies and scientific applications in order to provide clear recommendations to the scientific community what approaches are technicalla and scientifically feasible.

Feature Geo Analytics and Big Data Processing: Hybrid Approaches for Earth Science and Real-Time Decision Support

NASA Astrophysics Data System (ADS)

Wright, D. J.; Raad, M.; Hoel, E.; Park, M.; Mollenkopf, A.; Trujillo, R.

2016-12-01

Introduced is a new approach for processing spatiotemporal big data by leveraging distributed analytics and storage. A suite of temporally-aware analysis tools summarizes data nearby or within variable windows, aggregates points (e.g., for various sensor observations or vessel positions), reconstructs time-enabled points into tracks (e.g., for mapping and visualizing storm tracks), joins features (e.g., to find associations between features based on attributes, spatial relationships, temporal relationships or all three simultaneously), calculates point densities, finds hot spots (e.g., in species distributions), and creates space-time slices and cubes (e.g., in microweather applications with temperature, humidity, and pressure, or within human mobility studies). These "feature geo analytics" tools run in both batch and streaming spatial analysis mode as distributed computations across a cluster of servers on typical "big" data sets, where static data exist in traditional geospatial formats (e.g., shapefile) locally on a disk or file share, attached as static spatiotemporal big data stores, or streamed in near-real-time. In other words, the approach registers large datasets or data stores with ArcGIS Server, then distributes analysis across a cluster of machines for parallel processing. Several brief use cases will be highlighted based on a 16-node server cluster at 14 Gb RAM per node, allowing, for example, the buffering of over 8 million points or thousands of polygons in 1 minute. The approach is "hybrid" in that ArcGIS Server integrates open-source big data frameworks such as Apache Hadoop and Apache Spark on the cluster in order to run the analytics. In addition, the user may devise and connect custom open-source interfaces and tools developed in Python or Python Notebooks; the common denominator being the familiar REST API.

A Visual Analytics Paradigm Enabling Trillion-Edge Graph Exploration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wong, Pak C.; Haglin, David J.; Gillen, David S.

We present a visual analytics paradigm and a system prototype for exploring web-scale graphs. A web-scale graph is described as a graph with ~one trillion edges and ~50 billion vertices. While there is an aggressive R&D effort in processing and exploring web-scale graphs among internet vendors such as Facebook and Google, visualizing a graph of that scale still remains an underexplored R&D area. The paper describes a nontraditional peek-and-filter strategy that facilitates the exploration of a graph database of unprecedented size for visualization and analytics. We demonstrate that our system prototype can 1) preprocess a graph with ~25 billion edgesmore » in less than two hours and 2) support database query and visualization on the processed graph database afterward. Based on our computational performance results, we argue that we most likely will achieve the one trillion edge mark (a computational performance improvement of 40 times) for graph visual analytics in the near future.« less

BiNA: A Visual Analytics Tool for Biological Network Data

PubMed Central

Gerasch, Andreas; Faber, Daniel; Küntzer, Jan; Niermann, Peter; Kohlbacher, Oliver; Lenhof, Hans-Peter; Kaufmann, Michael

2014-01-01

Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA - the Biological Network Analyzer - a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/. PMID:24551056

A collaborative visual analytics suite for protein folding research.

PubMed

Harvey, William; Park, In-Hee; Rübel, Oliver; Pascucci, Valerio; Bremer, Peer-Timo; Li, Chenglong; Wang, Yusu

2014-09-01

Molecular dynamics (MD) simulation is a crucial tool for understanding principles behind important biochemical processes such as protein folding and molecular interaction. With the rapidly increasing power of modern computers, large-scale MD simulation experiments can be performed regularly, generating huge amounts of MD data. An important question is how to analyze and interpret such massive and complex data. One of the (many) challenges involved in analyzing MD simulation data computationally is the high-dimensionality of such data. Given a massive collection of molecular conformations, researchers typically need to rely on their expertise and prior domain knowledge in order to retrieve certain conformations of interest. It is not easy to make and test hypotheses as the data set as a whole is somewhat "invisible" due to its high dimensionality. In other words, it is hard to directly access and examine individual conformations from a sea of molecular structures, and to further explore the entire data set. There is also no easy and convenient way to obtain a global view of the data or its various modalities of biochemical information. To this end, we present an interactive, collaborative visual analytics tool for exploring massive, high-dimensional molecular dynamics simulation data sets. The most important utility of our tool is to provide a platform where researchers can easily and effectively navigate through the otherwise "invisible" simulation data sets, exploring and examining molecular conformations both as a whole and at individual levels. The visualization is based on the concept of a topological landscape, which is a 2D terrain metaphor preserving certain topological and geometric properties of the high dimensional protein energy landscape. In addition to facilitating easy exploration of conformations, this 2D terrain metaphor also provides a platform where researchers can visualize and analyze various properties (such as contact density) overlayed on the top of the 2D terrain. Finally, the software provides a collaborative environment where multiple researchers can assemble observations and biochemical events into storyboards and share them in real time over the Internet via a client-server architecture. The software is written in Scala and runs on the cross-platform Java Virtual Machine. Binaries and source code are available at http://www.aylasoftware.org and have been released under the GNU General Public License. Copyright © 2014 Elsevier Inc. All rights reserved.

Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop.

PubMed

Legg, Philip A; Chung, David H S; Parry, Matthew L; Bown, Rhodri; Jones, Mark W; Griffiths, Iwan W; Chen, Min

2013-12-01

Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance.

Lowering the Barrier to Reproducible Research by Publishing Provenance from Common Analytical Tools

NASA Astrophysics Data System (ADS)

Jones, M. B.; Slaughter, P.; Walker, L.; Jones, C. S.; Missier, P.; Ludäscher, B.; Cao, Y.; McPhillips, T.; Schildhauer, M.

2015-12-01

Scientific provenance describes the authenticity, origin, and processing history of research products and promotes scientific transparency by detailing the steps in computational workflows that produce derived products. These products include papers, findings, input data, software products to perform computations, and derived data and visualizations. The geosciences community values this type of information, and, at least theoretically, strives to base conclusions on computationally replicable findings. In practice, capturing detailed provenance is laborious and thus has been a low priority; beyond a lab notebook describing methods and results, few researchers capture and preserve detailed records of scientific provenance. We have built tools for capturing and publishing provenance that integrate into analytical environments that are in widespread use by geoscientists (R and Matlab). These tools lower the barrier to provenance generation by automating capture of critical information as researchers prepare data for analysis, develop, test, and execute models, and create visualizations. The 'recordr' library in R and the `matlab-dataone` library in Matlab provide shared functions to capture provenance with minimal changes to normal working procedures. Researchers can capture both scripted and interactive sessions, tag and manage these executions as they iterate over analyses, and then prune and publish provenance metadata and derived products to the DataONE federation of archival repositories. Provenance traces conform to the ProvONE model extension of W3C PROV, enabling interoperability across tools and languages. The capture system supports fine-grained versioning of science products and provenance traces. By assigning global identifiers such as DOIs, reseachers can cite the computational processes used to reach findings. And, finally, DataONE has built a web portal to search, browse, and clearly display provenance relationships between input data, the software used to execute analyses and models, and derived data and products that arise from these computations. This provenance is vital to interpretation and understanding of science, and provides an audit trail that researchers can use to understand and replicate computational workflows in the geosciences.

Lateral flow devices

DOEpatents

Mazumdar, Debapriya; Liu, Juewen; Lu, Yi

2010-09-21

An analytical test for an analyte comprises (a) a base, having a reaction area and a visualization area, (b) a capture species, on the base in the visualization area, comprising nucleic acid, and (c) analysis chemistry reagents, on the base in the reaction area. The analysis chemistry reagents comprise (i) a substrate comprising nucleic acid and a first label, and (ii) a reactor comprising nucleic acid. The analysis chemistry reagents can react with a sample comprising the analyte and water, to produce a visualization species comprising nucleic acid and the first label, and the capture species can bind the visualization species.

An automated protocol for performance benchmarking a widefield fluorescence microscope.

PubMed

Halter, Michael; Bier, Elianna; DeRose, Paul C; Cooksey, Gregory A; Choquette, Steven J; Plant, Anne L; Elliott, John T

2014-11-01

Widefield fluorescence microscopy is a highly used tool for visually assessing biological samples and for quantifying cell responses. Despite its widespread use in high content analysis and other imaging applications, few published methods exist for evaluating and benchmarking the analytical performance of a microscope. Easy-to-use benchmarking methods would facilitate the use of fluorescence imaging as a quantitative analytical tool in research applications, and would aid the determination of instrumental method validation for commercial product development applications. We describe and evaluate an automated method to characterize a fluorescence imaging system's performance by benchmarking the detection threshold, saturation, and linear dynamic range to a reference material. The benchmarking procedure is demonstrated using two different materials as the reference material, uranyl-ion-doped glass and Schott 475 GG filter glass. Both are suitable candidate reference materials that are homogeneously fluorescent and highly photostable, and the Schott 475 GG filter glass is currently commercially available. In addition to benchmarking the analytical performance, we also demonstrate that the reference materials provide for accurate day to day intensity calibration. Published 2014 Wiley Periodicals Inc. Published 2014 Wiley Periodicals Inc. This article is a US government work and, as such, is in the public domain in the United States of America.

Geovisualization applications to examine and explore high-density and hierarchical critical infrastructure data

NASA Astrophysics Data System (ADS)

Edsall, Robert; Hembree, Harvey

2018-05-01

The geospatial research and development team in the National and Homeland Security Division at Idaho National Laboratory was tasked with providing tools to derive insight from the substantial amount of data currently available - and continuously being produced - associated with the critical infrastructure of the US. This effort is in support of the Department of Homeland Security, whose mission includes the protection of this infrastructure and the enhancement of its resilience to hazards, both natural and human. We present geovisual-analytics-based approaches for analysis of vulnerabilities and resilience of critical infrastructure, designed so that decision makers, analysts, and infrastructure owners and managers can manage risk, prepare for hazards, and direct resources before and after an incident that might result in an interruption in service. Our designs are based on iterative discussions with DHS leadership and analysts, who in turn will use these tools to explore and communicate data in partnership with utility providers, law enforcement, and emergency response and recovery organizations, among others. In most cases these partners desire summaries of large amounts of data, but increasingly, our users seek the additional capability of focusing on, for example, a specific infrastructure sector, a particular geographic region, or time period, or of examining data in a variety of generalization or aggregation levels. These needs align well with tenets of in-formation-visualization design; in this paper, selected applications among those that we have designed are described and positioned within geovisualization, geovisual analytical, and information visualization frameworks.

Visual Analytics and Storytelling through Video

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wong, Pak C.; Perrine, Kenneth A.; Mackey, Patrick S.

2005-10-31

This paper supplements a video clip submitted to the Video Track of IEEE Symposium on Information Visualization 2005. The original video submission applies a two-way storytelling approach to demonstrate the visual analytics capabilities of a new visualization technique. The paper presents our video production philosophy, describes the plot of the video, explains the rationale behind the plot, and finally, shares our production experiences with our readers.

Genomics Portals: integrative web-platform for mining genomics data.

PubMed

Shinde, Kaustubh; Phatak, Mukta; Johannes, Freudenberg M; Chen, Jing; Li, Qian; Vineet, Joshi K; Hu, Zhen; Ghosh, Krishnendu; Meller, Jaroslaw; Medvedovic, Mario

2010-01-13

A large amount of experimental data generated by modern high-throughput technologies is available through various public repositories. Our knowledge about molecular interaction networks, functional biological pathways and transcriptional regulatory modules is rapidly expanding, and is being organized in lists of functionally related genes. Jointly, these two sources of information hold a tremendous potential for gaining new insights into functioning of living systems. Genomics Portals platform integrates access to an extensive knowledge base and a large database of human, mouse, and rat genomics data with basic analytical visualization tools. It provides the context for analyzing and interpreting new experimental data and the tool for effective mining of a large number of publicly available genomics datasets stored in the back-end databases. The uniqueness of this platform lies in the volume and the diversity of genomics data that can be accessed and analyzed (gene expression, ChIP-chip, ChIP-seq, epigenomics, computationally predicted binding sites, etc), and the integration with an extensive knowledge base that can be used in such analysis. The integrated access to primary genomics data, functional knowledge and analytical tools makes Genomics Portals platform a unique tool for interpreting results of new genomics experiments and for mining the vast amount of data stored in the Genomics Portals backend databases. Genomics Portals can be accessed and used freely at http://GenomicsPortals.org.

Genomics Portals: integrative web-platform for mining genomics data

PubMed Central

2010-01-01

Background A large amount of experimental data generated by modern high-throughput technologies is available through various public repositories. Our knowledge about molecular interaction networks, functional biological pathways and transcriptional regulatory modules is rapidly expanding, and is being organized in lists of functionally related genes. Jointly, these two sources of information hold a tremendous potential for gaining new insights into functioning of living systems. Results Genomics Portals platform integrates access to an extensive knowledge base and a large database of human, mouse, and rat genomics data with basic analytical visualization tools. It provides the context for analyzing and interpreting new experimental data and the tool for effective mining of a large number of publicly available genomics datasets stored in the back-end databases. The uniqueness of this platform lies in the volume and the diversity of genomics data that can be accessed and analyzed (gene expression, ChIP-chip, ChIP-seq, epigenomics, computationally predicted binding sites, etc), and the integration with an extensive knowledge base that can be used in such analysis. Conclusion The integrated access to primary genomics data, functional knowledge and analytical tools makes Genomics Portals platform a unique tool for interpreting results of new genomics experiments and for mining the vast amount of data stored in the Genomics Portals backend databases. Genomics Portals can be accessed and used freely at http://GenomicsPortals.org. PMID:20070909

Regulating outdoor advertisement boards; employing spatial decision support system to control urban visual pollution

NASA Astrophysics Data System (ADS)

Wakil, K.; Hussnain, MQ; Tahir, A.; Naeem, M. A.

2016-06-01

Unmanaged placement, size, location, structure and contents of outdoor advertisement boards have resulted in severe urban visual pollution and deterioration of the socio-physical living environment in urban centres of Pakistan. As per the regulatory instruments, the approval decision for a new advertisement installation is supposed to be based on the locational density of existing boards and their proximity or remoteness to certain land- uses. In cities, where regulatory tools for the control of advertisement boards exist, responsible authorities are handicapped in effective implementation due to the absence of geospatial analysis capacity. This study presents the development of a spatial decision support system (SDSS) for regularization of advertisement boards in terms of their location and placement. The knowledge module of the proposed SDSS is based on provisions and restrictions prescribed in regulatory documents. While the user interface allows visualization and scenario evaluation to understand if the new board will affect existing linear density on a particular road and if it violates any buffer restrictions around a particular land use. Technically the structure of the proposed SDSS is a web-based solution which includes open geospatial tools such as OpenGeo Suite, GeoExt, PostgreSQL, and PHP. It uses three key data sets including road network, locations of existing billboards and building parcels with land use information to perform the analysis. Locational suitability has been calculated using pairwise comparison through analytical hierarchy process (AHP) and weighted linear combination (WLC). Our results indicate that open geospatial tools can be helpful in developing an SDSS which can assist solving space related iterative decision challenges on outdoor advertisements. Employing such a system will result in effective implementation of regulations resulting in visual harmony and aesthetic improvement in urban communities.

Spectacle and SpecViz: New Spectral Analysis and Visualization Tools

NASA Astrophysics Data System (ADS)

Earl, Nicholas; Peeples, Molly; JDADF Developers

2018-01-01

A new era of spectroscopic exploration of our universe is being ushered in with advances in instrumentation and next-generation space telescopes. The advent of new spectroscopic instruments has highlighted a pressing need for tools scientists can use to analyze and explore these new data. We have developed Spectacle, a software package for analyzing both synthetic spectra from hydrodynamic simulations as well as real COS data with an aim of characterizing the behavior of the circumgalactic medium. It allows easy reduction of spectral data and analytic line generation capabilities. Currently, the package is focused on automatic determination of absorption regions and line identification with custom line list support, simultaneous line fitting using Voigt profiles via least-squares or MCMC methods, and multi-component modeling of blended features. Non-parametric measurements, such as equivalent widths, delta v90, and full-width half-max are available. Spectacle also provides the ability to compose compound models used to generate synthetic spectra allowing the user to define various LSF kernels, uncertainties, and to specify sampling.We also present updates to the visualization tool SpecViz, developed in conjunction with the JWST data analysis tools development team, to aid in the exploration of spectral data. SpecViz is an open source, Python-based spectral 1-D interactive visualization and analysis application built around high-performance interactive plotting. It supports handling general and instrument-specific data and includes advanced tool-sets for filtering and detrending one-dimensional data, along with the ability to isolate absorption regions using slicing and manipulate spectral features via spectral arithmetic. Multi-component modeling is also possible using a flexible model fitting tool-set that supports custom models to be used with various fitting routines. It also features robust user extensions such as custom data loaders and support for user-created plugins that add new functionality.This work was supported in part by HST AR #13919, HST GO #14268, and HST AR #14560.

Big Data in industry

NASA Astrophysics Data System (ADS)

Latinović, T. S.; Preradović, D. M.; Barz, C. R.; Latinović, M. T.; Petrica, P. P.; Pop-Vadean, A.

2016-08-01

The amount of data at the global level has grown exponentially. Along with this phenomena, we have a need for a new unit of measure like exabyte, zettabyte, and yottabyte as the last unit measures the amount of data. The growth of data gives a situation where the classic systems for the collection, storage, processing, and visualization of data losing the battle with a large amount, speed, and variety of data that is generated continuously. Many of data that is created by the Internet of Things, IoT (cameras, satellites, cars, GPS navigation, etc.). It is our challenge to come up with new technologies and tools for the management and exploitation of these large amounts of data. Big Data is a hot topic in recent years in IT circles. However, Big Data is recognized in the business world, and increasingly in the public administration. This paper proposes an ontology of big data analytics and examines how to enhance business intelligence through big data analytics as a service by presenting a big data analytics services-oriented architecture. This paper also discusses the interrelationship between business intelligence and big data analytics. The proposed approach in this paper might facilitate the research and development of business analytics, big data analytics, and business intelligence as well as intelligent agents.

DEVELOPMENTS IN GRworkbench

NASA Astrophysics Data System (ADS)

Moylan, Andrew; Scott, Susan M.; Searle, Anthony C.

2006-02-01

The software tool GRworkbench is an ongoing project in visual, numerical General Relativity at The Australian National University. Recently, GRworkbench has been significantly extended to facilitate numerical experimentation in analytically-defined space-times. The numerical differential geometric engine has been rewritten using functional programming techniques, enabling objects which are normally defined as functions in the formalism of differential geometry and General Relativity to be directly represented as function variables in the C++ code of GRworkbench. The new functional differential geometric engine allows for more accurate and efficient visualisation of objects in space-times and makes new, efficient computational techniques available. Motivated by the desire to investigate a recent scientific claim using GRworkbench, new tools for numerical experimentation have been implemented, allowing for the simulation of complex physical situations.

The Mochi project: a field theory approach to plasma dynamics and self-organization

NASA Astrophysics Data System (ADS)

You, Setthivoine; von der Linden, Jens; Lavine, Eric Sander; Card, Alexander; Carroll, Evan

2016-10-01

The Mochi project is designed to study the interaction between plasma flows and magnetic fields from the point-of-view of canonical flux tubes. The Mochi Labjet experiment is being commissioned after achieving first plasma. Analytical and numerical tools are being developed to visualize canonical flux tubes. One analytical tool described here is a field theory approach to plasma dynamics and self-organization. A redefinition of the Lagrangian of a multi-particle system in fields reformulates the single-particle, kinetic, and fluid equations governing fluid and plasma dynamics as a single set of generalized Maxwell's equations and Ohm's law for canonical force-fields. The Lagrangian includes new terms representing the coupling between the motion of particle distributions, between distributions and electromagnetic fields, with relativistic contributions. The formulation shows that the concepts of self-organization and canonical helicity transport are applicable across single-particle, kinetic, and fluid regimes, at classical and relativistic scales. The theory gives the basis for comparing canonical helicity change to energy change in general systems. This work is supported by by US DOE Grant DE-SC0010340.

From Data to Knowledge – Promising Analytical Tools and Techniques for Capture and Reuse of Corporate Knowledge and to Aid in the State Evaluation Process

DOE Office of Scientific and Technical Information (OSTI.GOV)

Danielson, Gary R.; Augustenborg, Elsa C.; Beck, Andrew E.

2010-10-29

The IAEA is challenged with limited availability of human resources for inspection and data analysis while proliferation threats increase. PNNL has a variety of IT solutions and techniques (at varying levels of maturity and development) that take raw data closer to useful knowledge, thereby assisting with and standardizing the analytical processes. This paper highlights some PNNL tools and techniques which are applicable to the international safeguards community, including: • Intelligent in-situ triage of data prior to reliable transmission to an analysis center resulting in the transmission of smaller and more relevant data sets • Capture of expert knowledge in re-usablemore » search strings tailored to specific mission outcomes • Image based searching fused with text based searching • Use of gaming to discover unexpected proliferation scenarios • Process modeling (e.g. Physical Model) as the basis for an information integration portal, which links to data storage locations along with analyst annotations, categorizations, geographic data, search strings and visualization outputs.« less

Supporting Communication and Coordination in Collaborative Sensemaking.

PubMed

Mahyar, Narges; Tory, Melanie

2014-12-01

When people work together to analyze a data set, they need to organize their findings, hypotheses, and evidence, share that information with their collaborators, and coordinate activities amongst team members. Sharing externalizations (recorded information such as notes) could increase awareness and assist with team communication and coordination. However, we currently know little about how to provide tool support for this sort of sharing. We explore how linked common work (LCW) can be employed within a `collaborative thinking space', to facilitate synchronous collaborative sensemaking activities in Visual Analytics (VA). Collaborative thinking spaces provide an environment for analysts to record, organize, share and connect externalizations. Our tool, CLIP, extends earlier thinking spaces by integrating LCW features that reveal relationships between collaborators' findings. We conducted a user study comparing CLIP to a baseline version without LCW. Results demonstrated that LCW significantly improved analytic outcomes at a collaborative intelligence task. Groups using CLIP were also able to more effectively coordinate their work, and held more discussion of their findings and hypotheses. LCW enabled them to maintain awareness of each other's activities and findings and link those findings to their own work, preventing disruptive oral awareness notifications.

Direct imaging of isofrequency contours in photonic structures

DOE PAGES

Regan, E. C.; Igarashi, Y.; Zhen, B.; ...

2016-11-25

The isofrequency contours of a photonic crystal are important for predicting and understanding exotic optical phenomena that are not apparent from high-symmetry band structure visualizations. We demonstrate a method to directly visualize the isofrequency contours of high-quality photonic crystal slabs that show quantitatively good agreement with numerical results throughout the visible spectrum. Our technique relies on resonance-enhanced photon scattering from generic fabrication disorder and surface roughness, so it can be applied to general photonic and plasmonic crystals or even quasi-crystals. We also present an analytical model of the scattering process, which explains the observation of isofrequency contours in our technique.more » Furthermore, the isofrequency contours provide information about the characteristics of the disorder and therefore serve as a feedback tool to improve fabrication processes.« less

In-line monitoring of pellet coating thickness growth by means of visual imaging.

PubMed

Oman Kadunc, Nika; Sibanc, Rok; Dreu, Rok; Likar, Boštjan; Tomaževič, Dejan

2014-08-15

Coating thickness is the most important attribute of coated pharmaceutical pellets as it directly affects release profiles and stability of the drug. Quality control of the coating process of pharmaceutical pellets is thus of utmost importance for assuring the desired end product characteristics. A visual imaging technique is presented and examined as a process analytic technology (PAT) tool for noninvasive continuous in-line and real time monitoring of coating thickness of pharmaceutical pellets during the coating process. Images of pellets were acquired during the coating process through an observation window of a Wurster coating apparatus. Image analysis methods were developed for fast and accurate determination of pellets' coating thickness during a coating process. The accuracy of the results for pellet coating thickness growth obtained in real time was evaluated through comparison with an off-line reference method and a good agreement was found. Information about the inter-pellet coating uniformity was gained from further statistical analysis of the measured pellet size distributions. Accuracy and performance analysis of the proposed method showed that visual imaging is feasible as a PAT tool for in-line and real time monitoring of the coating process of pharmaceutical pellets. Copyright © 2014 Elsevier B.V. All rights reserved.

An Integrated Web-Based 3d Modeling and Visualization Platform to Support Sustainable Cities

NASA Astrophysics Data System (ADS)

Amirebrahimi, S.; Rajabifard, A.

2012-07-01

Sustainable Development is found as the key solution to preserve the sustainability of cities in oppose to ongoing population growth and its negative impacts. This is complex and requires a holistic and multidisciplinary decision making. Variety of stakeholders with different backgrounds also needs to be considered and involved. Numerous web-based modeling and visualization tools have been designed and developed to support this process. There have been some success stories; however, majority failed to bring a comprehensive platform to support different aspects of sustainable development. In this work, in the context of SDI and Land Administration, CSDILA Platform - a 3D visualization and modeling platform -was proposed which can be used to model and visualize different dimensions to facilitate the achievement of sustainability, in particular, in urban context. The methodology involved the design of a generic framework for development of an analytical and visualization tool over the web. CSDILA Platform was then implemented via number of technologies based on the guidelines provided by the framework. The platform has a modular structure and uses Service-Oriented Architecture (SOA). It is capable of managing spatial objects in a 4D data store and can flexibly incorporate a variety of developed models using the platform's API. Development scenarios can be modeled and tested using the analysis and modeling component in the platform and the results are visualized in seamless 3D environment. The platform was further tested using number of scenarios and showed promising results and potentials to serve a wider need. In this paper, the design process of the generic framework, the implementation of CSDILA Platform and technologies used, and also findings and future research directions will be presented and discussed.

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase one, volume 4 : use of knowledge integrated visual analytics system in supporting bridge management.

DOT National Transportation Integrated Search

2009-12-01

The goals of integration should be: Supporting domain oriented data analysis through the use of : knowledge augmented visual analytics system. In this project, we focus on: : Providing interactive data exploration for bridge managements. : ...

Haptic over visual information in the distribution of visual attention after tool-use in near and far space.

PubMed

Park, George D; Reed, Catherine L

2015-10-01

Despite attentional prioritization for grasping space near the hands, tool-use appears to transfer attentional bias to the tool's end/functional part. The contributions of haptic and visual inputs to attentional distribution along a tool were investigated as a function of tool-use in near (Experiment 1) and far (Experiment 2) space. Visual attention was assessed with a 50/50, go/no-go, target discrimination task, while a tool was held next to targets appearing near the tool-occupied hand or tool-end. Target response times (RTs) and sensitivity (d-prime) were measured at target locations, before and after functional tool practice for three conditions: (1) open-tool: tool-end visible (visual + haptic inputs), (2) hidden-tool: tool-end visually obscured (haptic input only), and (3) short-tool: stick missing tool's length/end (control condition: hand occupied but no visual/haptic input). In near space, both open- and hidden-tool groups showed a tool-end, attentional bias (faster RTs toward tool-end) before practice; after practice, RTs near the hand improved. In far space, the open-tool group showed no bias before practice; after practice, target RTs near the tool-end improved. However, the hidden-tool group showed a consistent tool-end bias despite practice. Lack of short-tool group results suggested that hidden-tool group results were specific to haptic inputs. In conclusion, (1) allocation of visual attention along a tool due to tool practice differs in near and far space, and (2) visual attention is drawn toward the tool's end even when visually obscured, suggesting haptic input provides sufficient information for directing attention along the tool.

Next generation data harmonization

NASA Astrophysics Data System (ADS)

Armstrong, Chandler; Brown, Ryan M.; Chaves, Jillian; Czerniejewski, Adam; Del Vecchio, Justin; Perkins, Timothy K.; Rudnicki, Ron; Tauer, Greg

2015-05-01

Analysts are presented with a never ending stream of data sources. Often, subsets of data sources to solve problems are easily identified but the process to align data sets is time consuming. However, many semantic technologies do allow for fast harmonization of data to overcome these problems. These include ontologies that serve as alignment targets, visual tools and natural language processing that generate semantic graphs in terms of the ontologies, and analytics that leverage these graphs. This research reviews a developed prototype that employs all these approaches to perform analysis across disparate data sources documenting violent, extremist events.

[Application of the technique of analytical structure of project for the sub-project of websites catalog of the Virtual Health Library-Nursing].

PubMed

dos, Santos Luís Augusto; Marin, Heimar de Fátima; Marques, Isaac Rosa; Cunha, Isabel Cristina Kowal Olm

2007-01-01

This work intents, in a didactic form, to explain the benefits of use of a technique of project management, named Work Breakdown Structure: a graphical tool to identify the main results to be developed in a project. The real examples are applied to a sub-project of the Virtual Library in Health in Nursing (BVS-Enfermagem) to development of the Sites Catalogs. The benefits of graphical visualization for a major agreement between professionals of different expertise are presented.

From Particles and Point Clouds to Voxel Models: High Resolution Modeling of Dynamic Landscapes in Open Source GIS

NASA Astrophysics Data System (ADS)

Mitasova, H.; Hardin, E. J.; Kratochvilova, A.; Landa, M.

2012-12-01

Multitemporal data acquired by modern mapping technologies provide unique insights into processes driving land surface dynamics. These high resolution data also offer an opportunity to improve the theoretical foundations and accuracy of process-based simulations of evolving landforms. We discuss development of new generation of visualization and analytics tools for GRASS GIS designed for 3D multitemporal data from repeated lidar surveys and from landscape process simulations. We focus on data and simulation methods that are based on point sampling of continuous fields and lead to representation of evolving surfaces as series of raster map layers or voxel models. For multitemporal lidar data we present workflows that combine open source point cloud processing tools with GRASS GIS and custom python scripts to model and analyze dynamics of coastal topography (Figure 1) and we outline development of coastal analysis toolbox. The simulations focus on particle sampling method for solving continuity equations and its application for geospatial modeling of landscape processes. In addition to water and sediment transport models, already implemented in GIS, the new capabilities under development combine OpenFOAM for wind shear stress simulation with a new module for aeolian sand transport and dune evolution simulations. Comparison of observed dynamics with the results of simulations is supported by a new, integrated 2D and 3D visualization interface that provides highly interactive and intuitive access to the redesigned and enhanced visualization tools. Several case studies will be used to illustrate the presented methods and tools and demonstrate the power of workflows built with FOSS and highlight their interoperability.Figure 1. Isosurfaces representing evolution of shoreline and a z=4.5m contour between the years 1997-2011at Cape Hatteras, NC extracted from a voxel model derived from series of lidar-based DEMs.

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dasgupta, Aritra; Arendt, Dustin L.; Franklin, Lyndsey

State-of-the-art visual analytics models and frameworks mostly assume a static snapshot of the data, while in many cases it is a stream with constant updates and changes. Exploration of streaming data poses unique challenges as machine-level computations and abstractions need to be synchronized with the visual representation of the data and the temporally evolving human insights. In the visual analytics literature, we lack a thorough characterization of streaming data and analysis of the challenges associated with task abstraction, visualization design, and adaptation of the role of human-in-the-loop for exploration of data streams. We aim to fill this gap by conductingmore » a survey of the state-of-the-art in visual analytics of streaming data for systematically describing the contributions and shortcomings of current techniques and analyzing the research gaps that need to be addressed in the future. Our contributions are: i) problem characterization for identifying challenges that are unique to streaming data analysis tasks, ii) a survey and analysis of the state-of-the-art in streaming data visualization research with a focus on the visualization design space for dynamic data and the role of the human-in-the-loop, and iii) reflections on the design-trade-offs for streaming visual analytics techniques and their practical applicability in real-world application scenarios.« less

Increasing Access and Usability of Remote Sensing Data: The NASA Protected Area Archive

NASA Technical Reports Server (NTRS)

Geller, Gary N.

2004-01-01

Although remote sensing data are now widely available, much of it at low or no-cost, many managers of protected conservation areas do not have the expertise or tools to view or analyze it. Thus access to it by the protected area management community is effectively blocked. The Protected Area Archive will increase access to remote sensing data by creating collections of satellite images of protected areas and packaging them with simple-to-use visualization and analytical tools. The user can easily locate the area and image of interest on a map, then display, roam, and zoom the image. A set of simple tools will be provided so the user can explore the data and employ it to assist in management and monitoring of their area. The 'Phase 1 ' version requires only a Windows-based computer and basic computer skills, and may be of particular help to protected area managers in developing countries.

Utilizing Semantic Big Data for realizing a National-scale Infrastructure Vulnerability Analysis System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chinthavali, Supriya; Shankar, Mallikarjun

Critical Infrastructure systems(CIs) such as energy, water, transportation and communication are highly interconnected and mutually dependent in complex ways. Robust modeling of CIs interconnections is crucial to identify vulnerabilities in the CIs. We present here a national-scale Infrastructure Vulnerability Analysis System (IVAS) vision leveraging Se- mantic Big Data (SBD) tools, Big Data, and Geographical Information Systems (GIS) tools. We survey existing ap- proaches on vulnerability analysis of critical infrastructures and discuss relevant systems and tools aligned with our vi- sion. Next, we present a generic system architecture and discuss challenges including: (1) Constructing and manag- ing a CI network-of-networks graph,more » (2) Performing analytic operations at scale, and (3) Interactive visualization of ana- lytic output to generate meaningful insights. We argue that this architecture acts as a baseline to realize a national-scale network based vulnerability analysis system.« less

The Role of Teamwork in the Analysis of Big Data: A Study of Visual Analytics and Box Office Prediction.

PubMed

Buchanan, Verica; Lu, Yafeng; McNeese, Nathan; Steptoe, Michael; Maciejewski, Ross; Cooke, Nancy

2017-03-01

Historically, domains such as business intelligence would require a single analyst to engage with data, develop a model, answer operational questions, and predict future behaviors. However, as the problems and domains become more complex, organizations are employing teams of analysts to explore and model data to generate knowledge. Furthermore, given the rapid increase in data collection, organizations are struggling to develop practices for intelligence analysis in the era of big data. Currently, a variety of machine learning and data mining techniques are available to model data and to generate insights and predictions, and developments in the field of visual analytics have focused on how to effectively link data mining algorithms with interactive visuals to enable analysts to explore, understand, and interact with data and data models. Although studies have explored the role of single analysts in the visual analytics pipeline, little work has explored the role of teamwork and visual analytics in the analysis of big data. In this article, we present an experiment integrating statistical models, visual analytics techniques, and user experiments to study the role of teamwork in predictive analytics. We frame our experiment around the analysis of social media data for box office prediction problems and compare the prediction performance of teams, groups, and individuals. Our results indicate that a team's performance is mediated by the team's characteristics such as openness of individual members to others' positions and the type of planning that goes into the team's analysis. These findings have important implications for how organizations should create teams in order to make effective use of information from their analytic models.

Using Interactive Data Visualizations for Exploratory Analysis in Undergraduate Genomics Coursework: Field Study Findings and Guidelines

ERIC Educational Resources Information Center

Mirel, Barbara; Kumar, Anuj; Nong, Paige; Su, Gang; Meng, Fan

2016-01-01

Life scientists increasingly use visual analytics to explore large data sets and generate hypotheses. Undergraduate biology majors should be learning these same methods. Yet visual analytics is one of the most underdeveloped areas of undergraduate biology education. This study sought to determine the feasibility of undergraduate biology majors…

Literature and Product Review of Visual Analytics for Maritime Awareness

DTIC Science & Technology

2009-10-28

the user’s knowledge and experience. • Riveiro et al [107] provide a useful discussion of the cognitive process of anomaly detection based on...changes over time can be seen visually. • Wilkinson et al [140] suggests that we need visual analytics for three principal purposes: checking raw data...Predictions within the Current Plot • Yue et al [146] describe an AI blackboard-based agent that leverages interactive visualization and mixed

Big data in medical informatics: improving education through visual analytics.

PubMed

Vaitsis, Christos; Nilsson, Gunnar; Zary, Nabil

2014-01-01

A continuous effort to improve healthcare education today is currently driven from the need to create competent health professionals able to meet healthcare demands. Limited research reporting how educational data manipulation can help in healthcare education improvement. The emerging research field of visual analytics has the advantage to combine big data analysis and manipulation techniques, information and knowledge representation, and human cognitive strength to perceive and recognise visual patterns. The aim of this study was therefore to explore novel ways of representing curriculum and educational data using visual analytics. Three approaches of visualization and representation of educational data were presented. Five competencies at undergraduate medical program level addressed in courses were identified to inaccurately correspond to higher education board competencies. Different visual representations seem to have a potential in impacting on the ability to perceive entities and connections in the curriculum data.

Cryogenic Propellant Feed System Analytical Tool Development

NASA Technical Reports Server (NTRS)

Lusby, Brian S.; Miranda, Bruno M.; Collins, Jacob A.

2011-01-01

The Propulsion Systems Branch at NASA s Lyndon B. Johnson Space Center (JSC) has developed a parametric analytical tool to address the need to rapidly predict heat leak into propellant distribution lines based on insulation type, installation technique, line supports, penetrations, and instrumentation. The Propellant Feed System Analytical Tool (PFSAT) will also determine the optimum orifice diameter for an optional thermodynamic vent system (TVS) to counteract heat leak into the feed line and ensure temperature constraints at the end of the feed line are met. PFSAT was developed primarily using Fortran 90 code because of its number crunching power and the capability to directly access real fluid property subroutines in the Reference Fluid Thermodynamic and Transport Properties (REFPROP) Database developed by NIST. A Microsoft Excel front end user interface was implemented to provide convenient portability of PFSAT among a wide variety of potential users and its ability to utilize a user-friendly graphical user interface (GUI) developed in Visual Basic for Applications (VBA). The focus of PFSAT is on-orbit reaction control systems and orbital maneuvering systems, but it may be used to predict heat leak into ground-based transfer lines as well. PFSAT is expected to be used for rapid initial design of cryogenic propellant distribution lines and thermodynamic vent systems. Once validated, PFSAT will support concept trades for a variety of cryogenic fluid transfer systems on spacecraft, including planetary landers, transfer vehicles, and propellant depots, as well as surface-based transfer systems. The details of the development of PFSAT, its user interface, and the program structure will be presented.

SensePath: Understanding the Sensemaking Process Through Analytic Provenance.

PubMed

Nguyen, Phong H; Xu, Kai; Wheat, Ashley; Wong, B L William; Attfield, Simon; Fields, Bob

2016-01-01

Sensemaking is described as the process of comprehension, finding meaning and gaining insight from information, producing new knowledge and informing further action. Understanding the sensemaking process allows building effective visual analytics tools to make sense of large and complex datasets. Currently, it is often a manual and time-consuming undertaking to comprehend this: researchers collect observation data, transcribe screen capture videos and think-aloud recordings, identify recurring patterns, and eventually abstract the sensemaking process into a general model. In this paper, we propose a general approach to facilitate such a qualitative analysis process, and introduce a prototype, SensePath, to demonstrate the application of this approach with a focus on browser-based online sensemaking. The approach is based on a study of a number of qualitative research sessions including observations of users performing sensemaking tasks and post hoc analyses to uncover their sensemaking processes. Based on the study results and a follow-up participatory design session with HCI researchers, we decided to focus on the transcription and coding stages of thematic analysis. SensePath automatically captures user's sensemaking actions, i.e., analytic provenance, and provides multi-linked views to support their further analysis. A number of other requirements elicited from the design session are also implemented in SensePath, such as easy integration with existing qualitative analysis workflow and non-intrusive for participants. The tool was used by an experienced HCI researcher to analyze two sensemaking sessions. The researcher found the tool intuitive and considerably reduced analysis time, allowing better understanding of the sensemaking process.

CREATE-IP and CREATE-V: Data and Services Update

NASA Astrophysics Data System (ADS)

Carriere, L.; Potter, G. L.; Hertz, J.; Peters, J.; Maxwell, T. P.; Strong, S.; Shute, J.; Shen, Y.; Duffy, D.

2017-12-01

The NASA Center for Climate Simulation (NCCS) at the Goddard Space Flight Center and the Earth System Grid Federation (ESGF) are working together to build a uniform environment for the comparative study and use of a group of reanalysis datasets of particular importance to the research community. This effort is called the Collaborative REAnalysis Technical Environment (CREATE) and it contains two components: the CREATE-Intercomparison Project (CREATE-IP) and CREATE-V. This year's efforts included generating and publishing an atmospheric reanalysis ensemble mean and spread and improving the analytics available through CREATE-V. Related activities included adding access to subsets of the reanalysis data through ArcGIS and expanding the visualization tool to GMAO forecast data. This poster will present the access mechanisms to this data and use cases including example Jupyter Notebook code. The reanalysis ensemble was generated using two methods, first using standard Python tools for regridding, extracting levels and creating the ensemble mean and spread on a virtual server in the NCCS environment. The second was using a new analytics software suite, the Earth Data Analytics Services (EDAS), coupled with a high-performance Data Analytics and Storage System (DASS) developed at the NCCS. Results were compared to validate the EDAS methodologies, and the results, including time to process, will be presented. The ensemble includes selected 6 hourly and monthly variables, regridded to 1.25 degrees, with 24 common levels used for the 3D variables. Use cases for the new data and services will be presented, including the use of EDAS for the backend analytics on CREATE-V, the use of the GMAO forecast aerosol and cloud data in CREATE-V, and the ability to connect CREATE-V data to NCCS ArcGIS services.

A software tool for analyzing multichannel cochlear implant signals.

PubMed

Lai, Wai Kong; Bögli, Hans; Dillier, Norbert

2003-10-01

A useful and convenient means to analyze the radio frequency (RF) signals being sent by a speech processor to a cochlear implant would be to actually capture and display them with appropriate software. This is particularly useful for development or diagnostic purposes. sCILab (Swiss Cochlear Implant Laboratory) is such a PC-based software tool intended for the Nucleus family of Multichannel Cochlear Implants. Its graphical user interface provides a convenient and intuitive means for visualizing and analyzing the signals encoding speech information. Both numerical and graphic displays are available for detailed examination of the captured CI signals, as well as an acoustic simulation of these CI signals. sCILab has been used in the design and verification of new speech coding strategies, and has also been applied as an analytical tool in studies of how different parameter settings of existing speech coding strategies affect speech perception. As a diagnostic tool, it is also useful for troubleshooting problems with the external equipment of the cochlear implant systems.

Theoretical investigation of confocal microscopy using an elliptically polarized cylindrical vector laser beam: Visualization of quantum emitters near interfaces

NASA Astrophysics Data System (ADS)

Boichenko, Stepan

2018-04-01

We theoretically study laser-scanning confocal fluorescence microscopy using elliptically polarized cylindrical vector excitation light as a tool for visualization of arbitrarily oriented single quantum dipole emitters located (1) near planar surfaces enhancing fluorescence, (2) in a thin supported polymer film, (3) in a freestanding polymer film, and (4) in a dielectric planar microcavity. It is shown analytically that by using a tightly focused azimuthally polarized beam, it is possible to exclude completely the orientational dependence of the image intensity maximum of a quantum emitter that absorbs light as a pair of incoherent independent linear dipoles. For linear dipole quantum emitters, the orientational independence degree higher than 0.9 can normally be achieved (this quantity equal to 1 corresponds to completely excluded orientational dependence) if the collection efficiency of the microscope objective and the emitter's total quantum yield are not strongly orientationally dependent. Thus, the visualization of arbitrarily oriented single quantum emitters by means of the studied technique can be performed quite efficiently.

Rapid and visual detection of Leptospira in urine by LigB-LAMP assay with pre-addition of dye.

PubMed

Ali, Syed Atif; Kaur, Gurpreet; Boby, Nongthombam; Sabarinath, T; Solanki, Khushal; Pal, Dheeraj; Chaudhuri, Pallab

2017-12-01

Leptospirosis is considered to be the most widespread zoonotic disease caused by pathogenic species of Leptospira. The present study reports a novel set of primers targeting LigB gene for visual detection of pathogenic Leptospira in urine samples through Loop-mediated isothermal amplification (LAMP). The results were recorded by using Hydroxyl napthol blue (HNB), SYBR GREEN I and calcein. Analytical sensitivity of LAMP was as few as 10 leptospiral organisms in spiked urine samples from cattle and dog. LigB gene based LAMP, termed as LigB-LAMP, was found 10 times more sensitive than conventional PCR. The diagnostic specificity of LAMP was 100% when compared to SYBR green qPCR for detection of Leptospira in urine samples. Though qPCR was found more sensitive, the rapidity and simplicity in setting LAMP test followed by visual detection of Leptospira infection in clinical samples makes LigB-LAMP an alternative and favourable diagnostic tool in resource poor setting. Copyright © 2017 Elsevier Ltd. All rights reserved.

Eye-Tracking as a Tool to Evaluate Functional Ability in Everyday Tasks in Glaucoma.

PubMed

Kasneci, Enkelejda; Black, Alex A; Wood, Joanne M

2017-01-01

To date, few studies have investigated the eye movement patterns of individuals with glaucoma while they undertake everyday tasks in real-world settings. While some of these studies have reported possible compensatory gaze patterns in those with glaucoma who demonstrated good task performance despite their visual field loss, little is known about the complex interaction between field loss and visual scanning strategies and the impact on task performance and, consequently, on quality of life. We review existing approaches that have quantified the effect of glaucomatous visual field defects on the ability to undertake everyday activities through the use of eye movement analysis. Furthermore, we discuss current developments in eye-tracking technology and the potential for combining eye-tracking with virtual reality and advanced analytical approaches. Recent technological developments suggest that systems based on eye-tracking have the potential to assist individuals with glaucomatous loss to maintain or even improve their performance on everyday tasks and hence enhance their long-term quality of life. We discuss novel approaches for studying the visual search behavior of individuals with glaucoma that have the potential to assist individuals with glaucoma, through the use of personalized programs that take into consideration the individual characteristics of their remaining visual field and visual search behavior.

Eye-Tracking as a Tool to Evaluate Functional Ability in Everyday Tasks in Glaucoma

PubMed Central

Black, Alex A.

2017-01-01

To date, few studies have investigated the eye movement patterns of individuals with glaucoma while they undertake everyday tasks in real-world settings. While some of these studies have reported possible compensatory gaze patterns in those with glaucoma who demonstrated good task performance despite their visual field loss, little is known about the complex interaction between field loss and visual scanning strategies and the impact on task performance and, consequently, on quality of life. We review existing approaches that have quantified the effect of glaucomatous visual field defects on the ability to undertake everyday activities through the use of eye movement analysis. Furthermore, we discuss current developments in eye-tracking technology and the potential for combining eye-tracking with virtual reality and advanced analytical approaches. Recent technological developments suggest that systems based on eye-tracking have the potential to assist individuals with glaucomatous loss to maintain or even improve their performance on everyday tasks and hence enhance their long-term quality of life. We discuss novel approaches for studying the visual search behavior of individuals with glaucoma that have the potential to assist individuals with glaucoma, through the use of personalized programs that take into consideration the individual characteristics of their remaining visual field and visual search behavior. PMID:28293433

Hyperspectral Stimulated Raman Scattering Microscopy Unravels Aberrant Accumulation of Saturated Fat in Human Liver Cancer.

PubMed

Yan, Shuai; Cui, Sishan; Ke, Kun; Zhao, Bixing; Liu, Xiaolong; Yue, Shuhua; Wang, Ping

2018-06-05

Lipid metabolism is dysregulated in human cancers. The analytical tools that could identify and quantitatively map metabolites in unprocessed human tissues with submicrometer resolution are highly desired. Here, we implemented analytical hyperspectral stimulated Raman scattering microscopy to map the lipid metabolites in situ in normal and cancerous liver tissues from 24 patients. In contrast to the conventional wisdom that unsaturated lipid accumulation enhances tumor cell survival and proliferation, we unexpectedly visualized substantial amount of saturated fat accumulated in cancerous liver tissues, which was not seen in majority of their adjacent normal tissues. Further analysis by mass spectrometry confirmed significant high levels of glyceryl tripalmitate specifically in cancerous liver. These findings suggest that the aberrantly accumulated saturated fat may have great potential to be a metabolic biomarker for liver cancer.

Physiological and Anatomical Visual Analytics (PAVA) Background

EPA Pesticide Factsheets

The need to efficiently analyze human chemical disposition data from in vivo studies or in silico PBPK modeling efforts, and to see complex disposition data in a logical manner, has created a unique opportunity for visual analytics applid to PAD.

PlanetSense: A Real-time Streaming and Spatio-temporal Analytics Platform for Gathering Geo-spatial Intelligence from Open Source Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thakur, Gautam S; Bhaduri, Budhendra L; Piburn, Jesse O

Geospatial intelligence has traditionally relied on the use of archived and unvarying data for planning and exploration purposes. In consequence, the tools and methods that are architected to provide insight and generate projections only rely on such datasets. Albeit, if this approach has proven effective in several cases, such as land use identification and route mapping, it has severely restricted the ability of researchers to inculcate current information in their work. This approach is inadequate in scenarios requiring real-time information to act and to adjust in ever changing dynamic environments, such as evacuation and rescue missions. In this work, wemore » propose PlanetSense, a platform for geospatial intelligence that is built to harness the existing power of archived data and add to that, the dynamics of real-time streams, seamlessly integrated with sophisticated data mining algorithms and analytics tools for generating operational intelligence on the fly. The platform has four main components i) GeoData Cloud a data architecture for storing and managing disparate datasets; ii) Mechanism to harvest real-time streaming data; iii) Data analytics framework; iv) Presentation and visualization through web interface and RESTful services. Using two case studies, we underpin the necessity of our platform in modeling ambient population and building occupancy at scale.« less

Just-in-time Time Data Analytics and Visualization of Climate Simulations using the Bellerophon Framework

NASA Astrophysics Data System (ADS)

Anantharaj, V. G.; Venzke, J.; Lingerfelt, E.; Messer, B.

2015-12-01

Climate model simulations are used to understand the evolution and variability of earth's climate. Unfortunately, high-resolution multi-decadal climate simulations can take days to weeks to complete. Typically, the simulation results are not analyzed until the model runs have ended. During the course of the simulation, the output may be processed periodically to ensure that the model is preforming as expected. However, most of the data analytics and visualization are not performed until the simulation is finished. The lengthy time period needed for the completion of the simulation constrains the productivity of climate scientists. Our implementation of near real-time data visualization analytics capabilities allows scientists to monitor the progress of their simulations while the model is running. Our analytics software executes concurrently in a co-scheduling mode, monitoring data production. When new data are generated by the simulation, a co-scheduled data analytics job is submitted to render visualization artifacts of the latest results. These visualization output are automatically transferred to Bellerophon's data server located at ORNL's Compute and Data Environment for Science (CADES) where they are processed and archived into Bellerophon's database. During the course of the experiment, climate scientists can then use Bellerophon's graphical user interface to view animated plots and their associated metadata. The quick turnaround from the start of the simulation until the data are analyzed permits research decisions and projections to be made days or sometimes even weeks sooner than otherwise possible! The supercomputer resources used to run the simulation are unaffected by co-scheduling the data visualization jobs, so the model runs continuously while the data are visualized. Our just-in-time data visualization software looks to increase climate scientists' productivity as climate modeling moves into exascale era of computing.

Revealing 3D Ultrastructure and Morphology of Stem Cell Spheroids by Electron Microscopy.

PubMed

Jaros, Josef; Petrov, Michal; Tesarova, Marketa; Hampl, Ales

2017-01-01

Cell culture methods have been developed in efforts to produce biologically relevant systems for developmental and disease modeling, and appropriate analytical tools are essential. Knowledge of ultrastructural characteristics represents the basis to reveal in situ the cellular morphology, cell-cell interactions, organelle distribution, niches in which cells reside, and many more. The traditional method for 3D visualization of ultrastructural components, serial sectioning using transmission electron microscopy (TEM), is very labor-intensive due to contentious TEM slice preparation and subsequent image processing of the whole collection. In this chapter, we present serial block-face scanning electron microscopy, together with complex methodology for spheroid formation, contrasting of cellular compartments, image processing, and 3D visualization. The described technique is effective for detailed morphological analysis of stem cell spheroids, organoids, as well as organotypic cell cultures.

Frozen Stiff: Cartographic Design and Permafrost Mapping

NASA Astrophysics Data System (ADS)

Nelson, F. E.; Li, J.; Nyland, K. E.

2016-12-01

Maps are the primary vehicle used to communicate geographical relationships. Ironically, interest in the formal practice of cartography, the art and science of geographic visualization, has fallen significantly during a period when the sophistication and availability of GIS software has increased dramatically. Although the number of geographically oriented permafrost studies has increased significantly in recent years, little discussion about competing visualization strategies, map accuracy, and the psychophysical impact of cartographic design is evident in geocryological literature. Failure to use the full potential of the tools and techniques that contemporary cartographic and spatial-analytic theory makes possible affects our ability to effectively and accurately communicate the impacts and hazards associated with thawing permafrost, particularly in the context of global climate change. This presentation examines recent permafrost studies involving primarily small-scale (large area) mapping, and suggests cartographic strategies for rectifying existing problems.

Visualizing the BEC-BCS crossover in a two-dimensional Fermi gas: Pairing gaps and dynamical response functions from ab initio computations

NASA Astrophysics Data System (ADS)

Vitali, Ettore; Shi, Hao; Qin, Mingpu; Zhang, Shiwei

2017-12-01

Experiments with ultracold atoms provide a highly controllable laboratory setting with many unique opportunities for precision exploration of quantum many-body phenomena. The nature of such systems, with strong interaction and quantum entanglement, makes reliable theoretical calculations challenging. Especially difficult are excitation and dynamical properties, which are often the most directly relevant to experiment. We carry out exact numerical calculations, by Monte Carlo sampling of imaginary-time propagation of Slater determinants, to compute the pairing gap in the two-dimensional Fermi gas from first principles. Applying state-of-the-art analytic continuation techniques, we obtain the spectral function and the density and spin structure factors providing unique tools to visualize the BEC-BCS crossover. These quantities will allow for a direct comparison with experiments.

Generalized plasma dispersion function: One-solve-all treatment, visualizations, and application to Landau damping

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xie, Hua-Sheng

2013-09-15

A unified, fast, and effective approach is developed for numerical calculation of the well-known plasma dispersion function with extensions from Maxwellian distribution to almost arbitrary distribution functions, such as the δ, flat top, triangular, κ or Lorentzian, slowing down, and incomplete Maxwellian distributions. The singularity and analytic continuation problems are also solved generally. Given that the usual conclusion γ∝∂f{sub 0}/∂v is only a rough approximation when discussing the distribution function effects on Landau damping, this approach provides a useful tool for rigorous calculations of the linear wave and instability properties of plasma for general distribution functions. The results are alsomore » verified via a linear initial value simulation approach. Intuitive visualizations of the generalized plasma dispersion function are also provided.« less

Using 3D Printing for Rapid Prototyping of Characterization Tools for Investigating Powder Blend Behavior.

PubMed

Hirschberg, Cosima; Boetker, Johan P; Rantanen, Jukka; Pein-Hackelbusch, Miriam

2018-02-01

There is an increasing need to provide more detailed insight into the behavior of particulate systems. The current powder characterization tools are developed empirically and in many cases, modification of existing equipment is difficult. More flexible tools are needed to provide understanding of complex powder behavior, such as mixing process and segregation phenomenon. An approach based on the fast prototyping of new powder handling geometries and interfacing solutions for process analytical tools is reported. This study utilized 3D printing for rapid prototyping of customized geometries; overall goal was to assess mixing process of powder blends at small-scale with a combination of spectroscopic and mechanical monitoring. As part of the segregation evaluation studies, the flowability of three different paracetamol/filler-blends at different ratios was investigated, inter alia to define the percolation thresholds. Blends with a paracetamol wt% above the percolation threshold were subsequently investigated in relation to their segregation behavior. Rapid prototyping using 3D printing allowed designing two funnels with tailored flow behavior (funnel flow) of model formulations, which could be monitored with an in-line near-infrared (NIR) spectrometer. Calculating the root mean square (RMS) of the scores of the two first principal components of the NIR spectra visualized spectral variation as a function of process time. In a same setup, mechanical properties (basic flow energy) of the powder blend were monitored during blending. Rapid prototyping allowed for fast modification of powder testing geometries and easy interfacing with process analytical tools, opening new possibilities for more detailed powder characterization.

CI-KNOW: Cyberinfrastructure Knowledge Networks on the Web. A Social Network Enabled Recommender System for Locating Resources in Cyberinfrastructures

NASA Astrophysics Data System (ADS)

Green, H. D.; Contractor, N. S.; Yao, Y.

2006-12-01

A knowledge network is a multi-dimensional network created from the interactions and interconnections among the scientists, documents, data, analytic tools, and interactive collaboration spaces (like forums and wikis) associated with a collaborative environment. CI-KNOW is a suite of software tools that leverages automated data collection, social network theories, analysis techniques and algorithms to infer an individual's interests and expertise based on their interactions and activities within a knowledge network. The CI-KNOW recommender system mines the knowledge network associated with a scientific community's use of cyberinfrastructure tools and uses relational metadata to record connections among entities in the knowledge network. Recent developments in social network theories and methods provide the backbone for a modular system that creates recommendations from relational metadata. A network navigation portlet allows users to locate colleagues, documents, data or analytic tools in the knowledge network and to explore their networks through a visual, step-wise process. An internal auditing portlet offers administrators diagnostics to assess the growth and health of the entire knowledge network. The first instantiation of the prototype CI-KNOW system is part of the Environmental Cyberinfrastructure Demonstration project at the National Center for Supercomputing Applications, which supports the activities of hydrologic and environmental science communities (CLEANER and CUAHSI) under the umbrella of the WATERS network environmental observatory planning activities (http://cleaner.ncsa.uiuc.edu). This poster summarizes the key aspects of the CI-KNOW system, highlighting the key inputs, calculation mechanisms, and output modalities.

SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics

PubMed Central

Vartak, Manasi; Rahman, Sajjadur; Madden, Samuel; Parameswaran, Aditya; Polyzotis, Neoklis

2015-01-01

Data analysts often build visualizations as the first step in their analytical workflow. However, when working with high-dimensional datasets, identifying visualizations that show relevant or desired trends in data can be laborious. We propose SeeDB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, SeeDB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most “useful” or “interesting”. The two major obstacles in recommending interesting visualizations are (a) scale: evaluating a large number of candidate visualizations while responding within interactive time scales, and (b) utility: identifying an appropriate metric for assessing interestingness of visualizations. For the former, SeeDB introduces pruning optimizations to quickly identify high-utility visualizations and sharing optimizations to maximize sharing of computation across visualizations. For the latter, as a first step, we adopt a deviation-based metric for visualization utility, while indicating how we may be able to generalize it to other factors influencing utility. We implement SeeDB as a middleware layer that can run on top of any DBMS. Our experiments show that our framework can identify interesting visualizations with high accuracy. Our optimizations lead to multiple orders of magnitude speedup on relational row and column stores and provide recommendations at interactive time scales. Finally, we demonstrate via a user study the effectiveness of our deviation-based utility metric and the value of recommendations in supporting visual analytics. PMID:26779379

SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics.

PubMed

Vartak, Manasi; Rahman, Sajjadur; Madden, Samuel; Parameswaran, Aditya; Polyzotis, Neoklis

2015-09-01

Data analysts often build visualizations as the first step in their analytical workflow. However, when working with high-dimensional datasets, identifying visualizations that show relevant or desired trends in data can be laborious. We propose SeeDB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, SeeDB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most "useful" or "interesting". The two major obstacles in recommending interesting visualizations are (a) scale : evaluating a large number of candidate visualizations while responding within interactive time scales, and (b) utility : identifying an appropriate metric for assessing interestingness of visualizations. For the former, SeeDB introduces pruning optimizations to quickly identify high-utility visualizations and sharing optimizations to maximize sharing of computation across visualizations. For the latter, as a first step, we adopt a deviation-based metric for visualization utility, while indicating how we may be able to generalize it to other factors influencing utility. We implement SeeDB as a middleware layer that can run on top of any DBMS. Our experiments show that our framework can identify interesting visualizations with high accuracy. Our optimizations lead to multiple orders of magnitude speedup on relational row and column stores and provide recommendations at interactive time scales. Finally, we demonstrate via a user study the effectiveness of our deviation-based utility metric and the value of recommendations in supporting visual analytics.

Understanding Adherence and Prescription Patterns Using Large-Scale Claims Data.

PubMed

Bjarnadóttir, Margrét V; Malik, Sana; Onukwugha, Eberechukwu; Gooden, Tanisha; Plaisant, Catherine

2016-02-01

Advanced computing capabilities and novel visual analytics tools now allow us to move beyond the traditional cross-sectional summaries to analyze longitudinal prescription patterns and the impact of study design decisions. For example, design decisions regarding gaps and overlaps in prescription fill data are necessary for measuring adherence using prescription claims data. However, little is known regarding the impact of these decisions on measures of medication possession (e.g., medication possession ratio). The goal of the study was to demonstrate the use of visualization tools for pattern discovery, hypothesis generation, and study design. We utilized EventFlow, a novel discrete event sequence visualization software, to investigate patterns of prescription fills, including gaps and overlaps, utilizing large-scale healthcare claims data. The study analyzes data of individuals who had at least two prescriptions for one of five hypertension medication classes: ACE inhibitors, angiotensin II receptor blockers, beta blockers, calcium channel blockers, and diuretics. We focused on those members initiating therapy with diuretics (19.2%) who may have concurrently or subsequently take drugs in other classes as well. We identified longitudinal patterns in prescription fills for antihypertensive medications, investigated the implications of decisions regarding gap length and overlaps, and examined the impact on the average cost and adherence of the initial treatment episode. A total of 790,609 individuals are included in the study sample, 19.2% (N = 151,566) of whom started on diuretics first during the study period. The average age was 52.4 years and 53.1% of the population was female. When the allowable gap was zero, 34% of the population had continuous coverage and the average length of continuous coverage was 2 months. In contrast, when the allowable gap was 30 days, 69% of the population showed a single continuous prescription period with an average length of 5 months. The average prescription cost of the period of continuous coverage ranged from US$3.44 (when the maximum gap was 0 day) to US$9.08 (when the maximum gap was 30 days). Results were less impactful when considering overlaps. This proof-of-concept study illustrates the use of visual analytics tools in characterizing longitudinal medication possession. We find that prescription patterns and associated prescription costs are more influenced by allowable gap lengths than by definitions and treatment of overlap. Research using medication gaps and overlaps to define medication possession in prescription claims data should pay particular attention to the definition and use of gap lengths.

A new tool for the evaluation of the analytical procedure: Green Analytical Procedure Index.

PubMed

Płotka-Wasylka, J

2018-05-01

A new means for assessing analytical protocols relating to green analytical chemistry attributes has been developed. The new tool, called GAPI (Green Analytical Procedure Index), evaluates the green character of an entire analytical methodology, from sample collection to final determination, and was created using such tools as the National Environmental Methods Index (NEMI) or Analytical Eco-Scale to provide not only general but also qualitative information. In GAPI, a specific symbol with five pentagrams can be used to evaluate and quantify the environmental impact involved in each step of an analytical methodology, mainly from green through yellow to red depicting low, medium to high impact, respectively. The proposed tool was used to evaluate analytical procedures applied in the determination of biogenic amines in wine samples, and polycyclic aromatic hydrocarbon determination by EPA methods. GAPI tool not only provides an immediately perceptible perspective to the user/reader but also offers exhaustive information on evaluated procedures. Copyright © 2018 Elsevier B.V. All rights reserved.

Effects of Using Dynamic Mathematics Software on Preservice Mathematics Teachers' Spatial Visualization Skills: The Case of Spatial Analytic Geometry

ERIC Educational Resources Information Center

Kösa, Temel

2016-01-01

The purpose of this study was to investigate the effects of using dynamic geometry software on preservice mathematics teachers' spatial visualization skills and to determine whether spatial visualization skills can be a predictor of success in learning analytic geometry of space. The study used a quasi-experimental design with a control group.…

Empirical Analysis of the Subjective Impressions and Objective Measures of Domain Scientists’ Visual Analytic Judgments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dasgupta, Aritra; Burrows, Susannah M.; Han, Kyungsik

2017-05-08

Scientists often use specific data analysis and presentation methods familiar within their domain. But does high familiarity drive better analytical judgment? This question is especially relevant when familiar methods themselves can have shortcomings: many visualizations used conventionally for scientific data analysis and presentation do not follow established best practices. This necessitates new methods that might be unfamiliar yet prove to be more effective. But there is little empirical understanding of the relationships between scientists’ subjective impressions about familiar and unfamiliar visualizations and objective measures of their visual analytic judgments. To address this gap and to study these factors, we focusmore » on visualizations used for comparison of climate model performance. We report on a comprehensive survey-based user study with 47 climate scientists and present an analysis of : i) relationships among scientists’ familiarity, their perceived lev- els of comfort, confidence, accuracy, and objective measures of accuracy, and ii) relationships among domain experience, visualization familiarity, and post-study preference.« less

The Role of Motor Learning in Spatial Adaptation near a Tool

PubMed Central

Brown, Liana E.; Doole, Robert; Malfait, Nicole

2011-01-01

Some visual-tactile (bimodal) cells have visual receptive fields (vRFs) that overlap and extend moderately beyond the skin of the hand. Neurophysiological evidence suggests, however, that a vRF will grow to encompass a hand-held tool following active tool use but not after passive holding. Why does active tool use, and not passive holding, lead to spatial adaptation near a tool? We asked whether spatial adaptation could be the result of motor or visual experience with the tool, and we distinguished between these alternatives by isolating motor from visual experience with the tool. Participants learned to use a novel, weighted tool. The active training group received both motor and visual experience with the tool, the passive training group received visual experience with the tool, but no motor experience, and finally, a no-training control group received neither visual nor motor experience using the tool. After training, we used a cueing paradigm to measure how quickly participants detected targets, varying whether the tool was placed near or far from the target display. Only the active training group detected targets more quickly when the tool was placed near, rather than far, from the target display. This effect of tool location was not present for either the passive-training or control groups. These results suggest that motor learning influences how visual space around the tool is represented. PMID:22174944

Using Participatory System Dynamics Modeling to Examine the Local HIV Test and Treatment Care Continuum in Order to Reduce Community Viral Load.

PubMed

Weeks, Margaret R; Li, Jianghong; Lounsbury, David; Green, Helena Danielle; Abbott, Maryann; Berman, Marcie; Rohena, Lucy; Gonzalez, Rosely; Lang, Shawn; Mosher, Heather

2017-12-01

Achieving community-level goals to eliminate the HIV epidemic requires coordinated efforts through community consortia with a common purpose to examine and critique their own HIV testing and treatment (T&T) care system and build effective tools to guide their efforts to improve it. Participatory system dynamics (SD) modeling offers conceptual, methodological, and analytical tools to engage diverse stakeholders in systems conceptualization and visual mapping of dynamics that undermine community-level health outcomes and identify those that can be leveraged for systems improvement. We recruited and engaged a 25-member multi-stakeholder Task Force, whose members provide or utilize HIV-related services, to participate in SD modeling to examine and address problems of their local HIV T&T service system. Findings from the iterative model building sessions indicated Task Force members' increasingly complex understanding of the local HIV care system and demonstrated their improved capacity to visualize and critique multiple models of the HIV T&T service system and identify areas of potential leverage. Findings also showed members' enhanced communication and consensus in seeking deeper systems understanding and options for solutions. We discuss implications of using these visual SD models for subsequent simulation modeling of the T&T system and for other community applications to improve system effectiveness. © Society for Community Research and Action 2017.

Big Data Tools as Applied to ATLAS Event Data

NASA Astrophysics Data System (ADS)

Vukotic, I.; Gardner, R. W.; Bryant, L. A.

2017-10-01

Big Data technologies have proven to be very useful for storage, processing and visualization of derived metrics associated with ATLAS distributed computing (ADC) services. Logfiles, database records, and metadata from a diversity of systems have been aggregated and indexed to create an analytics platform for ATLAS ADC operations analysis. Dashboards, wide area data access cost metrics, user analysis patterns, and resource utilization efficiency charts are produced flexibly through queries against a powerful analytics cluster. Here we explore whether these techniques and associated analytics ecosystem can be applied to add new modes of open, quick, and pervasive access to ATLAS event data. Such modes would simplify access and broaden the reach of ATLAS public data to new communities of users. An ability to efficiently store, filter, search and deliver ATLAS data at the event and/or sub-event level in a widely supported format would enable or significantly simplify usage of machine learning environments and tools like Spark, Jupyter, R, SciPy, Caffe, TensorFlow, etc. Machine learning challenges such as the Higgs Boson Machine Learning Challenge, the Tracking challenge, Event viewers (VP1, ATLANTIS, ATLASrift), and still to be developed educational and outreach tools would be able to access the data through a simple REST API. In this preliminary investigation we focus on derived xAOD data sets. These are much smaller than the primary xAODs having containers, variables, and events of interest to a particular analysis. Being encouraged with the performance of Elasticsearch for the ADC analytics platform, we developed an algorithm for indexing derived xAOD event data. We have made an appropriate document mapping and have imported a full set of standard model W/Z datasets. We compare the disk space efficiency of this approach to that of standard ROOT files, the performance in simple cut flow type of data analysis, and will present preliminary results on its scaling characteristics with different numbers of clients, query complexity, and size of the data retrieved.

Analysis, Mining and Visualization Service at NCSA

NASA Astrophysics Data System (ADS)

Wilhelmson, R.; Cox, D.; Welge, M.

2004-12-01

NCSA's goal is to create a balanced system that fully supports high-end computing as well as: 1) high-end data management and analysis; 2) visualization of massive, highly complex data collections; 3) large databases; 4) geographically distributed Grid computing; and 5) collaboratories, all based on a secure computational environment and driven with workflow-based services. To this end NCSA has defined a new technology path that includes the integration and provision of cyberservices in support of data analysis, mining, and visualization. NCSA has begun to develop and apply a data mining system-NCSA Data-to-Knowledge (D2K)-in conjunction with both the application and research communities. NCSA D2K will enable the formation of model-based application workflows and visual programming interfaces for rapid data analysis. The Java-based D2K framework, which integrates analytical data mining methods with data management, data transformation, and information visualization tools, will be configurable from the cyberservices (web and grid services, tools, ..) viewpoint to solve a wide range of important data mining problems. This effort will use modules, such as a new classification methods for the detection of high-risk geoscience events, and existing D2K data management, machine learning, and information visualization modules. A D2K cyberservices interface will be developed to seamlessly connect client applications with remote back-end D2K servers, providing computational resources for data mining and integration with local or remote data stores. This work is being coordinated with SDSC's data and services efforts. The new NCSA Visualization embedded workflow environment (NVIEW) will be integrated with D2K functionality to tightly couple informatics and scientific visualization with the data analysis and management services. Visualization services will access and filter disparate data sources, simplifying tasks such as fusing related data from distinct sources into a coherent visual representation. This approach enables collaboration among geographically dispersed researchers via portals and front-end clients, and the coupling with data management services enables recording associations among datasets and building annotation systems into visualization tools and portals, giving scientists a persistent, shareable, virtual lab notebook. To facilitate provision of these cyberservices to the national community, NCSA will be providing a computational environment for large-scale data assimilation, analysis, mining, and visualization. This will be initially implemented on the new 512 processor shared memory SGI's recently purchased by NCSA. In addition to standard batch capabilities, NCSA will provide on-demand capabilities for those projects requiring rapid response (e.g., development of severe weather, earthquake events) for decision makers. It will also be used for non-sequential interactive analysis of data sets where it is important have access to large data volumes over space and time.

Visual Analytics for MOOC Data.

PubMed

Qu, Huamin; Chen, Qing

2015-01-01

With the rise of massive open online courses (MOOCs), tens of millions of learners can now enroll in more than 1,000 courses via MOOC platforms such as Coursera and edX. As a result, a huge amount of data has been collected. Compared with traditional education records, the data from MOOCs has much finer granularity and also contains new pieces of information. It is the first time in history that such comprehensive data related to learning behavior has become available for analysis. What roles can visual analytics play in this MOOC movement? The authors survey the current practice and argue that MOOCs provide an opportunity for visualization researchers and that visual analytics systems for MOOCs can benefit a range of end users such as course instructors, education researchers, students, university administrators, and MOOC providers.

MO-C-BRCD-03: The Role of Informatics in Medical Physics and Vice Versa.

PubMed

Andriole, K

2012-06-01

Like Medical Physics, Imaging Informatics encompasses concepts touching every aspect of the imaging chain from image creation, acquisition, management and archival, to image processing, analysis, display and interpretation. The two disciplines are in fact quite complementary, with similar goals to improve the quality of care provided to patients using an evidence-based approach, to assure safety in the clinical and research environments, to facilitate efficiency in the workplace, and to accelerate knowledge discovery. Use-cases describing several areas of informatics activity will be given to illustrate current limitations that would benefit from medical physicist participation, and conversely areas in which informaticists may contribute to the solution. Topics to be discussed include radiation dose monitoring, process management and quality control, display technologies, business analytics techniques, and quantitative imaging. Quantitative imaging is increasingly becoming an essential part of biomedicalresearch as well as being incorporated into clinical diagnostic activities. Referring clinicians are asking for more objective information to be gleaned from the imaging tests that they order so that they may make the best clinical management decisions for their patients. Medical Physicists may be called upon to identify existing issues as well as develop, validate and implement new approaches and technologies to help move the field further toward quantitative imaging methods for the future. Biomedical imaging informatics tools and techniques such as standards, integration, data mining, cloud computing and new systems architectures, ontologies and lexicons, data visualization and navigation tools, and business analytics applications can be used to overcome some of the existing limitations. 1. Describe what is meant by Medical Imaging Informatics and understand why the medical physicist should care. 2. Identify existing limitations in information technologies with respect to Medical Physics, and conversely see how Informatics may assist the medical physicist in filling some of the current gaps in their activities. 3. Understand general informatics concepts and areas of investigation including imaging and workflow standards, systems integration, computing architectures, ontologies, data mining and business analytics, data visualization and human-computer interface tools, and the importance of quantitative imaging for the future of Medical Physics and Imaging Informatics. 4. Become familiar with on-going efforts to address current challenges facing future research into and clinical implementation of quantitative imaging applications. © 2012 American Association of Physicists in Medicine.

Bonded composite to metal scarf joint performance in an aircraft landing gear drag strut. [for Boeing 747 aircraft

NASA Technical Reports Server (NTRS)

Howell, W. E.

1974-01-01

The structural performance of a boron-epoxy reinforced titanium drag strut, which contains a bonded scarf joint and was designed to the criteria of the Boeing 747 transport, was evaluated. An experimental and analytical investigation was conducted. The strut was exposed to two lifetimes of spectrum loading and was statically loaded to the tensile and compressive design ultimate loads. Throughout the test program no evidence of any damage in the drag strut was detected by strain gage measurements, ultrasonic inspection, or visual observation. An analytical study of the bonded joint was made using the NASA structural analysis computer program NASTRAN. A comparison of the strains predicted by the NASTRAN computer program with the experimentally determined values shows excellent agreement. The NASTRAN computer program is a viable tool for studying, in detail, the stresses and strains induced in a bonded joint.

Database and Analytical Tool Development for the Management of Data Derived from US DOE (NETL) Funded Fine Particulate (PM2.5) Research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robinson Khosah

2007-07-31

Advanced Technology Systems, Inc. (ATS) was contracted by the U. S. Department of Energy's National Energy Technology Laboratory (DOE-NETL) to develop a state-of-the-art, scalable and robust web-accessible database application to manage the extensive data sets resulting from the DOE-NETL-sponsored ambient air monitoring programs in the upper Ohio River valley region. The data management system was designed to include a web-based user interface that will allow easy access to the data by the scientific community, policy- and decision-makers, and other interested stakeholders, while providing detailed information on sampling, analytical and quality control parameters. In addition, the system will provide graphical analyticalmore » tools for displaying, analyzing and interpreting the air quality data. The system will also provide multiple report generation capabilities and easy-to-understand visualization formats that can be utilized by the media and public outreach/educational institutions. The project was conducted in two phases. Phase One included the following tasks: (1) data inventory/benchmarking, including the establishment of an external stakeholder group; (2) development of a data management system; (3) population of the database; (4) development of a web-based data retrieval system, and (5) establishment of an internal quality assurance/quality control system on data management. Phase Two involved the development of a platform for on-line data analysis. Phase Two included the following tasks: (1) development of a sponsor and stakeholder/user website with extensive online analytical tools; (2) development of a public website; (3) incorporation of an extensive online help system into each website; and (4) incorporation of a graphical representation (mapping) system into each website. The project is now technically completed.« less

Mapping healthcare systems: a policy relevant analytic tool

PubMed Central

Sekhri Feachem, Neelam; Afshar, Ariana; Pruett, Cristina; Avanceña, Anton L.V.

2017-01-01

Abstract Background In the past decade, an international consensus on the value of well-functioning systems has driven considerable health systems research. This research falls into two broad categories. The first provides conceptual frameworks that take complex healthcare systems and create simplified constructs of interactions and functions. The second focuses on granular inputs and outputs. This paper presents a novel translational mapping tool – the University of California, San Francisco mapping tool (the Tool) - which bridges the gap between these two areas of research, creating a platform for multi-country comparative analysis. Methods Using the Murray-Frenk framework, we create a macro-level representation of a country's structure, focusing on how it finances and delivers healthcare. The map visually depicts the fundamental policy questions in healthcare system design: funding sources and amount spent through each source, purchasers, populations covered, provider categories; and the relationship between these entities. Results We use the Tool to provide a macro-level comparative analysis of the structure of India's and Thailand's healthcare systems. Conclusions As part of the systems strengthening arsenal, the Tool can stimulate debate about the merits and consequences of different healthcare systems structural designs, using a common framework that fosters multi-country comparative analyses. PMID:28541518

First GIS Analysis of Modern Stone Tools Used by Wild Chimpanzees (Pan troglodytes verus) in Bossou, Guinea, West Africa

PubMed Central

Arroyo, Adrian; Matsuzawa, Tetsuro; de la Torre, Ignacio

2015-01-01

Stone tool use by wild chimpanzees of West Africa offers a unique opportunity to explore the evolutionary roots of technology during human evolution. However, detailed analyses of chimpanzee stone artifacts are still lacking, thus precluding a comparison with the earliest archaeological record. This paper presents the first systematic study of stone tools used by wild chimpanzees to crack open nuts in Bossou (Guinea-Conakry), and applies pioneering analytical techniques to such artifacts. Automatic morphometric GIS classification enabled to create maps of use wear over the stone tools (anvils, hammers, and hammers/ anvils), which were blind tested with GIS spatial analysis of damage patterns identified visually. Our analysis shows that chimpanzee stone tool use wear can be systematized and specific damage patterns discerned, allowing to discriminate between active and passive pounders in lithic assemblages. In summary, our results demonstrate the heuristic potential of combined suites of GIS techniques for the analysis of battered artifacts, and have enabled creating a referential framework of analysis in which wild chimpanzee battered tools can for the first time be directly compared to the early archaeological record. PMID:25793642

Network analysis of corticocortical connections reveals ventral and dorsal processing streams in mouse visual cortex

PubMed Central

Wang, Quanxin; Sporns, Olaf; Burkhalter, Andreas

2012-01-01

Much of the information used for visual perception and visually guided actions is processed in complex networks of connections within the cortex. To understand how this works in the normal brain and to determine the impact of disease, mice are promising models. In primate visual cortex, information is processed in a dorsal stream specialized for visuospatial processing and guided action and a ventral stream for object recognition. Here, we traced the outputs of 10 visual areas and used quantitative graph analytic tools of modern network science to determine, from the projection strengths in 39 cortical targets, the community structure of the network. We found a high density of the cortical graph that exceeded that previously shown in monkey. Each source area showed a unique distribution of projection weights across its targets (i.e. connectivity profile) that was well-fit by a lognormal function. Importantly, the community structure was strongly dependent on the location of the source area: outputs from medial/anterior extrastriate areas were more strongly linked to parietal, motor and limbic cortex, whereas lateral extrastriate areas were preferentially connected to temporal and parahippocampal cortex. These two subnetworks resemble dorsal and ventral cortical streams in primates, demonstrating that the basic layout of cortical networks is conserved across species. PMID:22457489

Thermodynamics of Gas Turbine Cycles with Analytic Derivatives in OpenMDAO

NASA Technical Reports Server (NTRS)

Gray, Justin; Chin, Jeffrey; Hearn, Tristan; Hendricks, Eric; Lavelle, Thomas; Martins, Joaquim R. R. A.

2016-01-01

A new equilibrium thermodynamics analysis tool was built based on the CEA method using the OpenMDAO framework. The new tool provides forward and adjoint analytic derivatives for use with gradient based optimization algorithms. The new tool was validated against the original CEA code to ensure an accurate analysis and the analytic derivatives were validated against finite-difference approximations. Performance comparisons between analytic and finite difference methods showed a significant speed advantage for the analytic methods. To further test the new analysis tool, a sample optimization was performed to find the optimal air-fuel equivalence ratio, , maximizing combustion temperature for a range of different pressures. Collectively, the results demonstrate the viability of the new tool to serve as the thermodynamic backbone for future work on a full propulsion modeling tool.

Collaborative visual analytics of radio surveys in the Big Data era

NASA Astrophysics Data System (ADS)

Vohl, Dany; Fluke, Christopher J.; Hassan, Amr H.; Barnes, David G.; Kilborn, Virginia A.

2017-06-01

Radio survey datasets comprise an increasing number of individual observations stored as sets of multidimensional data. In large survey projects, astronomers commonly face limitations regarding: 1) interactive visual analytics of sufficiently large subsets of data; 2) synchronous and asynchronous collaboration; and 3) documentation of the discovery workflow. To support collaborative data inquiry, we present encube, a large-scale comparative visual analytics framework. encube can utilise advanced visualization environments such as the CAVE2 (a hybrid 2D and 3D virtual reality environment powered with a 100 Tflop/s GPU-based supercomputer and 84 million pixels) for collaborative analysis of large subsets of data from radio surveys. It can also run on standard desktops, providing a capable visual analytics experience across the display ecology. encube is composed of four primary units enabling compute-intensive processing, advanced visualisation, dynamic interaction, parallel data query, along with data management. Its modularity will make it simple to incorporate astronomical analysis packages and Virtual Observatory capabilities developed within our community. We discuss how encube builds a bridge between high-end display systems (such as CAVE2) and the classical desktop, preserving all traces of the work completed on either platform - allowing the research process to continue wherever you are.

Analytical Web Tool for CERES Products

NASA Astrophysics Data System (ADS)

Mitrescu, C.; Chu, C.; Doelling, D.

2012-12-01

The CERES project provides the community climate quality observed TOA fluxes, consistent cloud properties, and computed profile and surface fluxes. The 11-year long data set proves invaluable for remote sensing and climate modeling communities for annual global mean energy, meridianal heat transport, consistent cloud and fluxes and climate trends studies. Moreover, a broader audience interested in Earth's radiative properties such as green energy, health and environmental companies have showed their interest in CERES derived products. A few years ago, the CERES team start developing a new web-based Ordering Tool tailored for this wide diversity of users. Recognizing the potential that web-2.0 technologies can offer to both Quality Control (QC) and scientific data visualization and manipulation, the CERES team began introducing a series of specialized functions that addresses the above. As such, displaying an attractive, easy to use modern web-based format, the Ordering Tool added the following analytical functions: i) 1-D Histograms to display the distribution of the data field to identify outliers that are useful for QC purposes; ii) an "Anomaly" map that shows the regional differences between the current month and the climatological monthly mean; iii) a 2-D Histogram that can identify either potential problems with the data (i.e. QC function) or provides a global view of trends and/or correlations between various CERES flux, cloud, aerosol, and atmospheric properties. The large volume and diversity of data, together with the on-the-fly execution were the main challenges that had to be tackle with. Depending on the application, the execution was done on either the browser side or the server side with the help of auxiliary files. Additional challenges came from the use of various open source applications, the multitude of CERES products and the seamless transition from previous development. For the future, we plan on expanding the analytical capabilities of the Ordering Tool and add/combine more CERES products to meet the growing data demand.

Investigating Analytic Tools for e-Book Design in Early Literacy Learning

ERIC Educational Resources Information Center

Roskos, Kathleen; Brueck, Jeremy; Widman, Sarah

2009-01-01

Toward the goal of better e-book design to support early literacy learning, this study investigates analytic tools for examining design qualities of e-books for young children. Three research-based analytic tools related to e-book design were applied to a mixed genre collection of 50 e-books from popular online sites. Tool performance varied…

A visual identification key utilizing both gestalt and analytic approaches to identification of Carices present in North America (Plantae, Cyperaceae)

PubMed Central

2013-01-01

Abstract Images are a critical part of the identification process because they enable direct, immediate and relatively unmediated comparisons between a specimen being identified and one or more reference specimens. The Carices Interactive Visual Identification Key (CIVIK) is a novel tool for identification of North American Carex species, the largest vascular plant genus in North America, and two less numerous closely-related genera, Cymophyllus and Kobresia. CIVIK incorporates 1288 high-resolution tiled image sets that allow users to zoom in to view minute structures that are crucial at times for identification in these genera. Morphological data are derived from the earlier Carex Interactive Identification Key (CIIK) which in turn used data from the Flora of North America treatments. In this new iteration, images can be viewed in a grid or histogram format, allowing multiple representations of data. In both formats the images are fully zoomable. PMID:24723777

Evaluating the decision accuracy and speed of clinical data visualizations.

PubMed

Pieczkiewicz, David S; Finkelstein, Stanley M

2010-01-01

Clinicians face an increasing volume of biomedical data. Assessing the efficacy of systems that enable accurate and timely clinical decision making merits corresponding attention. This paper discusses the multiple-reader multiple-case (MRMC) experimental design and linear mixed models as means of assessing and comparing decision accuracy and latency (time) for decision tasks in which clinician readers must interpret visual displays of data. These tools can assess and compare decision accuracy and latency (time). These experimental and statistical techniques, used extensively in radiology imaging studies, offer a number of practical and analytic advantages over more traditional quantitative methods such as percent-correct measurements and ANOVAs, and are recommended for their statistical efficiency and generalizability. An example analysis using readily available, free, and commercial statistical software is provided as an appendix. While these techniques are not appropriate for all evaluation questions, they can provide a valuable addition to the evaluative toolkit of medical informatics research.

Thinking with Crocodiles: An Iconic Animal at the Intersection of Early-Modern Religion and Natural Philosophy.

PubMed

Weinreich, Spencer J

2015-01-01

This paper seeks to explore how culturally and religiously significant animals could shape discourses in which they were deployed, taking the crocodile as its case study. Beginning with the textual and visual traditions linking the crocodile with Africa and the Middle East, I read sixteenth- and seventeenth-century travel narratives categorizing American reptiles as "crocodiles" rather than "alligators," as attempts to mitigate the disruptive strangeness of the Americas. The second section draws on Ann Blair's study of "Mosaic Philosophy" to examine scholarly debates over the taxonomic identity of the biblical Leviathan. I argue that the language and analytical tools of natural philosophy progressively permeated religious discourse. Finally, a survey of more than 25 extant examples of the premodern practice of displaying crocodiles in churches, as well as other crocodilian elements in Christian iconography, provides an explanation for the ubiquity of crocodiles in Wunderkammern, as natural philosophy appropriated ecclesial visual vocabularies.

Twelve tips to promote successful development of a learner performance dashboard within a medical education program.

PubMed

Boscardin, Christy; Fergus, Kirkpatrick B; Hellevig, Bonnie; Hauer, Karen E

2017-11-09

Easily accessible and interpretable performance data constitute critical feedback for learners that facilitate informed self-assessment and learning planning. To provide this feedback, there has been a proliferation of educational dashboards in recent years. An educational (learner) dashboard systematically delivers timely and continuous feedback on performance and can provide easily visualized and interpreted performance data. In this paper, we provide practical tips for developing a functional, user-friendly individual learner performance dashboard and literature review of dashboard development, assessment theory, and users' perspectives. Considering key design principles and maximizing current technological advances in data visualization techniques can increase dashboard utility and enhance the user experience. By bridging current technology with assessment strategies that support learning, educators can continue to improve the field of learning analytics and design of information management tools such as dashboards in support of improved learning outcomes.

Streaming Visual Analytics Workshop Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, Kristin A.; Burtner, Edwin R.; Kritzstein, Brian P.

How can we best enable users to understand complex emerging events and make appropriate assessments from streaming data? This was the central question addressed at a three-day workshop on streaming visual analytics. This workshop was organized by Pacific Northwest National Laboratory for a government sponsor. It brought together forty researchers and subject matter experts from government, industry, and academia. This report summarizes the outcomes from that workshop. It describes elements of the vision for a streaming visual analytic environment and set of important research directions needed to achieve this vision. Streaming data analysis is in many ways the analysis andmore » understanding of change. However, current visual analytics systems usually focus on static data collections, meaning that dynamically changing conditions are not appropriately addressed. The envisioned mixed-initiative streaming visual analytics environment creates a collaboration between the analyst and the system to support the analysis process. It raises the level of discourse from low-level data records to higher-level concepts. The system supports the analyst’s rapid orientation and reorientation as situations change. It provides an environment to support the analyst’s critical thinking. It infers tasks and interests based on the analyst’s interactions. The system works as both an assistant and a devil’s advocate, finding relevant data and alerts as well as considering alternative hypotheses. Finally, the system supports sharing of findings with others. Making such an environment a reality requires research in several areas. The workshop discussions focused on four broad areas: support for critical thinking, visual representation of change, mixed-initiative analysis, and the use of narratives for analysis and communication.« less

Big data and visual analytics in anaesthesia and health care.

PubMed

Simpao, A F; Ahumada, L M; Rehman, M A

2015-09-01

Advances in computer technology, patient monitoring systems, and electronic health record systems have enabled rapid accumulation of patient data in electronic form (i.e. big data). Organizations such as the Anesthesia Quality Institute and Multicenter Perioperative Outcomes Group have spearheaded large-scale efforts to collect anaesthesia big data for outcomes research and quality improvement. Analytics--the systematic use of data combined with quantitative and qualitative analysis to make decisions--can be applied to big data for quality and performance improvements, such as predictive risk assessment, clinical decision support, and resource management. Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces, and it can facilitate performance of cognitive activities involving big data. Ongoing integration of big data and analytics within anaesthesia and health care will increase demand for anaesthesia professionals who are well versed in both the medical and the information sciences. © The Author 2015. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Advancing Collaboration through Hydrologic Data and Model Sharing

NASA Astrophysics Data System (ADS)

Tarboton, D. G.; Idaszak, R.; Horsburgh, J. S.; Ames, D. P.; Goodall, J. L.; Band, L. E.; Merwade, V.; Couch, A.; Hooper, R. P.; Maidment, D. R.; Dash, P. K.; Stealey, M.; Yi, H.; Gan, T.; Castronova, A. M.; Miles, B.; Li, Z.; Morsy, M. M.

2015-12-01

HydroShare is an online, collaborative system for open sharing of hydrologic data, analytical tools, and models. It supports the sharing of and collaboration around "resources" which are defined primarily by standardized metadata, content data models for each resource type, and an overarching resource data model based on the Open Archives Initiative's Object Reuse and Exchange (OAI-ORE) standard and a hierarchical file packaging system called "BagIt". HydroShare expands the data sharing capability of the CUAHSI Hydrologic Information System by broadening the classes of data accommodated to include geospatial and multidimensional space-time datasets commonly used in hydrology. HydroShare also includes new capability for sharing models, model components, and analytical tools and will take advantage of emerging social media functionality to enhance information about and collaboration around hydrologic data and models. It also supports web services and server/cloud based computation operating on resources for the execution of hydrologic models and analysis and visualization of hydrologic data. HydroShare uses iRODS as a network file system for underlying storage of datasets and models. Collaboration is enabled by casting datasets and models as "social objects". Social functions include both private and public sharing, formation of collaborative groups of users, and value-added annotation of shared datasets and models. The HydroShare web interface and social media functions were developed using the Django web application framework coupled to iRODS. Data visualization and analysis is supported through the Tethys Platform web GIS software stack. Links to external systems are supported by RESTful web service interfaces to HydroShare's content. This presentation will introduce the HydroShare functionality developed to date and describe ongoing development of functionality to support collaboration and integration of data and models.

Map LineUps: Effects of spatial structure on graphical inference.

PubMed

Beecham, Roger; Dykes, Jason; Meulemans, Wouter; Slingsby, Aidan; Turkay, Cagatay; Wood, Jo

2017-01-01

Fundamental to the effective use of visualization as an analytic and descriptive tool is the assurance that presenting data visually provides the capability of making inferences from what we see. This paper explores two related approaches to quantifying the confidence we may have in making visual inferences from mapped geospatial data. We adapt Wickham et al.'s 'Visual Line-up' method as a direct analogy with Null Hypothesis Significance Testing (NHST) and propose a new approach for generating more credible spatial null hypotheses. Rather than using as a spatial null hypothesis the unrealistic assumption of complete spatial randomness, we propose spatially autocorrelated simulations as alternative nulls. We conduct a set of crowdsourced experiments (n=361) to determine the just noticeable difference (JND) between pairs of choropleth maps of geographic units controlling for spatial autocorrelation (Moran's I statistic) and geometric configuration (variance in spatial unit area). Results indicate that people's abilities to perceive differences in spatial autocorrelation vary with baseline autocorrelation structure and the geometric configuration of geographic units. These results allow us, for the first time, to construct a visual equivalent of statistical power for geospatial data. Our JND results add to those provided in recent years by Klippel et al. (2011), Harrison et al. (2014) and Kay & Heer (2015) for correlation visualization. Importantly, they provide an empirical basis for an improved construction of visual line-ups for maps and the development of theory to inform geospatial tests of graphical inference.

Immersive Visual Analytics for Transformative Neutron Scattering Science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A; Daniel, Jamison R; Drouhard, Margaret

The ORNL Spallation Neutron Source (SNS) provides the most intense pulsed neutron beams in the world for scientific research and development across a broad range of disciplines. SNS experiments produce large volumes of complex data that are analyzed by scientists with varying degrees of experience using 3D visualization and analysis systems. However, it is notoriously difficult to achieve proficiency with 3D visualizations. Because 3D representations are key to understanding the neutron scattering data, scientists are unable to analyze their data in a timely fashion resulting in inefficient use of the limited and expensive SNS beam time. We believe a moremore » intuitive interface for exploring neutron scattering data can be created by combining immersive virtual reality technology with high performance data analytics and human interaction. In this paper, we present our initial investigations of immersive visualization concepts as well as our vision for an immersive visual analytics framework that could lower the barriers to 3D exploratory data analysis of neutron scattering data at the SNS.« less

A Software Developer’s Guide to Informal Evaluation of Visual Analytics Environments Using VAST Challenge Information

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, Kristin A.; Scholtz, Jean; Whiting, Mark A.

The VAST Challenge has been a popular venue for academic and industry participants for over ten years. Many participants comment that the majority of their time in preparing VAST Challenge entries is discovering elements in their software environments that need to be redesigned in order to solve the given task. Fortunately, there is no need to wait until the VAST Challenge is announced to test out software systems. The Visual Analytics Benchmark Repository contains all past VAST Challenge tasks, data, solutions and submissions. This paper details the various types of evaluations that may be conducted using the Repository information. Inmore » this paper we describe how developers can do informal evaluations of various aspects of their visual analytics environments using VAST Challenge information. Aspects that can be evaluated include the appropriateness of the software for various tasks, the various data types and formats that can be accommodated, the effectiveness and efficiency of the process supported by the software, and the intuitiveness of the visualizations and interactions. Researchers can compare their visualizations and interactions to those submitted to determine novelty. In addition, the paper provides pointers to various guidelines that software teams can use to evaluate the usability of their software. While these evaluations are not a replacement for formal evaluation methods, this information can be extremely useful during the development of visual analytics environments.« less

The Role of Visual Learning in Improving Students' High-Order Thinking Skills

ERIC Educational Resources Information Center

Raiyn, Jamal

2016-01-01

Various concepts have been introduced to improve students' analytical thinking skills based on problem based learning (PBL). This paper introduces a new concept to increase student's analytical thinking skills based on a visual learning strategy. Such a strategy has three fundamental components: a teacher, a student, and a learning process. The…

Development of the Veritas plot and its application in cardiac surgery: an evidence-synthesis graphic tool for the clinician to assess multiple meta-analyses reporting on a common outcome.

PubMed

Panesar, Sukhmeet S; Rao, Christopher; Vecht, Joshua A; Mirza, Saqeb B; Netuveli, Gopalakrishnan; Morris, Richard; Rosenthal, Joe; Darzi, Ara; Athanasiou, Thanos

2009-10-01

Meta-analyses may be prone to generating misleading results because of a paucity of experimental studies (especially in surgery); publication bias; and heterogeneity in study design, intervention and the patient population of included studies. When investigating a specific clinical or scientific question on which several relevant meta-analyses may have been published, value judgments must be applied to determine which analysis represents the most robust evidence. These value judgments should be specifically acknowledged. We designed the Veritas plot to explicitly explore important elements of quality and to facilitate decision-making by highlighting specific areas in which meta-analyses are found to be deficient. Furthermore, as a graphic tool, it may be more intuitive than when similar data are presented in a tabular or text format. The Veritas plot is an adaption of the radar plot, a graphic tool for the description of multiattribute data. Key elements of meta-analytical quality such as heterogeneity, publication bias and study design are assessed. Existing qualitative methods such as the Assessment of Multiple Systematic Reviews (AMSTAR) tool have been incorporated in addition to important considerations when interpreting surgical meta-analyses such as the year of publication and population characteristics. To demonstrate the potential of the Veritas plot to inform clinical practice, we apply the Veritas plot to the meta-analytical literature comparing the incidence of 30-day stroke in off-pump coronary artery bypass surgery and conventional coronary artery bypass surgery. We demonstrate that a visually-stimulating and practical evidence-synthesis tool can direct the clinician and scientist to a particular meta-analytical study to inform clinical practice. The Veritas plot is also cumulative and allowed us to assess the quality of evidence over time. We have presented a practical graphic application for scientists and clinicians to identify and interpret variability in meta-analyses. Although further validation of the Veritas plot is required, it may have the potential to contribute to the implementation of evidence-based practice.

A Graphics Design Framework to Visualize Multi-Dimensional Economic Datasets

ERIC Educational Resources Information Center

Chandramouli, Magesh; Narayanan, Badri; Bertoline, Gary R.

2013-01-01

This study implements a prototype graphics visualization framework to visualize multidimensional data. This graphics design framework serves as a "visual analytical database" for visualization and simulation of economic models. One of the primary goals of any kind of visualization is to extract useful information from colossal volumes of…

33 CFR 385.33 - Revisions to models and analytical tools.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Management District, and other non-Federal sponsors shall rely on the best available science including models..., and assessment of projects. The selection of models and analytical tools shall be done in consultation... system-wide simulation models and analytical tools used in the evaluation and assessment of projects, and...

Unlocking Proteomic Heterogeneity in Complex Diseases through Visual Analytics

PubMed Central

Bhavnani, Suresh K.; Dang, Bryant; Bellala, Gowtham; Divekar, Rohit; Visweswaran, Shyam; Brasier, Allan; Kurosky, Alex

2015-01-01

Despite years of preclinical development, biological interventions designed to treat complex diseases like asthma often fail in phase III clinical trials. These failures suggest that current methods to analyze biomedical data might be missing critical aspects of biological complexity such as the assumption that cases and controls come from homogeneous distributions. Here we discuss why and how methods from the rapidly evolving field of visual analytics can help translational teams (consisting of biologists, clinicians, and bioinformaticians) to address the challenge of modeling and inferring heterogeneity in the proteomic and phenotypic profiles of patients with complex diseases. Because a primary goal of visual analytics is to amplify the cognitive capacities of humans for detecting patterns in complex data, we begin with an overview of the cognitive foundations for the field of visual analytics. Next, we organize the primary ways in which a specific form of visual analytics called networks have been used to model and infer biological mechanisms, which help to identify the properties of networks that are particularly useful for the discovery and analysis of proteomic heterogeneity in complex diseases. We describe one such approach called subject-protein networks, and demonstrate its application on two proteomic datasets. This demonstration provides insights to help translational teams overcome theoretical, practical, and pedagogical hurdles for the widespread use of subject-protein networks for analyzing molecular heterogeneities, with the translational goal of designing biomarker-based clinical trials, and accelerating the development of personalized approaches to medicine. PMID:25684269

Using Galaxy to Perform Large-Scale Interactive Data Analyses

PubMed Central

Hillman-Jackson, Jennifer; Clements, Dave; Blankenberg, Daniel; Taylor, James; Nekrutenko, Anton

2012-01-01

Innovations in biomedical research technologies continue to provide experimental biologists with novel and increasingly large genomic and high-throughput data resources to be analyzed. As creating and obtaining data has become easier, the key decision faced by many researchers is a practical one: where and how should an analysis be performed? Datasets are large and analysis tool set-up and use is riddled with complexities outside of the scope of core research activities. The authors believe that Galaxy (galaxyproject.org) provides a powerful solution that simplifies data acquisition and analysis in an intuitive web-application, granting all researchers access to key informatics tools previously only available to computational specialists working in Unix-based environments. We will demonstrate through a series of biomedically relevant protocols how Galaxy specifically brings together 1) data retrieval from public and private sources, for example, UCSC’s Eukaryote and Microbial Genome Browsers (genome.ucsc.edu), 2) custom tools (wrapped Unix functions, format standardization/conversions, interval operations) and 3rd party analysis tools, for example, Bowtie/Tuxedo Suite (bowtie-bio.sourceforge.net), Lastz (www.bx.psu.edu/~rsharris/lastz/), SAMTools (samtools.sourceforge.net), FASTX-toolkit (hannonlab.cshl.edu/fastx_toolkit), and MACS (liulab.dfci.harvard.edu/MACS), and creates results formatted for visualization in tools such as the Galaxy Track Browser (GTB, galaxyproject.org/wiki/Learn/Visualization), UCSC Genome Browser (genome.ucsc.edu), Ensembl (www.ensembl.org), and GeneTrack (genetrack.bx.psu.edu). Galaxy rapidly has become the most popular choice for integrated next generation sequencing (NGS) analytics and collaboration, where users can perform, document, and share complex analysis within a single interface in an unprecedented number of ways. PMID:18428782

GENEASE: Real time bioinformatics tool for multi-omics and disease ontology exploration, analysis and visualization.

PubMed

Ghandikota, Sudhir; Hershey, Gurjit K Khurana; Mersha, Tesfaye B

2018-03-24

Advances in high-throughput sequencing technologies have made it possible to generate multiple omics data at an unprecedented rate and scale. The accumulation of these omics data far outpaces the rate at which biologists can mine and generate new hypothesis to test experimentally. There is an urgent need to develop a myriad of powerful tools to efficiently and effectively search and filter these resources to address specific post-GWAS functional genomics questions. However, to date, these resources are scattered across several databases and often lack a unified portal for data annotation and analytics. In addition, existing tools to analyze and visualize these databases are highly fragmented, resulting researchers to access multiple applications and manual interventions for each gene or variant in an ad hoc fashion until all the questions are answered. In this study, we present GENEASE, a web-based one-stop bioinformatics tool designed to not only query and explore multi-omics and phenotype databases (e.g., GTEx, ClinVar, dbGaP, GWAS Catalog, ENCODE, Roadmap Epigenomics, KEGG, Reactome, Gene and Phenotype Ontology) in a single web interface but also to perform seamless post genome-wide association downstream functional and overlap analysis for non-coding regulatory variants. GENEASE accesses over 50 different databases in public domain including model organism-specific databases to facilitate gene/variant and disease exploration, enrichment and overlap analysis in real time. It is a user-friendly tool with point-and-click interface containing links for support information including user manual and examples. GENEASE can be accessed freely at http://research.cchmc.org/mershalab/genease_new/login.html. Tesfaye.Mersha@cchmc.org, Sudhir.Ghandikota@cchmc.org. Supplementary data are available at Bioinformatics online.

Iterating between Tools to Create and Edit Visualizations.

PubMed

Bigelow, Alex; Drucker, Steven; Fisher, Danyel; Meyer, Miriah

2017-01-01

A common workflow for visualization designers begins with a generative tool, like D3 or Processing, to create the initial visualization; and proceeds to a drawing tool, like Adobe Illustrator or Inkscape, for editing and cleaning. Unfortunately, this is typically a one-way process: once a visualization is exported from the generative tool into a drawing tool, it is difficult to make further, data-driven changes. In this paper, we propose a bridge model to allow designers to bring their work back from the drawing tool to re-edit in the generative tool. Our key insight is to recast this iteration challenge as a merge problem - similar to when two people are editing a document and changes between them need to reconciled. We also present a specific instantiation of this model, a tool called Hanpuku, which bridges between D3 scripts and Illustrator. We show several examples of visualizations that are iteratively created using Hanpuku in order to illustrate the flexibility of the approach. We further describe several hypothetical tools that bridge between other visualization tools to emphasize the generality of the model.

The science of visual analysis at extreme scale

NASA Astrophysics Data System (ADS)

Nowell, Lucy T.

2011-01-01

Driven by market forces and spanning the full spectrum of computational devices, computer architectures are changing in ways that present tremendous opportunities and challenges for data analysis and visual analytic technologies. Leadership-class high performance computing system will have as many as a million cores by 2020 and support 10 billion-way concurrency, while laptop computers are expected to have as many as 1,000 cores by 2015. At the same time, data of all types are increasing exponentially and automated analytic methods are essential for all disciplines. Many existing analytic technologies do not scale to make full use of current platforms and fewer still are likely to scale to the systems that will be operational by the end of this decade. Furthermore, on the new architectures and for data at extreme scales, validating the accuracy and effectiveness of analytic methods, including visual analysis, will be increasingly important.

Survey of visualization and analysis tools

NASA Technical Reports Server (NTRS)

Meyer, P. J.

1994-01-01

A large number of commercially available visualization and analysis tools are available to the researcher. Some of the strengths and limitations of some of these tools, from the viewpoint of the earth sciences discipline, are discussed. Visualization and analysis tools fall into one of two categories: those that are designed to a specific purpose and are non-extensive and those that are generic visual programming tools that are extensible. Most of the extensible packages examined incorporate a data flow paradigm.

Matisse: A Visual Analytics System for Exploring Emotion Trends in Social Media Text Streams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A; Drouhard, Margaret MEG G; Beaver, Justin M

Dynamically mining textual information streams to gain real-time situational awareness is especially challenging with social media systems where throughput and velocity properties push the limits of a static analytical approach. In this paper, we describe an interactive visual analytics system, called Matisse, that aids with the discovery and investigation of trends in streaming text. Matisse addresses the challenges inherent to text stream mining through the following technical contributions: (1) robust stream data management, (2) automated sentiment/emotion analytics, (3) interactive coordinated visualizations, and (4) a flexible drill-down interaction scheme that accesses multiple levels of detail. In addition to positive/negative sentiment prediction,more » Matisse provides fine-grained emotion classification based on Valence, Arousal, and Dominance dimensions and a novel machine learning process. Information from the sentiment/emotion analytics are fused with raw data and summary information to feed temporal, geospatial, term frequency, and scatterplot visualizations using a multi-scale, coordinated interaction model. After describing these techniques, we conclude with a practical case study focused on analyzing the Twitter sample stream during the week of the 2013 Boston Marathon bombings. The case study demonstrates the effectiveness of Matisse at providing guided situational awareness of significant trends in social media streams by orchestrating computational power and human cognition.« less

Developing Healthcare Data Analytics APPs with Open Data Science Tools.

PubMed

Hao, Bibo; Sun, Wen; Yu, Yiqin; Xie, Guotong

2017-01-01

Recent advances in big data analytics provide more flexible, efficient, and open tools for researchers to gain insight from healthcare data. Whilst many tools require researchers to develop programs with programming languages like Python, R and so on, which is not a skill set grasped by many researchers in the healthcare data analytics area. To make data science more approachable, we explored existing tools and developed a practice that can help data scientists convert existing analytics pipelines to user-friendly analytics APPs with rich interactions and features of real-time analysis. With this practice, data scientists can develop customized analytics pipelines as APPs in Jupyter Notebook and disseminate them to other researchers easily, and researchers can benefit from the shared notebook to perform analysis tasks or reproduce research results much more easily.

MeRy-B: a web knowledgebase for the storage, visualization, analysis and annotation of plant NMR metabolomic profiles

PubMed Central

2011-01-01

Background Improvements in the techniques for metabolomics analyses and growing interest in metabolomic approaches are resulting in the generation of increasing numbers of metabolomic profiles. Platforms are required for profile management, as a function of experimental design, and for metabolite identification, to facilitate the mining of the corresponding data. Various databases have been created, including organism-specific knowledgebases and analytical technique-specific spectral databases. However, there is currently no platform meeting the requirements for both profile management and metabolite identification for nuclear magnetic resonance (NMR) experiments. Description MeRy-B, the first platform for plant 1H-NMR metabolomic profiles, is designed (i) to provide a knowledgebase of curated plant profiles and metabolites obtained by NMR, together with the corresponding experimental and analytical metadata, (ii) for queries and visualization of the data, (iii) to discriminate between profiles with spectrum visualization tools and statistical analysis, (iv) to facilitate compound identification. It contains lists of plant metabolites and unknown compounds, with information about experimental conditions, the factors studied and metabolite concentrations for several plant species, compiled from more than one thousand annotated NMR profiles for various organs or tissues. Conclusion MeRy-B manages all the data generated by NMR-based plant metabolomics experiments, from description of the biological source to identification of the metabolites and determinations of their concentrations. It is the first database allowing the display and overlay of NMR metabolomic profiles selected through queries on data or metadata. MeRy-B is available from http://www.cbib.u-bordeaux2.fr/MERYB/index.php. PMID:21668943

Big data and high-performance analytics in structural health monitoring for bridge management

NASA Astrophysics Data System (ADS)

Alampalli, Sharada; Alampalli, Sandeep; Ettouney, Mohammed

2016-04-01

Structural Health Monitoring (SHM) can be a vital tool for effective bridge management. Combining large data sets from multiple sources to create a data-driven decision-making framework is crucial for the success of SHM. This paper presents a big data analytics framework that combines multiple data sets correlated with functional relatedness to convert data into actionable information that empowers risk-based decision-making. The integrated data environment incorporates near real-time streams of semi-structured data from remote sensors, historical visual inspection data, and observations from structural analysis models to monitor, assess, and manage risks associated with the aging bridge inventories. Accelerated processing of dataset is made possible by four technologies: cloud computing, relational database processing, support from NOSQL database, and in-memory analytics. The framework is being validated on a railroad corridor that can be subjected to multiple hazards. The framework enables to compute reliability indices for critical bridge components and individual bridge spans. In addition, framework includes a risk-based decision-making process that enumerate costs and consequences of poor bridge performance at span- and network-levels when rail networks are exposed to natural hazard events such as floods and earthquakes. Big data and high-performance analytics enable insights to assist bridge owners to address problems faster.

SmartR: an open-source platform for interactive visual analytics for translational research data

PubMed Central

Herzinger, Sascha; Gu, Wei; Satagopam, Venkata; Eifes, Serge; Rege, Kavita; Barbosa-Silva, Adriano; Schneider, Reinhard

2017-01-01

Abstract Summary: In translational research, efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre-clinical or OMICS data combined with strong visual analytical capabilities will significantly accelerate the scientific progress by making data more accessible and hypothesis generation easier. The open data warehouse tranSMART is capable of storing a variety of data types and has a growing user community including both academic institutions and pharmaceutical companies. tranSMART, however, currently lacks interactive and dynamic visual analytics and does not permit any post-processing interaction or exploration. For this reason, we developed SmartR, a plugin for tranSMART, that equips the platform not only with several dynamic visual analytical workflows, but also provides its own framework for the addition of new custom workflows. Modern web technologies such as D3.js or AngularJS were used to build a set of standard visualizations that were heavily improved with dynamic elements. Availability and Implementation: The source code is licensed under the Apache 2.0 License and is freely available on GitHub: https://github.com/transmart/SmartR. Contact: reinhard.schneider@uni.lu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28334291

SmartR: an open-source platform for interactive visual analytics for translational research data.

PubMed

Herzinger, Sascha; Gu, Wei; Satagopam, Venkata; Eifes, Serge; Rege, Kavita; Barbosa-Silva, Adriano; Schneider, Reinhard

2017-07-15

In translational research, efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre-clinical or OMICS data combined with strong visual analytical capabilities will significantly accelerate the scientific progress by making data more accessible and hypothesis generation easier. The open data warehouse tranSMART is capable of storing a variety of data types and has a growing user community including both academic institutions and pharmaceutical companies. tranSMART, however, currently lacks interactive and dynamic visual analytics and does not permit any post-processing interaction or exploration. For this reason, we developed SmartR , a plugin for tranSMART, that equips the platform not only with several dynamic visual analytical workflows, but also provides its own framework for the addition of new custom workflows. Modern web technologies such as D3.js or AngularJS were used to build a set of standard visualizations that were heavily improved with dynamic elements. The source code is licensed under the Apache 2.0 License and is freely available on GitHub: https://github.com/transmart/SmartR . reinhard.schneider@uni.lu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Automated Comparative Metabolite Profiling of Large LC-ESIMS Data Sets in an ACD/MS Workbook Suite Add-in, and Data Clustering on a New Open-Source Web Platform FreeClust.

PubMed

Božičević, Alen; Dobrzyński, Maciej; De Bie, Hans; Gafner, Frank; Garo, Eliane; Hamburger, Matthias

2017-12-05

The technological development of LC-MS instrumentation has led to significant improvements of performance and sensitivity, enabling high-throughput analysis of complex samples, such as plant extracts. Most software suites allow preprocessing of LC-MS chromatograms to obtain comprehensive information on single constituents. However, more advanced processing needs, such as the systematic and unbiased comparative metabolite profiling of large numbers of complex LC-MS chromatograms remains a challenge. Currently, users have to rely on different tools to perform such data analyses. We developed a two-step protocol comprising a comparative metabolite profiling tool integrated in ACD/MS Workbook Suite, and a web platform developed in R language designed for clustering and visualization of chromatographic data. Initially, all relevant chromatographic and spectroscopic data (retention time, molecular ions with the respective ion abundance, and sample names) are automatically extracted and assembled in an Excel spreadsheet. The file is then loaded into an online web application that includes various statistical algorithms and provides the user with tools to compare and visualize the results in intuitive 2D heatmaps. We applied this workflow to LC-ESIMS profiles obtained from 69 honey samples. Within few hours of calculation with a standard PC, honey samples were preprocessed and organized in clusters based on their metabolite profile similarities, thereby highlighting the common metabolite patterns and distributions among samples. Implementation in the ACD/Laboratories software package enables ulterior integration of other analytical data, and in silico prediction tools for modern drug discovery.

Visual Analytics of Surveillance Data on Foodborne Vibriosis, United States, 1973–2010

PubMed Central

Sims, Jennifer N.; Isokpehi, Raphael D.; Cooper, Gabrielle A.; Bass, Michael P.; Brown, Shyretha D.; St John, Alison L.; Gulig, Paul A.; Cohly, Hari H.P.

2011-01-01

Foodborne illnesses caused by microbial and chemical contaminants in food are a substantial health burden worldwide. In 2007, human vibriosis (non-cholera Vibrio infections) became a notifiable disease in the United States. In addition, Vibrio species are among the 31 major known pathogens transmitted through food in the United States. Diverse surveillance systems for foodborne pathogens also track outbreaks, illnesses, hospitalization and deaths due to non-cholera vibrios. Considering the recognition of vibriosis as a notifiable disease in the United States and the availability of diverse surveillance systems, there is a need for the development of easily deployed visualization and analysis approaches that can combine diverse data sources in an interactive manner. Current efforts to address this need are still limited. Visual analytics is an iterative process conducted via visual interfaces that involves collecting information, data preprocessing, knowledge representation, interaction, and decision making. We have utilized public domain outbreak and surveillance data sources covering 1973 to 2010, as well as visual analytics software to demonstrate integrated and interactive visualizations of data on foodborne outbreaks and surveillance of Vibrio species. Through the data visualization, we were able to identify unique patterns and/or novel relationships within and across datasets regarding (i) causative agent; (ii) foodborne outbreaks and illness per state; (iii) location of infection; (iv) vehicle (food) of infection; (v) anatomical site of isolation of Vibrio species; (vi) patients and complications of vibriosis; (vii) incidence of laboratory-confirmed vibriosis and V. parahaemolyticus outbreaks. The additional use of emerging visual analytics approaches for interaction with data on vibriosis, including non-foodborne related disease, can guide disease control and prevention as well as ongoing outbreak investigations. PMID:22174586

Estimating ankle rotational constraints from anatomic structure

NASA Astrophysics Data System (ADS)

Baker, H. H.; Bruckner, Janice S.; Langdon, John H.

1992-09-01

Three-dimensional biomedical data obtained through tomography provide exceptional views of biological anatomy. While visualization is one of the primary purposes for obtaining these data, other more quantitative and analytic uses are possible. These include modeling of tissue properties and interrelationships, simulation of physical processes, interactive surgical investigation, and analysis of kinematics and dynamics. As an application of our research in modeling tissue structure and function, we have been working to develop interactive and automated tools for studying joint geometry and kinematics. We focus here on discrimination of morphological variations in the foot and determining the implications of these on both hominid bipedal evolution and physical therapy treatment for foot disorders.

Electroencephalographic monitoring of complex mental tasks

NASA Technical Reports Server (NTRS)

Guisado, Raul; Montgomery, Richard; Montgomery, Leslie; Hickey, Chris

1992-01-01

Outlined here is the development of neurophysiological procedures to monitor operators during the performance of cognitive tasks. Our approach included the use of electroencepalographic (EEG) and rheoencephalographic (REG) techniques to determine changes in cortical function associated with cognition in the operator's state. A two channel tetrapolar REG, a single channel forearm impedance plethysmograph, a Lead I electrocardiogram (ECG) and a 21 channel EEG were used to measure subject responses to various visual-motor cognitive tasks. Testing, analytical, and display procedures for EEG and REG monitoring were developed that extend the state of the art and provide a valuable tool for the study of cerebral circulatory and neural activity during cognition.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cottam, Joseph A.; Blaha, Leslie M.

Systems have biases. Their interfaces naturally guide a user toward specific patterns of action. For example, modern word-processors and spreadsheets are both capable of taking word wrapping, checking spelling, storing tables, and calculating formulas. You could write a paper in a spreadsheet or could do simple business modeling in a word-processor. However, their interfaces naturally communicate which function they are designed for. Visual analytic interfaces also have biases. In this paper, we outline why simple Markov models are a plausible tool for investigating that bias and how they might be applied. We also discuss some anticipated difficulties in such modelingmore » and touch briefly on what some Markov model extensions might provide.« less

Factors Influencing Beliefs for Adoption of a Learning Analytics Tool: An Empirical Study

ERIC Educational Resources Information Center

Ali, Liaqat; Asadi, Mohsen; Gasevic, Dragan; Jovanovic, Jelena; Hatala, Marek

2013-01-01

Present research and development offer various learning analytics tools providing insights into different aspects of learning processes. Adoption of a specific tool for practice is based on how its learning analytics are perceived by educators to support their pedagogical and organizational goals. In this paper, we propose and empirically validate…

A Web Portal-Based Time-Aware KML Animation Tool for Exploring Spatiotemporal Dynamics of Hydrological Events

NASA Astrophysics Data System (ADS)

Bao, X.; Cai, X.; Liu, Y.

2009-12-01

Understanding spatiotemporal dynamics of hydrological events such as storms and droughts is highly valuable for decision making on disaster mitigation and recovery. Virtual Globe-based technologies such as Google Earth and Open Geospatial Consortium KML standards show great promises for collaborative exploration of such events using visual analytical approaches. However, currently there are two barriers for wider usage of such approaches. First, there lacks an easy way to use open source tools to convert legacy or existing data formats such as shapefiles, geotiff, or web services-based data sources to KML and to produce time-aware KML files. Second, an integrated web portal-based time-aware animation tool is currently not available. Thus users usually share their files in the portal but have no means to visually explore them without leaving the portal environment which the users are familiar with. We develop a web portal-based time-aware KML animation tool for viewing extreme hydrologic events. The tool is based on Google Earth JavaScript API and Java Portlet standard 2.0 JSR-286, and it is currently deployable in one of the most popular open source portal frameworks, namely Liferay. We have also developed an open source toolkit kml-soc-ncsa (http://code.google.com/p/kml-soc-ncsa/) to facilitate the conversion of multiple formats into KML and the creation of time-aware KML files. We illustrate our tool using some example cases, in which drought and storm events with both time and space dimension can be explored in this web-based KML animation portlet. The tool provides an easy-to-use web browser-based portal environment for multiple users to collaboratively share and explore their time-aware KML files as well as improving the understanding of the spatiotemporal dynamics of the hydrological events.

Hasse diagram as a green analytical metrics tool: ranking of methods for benzo[a]pyrene determination in sediments.

PubMed

Bigus, Paulina; Tsakovski, Stefan; Simeonov, Vasil; Namieśnik, Jacek; Tobiszewski, Marek

2016-05-01

This study presents an application of the Hasse diagram technique (HDT) as the assessment tool to select the most appropriate analytical procedures according to their greenness or the best analytical performance. The dataset consists of analytical procedures for benzo[a]pyrene determination in sediment samples, which were described by 11 variables concerning their greenness and analytical performance. Two analyses with the HDT were performed-the first one with metrological variables and the second one with "green" variables as input data. Both HDT analyses ranked different analytical procedures as the most valuable, suggesting that green analytical chemistry is not in accordance with metrology when benzo[a]pyrene in sediment samples is determined. The HDT can be used as a good decision support tool to choose the proper analytical procedure concerning green analytical chemistry principles and analytical performance merits.

Deriving Earth Science Data Analytics Tools/Techniques Requirements

NASA Astrophysics Data System (ADS)

Kempler, S. J.

2015-12-01

Data Analytics applications have made successful strides in the business world where co-analyzing extremely large sets of independent variables have proven profitable. Today, most data analytics tools and techniques, sometimes applicable to Earth science, have targeted the business industry. In fact, the literature is nearly absent of discussion about Earth science data analytics. Earth science data analytics (ESDA) is the process of examining large amounts of data from a variety of sources to uncover hidden patterns, unknown correlations, and other useful information. ESDA is most often applied to data preparation, data reduction, and data analysis. Co-analysis of increasing number and volume of Earth science data has become more prevalent ushered by the plethora of Earth science data sources generated by US programs, international programs, field experiments, ground stations, and citizen scientists. Through work associated with the Earth Science Information Partners (ESIP) Federation, ESDA types have been defined in terms of data analytics end goals. Goals of which are very different than those in business, requiring different tools and techniques. A sampling of use cases have been collected and analyzed in terms of data analytics end goal types, volume, specialized processing, and other attributes. The goal of collecting these use cases is to be able to better understand and specify requirements for data analytics tools and techniques yet to be implemented. This presentation will describe the attributes and preliminary findings of ESDA use cases, as well as provide early analysis of data analytics tools/techniques requirements that would support specific ESDA type goals. Representative existing data analytics tools/techniques relevant to ESDA will also be addressed.

Integrated Data Visualization and Virtual Reality Tool

NASA Technical Reports Server (NTRS)

Dryer, David A.

1998-01-01

The Integrated Data Visualization and Virtual Reality Tool (IDVVRT) Phase II effort was for the design and development of an innovative Data Visualization Environment Tool (DVET) for NASA engineers and scientists, enabling them to visualize complex multidimensional and multivariate data in a virtual environment. The objectives of the project were to: (1) demonstrate the transfer and manipulation of standard engineering data in a virtual world; (2) demonstrate the effects of design and changes using finite element analysis tools; and (3) determine the training and engineering design and analysis effectiveness of the visualization system.

The Development of a Visual-Perceptual Chemistry Specific (VPCS) Assessment Tool

ERIC Educational Resources Information Center

Oliver-Hoyo, Maria; Sloan, Caroline

2014-01-01

The development of the Visual-Perceptual Chemistry Specific (VPCS) assessment tool is based on items that align to eight visual-perceptual skills considered as needed by chemistry students. This tool includes a comprehensive range of visual operations and presents items within a chemistry context without requiring content knowledge to solve…

SnapShot: Visualization to Propel Ice Hockey Analytics.

PubMed

Pileggi, H; Stolper, C D; Boyle, J M; Stasko, J T

2012-12-01

Sports analysts live in a world of dynamic games flattened into tables of numbers, divorced from the rinks, pitches, and courts where they were generated. Currently, these professional analysts use R, Stata, SAS, and other statistical software packages for uncovering insights from game data. Quantitative sports consultants seek a competitive advantage both for their clients and for themselves as analytics becomes increasingly valued by teams, clubs, and squads. In order for the information visualization community to support the members of this blossoming industry, it must recognize where and how visualization can enhance the existing analytical workflow. In this paper, we identify three primary stages of today's sports analyst's routine where visualization can be beneficially integrated: 1) exploring a dataspace; 2) sharing hypotheses with internal colleagues; and 3) communicating findings to stakeholders.Working closely with professional ice hockey analysts, we designed and built SnapShot, a system to integrate visualization into the hockey intelligence gathering process. SnapShot employs a variety of information visualization techniques to display shot data, yet given the importance of a specific hockey statistic, shot length, we introduce a technique, the radial heat map. Through a user study, we received encouraging feedback from several professional analysts, both independent consultants and professional team personnel.

Fault diagnosis in orbital refueling operations

NASA Technical Reports Server (NTRS)

Boy, Guy A.

1988-01-01

Usually, operation manuals are provided for helping astronauts during space operations. These manuals include normal and malfunction procedures. Transferring operation manual knowledge into a computerized form is not a trivial task. This knowledge is generally written by designers or operation engineers and is often quite different from the user logic. The latter is usually a compiled version of the former. Experiments are in progress to assess the user logic. HORSES (Human - Orbital Refueling System - Expert System) is an attempt to include both of these logics in the same tool. It is designed to assist astronauts during monitoring and diagnosis tasks. Basically, HORSES includes a situation recognition level coupled to an analytical diagnoser, and a meta-level working on both of the previous levels. HORSES is a good tool for modeling task models and is also more broadly useful for knowledge design. The presentation is represented by abstract and overhead visuals only.

A reversible fluorescent probe for real-time live-cell imaging and quantification of endogenous hydropolysulfides.

PubMed

Umezawa, Keitaro; Kamiya, Mako; Urano, Yasuteru

2018-05-23

The chemical biology of reactive sulfur species, including hydropolysulfides, has been a subject undergoing intense study in recent years, but further understanding of their 'intact' function in living cells has been limited due to a lack of appropriate analytical tools. In order to overcome this limitation, we developed a new type of fluorescent probe which reversibly and selectively reacts to hydropolysulfides. The probe enables live-cell visualization and quantification of endogenous hydropolysulfides without interference from intrinsic thiol species such as glutathione. Additionally, real-time reversible monitoring of oxidative-stress-induced fluctuation of intrinsic hydropolysulfides has been achieved with a temporal resolution in the order of seconds, a result which has not yet been realized using conventional methods. These results reveal the probe's versatility as a new fluorescence imaging tool to understand the function of intracellular hydropolysulfides. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Update on Genomic Databases and Resources at the National Center for Biotechnology Information.

PubMed

Tatusova, Tatiana

2016-01-01

The National Center for Biotechnology Information (NCBI), as a primary public repository of genomic sequence data, collects and maintains enormous amounts of heterogeneous data. Data for genomes, genes, gene expressions, gene variation, gene families, proteins, and protein domains are integrated with the analytical, search, and retrieval resources through the NCBI website, text-based search and retrieval system, provides a fast and easy way to navigate across diverse biological databases.Comparative genome analysis tools lead to further understanding of evolution processes quickening the pace of discovery. Recent technological innovations have ignited an explosion in genome sequencing that has fundamentally changed our understanding of the biology of living organisms. This huge increase in DNA sequence data presents new challenges for the information management system and the visualization tools. New strategies have been designed to bring an order to this genome sequence shockwave and improve the usability of associated data.

Intelligent platforms for disease assessment: novel approaches in functional echocardiography.

PubMed

Sengupta, Partho P

2013-11-01

Accelerating trends in the dynamic digital era (from 2004 onward) has resulted in the emergence of novel parametric imaging tools that allow easy and accurate extraction of quantitative information from cardiac images. This review principally attempts to heighten the awareness of newer emerging paradigms that may advance acquisition, visualization and interpretation of the large functional data sets obtained during cardiac ultrasound imaging. Incorporation of innovative cognitive software that allow advanced pattern recognition and disease forecasting will likely transform the human-machine interface and interpretation process to achieve a more efficient and effective work environment. Novel technologies for automation and big data analytics that are already active in other fields need to be rapidly adapted to the health care environment with new academic-industry collaborations to enrich and accelerate the delivery of newer decision making tools for enhancing patient care. Copyright © 2013. Published by Elsevier Inc.

Mapping healthcare systems: a policy relevant analytic tool.

PubMed

Sekhri Feachem, Neelam; Afshar, Ariana; Pruett, Cristina; Avanceña, Anton L V

2017-07-01

In the past decade, an international consensus on the value of well-functioning systems has driven considerable health systems research. This research falls into two broad categories. The first provides conceptual frameworks that take complex healthcare systems and create simplified constructs of interactions and functions. The second focuses on granular inputs and outputs. This paper presents a novel translational mapping tool - the University of California, San Francisco mapping tool (the Tool) - which bridges the gap between these two areas of research, creating a platform for multi-country comparative analysis. Using the Murray-Frenk framework, we create a macro-level representation of a country's structure, focusing on how it finances and delivers healthcare. The map visually depicts the fundamental policy questions in healthcare system design: funding sources and amount spent through each source, purchasers, populations covered, provider categories; and the relationship between these entities. We use the Tool to provide a macro-level comparative analysis of the structure of India's and Thailand's healthcare systems. As part of the systems strengthening arsenal, the Tool can stimulate debate about the merits and consequences of different healthcare systems structural designs, using a common framework that fosters multi-country comparative analyses. © The Author 2017. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene.

Time-resolved fluorescence microscopy (FLIM) as an analytical tool in skin nanomedicine.

PubMed

Alexiev, Ulrike; Volz, Pierre; Boreham, Alexander; Brodwolf, Robert

2017-07-01

The emerging field of nanomedicine provides new approaches for the diagnosis and treatment of diseases, for symptom relief, and for monitoring of disease progression. Topical application of drug-loaded nanoparticles for the treatment of skin disorders is a promising strategy to overcome the stratum corneum, the upper layer of the skin, which represents an effective physical and biochemical barrier. The understanding of drug penetration into skin and enhanced penetration into skin facilitated by nanocarriers requires analytical tools that ideally allow to visualize the skin, its morphology, the drug carriers, drugs, their transport across the skin and possible interactions, as well as effects of the nanocarriers within the different skin layers. Here, we review some recent developments in the field of fluorescence microscopy, namely Fluorescence Lifetime Imaging Microscopy (FLIM)), for improved characterization of nanocarriers, their interactions and penetration into skin. In particular, FLIM allows for the discrimination of target molecules, e.g. fluorescently tagged nanocarriers, against the autofluorescent tissue background and, due to the environmental sensitivity of the fluorescence lifetime, also offers insights into the local environment of the nanoparticle and its interactions with other biomolecules. Thus, FLIM shows the potential to overcome several limits of intensity based microscopy. Copyright © 2017 Elsevier B.V. All rights reserved.

An architecture for genomics analysis in a clinical setting using Galaxy and Docker

PubMed Central

Digan, W; Countouris, H; Barritault, M; Baudoin, D; Laurent-Puig, P; Blons, H; Burgun, A

2017-01-01

Abstract Next-generation sequencing is used on a daily basis to perform molecular analysis to determine subtypes of disease (e.g., in cancer) and to assist in the selection of the optimal treatment. Clinical bioinformatics handles the manipulation of the data generated by the sequencer, from the generation to the analysis and interpretation. Reproducibility and traceability are crucial issues in a clinical setting. We have designed an approach based on Docker container technology and Galaxy, the popular bioinformatics analysis support open-source software. Our solution simplifies the deployment of a small-size analytical platform and simplifies the process for the clinician. From the technical point of view, the tools embedded in the platform are isolated and versioned through Docker images. Along the Galaxy platform, we also introduce the AnalysisManager, a solution that allows single-click analysis for biologists and leverages standardized bioinformatics application programming interfaces. We added a Shiny/R interactive environment to ease the visualization of the outputs. The platform relies on containers and ensures the data traceability by recording analytical actions and by associating inputs and outputs of the tools to EDAM ontology through ReGaTe. The source code is freely available on Github at https://github.com/CARPEM/GalaxyDocker. PMID:29048555

An architecture for genomics analysis in a clinical setting using Galaxy and Docker.

PubMed

Digan, W; Countouris, H; Barritault, M; Baudoin, D; Laurent-Puig, P; Blons, H; Burgun, A; Rance, B

2017-11-01

Next-generation sequencing is used on a daily basis to perform molecular analysis to determine subtypes of disease (e.g., in cancer) and to assist in the selection of the optimal treatment. Clinical bioinformatics handles the manipulation of the data generated by the sequencer, from the generation to the analysis and interpretation. Reproducibility and traceability are crucial issues in a clinical setting. We have designed an approach based on Docker container technology and Galaxy, the popular bioinformatics analysis support open-source software. Our solution simplifies the deployment of a small-size analytical platform and simplifies the process for the clinician. From the technical point of view, the tools embedded in the platform are isolated and versioned through Docker images. Along the Galaxy platform, we also introduce the AnalysisManager, a solution that allows single-click analysis for biologists and leverages standardized bioinformatics application programming interfaces. We added a Shiny/R interactive environment to ease the visualization of the outputs. The platform relies on containers and ensures the data traceability by recording analytical actions and by associating inputs and outputs of the tools to EDAM ontology through ReGaTe. The source code is freely available on Github at https://github.com/CARPEM/GalaxyDocker. © The Author 2017. Published by Oxford University Press.

Towards a Web-Enabled Geovisualization and Analytics Platform for the Energy and Water Nexus

NASA Astrophysics Data System (ADS)

Sanyal, J.; Chandola, V.; Sorokine, A.; Allen, M.; Berres, A.; Pang, H.; Karthik, R.; Nugent, P.; McManamay, R.; Stewart, R.; Bhaduri, B. L.

2017-12-01

Interactive data analytics are playing an increasingly vital role in the generation of new, critical insights regarding the complex dynamics of the energy/water nexus (EWN) and its interactions with climate variability and change. Integration of impacts, adaptation, and vulnerability (IAV) science with emerging, and increasingly critical, data science capabilities offers a promising potential to meet the needs of the EWN community. To enable the exploration of pertinent research questions, a web-based geospatial visualization platform is being built that integrates a data analysis toolbox with advanced data fusion and data visualization capabilities to create a knowledge discovery framework for the EWN. The system, when fully built out, will offer several geospatial visualization capabilities including statistical visual analytics, clustering, principal-component analysis, dynamic time warping, support uncertainty visualization and the exploration of data provenance, as well as support machine learning discoveries to render diverse types of geospatial data and facilitate interactive analysis. Key components in the system architecture includes NASA's WebWorldWind, the Globus toolkit, postgresql, as well as other custom built software modules.

EmailTime: visual analytics and statistics for temporal email

NASA Astrophysics Data System (ADS)

Erfani Joorabchi, Minoo; Yim, Ji-Dong; Shaw, Christopher D.

2011-01-01

Although the discovery and analysis of communication patterns in large and complex email datasets are difficult tasks, they can be a valuable source of information. We present EmailTime, a visual analysis tool of email correspondence patterns over the course of time that interactively portrays personal and interpersonal networks using the correspondence in the email dataset. Our approach is to put time as a primary variable of interest, and plot emails along a time line. EmailTime helps email dataset explorers interpret archived messages by providing zooming, panning, filtering and highlighting etc. To support analysis, it also measures and visualizes histograms, graph centrality and frequency on the communication graph that can be induced from the email collection. This paper describes EmailTime's capabilities, along with a large case study with Enron email dataset to explore the behaviors of email users within different organizational positions from January 2000 to December 2001. We defined email behavior as the email activity level of people regarding a series of measured metrics e.g. sent and received emails, numbers of email addresses, etc. These metrics were calculated through EmailTime. Results showed specific patterns in the use email within different organizational positions. We suggest that integrating both statistics and visualizations in order to display information about the email datasets may simplify its evaluation.

Using Visual Simulation Tools And Learning Outcomes-Based Curriculum To Help Transportation Engineering Students And Practitioners To Better Understand And Design Traffic Signal Control Systems

DOT National Transportation Integrated Search

2012-06-01

The use of visual simulation tools to convey complex concepts has become a useful tool in education as well as in research. : This report describes a project that developed curriculum and visualization tools to train transportation engineering studen...

Communications Effects Server (CES) Model for Systems Engineering Research

DTIC Science & Technology

2012-01-31

Visualization Tool Interface «logical» HLA Tool Interface «logical» DIS Tool Interface «logical» STK Tool Interface «module» Execution Kernels «logical...interoperate with STK when running simulations. GUI Components  Architect – The Architect represents the main network design and visualization ...interest» CES «block» Third Party Visualization Tool «block» Third Party Analysis Tool «block» Third Party Text Editor «block» HLA Tools Analyst User Army

Perspectives on making big data analytics work for oncology.

PubMed

El Naqa, Issam

2016-12-01

Oncology, with its unique combination of clinical, physical, technological, and biological data provides an ideal case study for applying big data analytics to improve cancer treatment safety and outcomes. An oncology treatment course such as chemoradiotherapy can generate a large pool of information carrying the 5Vs hallmarks of big data. This data is comprised of a heterogeneous mixture of patient demographics, radiation/chemo dosimetry, multimodality imaging features, and biological markers generated over a treatment period that can span few days to several weeks. Efforts using commercial and in-house tools are underway to facilitate data aggregation, ontology creation, sharing, visualization and varying analytics in a secure environment. However, open questions related to proper data structure representation and effective analytics tools to support oncology decision-making need to be addressed. It is recognized that oncology data constitutes a mix of structured (tabulated) and unstructured (electronic documents) that need to be processed to facilitate searching and subsequent knowledge discovery from relational or NoSQL databases. In this context, methods based on advanced analytics and image feature extraction for oncology applications will be discussed. On the other hand, the classical p (variables)≫n (samples) inference problem of statistical learning is challenged in the Big data realm and this is particularly true for oncology applications where p-omics is witnessing exponential growth while the number of cancer incidences has generally plateaued over the past 5-years leading to a quasi-linear growth in samples per patient. Within the Big data paradigm, this kind of phenomenon may yield undesirable effects such as echo chamber anomalies, Yule-Simpson reversal paradox, or misleading ghost analytics. In this work, we will present these effects as they pertain to oncology and engage small thinking methodologies to counter these effects ranging from incorporating prior knowledge, using information-theoretic techniques to modern ensemble machine learning approaches or combination of these. We will particularly discuss the pros and cons of different approaches to improve mining of big data in oncology. Copyright © 2016 Elsevier Inc. All rights reserved.

A Paper-Based Electrochromic Array for Visualized Electrochemical Sensing.

PubMed

Zhang, Fengling; Cai, Tianyi; Ma, Liang; Zhan, Liyuan; Liu, Hong

2017-01-31

We report a battery-powered, paper-based electrochromic array for visualized electrochemical sensing. The paper-based sensing system consists of six parallel electrochemical cells, which are powered by an aluminum-air battery. Each single electrochemical cell uses a Prussian Blue spot electrodeposited on an indium-doped tin oxide thin film as the electrochromic indicator. Each electrochemical cell is preloaded with increasing amounts of analyte. The sample activates the battery for the sensing. Both the preloaded analyte and the analyte in the sample initiate the color change of Prussian Blue to Prussian White. With a reaction time of 60 s, the number of electrochemical cells with complete color changes is correlated to the concentration of analyte in the sample. As a proof-of-concept analyte, lactic acid was detected semi-quantitatively using the naked eye.

Insight solutions are correct more often than analytic solutions

PubMed Central

Salvi, Carola; Bricolo, Emanuela; Kounios, John; Bowden, Edward; Beeman, Mark

2016-01-01

How accurate are insights compared to analytical solutions? In four experiments, we investigated how participants’ solving strategies influenced their solution accuracies across different types of problems, including one that was linguistic, one that was visual and two that were mixed visual-linguistic. In each experiment, participants’ self-judged insight solutions were, on average, more accurate than their analytic ones. We hypothesised that insight solutions have superior accuracy because they emerge into consciousness in an all-or-nothing fashion when the unconscious solving process is complete, whereas analytic solutions can be guesses based on conscious, prematurely terminated, processing. This hypothesis is supported by the finding that participants’ analytic solutions included relatively more incorrect responses (i.e., errors of commission) than timeouts (i.e., errors of omission) compared to their insight responses. PMID:27667960

State Health Mapper: An Interactive, Web-Based Tool for Physician Workforce Planning, Recruitment, and Health Services Research.

PubMed

Krause, Denise D

2015-11-01

Health rankings in Mississippi are abysmal. Mississippi also has fewer physicians to serve its population compared with all other states. Many residents of this predominately rural state do not have access to healthcare providers. To better understand the demographics and distribution of the current health workforce in Mississippi, the main objective of the study was to design a Web-based, spatial, interactive application to visualize and explore the physician workforce. A Web application was designed to assist in health workforce planning. Secondary datasets of licensure and population information were obtained, and live feeds from licensure systems are being established. Several technologies were used to develop an intuitive, user-friendly application. Custom programming was completed in JavaScript so the application could run on most platforms, including mobile devices. The application allows users to identify and query geographic locations of individual or aggregated physicians based on attributes included in the licensure data, to perform drive time or buffer analyses, and to explore sociodemographic population data by geographic area of choice. This Web-based application with analytical tools visually represents the physician workforce licensed in Mississippi and its attributes, and provides access to much-needed information for statewide health workforce planning and research. The success of the application is not only based on the practicality of the tool but also on its ease of use. Feedback has been positive and has come from a wide variety of organizations across the state.

An analytical model for the celestial distribution of polarized light, accounting for polarization singularities, wavelength and atmospheric turbidity

NASA Astrophysics Data System (ADS)

Wang, Xin; Gao, Jun; Fan, Zhiguo; Roberts, Nicholas W.

2016-06-01

We present a computationally inexpensive analytical model for simulating celestial polarization patterns in variable conditions. We combine both the singularity theory of Berry et al (2004 New J. Phys. 6 162) and the intensity model of Perez et al (1993 Sol. Energy 50 235-245) such that our single model describes three key sets of data: (1) the overhead distribution of the degree of polarization as well as the existence of neutral points in the sky; (2) the change in sky polarization as a function of the turbidity of the atmosphere; and (3) sky polarization patterns as a function of wavelength, calculated in this work from the ultra-violet to the near infra-red. To verify the performance of our model we generate accurate reference data using a numerical radiative transfer model and statistical comparisons between these two methods demonstrate no significant difference in almost all situations. The development of our analytical model provides a novel method for efficiently calculating the overhead skylight polarization pattern. This provides a new tool of particular relevance for our understanding of animals that use the celestial polarization pattern as a source of visual information.

Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets.

PubMed

Nariya, Maulik K; Kim, Jae Hyun; Xiong, Jian; Kleindl, Peter A; Hewarathna, Asha; Fisher, Adam C; Joshi, Sangeeta B; Schöneich, Christian; Forrest, M Laird; Middaugh, C Russell; Volkin, David B; Deeds, Eric J

2017-11-01

There is growing interest in generating physicochemical and biological analytical data sets to compare complex mixture drugs, for example, products from different manufacturers. In this work, we compare various crofelemer samples prepared from a single lot by filtration with varying molecular weight cutoffs combined with incubation for different times at different temperatures. The 2 preceding articles describe experimental data sets generated from analytical characterization of fractionated and degraded crofelemer samples. In this work, we use data mining techniques such as principal component analysis and mutual information scores to help visualize the data and determine discriminatory regions within these large data sets. The mutual information score identifies chemical signatures that differentiate crofelemer samples. These signatures, in many cases, would likely be missed by traditional data analysis tools. We also found that supervised learning classifiers robustly discriminate samples with around 99% classification accuracy, indicating that mathematical models of these physicochemical data sets are capable of identifying even subtle differences in crofelemer samples. Data mining and machine learning techniques can thus identify fingerprint-type attributes of complex mixture drugs that may be used for comparative characterization of products. Copyright © 2017 American Pharmacists Association®. All rights reserved.

The Benefits and Complexities of Operating Geographic Information Systems (GIS) in a High Performance Computing (HPC) Environment

NASA Astrophysics Data System (ADS)

Shute, J.; Carriere, L.; Duffy, D.; Hoy, E.; Peters, J.; Shen, Y.; Kirschbaum, D.

2017-12-01

The NASA Center for Climate Simulation (NCCS) at the Goddard Space Flight Center is building and maintaining an Enterprise GIS capability for its stakeholders, to include NASA scientists, industry partners, and the public. This platform is powered by three GIS subsystems operating in a highly-available, virtualized environment: 1) the Spatial Analytics Platform is the primary NCCS GIS and provides users discoverability of the vast DigitalGlobe/NGA raster assets within the NCCS environment; 2) the Disaster Mapping Platform provides mapping and analytics services to NASA's Disaster Response Group; and 3) the internal (Advanced Data Analytics Platform/ADAPT) enterprise GIS provides users with the full suite of Esri and open source GIS software applications and services. All systems benefit from NCCS's cutting edge infrastructure, to include an InfiniBand network for high speed data transfers; a mixed/heterogeneous environment featuring seamless sharing of information between Linux and Windows subsystems; and in-depth system monitoring and warning systems. Due to its co-location with the NCCS Discover High Performance Computing (HPC) environment and the Advanced Data Analytics Platform (ADAPT), the GIS platform has direct access to several large NCCS datasets including DigitalGlobe/NGA, Landsat, MERRA, and MERRA2. Additionally, the NCCS ArcGIS Desktop Windows virtual machines utilize existing NetCDF and OPeNDAP assets for visualization, modelling, and analysis - thus eliminating the need for data duplication. With the advent of this platform, Earth scientists have full access to vast data repositories and the industry-leading tools required for successful management and analysis of these multi-petabyte, global datasets. The full system architecture and integration with scientific datasets will be presented. Additionally, key applications and scientific analyses will be explained, to include the NASA Global Landslide Catalog (GLC) Reporter crowdsourcing application, the NASA GLC Viewer discovery and analysis tool, the DigitalGlobe/NGA Data Discovery Tool, the NASA Disaster Response Group Mapping Platform (https://maps.disasters.nasa.gov), and support for NASA's Arctic - Boreal Vulnerability Experiment (ABoVE).

Implementation of Information Management System for Radiation Safety of Personnel at the Russian Northwest Center for Radioactive Waste Management 'SevRAO' - 13131

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chizhov, K.; Simakov, A.; Seregin, V.

2013-07-01

The report is an overview of the information-analytical system designed to assure radiation safety of workers. The system was implemented in the Northwest Radioactive Waste Management Center 'SevRAO' (which is a branch of the Federal State Unitary Enterprise 'Radioactive Waste Management Enterprise RosRAO'). The center is located in the Northwest Russia. In respect to 'SevRAO', the Federal Medical-Biological Agency is the regulatory body, which deals with issues of radiation control. The main document to regulate radiation control is 'Reference levels of radiation factors in radioactive wastes management center'. This document contains about 250 parameters. We have developed a software toolmore » to simplify control of these parameters. The software includes: input interface, the database, dose calculating module and analytical block. Input interface is used to enter radiation environment data. Dose calculating module calculates the dose on the route. Analytical block optimizes and analyzes radiation situation maps. Much attention is paid to the GUI and graphical representation of results. The operator can enter the route at the industrial site or watch the fluctuations of the dose rate field on the map. Most of the results are presented in a visual form. Here we present some analytical tasks, such as comparison of the dose rate in some point with control levels at this point, to be solved for the purpose of radiation safety control. The program helps to identify points making the largest contribution to the collective dose of the personnel. The tool can automatically calculate the route with the lowest dose, compare and choose the best route. The program uses several options to visualize the radiation environment at the industrial site. This system will be useful for radiation monitoring services during the operation, planning of works and development of scenarios. The paper presents some applications of this system on real data over three years - from March 2009 to February 2012. (authors)« less

AuthorSynth: a collaboration network and behaviorally-based visualization tool of activation reports from the neuroscience literature.

PubMed

Sochat, Vanessa V

2015-01-01

Targeted collaboration is becoming more challenging with the ever-increasing number of publications, conferences, and academic responsibilities that the modern-day researcher must synthesize. Specifically, the field of neuroimaging had roughly 10,000 new papers in PubMed for the year 2013, presenting tens of thousands of international authors, each a potential collaborator working on some sub-domain in the field. To remove the burden of synthesizing an entire corpus of publications, talks, and conference interactions to find and assess collaborations, we combine meta-analytical neuroimaging informatics methods with machine learning and network analysis toward this goal. We present "AuthorSynth," a novel application prototype that includes (1) a collaboration network to identify researchers with similar results reported in the literature; and (2) a 2D plot-"brain lattice"-to visually summarize a single author's contribution to the field, and allow for searching of authors based on behavioral terms. This method capitalizes on intelligent synthesis of the neuroimaging literature, and demonstrates that data-driven approaches can be used to confirm existing collaborations, reveal potential ones, and identify gaps in published knowledge. We believe this tool exemplifies how methods from neuroimaging informatics can better inform researchers about progress and knowledge in the field, and enhance the modern workflow of finding collaborations.

A web-based multicriteria evaluation of spatial trade-offs between environmental and economic implications from hydraulic fracturing in a shale gas region in Ohio.

PubMed

Liu, X; Gorsevski, P V; Yacobucci, M M; Onasch, C M

2016-06-01

Planning of shale gas infrastructure and drilling sites for hydraulic fracturing has important spatial implications. The evaluation of conflicting and competing objectives requires an explicit consideration of multiple criteria as they have important environmental and economic implications. This study presents a web-based multicriteria spatial decision support system (SDSS) prototype with a flexible and user-friendly interface that could provide educational or decision-making capabilities with respect to hydraulic fracturing site selection in eastern Ohio. One of the main features of this SDSS is to emphasize potential trade-offs between important factors of environmental and economic ramifications from hydraulic fracturing activities using a weighted linear combination (WLC) method. In the prototype, the GIS-enabled analytical components allow spontaneous visualization of available alternatives on maps which provide value-added features for decision support processes and derivation of final decision maps. The SDSS prototype also facilitates nonexpert participation capabilities using a mapping module, decision-making tool, group decision module, and social media sharing tools. The logical flow of successively presented forms and standardized criteria maps is used to generate visualization of trade-off scenarios and alternative solutions tailored to individual user's preferences that are graphed for subsequent decision-making.

The Multisensory Attentional Consequences of Tool Use: A Functional Magnetic Resonance Imaging Study

PubMed Central

Holmes, Nicholas P.; Spence, Charles; Hansen, Peter C.; Mackay, Clare E.; Calvert, Gemma A.

2008-01-01

Background Tool use in humans requires that multisensory information is integrated across different locations, from objects seen to be distant from the hand, but felt indirectly at the hand via the tool. We tested the hypothesis that using a simple tool to perceive vibrotactile stimuli results in the enhanced processing of visual stimuli presented at the distal, functional part of the tool. Such a finding would be consistent with a shift of spatial attention to the location where the tool is used. Methodology/Principal Findings We tested this hypothesis by scanning healthy human participants' brains using functional magnetic resonance imaging, while they used a simple tool to discriminate between target vibrations, accompanied by congruent or incongruent visual distractors, on the same or opposite side to the tool. The attentional hypothesis was supported: BOLD response in occipital cortex, particularly in the right hemisphere lingual gyrus, varied significantly as a function of tool position, increasing contralaterally, and decreasing ipsilaterally to the tool. Furthermore, these modulations occurred despite the fact that participants were repeatedly instructed to ignore the visual stimuli, to respond only to the vibrotactile stimuli, and to maintain visual fixation centrally. In addition, the magnitude of multisensory (visual-vibrotactile) interactions in participants' behavioural responses significantly predicted the BOLD response in occipital cortical areas that were also modulated as a function of both visual stimulus position and tool position. Conclusions/Significance These results show that using a simple tool to locate and to perceive vibrotactile stimuli is accompanied by a shift of spatial attention to the location where the functional part of the tool is used, resulting in enhanced processing of visual stimuli at that location, and decreased processing at other locations. This was most clearly observed in the right hemisphere lingual gyrus. Such modulations of visual processing may reflect the functional importance of visuospatial information during human tool use. PMID:18958150

Scanning electron microscopy as an analytical tool for the study of calcified intrauterine contraceptive devices

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khan, S.R.; Wilkinson, E.J.

Within the endometrial cavity intrauterine contraceptive devices (IUDs) become encrusted with cellular, acellular, and fibrillar substances. Scanning electron microscopy was used to study the crust. Cellular material consisted mainly of blood cells and various types of bacteria. The fibrillar material appeared to be fibrin which was omnipresent in the crust and formed a thin layer immediately over the IUD surface. X-ray microanalysis of the acellular component of the crust revealed the presence of calcium. No other major peaks were identified. Near the IUD surface characteristic calcium phosphate crystals were present. Their microanalysis showed peaks for calcium and phosphorus. X-ray diffractionmore » of the crust however, showed it to contain only calcite. It is through the use of scanning electron microscopy that calcium phosphate has been detected in the IUD crust and a fibrillar layer has been visualized on the IUD surface. This study further demonstrates the effectiveness of SEM analytical techniques in the area of biomedical research.« less

Creating value in health care through big data: opportunities and policy implications.

PubMed

Roski, Joachim; Bo-Linn, George W; Andrews, Timothy A

2014-07-01

Big data has the potential to create significant value in health care by improving outcomes while lowering costs. Big data's defining features include the ability to handle massive data volume and variety at high velocity. New, flexible, and easily expandable information technology (IT) infrastructure, including so-called data lakes and cloud data storage and management solutions, make big-data analytics possible. However, most health IT systems still rely on data warehouse structures. Without the right IT infrastructure, analytic tools, visualization approaches, work flows, and interfaces, the insights provided by big data are likely to be limited. Big data's success in creating value in the health care sector may require changes in current polices to balance the potential societal benefits of big-data approaches and the protection of patients' confidentiality. Other policy implications of using big data are that many current practices and policies related to data use, access, sharing, privacy, and stewardship need to be revised. Project HOPE—The People-to-People Health Foundation, Inc.

QFD-ANP Approach for the Conceptual Design of Research Vessels: A Case Study

NASA Astrophysics Data System (ADS)

Venkata Subbaiah, Kambagowni; Yeshwanth Sai, Koneru; Suresh, Challa

2016-10-01

Conceptual design is a subset of concept art wherein a new idea of product is created instead of a visual representation which would directly be used in a final product. The purpose is to understand the needs of conceptual design which are being used in engineering designs and to clarify the current conceptual design practice. Quality function deployment (QFD) is a customer oriented design approach for developing new or improved products and services to enhance customer satisfaction. House of quality (HOQ) has been traditionally used as planning tool of QFD which translates customer requirements (CRs) into design requirements (DRs). Factor analysis is carried out in order to reduce the CR portions of HOQ. The analytical hierarchical process is employed to obtain the priority ratings of CR's which are used in constructing HOQ. This paper mainly discusses about the conceptual design of an oceanographic research vessel using analytical network process (ANP) technique. Finally the QFD-ANP integrated methodology helps to establish the importance ratings of DRs.

IBiSA_Tools: A Computational Toolkit for Ion-Binding State Analysis in Molecular Dynamics Trajectories of Ion Channels.

PubMed

Kasahara, Kota; Kinoshita, Kengo

2016-01-01

Ion conduction mechanisms of ion channels are a long-standing conundrum. Although the molecular dynamics (MD) method has been extensively used to simulate ion conduction dynamics at the atomic level, analysis and interpretation of MD results are not straightforward due to complexity of the dynamics. In our previous reports, we proposed an analytical method called ion-binding state analysis to scrutinize and summarize ion conduction mechanisms by taking advantage of a variety of analytical protocols, e.g., the complex network analysis, sequence alignment, and hierarchical clustering. This approach effectively revealed the ion conduction mechanisms and their dependence on the conditions, i.e., ion concentration and membrane voltage. Here, we present an easy-to-use computational toolkit for ion-binding state analysis, called IBiSA_tools. This toolkit consists of a C++ program and a series of Python and R scripts. From the trajectory file of MD simulations and a structure file, users can generate several images and statistics of ion conduction processes. A complex network named ion-binding state graph is generated in a standard graph format (graph modeling language; GML), which can be visualized by standard network analyzers such as Cytoscape. As a tutorial, a trajectory of a 50 ns MD simulation of the Kv1.2 channel is also distributed with the toolkit. Users can trace the entire process of ion-binding state analysis step by step. The novel method for analysis of ion conduction mechanisms of ion channels can be easily used by means of IBiSA_tools. This software is distributed under an open source license at the following URL: http://www.ritsumei.ac.jp/~ktkshr/ibisa_tools/.

Visualization Tools for Teaching Computer Security

ERIC Educational Resources Information Center

Yuan, Xiaohong; Vega, Percy; Qadah, Yaseen; Archer, Ricky; Yu, Huiming; Xu, Jinsheng

2010-01-01

Using animated visualization tools has been an important teaching approach in computer science education. We have developed three visualization and animation tools that demonstrate various information security concepts and actively engage learners. The information security concepts illustrated include: packet sniffer and related computer network…

Visual illusion of tool use recalibrates tactile perception

PubMed Central

Miller, Luke E.; Longo, Matthew R.; Saygin, Ayse P.

2018-01-01

Brief use of a tool recalibrates multisensory representations of the user’s body, a phenomenon called tool embodiment. Despite two decades of research, little is known about its boundary conditions. It has been widely argued that embodiment requires active tool use, suggesting a critical role for somatosensory and motor feedback. The present study used a visual illusion to cast doubt on this view. We used a mirror-based setup to induce a visual experience of tool use with an arm that was in fact stationary. Following illusory tool use, tactile perception was recalibrated on this stationary arm, and with equal magnitude as physical use. Recalibration was not found following illusory passive tool holding, and could not be accounted for by sensory conflict or general interhemispheric plasticity. These results suggest visual tool-use signals play a critical role in driving tool embodiment. PMID:28196765

Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention

PubMed Central

Fisher, Brian; Smith, Jennifer; Pike, Ian

2017-01-01

Background: Accurate understanding of complex health data is critical in order to deal with wicked health problems and make timely decisions. Wicked problems refer to ill-structured and dynamic problems that combine multidimensional elements, which often preclude the conventional problem solving approach. This pilot study introduces visual analytics (VA) methods to multi-stakeholder decision-making sessions about child injury prevention; Methods: Inspired by the Delphi method, we introduced a novel methodology—group analytics (GA). GA was pilot-tested to evaluate the impact of collaborative visual analytics on facilitating problem solving and supporting decision-making. We conducted two GA sessions. Collected data included stakeholders’ observations, audio and video recordings, questionnaires, and follow up interviews. The GA sessions were analyzed using the Joint Activity Theory protocol analysis methods; Results: The GA methodology triggered the emergence of ‘common ground’ among stakeholders. This common ground evolved throughout the sessions to enhance stakeholders’ verbal and non-verbal communication, as well as coordination of joint activities and ultimately collaboration on problem solving and decision-making; Conclusions: Understanding complex health data is necessary for informed decisions. Equally important, in this case, is the use of the group analytics methodology to achieve ‘common ground’ among diverse stakeholders about health data and their implications. PMID:28895928

Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention.

PubMed

Al-Hajj, Samar; Fisher, Brian; Smith, Jennifer; Pike, Ian

2017-09-12

Background : Accurate understanding of complex health data is critical in order to deal with wicked health problems and make timely decisions. Wicked problems refer to ill-structured and dynamic problems that combine multidimensional elements, which often preclude the conventional problem solving approach. This pilot study introduces visual analytics (VA) methods to multi-stakeholder decision-making sessions about child injury prevention; Methods : Inspired by the Delphi method, we introduced a novel methodology-group analytics (GA). GA was pilot-tested to evaluate the impact of collaborative visual analytics on facilitating problem solving and supporting decision-making. We conducted two GA sessions. Collected data included stakeholders' observations, audio and video recordings, questionnaires, and follow up interviews. The GA sessions were analyzed using the Joint Activity Theory protocol analysis methods; Results : The GA methodology triggered the emergence of ' common g round ' among stakeholders. This common ground evolved throughout the sessions to enhance stakeholders' verbal and non-verbal communication, as well as coordination of joint activities and ultimately collaboration on problem solving and decision-making; Conclusion s : Understanding complex health data is necessary for informed decisions. Equally important, in this case, is the use of the group analytics methodology to achieve ' common ground' among diverse stakeholders about health data and their implications.

Farm Management Support on Cloud Computing Platform: A System for Cropland Monitoring Using Multi-Source Remotely Sensed Data

NASA Astrophysics Data System (ADS)

Coburn, C. A.; Qin, Y.; Zhang, J.; Staenz, K.

2015-12-01

Food security is one of the most pressing issues facing humankind. Recent estimates predict that over one billion people don't have enough food to meet their basic nutritional needs. The ability of remote sensing tools to monitor and model crop production and predict crop yield is essential for providing governments and farmers with vital information to ensure food security. Google Earth Engine (GEE) is a cloud computing platform, which integrates storage and processing algorithms for massive remotely sensed imagery and vector data sets. By providing the capabilities of storing and analyzing the data sets, it provides an ideal platform for the development of advanced analytic tools for extracting key variables used in regional and national food security systems. With the high performance computing and storing capabilities of GEE, a cloud-computing based system for near real-time crop land monitoring was developed using multi-source remotely sensed data over large areas. The system is able to process and visualize the MODIS time series NDVI profile in conjunction with Landsat 8 image segmentation for crop monitoring. With multi-temporal Landsat 8 imagery, the crop fields are extracted using the image segmentation algorithm developed by Baatz et al.[1]. The MODIS time series NDVI data are modeled by TIMESAT [2], a software package developed for analyzing time series of satellite data. The seasonality of MODIS time series data, for example, the start date of the growing season, length of growing season, and NDVI peak at a field-level are obtained for evaluating the crop-growth conditions. The system fuses MODIS time series NDVI data and Landsat 8 imagery to provide information of near real-time crop-growth conditions through the visualization of MODIS NDVI time series and comparison of multi-year NDVI profiles. Stakeholders, i.e., farmers and government officers, are able to obtain crop-growth information at crop-field level online. This unique utilization of GEE in combination with advanced analytic and extraction techniques provides a vital remote sensing tool for decision makers and scientists with a high-degree of flexibility to adapt to different uses.

Big Data Geo-Analytical Tool Development for Spatial Analysis Uncertainty Visualization and Quantification Needs

NASA Astrophysics Data System (ADS)

Rose, K.; Bauer, J. R.; Baker, D. V.

2015-12-01

As big data computing capabilities are increasingly paired with spatial analytical tools and approaches, there is a need to ensure uncertainty associated with the datasets used in these analyses is adequately incorporated and portrayed in results. Often the products of spatial analyses, big data and otherwise, are developed using discontinuous, sparse, and often point-driven data to represent continuous phenomena. Results from these analyses are generally presented without clear explanations of the uncertainty associated with the interpolated values. The Variable Grid Method (VGM) offers users with a flexible approach designed for application to a variety of analyses where users there is a need to study, evaluate, and analyze spatial trends and patterns while maintaining connection to and communicating the uncertainty in the underlying spatial datasets. The VGM outputs a simultaneous visualization representative of the spatial data analyses and quantification of underlying uncertainties, which can be calculated using data related to sample density, sample variance, interpolation error, uncertainty calculated from multiple simulations. In this presentation we will show how we are utilizing Hadoop to store and perform spatial analysis through the development of custom Spark and MapReduce applications that incorporate ESRI Hadoop libraries. The team will present custom 'Big Data' geospatial applications that run on the Hadoop cluster and integrate with ESRI ArcMap with the team's probabilistic VGM approach. The VGM-Hadoop tool has been specially built as a multi-step MapReduce application running on the Hadoop cluster for the purpose of data reduction. This reduction is accomplished by generating multi-resolution, non-overlapping, attributed topology that is then further processed using ESRI's geostatistical analyst to convey a probabilistic model of a chosen study region. Finally, we will share our approach for implementation of data reduction and topology generation via custom multi-step Hadoop applications, performance benchmarking comparisons, and Hadoop-centric opportunities for greater parallelization of geospatial operations. The presentation includes examples of the approach being applied to a range of subsurface, geospatial studies (e.g. induced seismicity risk).

Leveraging multidisciplinarity in a visual analytics graduate course.

PubMed

Elmqvist, Niklas; Ebert, David S

2012-01-01

Demand is growing in engineering, business, science, research, and industry for students with visual analytics expertise. However, teaching VA is challenging owing to the multidisciplinary nature of the topic, students' diverse backgrounds, and the corresponding requirements for instructors. This article reports best practices from a VA graduate course at Purdue University, where instructors leveraged these challenges to their advantage instead of trying to mitigate them.

Analytical Thinking, Analytical Action: Using Prelab Video Demonstrations and e-Quizzes to Improve Undergraduate Preparedness for Analytical Chemistry Practical Classes

ERIC Educational Resources Information Center

Jolley, Dianne F.; Wilson, Stephen R.; Kelso, Celine; O'Brien, Glennys; Mason, Claire E.

2016-01-01

This project utilizes visual and critical thinking approaches to develop a higher-education synergistic prelab training program for a large second-year undergraduate analytical chemistry class, directing more of the cognitive learning to the prelab phase. This enabled students to engage in more analytical thinking prior to engaging in the…

Data-Driven Geospatial Visual Analytics for Real-Time Urban Flooding Decision Support

NASA Astrophysics Data System (ADS)

Liu, Y.; Hill, D.; Rodriguez, A.; Marini, L.; Kooper, R.; Myers, J.; Wu, X.; Minsker, B. S.

2009-12-01

Urban flooding is responsible for the loss of life and property as well as the release of pathogens and other pollutants into the environment. Previous studies have shown that spatial distribution of intense rainfall significantly impacts the triggering and behavior of urban flooding. However, no general purpose tools yet exist for deriving rainfall data and rendering them in real-time at the resolution of hydrologic units used for analyzing urban flooding. This paper presents a new visual analytics system that derives and renders rainfall data from the NEXRAD weather radar system at the sewershed (i.e. urban hydrologic unit) scale in real-time for a Chicago stormwater management project. We introduce a lightweight Web 2.0 approach which takes advantages of scientific workflow management and publishing capabilities developed at NCSA (National Center for Supercomputing Applications), streaming data-aware semantic content management repository, web-based Google Earth/Map and time-aware KML (Keyhole Markup Language). A collection of polygon-based virtual sensors is created from the NEXRAD Level II data using spatial, temporal and thematic transformations at the sewershed level in order to produce persistent virtual rainfall data sources for the animation. Animated color-coded rainfall map in the sewershed can be played in real-time as a movie using time-aware KML inside the web browser-based Google Earth for visually analyzing the spatiotemporal patterns of the rainfall intensity in the sewershed. Such system provides valuable information for situational awareness and improved decision support during extreme storm events in an urban area. Our further work includes incorporating additional data (such as basement flooding events data) or physics-based predictive models that can be used for more integrated data-driven decision support.

Podium: Ranking Data Using Mixed-Initiative Visual Analytics.

PubMed

Wall, Emily; Das, Subhajit; Chawla, Ravish; Kalidindi, Bharath; Brown, Eli T; Endert, Alex

2018-01-01

People often rank and order data points as a vital part of making decisions. Multi-attribute ranking systems are a common tool used to make these data-driven decisions. Such systems often take the form of a table-based visualization in which users assign weights to the attributes representing the quantifiable importance of each attribute to a decision, which the system then uses to compute a ranking of the data. However, these systems assume that users are able to quantify their conceptual understanding of how important particular attributes are to a decision. This is not always easy or even possible for users to do. Rather, people often have a more holistic understanding of the data. They form opinions that data point A is better than data point B but do not necessarily know which attributes are important. To address these challenges, we present a visual analytic application to help people rank multi-variate data points. We developed a prototype system, Podium, that allows users to drag rows in the table to rank order data points based on their perception of the relative value of the data. Podium then infers a weighting model using Ranking SVM that satisfies the user's data preferences as closely as possible. Whereas past systems help users understand the relationships between data points based on changes to attribute weights, our approach helps users to understand the attributes that might inform their understanding of the data. We present two usage scenarios to describe some of the potential uses of our proposed technique: (1) understanding which attributes contribute to a user's subjective preferences for data, and (2) deconstructing attributes of importance for existing rankings. Our proposed approach makes powerful machine learning techniques more usable to those who may not have expertise in these areas.

Screening methods for post-stroke visual impairment: a systematic review.

PubMed

Hanna, Kerry Louise; Hepworth, Lauren Rachel; Rowe, Fiona

2017-12-01

To provide a systematic overview of the various tools available to screen for post-stroke visual impairment. A review of the literature was conducted including randomised controlled trials, controlled trials, cohort studies, observational studies, systematic reviews and retrospective medical note reviews. All languages were included and translation was obtained. Participants included adults ≥18 years old diagnosed with a visual impairment as a direct cause of a stroke. We searched a broad range of scholarly online resources and hand-searched articles registers of published, unpublished and on-going trials. Search terms included a variety of MESH terms and alternatives in relation to stroke and visual conditions. Study selection was performed by two authors independently. The quality of the evidence and risk of bias were assessed using the STROBE, GRACE and PRISMA statements. A total of 25 articles (n = 2924) were included in this review. Articles appraised reported on tools screening solely for visual impairments or for general post-stroke disabilities inclusive of vision. The majority of identified tools screen for visual perception including visual neglect (VN), with few screening for visual acuity (VA), visual field (VF) loss or ocular motility (OM) defects. Six articles reported on nine screening tools which combined visual screening assessment alongside screening for general stroke disabilities. Of these, three included screening for VA; three screened for VF loss; three screened for OM defects and all screened for VN. Two tools screened for all visual impairments. A further 19 articles were found which reported on individual vision screening tests in stroke populations; two for VF loss; 11 for VN and six for other visual perceptual defects. Most tools cannot accurately account for those with aphasia or communicative deficits, which are common problems following a stroke. There is currently no standardised visual screening tool which can accurately assess all potential post-stroke visual impairments. The current tools screen for only a number of potential stroke-related impairments, which means many visual defects may be missed. The sensitivity of those which screen for all impairments is significantly lowered when patients are unable to report their visual symptoms. Future research is required to develop a tool capable of assessing stroke patients which encompasses all potential visual deficits and can also be easily performed by both the patients and administered by health care professionals in order to ensure all stroke survivors with visual impairment are accurately identified and managed. Implications for Rehabilitation Over 65% of stroke survivors will suffer from a visual impairment, whereas 45% of stroke units do not assess vision. Visual impairment significantly reduces the quality of life, such as being unable to return to work, driving and depression. This review outlines the available screening methods to accurately identify stroke survivors with visual impairments. Identifying visual impairment after stroke can aid general rehabilitation and thus, improve the quality of life for these patients.

Spectral mapping tools from the earth sciences applied to spectral microscopy data.

PubMed

Harris, A Thomas

2006-08-01

Spectral imaging, originating from the field of earth remote sensing, is a powerful tool that is being increasingly used in a wide variety of applications for material identification. Several workers have used techniques like linear spectral unmixing (LSU) to discriminate materials in images derived from spectral microscopy. However, many spectral analysis algorithms rely on assumptions that are often violated in microscopy applications. This study explores algorithms originally developed as improvements on early earth imaging techniques that can be easily translated for use with spectral microscopy. To best demonstrate the application of earth remote sensing spectral analysis tools to spectral microscopy data, earth imaging software was used to analyze data acquired with a Leica confocal microscope with mechanical spectral scanning. For this study, spectral training signatures (often referred to as endmembers) were selected with the ENVI (ITT Visual Information Solutions, Boulder, CO) "spectral hourglass" processing flow, a series of tools that use the spectrally over-determined nature of hyperspectral data to find the most spectrally pure (or spectrally unique) pixels within the data set. This set of endmember signatures was then used in the full range of mapping algorithms available in ENVI to determine locations, and in some cases subpixel abundances of endmembers. Mapping and abundance images showed a broad agreement between the spectral analysis algorithms, supported through visual assessment of output classification images and through statistical analysis of the distribution of pixels within each endmember class. The powerful spectral analysis algorithms available in COTS software, the result of decades of research in earth imaging, are easily translated to new sources of spectral data. Although the scale between earth imagery and spectral microscopy is radically different, the problem is the same: mapping material locations and abundances based on unique spectral signatures. (c) 2006 International Society for Analytical Cytology.

The 3rd DBCLS BioHackathon: improving life science data integration with Semantic Web technologies.

PubMed

Katayama, Toshiaki; Wilkinson, Mark D; Micklem, Gos; Kawashima, Shuichi; Yamaguchi, Atsuko; Nakao, Mitsuteru; Yamamoto, Yasunori; Okamoto, Shinobu; Oouchida, Kenta; Chun, Hong-Woo; Aerts, Jan; Afzal, Hammad; Antezana, Erick; Arakawa, Kazuharu; Aranda, Bruno; Belleau, Francois; Bolleman, Jerven; Bonnal, Raoul Jp; Chapman, Brad; Cock, Peter Ja; Eriksson, Tore; Gordon, Paul Mk; Goto, Naohisa; Hayashi, Kazuhiro; Horn, Heiko; Ishiwata, Ryosuke; Kaminuma, Eli; Kasprzyk, Arek; Kawaji, Hideya; Kido, Nobuhiro; Kim, Young Joo; Kinjo, Akira R; Konishi, Fumikazu; Kwon, Kyung-Hoon; Labarga, Alberto; Lamprecht, Anna-Lena; Lin, Yu; Lindenbaum, Pierre; McCarthy, Luke; Morita, Hideyuki; Murakami, Katsuhiko; Nagao, Koji; Nishida, Kozo; Nishimura, Kunihiro; Nishizawa, Tatsuya; Ogishima, Soichi; Ono, Keiichiro; Oshita, Kazuki; Park, Keun-Joon; Prins, Pjotr; Saito, Taro L; Samwald, Matthias; Satagopam, Venkata P; Shigemoto, Yasumasa; Smith, Richard; Splendiani, Andrea; Sugawara, Hideaki; Taylor, James; Vos, Rutger A; Withers, David; Yamasaki, Chisato; Zmasek, Christian M; Kawamoto, Shoko; Okubo, Kosaku; Asai, Kiyoshi; Takagi, Toshihisa

2013-02-11

BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research. The theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces. We discussed on topics including guidelines for designing semantic data and interoperability of resources. We consequently developed tools and clients for analysis and visualization. We provide a meeting report from BioHackathon 2010, in which we describe the discussions, decisions, and breakthroughs made as we moved towards compliance with Semantic Web technologies - from source provider, through middleware, to the end-consumer.

The 3rd DBCLS BioHackathon: improving life science data integration with Semantic Web technologies

PubMed Central

2013-01-01

Background BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research. Results The theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces. We discussed on topics including guidelines for designing semantic data and interoperability of resources. We consequently developed tools and clients for analysis and visualization. Conclusion We provide a meeting report from BioHackathon 2010, in which we describe the discussions, decisions, and breakthroughs made as we moved towards compliance with Semantic Web technologies - from source provider, through middleware, to the end-consumer. PMID:23398680

KNIME for reproducible cross-domain analysis of life science data.

PubMed

Fillbrunn, Alexander; Dietz, Christian; Pfeuffer, Julianus; Rahn, René; Landrum, Gregory A; Berthold, Michael R

2017-11-10

Experiments in the life sciences often involve tools from a variety of domains such as mass spectrometry, next generation sequencing, or image processing. Passing the data between those tools often involves complex scripts for controlling data flow, data transformation, and statistical analysis. Such scripts are not only prone to be platform dependent, they also tend to grow as the experiment progresses and are seldomly well documented, a fact that hinders the reproducibility of the experiment. Workflow systems such as KNIME Analytics Platform aim to solve these problems by providing a platform for connecting tools graphically and guaranteeing the same results on different operating systems. As an open source software, KNIME allows scientists and programmers to provide their own extensions to the scientific community. In this review paper we present selected extensions from the life sciences that simplify data exploration, analysis, and visualization and are interoperable due to KNIME's unified data model. Additionally, we name other workflow systems that are commonly used in the life sciences and highlight their similarities and differences to KNIME. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Using Chemistry and Color To Analyze Household Products: A 10-12 Hour Laboratory Project at the General Chemistry Level

NASA Astrophysics Data System (ADS)

Bosma, Wayne B.

1998-02-01

A general chemistry experiment is described in which the students use UV/Visible spectrometry as an analytical tool, for both compound identification and pH measurement. In the first portion of the experiment, the students compare spectra to determine which FD and C dyes are contained in household products. They furthermore use chromatography to separate the dyes in grape Kool-Aid, and analyze the products with the spectrometer. In the second portion of the experiment, the students use Beer's Law to determine the pH of solutions containing an acid/base indicator. The experiments are visually stimulating and provide a solid introduction to spectroscopy and perceived color.

Sensory techniques for measuring differences in California navel oranges treated with doses of gamma-radiation below 0. 6 Kgray

DOE Office of Scientific and Technical Information (OSTI.GOV)

O'Mahony, M.; Goldstein, L.R.

Navel oranges from California were given low post harvest doses of gamma radiation: 0.32-0.37 and 0.52-0.60 KGy (32-37 and 52-60 Krad); they were compared with nonirradiated controls for visual appearance, flavor by mouth, odor, taste and taste after sweetness suppression by gymnema sylvestre. Practiced judges were used as an analytical tool, with minimum cross-sensory interference, while untrained subjects were used to determine whether changes might be distinguished by nonexperts. Differences were found in appearance, flavor, taste and odor although they were less extreme at the lower dose. Untrained judges could discriminate the juice at the higher irradiation level only.

The Earth Science Research Network as Seen Through Network Analysis of the AGU

NASA Astrophysics Data System (ADS)

Narock, T.; Hasnain, S.; Stephan, R.

2017-12-01

Scientometrics is the science of science. Scientometric research includes measurements of impact, mapping of scientific fields, and the production of indicators for use in policy and management. We have leveraged network analysis in a scientometric study of the American Geophysical Union (AGU). Data from the AGU's Linked Data Abstract Browser was used to create a visualization and analytics tools to explore the Earth science's research network. Our application applies network theory to look at network structure within the various AGU sections, identify key individuals and communities related to Earth science topics, and examine multi-disciplinary collaboration across sections. Opportunities to optimize Earth science output, as well as policy and outreach applications, are discussed.

Database and Analytical Tool Development for the Management of Data Derived from US DOE (NETL) Funded Fine Particulate (PM2.5) Research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robinson P. Khosah; Frank T. Alex

2007-02-11

Advanced Technology Systems, Inc. (ATS) was contracted by the U. S. Department of Energy's National Energy Technology Laboratory (DOE-NETL) to develop a state-of-the-art, scalable and robust web-accessible database application to manage the extensive data sets resulting from the DOE-NETL-sponsored ambient air monitoring programs in the upper Ohio River valley region. The data management system was designed to include a web-based user interface that will allow easy access to the data by the scientific community, policy- and decision-makers, and other interested stakeholders, while providing detailed information on sampling, analytical and quality control parameters. In addition, the system will provide graphical analyticalmore » tools for displaying, analyzing and interpreting the air quality data. The system will also provide multiple report generation capabilities and easy-to-understand visualization formats that can be utilized by the media and public outreach/educational institutions. The project is being conducted in two phases. Phase One includes the following tasks: (1) data inventory/benchmarking, including the establishment of an external stakeholder group; (2) development of a data management system; (3) population of the database; (4) development of a web-based data retrieval system, and (5) establishment of an internal quality assurance/quality control system on data management. Phase Two, which is currently underway, involves the development of a platform for on-line data analysis. Phase Two includes the following tasks: (1) development of a sponsor and stakeholder/user website with extensive online analytical tools; (2) development of a public website; (3) incorporation of an extensive online help system into each website; and (4) incorporation of a graphical representation (mapping) system into each website. The project is now into its forty-eighth month of development activities.« less

DATABASE AND ANALYTICAL TOOL DEVELOPMENT FOR THE MANAGEMENT OF DATA DERIVED FROM US DOE (NETL) FUNDED FINE PARTICULATE (PM 2.5) RESEARCH

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robinson P. Khosah; Charles G. Crawford

2006-02-11

Advanced Technology Systems, Inc. (ATS) was contracted by the U. S. Department of Energy's National Energy Technology Laboratory (DOE-NETL) to develop a state-of-the-art, scalable and robust web-accessible database application to manage the extensive data sets resulting from the DOE-NETL-sponsored ambient air monitoring programs in the upper Ohio River valley region. The data management system was designed to include a web-based user interface that will allow easy access to the data by the scientific community, policy- and decision-makers, and other interested stakeholders, while providing detailed information on sampling, analytical and quality control parameters. In addition, the system will provide graphical analyticalmore » tools for displaying, analyzing and interpreting the air quality data. The system will also provide multiple report generation capabilities and easy-to-understand visualization formats that can be utilized by the media and public outreach/educational institutions. The project is being conducted in two phases. Phase One includes the following tasks: (1) data inventory/benchmarking, including the establishment of an external stakeholder group; (2) development of a data management system; (3) population of the database; (4) development of a web-based data retrieval system, and (5) establishment of an internal quality assurance/quality control system on data management. Phase Two, which is currently underway, involves the development of a platform for on-line data analysis. Phase Two includes the following tasks: (1) development of a sponsor and stakeholder/user website with extensive online analytical tools; (2) development of a public website; (3) incorporation of an extensive online help system into each website; and (4) incorporation of a graphical representation (mapping) system into each website. The project is now into its forty-second month of development activities.« less

DATABASE AND ANALYTICAL TOOL DEVELOPMENT FOR THE MANAGEMENT OF DATA DERIVED FROM US DOE (NETL) FUNDED FINE PARTICULATE (PM2.5) RESEARCH

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robinson P. Khosah; Charles G. Crawford

Advanced Technology Systems, Inc. (ATS) was contracted by the U. S. Department of Energy's National Energy Technology Laboratory (DOE-NETL) to develop a state-of-the-art, scalable and robust web-accessible database application to manage the extensive data sets resulting from the DOE-NETL-sponsored ambient air monitoring programs in the upper Ohio River valley region. The data management system was designed to include a web-based user interface that will allow easy access to the data by the scientific community, policy- and decision-makers, and other interested stakeholders, while providing detailed information on sampling, analytical and quality control parameters. In addition, the system will provide graphical analyticalmore » tools for displaying, analyzing and interpreting the air quality data. The system will also provide multiple report generation capabilities and easy-to-understand visualization formats that can be utilized by the media and public outreach/educational institutions. The project is being conducted in two phases. Phase 1, which is currently in progress and will take twelve months to complete, will include the following tasks: (1) data inventory/benchmarking, including the establishment of an external stakeholder group; (2) development of a data management system; (3) population of the database; (4) development of a web-based data retrieval system, and (5) establishment of an internal quality assurance/quality control system on data management. In Phase 2, which will be completed in the second year of the project, a platform for on-line data analysis will be developed. Phase 2 will include the following tasks: (1) development of a sponsor and stakeholder/user website with extensive online analytical tools; (2) development of a public website; (3) incorporation of an extensive online help system into each website; and (4) incorporation of a graphical representation (mapping) system into each website. The project is now into its eleventh month of Phase 1 development activities.« less

ETE: a python Environment for Tree Exploration.

PubMed

Huerta-Cepas, Jaime; Dopazo, Joaquín; Gabaldón, Toni

2010-01-13

Many bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale. Here we present the Environment for Tree Exploration (ETE), a python programming toolkit that assists in the automated manipulation, analysis and visualization of hierarchical trees. ETE libraries provide a broad set of tree handling options as well as specific methods to analyze phylogenetic and clustering trees. Among other features, ETE allows for the independent analysis of tree partitions, has support for the extended newick format, provides an integrated node annotation system and permits to link trees to external data such as multiple sequence alignments or numerical arrays. In addition, ETE implements a number of built-in analytical tools, including phylogeny-based orthology prediction and cluster validation techniques. Finally, ETE's programmable tree drawing engine can be used to automate the graphical rendering of trees with customized node-specific visualizations. ETE provides a complete set of methods to manipulate tree data structures that extends current functionality in other bioinformatic toolkits of a more general purpose. ETE is free software and can be downloaded from http://ete.cgenomics.org.

ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data

PubMed Central

Gardeux, Vincent; David, Fabrice P. A.; Shajkofci, Adrian; Schwalie, Petra C.; Deplancke, Bart

2017-01-01

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet these groups often lack the expertise to handle complex scRNA-seq datasets. Results We developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types. Availability and implementation The tool is freely available at asap.epfl.ch and R/Python scripts are available at github.com/DeplanckeLab/ASAP. Contact bart.deplancke@epfl.ch Supplementary information Supplementary data are available at Bioinformatics online. PMID:28541377

ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data.

PubMed

Gardeux, Vincent; David, Fabrice P A; Shajkofci, Adrian; Schwalie, Petra C; Deplancke, Bart

2017-10-01

Single-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet these groups often lack the expertise to handle complex scRNA-seq datasets. We developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types. The tool is freely available at asap.epfl.ch and R/Python scripts are available at github.com/DeplanckeLab/ASAP. bart.deplancke@epfl.ch. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

ETE: a python Environment for Tree Exploration

PubMed Central

2010-01-01

Background Many bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale. Results Here we present the Environment for Tree Exploration (ETE), a python programming toolkit that assists in the automated manipulation, analysis and visualization of hierarchical trees. ETE libraries provide a broad set of tree handling options as well as specific methods to analyze phylogenetic and clustering trees. Among other features, ETE allows for the independent analysis of tree partitions, has support for the extended newick format, provides an integrated node annotation system and permits to link trees to external data such as multiple sequence alignments or numerical arrays. In addition, ETE implements a number of built-in analytical tools, including phylogeny-based orthology prediction and cluster validation techniques. Finally, ETE's programmable tree drawing engine can be used to automate the graphical rendering of trees with customized node-specific visualizations. Conclusions ETE provides a complete set of methods to manipulate tree data structures that extends current functionality in other bioinformatic toolkits of a more general purpose. ETE is free software and can be downloaded from http://ete.cgenomics.org. PMID:20070885

Learn to Teach Chemistry Using Visual Media Tools

ERIC Educational Resources Information Center

Turkoguz, Suat

2012-01-01

The aim of this study was to investigate undergraduate students' attitudes to using visual media tools in the chemistry laboratory. One hundred and fifteen undergraduates studying science education at Dokuz Eylul University, Turkey participated in the study. They video-recorded chemistry experiments with visual media tools and assessed them on a…

TopicLens: Efficient Multi-Level Visual Topic Exploration of Large-Scale Document Collections.

PubMed

Kim, Minjeong; Kang, Kyeongpil; Park, Deokgun; Choo, Jaegul; Elmqvist, Niklas

2017-01-01

Topic modeling, which reveals underlying topics of a document corpus, has been actively adopted in visual analytics for large-scale document collections. However, due to its significant processing time and non-interactive nature, topic modeling has so far not been tightly integrated into a visual analytics workflow. Instead, most such systems are limited to utilizing a fixed, initial set of topics. Motivated by this gap in the literature, we propose a novel interaction technique called TopicLens that allows a user to dynamically explore data through a lens interface where topic modeling and the corresponding 2D embedding are efficiently computed on the fly. To support this interaction in real time while maintaining view consistency, we propose a novel efficient topic modeling method and a semi-supervised 2D embedding algorithm. Our work is based on improving state-of-the-art methods such as nonnegative matrix factorization and t-distributed stochastic neighbor embedding. Furthermore, we have built a web-based visual analytics system integrated with TopicLens. We use this system to measure the performance and the visualization quality of our proposed methods. We provide several scenarios showcasing the capability of TopicLens using real-world datasets.

Web-based visual analysis for high-throughput genomics

PubMed Central

2013-01-01

Background Visualization plays an essential role in genomics research by making it possible to observe correlations and trends in large datasets as well as communicate findings to others. Visual analysis, which combines visualization with analysis tools to enable seamless use of both approaches for scientific investigation, offers a powerful method for performing complex genomic analyses. However, there are numerous challenges that arise when creating rich, interactive Web-based visualizations/visual analysis applications for high-throughput genomics. These challenges include managing data flow from Web server to Web browser, integrating analysis tools and visualizations, and sharing visualizations with colleagues. Results We have created a platform simplifies the creation of Web-based visualization/visual analysis applications for high-throughput genomics. This platform provides components that make it simple to efficiently query very large datasets, draw common representations of genomic data, integrate with analysis tools, and share or publish fully interactive visualizations. Using this platform, we have created a Circos-style genome-wide viewer, a generic scatter plot for correlation analysis, an interactive phylogenetic tree, a scalable genome browser for next-generation sequencing data, and an application for systematically exploring tool parameter spaces to find good parameter values. All visualizations are interactive and fully customizable. The platform is integrated with the Galaxy (http://galaxyproject.org) genomics workbench, making it easy to integrate new visual applications into Galaxy. Conclusions Visualization and visual analysis play an important role in high-throughput genomics experiments, and approaches are needed to make it easier to create applications for these activities. Our framework provides a foundation for creating Web-based visualizations and integrating them into Galaxy. Finally, the visualizations we have created using the framework are useful tools for high-throughput genomics experiments. PMID:23758618

Analytical Tools in School Finance Reform.

ERIC Educational Resources Information Center

Johns, R. L.

This paper discusses the problem of analyzing variations in the educational opportunities provided by different school districts and describes how to assess the impact of school finance alternatives through use of various analytical tools. The author first examines relatively simple analytical methods, including calculation of per-pupil…

SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance.

PubMed

Sacha, Dominik; Kraus, Matthias; Bernard, Jurgen; Behrisch, Michael; Schreck, Tobias; Asano, Yuki; Keim, Daniel A

2018-01-01

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

Learning Visualization Strategies: A qualitative investigation

NASA Astrophysics Data System (ADS)

Halpern, Daniel; Oh, Kyong Eun; Tremaine, Marilyn; Chiang, James; Bemis, Karen; Silver, Deborah

2015-12-01

The following study investigates the range of strategies individuals develop to infer and interpret cross-sections of three-dimensional objects. We focus on the identification of mental representations and problem-solving processes made by 11 individuals with the goal of building training applications that integrate the strategies developed by the participants in our study. Our results suggest that although spatial transformation and perspective-taking techniques are useful for visualizing cross-section problems, these visual processes are augmented by analytical thinking. Further, our study shows that participants employ general analytic strategies for extended periods which evolve through practice into a set of progressively more expert strategies. Theoretical implications are discussed and five main findings are recommended for integration into the design of education software that facilitates visual learning and comprehension.

Visual Illusions: An Interesting Tool to Investigate Developmental Dyslexia and Autism Spectrum Disorder

PubMed Central

Gori, Simone; Molteni, Massimo; Facoetti, Andrea

2016-01-01

A visual illusion refers to a percept that is different in some aspect from the physical stimulus. Illusions are a powerful non-invasive tool for understanding the neurobiology of vision, telling us, indirectly, how the brain processes visual stimuli. There are some neurodevelopmental disorders characterized by visual deficits. Surprisingly, just a few studies investigated illusory perception in clinical populations. Our aim is to review the literature supporting a possible role for visual illusions in helping us understand the visual deficits in developmental dyslexia and autism spectrum disorder. Future studies could develop new tools – based on visual illusions – to identify an early risk for neurodevelopmental disorders. PMID:27199702

Toward a Shared Vocabulary for Visual Analysis: An Analytic Toolkit for Deconstructing the Visual Design of Graphic Novels

ERIC Educational Resources Information Center

Connors, Sean P.

2012-01-01

Literacy educators might advocate using graphic novels to develop students' visual literacy skills, but teachers who lack a vocabulary for engaging in close analysis of visual texts may be reluctant to teach them. Recognizing this, teacher educators should equip preservice teachers with a vocabulary for analyzing visual texts. This article…

StreamSqueeze: a dynamic stream visualization for monitoring of event data

NASA Astrophysics Data System (ADS)

Mansmann, Florian; Krstajic, Milos; Fischer, Fabian; Bertini, Enrico

2012-01-01

While in clear-cut situations automated analytical solution for data streams are already in place, only few visual approaches have been proposed in the literature for exploratory analysis tasks on dynamic information. However, due to the competitive or security-related advantages that real-time information gives in domains such as finance, business or networking, we are convinced that there is a need for exploratory visualization tools for data streams. Under the conditions that new events have higher relevance and that smooth transitions enable traceability of items, we propose a novel dynamic stream visualization called StreamSqueeze. In this technique the degree of interest of recent items is expressed through an increase in size and thus recent events can be shown with more details. The technique has two main benefits: First, the layout algorithm arranges items in several lists of various sizes and optimizes the positions within each list so that the transition of an item from one list to the other triggers least visual changes. Second, the animation scheme ensures that for 50 percent of the time an item has a static screen position where reading is most effective and then continuously shrinks and moves to the its next static position in the subsequent list. To demonstrate the capability of our technique, we apply it to large and high-frequency news and syslog streams and show how it maintains optimal stability of the layout under the conditions given above.

Analyzing Two-Phase Single-Case Data with Non-overlap and Mean Difference Indices: Illustration, Software Tools, and Alternatives.

PubMed

Manolov, Rumen; Losada, José L; Chacón-Moscoso, Salvador; Sanduvete-Chaves, Susana

2016-01-01

Two-phase single-case designs, including baseline evaluation followed by an intervention, represent the most clinically straightforward option for combining professional practice and research. However, unless they are part of a multiple-baseline schedule, such designs do not allow demonstrating a causal relation between the intervention and the behavior. Although the statistical options reviewed here cannot help overcoming this methodological limitation, we aim to make practitioners and applied researchers aware of the available appropriate options for extracting maximum information from the data. In the current paper, we suggest that the evaluation of behavioral change should include visual and quantitative analyses, complementing the substantive criteria regarding the practical importance of the behavioral change. Specifically, we emphasize the need to use structured criteria for visual analysis, such as the ones summarized in the What Works Clearinghouse Standards, especially if such criteria are complemented by visual aids, as illustrated here. For quantitative analysis, we focus on the non-overlap of all pairs and the slope and level change procedure, as they offer straightforward information and have shown reasonable performance. An illustration is provided of the use of these three pieces of information: visual, quantitative, and substantive. To make the use of visual and quantitative analysis feasible, open source software is referred to and demonstrated. In order to provide practitioners and applied researchers with a more complete guide, several analytical alternatives are commented on pointing out the situations (aims, data patterns) for which these are potentially useful.

Analyzing Two-Phase Single-Case Data with Non-overlap and Mean Difference Indices: Illustration, Software Tools, and Alternatives

PubMed Central

Manolov, Rumen; Losada, José L.; Chacón-Moscoso, Salvador; Sanduvete-Chaves, Susana

2016-01-01

Two-phase single-case designs, including baseline evaluation followed by an intervention, represent the most clinically straightforward option for combining professional practice and research. However, unless they are part of a multiple-baseline schedule, such designs do not allow demonstrating a causal relation between the intervention and the behavior. Although the statistical options reviewed here cannot help overcoming this methodological limitation, we aim to make practitioners and applied researchers aware of the available appropriate options for extracting maximum information from the data. In the current paper, we suggest that the evaluation of behavioral change should include visual and quantitative analyses, complementing the substantive criteria regarding the practical importance of the behavioral change. Specifically, we emphasize the need to use structured criteria for visual analysis, such as the ones summarized in the What Works Clearinghouse Standards, especially if such criteria are complemented by visual aids, as illustrated here. For quantitative analysis, we focus on the non-overlap of all pairs and the slope and level change procedure, as they offer straightforward information and have shown reasonable performance. An illustration is provided of the use of these three pieces of information: visual, quantitative, and substantive. To make the use of visual and quantitative analysis feasible, open source software is referred to and demonstrated. In order to provide practitioners and applied researchers with a more complete guide, several analytical alternatives are commented on pointing out the situations (aims, data patterns) for which these are potentially useful. PMID:26834691

ClipCard: Sharable, Searchable Visual Metadata Summaries on the Cloud to Render Big Data Actionable

NASA Astrophysics Data System (ADS)

Saripalli, P.; Davis, D.; Cunningham, R.

2013-12-01

Research firm IDC estimates that approximately 90 percent of the Enterprise Big Data go un-analyzed, as 'dark data' - an enormous corpus of undiscovered, untagged information residing on data warehouses, servers and Storage Area Networks (SAN). In the geosciences, these data range from unpublished model runs to vast survey data assets to raw sensor data. Many of these are now being collected instantaneously, at a greater volume and in new data formats. Not all of these data can be analyzed, nor processed in real time, and their features may not be well described at the time of collection. These dark data are a serious data management problem for science organizations of all types, especially ones with mandated or required data reporting and compliance requirements. Additionally, data curators and scientists are encouraged to quantify the impact of their data holdings as a way to measure research success. Deriving actionable insights is the foremost goal of Big Data Analytics (BDA), which is especially true with geoscience, given its direct impact on most of the pressing global issues. Clearly, there is a pressing need for innovative approaches to making dark data discoverable, measurable, and actionable. We report on ClipCard, a Cloud-based SaaS analytic platform for instant summarization, quick search, visualization and easy sharing of metadata summaries form the Dark Data at hierarchical levels of detail, thus rendering it 'white', i.e., actionable. We present a use case of the ClipCard platform, a cloud-based application which helps generate (abstracted) visual metadata summaries and meta-analytics for environmental data at hierarchical scales within and across big data containers. These summaries and analyses provide important new tools for managing big data and simplifying collaboration through easy to deploy sharing APIs. The ClipCard application solves a growing data management bottleneck by helping enterprises and large organizations to summarize, search, discover, and share the potential in their unused data and information assets. Using Cloud as the base platform enables wider reach, quick dissemination and easy sharing of the metadata summaries, without actually storing or sharing the original data assets per se.

Application of Andrew's Plots to Visualization of Multidimensional Data

ERIC Educational Resources Information Center

Grinshpun, Vadim

2016-01-01

Importance: The article raises a point of visual representation of big data, recently considered to be demanded for many scientific and real-life applications, and analyzes particulars for visualization of multi-dimensional data, giving examples of the visual analytics-related problems. Objectives: The purpose of this paper is to study application…

The Tools, Approaches and Applications of Visual Literacy in the Visual Arts Department of Cross River University of Technology, Calabar, Nigeria

ERIC Educational Resources Information Center

Ecoma, Victor

2016-01-01

The paper reflects upon the tools, approaches and applications of visual literacy in the Visual Arts Department of Cross River University of Technology, Calabar, Nigeria. The objective of the discourse is to examine how the visual arts training and practice equip students with skills in visual literacy through methods of production, materials and…

Autonomous cloud based site monitoring through hydro geophysical data assimilation, processing and result delivery

NASA Astrophysics Data System (ADS)

Versteeg, R.; Johnson, D. V.; Rodzianko, A.; Zhou, H.; Dafflon, B.; Leger, E.; de Kleine, M.

2017-12-01

Understanding of processes in the shallow subsurface requires that geophysical, biogeochemical, hydrological and remote sensing datasets are assimilated, processed and interpreted. Multiple enabling software capabilities for process understanding have been developed by the science community. These include information models (ODM2), reactive transport modeling (PFLOTRAN, Modflow, CLM, Landlab), geophysical inversion (E4D, BERT), parameter estimation (PEST, DAKOTA), visualization (ViSiT, Paraview, D3, QGIS) as well as numerous tools written in python and R for petrophysical mapping, stochastic modeling, data analysis and so on. These capabilities use data collected using sensors and analytical tools developed by multiple manufacturers which produce many different measurements. While scientists obviously leverage tools, capabilities and lessons learned from one site at other sites, the current approach to site characterization and monitoring is very labor intensive and does not scale well. Our objective is to be able to monitor many (hundreds - thousands) of sites. This requires that monitoring can be done in a near time, affordable, auditable and essentially autonomous manner. For this we have developed a modular vertically integrated cloud based software framework which was designed from the ground up for effective site and process monitoring. This software framework (PAF - Predictive Assimilation Framework) is multitenant software and provides automation of data ingestion, processing and visualization of hydrological, geochemical and geophysical (ERT/DTS) data. The core organizational element of PAF is a project/user one in which capabilities available to users are controlled by a combination of available data and access permissions. All PAF capabilities are exposed through APIs, making it easy to quickly add new components. PAF is fully integrated with newly developed autonomous electrical geophysical hardware and thus allows for automation of electrical geophysical ingestion and processing and the ability for co analysis and visualization of the raw and processed data with other data of interest (e.g. soil temperature, soil moisture, precipitation). We will demonstrate current PAF capabilities and discuss future efforts.

[application of the analytical transmission electron microscopy techniques for detection, identification and visualization of localization of nanoparticles of titanium and cerium oxides in mammalian cells].

PubMed

Shebanova, A S; Bogdanov, A G; Ismagulova, T T; Feofanov, A V; Semenyuk, P I; Muronets, V I; Erokhina, M V; Onishchenko, G E; Kirpichnikov, M P; Shaitan, K V

2014-01-01

This work represents the results of the study on applicability of the modern methods of analytical transmission electron microscopy for detection, identification and visualization of localization of nanoparticles of titanium and cerium oxides in A549 cell, human lung adenocarcinoma cell line. A comparative analysis of images of the nanoparticles in the cells obtained in the bright field mode of transmission electron microscopy, under dark-field scanning transmission electron microscopy and high-angle annular dark field scanning transmission electron was performed. For identification of nanoparticles in the cells the analytical techniques, energy-dispersive X-ray spectroscopy and electron energy loss spectroscopy, were compared when used in the mode of obtaining energy spectrum from different particles and element mapping. It was shown that the method for electron tomography is applicable to confirm that nanoparticles are localized in the sample but not coated by contamination. The possibilities and fields of utilizing different techniques for analytical transmission electron microscopy for detection, visualization and identification of nanoparticles in the biological samples are discussed.

VAP/VAT: video analytics platform and test bed for testing and deploying video analytics

NASA Astrophysics Data System (ADS)

Gorodnichy, Dmitry O.; Dubrofsky, Elan

2010-04-01

Deploying Video Analytics in operational environments is extremely challenging. This paper presents a methodological approach developed by the Video Surveillance and Biometrics Section (VSB) of the Science and Engineering Directorate (S&E) of the Canada Border Services Agency (CBSA) to resolve these problems. A three-phase approach to enable VA deployment within an operational agency is presented and the Video Analytics Platform and Testbed (VAP/VAT) developed by the VSB section is introduced. In addition to allowing the integration of third party and in-house built VA codes into an existing video surveillance infrastructure, VAP/VAT also allows the agency to conduct an unbiased performance evaluation of the cameras and VA software available on the market. VAP/VAT consists of two components: EventCapture, which serves to Automatically detect a "Visual Event", and EventBrowser, which serves to Display & Peruse of "Visual Details" captured at the "Visual Event". To deal with Open architecture as well as with Closed architecture cameras, two video-feed capture mechanisms have been developed within the EventCapture component: IPCamCapture and ScreenCapture.

A Strategy for Uncertainty Visualization Design

DTIC Science & Technology

2009-10-01

143–156, Magdeburg , Germany . [11] Thomson, J., Hetzler, E., MacEachren, A., Gahegan, M. and Pavel, M. (2005), A Typology for Visualizing Uncertainty...and Stasko [20] to bridge analytic gaps in visualization design, when tasks in the strategy overlap (and therefore complement) design frameworks

Miniaturized Temperature-Controlled Planar Chromatography (Micro-TLC) as a Versatile Technique for Fast Screening of Micropollutants and Biomarkers Derived from Surface Water Ecosystems and During Technological Processes of Wastewater Treatment.

PubMed

Ślączka-Wilk, Magdalena M; Włodarczyk, Elżbieta; Kaleniecka, Aleksandra; Zarzycki, Paweł K

2017-07-01

There is increasing interest in the development of simple analytical systems enabling the fast screening of target components in complex samples. A number of newly invented protocols are based on quasi separation techniques involving microfluidic paper-based analytical devices and/or micro total analysis systems. Under such conditions, the quantification of target components can be performed mainly due to selective detection. The main goal of this paper is to demonstrate that miniaturized planar chromatography has the capability to work as an efficient separation and quantification tool for the analysis of multiple targets within complex environmental samples isolated and concentrated using an optimized SPE method. In particular, we analyzed various samples collected from surface water ecosystems (lakes, rivers, and the Baltic Sea of Middle Pomerania in the northern part of Poland) in different seasons, as well as samples collected during key wastewater technological processes (originating from the "Jamno" wastewater treatment plant in Koszalin, Poland). We documented that the multiple detection of chromatographic spots on RP-18W microplates-under visible light, fluorescence, and fluorescence quenching conditions, and using the visualization reagent phosphomolybdic acid-enables fast and robust sample classification. The presented data reveal that the proposed micro-TLC system is useful, inexpensive, and can be considered as a complementary method for the fast control of treated sewage water discharged by a municipal wastewater treatment plant, particularly for the detection of low-molecular mass micropollutants with polarity ranging from estetrol to progesterone, as well as chlorophyll-related dyes. Due to the low consumption of mobile phases composed of water-alcohol binary mixtures (less than 1 mL/run for the simultaneous separation of up to nine samples), this method can be considered an environmentally friendly and green chemistry analytical tool. The described analytical protocol can be complementary to those involving classical column chromatography (HPLC) or various planar microfluidic devices.

A Grammar-based Approach for Modeling User Interactions and Generating Suggestions During the Data Exploration Process.

PubMed

Dabek, Filip; Caban, Jesus J

2017-01-01

Despite the recent popularity of visual analytics focusing on big data, little is known about how to support users that use visualization techniques to explore multi-dimensional datasets and accomplish specific tasks. Our lack of models that can assist end-users during the data exploration process has made it challenging to learn from the user's interactive and analytical process. The ability to model how a user interacts with a specific visualization technique and what difficulties they face are paramount in supporting individuals with discovering new patterns within their complex datasets. This paper introduces the notion of visualization systems understanding and modeling user interactions with the intent of guiding a user through a task thereby enhancing visual data exploration. The challenges faced and the necessary future steps to take are discussed; and to provide a working example, a grammar-based model is presented that can learn from user interactions, determine the common patterns among a number of subjects using a K-Reversible algorithm, build a set of rules, and apply those rules in the form of suggestions to new users with the goal of guiding them along their visual analytic process. A formal evaluation study with 300 subjects was performed showing that our grammar-based model is effective at capturing the interactive process followed by users and that further research in this area has the potential to positively impact how users interact with a visualization system.

Urban Space Explorer: A Visual Analytics System for Urban Planning.

PubMed

Karduni, Alireza; Cho, Isaac; Wessel, Ginette; Ribarsky, William; Sauda, Eric; Dou, Wenwen

2017-01-01

Understanding people's behavior is fundamental to many planning professions (including transportation, community development, economic development, and urban design) that rely on data about frequently traveled routes, places, and social and cultural practices. Based on the results of a practitioner survey, the authors designed Urban Space Explorer, a visual analytics system that utilizes mobile social media to enable interactive exploration of public-space-related activity along spatial, temporal, and semantic dimensions.

Text Stream Trend Analysis using Multiscale Visual Analytics with Applications to Social Media Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A; Beaver, Justin M; BogenII, Paul L.

In this paper, we introduce a new visual analytics system, called Matisse, that allows exploration of global trends in textual information streams with specific application to social media platforms. Despite the potential for real-time situational awareness using these services, interactive analysis of such semi-structured textual information is a challenge due to the high-throughput and high-velocity properties. Matisse addresses these challenges through the following contributions: (1) robust stream data management, (2) automated sen- timent/emotion analytics, (3) inferential temporal, geospatial, and term-frequency visualizations, and (4) a flexible drill-down interaction scheme that progresses from macroscale to microscale views. In addition to describing thesemore » contributions, our work-in-progress paper concludes with a practical case study focused on the analysis of Twitter 1% sample stream information captured during the week of the Boston Marathon bombings.« less

Visual analytics as a translational cognitive science.

PubMed

Fisher, Brian; Green, Tera Marie; Arias-Hernández, Richard

2011-07-01

Visual analytics is a new interdisciplinary field of study that calls for a more structured scientific approach to understanding the effects of interaction with complex graphical displays on human cognitive processes. Its primary goal is to support the design and evaluation of graphical information systems that better support cognitive processes in areas as diverse as scientific research and emergency management. The methodologies that make up this new field are as yet ill defined. This paper proposes a pathway for development of visual analytics as a translational cognitive science that bridges fundamental research in human/computer cognitive systems and design and evaluation of information systems in situ. Achieving this goal will require the development of enhanced field methods for conceptual decomposition of human/computer cognitive systems that maps onto laboratory studies, and improved methods for conducting laboratory investigations that might better map onto real-world cognitive processes in technology-rich environments. Copyright © 2011 Cognitive Science Society, Inc.

BiSet: Semantic Edge Bundling with Biclusters for Sensemaking.

PubMed

Sun, Maoyuan; Mi, Peng; North, Chris; Ramakrishnan, Naren

2016-01-01

Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to discover three suspicious people who all visited the same four cities. Existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. In this paper, we present BiSet, a visual analytics technique to support interactive exploration of coordinated relationships. In BiSet, we model coordinated relationships as biclusters and algorithmically mine them from a dataset. Then, we visualize the biclusters in context as bundled edges between sets of related entities. Thus, bundles enable analysts to infer task-oriented semantic insights about potentially coordinated activities. We make bundles as first class objects and add a new layer, "in-between", to contain these bundle objects. Based on this, bundles serve to organize entities represented in lists and visually reveal their membership. Users can interact with edge bundles to organize related entities, and vice versa, for sensemaking purposes. With a usage scenario, we demonstrate how BiSet supports the exploration of coordinated relationships in text analytics.

Visual Detection Under Uncertainty Operates Via an Early Static, Not Late Dynamic, Non-Linearity

PubMed Central

Neri, Peter

2010-01-01

Signals in the environment are rarely specified exactly: our visual system may know what to look for (e.g., a specific face), but not its exact configuration (e.g., where in the room, or in what orientation). Uncertainty, and the ability to deal with it, is a fundamental aspect of visual processing. The MAX model is the current gold standard for describing how human vision handles uncertainty: of all possible configurations for the signal, the observer chooses the one corresponding to the template associated with the largest response. We propose an alternative model in which the MAX operation, which is a dynamic non-linearity (depends on multiple inputs from several stimulus locations) and happens after the input stimulus has been matched to the possible templates, is replaced by an early static non-linearity (depends only on one input corresponding to one stimulus location) which is applied before template matching. By exploiting an integrated set of analytical and experimental tools, we show that this model is able to account for a number of empirical observations otherwise unaccounted for by the MAX model, and is more robust with respect to the realistic limitations imposed by the available neural hardware. We then discuss how these results, currently restricted to a simple visual detection task, may extend to a wider range of problems in sensory processing. PMID:21212835

Visual Analysis of Cloud Computing Performance Using Behavioral Lines.

PubMed

Muelder, Chris; Zhu, Biao; Chen, Wei; Zhang, Hongxin; Ma, Kwan-Liu

2016-02-29

Cloud computing is an essential technology to Big Data analytics and services. A cloud computing system is often comprised of a large number of parallel computing and storage devices. Monitoring the usage and performance of such a system is important for efficient operations, maintenance, and security. Tracing every application on a large cloud system is untenable due to scale and privacy issues. But profile data can be collected relatively efficiently by regularly sampling the state of the system, including properties such as CPU load, memory usage, network usage, and others, creating a set of multivariate time series for each system. Adequate tools for studying such large-scale, multidimensional data are lacking. In this paper, we present a visual based analysis approach to understanding and analyzing the performance and behavior of cloud computing systems. Our design is based on similarity measures and a layout method to portray the behavior of each compute node over time. When visualizing a large number of behavioral lines together, distinct patterns often appear suggesting particular types of performance bottleneck. The resulting system provides multiple linked views, which allow the user to interactively explore the data by examining the data or a selected subset at different levels of detail. Our case studies, which use datasets collected from two different cloud systems, show that this visual based approach is effective in identifying trends and anomalies of the systems.

Iontophoresis and Flame Photometry: A Hybrid Interdisciplinary Experiment

ERIC Educational Resources Information Center

Sharp, Duncan; Cottam, Linzi; Bradley, Sarah; Brannigan, Jeanie; Davis, James

2010-01-01

The combination of reverse iontophoresis and flame photometry provides an engaging analytical experiment that gives first-year undergraduate students a flavor of modern drug delivery and analyte extraction techniques while reinforcing core analytical concepts. The experiment provides a highly visual demonstration of the iontophoresis technique and…

TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data.

PubMed

Huang, Xiaoke; Zhao, Ye; Yang, Jing; Zhang, Chong; Ma, Chao; Ye, Xinyue

2016-01-01

We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys urban transportation dynamics which can be discovered by applying graph analysis algorithms. To support interactive, multiscale visual analytics, a graph partitioning algorithm is applied to create region-level graphs which have smaller size than the original street-level graph. Graph centralities, including Pagerank and betweenness, are computed to characterize the time-varying importance of different urban regions. The centralities are visualized by three coordinated views including a node-link graph view, a map view and a temporal information view. Users can interactively examine the importance of streets to discover and assess city traffic patterns. We have implemented a fully working prototype of this approach and evaluated it using massive taxi trajectories of Shenzhen, China. TrajGraph's capability in revealing the importance of city streets was evaluated by comparing the calculated centralities with the subjective evaluations from a group of drivers in Shenzhen. Feedback from a domain expert was collected. The effectiveness of the visual interface was evaluated through a formal user study. We also present several examples and a case study to demonstrate the usefulness of TrajGraph in urban transportation analysis.

Projected 21st century coastal flooding in the Southern California Bight. Part 2: Tools for assessing climate change-driven coastal hazards and socio-economic impacts

USGS Publications Warehouse

Erikson, Li; Barnard, Patrick; O'Neill, Andrea; Wood, Nathan J.; Jones, Jeanne M.; Finzi Hart, Juliette; Vitousek, Sean; Limber, Patrick; Hayden, Maya; Fitzgibbon, Michael; Lovering, Jessica; Foxgrover, Amy C.

2018-01-01

This paper is the second of two that describes the Coastal Storm Modeling System (CoSMoS) approach for quantifying physical hazards and socio-economic hazard exposure in coastal zones affected by sea-level rise and changing coastal storms. The modelling approach, presented in Part 1, downscales atmospheric global-scale projections to local scale coastal flood impacts by deterministically computing the combined hazards of sea-level rise, waves, storm surges, astronomic tides, fluvial discharges, and changes in shoreline positions. The method is demonstrated through an application to Southern California, United States, where the shoreline is a mix of bluffs, beaches, highly managed coastal communities, and infrastructure of high economic value. Results show that inclusion of 100-year projected coastal storms will increase flooding by 9–350% (an additional average 53.0 ± 16.0 km2) in addition to a 25–500 cm sea-level rise. The greater flooding extents translate to a 55–110% increase in residential impact and a 40–90% increase in building replacement costs. To communicate hazards and ranges in socio-economic exposures to these hazards, a set of tools were collaboratively designed and tested with stakeholders and policy makers; these tools consist of two web-based mapping and analytic applications as well as virtual reality visualizations. To reach a larger audience and enhance usability of the data, outreach and engagement included workshop-style trainings for targeted end-users and innovative applications of the virtual reality visualizations.

Model-Based Reasoning: Using Visual Tools to Reveal Student Learning

ERIC Educational Resources Information Center

Luckie, Douglas; Harrison, Scott H.; Ebert-May, Diane

2011-01-01

Using visual models is common in science and should become more common in classrooms. Our research group has developed and completed studies on the use of a visual modeling tool, the Concept Connector. This modeling tool consists of an online concept mapping Java applet that has automatic scoring functions we refer to as Robograder. The Concept…

A Visual Training Tool for Teaching Kanji to Children with Developmental Dyslexia

ERIC Educational Resources Information Center

Ikeshita-Yamazoe, Hanae; Miyao, Masutomo

2016-01-01

We developed a visual training tool to assist children with developmental dyslexia in learning to recognize and understand Chinese characters (kanji). The visual training tool presents the strokes of a kanji character as separate shapes and requires students to use these fragments to construct the character. Two types of experiments were conducted…

An Exploratory Study of Interactivity in Visualization Tools: "Flow" of Interaction

ERIC Educational Resources Information Center

Liang, Hai-Ning; Parsons, Paul C.; Wu, Hsien-Chi; Sedig, Kamran

2010-01-01

This paper deals with the design of interactivity in visualization tools. There are several factors that can be used to guide the analysis and design of the interactivity of these tools. One such factor is flow, which is concerned with the duration of interaction with visual representations of information--interaction being the actions performed…

AR4VI: AR as an Accessibility Tool for People with Visual Impairments

PubMed Central

Coughlan, James M.; Miele, Joshua

2017-01-01

Although AR technology has been largely dominated by visual media, a number of AR tools using both visual and auditory feedback have been developed specifically to assist people with low vision or blindness – an application domain that we term Augmented Reality for Visual Impairment (AR4VI). We describe two AR4VI tools developed at Smith-Kettlewell, as well as a number of pre-existing examples. We emphasize that AR4VI is a powerful tool with the potential to remove or significantly reduce a range of accessibility barriers. Rather than being restricted to use by people with visual impairments, AR4VI is a compelling universal design approach offering benefits for mainstream applications as well. PMID:29303163

AR4VI: AR as an Accessibility Tool for People with Visual Impairments.

PubMed

Coughlan, James M; Miele, Joshua

2017-10-01

Although AR technology has been largely dominated by visual media, a number of AR tools using both visual and auditory feedback have been developed specifically to assist people with low vision or blindness - an application domain that we term Augmented Reality for Visual Impairment (AR4VI). We describe two AR4VI tools developed at Smith-Kettlewell, as well as a number of pre-existing examples. We emphasize that AR4VI is a powerful tool with the potential to remove or significantly reduce a range of accessibility barriers. Rather than being restricted to use by people with visual impairments, AR4VI is a compelling universal design approach offering benefits for mainstream applications as well.

Visualization of the Mode Shapes of Pressure Oscillation in a Cylindrical Cavity

DOE Office of Scientific and Technical Information (OSTI.GOV)

He, Xin; Qi, Yunliang; Wang, Zhi

Our work describes a novel experimental method to visualize the mode shapes of pressure oscillation in a cylindrical cavity. Acoustic resonance in a cavity is a grand old problem that has been under investigation (using both analytical and numerical methods) for more than a century. In this article, a novel method based on high speed imaging of combustion chemiluminescence was presented to visualize the mode shapes of pressure oscillation in a cylindrical cavity. By generating high-temperature combustion gases and strong pressure waves simultaneously in a cylindrical cavity, the pressure oscillation can be inferred due to the chemiluminescence emissions of themore » combustion products. We can then visualized the mode shapes by reconstructing the images based on the amplitudes of the luminosity spectrum at the corresponding resonant frequencies. Up to 11 resonant mode shapes were clearly visualized, each matching very well with the analytical solutions.« less

Combining computational analyses and interactive visualization for document exploration and sensemaking in jigsaw.

PubMed

Görg, Carsten; Liu, Zhicheng; Kihm, Jaeyeon; Choo, Jaegul; Park, Haesun; Stasko, John

2013-10-01

Investigators across many disciplines and organizations must sift through large collections of text documents to understand and piece together information. Whether they are fighting crime, curing diseases, deciding what car to buy, or researching a new field, inevitably investigators will encounter text documents. Taking a visual analytics approach, we integrate multiple text analysis algorithms with a suite of interactive visualizations to provide a flexible and powerful environment that allows analysts to explore collections of documents while sensemaking. Our particular focus is on the process of integrating automated analyses with interactive visualizations in a smooth and fluid manner. We illustrate this integration through two example scenarios: an academic researcher examining InfoVis and VAST conference papers and a consumer exploring car reviews while pondering a purchase decision. Finally, we provide lessons learned toward the design and implementation of visual analytics systems for document exploration and understanding.

Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB.

PubMed

Chatziioannou, Aristotelis; Moulos, Panagiotis; Kolisis, Fragiskos N

2009-10-27

The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods. We describe here Gene ARMADA (Automated Robust MicroArray Data Analysis), a MATLAB implemented platform with a Graphical User Interface. This suite integrates all steps of microarray data analysis including automated data import, noise correction and filtering, normalization, statistical selection of differentially expressed genes, clustering, classification and annotation. In its current version, Gene ARMADA fully supports 2 coloured cDNA and Affymetrix oligonucleotide arrays, plus custom arrays for which experimental details are given in tabular form (Excel spreadsheet, comma separated values, tab-delimited text formats). It also supports the analysis of already processed results through its versatile import editor. Besides being fully automated, Gene ARMADA incorporates numerous functionalities of the Statistics and Bioinformatics Toolboxes of MATLAB. In addition, it provides numerous visualization and exploration tools plus customizable export data formats for seamless integration by other analysis tools or MATLAB, for further processing. Gene ARMADA requires MATLAB 7.4 (R2007a) or higher and is also distributed as a stand-alone application with MATLAB Component Runtime. Gene ARMADA provides a highly adaptable, integrative, yet flexible tool which can be used for automated quality control, analysis, annotation and visualization of microarray data, constituting a starting point for further data interpretation and integration with numerous other tools.

Empirical Analysis of the Subjective Impressions and Objective Measures of Domain Scientists’ Analytical Judgment Using Visualizations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dasgupta, Aritra; Burrows, Susannah M.; Han, Kyungsik

Scientists working in a particular domain often adhere to conventional data analysis and presentation methods and this leads to familiarity with these methods over time. But does high familiarity always lead to better analytical judgment? This question is especially relevant when visualizations are used in scientific tasks, as there can be discrepancies between visualization best practices and domain conventions. However, there is little empirical evidence of the relationships between scientists’ subjective impressions about familiar and unfamiliar visualizations and objective measures of their effect on scientific judgment. To address this gap and to study these factors, we focus on the climatemore » science domain, specifically on visualizations used for comparison of model performance. We present a comprehensive user study with 47 climate scientists where we explored the following factors: i) relationships between scientists’ familiarity, their perceived levels of com- fort, confidence, accuracy, and objective measures of accuracy, and ii) relationships among domain experience, visualization familiarity, and post-study preference.« less

Thinking graphically: Connecting vision and cognition during graph comprehension.

PubMed

Ratwani, Raj M; Trafton, J Gregory; Boehm-Davis, Deborah A

2008-03-01

Task analytic theories of graph comprehension account for the perceptual and conceptual processes required to extract specific information from graphs. Comparatively, the processes underlying information integration have received less attention. We propose a new framework for information integration that highlights visual integration and cognitive integration. During visual integration, pattern recognition processes are used to form visual clusters of information; these visual clusters are then used to reason about the graph during cognitive integration. In 3 experiments, the processes required to extract specific information and to integrate information were examined by collecting verbal protocol and eye movement data. Results supported the task analytic theories for specific information extraction and the processes of visual and cognitive integration for integrative questions. Further, the integrative processes scaled up as graph complexity increased, highlighting the importance of these processes for integration in more complex graphs. Finally, based on this framework, design principles to improve both visual and cognitive integration are described. PsycINFO Database Record (c) 2008 APA, all rights reserved

How I Learned to Swim: The Visual Journal as a Companion to Creative Inquiry

ERIC Educational Resources Information Center

Scott Shields, Sara

2016-01-01

In this paper, I discuss my engagement with a visual journal as a companion to creative research practice during my dissertation research. Grounded in arts based research methodologies; I explore visual journals in relationship to research, reflection and analytic processes. I begin with a discussion of the visual journal as an artifact of…

VCS: Tool for Visualizing Copy Number Variation and Single Nucleotide Polymorphism.

PubMed

Kim, HyoYoung; Sung, Samsun; Cho, Seoae; Kim, Tae-Hun; Seo, Kangseok; Kim, Heebal

2014-12-01

Copy number variation (CNV) or single nucleotide phlyorphism (SNP) is useful genetic resource to aid in understanding complex phenotypes or deseases susceptibility. Although thousands of CNVs and SNPs are currently avaliable in the public databases, they are somewhat difficult to use for analyses without visualization tools. We developed a web-based tool called the VCS (visualization of CNV or SNP) to visualize the CNV or SNP detected. The VCS tool can assist to easily interpret a biological meaning from the numerical value of CNV and SNP. The VCS provides six visualization tools: i) the enrichment of genome contents in CNV; ii) the physical distribution of CNV or SNP on chromosomes; iii) the distribution of log2 ratio of CNVs with criteria of interested; iv) the number of CNV or SNP per binning unit; v) the distribution of homozygosity of SNP genotype; and vi) cytomap of genes within CNV or SNP region.

Experiences in using DISCUS for visualizing human communication

NASA Astrophysics Data System (ADS)

Groehn, Matti; Nieminen, Marko; Haho, Paeivi; Smeds, Riitta

2000-02-01

In this paper, we present further improvement to the DISCUS software that can be used to record and analyze the flow and constants of business process simulation session discussion. The tool was initially introduced in 'visual data exploration and analysis IV' conference. The initial features of the tool enabled the visualization of discussion flow in business process simulation sessions and the creation of SOM analyses. The improvements of the tool consists of additional visualization possibilities that enable quick on-line analyses and improved graphical statistics. We have also created the very first interface to audio data and implemented two ways to visualize it. We also outline additional possibilities to use the tool in other application areas: these include usability testing and the possibility to use the tool for capturing design rationale in a product development process. The data gathered with DISCUS may be used in other applications, and further work may be done with data ming techniques.

Near Real Time Analytics of Human Sensor Networks in the Realm of Big Data

NASA Astrophysics Data System (ADS)

Aulov, O.; Halem, M.

2012-12-01

With the prolific development of social media, emergency responders have an increasing interest in harvesting social media from outlets such as Flickr, Twitter, and Facebook, in order to assess the scale and specifics of extreme events including wild fires, earthquakes, terrorist attacks, oil spills, etc. A number of experimental platforms have successfully been implemented to demonstrate the utilization of social media data in extreme events, including Twitter Earthquake Detector, which relied on tweets for earthquake monitoring; AirTwitter, which used tweets for air quality reporting; and our previous work, using Flickr data as boundary value forcings to improve the forecast of oil beaching in the aftermath of the Deepwater Horizon oil spill. The majority of these platforms addressed a narrow, specific type of emergency and harvested data from a particular outlet. We demonstrate an interactive framework for monitoring, mining and analyzing a plethora of heterogeneous social media sources for a diverse range of extreme events. Our framework consists of three major parts: a real time social media aggregator, a data processing and analysis engine, and a web-based visualization and reporting tool. The aggregator gathers tweets, Facebook comments from fan pages, Google+ posts, forum discussions, blog posts (such as LiveJournal and Blogger.com), images from photo-sharing platforms (such as Flickr, Picasa), videos from video-sharing platforms (youtube, Vimeo), and so forth. The data processing and analysis engine pre-processes the aggregated information and annotates it with geolocation and sentiment information. In many cases, the metadata of the social media posts does not contain geolocation information—-however, a human reader can easily guess from the body of the text what location is discussed. We are automating this task by use of Named Entity Recognition (NER) algorithms and a gazetteer service. The visualization and reporting tool provides a web-based, user-friendly interface that provides time-series analysis and plotting tools, geo-spacial visualization tools with interactive maps, and cause-effect inference tools. We demonstrate how we address big data challenges of monitoring, aggregating and analyzing vast amounts of social media data at a near realtime. As a result, our framework not only allows emergency responders to augment their situational awareness with social media information, but can also allow them to extract geophysical data and incorporate it into their analysis models.

A Progressive Approach to Teaching Analytics in the Marketing Curriculum

ERIC Educational Resources Information Center

Liu, Yiyuan; Levin, Michael A.

2018-01-01

With the emerging use of analytics tools and methodologies in marketing, marketing educators have provided students training and experiences beyond the soft skills associated with understanding consumer behavior. Previous studies have only discussed how to apply analytics in course designs, tools, and related practices. However, there is a lack of…

Penetrating the Fog: Analytics in Learning and Education

ERIC Educational Resources Information Center

Siemens, George; Long, Phil

2011-01-01

Attempts to imagine the future of education often emphasize new technologies--ubiquitous computing devices, flexible classroom designs, and innovative visual displays. But the most dramatic factor shaping the future of higher education is something that people cannot actually touch or see: "big data and analytics." Learning analytics is still in…

Be the Data: Embodied Visual Analytics

ERIC Educational Resources Information Center

Chen, Xin; Self, Jessica Zeitz; House, Leanna; Wenskovitch, John; Sun, Maoyuan; Wycoff, Nathan; Evia, Jane Robertson; Leman, Scotland; North, Chris

2018-01-01

With the rise of big data, it is becoming increasingly important to educate groups of students at many educational levels about data analytics. In particular, students without a strong mathematical background may have an unenthusiastic attitude towards high-dimensional data and find it challenging to understand relevant complex analytical methods,…

Median of patient results as a tool for assessment of analytical stability.

PubMed

Jørgensen, Lars Mønster; Hansen, Steen Ingemann; Petersen, Per Hyltoft; Sölétormos, György

2015-06-15

In spite of the well-established external quality assessment and proficiency testing surveys of analytical quality performance in laboratory medicine, a simple tool to monitor the long-term analytical stability as a supplement to the internal control procedures is often needed. Patient data from daily internal control schemes was used for monthly appraisal of the analytical stability. This was accomplished by using the monthly medians of patient results to disclose deviations from analytical stability, and by comparing divergences with the quality specifications for allowable analytical bias based on biological variation. Seventy five percent of the twenty analytes achieved on two COBASs INTEGRA 800 instruments performed in accordance with the optimum and with the desirable specifications for bias. Patient results applied in analytical quality performance control procedures are the most reliable sources of material as they represent the genuine substance of the measurements and therefore circumvent the problems associated with non-commutable materials in external assessment. Patient medians in the monthly monitoring of analytical stability in laboratory medicine are an inexpensive, simple and reliable tool to monitor the steadiness of the analytical practice. Copyright © 2015 Elsevier B.V. All rights reserved.

Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations

DOE PAGES

Matzen, Laura E.; Haass, Michael J.; Divis, Kristin M.; ...

2017-08-29

Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations. Visual saliency models are typically based on the properties of the human visual cortex and predict which areas of a scene havemore » visual features (e.g. color, luminance, edges) that are likely to draw a viewer's attention. While these models can accurately predict where viewers will look in a natural scene, they typically do not perform well for abstract data visualizations. In this paper, we discuss the reasons for the poor performance of existing saliency models when applied to data visualizations. We introduce the Data Visualization Saliency (DVS) model, a saliency model tailored to address some of these weaknesses, and we test the performance of the DVS model and existing saliency models by comparing the saliency maps produced by the models to eye tracking data obtained from human viewers. In conclusion, we describe how modified saliency models could be used as general tools for assessing the effectiveness of visualizations, including the strengths and weaknesses of this approach.« less

Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matzen, Laura E.; Haass, Michael J.; Divis, Kristin M.

Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations. Visual saliency models are typically based on the properties of the human visual cortex and predict which areas of a scene havemore » visual features (e.g. color, luminance, edges) that are likely to draw a viewer's attention. While these models can accurately predict where viewers will look in a natural scene, they typically do not perform well for abstract data visualizations. In this paper, we discuss the reasons for the poor performance of existing saliency models when applied to data visualizations. We introduce the Data Visualization Saliency (DVS) model, a saliency model tailored to address some of these weaknesses, and we test the performance of the DVS model and existing saliency models by comparing the saliency maps produced by the models to eye tracking data obtained from human viewers. In conclusion, we describe how modified saliency models could be used as general tools for assessing the effectiveness of visualizations, including the strengths and weaknesses of this approach.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, Kris A.; Scholtz, Jean; Whiting, Mark A.

The VAST Challenge has been a popular venue for academic and industry participants for over ten years. Many participants comment that the majority of their time in preparing VAST Challenge entries is discovering elements in their software environments that need to be redesigned in order to solve the given task. Fortunately, there is no need to wait until the VAST Challenge is announced to test out software systems. The Visual Analytics Benchmark Repository contains all past VAST Challenge tasks, data, solutions and submissions. This paper details the various types of evaluations that may be conducted using the Repository information. Inmore » this paper we describe how developers can do informal evaluations of various aspects of their visual analytics environments using VAST Challenge information. Aspects that can be evaluated include the appropriateness of the software for various tasks, the various data types and formats that can be accommodated, the effectiveness and efficiency of the process supported by the software, and the intuitiveness of the visualizations and interactions. Researchers can compare their visualizations and interactions to those submitted to determine novelty. In addition, the paper provides pointers to various guidelines that software teams can use to evaluate the usability of their software. While these evaluations are not a replacement for formal evaluation methods, this information can be extremely useful during the development of visual analytics environments.« less

The Top 10 Challenges in Extreme-Scale Visual Analytics

PubMed Central

Wong, Pak Chung; Shen, Han-Wei; Johnson, Christopher R.; Chen, Chaomei; Ross, Robert B.

2013-01-01

In this issue of CG&A, researchers share their R&D findings and results on applying visual analytics (VA) to extreme-scale data. Having surveyed these articles and other R&D in this field, we’ve identified what we consider the top challenges of extreme-scale VA. To cater to the magazine’s diverse readership, our discussion evaluates challenges in all areas of the field, including algorithms, hardware, software, engineering, and social issues. PMID:24489426

The 3D widgets for exploratory scientific visualization

NASA Technical Reports Server (NTRS)

Herndon, Kenneth P.; Meyer, Tom

1995-01-01

Computational fluid dynamics (CFD) techniques are used to simulate flows of fluids like air or water around such objects as airplanes and automobiles. These techniques usually generate very large amounts of numerical data which are difficult to understand without using graphical scientific visualization techniques. There are a number of commercial scientific visualization applications available today which allow scientists to control visualization tools via textual and/or 2D user interfaces. However, these user interfaces are often difficult to use. We believe that 3D direct-manipulation techniques for interactively controlling visualization tools will provide opportunities for powerful and useful interfaces with which scientists can more effectively explore their datasets. A few systems have been developed which use these techniques. In this paper, we will present a variety of 3D interaction techniques for manipulating parameters of visualization tools used to explore CFD datasets, and discuss in detail various techniques for positioning tools in a 3D scene.

Naturalistic Decision Making for Power System Operators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greitzer, Frank L.; Podmore, Robin; Robinson, Marck

2010-02-01

Motivation – Investigations of large-scale outages in the North American interconnected electric system often attribute the causes to three T’s: Trees, Training and Tools. To document and understand the mental processes used by expert operators when making critical decisions, a naturalistic decision making (NDM) model was developed. Transcripts of conversations were analyzed to reveal and assess NDM-based performance criteria. Findings/Design – An item analysis indicated that the operators’ Situation Awareness Levels, mental models, and mental simulations can be mapped at different points in the training scenario. This may identify improved training methods or analytical/ visualization tools. Originality/Value – This studymore » applies for the first time, the concepts of Recognition Primed Decision Making, Situation Awareness Levels and Cognitive Task Analysis to training of electric power system operators. Take away message – The NDM approach provides a viable framework for systematic training management to accelerate learning in simulator-based training scenarios for power system operators and teams.« less

Lipidomics informatics for life-science.

PubMed

Schwudke, D; Shevchenko, A; Hoffmann, N; Ahrends, R

2017-11-10

Lipidomics encompasses analytical approaches that aim to identify and quantify the complete set of lipids, defined as lipidome in a given cell, tissue or organism as well as their interactions with other molecules. The majority of lipidomics workflows is based on mass spectrometry and has been proven as a powerful tool in system biology in concert with other Omics disciplines. Unfortunately, bioinformatics infrastructures for this relatively young discipline are limited only to some specialists. Search engines, quantification algorithms, visualization tools and databases developed by the 'Lipidomics Informatics for Life-Science' (LIFS) partners will be restructured and standardized to provide broad access to these specialized bioinformatics pipelines. There are many medical challenges related to lipid metabolic alterations that will be fostered by capacity building suggested by LIFS. LIFS as member of the 'German Network for Bioinformatics' (de.NBI) node for 'Bioinformatics for Proteomics' (BioInfra.Prot) and will provide access to the described software as well as to tutorials and consulting services via a unified web-portal. Copyright © 2017 Elsevier B.V. All rights reserved.

On the importance of mathematical methods for analysis of MALDI-imaging mass spectrometry data.

PubMed

Trede, Dennis; Kobarg, Jan Hendrik; Oetjen, Janina; Thiele, Herbert; Maass, Peter; Alexandrov, Theodore

2012-03-21

In the last decade, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS), also called as MALDI-imaging, has proven its potential in proteomics and was successfully applied to various types of biomedical problems, in particular to histopathological label-free analysis of tissue sections. In histopathology, MALDI-imaging is used as a general analytic tool revealing the functional proteomic structure of tissue sections, and as a discovery tool for detecting new biomarkers discriminating a region annotated by an experienced histologist, in particular, for cancer studies. A typical MALDI-imaging data set contains 10⁸ to 10⁹ intensity values occupying more than 1 GB. Analysis and interpretation of such huge amount of data is a mathematically, statistically and computationally challenging problem. In this paper we overview some computational methods for analysis of MALDI-imaging data sets. We discuss the importance of data preprocessing, which typically includes normalization, baseline removal and peak picking, and hightlight the importance of image denoising when visualizing IMS data.

On the Importance of Mathematical Methods for Analysis of MALDI-Imaging Mass Spectrometry Data.

PubMed

Trede, Dennis; Kobarg, Jan Hendrik; Oetjen, Janina; Thiele, Herbert; Maass, Peter; Alexandrov, Theodore

2012-03-01

In the last decade, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS), also called as MALDI-imaging, has proven its potential in proteomics and was successfully applied to various types of biomedical problems, in particular to histopathological label-free analysis of tissue sections. In histopathology, MALDI-imaging is used as a general analytic tool revealing the functional proteomic structure of tissue sections, and as a discovery tool for detecting new biomarkers discriminating a region annotated by an experienced histologist, in particular, for cancer studies. A typical MALDI-imaging data set contains 108 to 109 intensity values occupying more than 1 GB. Analysis and interpretation of such huge amount of data is a mathematically, statistically and computationally challenging problem. In this paper we overview some computational methods for analysis of MALDI-imaging data sets. We discuss the importance of data preprocessing, which typically includes normalization, baseline removal and peak picking, and hightlight the importance of image denoising when visualizing IMS data.

Conversion of National Health Insurance Service-National Sample Cohort (NHIS-NSC) Database into Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM).

PubMed

You, Seng Chan; Lee, Seongwon; Cho, Soo-Yeon; Park, Hojun; Jung, Sungjae; Cho, Jaehyeong; Yoon, Dukyong; Park, Rae Woong

2017-01-01

It is increasingly necessary to generate medical evidence applicable to Asian people compared to those in Western countries. Observational Health Data Sciences a Informatics (OHDSI) is an international collaborative which aims to facilitate generating high-quality evidence via creating and applying open-source data analytic solutions to a large network of health databases across countries. We aimed to incorporate Korean nationwide cohort data into the OHDSI network by converting the national sample cohort into Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM). The data of 1.13 million subjects was converted to OMOP-CDM, resulting in average 99.1% conversion rate. The ACHILLES, open-source OMOP-CDM-based data profiling tool, was conducted on the converted database to visualize data-driven characterization and access the quality of data. The OMOP-CDM version of National Health Insurance Service-National Sample Cohort (NHIS-NSC) can be a valuable tool for multiple aspects of medical research by incorporation into the OHDSI research network.

Continuous whole-system monitoring toward rapid understanding of production HPC applications and systems

DOE PAGES

Agelastos, Anthony; Allan, Benjamin; Brandt, Jim; ...

2016-05-18

A detailed understanding of HPC applications’ resource needs and their complex interactions with each other and HPC platform resources are critical to achieving scalability and performance. Such understanding has been difficult to achieve because typical application profiling tools do not capture the behaviors of codes under the potentially wide spectrum of actual production conditions and because typical monitoring tools do not capture system resource usage information with high enough fidelity to gain sufficient insight into application performance and demands. In this paper we present both system and application profiling results based on data obtained through synchronized system wide monitoring onmore » a production HPC cluster at Sandia National Laboratories (SNL). We demonstrate analytic and visualization techniques that we are using to characterize application and system resource usage under production conditions for better understanding of application resource needs. Furthermore, our goals are to improve application performance (through understanding application-to-resource mapping and system throughput) and to ensure that future system capabilities match their intended workloads.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Agelastos, Anthony; Allan, Benjamin; Brandt, Jim

A detailed understanding of HPC applications’ resource needs and their complex interactions with each other and HPC platform resources are critical to achieving scalability and performance. Such understanding has been difficult to achieve because typical application profiling tools do not capture the behaviors of codes under the potentially wide spectrum of actual production conditions and because typical monitoring tools do not capture system resource usage information with high enough fidelity to gain sufficient insight into application performance and demands. In this paper we present both system and application profiling results based on data obtained through synchronized system wide monitoring onmore » a production HPC cluster at Sandia National Laboratories (SNL). We demonstrate analytic and visualization techniques that we are using to characterize application and system resource usage under production conditions for better understanding of application resource needs. Furthermore, our goals are to improve application performance (through understanding application-to-resource mapping and system throughput) and to ensure that future system capabilities match their intended workloads.« less

Do Bedside Visual Tools Improve Patient and Caregiver Satisfaction? A Systematic Review of the Literature.

PubMed

Goyal, Anupama A; Tur, Komalpreet; Mann, Jason; Townsend, Whitney; Flanders, Scott A; Chopra, Vineet

2017-11-01

Although common, the impact of low-cost bedside visual tools, such as whiteboards, on patient care is unclear. To systematically review the literature and assess the influence of bedside visual tools on patient satisfaction. Medline, Embase, SCOPUS, Web of Science, CINAHL, and CENTRAL. Studies of adult or pediatric hospitalized patients reporting physician identification, understanding of provider roles, patient-provider communication, and satisfaction with care from the use of visual tools were included. Outcomes were categorized as positive, negative, or neutral based on survey responses for identification, communication, and satisfaction. Two reviewers screened studies, extracted data, and assessed the risk of study bias. Sixteen studies met the inclusion criteria. Visual tools included whiteboards (n = 4), physician pictures (n = 7), whiteboard and picture (n = 1), electronic medical record-based patient portals (n = 3), and formatted notepads (n = 1). Tools improved patients' identification of providers (13/13 studies). The impact on understanding the providers' roles was largely positive (8/10 studies). Visual tools improved patient-provider communication (4/5 studies) and satisfaction (6/8 studies). In adults, satisfaction varied between positive with the use of whiteboards (2/5 studies) and neutral with pictures (1/5 studies). Satisfaction related to pictures in pediatric patients was either positive (1/3 studies) or neutral (1/3 studies). Differences in tool format (individual pictures vs handouts with pictures of all providers) and study design (randomized vs cohort) may explain variable outcomes. The use of bedside visual tools appears to improve patient recognition of providers and patient-provider communication. Future studies that include better design and outcome assessment are necessary before widespread use can be recommended. © 2017 Society of Hospital Medicine

Visual Impairment Screening Assessment (VISA) tool: pilot validation.

PubMed

Rowe, Fiona J; Hepworth, Lauren R; Hanna, Kerry L; Howard, Claire

2018-03-06

To report and evaluate a new Vision Impairment Screening Assessment (VISA) tool intended for use by the stroke team to improve identification of visual impairment in stroke survivors. Prospective case cohort comparative study. Stroke units at two secondary care hospitals and one tertiary centre. 116 stroke survivors were screened, 62 by naïve and 54 by non-naïve screeners. Both the VISA screening tool and the comprehensive specialist vision assessment measured case history, visual acuity, eye alignment, eye movements, visual field and visual inattention. Full completion of VISA tool and specialist vision assessment was achieved for 89 stroke survivors. Missing data for one or more sections typically related to patient's inability to complete the assessment. Sensitivity and specificity of the VISA screening tool were 90.24% and 85.29%, respectively; the positive and negative predictive values were 93.67% and 78.36%, respectively. Overall agreement was significant; k=0.736. Lowest agreement was found for screening of eye movement and visual inattention deficits. This early validation of the VISA screening tool shows promise in improving detection accuracy for clinicians involved in stroke care who are not specialists in vision problems and lack formal eye training, with potential to lead to more prompt referral with fewer false positives and negatives. Pilot validation indicates acceptability of the VISA tool for screening of visual impairment in stroke survivors. Sensitivity and specificity were high indicating the potential accuracy of the VISA tool for screening purposes. Results of this study have guided the revision of the VISA screening tool ahead of full clinical validation. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Audio-video decision support for patients: the documentary genré as a basis for decision aids.

PubMed

Volandes, Angelo E; Barry, Michael J; Wood, Fiona; Elwyn, Glyn

2013-09-01

Decision support tools are increasingly using audio-visual materials. However, disagreement exists about the use of audio-visual materials as they may be subjective and biased. This is a literature review of the major texts for documentary film studies to extrapolate issues of objectivity and bias from film to decision support tools. The key features of documentary films are that they attempt to portray real events and that the attempted reality is always filtered through the lens of the filmmaker. The same key features can be said of decision support tools that use audio-visual materials. Three concerns arising from documentary film studies as they apply to the use of audio-visual materials in decision support tools include whose perspective matters (stakeholder bias), how to choose among audio-visual materials (selection bias) and how to ensure objectivity (editorial bias). Decision science needs to start a debate about how audio-visual materials are to be used in decision support tools. Simply because audio-visual materials may be subjective and open to bias does not mean that we should not use them. Methods need to be found to ensure consensus around balance and editorial control, such that audio-visual materials can be used. © 2011 John Wiley & Sons Ltd.

Audio‐video decision support for patients: the documentary genré as a basis for decision aids

PubMed Central

Volandes, Angelo E.; Barry, Michael J.; Wood, Fiona; Elwyn, Glyn

2011-01-01

Abstract Objective  Decision support tools are increasingly using audio‐visual materials. However, disagreement exists about the use of audio‐visual materials as they may be subjective and biased. Methods  This is a literature review of the major texts for documentary film studies to extrapolate issues of objectivity and bias from film to decision support tools. Results  The key features of documentary films are that they attempt to portray real events and that the attempted reality is always filtered through the lens of the filmmaker. The same key features can be said of decision support tools that use audio‐visual materials. Three concerns arising from documentary film studies as they apply to the use of audio‐visual materials in decision support tools include whose perspective matters (stakeholder bias), how to choose among audio‐visual materials (selection bias) and how to ensure objectivity (editorial bias). Discussion  Decision science needs to start a debate about how audio‐visual materials are to be used in decision support tools. Simply because audio‐visual materials may be subjective and open to bias does not mean that we should not use them. Conclusion  Methods need to be found to ensure consensus around balance and editorial control, such that audio‐visual materials can be used. PMID:22032516