Advanced Research and Data Methods in Women's Health: Big Data Analytics, Adaptive Studies, and the Road Ahead.

PubMed

Macedonia, Christian R; Johnson, Clark T; Rajapakse, Indika

2017-02-01

Technical advances in science have had broad implications in reproductive and women's health care. Recent innovations in population-level data collection and storage have made available an unprecedented amount of data for analysis while computational technology has evolved to permit processing of data previously thought too dense to study. "Big data" is a term used to describe data that are a combination of dramatically greater volume, complexity, and scale. The number of variables in typical big data research can readily be in the thousands, challenging the limits of traditional research methodologies. Regardless of what it is called, advanced data methods, predictive analytics, or big data, this unprecedented revolution in scientific exploration has the potential to dramatically assist research in obstetrics and gynecology broadly across subject matter. Before implementation of big data research methodologies, however, potential researchers and reviewers should be aware of strengths, strategies, study design methods, and potential pitfalls. Examination of big data research examples contained in this article provides insight into the potential and the limitations of this data science revolution and practical pathways for its useful implementation.

Business and Science - Big Data, Big Picture

NASA Astrophysics Data System (ADS)

Rosati, A.

2013-12-01

Data Science is more than the creation, manipulation, and transformation of data. It is more than Big Data. The business world seems to have a hold on the term 'data science' and, for now, they define what it means. But business is very different than science. In this talk, I address how large datasets, Big Data, and data science are conceptually different in business and science worlds. I focus on the types of questions each realm asks, the data needed, and the consequences of findings. Gone are the days of datasets being created or collected to serve only one purpose or project. The trick with data reuse is to become familiar enough with a dataset to be able to combine it with other data and extract accurate results. As a Data Curator for the Advanced Cooperative Arctic Data and Information Service (ACADIS), my specialty is communication. Our team enables Arctic sciences by ensuring datasets are well documented and can be understood by reusers. Previously, I served as a data community liaison for the North American Regional Climate Change Assessment Program (NARCCAP). Again, my specialty was communicating complex instructions and ideas to a broad audience of data users. Before entering the science world, I was an entrepreneur. I have a bachelor's degree in economics and a master's degree in environmental social science. I am currently pursuing a Ph.D. in Geography. Because my background has embraced both the business and science worlds, I would like to share my perspectives on data, data reuse, data documentation, and the presentation or communication of findings. My experiences show that each can inform and support the other.

Rethinking big data: A review on the data quality and usage issues

NASA Astrophysics Data System (ADS)

Liu, Jianzheng; Li, Jie; Li, Weifeng; Wu, Jiansheng

2016-05-01

The recent explosive publications of big data studies have well documented the rise of big data and its ongoing prevalence. Different types of ;big data; have emerged and have greatly enriched spatial information sciences and related fields in terms of breadth and granularity. Studies that were difficult to conduct in the past time due to data availability can now be carried out. However, big data brings lots of ;big errors; in data quality and data usage, which cannot be used as a substitute for sound research design and solid theories. We indicated and summarized the problems faced by current big data studies with regard to data collection, processing and analysis: inauthentic data collection, information incompleteness and noise of big data, unrepresentativeness, consistency and reliability, and ethical issues. Cases of empirical studies are provided as evidences for each problem. We propose that big data research should closely follow good scientific practice to provide reliable and scientific ;stories;, as well as explore and develop techniques and methods to mitigate or rectify those 'big-errors' brought by big data.

Semantic Web technologies for the big data in life sciences.

PubMed

Wu, Hongyan; Yamaguchi, Atsuko

2014-08-01

The life sciences field is entering an era of big data with the breakthroughs of science and technology. More and more big data-related projects and activities are being performed in the world. Life sciences data generated by new technologies are continuing to grow in not only size but also variety and complexity, with great speed. To ensure that big data has a major influence in the life sciences, comprehensive data analysis across multiple data sources and even across disciplines is indispensable. The increasing volume of data and the heterogeneous, complex varieties of data are two principal issues mainly discussed in life science informatics. The ever-evolving next-generation Web, characterized as the Semantic Web, is an extension of the current Web, aiming to provide information for not only humans but also computers to semantically process large-scale data. The paper presents a survey of big data in life sciences, big data related projects and Semantic Web technologies. The paper introduces the main Semantic Web technologies and their current situation, and provides a detailed analysis of how Semantic Web technologies address the heterogeneous variety of life sciences big data. The paper helps to understand the role of Semantic Web technologies in the big data era and how they provide a promising solution for the big data in life sciences.

Cool Cosmology: ``WHISPER" better than ``BANG"

NASA Astrophysics Data System (ADS)

Carr, Paul

2007-10-01

Cosmologist Fred Hoyle coined ``big bang'' as a term of derision for Belgian priest George Lemaitre's prediction that the universe had originated from the expansion of a ``primeval atom'' in space-time. Hoyle referred to Lamaitre's hypothesis sarcastically as ``this big bang idea'' during a program broadcast on March 28, 1949 on the BBC. Hoyle's continuous creation or steady state theory can not explain the microwave background radiation or cosmic whisper discovered by Penzias and Wilson in 1964. The expansion and subsequent cooling of Lemaitre's hot ``primeval atom'' explains the whisper. ``Big bang'' makes no physical sense, as there was no matter (or space) to carry the sound that Hoyle's term implies. The ``big bang'' is a conjecture. New discoveries may be able to predict the observed ``whispering cosmos'' as well as dark matter and the nature of dark energy. The ``whispering universe'' is cooler cosmology than the big bang. Reference: Carr, Paul H. 2006. ``From the 'Music of the Spheres' to the 'Whispering Cosmos.' '' Chapter 3 of Beauty in Science and Spirit. Beech River Books. Center Ossipee, NH, http://www.MirrorOfNature.org.

Big Biology: Supersizing Science During the Emergence of the 21st Century

PubMed Central

Vermeulen, Niki

2017-01-01

Ist Biologie das jüngste Mitglied in der Familie von Big Science? Die vermehrte Zusammenarbeit in der biologischen Forschung wurde in der Folge des Human Genome Project zwar zum Gegenstand hitziger Diskussionen, aber Debatten und Reflexionen blieben meist im Polemischen verhaftet und zeigten eine begrenzte Wertschätzung für die Vielfalt und Erklärungskraft des Konzepts von Big Science. Zur gleichen Zeit haben Wissenschafts- und Technikforscher/innen in ihren Beschreibungen des Wandels der Forschungslandschaft die Verwendung des Begriffs Big Science gemieden. Dieser interdisziplinäre Artikel kombiniert eine begriffliche Analyse des Konzepts von Big Science mit unterschiedlichen Daten und Ideen aus einer Multimethodenuntersuchung mehrerer großer Forschungsprojekte in der Biologie. Ziel ist es, ein empirisch fundiertes, nuanciertes und analytisch nützliches Verständnis von Big Biology zu entwickeln und die normativen Debatten mit ihren einfachen Dichotomien und rhetorischen Positionen hinter sich zu lassen. Zwar kann das Konzept von Big Science als eine Mode in der Wissenschaftspolitik gesehen werden – inzwischen vielleicht sogar als ein altmodisches Konzept –, doch lautet meine innovative Argumentation, dass dessen analytische Verwendung unsere Aufmerksamkeit auf die Ausweitung der Zusammenarbeit in den Biowissenschaften lenkt. Die Analyse von Big Biology zeigt Unterschiede zu Big Physics und anderen Formen von Big Science, namentlich in den Mustern der Forschungsorganisation, der verwendeten Technologien und der gesellschaftlichen Zusammenhänge, in denen sie tätig ist. So können Reflexionen über Big Science, Big Biology und ihre Beziehungen zur Wissensproduktion die jüngsten Behauptungen über grundlegende Veränderungen in der Life Science-Forschung in einen historischen Kontext stellen. PMID:27215209

Big data in biomedicine.

PubMed

Costa, Fabricio F

2014-04-01

The increasing availability and growth rate of biomedical information, also known as 'big data', provides an opportunity for future personalized medicine programs that will significantly improve patient care. Recent advances in information technology (IT) applied to biomedicine are changing the landscape of privacy and personal information, with patients getting more control of their health information. Conceivably, big data analytics is already impacting health decisions and patient care; however, specific challenges need to be addressed to integrate current discoveries into medical practice. In this article, I will discuss the major breakthroughs achieved in combining omics and clinical health data in terms of their application to personalized medicine. I will also review the challenges associated with using big data in biomedicine and translational science. Copyright © 2013 Elsevier Ltd. All rights reserved.

Big Science and Big Big Science

ERIC Educational Resources Information Center

Marshall, Steve

2012-01-01

In his introduction to the science shows feature in "Primary Science" 115, Ian B. Dunne asks the question "Why have science shows?" He lists a host of very sound reasons, starting with because "science is fun" so why not engage and entertain, inspire, grab attention and encourage them to learn? He goes onto to state that: "Even in today's…

Nursing Needs Big Data and Big Data Needs Nursing.

PubMed

Brennan, Patricia Flatley; Bakken, Suzanne

2015-09-01

Contemporary big data initiatives in health care will benefit from greater integration with nursing science and nursing practice; in turn, nursing science and nursing practice has much to gain from the data science initiatives. Big data arises secondary to scholarly inquiry (e.g., -omics) and everyday observations like cardiac flow sensors or Twitter feeds. Data science methods that are emerging ensure that these data be leveraged to improve patient care. Big data encompasses data that exceed human comprehension, that exist at a volume unmanageable by standard computer systems, that arrive at a velocity not under the control of the investigator and possess a level of imprecision not found in traditional inquiry. Data science methods are emerging to manage and gain insights from big data. The primary methods included investigation of emerging federal big data initiatives, and exploration of exemplars from nursing informatics research to benchmark where nursing is already poised to participate in the big data revolution. We provide observations and reflections on experiences in the emerging big data initiatives. Existing approaches to large data set analysis provide a necessary but not sufficient foundation for nursing to participate in the big data revolution. Nursing's Social Policy Statement guides a principled, ethical perspective on big data and data science. There are implications for basic and advanced practice clinical nurses in practice, for the nurse scientist who collaborates with data scientists, and for the nurse data scientist. Big data and data science has the potential to provide greater richness in understanding patient phenomena and in tailoring interventional strategies that are personalized to the patient. © 2015 Sigma Theta Tau International.

Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm.

PubMed

Huang, Xiuzhen; Jennings, Steven F; Bruce, Barry; Buchan, Alison; Cai, Liming; Chen, Pengyin; Cramer, Carole L; Guan, Weihua; Hilgert, Uwe Kk; Jiang, Hongmei; Li, Zenglu; McClure, Gail; McMullen, Donald F; Nanduri, Bindu; Perkins, Andy; Rekepalli, Bhanu; Salem, Saeed; Specker, Jennifer; Walker, Karl; Wunsch, Donald; Xiong, Donghai; Zhang, Shuzhong; Zhang, Yu; Zhao, Zhongming; Moore, Jason H

2015-01-01

Whether your interests lie in scientific arenas, the corporate world, or in government, you have certainly heard the praises of big data: Big data will give you new insights, allow you to become more efficient, and/or will solve your problems. While big data has had some outstanding successes, many are now beginning to see that it is not the Silver Bullet that it has been touted to be. Here our main concern is the overall impact of big data; the current manifestation of big data is constructing a Maginot Line in science in the 21st century. Big data is not "lots of data" as a phenomena anymore; The big data paradigm is putting the spirit of the Maginot Line into lots of data. Big data overall is disconnecting researchers and science challenges. We propose No-Boundary Thinking (NBT), applying no-boundary thinking in problem defining to address science challenges.

Characterizing the changes in teaching practice during first semester implementation of an argument-based inquiry approach in a middle school science classroom

NASA Astrophysics Data System (ADS)

Pinney, Brian Robert John

The purpose of this study was to characterize ways in which teaching practice in classroom undergoing first semester implementation of an argument-based inquiry approach changes in whole-class discussion. Being that argument is explicitly called for in the Next Generation Science Standards and is currently a rare practice in teaching, many teachers will have to transform their teaching practice for inclusion of this feature. Most studies on Argument-Based Inquiry (ABI) agree that development of argument does not come easily and is only acquired through practice. Few studies have examined the ways in which teaching practice changes in relation to the big idea or disciplinary core idea (NGSS), the development of dialogue, and/or the development of argument during first semester implementation of an argument-based inquiry approach. To explore these areas, this study posed three primary research questions: (1) How does a teacher in his first semester of Science Writing Heuristic professional development make use of the "big idea"?, (1a) Is the indicated big idea consistent with NGSS core concepts?, (2) How did the dialogue in whole-class discussion change during the first semester of argument-based inquiry professional development?, (3) How did the argument in whole-class discussion change during the first semester of argument-based inquiry professional development? This semester-long study that took place in a middle school in a rural Midwestern city was grounded in interactive constructivism, and utilized a qualitative design to identify the ways in which the teacher utilized big ideas and how dialogue and argumentative dialogue developed over time. The purposefully selected teacher in this study provided a unique situation where he was in his first semester of professional development using the Science Writing Heuristic Approach to argument-based inquiry with 19 students who had two prior years' experience in ABI. Multiple sources of data were collected, including classroom video with transcripts, teacher interview, researcher field notes, student journals, teacher lesson plans from previous years, and a student questionnaire. Data analysis used a basic qualitative approach. The results showed (1) only the first time period had a true big idea, while the other two units contained topics, (2) each semester contained a similar use for the given big idea, though its role in the class was reduced after the opening activity, (3) the types of teacher questions shifted toward students explaining their comprehension of ideas and more students were involved in discussing each idea and for more turns of talk than in earlier time periods, (4) understanding science term definitions became more prominent later in the semester, with more stating science terms occurring earlier in the semester, (5) no significant changes were seen to the use of argument or claims and evidence throughout the study. The findings have informed theory and practice about science argumentation, the practice of whole-class dialogue, and the understanding of practice along four aspects: (1) apparent lack of understanding about big ideas and how to utilize them as the central organizing feature of a unit, (2) independent development of dialogue and argument, (3) apparent lack of understanding about the structure of argument and use of basic terminology with argument and big ideas, (4) challenges of ABI implementation. This study provides insight into the importance of prolonged and persistent professional development with ABI in teaching practice.

Big Machines and Big Science: 80 Years of Accelerators at Stanford

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

Loew, Gregory

2008-12-16

Longtime SLAC physicist Greg Loew will present a trip through SLAC's origins, highlighting its scientific achievements, and provide a glimpse of the lab's future in 'Big Machines and Big Science: 80 Years of Accelerators at Stanford.'

The Rise of Big Data in Oncology.

PubMed

Fessele, Kristen L

2018-05-01

To describe big data and data science in the context of oncology nursing care. Peer-reviewed and lay publications. The rapid expansion of real-world evidence from sources such as the electronic health record, genomic sequencing, administrative claims and other data sources has outstripped the ability of clinicians and researchers to manually review and analyze it. To promote high-quality, high-value cancer care, big data platforms must be constructed from standardized data sources to support extraction of meaningful, comparable insights. Nurses must advocate for the use of standardized vocabularies and common data elements that represent terms and concepts that are meaningful to patient care. Copyright © 2018 Elsevier Inc. All rights reserved.

Observatories, think tanks, and community models in the hydrologic and environmental sciences: How does it affect me?

NASA Astrophysics Data System (ADS)

Torgersen, Thomas

2006-06-01

Multiple issues in hydrologic and environmental sciences are now squarely in the public focus and require both government and scientific study. Two facts also emerge: (1) The new approach being touted publicly for advancing the hydrologic and environmental sciences is the establishment of community-operated "big science" (observatories, think tanks, community models, and data repositories). (2) There have been important changes in the business of science over the last 20 years that make it important for the hydrologic and environmental sciences to demonstrate the "value" of public investment in hydrological and environmental science. Given that community-operated big science (observatories, think tanks, community models, and data repositories) could become operational, I argue that such big science should not mean a reduction in the importance of single-investigator science. Rather, specific linkages between the large-scale, team-built, community-operated big science and the single investigator should provide context data, observatory data, and systems models for a continuing stream of hypotheses by discipline-based, specialized research and a strong rationale for continued, single-PI ("discovery-based") research. I also argue that big science can be managed to provide a better means of demonstrating the value of public investment in the hydrologic and environmental sciences. Decisions regarding policy will still be political, but big science could provide an integration of the best scientific understanding as a guide for the best policy.

Big Science for Growing Minds: Constructivist Classrooms for Young Thinkers. Early Childhood Education Series

ERIC Educational Resources Information Center

Brooks, Jacqueline Grennon

2011-01-01

Strong evidence from recent brain research shows that the intentional teaching of science is crucial in early childhood. "Big Science for Growing Minds" describes a groundbreaking curriculum that invites readers to rethink science education through a set of unifying concepts or "big ideas." Using an integrated learning approach, the author shows…

Toward a manifesto for the 'public understanding of big data'.

PubMed

Michael, Mike; Lupton, Deborah

2016-01-01

In this article, we sketch a 'manifesto' for the 'public understanding of big data'. On the one hand, this entails such public understanding of science and public engagement with science and technology-tinged questions as follows: How, when and where are people exposed to, or do they engage with, big data? Who are regarded as big data's trustworthy sources, or credible commentators and critics? What are the mechanisms by which big data systems are opened to public scrutiny? On the other hand, big data generate many challenges for public understanding of science and public engagement with science and technology: How do we address publics that are simultaneously the informant, the informed and the information of big data? What counts as understanding of, or engagement with, big data, when big data themselves are multiplying, fluid and recursive? As part of our manifesto, we propose a range of empirical, conceptual and methodological exhortations. We also provide Appendix 1 that outlines three novel methods for addressing some of the issues raised in the article. © The Author(s) 2015.

The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data.

PubMed

Margolis, Ronald; Derr, Leslie; Dunn, Michelle; Huerta, Michael; Larkin, Jennie; Sheehan, Jerry; Guyer, Mark; Green, Eric D

2014-01-01

Biomedical research has and will continue to generate large amounts of data (termed 'big data') in many formats and at all levels. Consequently, there is an increasing need to better understand and mine the data to further knowledge and foster new discovery. The National Institutes of Health (NIH) has initiated a Big Data to Knowledge (BD2K) initiative to maximize the use of biomedical big data. BD2K seeks to better define how to extract value from the data, both for the individual investigator and the overall research community, create the analytic tools needed to enhance utility of the data, provide the next generation of trained personnel, and develop data science concepts and tools that can be made available to all stakeholders. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Urgent Call for Nursing Big Data.

PubMed

Delaney, Connie W

2016-01-01

The purpose of this panel is to expand internationally a National Action Plan for sharable and comparable nursing data for quality improvement and big data science. There is an urgent need to assure that nursing has sharable and comparable data for quality improvement and big data science. A national collaborative - Nursing Knowledge and Big Data Science includes multi-stakeholder groups focused on a National Action Plan toward implementing and using sharable and comparable nursing big data. Panelists will share accomplishments and future plans with an eye toward international collaboration. This presentation is suitable for any audience attending the NI2016 conference.

The New Big Science: What's New, What's Not, and What's the Difference

NASA Astrophysics Data System (ADS)

Westfall, Catherine

2016-03-01

This talk will start with a brief recap of the development of the ``Big Science'' epitomized by high energy physics, that is, the science that flourished after WWII based on accelerators, teams, and price tags that grew ever larger. I will then explain the transformation that started in the 1980s and culminated in the 1990s when the Cold War ended and the next big machine needed to advance high energy physics, the multi-billion dollar Superconducting Supercollider (SSC), was cancelled. I will go on to outline the curious series of events that ushered in the New Big Science, a form of research well suited to a post-Cold War environment that valued practical rather than esoteric projects. To show the impact of the New Big Science I will describe how decisions were ``set into concrete'' during the development of experimental equipment at the Thomas Jefferson National Accelerator Facility in Newport News, Virginia.

A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science.

PubMed

Faghmous, James H; Kumar, Vipin

2014-09-01

Global climate change and its impact on human life has become one of our era's greatest challenges. Despite the urgency, data science has had little impact on furthering our understanding of our planet in spite of the abundance of climate data. This is a stark contrast from other fields such as advertising or electronic commerce where big data has been a great success story. This discrepancy stems from the complex nature of climate data as well as the scientific questions climate science brings forth. This article introduces a data science audience to the challenges and opportunities to mine large climate datasets, with an emphasis on the nuanced difference between mining climate data and traditional big data approaches. We focus on data, methods, and application challenges that must be addressed in order for big data to fulfill their promise with regard to climate science applications. More importantly, we highlight research showing that solely relying on traditional big data techniques results in dubious findings, and we instead propose a theory-guided data science paradigm that uses scientific theory to constrain both the big data techniques as well as the results-interpretation process to extract accurate insight from large climate data .

Clinical chemistry in higher dimensions: Machine-learning and enhanced prediction from routine clinical chemistry data.

PubMed

Richardson, Alice; Signor, Ben M; Lidbury, Brett A; Badrick, Tony

2016-11-01

Big Data is having an impact on many areas of research, not the least of which is biomedical science. In this review paper, big data and machine learning are defined in terms accessible to the clinical chemistry community. Seven myths associated with machine learning and big data are then presented, with the aim of managing expectation of machine learning amongst clinical chemists. The myths are illustrated with four examples investigating the relationship between biomarkers in liver function tests, enhanced laboratory prediction of hepatitis virus infection, the relationship between bilirubin and white cell count, and the relationship between red cell distribution width and laboratory prediction of anaemia. Copyright © 2016 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Perspectives on Policy and the Value of Nursing Science in a Big Data Era.

PubMed

Gephart, Sheila M; Davis, Mary; Shea, Kimberly

2018-01-01

As data volume explodes, nurse scientists grapple with ways to adapt to the big data movement without jeopardizing its epistemic values and theoretical focus that celebrate while acknowledging the authority and unity of its body of knowledge. In this article, the authors describe big data and emphasize ways that nursing science brings value to its study. Collective nursing voices that call for more nursing engagement in the big data era are answered with ways to adapt and integrate theoretical and domain expertise from nursing into data science.

Taking Advantage of the "Big Mo"--Momentum in Everyday English and Swedish and in Physics Teaching

ERIC Educational Resources Information Center

Haglund, Jesper; Jeppsson, Fredrik; Ahrenberg, Lars

2015-01-01

Science education research suggests that our everyday intuitions of motion and interaction of physical objects fit well with how physicists use the term "momentum". Corpus linguistics provides an easily accessible approach to study language in different domains, including everyday language. Analysis of language samples from English text…

Nursing Knowledge: Big Data Science-Implications for Nurse Leaders.

PubMed

Westra, Bonnie L; Clancy, Thomas R; Sensmeier, Joyce; Warren, Judith J; Weaver, Charlotte; Delaney, Connie W

2015-01-01

The integration of Big Data from electronic health records and other information systems within and across health care enterprises provides an opportunity to develop actionable predictive models that can increase the confidence of nursing leaders' decisions to improve patient outcomes and safety and control costs. As health care shifts to the community, mobile health applications add to the Big Data available. There is an evolving national action plan that includes nursing data in Big Data science, spearheaded by the University of Minnesota School of Nursing. For the past 3 years, diverse stakeholders from practice, industry, education, research, and professional organizations have collaborated through the "Nursing Knowledge: Big Data Science" conferences to create and act on recommendations for inclusion of nursing data, integrated with patient-generated, interprofessional, and contextual data. It is critical for nursing leaders to understand the value of Big Data science and the ways to standardize data and workflow processes to take advantage of newer cutting edge analytics to support analytic methods to control costs and improve patient quality and safety.

The Promise and Potential Perils of Big Data for Advancing Symptom Management Research in Populations at Risk for Health Disparities.

PubMed

Bakken, Suzanne; Reame, Nancy

2016-01-01

Symptom management research is a core area of nursing science and one of the priorities for the National Institute of Nursing Research, which specifically focuses on understanding the biological and behavioral aspects of symptoms such as pain and fatigue, with the goal of developing new knowledge and new strategies for improving patient health and quality of life. The types and volume of data related to the symptom experience, symptom management strategies, and outcomes are increasingly accessible for research. Traditional data streams are now complemented by consumer-generated (i.e., quantified self) and "omic" data streams. Thus, the data available for symptom science can be considered big data. The purposes of this chapter are to (a) briefly summarize the current drivers for the use of big data in research; (b) describe the promise of big data and associated data science methods for advancing symptom management research; (c) explicate the potential perils of big data and data science from the perspective of the ethical principles of autonomy, beneficence, and justice; and (d) illustrate strategies for balancing the promise and the perils of big data through a case study of a community at high risk for health disparities. Big data and associated data science methods offer the promise of multidimensional data sources and new methods to address significant research gaps in symptom management. If nurse scientists wish to apply big data and data science methods to advance symptom management research and promote health equity, they must carefully consider both the promise and perils.

Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics.

PubMed

Popescu, George V; Noutsos, Christos; Popescu, Sorina C

2016-01-01

In modern plant biology, progress is increasingly defined by the scientists' ability to gather and analyze data sets of high volume and complexity, otherwise known as "big data". Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.

From Big Data to Knowledge in the Social Sciences.

PubMed

Hesse, Bradford W; Moser, Richard P; Riley, William T

2015-05-01

One of the challenges associated with high-volume, diverse datasets is whether synthesis of open data streams can translate into actionable knowledge. Recognizing that challenge and other issues related to these types of data, the National Institutes of Health developed the Big Data to Knowledge or BD2K initiative. The concept of translating "big data to knowledge" is important to the social and behavioral sciences in several respects. First, a general shift to data-intensive science will exert an influence on all scientific disciplines, but particularly on the behavioral and social sciences given the wealth of behavior and related constructs captured by big data sources. Second, science is itself a social enterprise; by applying principles from the social sciences to the conduct of research, it should be possible to ameliorate some of the systemic problems that plague the scientific enterprise in the age of big data. We explore the feasibility of recalibrating the basic mechanisms of the scientific enterprise so that they are more transparent and cumulative; more integrative and cohesive; and more rapid, relevant, and responsive.

From Big Data to Knowledge in the Social Sciences

PubMed Central

Hesse, Bradford W.; Moser, Richard P.; Riley, William T.

2015-01-01

One of the challenges associated with high-volume, diverse datasets is whether synthesis of open data streams can translate into actionable knowledge. Recognizing that challenge and other issues related to these types of data, the National Institutes of Health developed the Big Data to Knowledge or BD2K initiative. The concept of translating “big data to knowledge” is important to the social and behavioral sciences in several respects. First, a general shift to data-intensive science will exert an influence on all scientific disciplines, but particularly on the behavioral and social sciences given the wealth of behavior and related constructs captured by big data sources. Second, science is itself a social enterprise; by applying principles from the social sciences to the conduct of research, it should be possible to ameliorate some of the systemic problems that plague the scientific enterprise in the age of big data. We explore the feasibility of recalibrating the basic mechanisms of the scientific enterprise so that they are more transparent and cumulative; more integrative and cohesive; and more rapid, relevant, and responsive. PMID:26294799

PANGAEA® - Data Publisher for Earth & Environmental Science - Research data enters scholarly communication and big data analysis

NASA Astrophysics Data System (ADS)

Diepenbroek, Michael; Schindler, Uwe; Riedel, Morris; Huber, Robert

2014-05-01

The ISCU World Data Center PANGAEA is an information system for acquisition, processing, long term storage, and publication of geo-referenced data related to earth science fields. Storing more than 350.000 data sets from all fields of geosciences it belongs to the largest archives for observational earth science data. Standard conform interfaces (ISO, OGC, W3C, OAI) enable access from a variety of data and information portals, among them the search engine of PANGAEA itself ((www.pangaea.de) and e.g. GBIF. All data sets in PANGAEA are citable, fully documented, and can be referenced via persistent identifiers (Digital Object Identifier - DOI) - a premise for data publication. Together with other ICSU World Data Centers (www.icsu-wds.org) and the Technical Information Library in Germany (TIB) PANGAEA had a share in the implementation of a DOI based registry for scientific data, which by now is supported by a worldwide consortium of libraries (www.datacite.org). A further milestone was building up strong co-operations with science publishers as Elsevier, Springer, Wiley, AGU, Nature and others. A common web service allows to reference supplementary data in PANGAEA directly from an articles abstract page (e.g. Science Direct). The next step with science publishers is to further integrate the editorial process for the publication of supplementary data with the publication procedures on the journal side. Data centric research efforts such as environmental modelling or big data analysing approaches represent new challenges for PANGAEA. Integrated data warehouse technologies are used for highly efficient retrievals and compilations of time slices or surface data matrixes on any measurement parameters out of the whole data continuum. Further, new and emerging big data approaches are currently investigated within PANGAEA to e.g. evaluate its usability for quality control or data clustering. PANGAEA is operated as a joint long term facility by MARUM at the University Bremen and the Alfred Wegener Institute for Polar and Marine Research (AWI). More than 80% of the funding results from project data management and the implementation of spatial data infrastructures (more than 160 International to national projects since the last 15 years - www.pangaea.de/projects).

A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science

PubMed Central

Kumar, Vipin

2014-01-01

Abstract Global climate change and its impact on human life has become one of our era's greatest challenges. Despite the urgency, data science has had little impact on furthering our understanding of our planet in spite of the abundance of climate data. This is a stark contrast from other fields such as advertising or electronic commerce where big data has been a great success story. This discrepancy stems from the complex nature of climate data as well as the scientific questions climate science brings forth. This article introduces a data science audience to the challenges and opportunities to mine large climate datasets, with an emphasis on the nuanced difference between mining climate data and traditional big data approaches. We focus on data, methods, and application challenges that must be addressed in order for big data to fulfill their promise with regard to climate science applications. More importantly, we highlight research showing that solely relying on traditional big data techniques results in dubious findings, and we instead propose a theory-guided data science paradigm that uses scientific theory to constrain both the big data techniques as well as the results-interpretation process to extract accurate insight from large climate data. PMID:25276499

Presenting the 'Big Ideas' of Science: Earth Science Examples.

ERIC Educational Resources Information Center

King, Chris

2001-01-01

Details an 'explanatory Earth story' on plate tectonics to show how such a 'story' can be developed in an earth science context. Presents five other stories in outline form. Explains the use of these stories as vehicles to present the big ideas of science. (DDR)

Big data science: A literature review of nursing research exemplars.

PubMed

Westra, Bonnie L; Sylvia, Martha; Weinfurter, Elizabeth F; Pruinelli, Lisiane; Park, Jung In; Dodd, Dianna; Keenan, Gail M; Senk, Patricia; Richesson, Rachel L; Baukner, Vicki; Cruz, Christopher; Gao, Grace; Whittenburg, Luann; Delaney, Connie W

Big data and cutting-edge analytic methods in nursing research challenge nurse scientists to extend the data sources and analytic methods used for discovering and translating knowledge. The purpose of this study was to identify, analyze, and synthesize exemplars of big data nursing research applied to practice and disseminated in key nursing informatics, general biomedical informatics, and nursing research journals. A literature review of studies published between 2009 and 2015. There were 650 journal articles identified in 17 key nursing informatics, general biomedical informatics, and nursing research journals in the Web of Science database. After screening for inclusion and exclusion criteria, 17 studies published in 18 articles were identified as big data nursing research applied to practice. Nurses clearly are beginning to conduct big data research applied to practice. These studies represent multiple data sources and settings. Although numerous analytic methods were used, the fundamental issue remains to define the types of analyses consistent with big data analytic methods. There are needs to increase the visibility of big data and data science research conducted by nurse scientists, further examine the use of state of the science in data analytics, and continue to expand the availability and use of a variety of scientific, governmental, and industry data resources. A major implication of this literature review is whether nursing faculty and preparation of future scientists (PhD programs) are prepared for big data and data science. Copyright © 2016 Elsevier Inc. All rights reserved.

A Systematic Review of Techniques and Sources of Big Data in the Healthcare Sector.

PubMed

Alonso, Susel Góngora; de la Torre Díez, Isabel; Rodrigues, Joel J P C; Hamrioui, Sofiane; López-Coronado, Miguel

2017-10-14

The main objective of this paper is to present a review of existing researches in the literature, referring to Big Data sources and techniques in health sector and to identify which of these techniques are the most used in the prediction of chronic diseases. Academic databases and systems such as IEEE Xplore, Scopus, PubMed and Science Direct were searched, considering the date of publication from 2006 until the present time. Several search criteria were established as 'techniques' OR 'sources' AND 'Big Data' AND 'medicine' OR 'health', 'techniques' AND 'Big Data' AND 'chronic diseases', etc. Selecting the paper considered of interest regarding the description of the techniques and sources of Big Data in healthcare. It found a total of 110 articles on techniques and sources of Big Data on health from which only 32 have been identified as relevant work. Many of the articles show the platforms of Big Data, sources, databases used and identify the techniques most used in the prediction of chronic diseases. From the review of the analyzed research articles, it can be noticed that the sources and techniques of Big Data used in the health sector represent a relevant factor in terms of effectiveness, since it allows the application of predictive analysis techniques in tasks such as: identification of patients at risk of reentry or prevention of hospital or chronic diseases infections, obtaining predictive models of quality.

The Need for a Definition of Big Data for Nursing Science: A Case Study of Disaster Preparedness.

PubMed

Wong, Ho Ting; Chiang, Vico Chung Lim; Choi, Kup Sze; Loke, Alice Yuen

2016-10-17

The rapid development of technology has made enormous volumes of data available and achievable anytime and anywhere around the world. Data scientists call this change a data era and have introduced the term "Big Data", which has drawn the attention of nursing scholars. Nevertheless, the concept of Big Data is quite fuzzy and there is no agreement on its definition among researchers of different disciplines. Without a clear consensus on this issue, nursing scholars who are relatively new to the concept may consider Big Data to be merely a dataset of a bigger size. Having a suitable definition for nurse researchers in their context of research and practice is essential for the advancement of nursing research. In view of the need for a better understanding on what Big Data is, the aim in this paper is to explore and discuss the concept. Furthermore, an example of a Big Data research study on disaster nursing preparedness involving six million patient records is used for discussion. The example demonstrates that a Big Data analysis can be conducted from many more perspectives than would be possible in traditional sampling, and is superior to traditional sampling. Experience gained from the process of using Big Data in this study will shed light on future opportunities for conducting evidence-based nursing research to achieve competence in disaster nursing.

The Need for a Definition of Big Data for Nursing Science: A Case Study of Disaster Preparedness

PubMed Central

Wong, Ho Ting; Chiang, Vico Chung Lim; Choi, Kup Sze; Loke, Alice Yuen

2016-01-01

The rapid development of technology has made enormous volumes of data available and achievable anytime and anywhere around the world. Data scientists call this change a data era and have introduced the term “Big Data”, which has drawn the attention of nursing scholars. Nevertheless, the concept of Big Data is quite fuzzy and there is no agreement on its definition among researchers of different disciplines. Without a clear consensus on this issue, nursing scholars who are relatively new to the concept may consider Big Data to be merely a dataset of a bigger size. Having a suitable definition for nurse researchers in their context of research and practice is essential for the advancement of nursing research. In view of the need for a better understanding on what Big Data is, the aim in this paper is to explore and discuss the concept. Furthermore, an example of a Big Data research study on disaster nursing preparedness involving six million patient records is used for discussion. The example demonstrates that a Big Data analysis can be conducted from many more perspectives than would be possible in traditional sampling, and is superior to traditional sampling. Experience gained from the process of using Big Data in this study will shed light on future opportunities for conducting evidence-based nursing research to achieve competence in disaster nursing. PMID:27763525

'Big data' in pharmaceutical science: challenges and opportunities.

PubMed

Dossetter, Al G; Ecker, Gerhard; Laverty, Hugh; Overington, John

2014-05-01

Future Medicinal Chemistry invited a selection of experts to express their views on the current impact of big data in drug discovery and design, as well as speculate on future developments in the field. The topics discussed include the challenges of implementing big data technologies, maintaining the quality and privacy of data sets, and how the industry will need to adapt to welcome the big data era. Their enlightening responses provide a snapshot of the many and varied contributions being made by big data to the advancement of pharmaceutical science.

KSC-2014-3457

NASA Image and Video Library

2014-08-10

CAPE CANAVERAL, Fla. – A “supermoon” breaks through the clouds over Launch Complex 39 at NASA’s Kennedy Space Center in Florida. The scientific term for the supermoon phenomenon is "perigee moon." Full moons vary in size because of the oval shape of the moon's orbit. The moon follows an elliptical path around Earth with one side about 50,000 kilometers closer than the other. Full moons that occur on the perigee side of the moon's orbit seem extra big and bright. For additional information, visit http://science.nasa.gov/science-news/science-at-nasa/2014/10jul_supermoons/. Photo credit: NASA/Ben Smegelsky

Who's Afraid of the Big Bad Methods? Methodological Games and Role Play

ERIC Educational Resources Information Center

Kollars, Nina; Rosen, Amanda M.

2017-01-01

In terms of gamification within political science, some fields-particularly international relations and American politics--have received more attention than others. One of the most underserved parts of the discipline is research methods; a course that, coincidentally, is frequently cited as one that instructors hate to teach and students hate to…

Forget the hype or reality. Big data presents new opportunities in Earth Science.

NASA Astrophysics Data System (ADS)

Lee, T. J.

2015-12-01

Earth science is arguably one of the most mature science discipline which constantly acquires, curates, and utilizes a large volume of data with diverse variety. We deal with big data before there is big data. For example, while developing the EOS program in the 1980s, the EOS data and information system (EOSDIS) was developed to manage the vast amount of data acquired by the EOS fleet of satellites. EOSDIS continues to be a shining example of modern science data systems in the past two decades. With the explosion of internet, the usage of social media, and the provision of sensors everywhere, the big data era has bring new challenges. First, Goggle developed the search algorithm and a distributed data management system. The open source communities quickly followed up and developed Hadoop file system to facility the map reduce workloads. The internet continues to generate tens of petabytes of data every day. There is a significant shortage of algorithms and knowledgeable manpower to mine the data. In response, the federal government developed the big data programs that fund research and development projects and training programs to tackle these new challenges. Meanwhile, comparatively to the internet data explosion, Earth science big data problem has become quite small. Nevertheless, the big data era presents an opportunity for Earth science to evolve. We learned about the MapReduce algorithms, in memory data mining, machine learning, graph analysis, and semantic web technologies. How do we apply these new technologies to our discipline and bring the hype to Earth? In this talk, I will discuss how we might want to apply some of the big data technologies to our discipline and solve many of our challenging problems. More importantly, I will propose new Earth science data system architecture to enable new type of scientific inquires.

Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives

PubMed Central

Miron-Shatz, T.; Lau, A. Y. S.; Paton, C.

2014-01-01

Summary Objectives As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges. Methods A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale. Results Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to “small data” would also be useful. PMID:25123717

Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives. Contribution of the IMIA Social Media Working Group.

PubMed

Hansen, M M; Miron-Shatz, T; Lau, A Y S; Paton, C

2014-08-15

As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges. A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale. Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts: The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to "small data" would also be useful.

The Ethics of Big Data and Nursing Science.

PubMed

Milton, Constance L

2017-10-01

Big data is a scientific, social, and technological trend referring to the process and size of datasets available for analysis. Ethical implications arise as healthcare disciplines, including nursing, struggle over questions of informed consent, privacy, ownership of data, and its possible use in epistemology. The author offers straight-thinking possibilities for the use of big data in nursing science.

Big Data Analytics for Disaster Preparedness and Response of Mobile Communication Infrastructure during Natural Hazards

NASA Astrophysics Data System (ADS)

Zhong, L.; Takano, K.; Ji, Y.; Yamada, S.

2015-12-01

The disruption of telecommunications is one of the most critical disasters during natural hazards. As the rapid expanding of mobile communications, the mobile communication infrastructure plays a very fundamental role in the disaster response and recovery activities. For this reason, its disruption will lead to loss of life and property, due to information delays and errors. Therefore, disaster preparedness and response of mobile communication infrastructure itself is quite important. In many cases of experienced disasters, the disruption of mobile communication networks is usually caused by the network congestion and afterward long-term power outage. In order to reduce this disruption, the knowledge of communication demands during disasters is necessary. And big data analytics will provide a very promising way to predict the communication demands by analyzing the big amount of operational data of mobile users in a large-scale mobile network. Under the US-Japan collaborative project on 'Big Data and Disaster Research (BDD)' supported by the Japan Science and Technology Agency (JST) and National Science Foundation (NSF), we are going to investigate the application of big data techniques in the disaster preparedness and response of mobile communication infrastructure. Specifically, in this research, we have considered to exploit the big amount of operational information of mobile users for predicting the communications needs in different time and locations. By incorporating with other data such as shake distribution of an estimated major earthquake and the power outage map, we are able to provide the prediction information of stranded people who are difficult to confirm safety or ask for help due to network disruption. In addition, this result could further facilitate the network operators to assess the vulnerability of their infrastructure and make suitable decision for the disaster preparedness and response. In this presentation, we are going to introduce the results we obtained based on the big data analytics of mobile user statistical information and discuss the implications of these results.

Big Data and Data Science in Critical Care.

PubMed

Sanchez-Pinto, L Nelson; Luo, Yuan; Churpek, Matthew M

2018-05-09

The digitalization of the health-care system has resulted in a deluge of clinical Big Data and has prompted the rapid growth of data science in medicine. Data science, which is the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. The availability of large amounts of data in the ICU, the need for better evidence-based care, and the complexity of critical illness makes the use of data science techniques and data-driven research particularly appealing to intensivists. Despite the increasing number of studies and publications in the field, thus far there have been few examples of data science projects that have resulted in successful implementations of data-driven systems in the ICU. However, given the expected growth in the field, intensivists should be familiar with the opportunities and challenges of Big Data and data science. The present article reviews the definitions, types of algorithms, applications, challenges, and future of Big Data and data science in critical care. Copyright © 2018 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Meteor Observations as Big Data Citizen Science

NASA Astrophysics Data System (ADS)

Gritsevich, M.; Vinkovic, D.; Schwarz, G.; Nina, A.; Koschny, D.; Lyytinen, E.

2016-12-01

Meteor science represents an excellent example of the citizen science project, where progress in the field has been largely determined by amateur observations. Over the last couple of decades technological advancements in observational techniques have yielded drastic improvements in the quality, quantity and diversity of meteor data, while even more ambitious instruments are about to become operational. This empowers meteor science to boost its experimental and theoretical horizons and seek more advanced scientific goals. We review some of the developments that push meteor science into the Big Data era that requires more complex methodological approaches through interdisciplinary collaborations with other branches of physics and computer science. We argue that meteor science should become an integral part of large surveys in astronomy, aeronomy and space physics, and tackle the complexity of micro-physics of meteor plasma and its interaction with the atmosphere. The recent increased interest in meteor science triggered by the Chelyabinsk fireball helps in building the case for technologically and logistically more ambitious meteor projects. This requires developing new methodological approaches in meteor research, with Big Data science and close collaboration between citizen science, geoscience and astronomy as critical elements. We discuss possibilities for improvements and promote an opportunity for collaboration in meteor science within the currently established BigSkyEarth http://bigskyearth.eu/ network.

Bio and health informatics meets cloud : BioVLab as an example.

PubMed

Chae, Heejoon; Jung, Inuk; Lee, Hyungro; Marru, Suresh; Lee, Seong-Whan; Kim, Sun

2013-01-01

The exponential increase of genomic data brought by the advent of the next or the third generation sequencing (NGS) technologies and the dramatic drop in sequencing cost have driven biological and medical sciences to data-driven sciences. This revolutionary paradigm shift comes with challenges in terms of data transfer, storage, computation, and analysis of big bio/medical data. Cloud computing is a service model sharing a pool of configurable resources, which is a suitable workbench to address these challenges. From the medical or biological perspective, providing computing power and storage is the most attractive feature of cloud computing in handling the ever increasing biological data. As data increases in size, many research organizations start to experience the lack of computing power, which becomes a major hurdle in achieving research goals. In this paper, we review the features of publically available bio and health cloud systems in terms of graphical user interface, external data integration, security and extensibility of features. We then discuss about issues and limitations of current cloud systems and conclude with suggestion of a biological cloud environment concept, which can be defined as a total workbench environment assembling computational tools and databases for analyzing bio/medical big data in particular application domains.

Biosecurity in the age of Big Data: a conversation with the FBI

PubMed Central

Kozminski, Keith G.

2015-01-01

New scientific frontiers and emerging technologies within the life sciences pose many global challenges to society. Big Data is a premier example, especially with respect to individual, national, and international security. Here a Special Agent of the Federal Bureau of Investigation discusses the security implications of Big Data and the need for security in the life sciences. PMID:26543195

Big Data in Health: a Literature Review from the Year 2005.

PubMed

de la Torre Díez, Isabel; Cosgaya, Héctor Merino; Garcia-Zapirain, Begoña; López-Coronado, Miguel

2016-09-01

The information stored in healthcare systems has increased over the last ten years, leading it to be considered Big Data. There is a wealth of health information ready to be analysed. However, the sheer volume raises a challenge for traditional methods. The aim of this article is to conduct a cutting-edge study on Big Data in healthcare from 2005 to the present. This literature review will help researchers to know how Big Data has developed in the health industry and open up new avenues for research. Information searches have been made on various scientific databases such as Pubmed, Science Direct, Scopus and Web of Science for Big Data in healthcare. The search criteria were "Big Data" and "health" with a date range from 2005 to the present. A total of 9724 articles were found on the databases. 9515 articles were discarded as duplicates or for not having a title of interest to the study. 209 articles were read, with the resulting decision that 46 were useful for this study. 52.6 % of the articles used were found in Science Direct, 23.7 % in Pubmed, 22.1 % through Scopus and the remaining 2.6 % through the Web of Science. Big Data has undergone extremely high growth since 2011 and its use is becoming compulsory in developed nations and in an increasing number of developing nations. Big Data is a step forward and a cost reducer for public and private healthcare.

KSC-2014-3456

NASA Image and Video Library

2014-08-10

CAPE CANAVERAL, Fla. – Clouds over Launch Complex 39 at NASA’s Kennedy Space Center in Florida threaten to obscure the view of the “supermoon” forecast to light up the sky. The scientific term for the supermoon phenomenon is "perigee moon." Full moons vary in size because of the oval shape of the moon's orbit. The moon follows an elliptical path around Earth with one side about 50,000 kilometers closer than the other. Full moons that occur on the perigee side of the moon's orbit seem extra big and bright. For additional information, visit http://science.nasa.gov/science-news/science-at-nasa/2014/10jul_supermoons/. Photo credit: NASA/Ben Smegelsky

KSC-2014-3453

NASA Image and Video Library

2014-08-10

CAPE CANAVERAL, Fla. – Night falls over the turn basin in Launch Complex 39 at NASA’s Kennedy Space Center in Florida, bringing with it expectations of the appearance of a “supermoon.” The scientific term for the supermoon phenomenon is "perigee moon." Full moons vary in size because of the oval shape of the moon's orbit. The moon follows an elliptical path around Earth with one side about 50,000 kilometers closer than the other. Full moons that occur on the perigee side of the moon's orbit seem extra big and bright. For additional information, visit http://science.nasa.gov/science-news/science-at-nasa/2014/10jul_supermoons/. Photo credit: NASA/Ben Smegelsky

Astrophysics and Big Data: Challenges, Methods, and Tools

NASA Astrophysics Data System (ADS)

Garofalo, Mauro; Botta, Alessio; Ventre, Giorgio

2017-06-01

Nowadays there is no field research which is not flooded with data. Among the sciences, astrophysics has always been driven by the analysis of massive amounts of data. The development of new and more sophisticated observation facilities, both ground-based and spaceborne, has led data more and more complex (Variety), an exponential growth of both data Volume (i.e., in the order of petabytes), and Velocity in terms of production and transmission. Therefore, new and advanced processing solutions will be needed to process this huge amount of data. We investigate some of these solutions, based on machine learning models as well as tools and architectures for Big Data analysis that can be exploited in the astrophysical context.

Detection and Characterisation of Meteors as a Big Data Citizen Science project

NASA Astrophysics Data System (ADS)

Gritsevich, M.

2017-12-01

Out of a total around 50,000 meteorites currently known to science, the atmospheric passage was recorded instrumentally in only 30 cases with the potential to derive their atmospheric trajectories and pre-impact heliocentric orbits. Similarly, while the observations of meteors, add thousands of new entries per month to existing databases, it is extremely rare they lead to meteorite recovery. Meteor studies thus represent an excellent example of the Big Data citizen science project, where progress in the field largely depends on the prompt identification and characterisation of meteor events as well as on extensive and valuable contributions by amateur observers. Over the last couple of decades technological advancements in observational techniques have yielded drastic improvements in the quality, quantity and diversity of meteor data, while even more ambitious instruments are about to become operational. This empowers meteor science to boost its experimental and theoretical horizons and seek more advanced scientific goals. We review some of the developments that push meteor science into the Big Data era that requires more complex methodological approaches through interdisciplinary collaborations with other branches of physics and computer science. We argue that meteor science should become an integral part of large surveys in astronomy, aeronomy and space physics, and tackle the complexity of micro-physics of meteor plasma and its interaction with the atmosphere. The recent increased interest in meteor science triggered by the Chelyabinsk fireball helps in building the case for technologically and logistically more ambitious meteor projects. This requires developing new methodological approaches in meteor research, with Big Data science and close collaboration between citizen science, geoscience and astronomy as critical elements. We discuss possibilities for improvements and promote an opportunity for collaboration in meteor science within the currently established EU COST BigSkyEarth http://bigskyearth.eu/ network.

Data science, learning, and applications to biomedical and health sciences.

PubMed

Adam, Nabil R; Wieder, Robert; Ghosh, Debopriya

2017-01-01

The last decade has seen an unprecedented increase in the volume and variety of electronic data related to research and development, health records, and patient self-tracking, collectively referred to as Big Data. Properly harnessed, Big Data can provide insights and drive discovery that will accelerate biomedical advances, improve patient outcomes, and reduce costs. However, the considerable potential of Big Data remains unrealized owing to obstacles including a limited ability to standardize and consolidate data and challenges in sharing data, among a variety of sources, providers, and facilities. Here, we discuss some of these challenges and potential solutions, as well as initiatives that are already underway to take advantage of Big Data. © 2017 New York Academy of Sciences.

The Sociology of Traditional, Complementary and Alternative Medicine

PubMed Central

Gale, Nicola

2014-01-01

Complementary and alternative medicine (CAM) and traditional medicine (TM) are important social phenomena. This article reviews the sociological literature on the topic. First, it addresses the question of terminology, arguing that the naming process is a glimpse into the complexities of power and history that characterize the field. Second, focusing on the last 15 years of scholarship, it considers how sociological research on users and practitioners of TM/CAM has developed in that time. Third, it addresses two newer strands of work termed here the ‘big picture’ and the ‘big question’. The big picture includes concepts that offer interpretation of what is happening at a societal level to constrain and enable observed patterns of social practice (pluralism, integration, hybridity and activism). The big question, ‘Does it work?’, is one of epistemology and focuses on two developing fields of critical enquiry – first, social critiques of medical science knowledge production and, second, attempts to explain the nature of interventions, i.e. how they work. Finally, the article examines the role of sociology moving forward. PMID:25177359

Big physics quartet win government backing

NASA Astrophysics Data System (ADS)

Banks, Michael

2014-09-01

Four major physics-based projects are among 10 to have been selected by Japan’s Ministry of Education, Culture, Sports, Science and Technology for funding in the coming decade as part of its “roadmap” of big-science projects.

Can Any Good Thing Come out of Nazareth? (John 1:46) 1999 George C. Pimentel Award, sponsored by Union Carbide Corporation

NASA Astrophysics Data System (ADS)

Orna, Mary Virginia

1999-09-01

We are in the era of Big Science, which also means big institutions where the Big Science is done. However, higher education in the United States is unique in that parallel to the array of big institutions is a system of small liberal arts and sciences colleges where students receive the personal attention and faculty contact that is often not possible at larger institutions. While these smaller institutions are limited in resources and finances, studies have shown that they contribute a disproportionately higher number of leaders across a spectrum of disciplines, including chemistry. This address summarizes my personal odyssey and the reasons for the award. In it, I emphasize the advantages enjoyed by liberal arts and sciences students and faculty that enable them to overcome the view that great things can only be done in large, cosmopolitan settings.

IS Programs Responding to Industry Demands for Data Scientists: A Comparison between 2011-2016

ERIC Educational Resources Information Center

Mills, Robert J.; Chudoba, Katherine M.; Olsen, David H.

2016-01-01

The term data scientist has only been in common use since 2008, but in 2016 it is considered one of the top careers in the United States. The purpose of this paper is to explore the growth of data science content areas such as analytics, business intelligence, and big data in AACSB Information Systems (IS) programs between 2011 and 2016. A…

Focus: new perspectives on science and the Cold War. Introduction.

PubMed

Heyck, Hunter; Kaiser, David

2010-06-01

Twenty years after the fall of the Berlin Wall, the Cold War looks ever more like a slice of history rather than a contemporary reality. During those same twenty years, scholarship on science, technology, and the state during the Cold War era has expanded dramatically. Building on major studies of physics in the American context--often couched in terms of "big science"--recent work has broached scientific efforts in other domains as well, scrutinizing Cold War scholarship in increasingly international and comparative frameworks. The essays in this Focus section take stock of current thinking about science and the Cold War, revisiting the question of how best to understand tangled (and sometimes surprising) relationships between government patronage and the world of ideas.

From darwin to the census of marine life: marine biology as big science.

PubMed

Vermeulen, Niki

2013-01-01

With the development of the Human Genome Project, a heated debate emerged on biology becoming 'big science'. However, biology already has a long tradition of collaboration, as natural historians were part of the first collective scientific efforts: exploring the variety of life on earth. Such mappings of life still continue today, and if field biology is gradually becoming an important subject of studies into big science, research into life in the world's oceans is not taken into account yet. This paper therefore explores marine biology as big science, presenting the historical development of marine research towards the international 'Census of Marine Life' (CoML) making an inventory of life in the world's oceans. Discussing various aspects of collaboration--including size, internationalisation, research practice, technological developments, application, and public communication--I will ask if CoML still resembles traditional collaborations to collect life. While showing both continuity and change, I will argue that marine biology is a form of natural history: a specific way of working together in biology that has transformed substantially in interaction with recent developments in the life sciences and society. As a result, the paper does not only give an overview of transformations towards large scale research in marine biology, but also shines a new light on big biology, suggesting new ways to deepen the understanding of collaboration in the life sciences by distinguishing between different 'collective ways of knowing'.

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

PubMed Central

Zhang, Xinzhi; Pérez-Stable, Eliseo J.; Bourne, Philip E.; Peprah, Emmanuel; Duru, O. Kenrik; Breen, Nancy; Berrigan, David; Wood, Fred; Jackson, James S.; Wong, David W.S.; Denny, Joshua

2017-01-01

Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them. PMID:28439179

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century.

PubMed

Zhang, Xinzhi; Pérez-Stable, Eliseo J; Bourne, Philip E; Peprah, Emmanuel; Duru, O Kenrik; Breen, Nancy; Berrigan, David; Wood, Fred; Jackson, James S; Wong, David W S; Denny, Joshua

2017-01-01

Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them.

The opportunities and ethics of big data: practical priorities for a national Council of Data Ethics.

PubMed

Varley-Winter, Olivia; Shah, Hetan

2016-12-28

In order to generate the gains that can come from analysing and linking big datasets, data holders need to consider the ethical frameworks, principles and applications that help to maintain public trust. In the USA, the National Science Foundation helped to set up a Council for Big Data, Ethics and Society, of which there is no equivalent in the UK. In November 2015, the Royal Statistical Society convened a workshop of 28 participants from government, academia and the private sector, and discussed the practical priorities that might be assisted by a new Council of Data Ethics in the UK. This article draws together the views from that meeting. Priorities for policy-makers and others include seeking a public mandate and informing the terms of the social contract for use of data; building professional competence and due diligence on data protection; appointment of champions who are competent to address public concerns; and transparency, across all dimensions. For government data, further priorities include improvements to data access, and development of data infrastructure. In conclusion, we support the establishment of a national Data Ethics Council, alongside wider and deeper engagement of the public to address data ethics dilemmas.This article is part of the themed issue 'The ethical impact of data science'. © 2016 The Author(s).

Using Ethical Reasoning to Amplify the Reach and Resonance of Professional Codes of Conduct in Training Big Data Scientists.

PubMed

Tractenberg, Rochelle E; Russell, Andrew J; Morgan, Gregory J; FitzGerald, Kevin T; Collmann, Jeff; Vinsel, Lee; Steinmann, Michael; Dolling, Lisa M

2015-12-01

The use of Big Data--however the term is defined--involves a wide array of issues and stakeholders, thereby increasing numbers of complex decisions around issues including data acquisition, use, and sharing. Big Data is becoming a significant component of practice in an ever-increasing range of disciplines; however, since it is not a coherent "discipline" itself, specific codes of conduct for Big Data users and researchers do not exist. While many institutions have created, or will create, training opportunities (e.g., degree programs, workshops) to prepare people to work in and around Big Data, insufficient time, space, and thought have been dedicated to training these people to engage with the ethical, legal, and social issues in this new domain. Since Big Data practitioners come from, and work in, diverse contexts, neither a relevant professional code of conduct nor specific formal ethics training are likely to be readily available. This normative paper describes an approach to conceptualizing ethical reasoning and integrating it into training for Big Data use and research. Our approach is based on a published framework that emphasizes ethical reasoning rather than topical knowledge. We describe the formation of professional community norms from two key disciplines that contribute to the emergent field of Big Data: computer science and statistics. Historical analogies from these professions suggest strategies for introducing trainees and orienting practitioners both to ethical reasoning and to a code of professional conduct itself. We include two semester course syllabi to strengthen our thesis that codes of conduct (including and beyond those we describe) can be harnessed to support the development of ethical reasoning in, and a sense of professional identity among, Big Data practitioners.

Big data in forensic science and medicine.

PubMed

Lefèvre, Thomas

2018-07-01

In less than a decade, big data in medicine has become quite a phenomenon and many biomedical disciplines got their own tribune on the topic. Perspectives and debates are flourishing while there is a lack for a consensual definition for big data. The 3Vs paradigm is frequently evoked to define the big data principles and stands for Volume, Variety and Velocity. Even according to this paradigm, genuine big data studies are still scarce in medicine and may not meet all expectations. On one hand, techniques usually presented as specific to the big data such as machine learning techniques are supposed to support the ambition of personalized, predictive and preventive medicines. These techniques are mostly far from been new and are more than 50 years old for the most ancient. On the other hand, several issues closely related to the properties of big data and inherited from other scientific fields such as artificial intelligence are often underestimated if not ignored. Besides, a few papers temper the almost unanimous big data enthusiasm and are worth attention since they delineate what is at stakes. In this context, forensic science is still awaiting for its position papers as well as for a comprehensive outline of what kind of contribution big data could bring to the field. The present situation calls for definitions and actions to rationally guide research and practice in big data. It is an opportunity for grounding a true interdisciplinary approach in forensic science and medicine that is mainly based on evidence. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Machine learning for Big Data analytics in plants.

PubMed

Ma, Chuang; Zhang, Hao Helen; Wang, Xiangfeng

2014-12-01

Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences. Copyright © 2014 Elsevier Ltd. All rights reserved.

"Air Toxics under the Big Sky": Examining the Effectiveness of Authentic Scientific Research on High School Students' Science Skills and Interest

ERIC Educational Resources Information Center

Ward, Tony J.; Delaloye, Naomi; Adams, Earle Raymond; Ware, Desirae; Vanek, Diana; Knuth, Randy; Hester, Carolyn Laurie; Marra, Nancy Noel; Holian, Andrij

2016-01-01

"Air Toxics Under the Big Sky" is an environmental science outreach/education program that incorporates the Next Generation Science Standards (NGSS) 8 Practices with the goal of promoting knowledge and understanding of authentic scientific research in high school classrooms through air quality research. This research explored: (1)…

Systems Biology and Cancer Prevention: All Options on the Table

PubMed Central

Rosenfeld, Simon; Kapetanovic, Izet

2008-01-01

In this paper, we outline the status quo and approaches to further development of the systems biology concepts with focus on applications in cancer prevention science. We discuss the biological aspects of cancer research that are of primary importance in cancer prevention, motivations for their mathematical modeling and some recent advances in computational oncology. We also make an attempt to outline in big conceptual terms the contours of future work aimed at creation of large-scale computational and informational infrastructure for using as a routine tool in cancer prevention science and decision making. PMID:19787092

A Shovel-Ready Solution to Fill the Nursing Data Gap in the Interdisciplinary Clinical Picture.

PubMed

Keenan, Gail M; Lopez, Karen Dunn; Sousa, Vanessa E C; Stifter, Janet; Macieira, Tamara G R; Boyd, Andrew D; Yao, Yingwei; Herdman, T Heather; Moorhead, Sue; McDaniel, Anna; Wilkie, Diana J

2018-01-01

To critically evaluate 2014 American Academy of Nursing (AAN) call-to-action plan for generating interoperable nursing data. Healthcare literature. AAN's plan will not generate the nursing data needed to participate in big data science initiatives in the short term because Logical Observation Identifiers Names and Codes and Systematized Nomenclature of Medicine - Clinical Terms are not yet ripe for generating interoperable data. Well-tested viable alternatives exist. Authors present recommendations for revisions to AAN's plan and an evidence-based alternative to generating interoperable nursing data in the near term. These revisions can ultimately lead to the proposed terminology goals of the AAN's plan in the long term. © 2017 NANDA International, Inc.

Nursing Management Minimum Data Set: Cost-Effective Tool To Demonstrate the Value of Nurse Staffing in the Big Data Science Era.

PubMed

Pruinelli, Lisiane; Delaney, Connie W; Garciannie, Amy; Caspers, Barbara; Westra, Bonnie L

2016-01-01

There is a growing body of evidence of the relationship of nurse staffing to patient, nurse, and financial outcomes. With the advent of big data science and developing big data analytics in nursing, data science with the reuse of big data is emerging as a timely and cost-effective approach to demonstrate nursing value. The Nursing Management Minimum Date Set (NMMDS) provides standard administrative data elements, definitions, and codes to measure the context where care is delivered and, consequently, the value of nursing. The integration of the NMMDS elements in the current health system provides evidence for nursing leaders to measure and manage decisions, leading to better patient, staffing, and financial outcomes. It also enables the reuse of data for clinical scholarship and research.

A glossary for big data in population and public health: discussion and commentary on terminology and research methods.

PubMed

Fuller, Daniel; Buote, Richard; Stanley, Kevin

2017-11-01

The volume and velocity of data are growing rapidly and big data analytics are being applied to these data in many fields. Population and public health researchers may be unfamiliar with the terminology and statistical methods used in big data. This creates a barrier to the application of big data analytics. The purpose of this glossary is to define terms used in big data and big data analytics and to contextualise these terms. We define the five Vs of big data and provide definitions and distinctions for data mining, machine learning and deep learning, among other terms. We provide key distinctions between big data and statistical analysis methods applied to big data. We contextualise the glossary by providing examples where big data analysis methods have been applied to population and public health research problems and provide brief guidance on how to learn big data analysis methods. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

News Conference: The Big Bangor Day Meeting Lecture: Charterhouse plays host to a physics day Festival: Science on Stage festival 2013 arrives in Poland Event: Scottish Physics Teachers' Summer School Meeting: Researchers and educators meet at Lund University Conference: Exeter marks the spot Recognition: European Physical Society uncovers an historic site Education: Initial teacher education undergoes big changes Forthcoming events

NASA Astrophysics Data System (ADS)

2013-09-01

Conference: The Big Bangor Day Meeting Lecture: Charterhouse plays host to a physics day Festival: Science on Stage festival 2013 arrives in Poland Event: Scottish Physics Teachers' Summer School Meeting: Researchers and educators meet at Lund University Conference: Exeter marks the spot Recognition: European Physical Society uncovers an historic site Education: Initial teacher education undergoes big changes Forthcoming events

Assessing Conceptual Understanding via Literacy-Infused, Inquiry-Based Science among Middle School English Learners and Economically-Challenged Students

ERIC Educational Resources Information Center

Lara-Alecio, Rafael; Irby, Beverly J.; Tong, Fuhui; Guerrero, Cindy; Koch, Janice; Sutton-Jones, Kara L.

2018-01-01

The overarching purpose of our study was to compare performances of treatment and control condition students who completed a literacy-infused, inquiry-based science intervention through sixth grade as measured by a big idea assessment tool which we refer to as the Big Ideas in Science Assessment (BISA). First, we determine the concurrent validity…

Big-Data-Driven Stem Cell Science and Tissue Engineering: Vision and Unique Opportunities.

PubMed

Del Sol, Antonio; Thiesen, Hans J; Imitola, Jaime; Carazo Salas, Rafael E

2017-02-02

Achieving the promises of stem cell science to generate precise disease models and designer cell samples for personalized therapeutics will require harnessing pheno-genotypic cell-level data quantitatively and predictively in the lab and clinic. Those requirements could be met by developing a Big-Data-driven stem cell science strategy and community. Copyright © 2017 Elsevier Inc. All rights reserved.

The BIG Data Center: from deposition to integration to translation

PubMed Central

2017-01-01

Biological data are generated at unprecedentedly exponential rates, posing considerable challenges in big data deposition, integration and translation. The BIG Data Center, established at Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, provides a suite of database resources, including (i) Genome Sequence Archive, a data repository specialized for archiving raw sequence reads, (ii) Gene Expression Nebulas, a data portal of gene expression profiles based entirely on RNA-Seq data, (iii) Genome Variation Map, a comprehensive collection of genome variations for featured species, (iv) Genome Warehouse, a centralized resource housing genome-scale data with particular focus on economically important animals and plants, (v) Methylation Bank, an integrated database of whole-genome single-base resolution methylomes and (vi) Science Wikis, a central access point for biological wikis developed for community annotations. The BIG Data Center is dedicated to constructing and maintaining biological databases through big data integration and value-added curation, conducting basic research to translate big data into big knowledge and providing freely open access to a variety of data resources in support of worldwide research activities in both academia and industry. All of these resources are publicly available and can be found at http://bigd.big.ac.cn. PMID:27899658

Examining the Big-Fish-Little-Pond Effect on Students' Self-Concept of Learning Science in Taiwan Based on the TIMSS Databases

ERIC Educational Resources Information Center

Liou, Pey-Yan

2014-01-01

The purpose of this study is to examine the relationship between student self-concept and achievement in science in Taiwan based on the big-fish-little-pond effect (BFLPE) model using the Trends in International Mathematics and Science Study (TIMSS) 2003 and 2007 databases. Hierarchical linear modeling was used to examine the effects of the…

A Proposed Concentration Curriculum Design for Big Data Analytics for Information Systems Students

ERIC Educational Resources Information Center

Molluzzo, John C.; Lawler, James P.

2015-01-01

Big Data is becoming a critical component of the Information Systems curriculum. Educators are enhancing gradually the concentration curriculum for Big Data in schools of computer science and information systems. This paper proposes a creative curriculum design for Big Data Analytics for a program at a major metropolitan university. The design…

Perspective: Materials informatics and big data: Realization of the "fourth paradigm" of science in materials science

NASA Astrophysics Data System (ADS)

Agrawal, Ankit; Choudhary, Alok

2016-05-01

Our ability to collect "big data" has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery. The need for data informatics is also emphasized by the Materials Genome Initiative (MGI), further boosting the emerging field of materials informatics. In this article, we look at how data-driven techniques are playing a big role in deciphering processing-structure-property-performance relationships in materials, with illustrative examples of both forward models (property prediction) and inverse models (materials discovery). Such analytics can significantly reduce time-to-insight and accelerate cost-effective materials discovery, which is the goal of MGI.

To See the Unseen: A History of Planetary Radar Astronomy

NASA Technical Reports Server (NTRS)

Butrica, Andrew J.

1996-01-01

This book relates the history of planetary radar astronomy from its origins in radar to the present day and secondarily to bring to light that history as a case of 'Big Equipment but not Big Science'. Chapter One sketches the emergence of radar astronomy as an ongoing scientific activity at Jodrell Bank, where radar research revealed that meteors were part of the solar system. The chief Big Science driving early radar astronomy experiments was ionospheric research. Chapter Two links the Cold War and the Space Race to the first radar experiments attempted on planetary targets, while recounting the initial achievements of planetary radar, namely, the refinement of the astronomical unit and the rotational rate and direction of Venus. Chapter Three discusses early attempts to organize radar astronomy and the efforts at MIT's Lincoln Laboratory, in conjunction with Harvard radio astronomers, to acquire antenna time unfettered by military priorities. Here, the chief Big Science influencing the development of planetary radar astronomy was radio astronomy. Chapter Four spotlights the evolution of planetary radar astronomy at the Jet Propulsion Laboratory, a NASA facility, at Cornell University's Arecibo Observatory, and at Jodrell Bank. A congeries of funding from the military, the National Science Foundation, and finally NASA marked that evolution, which culminated in planetary radar astronomy finding a single Big Science patron, NASA. Chapter Five analyzes planetary radar astronomy as a science using the theoretical framework provided by philosopher of science Thomas Kuhn. Chapter Six explores the shift in planetary radar astronomy beginning in the 1970s that resulted from its financial and institutional relationship with NASA Big Science. Chapter Seven addresses the Magellan mission and its relation to the evolution of planetary radar astronomy from a ground-based to a space-based activity. Chapters Eight and Nine discuss the research carried out at ground-based facilities by this transformed planetary radar astronomy, as well as the upgrading of the Arecibo and Goldstone radars. A technical essay appended to this book provides an overview of planetary radar techniques, especially range-Doppler mapping.

Earth Science Data Analysis in the Era of Big Data

NASA Technical Reports Server (NTRS)

Kuo, K.-S.; Clune, T. L.; Ramachandran, R.

2014-01-01

Anyone with even a cursory interest in information technology cannot help but recognize that "Big Data" is one of the most fashionable catchphrases of late. From accurate voice and facial recognition, language translation, and airfare prediction and comparison, to monitoring the real-time spread of flu, Big Data techniques have been applied to many seemingly intractable problems with spectacular successes. They appear to be a rewarding way to approach many currently unsolved problems. Few fields of research can claim a longer history with problems involving voluminous data than Earth science. The problems we are facing today with our Earth's future are more complex and carry potentially graver consequences than the examples given above. How has our climate changed? Beside natural variations, what is causing these changes? What are the processes involved and through what mechanisms are these connected? How will they impact life as we know it? In attempts to answer these questions, we have resorted to observations and numerical simulations with ever-finer resolutions, which continue to feed the "data deluge." Plausibly, many Earth scientists are wondering: How will Big Data technologies benefit Earth science research? As an example from the global water cycle, one subdomain among many in Earth science, how would these technologies accelerate the analysis of decades of global precipitation to ascertain the changes in its characteristics, to validate these changes in predictive climate models, and to infer the implications of these changes to ecosystems, economies, and public health? Earth science researchers need a viable way to harness the power of Big Data technologies to analyze large volumes and varieties of data with velocity and veracity. Beyond providing speedy data analysis capabilities, Big Data technologies can also play a crucial, albeit indirect, role in boosting scientific productivity by facilitating effective collaboration within an analysis environment. To illustrate the effects of combining a Big Data technology with an effective means of collaboration, we relate the (fictitious) experience of an early-career Earth science researcher a few years beyond the present, interlaced and contrasted with reminiscences of its recent past (i.e., the present).

Who Owns Educational Theory? Big Data, Algorithms and the Expert Power of Education Data Science

ERIC Educational Resources Information Center

Williamson, Ben

2017-01-01

"Education data science" is an emerging methodological field which possesses the algorithm-driven technologies required to generate insights and knowledge from educational big data. This article consists of an analysis of the Lytics Lab, Stanford University's laboratory for research and development in learning analytics, and the Center…

Big Data: Philosophy, Emergence, Crowdledge, and Science Education

ERIC Educational Resources Information Center

dos Santos, Renato P.

2015-01-01

Big Data already passed out of hype, is now a field that deserves serious academic investigation, and natural scientists should also become familiar with Analytics. On the other hand, there is little empirical evidence that any science taught in school is helping people to lead happier, more prosperous, or more politically well-informed lives. In…

Discourse, Power, and Knowledge in the Management of "Big Science": The Production of Consensus in a Nuclear Fusion Research Laboratory.

ERIC Educational Resources Information Center

Kinsella, William J.

1999-01-01

Extends a Foucauldian view of power/knowledge to the archetypical knowledge-intensive organization, the scientific research laboratory. Describes the discursive production of power/knowledge at the "big science" laboratory conducting nuclear fusion research and illuminates a critical incident in which the fusion research…

Big Images and Big Ideas!

ERIC Educational Resources Information Center

McCullagh, John; Greenwood, Julian

2011-01-01

In this digital age, is primary science being left behind? Computer microscopes provide opportunities to transform science lessons into highly exciting learning experiences and to shift enquiry and discovery back into the hands of the children. A class of 5- and 6-year-olds was just one group of children involved in the Digitally Resourced…

A Guided Inquiry on Hubble Plots and the Big Bang

ERIC Educational Resources Information Center

Forringer, Ted

2014-01-01

In our science for non-science majors course "21st Century Physics," we investigate modern "Hubble plots" (plots of velocity versus distance for deep space objects) in order to discuss the Big Bang, dark matter, and dark energy. There are two potential challenges that our students face when encountering these topics for the…

Measuring science or religion? A measurement analysis of the National Science Foundation sponsored science literacy scale 2006-2010.

PubMed

Roos, J Micah

2014-10-01

High scientific literacy is widely considered a public good. Methods of assessing public scientific knowledge or literacy are equally important. In an effort to measure lay scientific literacy in the United States, the National Science Foundation (NSF) science literacy scale has been a part of the last three waves of the General Social Survey. However, there has been debate over the validity of some survey items as indicators of science knowledge. While many researchers treat the NSF science scale as measuring a single dimension, previous work (Bann and Schwerin, 2004; Miller, 1998, 2004) suggests a bidimensional structure. This paper hypothesizes and tests a new measurement model for the NSF science knowledge scale and finds that two items about evolution and the big bang are more measures of a religious belief dimension termed "Young Earth Worldview" than they are measures of scientific knowledge. Results are replicated in seven samples. © The Author(s) 2013.

The community-driven BiG CZ software system for integration and analysis of bio- and geoscience data in the critical zone

NASA Astrophysics Data System (ADS)

Aufdenkampe, A. K.; Mayorga, E.; Horsburgh, J. S.; Lehnert, K. A.; Zaslavsky, I.; Valentine, D. W., Jr.; Richard, S. M.; Cheetham, R.; Meyer, F.; Henry, C.; Berg-Cross, G.; Packman, A. I.; Aronson, E. L.

2014-12-01

Here we present the prototypes of a new scientific software system designed around the new Observations Data Model version 2.0 (ODM2, https://github.com/UCHIC/ODM2) to substantially enhance integration of biological and Geological (BiG) data for Critical Zone (CZ) science. The CZ science community takes as its charge the effort to integrate theory, models and data from the multitude of disciplines collectively studying processes on the Earth's surface. The central scientific challenge of the CZ science community is to develop a "grand unifying theory" of the critical zone through a theory-model-data fusion approach, for which the key missing need is a cyberinfrastructure for seamless 4D visual exploration of the integrated knowledge (data, model outputs and interpolations) from all the bio and geoscience disciplines relevant to critical zone structure and function, similar to today's ability to easily explore historical satellite imagery and photographs of the earth's surface using Google Earth. This project takes the first "BiG" steps toward answering that need. The overall goal of this project is to co-develop with the CZ science and broader community, including natural resource managers and stakeholders, a web-based integration and visualization environment for joint analysis of cross-scale bio and geoscience processes in the critical zone (BiG CZ), spanning experimental and observational designs. We will: (1) Engage the CZ and broader community to co-develop and deploy the BiG CZ software stack; (2) Develop the BiG CZ Portal web application for intuitive, high-performance map-based discovery, visualization, access and publication of data by scientists, resource managers, educators and the general public; (3) Develop the BiG CZ Toolbox to enable cyber-savvy CZ scientists to access BiG CZ Application Programming Interfaces (APIs); and (4) Develop the BiG CZ Central software stack to bridge data systems developed for multiple critical zone domains into a single metadata catalog. The entire BiG CZ Software system is being developed on public repositories as a modular suite of open source software projects. It will be built around a new Observations Data Model Version 2.0 (ODM2) that has been developed by members of the BiG CZ project team, with community input, under separate funding.

From Darwin to the Census of Marine Life: Marine Biology as Big Science

PubMed Central

Vermeulen, Niki

2013-01-01

With the development of the Human Genome Project, a heated debate emerged on biology becoming ‘big science’. However, biology already has a long tradition of collaboration, as natural historians were part of the first collective scientific efforts: exploring the variety of life on earth. Such mappings of life still continue today, and if field biology is gradually becoming an important subject of studies into big science, research into life in the world's oceans is not taken into account yet. This paper therefore explores marine biology as big science, presenting the historical development of marine research towards the international ‘Census of Marine Life’ (CoML) making an inventory of life in the world's oceans. Discussing various aspects of collaboration – including size, internationalisation, research practice, technological developments, application, and public communication – I will ask if CoML still resembles traditional collaborations to collect life. While showing both continuity and change, I will argue that marine biology is a form of natural history: a specific way of working together in biology that has transformed substantially in interaction with recent developments in the life sciences and society. As a result, the paper does not only give an overview of transformations towards large scale research in marine biology, but also shines a new light on big biology, suggesting new ways to deepen the understanding of collaboration in the life sciences by distinguishing between different ‘collective ways of knowing’. PMID:23342119

Materials Data Science: Current Status and Future Outlook

NASA Astrophysics Data System (ADS)

Kalidindi, Surya R.; De Graef, Marc

2015-07-01

The field of materials science and engineering is on the cusp of a digital data revolution. After reviewing the nature of data science and Big Data, we discuss the features of materials data that distinguish them from data in other fields. We introduce the concept of process-structure-property (PSP) linkages and illustrate how the determination of PSPs is one of the main objectives of materials data science. Then we review a selection of materials databases, as well as important aspects of materials data management, such as storage hardware, archiving strategies, and data access strategies. We introduce the emerging field of materials data analytics, which focuses on data-driven approaches to extract and curate materials knowledge from available data sets. The critical need for materials e-collaboration platforms is highlighted, and we conclude the article with a number of suggestions regarding the near-term future of the materials data science field.

Power to the People: Data Citizens in the Age of Precision Medicine

PubMed Central

Evans, Barbara J.

2017-01-01

Twentieth-century bioethics celebrated individual autonomy but framed autonomy largely in terms of an individual’s power to make decisions and act alone. The most pressing challenges of big data science in the twenty-first century can only be resolved through collective action and common purpose. This Article surveys some of these challenges and asks how common purpose can ever emerge on the present bioethical and regulatory landscape. The solution may lie in embracing a broader concept of autonomy that empowers individuals to protect their interests by exercising meaningful rights of data citizenship. This Article argues that twentieth-century bioethics was a paternalistic, top-down affair in which self-proclaimed ethics experts set standards to protect research subjects portrayed as autonomous yet too vulnerable and disorganized to protect themselves. The time may be ripe for BioEXIT, a popular uprising of regular people seeking a meaningful voice in establishing citizen-led ethical and privacy standards to advance big-data science while addressing the concerns people feel about the privacy of their health data. PMID:29118898

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.

The BIG Data Center: from deposition to integration to translation.

PubMed

2017-01-04

Biological data are generated at unprecedentedly exponential rates, posing considerable challenges in big data deposition, integration and translation. The BIG Data Center, established at Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, provides a suite of database resources, including (i) Genome Sequence Archive, a data repository specialized for archiving raw sequence reads, (ii) Gene Expression Nebulas, a data portal of gene expression profiles based entirely on RNA-Seq data, (iii) Genome Variation Map, a comprehensive collection of genome variations for featured species, (iv) Genome Warehouse, a centralized resource housing genome-scale data with particular focus on economically important animals and plants, (v) Methylation Bank, an integrated database of whole-genome single-base resolution methylomes and (vi) Science Wikis, a central access point for biological wikis developed for community annotations. The BIG Data Center is dedicated to constructing and maintaining biological databases through big data integration and value-added curation, conducting basic research to translate big data into big knowledge and providing freely open access to a variety of data resources in support of worldwide research activities in both academia and industry. All of these resources are publicly available and can be found at http://bigd.big.ac.cn. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Communicating the Nature of Science through "The Big Bang Theory": Evidence from a Focus Group Study

ERIC Educational Resources Information Center

Li, Rashel; Orthia, Lindy A.

2016-01-01

In this paper, we discuss a little-studied means of communicating about or teaching the nature of science (NOS)--through fiction television. We report some results of focus group research which suggest that the American sitcom "The Big Bang Theory" (2007-present), whose main characters are mostly working scientists, has influenced…

A Big Bang Lab

ERIC Educational Resources Information Center

Scheider, Walter

2005-01-01

The February 2005 issue of The Science Teacher (TST) reminded everyone that by learning how scientists study stars, students gain an understanding of how science measures things that can not be set up in lab, either because they are too big, too far away, or happened in a very distant past. The authors of "How Far are the Stars?" show how the…

Big Data and Perioperative Nursing.

PubMed

Westra, Bonnie L; Peterson, Jessica J

2016-10-01

Big data are large volumes of digital data that can be collected from disparate sources and are challenging to analyze. These data are often described with the five "Vs": volume, velocity, variety, veracity, and value. Perioperative nurses contribute to big data through documentation in the electronic health record during routine surgical care, and these data have implications for clinical decision making, administrative decisions, quality improvement, and big data science. This article explores methods to improve the quality of perioperative nursing data and provides examples of how these data can be combined with broader nursing data for quality improvement. We also discuss a national action plan for nursing knowledge and big data science and how perioperative nurses can engage in collaborative actions to transform health care. Standardized perioperative nursing data has the potential to affect care far beyond the original patient. Copyright © 2016 AORN, Inc. Published by Elsevier Inc. All rights reserved.

The big data-big model (BDBM) challenges in ecological research

NASA Astrophysics Data System (ADS)

Luo, Y.

2015-12-01

The field of ecology has become a big-data science in the past decades due to development of new sensors used in numerous studies in the ecological community. Many sensor networks have been established to collect data. For example, satellites, such as Terra and OCO-2 among others, have collected data relevant on global carbon cycle. Thousands of field manipulative experiments have been conducted to examine feedback of terrestrial carbon cycle to global changes. Networks of observations, such as FLUXNET, have measured land processes. In particular, the implementation of the National Ecological Observatory Network (NEON), which is designed to network different kinds of sensors at many locations over the nation, will generate large volumes of ecological data every day. The raw data from sensors from those networks offer an unprecedented opportunity for accelerating advances in our knowledge of ecological processes, educating teachers and students, supporting decision-making, testing ecological theory, and forecasting changes in ecosystem services. Currently, ecologists do not have the infrastructure in place to synthesize massive yet heterogeneous data into resources for decision support. It is urgent to develop an ecological forecasting system that can make the best use of multiple sources of data to assess long-term biosphere change and anticipate future states of ecosystem services at regional and continental scales. Forecasting relies on big models that describe major processes that underlie complex system dynamics. Ecological system models, despite great simplification of the real systems, are still complex in order to address real-world problems. For example, Community Land Model (CLM) incorporates thousands of processes related to energy balance, hydrology, and biogeochemistry. Integration of massive data from multiple big data sources with complex models has to tackle Big Data-Big Model (BDBM) challenges. Those challenges include interoperability of multiple, heterogeneous data sets; intractability of structural complexity of big models; equifinality of model structure selection and parameter estimation; and computational demand of global optimization with Big Models.

Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders--promises and limitations.

PubMed

Zhao, Yihong; Castellanos, F Xavier

2016-03-01

Psychiatric science remains descriptive, with a categorical nosology intended to enhance interobserver reliability. Increased awareness of the mismatch between categorical classifications and the complexity of biological systems drives the search for novel frameworks including discovery science in Big Data. In this review, we provide an overview of incipient approaches, primarily focused on classically categorical diagnoses such as schizophrenia (SZ), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD), but also reference convincing, if focal, advances in cancer biology, to describe the challenges of Big Data and discovery science, and outline approaches being formulated to overcome existing obstacles. A paradigm shift from categorical diagnoses to a domain/structure-based nosology and from linear causal chains to complex causal network models of brain-behavior relationship is ongoing. This (r)evolution involves appreciating the complexity, dimensionality, and heterogeneity of neuropsychiatric data collected from multiple sources ('broad' data) along with data obtained at multiple levels of analysis, ranging from genes to molecules, cells, circuits, and behaviors ('deep' data). Both of these types of Big Data landscapes require the use and development of robust and powerful informatics and statistical approaches. Thus, we describe Big Data analysis pipelines and the promise and potential limitations in using Big Data approaches to study psychiatric disorders. We highlight key resources available for psychopathological studies and call for the application and development of Big Data approaches to dissect the causes and mechanisms of neuropsychiatric disorders and identify corresponding biomarkers for early diagnosis. © 2016 Association for Child and Adolescent Mental Health.

Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders: promises and limitations

PubMed Central

Zhao, Yihong; Castellanos, F. Xavier

2015-01-01

Background and Scope Psychiatric science remains descriptive, with a categorical nosology intended to enhance inter-observer reliability. Increased awareness of the mismatch between categorical classifications and the complexity of biological systems drives the search for novel frameworks including discovery science in Big Data. In this review, we provide an overview of incipient approaches, primarily focused on classically categorical diagnoses such as schizophrenia (SZ), autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), but also reference convincing, if focal, advances in cancer biology, to describe the challenges of Big Data and discovery science, and outline approaches being formulated to overcome existing obstacles. Findings A paradigm shift from categorical diagnoses to a domain/structure-based nosology and from linear causal chains to complex causal network models of brain-behavior relationship is ongoing. This (r)evolution involves appreciating the complexity, dimensionality and heterogeneity of neuropsychiatric data collected from multiple sources (“broad” data) along with data obtained at multiple levels of analysis, ranging from genes to molecules, cells, circuits and behaviors (“deep” data). Both of these types of Big Data landscapes require the use and development of robust and powerful informatics and statistical approaches. Thus, we describe Big Data analysis pipelines and the promise and potential limitations in using Big Data approaches to study psychiatric disorders. Conclusion We highlight key resources available for psychopathological studies and call for the application and development of Big Data approaches to dissect the causes and mechanisms of neuropsychiatric disorders and identify corresponding biomarkers for early diagnosis. PMID:26732133

A Demonstration of Big Data Technology for Data Intensive Earth Science (Invited)

NASA Astrophysics Data System (ADS)

Kuo, K.; Clune, T.; Ramachandran, R.; Rushing, J.; Fekete, G.; Lin, A.; Doan, K.; Oloso, A. O.; Duffy, D.

2013-12-01

Big Data technologies exhibit great potential to change the way we conduct scientific investigations, especially analysis of voluminous and diverse data sets. Obviously, not all Big Data technologies are applicable to all aspects of scientific data analysis. Our NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) project, Automated Event Service (AES), pioneers the exploration of Big Data technologies for data intensive Earth science. Since Earth science data are largely stored and manipulated in the form of multidimensional arrays, the project first evaluates array performance of several candidate Big Data technologies, including MapReduce (Hadoop), SciDB, and a custom-built Polaris system, which have one important feature in common: shared nothing architecture. The evaluation finds SicDB to be the most promising. In this presentation, we demonstrate SciDB using a couple of use cases, each operating on a distinct data set in the regular latitude-longitude grid. The first use case is the discovery and identification of blizzards using NASA's Modern Era Retrospective-analysis for Research and Application (MERRA) data sets. The other finds diurnal signals in the same 8-year period using SSMI data from three different instruments with different equator crossing times by correlating their retrieved parameters. In addition, the AES project is also developing a collaborative component to enable the sharing of event queries and results. Preliminary capabilities will be presented as well.

Personalized medicine beyond genomics: alternative futures in big data-proteomics, environtome and the social proteome.

PubMed

Özdemir, Vural; Dove, Edward S; Gürsoy, Ulvi K; Şardaş, Semra; Yıldırım, Arif; Yılmaz, Şenay Görücü; Ömer Barlas, I; Güngör, Kıvanç; Mete, Alper; Srivastava, Sanjeeva

2017-01-01

No field in science and medicine today remains untouched by Big Data, and psychiatry is no exception. Proteomics is a Big Data technology and a next generation biomarker, supporting novel system diagnostics and therapeutics in psychiatry. Proteomics technology is, in fact, much older than genomics and dates to the 1970s, well before the launch of the international Human Genome Project. While the genome has long been framed as the master or "elite" executive molecule in cell biology, the proteome by contrast is humble. Yet the proteome is critical for life-it ensures the daily functioning of cells and whole organisms. In short, proteins are the blue-collar workers of biology, the down-to-earth molecules that we cannot live without. Since 2010, proteomics has found renewed meaning and international attention with the launch of the Human Proteome Project and the growing interest in Big Data technologies such as proteomics. This article presents an interdisciplinary technology foresight analysis and conceptualizes the terms "environtome" and "social proteome". We define "environtome" as the entire complement of elements external to the human host, from microbiome, ambient temperature and weather conditions to government innovation policies, stock market dynamics, human values, political power and social norms that collectively shape the human host spatially and temporally. The "social proteome" is the subset of the environtome that influences the transition of proteomics technology to innovative applications in society. The social proteome encompasses, for example, new reimbursement schemes and business innovation models for proteomics diagnostics that depart from the "once-a-life-time" genotypic tests and the anticipated hype attendant to context and time sensitive proteomics tests. Building on the "nesting principle" for governance of complex systems as discussed by Elinor Ostrom, we propose here a 3-tiered organizational architecture for Big Data science such as proteomics. The proposed nested governance structure is comprised of (a) scientists, (b) ethicists, and (c) scholars in the nascent field of "ethics-of-ethics", and aims to cultivate a robust social proteome for personalized medicine. Ostrom often noted that such nested governance designs offer assurance that political power embedded in innovation processes is distributed evenly and is not concentrated disproportionately in a single overbearing stakeholder or person. We agree with this assessment and conclude by underscoring the synergistic value of social and biological proteomes to realize the full potentials of proteomics science for personalized medicine in psychiatry in the present era of Big Data.

Science Fiction and the Big Questions

NASA Astrophysics Data System (ADS)

O'Keefe, M.

Advocates of space science promote investment in science education and the development of new technologies necessary for space travel. Success in these areas requires an increase of interest and support among the general public. What role can entertainment media play in inspiring the public ­ especially young people ­ to support the development of space science? Such inspiration is badly needed. Science education and funding in the United States are in a state of crisis. This bleak situation exists during a boom in the popularity of science-oriented television shows and science fiction movies. This paper draws on interviews with professionals in science, technology, engineering and mathematics (STEM) fields, as well as students interested in those fields. The interviewees were asked about their lifelong media-viewing habits. Analysis of these interviews, along with examples from popular culture, suggests that science fiction can be a valuable tool for space advocates. Specifically, the aspects of character, story, and special effects can provide viewers with inspiration and a sense of wonder regarding space science and the prospect of long-term human space exploration.

BRIC Health Systems and Big Pharma: A Challenge for Health Policy and Management.

PubMed

Rodwin, Victor G; Fabre, Guilhem; Ayoub, Rafael F

2018-01-02

BRIC nations - Brazil, Russia, India, and China - represent 40% of the world's population, including a growing aging population and middle class with an increasing prevalence of chronic disease. Their healthcare systems increasingly rely on prescription drugs, but they differ from most other healthcare systems because healthcare expenditures in BRIC nations have exhibited the highest revenue growth rates for pharmaceutical multinational corporations (MNCs), Big Pharma. The response of BRIC nations to Big Pharma presents contrasting cases of how governments manage the tensions posed by rising public expectations and limited resources to satisfy them. Understanding these tensions represents an emerging area of research and an important challenge for all those who work in the field of health policy and management (HPAM). © 2018 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Big Data Science Education: A Case Study of a Project-Focused Introductory Course

ERIC Educational Resources Information Center

Saltz, Jeffrey; Heckman, Robert

2015-01-01

This paper reports on a case study of a project-focused introduction to big data science course. The pedagogy of the course leveraged boundary theory, where students were positioned to be at the boundary between a client's desire to understand their data and the academic class. The results of the case study demonstrate that using live clients…

Acquisition, Analysis, and Sharing of Data in 2015 and Beyond: A Survey of the Landscape: A Conference Report From the American Heart Association Data Summit 2015.

PubMed

Antman, Elliott M; Benjamin, Emelia J; Harrington, Robert A; Houser, Steven R; Peterson, Eric D; Bauman, Mary Ann; Brown, Nancy; Bufalino, Vincent; Califf, Robert M; Creager, Mark A; Daugherty, Alan; Demets, David L; Dennis, Bernard P; Ebadollahi, Shahram; Jessup, Mariell; Lauer, Michael S; Lo, Bernard; MacRae, Calum A; McConnell, Michael V; McCray, Alexa T; Mello, Michelle M; Mueller, Eric; Newburger, Jane W; Okun, Sally; Packer, Milton; Philippakis, Anthony; Ping, Peipei; Prasoon, Prad; Roger, Véronique L; Singer, Steve; Temple, Robert; Turner, Melanie B; Vigilante, Kevin; Warner, John; Wayte, Patrick

2015-11-05

A 1.5-day interactive forum was convened to discuss critical issues in the acquisition, analysis, and sharing of data in the field of cardiovascular and stroke science. The discussion will serve as the foundation for the American Heart Association's (AHA's) near-term and future strategies in the Big Data area. The concepts evolving from this forum may also inform other fields of medicine and science. A total of 47 participants representing stakeholders from 7 domains (patients, basic scientists, clinical investigators, population researchers, clinicians and healthcare system administrators, industry, and regulatory authorities) participated in the conference. Presentation topics included updates on data as viewed from conventional medical and nonmedical sources, building and using Big Data repositories, articulation of the goals of data sharing, and principles of responsible data sharing. Facilitated breakout sessions were conducted to examine what each of the 7 stakeholder domains wants from Big Data under ideal circumstances and the possible roles that the AHA might play in meeting their needs. Important areas that are high priorities for further study regarding Big Data include a description of the methodology of how to acquire and analyze findings, validation of the veracity of discoveries from such research, and integration into investigative and clinical care aspects of future cardiovascular and stroke medicine. Potential roles that the AHA might consider include facilitating a standards discussion (eg, tools, methodology, and appropriate data use), providing education (eg, healthcare providers, patients, investigators), and helping build an interoperable digital ecosystem in cardiovascular and stroke science. There was a consensus across stakeholder domains that Big Data holds great promise for revolutionizing the way cardiovascular and stroke research is conducted and clinical care is delivered; however, there is a clear need for the creation of a vision of how to use it to achieve the desired goals. Potential roles for the AHA center around facilitating a discussion of standards, providing education, and helping establish a cardiovascular digital ecosystem. This ecosystem should be interoperable and needs to interface with the rapidly growing digital object environment of the modern-day healthcare system. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Big data and clinicians: a review on the state of the science.

PubMed

Wang, Weiqi; Krishnan, Eswar

2014-01-17

In the past few decades, medically related data collection saw a huge increase, referred to as big data. These huge datasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important role in identifying causality of patient symptoms, in predicting hazards of disease incidence or reoccurrence, and in improving primary-care quality. The objective of this review was to provide an overview of the features of clinical big data, describe a few commonly employed computational algorithms, statistical methods, and software toolkits for data manipulation and analysis, and discuss the challenges and limitations in this realm. We conducted a literature review to identify studies on big data in medicine, especially clinical medicine. We used different combinations of keywords to search PubMed, Science Direct, Web of Knowledge, and Google Scholar for literature of interest from the past 10 years. This paper reviewed studies that analyzed clinical big data and discussed issues related to storage and analysis of this type of data. Big data is becoming a common feature of biological and clinical studies. Researchers who use clinical big data face multiple challenges, and the data itself has limitations. It is imperative that methodologies for data analysis keep pace with our ability to collect and store data.

New to Teaching: Small Changes Can Produce Big Results!

ERIC Educational Resources Information Center

Shenton, Megan

2017-01-01

In this article, Megan Shenton, a final-year trainee teacher at Nottinghom Trent University, describes using "The Big Question" in her science teaching in a move away from objectives. The Big Question is an innovative pedagogical choice, where instead of implementing a learning objective, a question is posed at the start of the session…

How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China

PubMed Central

Huang, Ying; Zhang, Yi; Youtie, Jan; Porter, Alan L.; Wang, Xuefeng

2016-01-01

How do funding agencies ramp-up their capabilities to support research in a rapidly emerging area? This paper addresses this question through a comparison of research proposals awarded by the US National Science Foundation (NSF) and the National Natural Science Foundation of China (NSFC) in the field of Big Data. Big data is characterized by its size and difficulties in capturing, curating, managing and processing it in reasonable periods of time. Although Big Data has its legacy in longstanding information technology research, the field grew very rapidly over a short period. We find that the extent of interdisciplinarity is a key aspect in how these funding agencies address the rise of Big Data. Our results show that both agencies have been able to marshal funding to support Big Data research in multiple areas, but the NSF relies to a greater extent on multi-program funding from different fields. We discuss how these interdisciplinary approaches reflect the research hot-spots and innovation pathways in these two countries. PMID:27219466

How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China.

PubMed

Huang, Ying; Zhang, Yi; Youtie, Jan; Porter, Alan L; Wang, Xuefeng

2016-01-01

How do funding agencies ramp-up their capabilities to support research in a rapidly emerging area? This paper addresses this question through a comparison of research proposals awarded by the US National Science Foundation (NSF) and the National Natural Science Foundation of China (NSFC) in the field of Big Data. Big data is characterized by its size and difficulties in capturing, curating, managing and processing it in reasonable periods of time. Although Big Data has its legacy in longstanding information technology research, the field grew very rapidly over a short period. We find that the extent of interdisciplinarity is a key aspect in how these funding agencies address the rise of Big Data. Our results show that both agencies have been able to marshal funding to support Big Data research in multiple areas, but the NSF relies to a greater extent on multi-program funding from different fields. We discuss how these interdisciplinary approaches reflect the research hot-spots and innovation pathways in these two countries.

The History of Science and Technology at Bell Labs

NASA Astrophysics Data System (ADS)

Bishop, David

2008-03-01

Over the last 80 years, Bell Labs has been one of the most scientifically and technologically productive research labs in the world. Inventions such as the transistor, laser, cell phone, solar cell, negative feedback amplifier, communications satellite and many others were made there. Scientific breakthroughs such as discovery of the Big Bang, the wave nature of the electron, electron localization and the fractional quantum hall effect were also made there making Bell Labs almost unique in terms of large impacts in both science and technology. In my talk, I will discuss the history of the lab, talk about the present and give some suggestions for how I see it evolving into the future.

F*** Yeah Fluid Dynamics: On science outreach and appealing to broad audiences

NASA Astrophysics Data System (ADS)

Sharp, Nicole

2015-11-01

Sharing scientific research with general audiences is important for scientists both in terms of educating the public and in pursuing funding opportunities. But it's not always apparent how to make a big splash. Over the past five years, fluid dynamics outreach blog FYFD has published more than 1300 articles and gained an audience of over 215,000 readers. The site appeals to a wide spectrum of readers in both age and field of study. This talk will utilize five years' worth of site content and reader feedback to examine what makes science appealing to general audiences and suggest methods researchers can use to shape their work's broader impact.

Functional connectomics from a "big data" perspective.

PubMed

Xia, Mingrui; He, Yong

2017-10-15

In the last decade, explosive growth regarding functional connectome studies has been observed. Accumulating knowledge has significantly contributed to our understanding of the brain's functional network architectures in health and disease. With the development of innovative neuroimaging techniques, the establishment of large brain datasets and the increasing accumulation of published findings, functional connectomic research has begun to move into the era of "big data", which generates unprecedented opportunities for discovery in brain science and simultaneously encounters various challenging issues, such as data acquisition, management and analyses. Big data on the functional connectome exhibits several critical features: high spatial and/or temporal precision, large sample sizes, long-term recording of brain activity, multidimensional biological variables (e.g., imaging, genetic, demographic, cognitive and clinic) and/or vast quantities of existing findings. We review studies regarding functional connectomics from a big data perspective, with a focus on recent methodological advances in state-of-the-art image acquisition (e.g., multiband imaging), analysis approaches and statistical strategies (e.g., graph theoretical analysis, dynamic network analysis, independent component analysis, multivariate pattern analysis and machine learning), as well as reliability and reproducibility validations. We highlight the novel findings in the application of functional connectomic big data to the exploration of the biological mechanisms of cognitive functions, normal development and aging and of neurological and psychiatric disorders. We advocate the urgent need to expand efforts directed at the methodological challenges and discuss the direction of applications in this field. Copyright © 2017 Elsevier Inc. All rights reserved.

From ecological records to big data: the invention of global biodiversity.

PubMed

Devictor, Vincent; Bensaude-Vincent, Bernadette

2016-12-01

This paper is a critical assessment of the epistemological impact of the systematic quantification of nature with the accumulation of big datasets on the practice and orientation of ecological science. We examine the contents of big databases and argue that it is not just accumulated information; records are translated into digital data in a process that changes their meanings. In order to better understand what is at stake in the 'datafication' process, we explore the context for the emergence and quantification of biodiversity in the 1980s, along with the concept of the global environment. In tracing the origin and development of the global biodiversity information facility (GBIF) we describe big data biodiversity projects as a techno-political construction dedicated to monitoring a new object: the global diversity. We argue that, biodiversity big data became a powerful driver behind the invention of the concept of the global environment, and a way to embed ecological science in the political agenda.

Individuals with greater science literacy and education have more polarized beliefs on controversial science topics.

PubMed

Drummond, Caitlin; Fischhoff, Baruch

2017-09-05

Although Americans generally hold science in high regard and respect its findings, for some contested issues, such as the existence of anthropogenic climate change, public opinion is polarized along religious and political lines. We ask whether individuals with more general education and greater science knowledge, measured in terms of science education and science literacy, display more (or less) polarized beliefs on several such issues. We report secondary analyses of a nationally representative dataset (the General Social Survey), examining the predictors of beliefs regarding six potentially controversial issues. We find that beliefs are correlated with both political and religious identity for stem cell research, the Big Bang, and human evolution, and with political identity alone on climate change. Individuals with greater education, science education, and science literacy display more polarized beliefs on these issues. We find little evidence of political or religious polarization regarding nanotechnology and genetically modified foods. On all six topics, people who trust the scientific enterprise more are also more likely to accept its findings. We discuss the causal mechanisms that might underlie the correlation between education and identity-based polarization.

The Whole Shebang: How Science Produced the Big Bang Model.

ERIC Educational Resources Information Center

Ferris, Timothy

2002-01-01

Offers an account of the accumulation of evidence that has led scientists to have confidence in the big bang theory of the creation of the universe. Discusses the early work of Ptolemy, Copernicus, Kepler, Galileo, and Newton, noting the rise of astrophysics, and highlighting the birth of the big bang model (the cosmic microwave background theory…

Big Sib Students' Perceptions of the Educational Environment at the School of Medical Sciences, Universiti Sains Malaysia, using Dundee Ready Educational Environment Measure (DREEM) Inventory.

PubMed

Arzuman, Hafiza; Yusoff, Muhamad Saiful Bahri; Chit, Som Phong

2010-07-01

A cross-sectional descriptive study was conducted among Big Sib students to explore their perceptions of the educational environment at the School of Medical Sciences, Universiti Sains Malaysia (USM) and its weak areas using the Dundee Ready Educational Environment Measure (DREEM) inventory. The DREEM inventory is a validated global instrument for measuring educational environments in undergraduate medical and health professional education. The English version of the DREEM inventory was administered to all Year 2 Big Sib students (n = 67) at a regular Big Sib session. The purpose of the study as well as confidentiality and ethical issues were explained to the students before the questionnaire was administered. The response rate was 62.7% (42 out of 67 students). The overall DREEM score was 117.9/200 (SD 14.6). The DREEM indicated that the Big Sib students' perception of educational environment of the medical school was more positive than negative. Nevertheless, the study also revealed some problem areas within the educational environment. This pilot study revealed that Big Sib students perceived a positive learning environment at the School of Medical Sciences, USM. It also identified some low-scored areas that require further exploration to pinpoint the exact problems. The relatively small study population selected from a particular group of students was the major limitation of the study. This small sample size also means that the study findings cannot be generalised.

Database Resources of the BIG Data Center in 2018

PubMed Central

Xu, Xingjian; Hao, Lili; Zhu, Junwei; Tang, Bixia; Zhou, Qing; Song, Fuhai; Chen, Tingting; Zhang, Sisi; Dong, Lili; Lan, Li; Wang, Yanqing; Sang, Jian; Hao, Lili; Liang, Fang; Cao, Jiabao; Liu, Fang; Liu, Lin; Wang, Fan; Ma, Yingke; Xu, Xingjian; Zhang, Lijuan; Chen, Meili; Tian, Dongmei; Li, Cuiping; Dong, Lili; Du, Zhenglin; Yuan, Na; Zeng, Jingyao; Zhang, Zhewen; Wang, Jinyue; Shi, Shuo; Zhang, Yadong; Pan, Mengyu; Tang, Bixia; Zou, Dong; Song, Shuhui; Sang, Jian; Xia, Lin; Wang, Zhennan; Li, Man; Cao, Jiabao; Niu, Guangyi; Zhang, Yang; Sheng, Xin; Lu, Mingming; Wang, Qi; Xiao, Jingfa; Zou, Dong; Wang, Fan; Hao, Lili; Liang, Fang; Li, Mengwei; Sun, Shixiang; Zou, Dong; Li, Rujiao; Yu, Chunlei; Wang, Guangyu; Sang, Jian; Liu, Lin; Li, Mengwei; Li, Man; Niu, Guangyi; Cao, Jiabao; Sun, Shixiang; Xia, Lin; Yin, Hongyan; Zou, Dong; Xu, Xingjian; Ma, Lina; Chen, Huanxin; Sun, Yubin; Yu, Lei; Zhai, Shuang; Sun, Mingyuan; Zhang, Zhang; Zhao, Wenming; Xiao, Jingfa; Bao, Yiming; Song, Shuhui; Hao, Lili; Li, Rujiao; Ma, Lina; Sang, Jian; Wang, Yanqing; Tang, Bixia; Zou, Dong; Wang, Fan

2018-01-01

Abstract The BIG Data Center at Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences provides freely open access to a suite of database resources in support of worldwide research activities in both academia and industry. With the vast amounts of omics data generated at ever-greater scales and rates, the BIG Data Center is continually expanding, updating and enriching its core database resources through big-data integration and value-added curation, including BioCode (a repository archiving bioinformatics tool codes), BioProject (a biological project library), BioSample (a biological sample library), Genome Sequence Archive (GSA, a data repository for archiving raw sequence reads), Genome Warehouse (GWH, a centralized resource housing genome-scale data), Genome Variation Map (GVM, a public repository of genome variations), Gene Expression Nebulas (GEN, a database of gene expression profiles based on RNA-Seq data), Methylation Bank (MethBank, an integrated databank of DNA methylomes), and Science Wikis (a series of biological knowledge wikis for community annotations). In addition, three featured web services are provided, viz., BIG Search (search as a service; a scalable inter-domain text search engine), BIG SSO (single sign-on as a service; a user access control system to gain access to multiple independent systems with a single ID and password) and Gsub (submission as a service; a unified submission service for all relevant resources). All of these resources are publicly accessible through the home page of the BIG Data Center at http://bigd.big.ac.cn. PMID:29036542

Making a Big Bang on the small screen

NASA Astrophysics Data System (ADS)

Thomas, Nick

2010-01-01

While the quality of some TV sitcoms can leave viewers feeling cheated out of 30 minutes of their lives, audiences and critics are raving about the science-themed US comedy The Big Bang Theory. First shown on the CBS network in 2007, the series focuses on two brilliant postdoc physicists, Leonard and Sheldon, who are totally absorbed by science. Adhering to the stereotype, they also share a fanatical interest in science fiction, video-gaming and comic books, but unfortunately lack the social skills required to connect with their 20-something nonacademic contemporaries.

Big Data and Clinicians: A Review on the State of the Science

PubMed Central

Wang, Weiqi

2014-01-01

Background In the past few decades, medically related data collection saw a huge increase, referred to as big data. These huge datasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important role in identifying causality of patient symptoms, in predicting hazards of disease incidence or reoccurrence, and in improving primary-care quality. Objective The objective of this review was to provide an overview of the features of clinical big data, describe a few commonly employed computational algorithms, statistical methods, and software toolkits for data manipulation and analysis, and discuss the challenges and limitations in this realm. Methods We conducted a literature review to identify studies on big data in medicine, especially clinical medicine. We used different combinations of keywords to search PubMed, Science Direct, Web of Knowledge, and Google Scholar for literature of interest from the past 10 years. Results This paper reviewed studies that analyzed clinical big data and discussed issues related to storage and analysis of this type of data. Conclusions Big data is becoming a common feature of biological and clinical studies. Researchers who use clinical big data face multiple challenges, and the data itself has limitations. It is imperative that methodologies for data analysis keep pace with our ability to collect and store data. PMID:25600256

The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery.

PubMed

Swan, Melanie

2013-06-01

A key contemporary trend emerging in big data science is the quantified self (QS)-individuals engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information as n=1 individuals or in groups. There are opportunities for big data scientists to develop new models to support QS data collection, integration, and analysis, and also to lead in defining open-access database resources and privacy standards for how personal data is used. Next-generation QS applications could include tools for rendering QS data meaningful in behavior change, establishing baselines and variability in objective metrics, applying new kinds of pattern recognition techniques, and aggregating multiple self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. The long-term vision of QS activity is that of a systemic monitoring approach where an individual's continuous personal information climate provides real-time performance optimization suggestions. There are some potential limitations related to QS activity-barriers to widespread adoption and a critique regarding scientific soundness-but these may be overcome. One interesting aspect of QS activity is that it is fundamentally a quantitative and qualitative phenomenon since it includes both the collection of objective metrics data and the subjective experience of the impact of these data. Some of this dynamic is being explored as the quantified self is becoming the qualified self in two new ways: by applying QS methods to the tracking of qualitative phenomena such as mood, and by understanding that QS data collection is just the first step in creating qualitative feedback loops for behavior change. In the long-term future, the quantified self may become additionally transformed into the extended exoself as data quantification and self-tracking enable the development of new sense capabilities that are not possible with ordinary senses. The individual body becomes a more knowable, calculable, and administrable object through QS activity, and individuals have an increasingly intimate relationship with data as it mediates the experience of reality.

Mash-up of techniques between data crawling/transfer, data preservation/stewardship and data processing/visualization technologies on a science cloud system designed for Earth and space science: a report of successful operation and science projects of the NICT Science Cloud

NASA Astrophysics Data System (ADS)

Murata, K. T.

2014-12-01

Data-intensive or data-centric science is 4th paradigm after observational and/or experimental science (1st paradigm), theoretical science (2nd paradigm) and numerical science (3rd paradigm). Science cloud is an infrastructure for 4th science methodology. The NICT science cloud is designed for big data sciences of Earth, space and other sciences based on modern informatics and information technologies [1]. Data flow on the cloud is through the following three techniques; (1) data crawling and transfer, (2) data preservation and stewardship, and (3) data processing and visualization. Original tools and applications of these techniques have been designed and implemented. We mash up these tools and applications on the NICT Science Cloud to build up customized systems for each project. In this paper, we discuss science data processing through these three steps. For big data science, data file deployment on a distributed storage system should be well designed in order to save storage cost and transfer time. We developed a high-bandwidth virtual remote storage system (HbVRS) and data crawling tool, NICTY/DLA and Wide-area Observation Network Monitoring (WONM) system, respectively. Data files are saved on the cloud storage system according to both data preservation policy and data processing plan. The storage system is developed via distributed file system middle-ware (Gfarm: GRID datafarm). It is effective since disaster recovery (DR) and parallel data processing are carried out simultaneously without moving these big data from storage to storage. Data files are managed on our Web application, WSDBank (World Science Data Bank). The big-data on the cloud are processed via Pwrake, which is a workflow tool with high-bandwidth of I/O. There are several visualization tools on the cloud; VirtualAurora for magnetosphere and ionosphere, VDVGE for google Earth, STICKER for urban environment data and STARStouch for multi-disciplinary data. There are 30 projects running on the NICT Science Cloud for Earth and space science. In 2003 56 refereed papers were published. At the end, we introduce a couple of successful results of Earth and space sciences using these three techniques carried out on the NICT Sciences Cloud. [1] http://sc-web.nict.go.jp

Science versus (?) Art: Human Perception of Other Worlds

NASA Astrophysics Data System (ADS)

Hartmann, William K.

1998-09-01

At the time of the Renaissance, science and art were mixed together as a way to understand the human relation to the larger cosmos. Leonardo da Vinci exemplifies this approach. In modern times, the two have become separate, and even antagonistic, ``two cultures." Scientists have increasingly been satisfied to present quantitative measures of phenomena, without ever asking what the measures mean in human terms. Examples include the nature of the lunar surface, asteroid colors and brightness of the Io aurora, as will be discussed. However, in presenting the "big picture" to the public, and even to other working scientists, it is useful to revisit the Renaissance paradigm. Artists are increasingly working with scientists to translate the understanding of other worlds to the public, and this creates many opportunities for education projects in schools, and for careers in public outreach and science journalism.

State autonomy, policy paralysis: paradoxes of institutions and culture in the French health care system.

PubMed

Rochaix, Lise; Wilsford, David

2005-01-01

In this article, we assess the recent performance of the French state at containing costs in health care using political science concepts such as path dependency and incentives, which are central to an economic approach. The article focuses on institutional capacities and cultural immobilism and attempts to lay bare the tensions at play in seizing (or not) opportunities for structural change. In particular, we attempt to delineate what constitutes real change in this policy arena (big reforms versus the accumulation of many small policy movements) and to understand the variables at play in the coming together of conjunctures that provide for the big, as well as the underlying structures that allow the accumulation of the small. Except in cases of favorable conjuncture, the analysis bodes very ill for nonincremental reform and, indeed, for significant change over the long term.

Integrated Science and Logistical Planning to Support Big Questions in Antarctic Science

NASA Astrophysics Data System (ADS)

Vaughan, D. G.; Stockings, T. M.

2015-12-01

Each year, British Antarctic Survey (BAS) supports an extensive programme of science at five Antarctic and sub-Antarctic stations, ranging from the tiny Bird Island Research Station at 54°S in the South Atlantic, to the massive, and fully re-locatable, Halley Research Station on Brunt Ice Shelf at 75°S. The BAS logistics hub, Rothera Research Station on the Antarctic Peninsula supports deployment of deep-field and airborne field campaigns through much of the Antarctic continent, and an innovative new UK polar research vessel is under design, and planned to enter service in the Southern Ocean in 2019. BAS's core science programme covering all aspects of physical, biological and geological science is delivered by our own science teams, but every year many other UK scientists and overseas collaborators also access BAS's Antarctic logistics to support their own programmes. As an integrated science and logistics provider, BAS is continuously reviewing its capabilities and operational procedures to ensure that the future long-term requirements of science are optimally supported. Current trends are towards providing the capacity for heavier remote operations and larger-scale field camps, increasing use of autonomous ocean and airborne platforms, and increasing opportunities to provide turnkey solutions for low-cost experimental deployments. This talk will review of expected trends in Antarctic science and the opportunities to conduct science in Antarctica. It will outline the anticipated logistic developments required to support future stakeholder-led and strategically-directed science programmes, and the long-term ambitions of our science communities indentified in several recent horizon-scanning activities.

MiTEP's Collaborative Field Course Design Process Based on Earth Science Literacy Principles

NASA Astrophysics Data System (ADS)

Engelmann, C. A.; Rose, W. I.; Huntoon, J. E.; Klawiter, M. F.; Hungwe, K.

2010-12-01

Michigan Technological University has developed a collaborative process for designing summer field courses for teachers as part of their National Science Foundation funded Math Science Partnership program, called the Michigan Teacher Excellence Program (MiTEP). This design process was implemented and then piloted during two two-week courses: Earth Science Institute I (ESI I) and Earth Science Institute II (ESI II). Participants consisted of a small group of Michigan urban science teachers who are members of the MiTEP program. The Earth Science Literacy Principles (ESLP) served as the framework for course design in conjunction with input from participating MiTEP teachers as well as research done on common teacher and student misconceptions in Earth Science. Research on the Earth Science misconception component, aligned to the ESLP, is more fully addressed in GSA Abstracts with Programs Vol. 42, No. 5. “Recognizing Earth Science Misconceptions and Reconstructing Knowledge through Conceptual-Change-Teaching”. The ESLP were released to the public in January 2009 by the Earth Science Literacy Organizing Committee and can be found at http://www.earthscienceliteracy.org/index.html. Each day of the first nine days of both Institutes was focused on one of the nine ESLP Big Ideas; the tenth day emphasized integration of concepts across all of the ESLP Big Ideas. Throughout each day, Michigan Tech graduate student facilitators and professors from Michigan Tech and Grand Valley State University consistantly focused teaching and learning on the day's Big Idea. Many Earth Science experts from Michigan Tech and Grand Valley State University joined the MiTEP teachers in the field or on campus, giving presentations on the latest research in their area that was related to that Big Idea. Field sites were chosen for their unique geological features as well as for the “sense of place” each site provided. Preliminary research findings indicate that this collaborative design process piloted as ESI I and ESI II was successful in improving MiTEP teacher understanding of Earth Science content and that it was helpful to use the ESLP framework. Ultimately, a small sample of student scores will look at the impact on student learning in the MiTEP teacher classrooms.

samiDB: A Prototype Data Archive for Big Science Exploration

NASA Astrophysics Data System (ADS)

Konstantopoulos, I. S.; Green, A. W.; Cortese, L.; Foster, C.; Scott, N.

2015-04-01

samiDB is an archive, database, and query engine to serve the spectra, spectral hypercubes, and high-level science products that make up the SAMI Galaxy Survey. Based on the versatile Hierarchical Data Format (HDF5), samiDB does not depend on relational database structures and hence lightens the setup and maintenance load imposed on science teams by metadata tables. The code, written in Python, covers the ingestion, querying, and exporting of data as well as the automatic setup of an HTML schema browser. samiDB serves as a maintenance-light data archive for Big Science and can be adopted and adapted by science teams that lack the means to hire professional archivists to set up the data back end for their projects.

Data Mining Citizen Science Results

NASA Astrophysics Data System (ADS)

Borne, K. D.

2012-12-01

Scientific discovery from big data is enabled through multiple channels, including data mining (through the application of machine learning algorithms) and human computation (commonly implemented through citizen science tasks). We will describe the results of new data mining experiments on the results from citizen science activities. Discovering patterns, trends, and anomalies in data are among the powerful contributions of citizen science. Establishing scientific algorithms that can subsequently re-discover the same types of patterns, trends, and anomalies in automatic data processing pipelines will ultimately result from the transformation of those human algorithms into computer algorithms, which can then be applied to much larger data collections. Scientific discovery from big data is thus greatly amplified through the marriage of data mining with citizen science.

Big Data on the Big Screen

NASA Image and Video Library

2013-10-17

The center of the Milky Way galaxy imaged by NASA Spitzer Space Telescope is displayed on a quarter-of-a-billion-pixel, high-definition 23-foot-wide 7-meter LCD science visualization screen at NASA Ames Research Center.

LLNL's Big Science Capabilities Help Spur Over $796 Billion in U.S. Economic Activity Sequencing the Human Genome

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

Stewart, Jeffrey S.

LLNL’s successful history of taking on big science projects spans beyond national security and has helped create billions of dollars per year in new economic activity. One example is LLNL’s role in helping sequence the human genome. Over $796 billion in new economic activity in over half a dozen fields has been documented since LLNL successfully completed this Grand Challenge.

[Social change and sciences in the 20th century].

PubMed

Garamvölgyi, J

1995-12-05

The symbiotic interdependence of state, economy and science is one of the most significant structural characteristics of the 20th century. This development results from inherent scientific as well as from social procedures and needs, and it has been favoured by the two World Wars, culminating in the Cold War. This led to new structures: institutions of large scale research, think tanks, and the military-industrial complex. Big government, big business, and big science are depending on each other. Parallel to the new way of thinking in physics (Einstein, Bohr and others), finally accomplished by the revolution in cybernetics (Wiener), the traditional borders between disciplines have been overcome. The production of new knowledge is now of primary importance. Today, information proves to be one of the strategic resources which determines prosperity, power and prestige as well as success in economic and political markets.

Biosecurity in the age of Big Data: a conversation with the FBI.

PubMed

You, Edward; Kozminski, Keith G

2015-11-05

New scientific frontiers and emerging technologies within the life sciences pose many global challenges to society. Big Data is a premier example, especially with respect to individual, national, and international security. Here a Special Agent of the Federal Bureau of Investigation discusses the security implications of Big Data and the need for security in the life sciences. © 2015 Kozminski. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Big Outcrops and Big Ideas in Earth Science K-8 Professional Development

NASA Astrophysics Data System (ADS)

Baldwin, K. A.; Cooper, C. M.; Cavagnetto, A.; Morrison, J.; Adesope, O.

2014-12-01

Washington State has recently adopted the Next Generation Science Standards (NGSS) and state leaders are now working toward supporting teachers' implementation of the new standards and the pedagogical practices that support them. This poster encompasses one of one such professional development (PD) effort. The Enhancing Understanding of Concepts and Processes of Science (EUCAPS) project serves 31 K-8 in-service teachers in two southeast Washington school districts. In year two of this three year PD project, in-service teachers explored the Earth sciences and pedagogical approaches such as the Science Writing Heuristic, concept mapping, and activities which emphasized the epistemic nature of science. The goals of the EUCAPS PD project are to increase in-service teachers' big ideas in science and to provide support to in-service teachers as they transition to the NGSS. Teachers used concepts maps to document their knowledge of Earth science processes before and after visiting a local field site in Lewiston, Idaho. In the context of immersive inquiries, teachers collected field-based evidence to support their claims about the geological history of the field site. Teachers presented their claims and evidence to their peers in the form a story about the local geologic history. This poster will present an overview of the PD as well as provide examples of teacher's work and alignment with the NGSS.

Findability of Federal Research Data

NASA Astrophysics Data System (ADS)

Hourcle, J. A.

2013-12-01

Findability of Federal Research Data Although many of the federal agencies have been providing access to scientific research data for years if not decades, the findability of the data has been quite lacking. Many discipline-wide efforts have been made in the big science communities, such as PDS for planetary science and the VOs in night time astronomy and heliophysics, but there is a lack of single entry point for someone looking for data. The science.gov website contains links to many of these big-science search systems, but doesn't differentiate between links to science quality data and websites or browse products, making it more difficult to search specifically for data. The data.gov website is a useful repository for PIs of small science data to stash their data, particularly as it allows for interested parties to interact with tabular data. Unfortunately, as each group thinks of their data differently, much of what's now in the system is a mess; collections of data being tracked as individual records with no relationships between them. Big science projects also get tracked as single records, potentially with only a single record for missions with multiple instruments and significantly different data series. We present recommendations on how to improve the findability of federal research data on data.gov, based on years of working on the Virtual Solar Observatory and withing the science informatics community.

"small problems, Big Trouble": An Art and Science Collaborative Exhibition Reflecting Seemingly small problems Leading to Big Threats

NASA Astrophysics Data System (ADS)

Waller, J. L.; Brey, J. A.

2014-12-01

"small problems, Big Trouble" (spBT) is an exhibition of artist Judith Waller's paintings accompanied by text panels written by Earth scientist Dr. James A. Brey and several science researchers and educators. The text panels' message is as much the focus of the show as the art--true interdisciplinarity! Waller and Brey's history of art and earth science collaborations include the successful exhibition "Layers: Places in Peril". New in spBT is extended collaboration with other scientists in order to create awareness of geoscience and other subjects (i.e. soil, parasites, dust, pollutants, invasive species, carbon, ground water contaminants, solar wind) small in scale which pose significant threats. The paintings are the size of a mirror, a symbol suggesting the problems depicted are those we increasingly need to face, noting our collective reflections of shared current and future reality. Naturalistic rendering and abstract form in the art helps reach a broad audience including those familiar with art and those familiar with science. The goal is that gallery visitors gain greater appreciation and understanding of both—and of the sober content of the show as a whole. "small problems, Big Trouble" premiers in Wisconsin April, 2015. As in previous collaborations, Waller and Brey actively utilize art and science (specifically geoscience) as an educational vehicle for active student learning. Planned are interdisciplinary university and area high school activities linked through spBT. The exhibition in a public gallery offers a means to enhance community awareness of and action on scientific issues through art's power to engage people on an emotional level. This AGU presentation includes a description of past Waller and Brey activities: incorporating art and earth science in lab and studio classrooms, producing gallery and museum exhibitions and delivering workshops and other presentations. They also describe how walking the paths of several past earth science disasters continues to inspire new chapters in their "Layers: Places in Peril" exhibit! A slide show includes images of paintings for "small problems, Big Trouble". Brey and Waller will lead a discussion on their process of incorporating broader collaboration with geoscientists and others in an educational art exhibition.

Database Resources of the BIG Data Center in 2018.

PubMed

2018-01-04

The BIG Data Center at Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences provides freely open access to a suite of database resources in support of worldwide research activities in both academia and industry. With the vast amounts of omics data generated at ever-greater scales and rates, the BIG Data Center is continually expanding, updating and enriching its core database resources through big-data integration and value-added curation, including BioCode (a repository archiving bioinformatics tool codes), BioProject (a biological project library), BioSample (a biological sample library), Genome Sequence Archive (GSA, a data repository for archiving raw sequence reads), Genome Warehouse (GWH, a centralized resource housing genome-scale data), Genome Variation Map (GVM, a public repository of genome variations), Gene Expression Nebulas (GEN, a database of gene expression profiles based on RNA-Seq data), Methylation Bank (MethBank, an integrated databank of DNA methylomes), and Science Wikis (a series of biological knowledge wikis for community annotations). In addition, three featured web services are provided, viz., BIG Search (search as a service; a scalable inter-domain text search engine), BIG SSO (single sign-on as a service; a user access control system to gain access to multiple independent systems with a single ID and password) and Gsub (submission as a service; a unified submission service for all relevant resources). All of these resources are publicly accessible through the home page of the BIG Data Center at http://bigd.big.ac.cn. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

The Big Challenge in Big Earth Science Data: Maturing to Transdisciplinary Data Platforms that are Relevant to Government, Research and Industry

NASA Astrophysics Data System (ADS)

Wyborn, Lesley; Evans, Ben

2016-04-01

Collecting data for the Earth Sciences has a particularly long history going back centuries. Initially scientific data came only from simple human observations recorded by pen on paper. Scientific instruments soon supplemented data capture, and as these instruments became more capable (e.g, automation, more information captured, generation of digitally-born outputs), Earth Scientists entered the 'Big Data' era where progressively data became too big to store and process locally in the old style vaults. To date, most funding initiatives for collection and storage of large volume data sets in the Earth Sciences have been specialised within a single discipline (e.g., climate, geophysics, and Earth Observation) or specific to an individual institution. To undertake interdisciplinary research, it is hard for users to integrate data from these individual repositories mainly due to limitations on physical access to/movement of the data, and/or data being organised without enough information to make sense of it without discipline specialised knowledge. Smaller repositories have also gradually been seen as inefficient in terms of the cost to manage and access (including scarce skills) and effective implementation of new technology and techniques. Within the last decade, the trend is towards fewer and larger data repositories that increasingly are collocated with HPC/cloud resources. There has also been a growing recognition that digital data can be a valuable resource that can be reused and repurposed - publicly funded data from either the academic of government sector is seen as a shared resource, and that efficiencies can be gained by co-location. These new, highly capable, 'transdisciplinary' data repositories are emerging as a fundamental 'infrastructure' both for research and other innovation. The sharing of academic and government data resources on the same infrastructures is enabling new research programmes that will enable integration beyond the traditional physical scientific domain silos, including into the humanities and social sciences. Furthermore there is increasing desire for these 'Big Data' data infrastructures to prove their value not only as platforms for scientific discovery, but to also support the development of evidence-based government policies, economic growth, and private-sector opportunities. The capacity of these transdisciplinary data repositories leads to many new exciting opportunities for the next generation of large-scale data integration, but there is an emerging suite of data challenges that now need to be tackled. Many large volume data sets have historically been developed within traditional domain silos and issues such as difference of standards (informal and formal), the data conventions, the lack of controlled or even uniform vocabularies, the non-existent/not machine-accessible semantic information, and bespoke or unclear copyrights and licensing are becoming apparent. The different perspectives and approaches of the various communities have also started to come to the fore; particularly the dominant file based approach of the big data generating science communities versus the database approach of the point observational communities; and the multidimensional approach of the climate and oceans community versus the traditional 2D approach of the GIS/spatial community. Addressing such challenges is essential to fully unlock online access to all relevant data to enable the maturing of research to the transdisciplinary paradigm.

Big data, open science and the brain: lessons learned from genomics.

PubMed

Choudhury, Suparna; Fishman, Jennifer R; McGowan, Michelle L; Juengst, Eric T

2014-01-01

The BRAIN Initiative aims to break new ground in the scale and speed of data collection in neuroscience, requiring tools to handle data in the magnitude of yottabytes (10(24)). The scale, investment and organization of it are being compared to the Human Genome Project (HGP), which has exemplified "big science" for biology. In line with the trend towards Big Data in genomic research, the promise of the BRAIN Initiative, as well as the European Human Brain Project, rests on the possibility to amass vast quantities of data to model the complex interactions between the brain and behavior and inform the diagnosis and prevention of neurological disorders and psychiatric disease. Advocates of this "data driven" paradigm in neuroscience argue that harnessing the large quantities of data generated across laboratories worldwide has numerous methodological, ethical and economic advantages, but it requires the neuroscience community to adopt a culture of data sharing and open access to benefit from them. In this article, we examine the rationale for data sharing among advocates and briefly exemplify these in terms of new "open neuroscience" projects. Then, drawing on the frequently invoked model of data sharing in genomics, we go on to demonstrate the complexities of data sharing, shedding light on the sociological and ethical challenges within the realms of institutions, researchers and participants, namely dilemmas around public/private interests in data, (lack of) motivation to share in the academic community, and potential loss of participant anonymity. Our paper serves to highlight some foreseeable tensions around data sharing relevant to the emergent "open neuroscience" movement.

Creation a Geo Big Data Outreach and Training Collaboratory for Wildfire Community

NASA Astrophysics Data System (ADS)

Altintas, I.; Sale, J.; Block, J.; Cowart, C.; Crawl, D.

2015-12-01

A major challenge for the geoscience community is the training and education of current and next generation big data geoscientists. In wildfire research, there are an increasing number of tools, middleware and techniques to use for data science related to wildfires. The necessary computing infrastructures are often within reach and most of the software tools for big data are freely available. But what has been lacking is a transparent platform and training program to produce data science experts who can use these integrated tools effectively. Scientists well versed to take advantage of big data technologies in geoscience applications is of critical importance to the future of research and knowledge advancement. To address this critical need, we are developing learning modules to teach process-based thinking to capture the value of end-to-end systems of reusable blocks of knowledge and integrate the tools and technologies used in big data analysis in an intuitive manner. WIFIRE is an end-to-end cyberinfrastructure for dynamic data-driven simulation, prediction and visualization of wildfire behavior.To this end, we are openly extending an environment we have built for "big data training" (biobigdata.ucsd.edu) to similar MOOC-based approaches to the wildfire community. We are building an environment that includes training modules for distributed platforms and systems, Big Data concepts, and scalable workflow tools, along with other basics of data science including data management, reproducibility and sharing of results. We also plan to provide teaching modules with analytical and dynamic data-driven wildfire behavior modeling case studies which address the needs not only of standards-based K-12 science education but also the needs of a well-educated and informed citizenry.Another part our outreach mission is to educate our community on all aspects of wildfire research. One of the most successful ways of accomplishing this is through high school and undergraduate student internships. Students have worked closely with WIFIRE researchers on various projects including the development of statistical models of wildfire ignition probabilities for southern California, and the development of a smartphone app for crowd-sourced wildfire reporting through social networks such as Twitter and Facebook.

Reviews

NASA Astrophysics Data System (ADS)

2004-01-01

BOOK REVIEWS (99) Complete A-Z Physics Handbook Science Magic in the Kitchen The Science of Cooking Science Experiments You Can Eat WEB WATCH (101) These journal themes are pasta joke Microwave oven Web links CD REVIEW (104) Electricity and Magnetism, KS3 Big Science Comics

Technical challenges for big data in biomedicine and health: data sources, infrastructure, and analytics.

PubMed

Peek, N; Holmes, J H; Sun, J

2014-08-15

To review technical and methodological challenges for big data research in biomedicine and health. We discuss sources of big datasets, survey infrastructures for big data storage and big data processing, and describe the main challenges that arise when analyzing big data. The life and biomedical sciences are massively contributing to the big data revolution through secondary use of data that were collected during routine care and through new data sources such as social media. Efficient processing of big datasets is typically achieved by distributing computation over a cluster of computers. Data analysts should be aware of pitfalls related to big data such as bias in routine care data and the risk of false-positive findings in high-dimensional datasets. The major challenge for the near future is to transform analytical methods that are used in the biomedical and health domain, to fit the distributed storage and processing model that is required to handle big data, while ensuring confidentiality of the data being analyzed.

Neoliberal science, Chinese style: Making and managing the 'obesity epidemic'.

PubMed

Greenhalgh, Susan

2016-08-01

Science and Technology Studies has seen a growing interest in the commercialization of science. In this article, I track the role of corporations in the construction of the obesity epidemic, deemed one of the major public health threats of the century. Focusing on China, a rising superpower in the midst of rampant, state-directed neoliberalization, I unravel the process, mechanisms, and broad effects of the corporate invention of an obesity epidemic. Largely hidden from view, Western firms were central actors at every stage in the creation, definition, and governmental management of obesity as a Chinese disease. Two industry-funded global health entities and the exploitation of personal ties enabled actors to nudge the development of obesity science and policy along lines beneficial to large firms, while obscuring the nudging. From Big Pharma to Big Food and Big Soda, transnational companies have been profiting from the 'epidemic of Chinese obesity', while doing little to effectively treat or prevent it. The China case suggests how obesity might have been constituted an 'epidemic threat' in other parts of the world and underscores the need for global frameworks to guide the study of neoliberal science and policymaking.

Big Ideas in Volcanology-a new way to teach and think about the subject?

NASA Astrophysics Data System (ADS)

Rose, W. I.

2011-12-01

As intense work with identifying and presenting earth science to middle school science teachers in the MiTEP project advances, I have realized that tools used to connect with teachers and students of earth science in general and especially to promote higher levels of learning, should be advantageous in graduate teaching as well. In my last of 40 years of teaching graduate volcanology, I have finally organized the class around ideas based on Earth Science Literacy Principles and on common misconceptions. As such, I propose and fully explore the twelve "big ideas" of volcanology at the rate of one per week. This curricular organization highlights the ideas in volcanology that have major impact beyond volcanology itself and explores the roots and global ramifications of these ideas. Together they show how volcanology interfaces with the science world and the "real" world or how volcanologists interface with "real" people. In addition to big ideas we explore difficult and misunderstood concepts and the public misconceptions associated with each. The new organization and its focus on understanding relevant and far reaching concepts and hypotheses provides a refreshing context for advanced learning. It is planned to be the basis for an interactive website.

Unraveling the Complexities of Life Sciences Data.

PubMed

Higdon, Roger; Haynes, Winston; Stanberry, Larissa; Stewart, Elizabeth; Yandl, Gregory; Howard, Chris; Broomall, William; Kolker, Natali; Kolker, Eugene

2013-03-01

The life sciences have entered into the realm of big data and data-enabled science, where data can either empower or overwhelm. These data bring the challenges of the 5 Vs of big data: volume, veracity, velocity, variety, and value. Both independently and through our involvement with DELSA Global (Data-Enabled Life Sciences Alliance, DELSAglobal.org), the Kolker Lab ( kolkerlab.org ) is creating partnerships that identify data challenges and solve community needs. We specialize in solutions to complex biological data challenges, as exemplified by the community resource of MOPED (Model Organism Protein Expression Database, MOPED.proteinspire.org ) and the analysis pipeline of SPIRE (Systematic Protein Investigative Research Environment, PROTEINSPIRE.org ). Our collaborative work extends into the computationally intensive tasks of analysis and visualization of millions of protein sequences through innovative implementations of sequence alignment algorithms and creation of the Protein Sequence Universe tool (PSU). Pushing into the future together with our collaborators, our lab is pursuing integration of multi-omics data and exploration of biological pathways, as well as assigning function to proteins and porting solutions to the cloud. Big data have come to the life sciences; discovering the knowledge in the data will bring breakthroughs and benefits.

Big Data: An Opportunity for Collaboration with Computer Scientists on Data-Driven Science

NASA Astrophysics Data System (ADS)

Baru, C.

2014-12-01

Big data technologies are evolving rapidly, driven by the need to manage ever increasing amounts of historical data; process relentless streams of human and machine-generated data; and integrate data of heterogeneous structure from extremely heterogeneous sources of information. Big data is inherently an application-driven problem. Developing the right technologies requires an understanding of the applications domain. Though, an intriguing aspect of this phenomenon is that the availability of the data itself enables new applications not previously conceived of! In this talk, we will discuss how the big data phenomenon creates an imperative for collaboration among domain scientists (in this case, geoscientists) and computer scientists. Domain scientists provide the application requirements as well as insights about the data involved, while computer scientists help assess whether problems can be solved with currently available technologies or require adaptaion of existing technologies and/or development of new technologies. The synergy can create vibrant collaborations potentially leading to new science insights as well as development of new data technologies and systems. The area of interface between geosciences and computer science, also referred to as geoinformatics is, we believe, a fertile area for interdisciplinary research.

Climate Analytics-As-a-Service (CAaas), Advanced Information Systems, and Services to Accelerate the Climate Sciences.

NASA Astrophysics Data System (ADS)

McInerney, M.; Schnase, J. L.; Duffy, D.; Tamkin, G.; Nadeau, D.; Strong, S.; Thompson, J. H.; Sinno, S.; Lazar, D.

2014-12-01

The climate sciences represent a big data domain that is experiencing unprecedented growth. In our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with big data that ultimately product societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by cloud computing. Within this framework, cloud computing plays an important role; however, we see it as only one element in a constellation of capabilities that are essential to delivering climate analytics-as-a-service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the big data challenges in this domain. This poster will highlight specific examples of CAaaS using climate reanalysis data, high-performance cloud computing, map reduce, and the Climate Data Services API.

Phylomemetic patterns in science evolution--the rise and fall of scientific fields.

PubMed

Chavalarias, David; Cointet, Jean-Philippe

2013-01-01

We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields. We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns. Some structural properties of the scientific fields - in particular their density -, which are defined independently of the phylomemy reconstruction, are clearly correlated with their status and their fate in the phylomemy (like their age or their short term survival). Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.

Big Science! Big Problems?

ERIC Educational Resources Information Center

Beigel, Allan

1991-01-01

Lessons learned by the University of Arizona through participation in two major scientific projects, construction of an astronomical observatory and a super cyclotron, are discussed. Four criteria for institutional participation in such projects are outlined, including consistency with institutional mission, adequate resources, leadership, and…

Big Data from Europe's Natural Science Collections through DiSSCo

NASA Astrophysics Data System (ADS)

Addink, Wouter; Koureas, Dimitris; Casino, Ana

2017-04-01

DiSSCo, a Distributed System of Scientific Collections, will be a Research Infrastructure delivering big data describing the history of Planet Earth. Approximately 1.5 billion biological and geological specimens, representing the last 300 years of scientific study on the natural world, reside in collections all over Europe. These span 4.5 billion years of history, from the formation of the solar system to the present day. In the European landscape of environmental Research Infrastructures, different projects and landmarks describe services that aim at aggregating, monitoring, analysing and modelling geo-diversity information. The effectiveness of these services, however, is based on the quality and availability of primary reference data that today is scattered and uncomplete. DiSSCo provides the required bio-geographical, taxonomic and species trait data at the level of precision and accuracy required to enable and speed up research for the rapidly growing seven grand societal challenges that are priorities of the Europe 2020 strategy. DiSSCo enables better connections between collection data and observations in biodiversity observation networks, such as EU BON and GEOBON. This supports research areas like long term ecological research, for which the continuity and long term research is a strength of biological collections.

Measuring adolescent science motivation

NASA Astrophysics Data System (ADS)

Schumm, Maximiliane F.; Bogner, Franz X.

2016-02-01

To monitor science motivation, 232 tenth graders of the college preparatory level ('Gymnasium') completed the Science Motivation Questionnaire II (SMQ-II). Additionally, personality data were collected using a 10-item version of the Big Five Inventory. A subsequent exploratory factor analysis based on the eigenvalue-greater-than-one criterion, extracted a loading pattern, which in principle, followed the SMQ-II frame. Two items were dropped due to inappropriate loadings. The remaining SMQ-II seems to provide a consistent scale matching the findings in literature. Nevertheless, also possible shortcomings of the scale are discussed. Data showed a higher perceived self-determination in girls which seems compensated by their lower self-efficacy beliefs leading to equality of females and males in overall science motivation scores. Additionally, the Big Five personality traits and science motivation components show little relationship.

Beyond Einstein: from the Big Bang to black holes

NASA Astrophysics Data System (ADS)

White, Nicholas E.; Diaz, Alphonso V.

2004-01-01

How did the Universe begin? Does time have a beginning and an end? Does space have edges? Einstein's theory of relativity replied to these ancient questions with three startling predictions: that the Universe is expanding from a Big Bang; that black holes so distort space and time that time stops at their edges; and that a dark energy could be pulling space apart, sending galaxies forever beyond the edge of the visible Universe. Observations confirm these remarkable predictions, the last finding only four years ago. Yet Einstein's legacy is incomplete. His theory raises - but cannot answer - three profound questions: What powered the Big Bang? What happens to space, time and matter at the edge of a black hole? and, What is the mysterious dark energy pulling the Universe apart? The Beyond Einstein program within NASA's office of space science aims to answer these questions, employing a series of missions linked by powerful new technologies and complementary approaches to shared science goals. The program also serves as a potent force with which to enhance science education and science literacy.

What Difference Does Quantity Make? On the Epistemology of Big Data in Biology

PubMed Central

Leonelli, Sabina

2015-01-01

Is big data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of big data science does not lie in the sheer quantity of data involved, but rather in (1) the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community; and (2) the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this paper reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data; and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which big data need to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analyzing data given these developments, and the opportunities and worries associated to big data discourse and methods. PMID:25729586

What Difference Does Quantity Make? On the Epistemology of Big Data in Biology.

PubMed

Leonelli, Sabina

2014-06-01

Is big data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of big data science does not lie in the sheer quantity of data involved, but rather in (1) the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community; and (2) the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this paper reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data; and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which big data need to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analyzing data given these developments, and the opportunities and worries associated to big data discourse and methods.

DEVELOPING THE TRANSDISCIPLINARY AGING RESEARCH AGENDA: NEW DEVELOPMENTS IN BIG DATA.

PubMed

Callaghan, Christian William

2017-07-19

In light of dramatic advances in big data analytics and the application of these advances in certain scientific fields, new potentialities exist for breakthroughs in aging research. Translating these new potentialities to research outcomes for aging populations, however, remains a challenge, as underlying technologies which have enabled exponential increases in 'big data' have not yet enabled a commensurate era of 'big knowledge,' or similarly exponential increases in biomedical breakthroughs. Debates also reveal differences in the literature, with some arguing big data analytics heralds a new era associated with the 'end of theory' or which makes the scientific method obsolete, where correlation supercedes causation, whereby science can advance without theory and hypotheses testing. On the other hand, others argue theory cannot be subordinate to data, no matter how comprehensive data coverage can ultimately become. Given these two tensions, namely between exponential increases in data absent exponential increases in biomedical research outputs, and between the promise of comprehensive data coverage and data-driven inductive versus theory-driven deductive modes of enquiry, this paper seeks to provide a critical review of certain theory and literature that offers useful perspectives of certain developments in big data analytics and their theoretical implications for aging research. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Reviews Book: Extended Project Student Guide Book: My Inventions Book: ASE Guide to Research in Science Education Classroom Video: The Science of Starlight Software: SPARKvue Book: The Geek Manifesto Ebook: A Big Ball of Fire Apps

NASA Astrophysics Data System (ADS)

2014-05-01

WE RECOMMEND Level 3 Extended Project Student Guide A non-specialist, generally useful and nicely put together guide to project work ASE Guide to Research in Science Education Few words wasted in this handy introduction and reference The Science of Starlight Slow but steady DVD covers useful ground SPARKvue Impressive software now available as an app WORTH A LOOK My Inventions and Other Writings Science, engineering, autobiography, visions and psychic phenomena mixed in a strange but revealing concoction The Geek Manifesto: Why Science Matters More enthusiasm than science, but a good motivator and interesting A Big Ball of Fire: Your questions about the Sun answered Free iTunes download made by and for students goes down well APPS Collider visualises LHC experiments ... Science Museum app enhances school trips ... useful information for the Cambridge Science Festival

Individuals with greater science literacy and education have more polarized beliefs on controversial science topics

PubMed Central

2017-01-01

Although Americans generally hold science in high regard and respect its findings, for some contested issues, such as the existence of anthropogenic climate change, public opinion is polarized along religious and political lines. We ask whether individuals with more general education and greater science knowledge, measured in terms of science education and science literacy, display more (or less) polarized beliefs on several such issues. We report secondary analyses of a nationally representative dataset (the General Social Survey), examining the predictors of beliefs regarding six potentially controversial issues. We find that beliefs are correlated with both political and religious identity for stem cell research, the Big Bang, and human evolution, and with political identity alone on climate change. Individuals with greater education, science education, and science literacy display more polarized beliefs on these issues. We find little evidence of political or religious polarization regarding nanotechnology and genetically modified foods. On all six topics, people who trust the scientific enterprise more are also more likely to accept its findings. We discuss the causal mechanisms that might underlie the correlation between education and identity-based polarization. PMID:28827344

Big Data and Chemical Education

ERIC Educational Resources Information Center

Pence, Harry E.; Williams, Antony J.

2016-01-01

The amount of computerized information that organizations collect and process is growing so large that the term Big Data is commonly being used to describe the situation. Accordingly, Big Data is defined by a combination of the Volume, Variety, Velocity, and Veracity of the data being processed. Big Data tools are already having an impact in…

Disproof of Big Bang's Foundational Expansion Redshift Assumption Overthrows the Big Bang and Its No-Center Universe and Is Replaced by a Spherically Symmetric Model with Nearby Center with the 2.73 K CMR Explained by Vacuum Gravity and Doppler Effects

NASA Astrophysics Data System (ADS)

Gentry, Robert

2015-04-01

Big bang theory holds its central expansion redshift assumption quickly reduced the theorized radiation flash to ~ 1010 K, and then over 13.8 billion years reduced it further to the present 2.73 K CMR. Weinberg claims this 2.73 K value agrees with big bang theory so well that ``...we can be sure that this radiation was indeed left over from a time about a million years after the `big bang.' '' (TF3M, p180, 1993 ed.) Actually his conclusion is all based on big bang's in-flight wavelength expansion being a valid physical process. In fact all his surmising is nothing but science fiction because our disproof of GR-induced in-flight wavelength expansion [1] definitely proves the 2.73 K CMR could never have been the wavelength-expanded relic of any radiation, much less the presumed big bang's. This disproof of big bang's premier prediction is a death blow to the big bang as it is also to the idea that the redshifts in Hubble's redshift relation are expansion shifts; this negates Friedmann's everywhere-the-same, no-center universe concept and proves it does have a nearby Center, a place which can be identified in Psalm 103:19 and in Revelation 20:11 as the location of God's eternal throne. Widely published (Science, Nature, ARNS) evidence of Earth's fiat creation will also be presented. The research is supported by the God of Creation. This paper [1] is in for publication.

Opportunities and challenges of big data for the social sciences: The case of genomic data.

PubMed

Liu, Hexuan; Guo, Guang

2016-09-01

In this paper, we draw attention to one unique and valuable source of big data, genomic data, by demonstrating the opportunities they provide to social scientists. We discuss different types of large-scale genomic data and recent advances in statistical methods and computational infrastructure used to address challenges in managing and analyzing such data. We highlight how these data and methods can be used to benefit social science research. Copyright © 2016 Elsevier Inc. All rights reserved.

Big data for health.

PubMed

Andreu-Perez, Javier; Poon, Carmen C Y; Merrifield, Robert D; Wong, Stephen T C; Yang, Guang-Zhong

2015-07-01

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

Commentary: Leveraging discovery science to advance child and adolescent psychiatric research--a commentary on Zhao and Castellanos 2016.

PubMed

Mennes, Maarten

2016-03-01

'Big Data' and 'Population Imaging' are becoming integral parts of inspiring research aimed at delineating the biological underpinnings of psychiatric disorders. The scientific strategies currently associated with big data and population imaging are typically embedded in so-called discovery science, thereby pointing to the hypothesis-generating rather than hypothesis-testing nature of discovery science. In this issue, Yihong Zhao and F. Xavier Castellanos provide a compelling overview of strategies for discovery science aimed at progressing our understanding of neuropsychiatric disorders. In particular, they focus on efforts in genetic and neuroimaging research, which, together with extended behavioural testing, form the main pillars of psychopathology research. © 2016 Association for Child and Adolescent Mental Health.

Picture of the Week: Making the (reactive) case for explosives science

Science.gov Websites

: small organisms, big impacts Biocrusts: small organisms, big impacts View on Flickr Bismuth and tin on the rocks Bismuth and tin on the rocks View on Flickr Need to Know: Van Allen Belts Need to Know: Van

Streaming Swarm of Nano Space Probes for Modern Analytical Methods Applied to Planetary Science

NASA Astrophysics Data System (ADS)

Vizi, P. G.; Horvath, A. F.; Berczi, Sz.

2017-11-01

Streaming swarms gives possibilities to collect data from big fields in one time. The whole streaming fleet possible to behave like one big organization and can be realized as a planetary mission solution with stream type analytical methods.

Adding Big Data Analytics to GCSS-MC

DTIC Science & Technology

2014-09-30

TERMS Big Data , Hadoop , MapReduce, GCSS-MC 15. NUMBER OF PAGES 93 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified 18. SECURITY...10 2.5 Hadoop . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3 The Experiment Design 23 3.1 Why Add a Big Data Element...23 3.2 Adding a Big Data Element to GCSS-MC . . . . . . . . . . . . . . 24 3.3 Building a Hadoop Cluster

Computational science: shifting the focus from tools to models

PubMed Central

Hinsen, Konrad

2014-01-01

Computational techniques have revolutionized many aspects of scientific research over the last few decades. Experimentalists use computation for data analysis, processing ever bigger data sets. Theoreticians compute predictions from ever more complex models. However, traditional articles do not permit the publication of big data sets or complex models. As a consequence, these crucial pieces of information no longer enter the scientific record. Moreover, they have become prisoners of scientific software: many models exist only as software implementations, and the data are often stored in proprietary formats defined by the software. In this article, I argue that this emphasis on software tools over models and data is detrimental to science in the long term, and I propose a means by which this can be reversed. PMID:25309728

NASA Structure and Evolution of the Universe Theme: Science Overview

NASA Technical Reports Server (NTRS)

White, Nicholas E.; Margon, Bruce

2001-01-01

The NASA Office of Space Science Structure and Evolution of the Universe (SEU) theme covers a wide variety of scientific investigations, from the nearest bodies to the farthest observable distances just after the time of the Big Bang. SEU supports experiments that sense radiation of all wavelengths, together with particle and gravitational wave detection. Recently completed road mapping and strategic planning exercises have identified a number of near- and medium-term space initiatives for the 2003-2023 time frame. Each of these experiments pushes the state of the art technically, but will return incredible new insights on the formation and evolution of the universe, as well as probe fundamental laws of physics in regimes never before tested. The scientific goals and technological highlights of each mission are described.

The Big Five personality factors and psychological well-being following stroke: a systematic review.

PubMed

Dwan, Toni; Ownsworth, Tamara

2017-12-22

To identify and appraise studies investigating the relationship between the Big Five personality factors and psychological well-being following stroke and evidence for personality change. Systematic searches of six databases (PsychINFO, CINAHL, Ovid Medline, Cochrane, PubMed, and Web of Science) were conducted from inception to June 2017. Studies involving adult stroke samples that employed a validated measure of at least one of the Big Five personality factors were included. Two reviewers independently assessed the eligibility and methodological quality of studies. Eleven studies were identified that assessed associations between personality and psychological well-being after stroke (nine studies) or post-stroke personality change (two studies). A consistent finding was that higher neuroticism was significantly related to poorer psychological well-being. The evidence for the other Big Five factors was mixed. In terms of personality change, two cross-sectional studies reported high rates of elevated neuroticism (38-48%) and low extraversion (33-40%) relative to normative data. Different questionnaires and approaches to measuring personality (i.e., self vs. informant ratings, premorbid personality vs. current personality) complicated comparisons between studies. People high on neuroticism are at increased risk of poor psychological well-being after stroke. Prospective longitudinal studies are needed to address the limited research on post-stroke personality change. Implications for rehabilitation High neuroticism is associated with poorer psychological well-being after stroke. Assessing personality characteristics early after stroke may help to identify those at risk of poor psychological outcomes.

What Role for Law, Human Rights, and Bioethics in an Age of Big Data, Consortia Science, and Consortia Ethics? The Importance of Trustworthiness.

PubMed

Dove, Edward S; Özdemir, Vural

2015-09-01

The global bioeconomy is generating new paradigm-shifting practices of knowledge co-production, such as collective innovation; large-scale, data-driven global consortia science (Big Science); and consortia ethics (Big Ethics). These bioeconomic and sociotechnical practices can be forces for progressive social change, but they can also raise predicaments at the interface of law, human rights, and bioethics. In this article, we examine one such double-edged practice: the growing, multivariate exploitation of Big Data in the health sector, particularly by the private sector. Commercial exploitation of health data for knowledge-based products is a key aspect of the bioeconomy and is also a topic of concern among publics around the world. It is exacerbated in the current age of globally interconnected consortia science and consortia ethics, which is characterized by accumulating epistemic proximity, diminished academic independence, "extreme centrism", and conflicted/competing interests among innovation actors. Extreme centrism is of particular importance as a new ideology emerging from consortia science and consortia ethics; this relates to invariably taking a middle-of-the-road populist stance, even in the event of human rights breaches, so as to sustain the populist support needed for consortia building and collective innovation. What role do law, human rights, and bioethics-separate and together-have to play in addressing these predicaments and opportunities in early 21st century science and society? One answer we propose is an intertwined ethico-legal normative construct, namely trustworthiness . By considering trustworthiness as a central pillar at the intersection of law, human rights, and bioethics, we enable others to trust us, which in turns allows different actors (both nonprofit and for-profit) to operate more justly in consortia science and ethics, as well as to access and responsibly use health data for public benefit.

What Role for Law, Human Rights, and Bioethics in an Age of Big Data, Consortia Science, and Consortia Ethics? The Importance of Trustworthiness

PubMed Central

Dove, Edward S.; Özdemir, Vural

2015-01-01

The global bioeconomy is generating new paradigm-shifting practices of knowledge co-production, such as collective innovation; large-scale, data-driven global consortia science (Big Science); and consortia ethics (Big Ethics). These bioeconomic and sociotechnical practices can be forces for progressive social change, but they can also raise predicaments at the interface of law, human rights, and bioethics. In this article, we examine one such double-edged practice: the growing, multivariate exploitation of Big Data in the health sector, particularly by the private sector. Commercial exploitation of health data for knowledge-based products is a key aspect of the bioeconomy and is also a topic of concern among publics around the world. It is exacerbated in the current age of globally interconnected consortia science and consortia ethics, which is characterized by accumulating epistemic proximity, diminished academic independence, “extreme centrism”, and conflicted/competing interests among innovation actors. Extreme centrism is of particular importance as a new ideology emerging from consortia science and consortia ethics; this relates to invariably taking a middle-of-the-road populist stance, even in the event of human rights breaches, so as to sustain the populist support needed for consortia building and collective innovation. What role do law, human rights, and bioethics—separate and together—have to play in addressing these predicaments and opportunities in early 21st century science and society? One answer we propose is an intertwined ethico-legal normative construct, namely trustworthiness. By considering trustworthiness as a central pillar at the intersection of law, human rights, and bioethics, we enable others to trust us, which in turns allows different actors (both nonprofit and for-profit) to operate more justly in consortia science and ethics, as well as to access and responsibly use health data for public benefit. PMID:26345196

Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data.

PubMed

Dinov, Ivo D

2016-01-01

Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be 'team science'.

Beyond Einstein: From the Big Bang to Black Holes

NASA Astrophysics Data System (ADS)

White, N.

Beyond Einstein is a science-driven program of missions, education and outreach, and technology, to address three questions: What powered the Big Bang? What happens to space, time, and matter at the edge of a Black Hole? What is the mysterious Dark Energy pulling the universe apart? To address the science objectives, Beyond Einstein contains several interlinked elements. The strategic missions Constellation-X and LISA primarily investigate the nature of black holes. Constellation-X is a spectroscopic observatory that uses X-ray emitting atoms as clocks to follow the fate of matter falling into black holes. LISA will be the first space-based gravitational wave observatory uses gravitational waves to measure the dynamic structure of space and time around black holes. Moderate sized probes that are fully competed, peer-reviewed missions (300M-450M) launched every 3-5 years to address the focussed science goals: 1) Determine the nature of the Dark Energy that dominates the universe, 2) Search for the signature of the beginning of the Big Bang in the microwave background and 3) Take a census of Black Holes of all sizes and ages in the universe. The final element is a Technology Program to enable ultimate Vision Missions (after 2015) to directly detect gravitational waves echoing from the beginning of the Big Bang, and to directly image matter near the event horizon of a Black Hole. An associated Education and Public Outreach Program will inspire the next generation of scientists, and support national science standards and benchmarks.

The International Big History Association

ERIC Educational Resources Information Center

Duffy, Michael; Duffy, D'Neil

2013-01-01

IBHA, the International Big History Association, was organized in 2010 and "promotes the unified, interdisciplinary study and teaching of history of the Cosmos, Earth, Life, and Humanity." This is the vision that Montessori embraced long before the discoveries of modern science fleshed out the story of the evolving universe. "Big…

Hawaii

Atmospheric Science Data Center

2014-05-15

article title:  Big Island, Hawaii     View Larger ... Multi-angle Imaging SpectroRadiometer (MISR) images of the Big Island of Hawaii, April - June 2000. The images have been rotated so that ... NASA's Goddard Space Flight Center, Greenbelt, MD. The MISR data were obtained from the NASA Langley Research Center Atmospheric Science ...

Translating Big Data into Big Climate Ideas: Communicating Future Climate Scenarios to Increase Interdisciplinary Engagement.

EPA Science Inventory

Climate change has emerged as the significant environmental challenge of the 21st century. Therefore, understanding our changing world has forced researchers from many different fields of science to join together to tackle complicated research questions. The climate change resear...

Teaching Teachers: Bringing First-Rate Science to the Elementary Classroom. An NSTA Press Journals Collection.

ERIC Educational Resources Information Center

Smith, Betty, Ed.

This document presents a collection of papers published in the "Teaching Teachers" column in the elementary-level journal, "Science and Children." Contents include: (1) "Science is Part of the Big Picture: Teachers Become Science Learners" (Anita Greenwood); (2) "Reaching the Reluctant Science Teacher: Learning How To Teach Inquiry-Based Science"…

[Position Paper of The AG Digital Health DNVF on Digital Health Applications: Framework Conditions For Use in Health Care, Structural Development and Science].

PubMed

Vollmar, Horst Christian; Kramer, Ursula; Müller, Hardy; Griemmert, Maria; Noelle, Guido; Schrappe, Matthias

2017-12-01

The term "digital health" is currently the most comprehensive term that includes all information and communication technologies in healthcare, including e-health, mobile health, telemedicine, big data, health apps and others. Digital health can be seen as a good example of the use of the concept and methodology of health services research in the interaction between complex interventions and complex contexts. The position paper deals with 1) digital health as the subject of health services research; 2) digital health as a methodological and ethical challenge for health services research. The often-postulated benefits of digital health interventions should be demonstrated with good studies. First systematic evaluations of apps for "treatment support" show that risks are higher than benefits. The need for a rigorous proof applies even more to big data-assisted interventions that support decision-making in the treatment process with the support of artificial intelligence. Of course, from the point of view of health services research, it is worth participating as much as possible in data access available through digital health and "big data". However, there is the risk that a noncritical application of digital health and big data will lead to a return to a linear understanding of biomedical research, which, at best, accepts complex conditions assuming multivariate models but does not take complex facts into account. It is not just a matter of scientific ethical requirements in health services care research, for instance, better research instead of unnecessary research ("reducing waste"), but it is primarily a matter of anticipating the social consequences (system level) of scientific analysis and evaluation. This is both a challenge and an attractive option for health services research to present itself as a mature and responsible scientific discipline. © Georg Thieme Verlag KG Stuttgart · New York.

A General-purpose Framework for Parallel Processing of Large-scale LiDAR Data

NASA Astrophysics Data System (ADS)

Li, Z.; Hodgson, M.; Li, W.

2016-12-01

Light detection and ranging (LiDAR) technologies have proven efficiency to quickly obtain very detailed Earth surface data for a large spatial extent. Such data is important for scientific discoveries such as Earth and ecological sciences and natural disasters and environmental applications. However, handling LiDAR data poses grand geoprocessing challenges due to data intensity and computational intensity. Previous studies received notable success on parallel processing of LiDAR data to these challenges. However, these studies either relied on high performance computers and specialized hardware (GPUs) or focused mostly on finding customized solutions for some specific algorithms. We developed a general-purpose scalable framework coupled with sophisticated data decomposition and parallelization strategy to efficiently handle big LiDAR data. Specifically, 1) a tile-based spatial index is proposed to manage big LiDAR data in the scalable and fault-tolerable Hadoop distributed file system, 2) two spatial decomposition techniques are developed to enable efficient parallelization of different types of LiDAR processing tasks, and 3) by coupling existing LiDAR processing tools with Hadoop, this framework is able to conduct a variety of LiDAR data processing tasks in parallel in a highly scalable distributed computing environment. The performance and scalability of the framework is evaluated with a series of experiments conducted on a real LiDAR dataset using a proof-of-concept prototype system. The results show that the proposed framework 1) is able to handle massive LiDAR data more efficiently than standalone tools; and 2) provides almost linear scalability in terms of either increased workload (data volume) or increased computing nodes with both spatial decomposition strategies. We believe that the proposed framework provides valuable references on developing a collaborative cyberinfrastructure for processing big earth science data in a highly scalable environment.

A survey of big data research

PubMed Central

Fang, Hua; Zhang, Zhaoyang; Wang, Chanpaul Jin; Daneshmand, Mahmoud; Wang, Chonggang; Wang, Honggang

2015-01-01

Big data create values for business and research, but pose significant challenges in terms of networking, storage, management, analytics and ethics. Multidisciplinary collaborations from engineers, computer scientists, statisticians and social scientists are needed to tackle, discover and understand big data. This survey presents an overview of big data initiatives, technologies and research in industries and academia, and discusses challenges and potential solutions. PMID:26504265

Big Data Analytics for a Smart Green Infrastructure Strategy

NASA Astrophysics Data System (ADS)

Barrile, Vincenzo; Bonfa, Stefano; Bilotta, Giuliana

2017-08-01

As well known, Big Data is a term for data sets so large or complex that traditional data processing applications aren’t sufficient to process them. The term “Big Data” is referred to using predictive analytics. It is often related to user behavior analytics, or other advanced data analytics methods which from data extract value, and rarely to a particular size of data set. This is especially true for the huge amount of Earth Observation data that satellites constantly orbiting the earth daily transmit.

Managing Fleet Wide Sensory Data: Lessons Learned in Dealing with Volume, Velocity, Variety, Veracity, Value and Visibility

DTIC Science & Technology

2014-10-02

hadoop / Bradicich, T. & Orci, S. (2012). Moore’s Law of Big Data National Instruments Instrumentation News. December 2012...accurate and meaningful conclusions from such a large amount of data is a growing problem, and the term “ Big Data ” describes this phenomenon. Big Data ...is “ Big Data ”. 2. HISTORY OF BIG DATA The technology research firm International Data Corporation (IDC) recently performed a study on digital

Extragalactic astronomy: The universe beyond our galaxy

NASA Technical Reports Server (NTRS)

Jacobs, K. C.

1976-01-01

This single-topic brochure is for high school physical science teachers to use in introducing students to extragalactic astronomy. The material is presented in three parts: the fundamental content of extragalactic astronomy; modern discoveries delineated in greater detail; and a summary of the earlier discussions within the structure of the Big-Bang Theory of evolution. Each of the three sections is followed by student exercises (activities, laboratory projects, and questions-and-answers). The unit close with a glossary which explains unfamilar terms used in the text and a collection of teacher aids (literature references and audiovisual materials for utilization in further study).

The Human Genome Project: big science transforms biology and medicine.

PubMed

Hood, Leroy; Rowen, Lee

2013-01-01

The Human Genome Project has transformed biology through its integrated big science approach to deciphering a reference human genome sequence along with the complete sequences of key model organisms. The project exemplifies the power, necessity and success of large, integrated, cross-disciplinary efforts - so-called 'big science' - directed towards complex major objectives. In this article, we discuss the ways in which this ambitious endeavor led to the development of novel technologies and analytical tools, and how it brought the expertise of engineers, computer scientists and mathematicians together with biologists. It established an open approach to data sharing and open-source software, thereby making the data resulting from the project accessible to all. The genome sequences of microbes, plants and animals have revolutionized many fields of science, including microbiology, virology, infectious disease and plant biology. Moreover, deeper knowledge of human sequence variation has begun to alter the practice of medicine. The Human Genome Project has inspired subsequent large-scale data acquisition initiatives such as the International HapMap Project, 1000 Genomes, and The Cancer Genome Atlas, as well as the recently announced Human Brain Project and the emerging Human Proteome Project.

Challenges of Big Data in Educational Assessment

ERIC Educational Resources Information Center

Gibson, David C.; Webb, Mary; Ifenthaler, Dirk

2015-01-01

This paper briefly discusses four measurement challenges of data science or "big data" in educational assessments that are enabled by technology: 1. Dealing with change over time via time-based data. 2. How a digital performance space's relationships interact with learner actions, communications and products. 3. How layers of…

Expanding Evidence Approaches for Learning in a Digital World

ERIC Educational Resources Information Center

Means, Barbara; Anderson, Kea

2013-01-01

This report describes how big data and an evidence framework can align across five contexts of educational improvement. It explains that before working with big data, there is an important prerequisite: the proposed innovation should align with deeper learning objectives and should incorporate sound learning sciences principles. New curriculum…

BIG: a large-scale data integration tool for renal physiology.

PubMed

Zhao, Yue; Yang, Chin-Rang; Raghuram, Viswanathan; Parulekar, Jaya; Knepper, Mark A

2016-10-01

Due to recent advances in high-throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: "How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually?" This is the type of problem that has motivated the "Big-Data" revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/.

Precision Nutrition 4.0: A Big Data and Ethics Foresight Analysis--Convergence of Agrigenomics, Nutrigenomics, Nutriproteomics, and Nutrimetabolomics.

PubMed

Özdemir, Vural; Kolker, Eugene

2016-02-01

Nutrition is central to sustenance of good health, not to mention its role as a cultural object that brings together or draws lines among societies. Undoubtedly, understanding the future paths of nutrition science in the current era of Big Data remains firmly on science, technology, and innovation strategy agendas around the world. Nutrigenomics, the confluence of nutrition science with genomics, brought about a new focus on and legitimacy for "variability science" (i.e., the study of mechanisms of person-to-person and population differences in response to food, and the ways in which food variably impacts the host, for example, nutrient-related disease outcomes). Societal expectations, both public and private, and claims over genomics-guided and individually-tailored precision diets continue to proliferate. While the prospects of nutrition science, and nutrigenomics in particular, are established, there is a need to integrate the efforts in four Big Data domains that are naturally allied--agrigenomics, nutrigenomics, nutriproteomics, and nutrimetabolomics--that address complementary variability questions pertaining to individual differences in response to food-related environmental exposures. The joint use of these four omics knowledge domains, coined as Precision Nutrition 4.0 here, has sadly not been realized to date, but the potentials for such integrated knowledge innovation are enormous. Future personalized nutrition practices would benefit from a seamless planning of life sciences funding, research, and practice agendas from "farm to clinic to supermarket to society," and from "genome to proteome to metabolome." Hence, this innovation foresight analysis explains the already existing potentials waiting to be realized, and suggests ways forward for innovation in both technology and ethics foresight frames on precision nutrition. We propose the creation of a new Precision Nutrition Evidence Barometer for periodic, independent, and ongoing retrieval, screening, and aggregation of the relevant life sciences data. For innovation in Big Data ethics oversight, we suggest "nested governance" wherein the processes of knowledge production are made transparent in the continuum from life sciences and social sciences to humanities, and where each innovation actor reports to another accountability and transparency layer: scientists to ethicists, and ethicists to scholars in the emerging field of ethics-of-ethics. Such nested innovation ecosystems offer safety against innovation blind spots, calibrate visible/invisible power differences in the cultures of science or ethics, and ultimately, reducing the risk of "paper values"--what people say--and "real values"--what innovation actors actually do. We are optimistic that the convergence of nutrigenomics with nutriproteomics, nutrimetabolomics, and agrigenomics can build a robust, sustainable, and trustworthy precision nutrition 4.0 agenda, as articulated in this Big Data and ethics foresight analysis.

Bigfoot Field Manual

NASA Astrophysics Data System (ADS)

Campbell, J. L.; Burrows, S.; Gower, S. T.; Cohen, W. B.

1999-09-01

The BigFoot Project is funded by the Earth Science Enterprise to collect and organize data to be used in the EOS Validation Program. The data collected by the BigFoot Project are unique in being ground-based observations coincident with satellite overpasses. In addition to collecting data, the BigFoot project will develop and test new algorithms for scaling point measurements to the same spatial scales as the EOS satellite products. This BigFoot Field Manual Mill be used to achieve completeness and consistency of data collected at four initial BigFoot sites and at future sites that may collect similar validation data. Therefore, validation datasets submitted to the ORNL DAAC that have been compiled in a manner consistent with the field manual will be especially valuable in the validation program.

Solar-Terrestrial and Astronomical Research Network (STAR-Network) - A Meaningful Practice of New Cyberinfrastructure on Space Science

NASA Astrophysics Data System (ADS)

Hu, X.; Zou, Z.

2017-12-01

For the next decades, comprehensive big data application environment is the dominant direction of cyberinfrastructure development on space science. To make the concept of such BIG cyberinfrastructure (e.g. Digital Space) a reality, these aspects of capability should be focused on and integrated, which includes science data system, digital space engine, big data application (tools and models) and the IT infrastructure. In the past few years, CAS Chinese Space Science Data Center (CSSDC) has made a helpful attempt in this direction. A cloud-enabled virtual research platform on space science, called Solar-Terrestrial and Astronomical Research Network (STAR-Network), has been developed to serve the full lifecycle of space science missions and research activities. It integrated a wide range of disciplinary and interdisciplinary resources, to provide science-problem-oriented data retrieval and query service, collaborative mission demonstration service, mission operation supporting service, space weather computing and Analysis service and other self-help service. This platform is supported by persistent infrastructure, including cloud storage, cloud computing, supercomputing and so on. Different variety of resource are interconnected: the science data can be displayed on the browser by visualization tools, the data analysis tools and physical models can be drived by the applicable science data, the computing results can be saved on the cloud, for example. So far, STAR-Network has served a series of space science mission in China, involving Strategic Pioneer Program on Space Science (this program has invested some space science satellite as DAMPE, HXMT, QUESS, and more satellite will be launched around 2020) and Meridian Space Weather Monitor Project. Scientists have obtained some new findings by using the science data from these missions with STAR-Network's contribution. We are confident that STAR-Network is an exciting practice of new cyberinfrastructure architecture on space science.

Taking Advantage of the "Big Mo"—Momentum in Everyday English and Swedish and in Physics Teaching

NASA Astrophysics Data System (ADS)

Haglund, Jesper; Jeppsson, Fredrik; Ahrenberg, Lars

2015-06-01

Science education research suggests that our everyday intuitions of motion and interaction of physical objects fit well with how physicists use the term "momentum". Corpus linguistics provides an easily accessible approach to study language in different domains, including everyday language. Analysis of language samples from English text corpora reveals a trend of increasing metaphorical use of "momentum" in non-science domains, and through conceptual metaphor analysis, we show that the use of the word in everyday language, as opposed to for instance "force", is largely adequate from a physics point of view. In addition, "momentum" has recently been borrowed into Swedish as a metaphor in domains such as sports, politics and finance, with meanings similar to those in physics. As an implication for educational practice, we find support for the suggestion to introduce the term "momentum" to English-speaking pupils at an earlier age than what is typically done in the educational system today, thereby capitalising on their intuitions and experiences of everyday language. For Swedish-speaking pupils, and possibly also relevant to other languages, the parallel between "momentum" and the corresponding physics term in the students' mother tongue could be made explicit..

Big Data - What is it and why it matters.

PubMed

Tattersall, Andy; Grant, Maria J

2016-06-01

Big data, like MOOCs, altmetrics and open access, is a term that has been commonplace in the library community for some time yet, despite its prevalence, many in the library and information sector remain unsure of the relationship between big data and their roles. This editorial explores what big data could mean for the day-to-day practice of health library and information workers, presenting examples of big data in action, considering the ethics of accessing big data sets and the potential for new roles for library and information workers. © 2016 Health Libraries Group.

Measuring Adolescent Science Motivation

ERIC Educational Resources Information Center

Schumm, Maximiliane F.; Bogner, Franz X.

2016-01-01

To monitor science motivation, 232 tenth graders of the college preparatory level ("Gymnasium") completed the Science Motivation Questionnaire II (SMQ-II). Additionally, personality data were collected using a 10-item version of the Big Five Inventory. A subsequent exploratory factor analysis based on the eigenvalue-greater-than-one…

Technology for Mining the Big Data of MOOCs

ERIC Educational Resources Information Center

O'Reilly, Una-May; Veeramachaneni, Kalyan

2014-01-01

Because MOOCs bring big data to the forefront, they confront learning science with technology challenges. We describe an agenda for developing technology that enables MOOC analytics. Such an agenda needs to efficiently address the detailed, low level, high volume nature of MOOC data. It also needs to help exploit the data's capacity to reveal, in…

Big Ideas at the Center for Innovation in Education at Thomas College

ERIC Educational Resources Information Center

Prawat, Ted

2016-01-01

Schools and teachers are looking for innovative ways to teach the "big ideas" emerging in the core curricula, especially in STEAM fields (science technology, engineering, arts and math). As a result, learning environments that support digital learning and educational technology on various platforms and devices are taking on…

The Big Bang: UK Young Scientists' and Engineers' Fair 2010

ERIC Educational Resources Information Center

Allison, Simon

2010-01-01

The Big Bang: UK Young Scientists' and Engineers' Fair is an annual three-day event designed to promote science, technology, engineering and maths (STEM) careers to young people aged 7-19 through experiential learning. It is supported by stakeholders from business and industry, government and the community, and brings together people from various…

Evolution of the Air Toxics under the Big Sky Program

ERIC Educational Resources Information Center

Marra, Nancy; Vanek, Diana; Hester, Carolyn; Holian, Andrij; Ward, Tony; Adams, Earle; Knuth, Randy

2011-01-01

As a yearlong exploration of air quality and its relation to respiratory health, the "Air Toxics Under the Big Sky" program offers opportunities for students to learn and apply science process skills through self-designed inquiry-based research projects conducted within their communities. The program follows a systematic scope and sequence…

Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology

USDA-ARS?s Scientific Manuscript database

Most efforts to harness the power of big data for ecology and environmental sciences focus on data and metadata sharing, standardization, and accuracy. However, many scientists have not accepted the data deluge as an integral part of their research because the current scientific method is not scalab...

Mount Sharp Inside Gale Crater, Mars

NASA Image and Video Library

2012-03-28

Curiosity, the big rover of NASA Mars Science Laboratory mission, will land in August 2012 near the foot of a mountain inside Gale Crater. The mission project science group is calling the mountain Mount Sharp.

Combustion Science for Cleaner Fuels

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

Ahmed, Musahid

2014-10-17

Musahid Ahmed discusses how he and his team use the Advanced Light Source (ALS) to study combustion chemistry at our '8 Big Ideas' Science at the Theater event on October 8th, 2014, in Oakland, California.

Combustion Science for Cleaner Fuels

ScienceCinema

Ahmed, Musahid

2018-01-16

Musahid Ahmed discusses how he and his team use the Advanced Light Source (ALS) to study combustion chemistry at our '8 Big Ideas' Science at the Theater event on October 8th, 2014, in Oakland, California.

Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology.

PubMed

Salazar, Brittany M; Balczewski, Emily A; Ung, Choong Yong; Zhu, Shizhen

2016-12-27

Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring "big data" applications in pediatric oncology. Computational strategies derived from big data science-network- and machine learning-based modeling and drug repositioning-hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which "big data" and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.

The dynamics of big data and human rights: the case of scientific research.

PubMed

Vayena, Effy; Tasioulas, John

2016-12-28

In this paper, we address the complex relationship between big data and human rights. Because this is a vast terrain, we restrict our focus in two main ways. First, we concentrate on big data applications in scientific research, mostly health-related research. And, second, we concentrate on two human rights: the familiar right to privacy and the less well-known right to science. Our contention is that human rights interact in potentially complex ways with big data, not only constraining it, but also enabling it in various ways; and that such rights are dynamic in character, rather than fixed once and for all, changing in their implications over time in line with changes in the context we inhabit, and also as they interact among themselves in jointly responding to the opportunities and risks thrown up by a changing world. Understanding this dynamic interaction of human rights is crucial for formulating an ethic tailored to the realities-the new capabilities and risks-of the rapidly evolving digital environment.This article is part of the themed issue 'The ethical impact of data science'. © 2016 The Author(s).

BIG: a large-scale data integration tool for renal physiology

PubMed Central

Zhao, Yue; Yang, Chin-Rang; Raghuram, Viswanathan; Parulekar, Jaya

2016-01-01

Due to recent advances in high-throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: “How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually?” This is the type of problem that has motivated the “Big-Data” revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/. PMID:27279488

Commentary: Epidemiology in the era of big data.

PubMed

Mooney, Stephen J; Westreich, Daniel J; El-Sayed, Abdulrahman M

2015-05-01

Big Data has increasingly been promoted as a revolutionary development in the future of science, including epidemiology. However, the definition and implications of Big Data for epidemiology remain unclear. We here provide a working definition of Big Data predicated on the so-called "three V's": variety, volume, and velocity. From this definition, we argue that Big Data has evolutionary and revolutionary implications for identifying and intervening on the determinants of population health. We suggest that as more sources of diverse data become publicly available, the ability to combine and refine these data to yield valid answers to epidemiologic questions will be invaluable. We conclude that while epidemiology as practiced today will continue to be practiced in the Big Data future, a component of our field's future value lies in integrating subject matter knowledge with increased technical savvy. Our training programs and our visions for future public health interventions should reflect this future.

Epidemiology in the Era of Big Data

PubMed Central

Mooney, Stephen J; Westreich, Daniel J; El-Sayed, Abdulrahman M

2015-01-01

Big Data has increasingly been promoted as a revolutionary development in the future of science, including epidemiology. However, the definition and implications of Big Data for epidemiology remain unclear. We here provide a working definition of Big Data predicated on the so-called ‘3 Vs’: variety, volume, and velocity. From this definition, we argue that Big Data has evolutionary and revolutionary implications for identifying and intervening on the determinants of population health. We suggest that as more sources of diverse data become publicly available, the ability to combine and refine these data to yield valid answers to epidemiologic questions will be invaluable. We conclude that, while epidemiology as practiced today will continue to be practiced in the Big Data future, a component of our field’s future value lies in integrating subject matter knowledge with increased technical savvy. Our training programs and our visions for future public health interventions should reflect this future. PMID:25756221

Art, science, and immersion: data-driven experiences

NASA Astrophysics Data System (ADS)

West, Ruth G.; Monroe, Laura; Ford Morie, Jacquelyn; Aguilera, Julieta

2013-03-01

This panel and dialog-paper explores the potentials at the intersection of art, science, immersion and highly dimensional, "big" data to create new forms of engagement, insight and cultural forms. We will address questions such as: "What kinds of research questions can be identified at the intersection of art + science + immersive environments that can't be expressed otherwise?" "How is art+science+immersion distinct from state-of-the art visualization?" "What does working with immersive environments and visualization offer that other approaches don't or can't?" "Where does immersion fall short?" We will also explore current trends in the application of immersion for gaming, scientific data, entertainment, simulation, social media and other new forms of big data. We ask what expressive, arts-based approaches can contribute to these forms in the broad cultural landscape of immersive technologies.

Second BRITE-Constellation Science Conference: Small satellites—big science, Proceedings of the Polish Astronomical Society volume 5

NASA Astrophysics Data System (ADS)

Zwintz, Konstanze; Poretti, Ennio

2017-09-01

In 2016 the BRITE-Constellation mission had been operational for more than two years. At that time, several hundreds of bright stars of various types had been observed successfully in the two BRITE lters and astonishing new discoveries had been made. Therefore, the time was ripe to host the Second BRITE-Constellation Science Conference: Small satellites | big science" from August 22 to 26, 2016, in the beautiful Madonnensaal of the University of Innsbruck, Austria. With this conference, we brought together the scientic community interested in BRITE-Constellation, pro- vided an update on the status of the mission, presented and discussed latest scientic results, shared our experiences with the data, illustrated successful cooperations between professional and amateur ground-based observers and BRITE scientists, and explored new ideas for future BRITE-Constellation observations.

Big defensins, a diverse family of antimicrobial peptides that follows different patterns of expression in hemocytes of the oyster Crassostrea gigas.

PubMed

Rosa, Rafael D; Santini, Adrien; Fievet, Julie; Bulet, Philippe; Destoumieux-Garzón, Delphine; Bachère, Evelyne

2011-01-01

Big defensin is an antimicrobial peptide composed of a highly hydrophobic N-terminal region and a cationic C-terminal region containing six cysteine residues involved in three internal disulfide bridges. While big defensin sequences have been reported in various mollusk species, few studies have been devoted to their sequence diversity, gene organization and their expression in response to microbial infections. Using the high-throughput Digital Gene Expression approach, we have identified in Crassostrea gigas oysters several sequences coding for big defensins induced in response to a Vibrio infection. We showed that the oyster big defensin family is composed of three members (named Cg-BigDef1, Cg-BigDef2 and Cg-BigDef3) that are encoded by distinct genomic sequences. All Cg-BigDefs contain a hydrophobic N-terminal domain and a cationic C-terminal domain that resembles vertebrate β-defensins. Both domains are encoded by separate exons. We found that big defensins form a group predominantly present in mollusks and closer to vertebrate defensins than to invertebrate and fungi CSαβ-containing defensins. Moreover, we showed that Cg-BigDefs are expressed in oyster hemocytes only and follow different patterns of gene expression. While Cg-BigDef3 is non-regulated, both Cg-BigDef1 and Cg-BigDef2 transcripts are strongly induced in response to bacterial challenge. Induction was dependent on pathogen associated molecular patterns but not damage-dependent. The inducibility of Cg-BigDef1 was confirmed by HPLC and mass spectrometry, since ions with a molecular mass compatible with mature Cg-BigDef1 (10.7 kDa) were present in immune-challenged oysters only. From our biochemical data, native Cg-BigDef1 would result from the elimination of a prepropeptide sequence and the cyclization of the resulting N-terminal glutamine residue into a pyroglutamic acid. We provide here the first report showing that big defensins form a family of antimicrobial peptides diverse not only in terms of sequences but also in terms of genomic organization and regulation of gene expression.

Big Defensins, a Diverse Family of Antimicrobial Peptides That Follows Different Patterns of Expression in Hemocytes of the Oyster Crassostrea gigas

PubMed Central

Rosa, Rafael D.; Santini, Adrien; Fievet, Julie; Bulet, Philippe; Destoumieux-Garzón, Delphine; Bachère, Evelyne

2011-01-01

Background Big defensin is an antimicrobial peptide composed of a highly hydrophobic N-terminal region and a cationic C-terminal region containing six cysteine residues involved in three internal disulfide bridges. While big defensin sequences have been reported in various mollusk species, few studies have been devoted to their sequence diversity, gene organization and their expression in response to microbial infections. Findings Using the high-throughput Digital Gene Expression approach, we have identified in Crassostrea gigas oysters several sequences coding for big defensins induced in response to a Vibrio infection. We showed that the oyster big defensin family is composed of three members (named Cg-BigDef1, Cg-BigDef2 and Cg-BigDef3) that are encoded by distinct genomic sequences. All Cg-BigDefs contain a hydrophobic N-terminal domain and a cationic C-terminal domain that resembles vertebrate β-defensins. Both domains are encoded by separate exons. We found that big defensins form a group predominantly present in mollusks and closer to vertebrate defensins than to invertebrate and fungi CSαβ-containing defensins. Moreover, we showed that Cg-BigDefs are expressed in oyster hemocytes only and follow different patterns of gene expression. While Cg-BigDef3 is non-regulated, both Cg-BigDef1 and Cg-BigDef2 transcripts are strongly induced in response to bacterial challenge. Induction was dependent on pathogen associated molecular patterns but not damage-dependent. The inducibility of Cg-BigDef1 was confirmed by HPLC and mass spectrometry, since ions with a molecular mass compatible with mature Cg-BigDef1 (10.7 kDa) were present in immune-challenged oysters only. From our biochemical data, native Cg-BigDef1 would result from the elimination of a prepropeptide sequence and the cyclization of the resulting N-terminal glutamine residue into a pyroglutamic acid. Conclusions We provide here the first report showing that big defensins form a family of antimicrobial peptides diverse not only in terms of sequences but also in terms of genomic organization and regulation of gene expression. PMID:21980497

Research Data Alliance: Understanding Big Data Analytics Applications in Earth Science

NASA Astrophysics Data System (ADS)

Riedel, Morris; Ramachandran, Rahul; Baumann, Peter

2014-05-01

The Research Data Alliance (RDA) enables data to be shared across barriers through focused working groups and interest groups, formed of experts from around the world - from academia, industry and government. Its Big Data Analytics (BDA) interest groups seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. BDA seeks to analyze different scientific domain applications (e.g. earth science use cases) and their potential use of various big data analytics techniques. These techniques reach from hardware deployment models up to various different algorithms (e.g. machine learning algorithms such as support vector machines for classification). A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. This contribution will outline initial parts of such a classification and recommendations in the specific context of the field of Earth Sciences. Given lessons learned and experiences are based on a survey of use cases and also providing insights in a few use cases in detail.

Research Data Alliance: Understanding Big Data Analytics Applications in Earth Science

NASA Technical Reports Server (NTRS)

Riedel, Morris; Ramachandran, Rahul; Baumann, Peter

2014-01-01

The Research Data Alliance (RDA) enables data to be shared across barriers through focused working groups and interest groups, formed of experts from around the world - from academia, industry and government. Its Big Data Analytics (BDA) interest groups seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. BDA seeks to analyze different scientific domain applications (e.g. earth science use cases) and their potential use of various big data analytics techniques. These techniques reach from hardware deployment models up to various different algorithms (e.g. machine learning algorithms such as support vector machines for classification). A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. This contribution will outline initial parts of such a classification and recommendations in the specific context of the field of Earth Sciences. Given lessons learned and experiences are based on a survey of use cases and also providing insights in a few use cases in detail.

The Big Bang, Genesis, and Knocking on Heaven's Door

NASA Astrophysics Data System (ADS)

Gentry, Robert

2012-03-01

Michael Shermer recently upped the ante in the big bang-Genesis controversy by citing Lisa Randall's provocative claim (Science 334, 762 (2011)) that ``it is inconceivable that God could continue to intervene without introducing a material trace of his actions.'' So does Randall's and Shermer's agreement that no such evidence exists disprove God's existence? Not in my view because my 1970s Science, Nature and ARNS publications, and my article in the 1982 AAAS Western Division's Symposium Proceedings, Evolution Confronts Creation, all contain validation of God's existence via discovery of His Fingerprints of Creation and falsification of the big bang and geological evolution. These results came to wide public/scientific attention in my testimony at the 1981 Arkansas creation/evolution trial. There ACLU witness G Brent Dalrymple from the USGS -- and 2005 Medal of Science recipient from President Bush -- admitted I had discovered a tiny mystery (primordial polonium radiohalos) in granite rocks that indicated their almost instant creation. As a follow-up in 1992 and 1995 he sent out SOS letters to the entire AGU membership that the polonium halo evidence for fiat creation still existed and that someone needed to urgently find a naturalistic explanation for them. Is the physics community guilty of a Watergate-type cover-up of this discovery of God's existence and falsification of the big bang? For the answer see www.halos.tv.

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.

Algorithmic psychometrics and the scalable subject.

PubMed

Stark, Luke

2018-04-01

Recent public controversies, ranging from the 2014 Facebook 'emotional contagion' study to psychographic data profiling by Cambridge Analytica in the 2016 American presidential election, Brexit referendum and elsewhere, signal watershed moments in which the intersecting trajectories of psychology and computer science have become matters of public concern. The entangled history of these two fields grounds the application of applied psychological techniques to digital technologies, and an investment in applying calculability to human subjectivity. Today, a quantifiable psychological subject position has been translated, via 'big data' sets and algorithmic analysis, into a model subject amenable to classification through digital media platforms. I term this position the 'scalable subject', arguing it has been shaped and made legible by algorithmic psychometrics - a broad set of affordances in digital platforms shaped by psychology and the behavioral sciences. In describing the contours of this 'scalable subject', this paper highlights the urgent need for renewed attention from STS scholars on the psy sciences, and on a computational politics attentive to psychology, emotional expression, and sociality via digital media.

Big data in medical science--a biostatistical view.

PubMed

Binder, Harald; Blettner, Maria

2015-02-27

Inexpensive techniques for measurement and data storage now enable medical researchers to acquire far more data than can conveniently be analyzed by traditional methods. The expression "big data" refers to quantities on the order of magnitude of a terabyte (1012 bytes); special techniques must be used to evaluate such huge quantities of data in a scientifically meaningful way. Whether data sets of this size are useful and important is an open question that currently confronts medical science. In this article, we give illustrative examples of the use of analytical techniques for big data and discuss them in the light of a selective literature review. We point out some critical aspects that should be considered to avoid errors when large amounts of data are analyzed. Machine learning techniques enable the recognition of potentially relevant patterns. When such techniques are used, certain additional steps should be taken that are unnecessary in more traditional analyses; for example, patient characteristics should be differentially weighted. If this is not done as a preliminary step before similarity detection, which is a component of many data analysis operations, characteristics such as age or sex will be weighted no higher than any one out of 10 000 gene expression values. Experience from the analysis of conventional observational data sets can be called upon to draw conclusions about potential causal effects from big data sets. Big data techniques can be used, for example, to evaluate observational data derived from the routine care of entire populations, with clustering methods used to analyze therapeutically relevant patient subgroups. Such analyses can provide complementary information to clinical trials of the classic type. As big data analyses become more popular, various statistical techniques for causality analysis in observational data are becoming more widely available. This is likely to be of benefit to medical science, but specific adaptations will have to be made according to the requirements of the applications.

Bat ecology and public health surveillance for rabies in an urbanizing region of Colorado

USGS Publications Warehouse

O'Shea, T.J.; Neubaum, D.J.; Neubaum, M.A.; Cryan, P.M.; Ellison, L.E.; Stanley, T.R.; Rupprecht, C.E.; Pape, W.J.; Bowen, R.A.

2011-01-01

We describe use of Fort Collins, Colorado, and nearby areas by bats in 2001-2005, and link patterns in bat ecology with concurrent public health surveillance for rabies. Our analyses are based on evaluation of summary statistics, and information-theoretic support for results of simple logistic regression. Based on captures in mist nets, the city bat fauna differed from that of the adjacent mountains, and was dominated by big brown bats (Eptesicus fuscus). Species, age, and sex composition of bats submitted for rabies testing locally and along the urbanizing Front Range Corridor were similar to those of the mist-net captures and reflected the annual cycle of reproduction and activity of big brown bats. Few submissions occurred November- March, when these bats hibernated elsewhere. In summer females roosted in buildings in colonies and dominated health samples; fledging of young corresponded to a summer peak in health submissions with no increase in rabies prevalence. Roosting ecology of big brown bats in buildings was similar to that reported for natural sites, including colony size, roost-switching behavior, fidelity to roosts in a small area, and attributes important for roost selection. Attrition in roosts occurred from structural modifications of buildings to exclude colonies by citizens, but without major effects on long-term bat reproduction or survival. Bats foraged in areas set aside for nature conservation. A pattern of lower diversity in urban bat communities with dominance by big brown bats may occur widely in the USA, and is consistent with national public health records for rabies surveillance. ?? 2011 Springer Science+Business Media, LLC (outside the USA).

IEDA: Making Small Data BIG Through Interdisciplinary Partnerships Among Long-tail Domains

NASA Astrophysics Data System (ADS)

Lehnert, K. A.; Carbotte, S. M.; Arko, R. A.; Ferrini, V. L.; Hsu, L.; Song, L.; Ghiorso, M. S.; Walker, D. J.

2014-12-01

The Big Data world in the Earth Sciences so far exists primarily for disciplines that generate massive volumes of observational or computed data using large-scale, shared instrumentation such as global sensor networks, satellites, or high-performance computing facilities. These data are typically managed and curated by well-supported community data facilities that also provide the tools for exploring the data through visualization or statistical analysis. In many other domains, especially those where data are primarily acquired by individual investigators or small teams (known as 'Long-tail data'), data are poorly shared and integrated, lacking a community-based data infrastructure that ensures persistent access, quality control, standardization, and integration of data, as well as appropriate tools to fully explore and mine the data within the context of broader Earth Science datasets. IEDA (Integrated Earth Data Applications, www.iedadata.org) is a data facility funded by the US NSF to develop and operate data services that support data stewardship throughout the full life cycle of observational data in the solid earth sciences, with a focus on the data management needs of individual researchers. IEDA builds on a strong foundation of mature disciplinary data systems for marine geology and geophysics, geochemistry, and geochronology. These systems have dramatically advanced data resources in those long-tail Earth science domains. IEDA has strengthened these resources by establishing a consolidated, enterprise-grade infrastructure that is shared by the domain-specific data systems, and implementing joint data curation and data publication services that follow community standards. In recent years, other domain-specific data efforts have partnered with IEDA to take advantage of this infrastructure and improve data services to their respective communities with formal data publication, long-term preservation of data holdings, and better sustainability. IEDA hopes to foster such partnerships with streamlined data services, including user-friendly, single-point interfaces for data submission, discovery, and access across the partner systems to support interdisciplinary science.

Using Sentiment Analysis to Observe How Science is Communicated

NASA Astrophysics Data System (ADS)

Topping, David; Illingworth, Sam

2016-04-01

'Citizen Science' and 'Big data' are terms that are currently ubiquitous in the field of science communication. Whilst opinions differ as to what exactly constitutes a 'citizen', and how much information is needed in order for a data set to be considered truly 'big', what is apparent is that both of these fields have the potential to help revolutionise not just the way that science is communicated, but also the way that it is conducted. However, both the generation of sufficient data, and the efficiency of then analysing the data once it has been analysed need to be taken into account. Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. The process of sentiment analysis can be automated, providing that an adequate training set has been used, and that the nuances that are associated with a particular topic have been accounted for. Given the large amounts of data that are generated by social media posts, and the often-opinionated nature of these posts, they present an ideal source of data to both train with and then scrutinize using sentiment analysis. In this work we will demonstrate how sentiment analysis can be used to examine a large number of Twitter posts, and how a training set can be established to ensure consistency and accuracy in the automation. Following an explanation of the process, we will demonstrate how automated sentiment analysis can be used to categorise opinions in relation to a large-scale science festival, and will discuss if sentiment analysis can be used to tell us if there is a bias in these communications. We will also investigate if sentiment analysis can be used to replace more traditional, and invasive evaluation strategies, and how this approach can then be adopted to investigate other topics, both within scientific communication and in the wider scientific context.

Towards Personal Exposures: How Technology Is Changing Air Pollution and Health Research.

PubMed

Larkin, A; Hystad, P

2017-12-01

We present a review of emerging technologies and how these can transform personal air pollution exposure assessment and subsequent health research. Estimating personal air pollution exposures is currently split broadly into methods for modeling exposures for large populations versus measuring exposures for small populations. Air pollution sensors, smartphones, and air pollution models capitalizing on big/new data sources offer tremendous opportunity for unifying these approaches and improving long-term personal exposure prediction at scales needed for population-based research. A multi-disciplinary approach is needed to combine these technologies to not only estimate personal exposures for epidemiological research but also determine drivers of these exposures and new prevention opportunities. While available technologies can revolutionize air pollution exposure research, ethical, privacy, logistical, and data science challenges must be met before widespread implementations occur. Available technologies and related advances in data science can improve long-term personal air pollution exposure estimates at scales needed for population-based research. This will advance our ability to evaluate the impacts of air pollution on human health and develop effective prevention strategies.

Exploiting big data for critical care research.

PubMed

Docherty, Annemarie B; Lone, Nazir I

2015-10-01

Over recent years the digitalization, collection and storage of vast quantities of data, in combination with advances in data science, has opened up a new era of big data. In this review, we define big data, identify examples of critical care research using big data, discuss the limitations and ethical concerns of using these large datasets and finally consider scope for future research. Big data refers to datasets whose size, complexity and dynamic nature are beyond the scope of traditional data collection and analysis methods. The potential benefits to critical care are significant, with faster progress in improving health and better value for money. Although not replacing clinical trials, big data can improve their design and advance the field of precision medicine. However, there are limitations to analysing big data using observational methods. In addition, there are ethical concerns regarding maintaining confidentiality of patients who contribute to these datasets. Big data have the potential to improve medical care and reduce costs, both by individualizing medicine, and bringing together multiple sources of data about individual patients. As big data become increasingly mainstream, it will be important to maintain public confidence by safeguarding data security, governance and confidentiality.

Use of a metadata documentation and search tool for large data volumes: The NGEE arctic example

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

Devarakonda, Ranjeet; Hook, Leslie A; Killeffer, Terri S

The Online Metadata Editor (OME) is a web-based tool to help document scientific data in a well-structured, popular scientific metadata format. In this paper, we will discuss the newest tool that Oak Ridge National Laboratory (ORNL) has developed to generate, edit, and manage metadata and how it is helping data-intensive science centers and projects, such as the U.S. Department of Energy s Next Generation Ecosystem Experiments (NGEE) in the Arctic to prepare metadata and make their big data produce big science and lead to new discoveries.

Did God create our universe? Theological reflections on the Big Bang, inflation, and quantum cosmologies.

PubMed

Russell, R J

2001-12-01

The sciences and the humanities, including theology, form an epistemic hierarchy that ensures both constraint and irreducibility. At the same time, theological methodology is analogous to scientific methodology, though with several important differences. This model of interaction between science and theology can be seen illustrated in a consideration of the relation between contemporary cosmology (Big Bang cosmology, cosmic inflation, and quantum cosmology) and Christian systematic and natural theology. In light of developments in cosmology, the question of origins has become theologically less interesting than that of the cosmic evolution of a contingent universe.

Manufacturing and certification of a diffraction corrector for controlling the surface shape of the six-meter main mirror of the Big Azimuthal Telescope of the Russian Academy of Sciences

NASA Astrophysics Data System (ADS)

Nasyrov, R. K.; Poleshchuk, A. G.

2017-09-01

This paper describes the development and manufacture of diffraction corrector and imitator for the interferometric control of the surface shape of the 6-m main mirror of the Big Azimuthal Telescope of the Russian Academy of Sciences. The effect of errors in manufacture and adjustment on the quality of the measurement wavefront is studied. The corrector is controlled with the use of an off-axis diffraction imitator operating in a reflection mode. The measured error is smaller than 0.0138λ (RMS).

Linking Big and Small Data Across the Social, Engineering, and Earth Sciences

NASA Astrophysics Data System (ADS)

Chen, R. S.; de Sherbinin, A. M.; Levy, M. A.; Downs, R. R.

2014-12-01

The challenges of sustainable development cut across the social, health, ecological, engineering, and Earth sciences, across a wide range of spatial and temporal scales, and across the spectrum from basic to applied research and decision making. The rapidly increasing availability of data and information in digital form from a variety of data repositories, networks, and other sources provides new opportunities to link and integrate both traditional data holdings as well as emerging "big data" resources in ways that enable interdisciplinary research and facilitate the use of objective scientific data and information in society. Taking advantage of these opportunities not only requires improved technical and scientific data interoperability across disciplines, scales, and data types, but also concerted efforts to bridge gaps and barriers between key communities, institutions, and networks. Given the long time perspectives required in planning sustainable approaches to development, it is also imperative to address user requirements for long-term data continuity and stewardship by trustworthy repositories. We report here on lessons learned by CIESIN working on a range of sustainable development issues to integrate data across multiple repositories and networks. This includes CIESIN's roles in developing policy-relevant climate and environmental indicators, soil data for African agriculture, and exposure and risk measures for hazards, disease, and conflict, as well as CIESIN's participation in a range of national and international initiatives related both to sustainable development and to open data access, interoperability, and stewardship.

Visualization of Big Data Through Ship Maintenance Metrics Analysis for Fleet Maintenance and Revitalization

DTIC Science & Technology

2014-03-01

BIG DATA THROUGH SHIP MAINTENANCE METRICS ANALYSIS FOR FLEET MAINTENANCE AND REVITALIZATION by Isaac J. Donaldson March 2014 Thesis...March 2014 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE VISUALIZATION OF BIG DATA THROUGH SHIP MAINTENANCE METRICS...terms of the overall performance of ship maintenance processes is clearly a big data problem. The current process for presenting data on the more than

Big questions, big science: meeting the challenges of global ecology.

PubMed

Schimel, David; Keller, Michael

2015-04-01

Ecologists are increasingly tackling questions that require significant infrastucture, large experiments, networks of observations, and complex data and computation. Key hypotheses in ecology increasingly require more investment, and larger data sets to be tested than can be collected by a single investigator's or s group of investigator's labs, sustained for longer than a typical grant. Large-scale projects are expensive, so their scientific return on the investment has to justify the opportunity cost-the science foregone because resources were expended on a large project rather than supporting a number of individual projects. In addition, their management must be accountable and efficient in the use of significant resources, requiring the use of formal systems engineering and project management to mitigate risk of failure. Mapping the scientific method into formal project management requires both scientists able to work in the context, and a project implementation team sensitive to the unique requirements of ecology. Sponsoring agencies, under pressure from external and internal forces, experience many pressures that push them towards counterproductive project management but a scientific community aware and experienced in large project science can mitigate these tendencies. For big ecology to result in great science, ecologists must become informed, aware and engaged in the advocacy and governance of large ecological projects.

A framework for integrating and synthesizing data to ask and answer science questions in the Critical Zone

NASA Astrophysics Data System (ADS)

Bristol, S.

2014-12-01

In 2007, the U.S. Geological Survey (USGS) published a science strategy that resulted in an organizational pivot toward more focused attention on societal challenges and our ability to predict changes and study mitigation and resilience. The strategy described a number of global dynamics including climate and resource-related critical zone (CZ) impacts and emphasized the need for data integration as a significant underpinning for all of the science questions raised in the report. Organizational changes that came about as a result of the science strategy sparked a new entity called Core Science Systems, which has set as its mission the creation of a Modular Science Framework designed to seamlessly organize and integrate all data, information, and knowledge from the CZ. A part of this grand challenge is directly within the purview of the USGS mission and our science programs, while the data integration framework itself is part of a much larger global scientific cyberinfrastructure. This talk describes current research and development in pursuit of the USGS Modular Science Framework and how the work is being conducted in the context of the broader earth system sciences. Communities of practice under the banner of the Earth Science Information Partners are fostering working relationships vital to cohesion and interoperability between contributing institutions. The National Science Foundation's EarthCube and Cyberinfrastructure for the 21st Century initiatives are providing some of the necessary building blocks through foundational informatics and data science research. The U.S. Group on Earth Observations is providing leadership and coordination across agencies who operate earth observation systems. The White House Big Data Initiative is providing long term research and development vision to set the stage for sustainable, long term infrastructure across government data agencies. The end result will be a major building block of CZ science.

Entrenched Compartmentalisation and Students' Abilities and Levels of Interest in Science

ERIC Educational Resources Information Center

Billingsley, Berry; Nassaji, Mehdi; Abedin, Manzoorul

2017-01-01

This article explores the notion that asking and exploring so-called "big questions" could potentially increase the diversity and number of students who aspire to work in science and science-related careers. The focus is the premise that girls are more interested than boys in the relationships between science and other disciplines. The…

The Big Picture: Pre-Service Teachers' Perceptions of "Expert" Science Teachers

ERIC Educational Resources Information Center

McKinnon, Merryn; Perara, Sean

2015-01-01

This study adapted the Draw-A-Science-Teacher Test to compare 22 pre-service teachers' perceptions of their own strengths as science teachers against their perceived strengths of expert science teachers. The drawings identified a disconnection between theory and practice that we revisit in the literature. Our findings from this pilot study are…

Motivation of synthesis, with an example on groundwater quality sustainability

NASA Astrophysics Data System (ADS)

Fogg, G. E.; Labolle, E. M.

2007-12-01

Synthesis of ideas and theories from disparate disciplines is necessary for addressing the major problems faced by society. Such integration happens neither via edict nor via lofty declarations of what is needed or what is best. It happens mainly through two mechanisms: limited scope collaborations (e.g., ~2-3 investigators) in which the researchers believe deeply in their need for each other's expertise and much larger scope collaborations driven by the 'big idea.' Perhaps the strongest motivation for broad, effective synthesis is the 'big idea' that is sufficiently important and inspiring to marshal the appropriate collaborative efforts. Examples include the Manhattan Project, the quest for cancer cures, predicting effects of climate change, and groundwater quality sustainability. The latter is posed as an example of a 'big idea' that would potentially unify research efforts in both the sciences and social sciences toward a common, pressing objective.

Bringing the Tools of Big Science to Bear on Local Environmental Challenges

ERIC Educational Resources Information Center

Bronson, Scott; Jones, Keith W.; Brown, Maria

2013-01-01

We describe an interactive collaborative environmental education project that makes advanced laboratory facilities at Brookhaven National Laboratory accessible for one-year or multi-year science projects for the high school level. Cyber-enabled Environmental Science (CEES) utilizes web conferencing software to bring multi-disciplinary,…

Reflections on the Use of Tablet Technology

ERIC Educational Resources Information Center

Wise, Nicki; McGregor, Deb; Bird, James

2015-01-01

This article describes a recent Oxfordshire Big Science Event (BSE), which was combined with Science Week in Bure Park Primary School and involved a competition in which primary school children throughout Oxfordshire devised, carried out, and recorded data from science investigations to answer questions that interested them. Teams of children…

NASA EOSDIS Evolution in the BigData Era

NASA Technical Reports Server (NTRS)

Lynnes, Christopher

2015-01-01

NASA's EOSDIS system faces several challenges in the Big Data Era. Although volumes are large (but not unmanageably so), the variety of different data collections is daunting. That variety also brings with it a large and diverse user community. One key evolution EOSDIS is working toward is to enable more science analysis to be performed close to the data.

What's in a Relationship? An Examination of Social Capital, Race and Class in Mentoring Relationships

ERIC Educational Resources Information Center

Gaddis, S. Michael

2012-01-01

After 25 years of intense scrutiny, social capital remains an important yet highly debated concept in social science research. This research uses data from youth and mentors in several chapters of Big Brothers/Big Sisters to assess the importance of different mentoring relationship characteristics in creating positive outcomes among youths. The…

Air Toxics under the Big Sky: A Real-World Investigation to Engage High School Science Students

ERIC Educational Resources Information Center

Adams, Earle; Smith, Garon; Ward, Tony J.; Vanek, Diana; Marra, Nancy; Jones, David; Henthorn, Melissa; Striebel, Jim

2008-01-01

This paper describes a problem-based chemistry education model in which students perform scientific research on a local environmentally relevant problem. The project is a collaboration among The University of Montana and local high schools centered around Missoula, Montana. "Air Toxics under the Big Sky" involves high school students in collecting…

Close Encounters of the Best Kind: The Latest Sci-Fi

ERIC Educational Resources Information Center

Kunzel, Bonnie

2008-01-01

Not only is science fiction alive and well--it's flourishing. From the big screen (howdy, Wall-E) to the big books (like Suzanne Collins's The Hunger Games, which has attracted loads of prepublication praise), 2008 has been a great year for sci-fi. Publishers have released truckloads of new sci-fi titles this year, but what's particularly…

Small Core, Big Network: A Comprehensive Approach to GIS Teaching Practice Based on Digital Three-Dimensional Campus Reconstruction

ERIC Educational Resources Information Center

Cheng, Liang; Zhang, Wen; Wang, Jiechen; Li, Manchun; Zhong, Lishan

2014-01-01

Geographic information science (GIS) features a wide range of disciplines and has broad applicability. Challenges associated with rapidly developing GIS technology and the currently limited teaching and practice materials hinder universities from cultivating highly skilled GIS graduates. Based on the idea of "small core, big network," a…

429th Brookhaven Lecture

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

Robert P. Crease

2007-10-31

Robert P. Crease, historian for Brookhaven National Laboratory and Chair of the Philosophy Department at Stony Brook University, presents "How Big Science Came to Long Island: The Birth of Brookhaven Lab," covering the founding of the Laboratory, the key figures involved in starting BNL, and the many problems that had to be overcome in creating and designing its first big machines.

429th Brookhaven Lecture

ScienceCinema

Robert P. Crease

2017-12-09

Robert P. Crease, historian for Brookhaven National Laboratory and Chair of the Philosophy Department at Stony Brook University, presents "How Big Science Came to Long Island: The Birth of Brookhaven Lab," covering the founding of the Laboratory, the key figures involved in starting BNL, and the many problems that had to be overcome in creating and designing its first big machines.

Big Data, Big Problems: A Healthcare Perspective.

PubMed

Househ, Mowafa S; Aldosari, Bakheet; Alanazi, Abdullah; Kushniruk, Andre W; Borycki, Elizabeth M

2017-01-01

Much has been written on the benefits of big data for healthcare such as improving patient outcomes, public health surveillance, and healthcare policy decisions. Over the past five years, Big Data, and the data sciences field in general, has been hyped as the "Holy Grail" for the healthcare industry promising a more efficient healthcare system with the promise of improved healthcare outcomes. However, more recently, healthcare researchers are exposing the potential and harmful effects Big Data can have on patient care associating it with increased medical costs, patient mortality, and misguided decision making by clinicians and healthcare policy makers. In this paper, we review the current Big Data trends with a specific focus on the inadvertent negative impacts that Big Data could have on healthcare, in general, and specifically, as it relates to patient and clinical care. Our study results show that although Big Data is built up to be as a the "Holy Grail" for healthcare, small data techniques using traditional statistical methods are, in many cases, more accurate and can lead to more improved healthcare outcomes than Big Data methods. In sum, Big Data for healthcare may cause more problems for the healthcare industry than solutions, and in short, when it comes to the use of data in healthcare, "size isn't everything."

Crowd-Funded Micro-Grants for Genomics and “Big Data”: An Actionable Idea Connecting Small (Artisan) Science, Infrastructure Science, and Citizen Philanthropy

PubMed Central

Badr, Kamal F.; Dove, Edward S.; Endrenyi, Laszlo; Geraci, Christy Jo; Hotez, Peter J.; Milius, Djims; Neves-Pereira, Maria; Pang, Tikki; Rotimi, Charles N.; Sabra, Ramzi; Sarkissian, Christineh N.; Srivastava, Sanjeeva; Tims, Hesther; Zgheib, Nathalie K.; Kickbusch, Ilona

2013-01-01

Abstract Biomedical science in the 21st century is embedded in, and draws from, a digital commons and “Big Data” created by high-throughput Omics technologies such as genomics. Classic Edisonian metaphors of science and scientists (i.e., “the lone genius” or other narrow definitions of expertise) are ill equipped to harness the vast promises of the 21st century digital commons. Moreover, in medicine and life sciences, experts often under-appreciate the important contributions made by citizen scholars and lead users of innovations to design innovative products and co-create new knowledge. We believe there are a large number of users waiting to be mobilized so as to engage with Big Data as citizen scientists—only if some funding were available. Yet many of these scholars may not meet the meta-criteria used to judge expertise, such as a track record in obtaining large research grants or a traditional academic curriculum vitae. This innovation research article describes a novel idea and action framework: micro-grants, each worth $1000, for genomics and Big Data. Though a relatively small amount at first glance, this far exceeds the annual income of the “bottom one billion”—the 1.4 billion people living below the extreme poverty level defined by the World Bank ($1.25/day). We describe two types of micro-grants. Type 1 micro-grants can be awarded through established funding agencies and philanthropies that create micro-granting programs to fund a broad and highly diverse array of small artisan labs and citizen scholars to connect genomics and Big Data with new models of discovery such as open user innovation. Type 2 micro-grants can be funded by existing or new science observatories and citizen think tanks through crowd-funding mechanisms described herein. Type 2 micro-grants would also facilitate global health diplomacy by co-creating crowd-funded micro-granting programs across nation-states in regions facing political and financial instability, while sharing similar disease burdens, therapeutics, and diagnostic needs. We report the creation of ten Type 2 micro-grants for citizen science and artisan labs to be administered by the nonprofit Data-Enabled Life Sciences Alliance International (DELSA Global, Seattle). Our hope is that these micro-grants will spur novel forms of disruptive innovation and genomics translation by artisan scientists and citizen scholars alike. We conclude with a neglected voice from the global health frontlines, the American University of Iraq in Sulaimani, and suggest that many similar global regions are now poised for micro-grant enabled collective innovation to harness the 21st century digital commons. PMID:23574338

Crowd-funded micro-grants for genomics and "big data": an actionable idea connecting small (artisan) science, infrastructure science, and citizen philanthropy.

PubMed

Özdemir, Vural; Badr, Kamal F; Dove, Edward S; Endrenyi, Laszlo; Geraci, Christy Jo; Hotez, Peter J; Milius, Djims; Neves-Pereira, Maria; Pang, Tikki; Rotimi, Charles N; Sabra, Ramzi; Sarkissian, Christineh N; Srivastava, Sanjeeva; Tims, Hesther; Zgheib, Nathalie K; Kickbusch, Ilona

2013-04-01

Biomedical science in the 21(st) century is embedded in, and draws from, a digital commons and "Big Data" created by high-throughput Omics technologies such as genomics. Classic Edisonian metaphors of science and scientists (i.e., "the lone genius" or other narrow definitions of expertise) are ill equipped to harness the vast promises of the 21(st) century digital commons. Moreover, in medicine and life sciences, experts often under-appreciate the important contributions made by citizen scholars and lead users of innovations to design innovative products and co-create new knowledge. We believe there are a large number of users waiting to be mobilized so as to engage with Big Data as citizen scientists-only if some funding were available. Yet many of these scholars may not meet the meta-criteria used to judge expertise, such as a track record in obtaining large research grants or a traditional academic curriculum vitae. This innovation research article describes a novel idea and action framework: micro-grants, each worth $1000, for genomics and Big Data. Though a relatively small amount at first glance, this far exceeds the annual income of the "bottom one billion"-the 1.4 billion people living below the extreme poverty level defined by the World Bank ($1.25/day). We describe two types of micro-grants. Type 1 micro-grants can be awarded through established funding agencies and philanthropies that create micro-granting programs to fund a broad and highly diverse array of small artisan labs and citizen scholars to connect genomics and Big Data with new models of discovery such as open user innovation. Type 2 micro-grants can be funded by existing or new science observatories and citizen think tanks through crowd-funding mechanisms described herein. Type 2 micro-grants would also facilitate global health diplomacy by co-creating crowd-funded micro-granting programs across nation-states in regions facing political and financial instability, while sharing similar disease burdens, therapeutics, and diagnostic needs. We report the creation of ten Type 2 micro-grants for citizen science and artisan labs to be administered by the nonprofit Data-Enabled Life Sciences Alliance International (DELSA Global, Seattle). Our hope is that these micro-grants will spur novel forms of disruptive innovation and genomics translation by artisan scientists and citizen scholars alike. We conclude with a neglected voice from the global health frontlines, the American University of Iraq in Sulaimani, and suggest that many similar global regions are now poised for micro-grant enabled collective innovation to harness the 21(st) century digital commons.

The Big Splat, or How Our Moon Came to Be

NASA Astrophysics Data System (ADS)

MacKenzie, Dana

2003-03-01

The first popular book to explain the dramatic theory behind the Moon's genesis This lively science history relates one of the great recent breakthroughs in planetary astronomy-a successful theory of the birth of the Moon. Science journalist Dana Mackenzie traces the evolution of this theory, one little known outside the scientific community: a Mars-sized object collided with Earth some four billion years ago, and the remains of this colossal explosion-the Big Splat-came together to form the Moon. Beginning with notions of the Moon in ancient cosmologies, Mackenzie relates the fascinating history of lunar speculation, moving from Galileo and Kepler to George Darwin (son of Charles) and the Apollo astronauts, whose trips to the lunar surface helped solve one of the most enigmatic mysteries of the night sky: who hung the Moon? Dana Mackenzie (Santa Cruz, CA) is a freelance science journalist. His articles have appeared in such magazines as Science, Discover, American Scientist, The Sciences, and New Scientist.

Quest for Value in Big Earth Data

NASA Astrophysics Data System (ADS)

Kuo, Kwo-Sen; Oloso, Amidu O.; Rilee, Mike L.; Doan, Khoa; Clune, Thomas L.; Yu, Hongfeng

2017-04-01

Among all the V's of Big Data challenges, such as Volume, Variety, Velocity, Veracity, etc., we believe Value is the ultimate determinant, because a system delivering better value has a competitive edge over others. Although it is not straightforward to assess the value of scientific endeavors, we believe the ratio of scientific productivity increase to investment is a reasonable measure. Our research in Big Data approaches to data-intensive analysis for Earth Science has yielded some insights, as well as evidences, as to how optimal value might be attained. The first insight is that we should avoid, as much as possible, moving data through connections with relatively low bandwidth. That is, we recognize that moving data is expensive, albeit inevitable. They must at least be moved from the storage device into computer main memory and then to CPU registers for computation. When data must be moved it is better to move them via relatively high-bandwidth connections and avoid low-bandwidth ones. For this reason, a technology that can best exploit data locality will have an advantage over others. Data locality is easy to achieve and exploit with only one dataset. With multiple datasets, data colocation becomes important in addition to data locality. However, the organization of datasets can only be co-located for certain types of analyses. It is impossible for them to be co-located for all analyses. Therefore, our second insight is that we need to co-locate the datasets for the most commonly used analyses. In Earth Science, we believe the most common analysis requirement is "spatiotemporal coincidence". For example, when we analyze precipitation systems, we often would like to know the environment conditions "where and when" (i.e. at the same location and time) there is precipitation. This "where and when" indicates the "spatiotemporal coincidence" requirement. Thus, an associated insight is that datasets need to be partitioned per the physical dimensions, i.e. space and time, rather than their array index dimensions to achieve co-location for spatiotemporal coincidence. This leads further to the insight that, in terms of optimizing Value, achieving good scalability in Variety is more crucial than good scalability in Volume. Therefore, we will discuss our innovative approach to improving productivity by homogenizing the daunting varieties in Earth Science data to enable data co-location systematically. In addition, a Big Data system incorporating the capabilities described above has the potential to drastically shorten the data preparation period of machine learning, better facilitate automated machine learning operations, and further boost scientific productivity.

Research on Implementing Big Data: Technology, People, & Processes

ERIC Educational Resources Information Center

Rankin, Jenny Grant; Johnson, Margie; Dennis, Randall

2015-01-01

When many people hear the term "big data", they primarily think of a technology tool for the collection and reporting of data of high variety, volume, and velocity. However, the complexity of big data is not only the technology, but the supporting processes, policies, and people supporting it. This paper was written by three experts to…

76 FR 35909 - Temporary Concession Contract for Big South Fork National Recreation Area, TN/KY

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-20

... Recreation Area, TN/KY. SUMMARY: Pursuant to 36 CFR 51.24, public notice is hereby given that the National... Concession Contract for Big South Fork National Recreation Area, TN/KY AGENCY: National Park Service... services within Big South Fork National Recreation Area, Tennessee and Kentucky, for a term not to exceed 3...

Big Data: Next-Generation Machines for Big Science

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

Hack, James J.; Papka, Michael E.

Addressing the scientific grand challenges identified by the US Department of Energy’s (DOE’s) Office of Science’s programs alone demands a total leadership-class computing capability of 150 to 400 Pflops by the end of this decade. The successors to three of the DOE’s most powerful leadership-class machines are set to arrive in 2017 and 2018—the products of the Collaboration Oak Ridge Argonne Livermore (CORAL) initiative, a national laboratory–industry design/build approach to engineering nextgeneration petascale computers for grand challenge science. These mission-critical machines will enable discoveries in key scientific fields such as energy, biotechnology, nanotechnology, materials science, and high-performance computing, and servemore » as a milestone on the path to deploying exascale computing capabilities.« less

Applying science and mathematics to big data for smarter buildings.

PubMed

Lee, Young M; An, Lianjun; Liu, Fei; Horesh, Raya; Chae, Young Tae; Zhang, Rui

2013-08-01

Many buildings are now collecting a large amount of data on operations, energy consumption, and activities through systems such as a building management system (BMS), sensors, and meters (e.g., submeters and smart meters). However, the majority of data are not utilized and are thrown away. Science and mathematics can play an important role in utilizing these big data and accurately assessing how energy is consumed in buildings and what can be done to save energy, make buildings energy efficient, and reduce greenhouse gas (GHG) emissions. This paper discusses an analytical tool that has been developed to assist building owners, facility managers, operators, and tenants of buildings in assessing, benchmarking, diagnosing, tracking, forecasting, and simulating energy consumption in building portfolios. © 2013 New York Academy of Sciences.

Big data in fashion industry

NASA Astrophysics Data System (ADS)

Jain, S.; Bruniaux, J.; Zeng, X.; Bruniaux, P.

2017-10-01

Significant work has been done in the field of big data in last decade. The concept of big data includes analysing voluminous data to extract valuable information. In the fashion world, big data is increasingly playing a part in trend forecasting, analysing consumer behaviour, preference and emotions. The purpose of this paper is to introduce the term fashion data and why it can be considered as big data. It also gives a broad classification of the types of fashion data and briefly defines them. Also, the methodology and working of a system that will use this data is briefly described.

Gender Differences in Achievement in Calculating Reacting Masses from Chemical Equations among Secondary School Students in Makurdi Metropols

ERIC Educational Resources Information Center

Eriba, Joel O.; Ande, Sesugh

2006-01-01

Over the years there exists gender inequality in science achievement among senior secondary school students the world over. It is observed that the males score higher than the females in science and science- related examinations. This has created a big psychological alienation or depression in the minds of female students towards science and…

Small Bodies, Big Concepts: Bringing Visual Analysis into the Middle School Classroom

NASA Astrophysics Data System (ADS)

Cobb, W. H.; Lebofsky, L. A.; Ristvey, J. D.; Buxner, S.; Weeks, S.; Zolensky, M. E.

2012-03-01

Multi-disciplinary PD model digs into high-end planetary science backed by a pedagogical framework, Designing Effective Science Instruction. NASA activities are sequenced to promote visual analysis of emerging data from Discovery Program missions.

The Big Science Questions About Mercury's Ice-Bearing Polar Deposits After MESSENGER

NASA Astrophysics Data System (ADS)

Chabot, N. L.; Lawrence, D. J.

2018-05-01

Mercury’s polar deposits provide many well-characterized locations that are known to have large expanses of exposed water ice and/or other volatile materials — presenting unique opportunities to address fundamental science questions.

Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification.

PubMed

d'Alessandro, Brian; O'Neil, Cathy; LaGatta, Tom

2017-06-01

Recent research has helped to cultivate growing awareness that machine-learning systems fueled by big data can create or exacerbate troubling disparities in society. Much of this research comes from outside of the practicing data science community, leaving its members with little concrete guidance to proactively address these concerns. This article introduces issues of discrimination to the data science community on its own terms. In it, we tour the familiar data-mining process while providing a taxonomy of common practices that have the potential to produce unintended discrimination. We also survey how discrimination is commonly measured, and suggest how familiar development processes can be augmented to mitigate systems' discriminatory potential. We advocate that data scientists should be intentional about modeling and reducing discriminatory outcomes. Without doing so, their efforts will result in perpetuating any systemic discrimination that may exist, but under a misleading veil of data-driven objectivity.

Analyzing Big Data in Psychology: A Split/Analyze/Meta-Analyze Approach

PubMed Central

Cheung, Mike W.-L.; Jak, Suzanne

2016-01-01

Big data is a field that has traditionally been dominated by disciplines such as computer science and business, where mainly data-driven analyses have been performed. Psychology, a discipline in which a strong emphasis is placed on behavioral theories and empirical research, has the potential to contribute greatly to the big data movement. However, one challenge to psychologists—and probably the most crucial one—is that most researchers may not have the necessary programming and computational skills to analyze big data. In this study we argue that psychologists can also conduct big data research and that, rather than trying to acquire new programming and computational skills, they should focus on their strengths, such as performing psychometric analyses and testing theories using multivariate analyses to explain phenomena. We propose a split/analyze/meta-analyze approach that allows psychologists to easily analyze big data. Two real datasets are used to demonstrate the proposed procedures in R. A new research agenda related to the analysis of big data in psychology is outlined at the end of the study. PMID:27242639

Analyzing Big Data in Psychology: A Split/Analyze/Meta-Analyze Approach.

PubMed

Cheung, Mike W-L; Jak, Suzanne

2016-01-01

Big data is a field that has traditionally been dominated by disciplines such as computer science and business, where mainly data-driven analyses have been performed. Psychology, a discipline in which a strong emphasis is placed on behavioral theories and empirical research, has the potential to contribute greatly to the big data movement. However, one challenge to psychologists-and probably the most crucial one-is that most researchers may not have the necessary programming and computational skills to analyze big data. In this study we argue that psychologists can also conduct big data research and that, rather than trying to acquire new programming and computational skills, they should focus on their strengths, such as performing psychometric analyses and testing theories using multivariate analyses to explain phenomena. We propose a split/analyze/meta-analyze approach that allows psychologists to easily analyze big data. Two real datasets are used to demonstrate the proposed procedures in R. A new research agenda related to the analysis of big data in psychology is outlined at the end of the study.

IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research.

PubMed

Chen, Ying; Elenee Argentinis, J D; Weber, Griff

2016-04-01

Life sciences researchers are under pressure to innovate faster than ever. Big data offer the promise of unlocking novel insights and accelerating breakthroughs. Ironically, although more data are available than ever, only a fraction is being integrated, understood, and analyzed. The challenge lies in harnessing volumes of data, integrating the data from hundreds of sources, and understanding their various formats. New technologies such as cognitive computing offer promise for addressing this challenge because cognitive solutions are specifically designed to integrate and analyze big datasets. Cognitive solutions can understand different types of data such as lab values in a structured database or the text of a scientific publication. Cognitive solutions are trained to understand technical, industry-specific content and use advanced reasoning, predictive modeling, and machine learning techniques to advance research faster. Watson, a cognitive computing technology, has been configured to support life sciences research. This version of Watson includes medical literature, patents, genomics, and chemical and pharmacological data that researchers would typically use in their work. Watson has also been developed with specific comprehension of scientific terminology so it can make novel connections in millions of pages of text. Watson has been applied to a few pilot studies in the areas of drug target identification and drug repurposing. The pilot results suggest that Watson can accelerate identification of novel drug candidates and novel drug targets by harnessing the potential of big data. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

What’s So Different about Big Data?. A Primer for Clinicians Trained to Think Epidemiologically

PubMed Central

Liu, Vincent

2014-01-01

The Big Data movement in computer science has brought dramatic changes in what counts as data, how those data are analyzed, and what can be done with those data. Although increasingly pervasive in the business world, it has only recently begun to influence clinical research and practice. As Big Data draws from different intellectual traditions than clinical epidemiology, the ideas may be less familiar to practicing clinicians. There is an increasing role of Big Data in health care, and it has tremendous potential. This Demystifying Data Seminar identifies four main strands in Big Data relevant to health care. The first is the inclusion of many new kinds of data elements into clinical research and operations, in a volume not previously routinely used. Second, Big Data asks different kinds of questions of data and emphasizes the usefulness of analyses that are explicitly associational but not causal. Third, Big Data brings new analytic approaches to bear on these questions. And fourth, Big Data embodies a new set of aspirations for a breaking down of distinctions between research data and operational data and their merging into a continuously learning health system. PMID:25102315

What's so different about big data?. A primer for clinicians trained to think epidemiologically.

PubMed

Iwashyna, Theodore J; Liu, Vincent

2014-09-01

The Big Data movement in computer science has brought dramatic changes in what counts as data, how those data are analyzed, and what can be done with those data. Although increasingly pervasive in the business world, it has only recently begun to influence clinical research and practice. As Big Data draws from different intellectual traditions than clinical epidemiology, the ideas may be less familiar to practicing clinicians. There is an increasing role of Big Data in health care, and it has tremendous potential. This Demystifying Data Seminar identifies four main strands in Big Data relevant to health care. The first is the inclusion of many new kinds of data elements into clinical research and operations, in a volume not previously routinely used. Second, Big Data asks different kinds of questions of data and emphasizes the usefulness of analyses that are explicitly associational but not causal. Third, Big Data brings new analytic approaches to bear on these questions. And fourth, Big Data embodies a new set of aspirations for a breaking down of distinctions between research data and operational data and their merging into a continuously learning health system.

Big questions, big science: meeting the challenges of global ecology

Treesearch

David Schimel; Michael Keller

2015-01-01

Ecologists are increasingly tackling questions that require significant infrastucture, large experiments, networks of observations, and complex data and computation. Key hypotheses in ecology increasingly require more investment, and larger data sets to be tested than can be collected by a single investigator’s or s group of investigator’s labs, sustained for longer...

Examining Big-Fish-Little-Pond-Effects across 49 Countries: A Multilevel Latent Variable Modelling Approach

ERIC Educational Resources Information Center

Wang, Ze

2015-01-01

Using data from the Trends in International Mathematics and Science Study (TIMSS) 2007, this study examined the big-fish-little-pond-effects (BFLPEs) in 49 countries. In this study, the effect of math ability on math self-concept was decomposed into a within- and a between-level components using implicit mean centring and the complex data…

KNMI DataLab experiences in serving data-driven innovations

NASA Astrophysics Data System (ADS)

Noteboom, Jan Willem; Sluiter, Raymond

2016-04-01

Climate change research and innovations in weather forecasting rely more and more on (Big) data. Besides increasing data from traditional sources (such as observation networks, radars and satellites), the use of open data, crowd sourced data and the Internet of Things (IoT) is emerging. To deploy these sources of data optimally in our services and products, KNMI has established a DataLab to serve data-driven innovations in collaboration with public and private sector partners. Big data management, data integration, data analytics including machine learning and data visualization techniques are playing an important role in the DataLab. Cross-domain data-driven innovations that arise from public-private collaborative projects and research programmes can be explored, experimented and/or piloted by the KNMI DataLab. Furthermore, advice can be requested on (Big) data techniques and data sources. In support of collaborative (Big) data science activities, scalable environments are offered with facilities for data integration, data analysis and visualization. In addition, Data Science expertise is provided directly or from a pool of internal and external experts. At the EGU conference, gained experiences and best practices are presented in operating the KNMI DataLab to serve data-driven innovations for weather and climate applications optimally.

The Next Big Thing - Eric Haseltine

ScienceCinema

Eric Haseltine

2017-12-09

Eric Haseltine, Haseltine Partners president and former chief of Walt Disney Imagineering, presented "The Next Big Thing," on Sept. 11, at the ORNL. He described the four "early warning signs" that a scientific breakthrough is imminent, and then suggested practical ways to turn these insights into breakthrough innovations. Haseltine is former director of research at the National Security Agency and associate director for science and technology for the director of National Intelligence, former executive vice president of Walt Disney Imagineering and director of engineering for Hughes Aircraft. He has 15 patents in optics, special effects and electronic media, and more than 100 publications in science and technical journals, the web and Discover Magazine.

Diverse Grains in Mars Sandstone Target Big Arm

NASA Image and Video Library

2015-07-01

This view of a sandstone target called "Big Arm" covers an area about 1.3 inches (33 millimeters) wide in detail that shows differing shapes and colors of sand grains in the stone. Three separate images taken by the Mars Hand Lens Imager (MAHLI) camera on NASA's Curiosity Mars rover, at different focus settings, were combined into this focus-merge view. The Big Arm target on lower Mount Sharp is at a location near "Marias Pass" where a mudstone bedrock is in contact with overlying sandstone bedrock. MAHLI recorded the component images on May 29, 2015, during the 999th Martian day, or sol, of Curiosity's work on Mars. The rounded shape of some grains visible here suggests they traveled long distances before becoming part of the sediment that later hardened into sandstone. Other grains are more angular and may have originated closer to the rock's current location. Lighter and darker grains may have different compositions. MAHLI was built by Malin Space Science Systems, San Diego. NASA's Jet Propulsion Laboratory, a division of the California Institute of Technology in Pasadena, manages the Mars Science Laboratory Project for the NASA Science Mission Directorate, Washington. http://photojournal.jpl.nasa.gov/catalog/PIA19677

The Human Genome Project: big science transforms biology and medicine

PubMed Central

2013-01-01

The Human Genome Project has transformed biology through its integrated big science approach to deciphering a reference human genome sequence along with the complete sequences of key model organisms. The project exemplifies the power, necessity and success of large, integrated, cross-disciplinary efforts - so-called ‘big science’ - directed towards complex major objectives. In this article, we discuss the ways in which this ambitious endeavor led to the development of novel technologies and analytical tools, and how it brought the expertise of engineers, computer scientists and mathematicians together with biologists. It established an open approach to data sharing and open-source software, thereby making the data resulting from the project accessible to all. The genome sequences of microbes, plants and animals have revolutionized many fields of science, including microbiology, virology, infectious disease and plant biology. Moreover, deeper knowledge of human sequence variation has begun to alter the practice of medicine. The Human Genome Project has inspired subsequent large-scale data acquisition initiatives such as the International HapMap Project, 1000 Genomes, and The Cancer Genome Atlas, as well as the recently announced Human Brain Project and the emerging Human Proteome Project. PMID:24040834

An evaluation of evaluative personality terms: a comparison of the big seven and five-factor model in predicting psychopathology.

PubMed

Durrett, Christine; Trull, Timothy J

2005-09-01

Two personality models are compared regarding their relationship with personality disorder (PD) symptom counts and with lifetime Axis I diagnoses. These models share 5 similar domains, and the Big 7 model also includes 2 domains assessing self-evaluation: positive and negative valence. The Big 7 model accounted for more variance in PDs than the 5-factor model, primarily because of the association of negative valence with most PDs. Although low-positive valence was associated with most Axis I diagnoses, the 5-factor model generally accounted for more variance in Axis I diagnoses than the Big 7 model. Some predicted associations between self-evaluation and psychopathology were not found, and unanticipated associations emerged. These findings are discussed regarding the utility of evaluative terms in clinical assessment.

PREDON Scientific Data Preservation 2014

NASA Astrophysics Data System (ADS)

Diaconu, C.; Kraml, S.; Surace, C.; Chateigner, D.; Libourel, T.; Laurent, A.; Lin, Y.; Schaming, M.; Benbernou, S.; Lebbah, M.; Boucon, D.; Cérin, C.; Azzag, H.; Mouron, P.; Nief, J.-Y.; Coutin, S.; Beckmann, V.

Scientific data collected with modern sensors or dedicated detectors exceed very often the perimeter of the initial scientific design. These data are obtained more and more frequently with large material and human efforts. A large class of scientific experiments are in fact unique because of their large scale, with very small chances to be repeated and to superseded by new experiments in the same domain: for instance high energy physics and astrophysics experiments involve multi-annual developments and a simple duplication of efforts in order to reproduce old data is simply not affordable. Other scientific experiments are in fact unique by nature: earth science, medical sciences etc. since the collected data is "time-stamped" and thereby non-reproducible by new experiments or observations. In addition, scientific data collection increased dramatically in the recent years, participating to the so-called "data deluge" and inviting for common reflection in the context of "big data" investigations. The new knowledge obtained using these data should be preserved long term such that the access and the re-use are made possible and lead to an enhancement of the initial investment. Data observatories, based on open access policies and coupled with multi-disciplinary techniques for indexing and mining may lead to truly new paradigms in science. It is therefore of outmost importance to pursue a coherent and vigorous approach to preserve the scientific data at long term. The preservation remains nevertheless a challenge due to the complexity of the data structure, the fragility of the custom-made software environments as well as the lack of rigorous approaches in workflows and algorithms. To address this challenge, the PREDON project has been initiated in France in 2012 within the MASTODONS program: a Big Data scientific challenge, initiated and supported by the Interdisciplinary Mission of the National Centre for Scientific Research (CNRS). PREDON is a study group formed by researchers from different disciplines and institutes. Several meetings and workshops lead to a rich exchange in ideas, paradigms and methods. The present document includes contributions of the participants to the PREDON Study Group, as well as invited papers, related to the scientific case, methodology and technology. This document should be read as a "facts finding" resource pointing to a concrete and significant scientific interest for long term research data preservation, as well as to cutting edge methods and technologies to achieve this goal. A sustained, coherent and long term action in the area of scientific data preservation would be highly beneficial.

First Born amplitude for transitions from a circular state to a state of large (l, m)

NASA Astrophysics Data System (ADS)

Dewangan, D. P.

2005-01-01

The use of cylindrical polar coordinates instead of the conventional spherical polar coordinates enables us to derive compact expressions of the first Born amplitude for some selected sets of transitions from an arbitrary initial circular \\big|\\psi_{n_i,n_i-1,n_i-1}\\big\\rangle state to a final \\big|\\psi_{n_f,l_f,m_f}\\big\\rangle state of large (lf, mf). The formulae for \\big|\\psi_{n_i,n_i-1,n_i-1}\\big\\rangle \\longrightarrow \\big|\\psi_{n_f,n_f-1,n_f-2}\\big\\rangle and \\big|\\psi_{n_i,n_i-1,n_i-1}\\big\\rangle \\longrightarrow \\big|\\psi_{n_f,n_f-1,n_f-3}\\big\\rangle transitions are expressed in terms of the Jacobi polynomials which serve as suitable starting points for constructing complete solutions over the bound energy levels of hydrogen-like atoms. The formulae for \\big|\\psi_{n_i,n_i-1,n_i-1}\\big\\rangle \\longrightarrow \\big|\\psi_{n_f,n_f-1,-(n_f-2)}\\big\\rangle and \\big|\\psi_{n_i,n_i-1,n_i-1}\\big\\rangle \\longrightarrow \\big|\\psi_{n_f,n_f-1,-(n_f-3)}\\big\\rangle transitions are in simple algebraic forms and are directly applicable to all possible values of ni and nf. It emerges that the method can be extended to evaluate the first Born amplitude for many other transitions involving states of large (l, m).

Bigfoot Field Manual, Version 2.1

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

Campbell, J.L.; Burrows, S.; Gower, S.T.

1999-09-01

The BigFoot Project is funded by the Earth Science Enterprise to collect and organize data to be used in the National Aeronautics and Space Administration's Earth Observing System (EOS) Validation Program. The data collected by the BigFoot Project are unique in being ground-based observations coincident with satellite overpasses. In addition to collecting data, the BigFoot project will develop and test new algorithms for scaling point measurements to the same spatial scales as the EOS satellite products. This BigFoot Field Manual will be used to achieve completeness and consistency of data collected at four initial BigFoot sites and at future sitesmore » that may collect similar validation data. Therefore, validation datasets submitted to the Oak Ridge National Laboratory Distributed Active Archive Center that have been compiled in a manner consistent with the field manual will be especially valuable in the validation program.« less

Aquatic Sciences and Its Appeal for Expeditionary Research Science Education

NASA Astrophysics Data System (ADS)

Aguilar, C.; Cuhel, R. L.

2016-02-01

Our multi-program team studies aim to develop specific "hard" and "soft" STEM skills that integrate, literally, both disciplinary and socio-economic aspects of students lives to include peer mentoring, advisement, enabling, and professional mentorship, as well as honestly productive, career-developing hands-on research. Specifically, we use Interdependent, multidisciplinary research experiences; Development and honing of specific disciplinary skill (you have to have something TO network); Use of skill in a team to produce big picture product; Interaction with varied, often outside professionals; in order to Finish with self-confidence and a marketable skill. In a given year our umbrella projects involve linked aquatic science disciplines: Analytical Chemistry; Geology; Geochemistry; Microbiology; Engineering (Remotely Operated Vehicles); and recently Policy (scientist-public engagement). We especially use expeditionary research activities aboard our research vessel in Lake Michigan, during which (a dozen at a time, from multiple programs) students: Experience ocean-scale research cruise activities; Apply a learned skill in real time to characterize a large lake; Participate in interdisciplinary teamwork; Learn interactions among biology, chemistry, geology, optics, physics for diverse aquatic habitats; and, importantly, Experience leadership as "Chief Scientist-for-a-station". These team efforts achieve beneficial outcomes: Develop self-confidence in application of skills; Enable expression of leadership capabilities; Provide opportunity to assess "love of big water"; Produce invaluable long-term dataset for the studied region (our benefit); and they are Often voted as a top influence for career decisions. These collectively have led to some positive outcomes for "historical" undergraduate participants - more than half in STEM graduate programs, only a few not still involved in a STEM career at some level, or involved as for example a lawyer in environmental policy.

Economics and econophysics in the era of Big Data

NASA Astrophysics Data System (ADS)

Cheong, Siew Ann

2016-12-01

There is an undeniable disconnect between theory-heavy economics and the real world, and some cross polination of ideas with econophysics, which is more balanced between data and models, might help economics along the way to become a truly scientific enterprise. With the coming of the era of Big Data, this transformation of economics into a data-driven science is becoming more urgent. In this article, I use the story of Kepler's discovery of his three laws of planetary motion to enlarge the framework of the scientific approach, from one that focuses on experimental sciences, to one that accommodates observational sciences, and further to one that embraces data mining and machine learning. I distinguish between the ontological values of Kepler's Laws vis-a-vis Newton's Laws, and argue that the latter is more fundamental because it is able to explain the former. I then argue that the fundamental laws of economics lie not in mathematical equations, but in models of adaptive economic agents. With this shift in mind set, it becomes possible to think about how interactions between agents can lead to the emergence of multiple stable states and critical transitions, and complex adaptive policies and regulations that might actually work in the real world. Finally, I discuss how Big Data, exploratory agent-based modeling, and predictive agent-based modeling can come together in a unified framework to make economics a true science.

25th Birthday Cern- Amphi

ScienceCinema

None

2017-12-09

Cérémonie du 25ème anniversaire du Cern avec 2 orateurs: le Prof.Weisskopf parle de la signification et le rôle du Cern et le Prof.Casimir(?) fait un exposé sur les rélations entre la science pure et la science appliquée et la "big science" (science légère)

Using a Very Big Rocket to take Very Small Satellites to Very Far Places

NASA Technical Reports Server (NTRS)

Cohen, Barbara

2017-01-01

Planetary science cubesats are being built. Insight (2018) will carry 2 cubesats to provide communication links to Mars. EM-1 (2019) will carry 13 cubesat-class missions to further smallsat science and exploration capabilities. Planetary science cubesats have more in common with large planetary science missions than LEO cubesats- need to work closely with people who have deep-space mission experience

International Cooperation in Science. Science Policy Study--Hearings Volume 7. Hearings before the Task Force on Science Policy of the Committee on Science and Technology, House of Representatives, Ninety-Ninth Congress, First Session (June 18, 19, 20, 27, 1985). No. 50.

ERIC Educational Resources Information Center

Congress of the U.S., Washington, DC. House Committee on Science and Technology.

These hearings on international cooperation in science focused on three issues: (1) international cooperation in big science; (2) the impact of international cooperation on research priorities; and (3) coordination in management of international cooperative research. Witnesses presenting testimony and/or prepared statements were: Victor Weisskopf;…

Entering the 'big data' era in medicinal chemistry: molecular promiscuity analysis revisited.

PubMed

Hu, Ye; Bajorath, Jürgen

2017-06-01

The 'big data' concept plays an increasingly important role in many scientific fields. Big data involves more than unprecedentedly large volumes of data that become available. Different criteria characterizing big data must be carefully considered in computational data mining, as we discuss herein focusing on medicinal chemistry. This is a scientific discipline where big data is beginning to emerge and provide new opportunities. For example, the ability of many drugs to specifically interact with multiple targets, termed promiscuity, forms the molecular basis of polypharmacology, a hot topic in drug discovery. Compound promiscuity analysis is an area that is much influenced by big data phenomena. Different results are obtained depending on chosen data selection and confidence criteria, as we also demonstrate.

Big Fish in Little Ponds Aspire More: Mediation and Cross-Cultural Generalizability of School-Average Ability Effects on Self-Concept and Career Aspirations in Science

ERIC Educational Resources Information Center

Nagengast, Benjamin; Marsh, Herbert W.

2012-01-01

Being schooled with other high-achieving peers has a detrimental influence on students' self-perceptions: School-average and class-average achievement have a negative effect on academic self-concept and career aspirations--the big-fish-little-pond effect. Individual achievement, on the other hand, predicts academic self-concept and career…

On Enthusing Students about Big Data and Social Media Visualization and Analysis Using R, RStudio, and RMarkdown

ERIC Educational Resources Information Center

Stander, Julian; Dalla Valle, Luciana

2017-01-01

We discuss the learning goals, content, and delivery of a University of Plymouth intensive module delivered over four weeks entitled MATH1608PP Understanding Big Data from Social Networks, aimed at introducing students to a broad range of techniques used in modern Data Science. This module made use of R, accessed through RStudio, and some popular…

Rethinking climate change adaptation and place through a situated pathways framework: A case study from the Big Hole Valley, USA

Treesearch

Daniel J. Murphy; Laurie Yung; Carina Wyborn; Daniel R. Williams

2017-01-01

This paper critically examines the temporal and spatial dynamics of adaptation in climate change science and explores how dynamic notions of 'place' elucidate novel ways of understanding community vulnerability and adaptation. Using data gathered from a narrative scenario-building process carried out among communities of the Big Hole Valley in Montana, the...

Enabling Analytics in the Cloud for Earth Science Data

NASA Technical Reports Server (NTRS)

Ramachandran, Rahul; Lynnes, Christopher; Bingham, Andrew W.; Quam, Brandi M.

2018-01-01

The purpose of this workshop was to hold interactive discussions where providers, users, and other stakeholders could explore the convergence of three main elements in the rapidly developing world of technology: Big Data, Cloud Computing, and Analytics, [for earth science data].

The nonequilibrium quantum many-body problem as a paradigm for extreme data science

NASA Astrophysics Data System (ADS)

Freericks, J. K.; Nikolić, B. K.; Frieder, O.

2014-12-01

Generating big data pervades much of physics. But some problems, which we call extreme data problems, are too large to be treated within big data science. The nonequilibrium quantum many-body problem on a lattice is just such a problem, where the Hilbert space grows exponentially with system size and rapidly becomes too large to fit on any computer (and can be effectively thought of as an infinite-sized data set). Nevertheless, much progress has been made with computational methods on this problem, which serve as a paradigm for how one can approach and attack extreme data problems. In addition, viewing these physics problems from a computer-science perspective leads to new approaches that can be tried to solve more accurately and for longer times. We review a number of these different ideas here.

Exponential Growth and the Shifting Global Center of Gravity of Science Production, 1900-2011

ERIC Educational Resources Information Center

Zhang, Liang; Powell, Justin J. W.; Baker, David P.

2015-01-01

Long historical trends in scientific discovery led mid-20th century scientometricians to mark the advent of "big science"--extensive science production--and predicted that over the next few decades, the exponential growth would slow, resulting in lower rates of increase in production at the upper limit of a logistic curve. They were…

Don't Dumb Me down

ERIC Educational Resources Information Center

Goldacre, Ben

2007-01-01

In this article, the author talks about pseudoscientific quack, or a big science story in a national newspaper and explains why science in the media is so often pointless, simplistic, boring, or just plain wrong. It is the author's hypothesis that in their choice of stories, and the way they cover them, the media create a parody of science, for…

Thinking, Doing, Talking Science: Evaluation Report and Executive Summary

ERIC Educational Resources Information Center

Hanley, Pam; Slavin, Robert; Elliott, Louise

2015-01-01

Thinking, Doing, Talking Science (TDTS) is a programme that aims to make science lessons in primary schools more practical, creative and challenging. Teachers are trained in a repertoire of strategies that aim to encourage pupils to use higher order thinking skills. For example, pupils are posed 'Big Questions,' such as 'How do you know that the…

Differences in the Socio-Emotional Competency Profile in University Students from different Disciplinary Area

ERIC Educational Resources Information Center

Castejon, Juan Luis; Cantero, Ma. Pilar; Perez, Nelida

2008-01-01

Introduction: The main objective of this paper is to establish a profile of socio-emotional competencies characteristic of a sample of students from each of the big academic areas in higher education: legal sciences, social sciences, education, humanities, science and technology, and health. An additional objective was to analyse differences…

Proportional Reasoning Ability and Concepts of Scale: Surface Area to Volume Relationships in Science

ERIC Educational Resources Information Center

Taylor, Amy; Jones, Gail

2009-01-01

The "National Science Education Standards" emphasise teaching unifying concepts and processes such as basic functions of living organisms, the living environment, and scale. Scale influences science processes and phenomena across the domains. One of the big ideas of scale is that of surface area to volume. This study explored whether or not there…

Review of the National Research Council's Framework for K-12 Science Education

ERIC Educational Resources Information Center

Gross, Paul R.

2011-01-01

The new "Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas" is a big, comprehensive volume, carefully organized and heavily documented. It is the long-awaited product of the Committee on a Conceptual Framework for New K-12 Science Education Standards. As noted, it is a weighty document (more than 300…

"Mr. Database" : Jim Gray and the History of Database Technologies.

PubMed

Hanwahr, Nils C

2017-12-01

Although the widespread use of the term "Big Data" is comparatively recent, it invokes a phenomenon in the developments of database technology with distinct historical contexts. The database engineer Jim Gray, known as "Mr. Database" in Silicon Valley before his disappearance at sea in 2007, was involved in many of the crucial developments since the 1970s that constitute the foundation of exceedingly large and distributed databases. Jim Gray was involved in the development of relational database systems based on the concepts of Edgar F. Codd at IBM in the 1970s before he went on to develop principles of Transaction Processing that enable the parallel and highly distributed performance of databases today. He was also involved in creating forums for discourse between academia and industry, which influenced industry performance standards as well as database research agendas. As a co-founder of the San Francisco branch of Microsoft Research, Gray increasingly turned toward scientific applications of database technologies, e. g. leading the TerraServer project, an online database of satellite images. Inspired by Vannevar Bush's idea of the memex, Gray laid out his vision of a Personal Memex as well as a World Memex, eventually postulating a new era of data-based scientific discovery termed "Fourth Paradigm Science". This article gives an overview of Gray's contributions to the development of database technology as well as his research agendas and shows that central notions of Big Data have been occupying database engineers for much longer than the actual term has been in use.

Big data need big theory too

PubMed Central

Dougherty, Edward R.; Highfield, Roger R.

2016-01-01

The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales. Here, we point out the weaknesses of pure big data approaches with particular focus on biology and medicine, which fail to provide conceptual accounts for the processes to which they are applied. No matter their ‘depth’ and the sophistication of data-driven methods, such as artificial neural nets, in the end they merely fit curves to existing data. Not only do these methods invariably require far larger quantities of data than anticipated by big data aficionados in order to produce statistically reliable results, but they can also fail in circumstances beyond the range of the data used to train them because they are not designed to model the structural characteristics of the underlying system. We argue that it is vital to use theory as a guide to experimental design for maximal efficiency of data collection and to produce reliable predictive models and conceptual knowledge. Rather than continuing to fund, pursue and promote ‘blind’ big data projects with massive budgets, we call for more funding to be allocated to the elucidation of the multiscale and stochastic processes controlling the behaviour of complex systems, including those of life, medicine and healthcare. This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’. PMID:27698035

Big data need big theory too.

PubMed

Coveney, Peter V; Dougherty, Edward R; Highfield, Roger R

2016-11-13

The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales. Here, we point out the weaknesses of pure big data approaches with particular focus on biology and medicine, which fail to provide conceptual accounts for the processes to which they are applied. No matter their 'depth' and the sophistication of data-driven methods, such as artificial neural nets, in the end they merely fit curves to existing data. Not only do these methods invariably require far larger quantities of data than anticipated by big data aficionados in order to produce statistically reliable results, but they can also fail in circumstances beyond the range of the data used to train them because they are not designed to model the structural characteristics of the underlying system. We argue that it is vital to use theory as a guide to experimental design for maximal efficiency of data collection and to produce reliable predictive models and conceptual knowledge. Rather than continuing to fund, pursue and promote 'blind' big data projects with massive budgets, we call for more funding to be allocated to the elucidation of the multiscale and stochastic processes controlling the behaviour of complex systems, including those of life, medicine and healthcare.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'. © 2015 The Authors.

Big Questions: Missing Antimatter

ScienceCinema

Lincoln, Don

2018-06-08

Einstein's equation E = mc2 is often said to mean that energy can be converted into matter. More accurately, energy can be converted to matter and antimatter. During the first moments of the Big Bang, the universe was smaller, hotter and energy was everywhere. As the universe expanded and cooled, the energy converted into matter and antimatter. According to our best understanding, these two substances should have been created in equal quantities. However when we look out into the cosmos we see only matter and no antimatter. The absence of antimatter is one of the Big Mysteries of modern physics. In this video, Fermilab's Dr. Don Lincoln explains the problem, although doesn't answer it. The answer, as in all Big Mysteries, is still unknown and one of the leading research topics of contemporary science.

White House Science Fair

NASA Image and Video Library

2013-04-22

Director of Strategic Communications and Senior Science and Technology Policy Analyst, Office of Science and Technology Policy, Executive Office of the President, Rick Weiss, left, “Big Bang Theory” co-creator Bill Prady, center, and NASA Mars Curiosity Landing mission controller, Bobak "Mohawk Guy" Ferdowsi talk during the White House Science Fair held at the White House, April 22, 2013. The science fair celebrated student winners of a broad range of science, technology, engineering and math (STEM) competitions from across the country. Photo Credit: (NASA/Bill Ingalls)

An Information Literacy Partnership.

ERIC Educational Resources Information Center

Bielich, Paul; Page, Frederick

2002-01-01

Describes a pilot partnership formed by a science teacher and a science library media specialist between Detroit's Northwestern High School and the David Adamany Undergraduate Library at Wayne State University to develop student information literacy in high school. Discusses activities; teacher attitudes; introduction of the Big6 Skills; and…

Post-genomic science: cross-disciplinary and large-scale collaborative research and its organizational and technological challenges for the scientific research process.

PubMed

Welsh, Elaine; Jirotka, Marina; Gavaghan, David

2006-06-15

We examine recent developments in cross-disciplinary science and contend that a 'Big Science' approach is increasingly evident in the life sciences-facilitated by a breakdown of the traditional barriers between academic disciplines and the application of technologies across these disciplines. The first fruits of 'Big Biology' are beginning to be seen in, for example, genomics, (bio)-nanotechnology and systems biology. We suggest that this has profound implications for the research process and presents challenges both in technological design, in the provision of infrastructure and training, in the organization of research groups, and in providing suitable research funding mechanisms and reward systems. These challenges need to be addressed if the promise of this approach is to be fully realized. In this paper, we will draw on the work of social scientists to understand how these developments in science and technology relate to organizational culture, organizational change and the context of scientific work. We seek to learn from previous technological developments that seemed to offer similar potential for organizational and social change.

The Potential Improvement of Team-Working Skills in Biomedical and Natural Science Students Using a Problem-Based Learning Approach

ERIC Educational Resources Information Center

Nowrouzian, Forough L.; Farewell, Anne

2013-01-01

Teamwork has become an integral part of most organisations today, and it is clearly important in Science and other disciplines. In Science, research teams increase in size while the number of single-authored papers and patents decline. Team-work in laboratory sciences permits projects that are too big or complex for one individual to be tackled.…

Vectors into the Future of Mass and Interpersonal Communication Research: Big Data, Social Media, and Computational Social Science.

PubMed

Cappella, Joseph N

2017-10-01

Simultaneous developments in big data, social media, and computational social science have set the stage for how we think about and understand interpersonal and mass communication. This article explores some of the ways that these developments generate 4 hypothetical "vectors" - directions - into the next generation of communication research. These vectors include developments in network analysis, modeling interpersonal and social influence, recommendation systems, and the blurring of distinctions between interpersonal and mass audiences through narrowcasting and broadcasting. The methods and research in these arenas are occurring in areas outside the typical boundaries of the communication discipline but engage classic, substantive questions in mass and interpersonal communication.

The Next Big Thing - Eric Haseltine

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

Eric Haseltine

2009-09-16

Eric Haseltine, Haseltine Partners president and former chief of Walt Disney Imagineering, presented "The Next Big Thing," on Sept. 11, at the ORNL. He described the four "early warning signs" that a scientific breakthrough is imminent, and then suggested practical ways to turn these insights into breakthrough innovations. Haseltine is former director of research at the National Security Agency and associate director for science and technology for the director of National Intelligence, former executive vice president of Walt Disney Imagineering and director of engineering for Hughes Aircraft. He has 15 patents in optics, special effects and electronic media, and moremore » than 100 publications in science and technical journals, the web and Discover Magazine.« less

Entering the ‘big data’ era in medicinal chemistry: molecular promiscuity analysis revisited

PubMed Central

Hu, Ye; Bajorath, Jürgen

2017-01-01

The ‘big data’ concept plays an increasingly important role in many scientific fields. Big data involves more than unprecedentedly large volumes of data that become available. Different criteria characterizing big data must be carefully considered in computational data mining, as we discuss herein focusing on medicinal chemistry. This is a scientific discipline where big data is beginning to emerge and provide new opportunities. For example, the ability of many drugs to specifically interact with multiple targets, termed promiscuity, forms the molecular basis of polypharmacology, a hot topic in drug discovery. Compound promiscuity analysis is an area that is much influenced by big data phenomena. Different results are obtained depending on chosen data selection and confidence criteria, as we also demonstrate. PMID:28670471

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science through Cloud-Enabled Climate Analytics-as-a-Service

NASA Astrophysics Data System (ADS)

Schnase, J. L.; Duffy, D.; Tamkin, G. S.; Nadeau, D.; Thompson, J. H.; Grieg, C. M.; McInerney, M.; Webster, W. P.

2013-12-01

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS built on this principle. MERRA/AS enables MapReduce analytics over NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRA/AS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRA/AS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

NASA Technical Reports Server (NTRS)

Schnase, John L.; Duffy, Daniel Quinn; Tamkin, Glenn S.; Nadeau, Denis; Thompson, John H.; Grieg, Christina M.; McInerney, Mark A.; Webster, William P.

2014-01-01

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

The DKIST Data Center: Meeting the Data Challenges for Next-Generation, Ground-Based Solar Physics

NASA Astrophysics Data System (ADS)

Davey, A. R.; Reardon, K.; Berukoff, S. J.; Hays, T.; Spiess, D.; Watson, F. T.; Wiant, S.

2016-12-01

The Daniel K. Inouye Solar Telescope (DKIST) is under construction on the summit of Haleakalā in Maui, and scheduled to start science operations in 2020. The DKIST design includes a four-meter primary mirror coupled to an adaptive optics system, and a flexible instrumentation suite capable of delivering high-resolution optical and infrared observations of the solar chromosphere, photosphere, and corona. Through investigator-driven science proposals, the facility will generate an average of 8 TB of data daily, comprised of millions of images and hundreds of millions of metadata elements. The DKIST Data Center is responsible for the long-term curation and calibration of data received from the DKIST, and for distributing it to the user community for scientific use. Two key elements necessary to meet the inherent big data challenge are the development of flexible public/private cloud computing and coupled relational and non-relational data storage mechanisms. We discuss how this infrastructure is being designed to meet the significant expectation of automatic and manual calibration of ground-based solar physics data, and the maximization the data's utility through efficient, long-term data management practices implemented with prudent process definition and technology exploitation.

Big Science, Team Science, and Open Science for Neuroscience.

PubMed

Koch, Christof; Jones, Allan

2016-11-02

The Allen Institute for Brain Science is a non-profit private institution dedicated to basic brain science with an internal organization more commonly found in large physics projects-large teams generating complete, accurate and permanent resources for the mouse and human brain. It can also be viewed as an experiment in the sociology of neuroscience. We here describe some of the singular differences to more academic, PI-focused institutions. Copyright © 2016 Elsevier Inc. All rights reserved.

A genetic algorithm-based job scheduling model for big data analytics.

PubMed

Lu, Qinghua; Li, Shanshan; Zhang, Weishan; Zhang, Lei

Big data analytics (BDA) applications are a new category of software applications that process large amounts of data using scalable parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open-source big data analytics framework, which implements the MapReduce programming model to process big data with MapReduce jobs. Big data analytics jobs are often continuous and not mutually separated. The existing work mainly focuses on executing jobs in sequence, which are often inefficient and consume high energy. In this paper, we propose a genetic algorithm-based job scheduling model for big data analytics applications to improve the efficiency of big data analytics. To implement the job scheduling model, we leverage an estimation module to predict the performance of clusters when executing analytics jobs. We have evaluated the proposed job scheduling model in terms of feasibility and accuracy.

Introducing the Big Knowledge to Use (BK2U) challenge.

PubMed

Perl, Yehoshua; Geller, James; Halper, Michael; Ochs, Christopher; Zheng, Ling; Kapusnik-Uner, Joan

2017-01-01

The purpose of the Big Data to Knowledge initiative is to develop methods for discovering new knowledge from large amounts of data. However, if the resulting knowledge is so large that it resists comprehension, referred to here as Big Knowledge (BK), how can it be used properly and creatively? We call this secondary challenge, Big Knowledge to Use. Without a high-level mental representation of the kinds of knowledge in a BK knowledgebase, effective or innovative use of the knowledge may be limited. We describe summarization and visualization techniques that capture the big picture of a BK knowledgebase, possibly created from Big Data. In this research, we distinguish between assertion BK and rule-based BK (rule BK) and demonstrate the usefulness of summarization and visualization techniques of assertion BK for clinical phenotyping. As an example, we illustrate how a summary of many intracranial bleeding concepts can improve phenotyping, compared to the traditional approach. We also demonstrate the usefulness of summarization and visualization techniques of rule BK for drug-drug interaction discovery. © 2016 New York Academy of Sciences.

Considerations on Geospatial Big Data

NASA Astrophysics Data System (ADS)

LIU, Zhen; GUO, Huadong; WANG, Changlin

2016-11-01

Geospatial data, as a significant portion of big data, has recently gained the full attention of researchers. However, few researchers focus on the evolution of geospatial data and its scientific research methodologies. When entering into the big data era, fully understanding the changing research paradigm associated with geospatial data will definitely benefit future research on big data. In this paper, we look deep into these issues by examining the components and features of geospatial big data, reviewing relevant scientific research methodologies, and examining the evolving pattern of geospatial data in the scope of the four ‘science paradigms’. This paper proposes that geospatial big data has significantly shifted the scientific research methodology from ‘hypothesis to data’ to ‘data to questions’ and it is important to explore the generality of growing geospatial data ‘from bottom to top’. Particularly, four research areas that mostly reflect data-driven geospatial research are proposed: spatial correlation, spatial analytics, spatial visualization, and scientific knowledge discovery. It is also pointed out that privacy and quality issues of geospatial data may require more attention in the future. Also, some challenges and thoughts are raised for future discussion.

Big Data access and infrastructure for modern biology: case studies in data repository utility.

PubMed

Boles, Nathan C; Stone, Tyler; Bergeron, Charles; Kiehl, Thomas R

2017-01-01

Big Data is no longer solely the purview of big organizations with big resources. Today's routine tools and experimental methods can generate large slices of data. For example, high-throughput sequencing can quickly interrogate biological systems for the expression levels of thousands of different RNAs, examine epigenetic marks throughout the genome, and detect differences in the genomes of individuals. Multichannel electrophysiology platforms produce gigabytes of data in just a few minutes of recording. Imaging systems generate videos capturing biological behaviors over the course of days. Thus, any researcher now has access to a veritable wealth of data. However, the ability of any given researcher to utilize that data is limited by her/his own resources and skills for downloading, storing, and analyzing the data. In this paper, we examine the necessary resources required to engage Big Data, survey the state of modern data analysis pipelines, present a few data repository case studies, and touch on current institutions and programs supporting the work that relies on Big Data. © 2016 New York Academy of Sciences.

Personality Theories Facilitate Integrating the Five Principles and Deducing Hypotheses for Testing

ERIC Educational Resources Information Center

Maddi, Salvatore R.

2007-01-01

Comments on the original article "A New Big Five: Fundamental Principles for an Integrative Science of Personality," by Dan P. McAdams and Jennifer L. Pals (see record 2006-03947-002). In presenting their view of personality science, McAdams and Pals (April 2006) elaborated the importance of five principles for building an integrated science of…

Assessing Secondary and College Students' Implicit Assumptions about the Particulate Nature of Matter: Development and Validation of the Structure and Motion of Matter Survey

ERIC Educational Resources Information Center

Stains, Marilyne; Escriu-Sune, Marta; Alverez de Santizo, Myrna Lisseth Molina; Sevian, Hannah

2011-01-01

Development of learning progressions has been at the forefront of science education for several years. While understanding students' conceptual development toward "big ideas" in science is extremely valuable for researchers, science teachers can also benefit from assessment tools that diagnose their students' trajectories along the learning…

ARC-2009-ACD09-0261-015

NASA Image and Video Library

2009-12-10

Korean High Level Delegation Visit Ames Certer Director and various Senior staff: John Hines, Ames Center Chief Technologist (middel left) explains operations at the LADEE lab to Soon-Duk Bae, Deputy Director, Big Science Policy Division, Ministry of Educaiton, Science Technology, Young-Mok Hyun, Deputy Director, Space Development Division, Ministry of Educaiton, Science Technology, Seorium Lee, Senior Researcher, International Relations Korea Aerospace Research Institute.

Exascale computing and big data

DOE PAGES

Reed, Daniel A.; Dongarra, Jack

2015-06-25

Scientific discovery and engineering innovation requires unifying traditionally separated high-performance computing and big data analytics. The tools and cultures of high-performance computing and big data analytics have diverged, to the detriment of both; unification is essential to address a spectrum of major research domains. The challenges of scale tax our ability to transmit data, compute complicated functions on that data, or store a substantial part of it; new approaches are required to meet these challenges. Finally, the international nature of science demands further development of advanced computer architectures and global standards for processing data, even as international competition complicates themore » openness of the scientific process.« less

Exascale computing and big data

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

Reed, Daniel A.; Dongarra, Jack

Scientific discovery and engineering innovation requires unifying traditionally separated high-performance computing and big data analytics. The tools and cultures of high-performance computing and big data analytics have diverged, to the detriment of both; unification is essential to address a spectrum of major research domains. The challenges of scale tax our ability to transmit data, compute complicated functions on that data, or store a substantial part of it; new approaches are required to meet these challenges. Finally, the international nature of science demands further development of advanced computer architectures and global standards for processing data, even as international competition complicates themore » openness of the scientific process.« less

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture.

PubMed

Morota, Gota; Ventura, Ricardo V; Silva, Fabyano F; Koyama, Masanori; Fernando, Samodha C

2018-04-14

Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the use of tools to routinely monitor and collect information from animals and farms in a less laborious manner than before. These efforts have enabled the animal sciences to embark on information technology-driven discoveries to improve animal agriculture. However, the growing amount and complexity of data generated by fully automated, high-throughput data recording or phenotyping platforms, including digital images, sensor and sound data, unmanned systems, and information obtained from real-time noninvasive computer vision, pose challenges to the successful implementation of precision animal agriculture. The emerging fields of machine learning and data mining are expected to be instrumental in helping meet the daunting challenges facing global agriculture. Yet, their impact and potential in "big data" analysis have not been adequately appreciated in the animal science community, where this recognition has remained only fragmentary. To address such knowledge gaps, this article outlines a framework for machine learning and data mining and offers a glimpse into how they can be applied to solve pressing problems in animal sciences.

Taking a 'Big Data' approach to data quality in a citizen science project.

PubMed

Kelling, Steve; Fink, Daniel; La Sorte, Frank A; Johnston, Alison; Bruns, Nicholas E; Hochachka, Wesley M

2015-11-01

Data from well-designed experiments provide the strongest evidence of causation in biodiversity studies. However, for many species the collection of these data is not scalable to the spatial and temporal extents required to understand patterns at the population level. Only data collected from citizen science projects can gather sufficient quantities of data, but data collected from volunteers are inherently noisy and heterogeneous. Here we describe a 'Big Data' approach to improve the data quality in eBird, a global citizen science project that gathers bird observations. First, eBird's data submission design ensures that all data meet high standards of completeness and accuracy. Second, we take a 'sensor calibration' approach to measure individual variation in eBird participant's ability to detect and identify birds. Third, we use species distribution models to fill in data gaps. Finally, we provide examples of novel analyses exploring population-level patterns in bird distributions.

Epidemiology in wonderland: Big Data and precision medicine.

PubMed

Saracci, Rodolfo

2018-03-01

Big Data and precision medicine, two major contemporary challenges for epidemiology, are critically examined from two different angles. In Part 1 Big Data collected for research purposes (Big research Data) and Big Data used for research although collected for other primary purposes (Big secondary Data) are discussed in the light of the fundamental common requirement of data validity, prevailing over "bigness". Precision medicine is treated developing the key point that high relative risks are as a rule required to make a variable or combination of variables suitable for prediction of disease occurrence, outcome or response to treatment; the commercial proliferation of allegedly predictive tests of unknown or poor validity is commented. Part 2 proposes a "wise epidemiology" approach to: (a) choosing in a context imprinted by Big Data and precision medicine-epidemiological research projects actually relevant to population health, (b) training epidemiologists, (c) investigating the impact on clinical practices and doctor-patient relation of the influx of Big Data and computerized medicine and (d) clarifying whether today "health" may be redefined-as some maintain in purely technological terms.

The NASA Beyond Einstein Program

NASA Technical Reports Server (NTRS)

White, Nicholas E.

2006-01-01

Einstein's legacy is incomplete, his theory of General relativity raises -- but cannot answer --three profound questions: What powered the big bang? What happens to space, time, and matter at the edge of a black hole? and What is the mysterious dark energy pulling the Universe apart? The Beyond Einstein program within NASA's Office of Space Science aims to answer these questions, employing a series of missions linked by powerful new technologies and complementary approaches towards shared science goals. The Beyond Einstein program has three linked elements which advance science and technology towards two visions; to detect directly gravitational wave signals from the earliest possible moments of the BIg Bang, and to image the event horizon of a black hole. The central element is a pair of Einstein Great Observatories, Constellation-X and LISA. Constellation-X is a powerful new X-ray observatory dedicated to X-Ray Spectroscopy. LISA is the first spaced based gravitational wave detector. These powerful facilities will blaze new paths to the questions about black holes, the Big Bang and dark energy. The second element is a series of competitively selected Einstein Probes, each focused on one of the science questions and includes a mission dedicated resolving the Dark Energy mystery. The third element is a program of technology development, theoretical studies and education. The Beyond Einstein program is a new element in the proposed NASA budget for 2004. This talk will give an overview of the program and the missions contained within it.

The association between plasma big endothelin-1 levels at admission and long-term outcomes in patients with atrial fibrillation.

PubMed

Wu, Shuang; Yang, Yan-Min; Zhu, Jun; Ren, Jia-Meng; Wang, Juan; Zhang, Han; Shao, Xing-Hui

2018-05-01

The prognostic role of big endothelin-1 (ET-1) in atrial fibrillation (AF) is unclear. We aimed to assess its predictive value in patients with AF. A total of 716 AF patients were enrolled and divided into two groups based on the optimal cut-off value of big ET-1 in predicting all-cause mortality. The primary outcomes were all-cause mortality and major adverse events (MAEs). Cox regression analysis and net reclassification improvement (NRI) analysis were performed to assess the predictive value of big ET-1 on outcomes. With the optimal cut-off value of 0.55 pmol/L, 326 patients were classified into the high big ET-1 levels group. Cardiac dysfunction and left atrial dilation were factors related to high big ET-1 levels. During a median follow-up of 3 years, patients with big ET-1 ≥ 0.55 pmol/L had notably higher risk of all-cause death (44.8% vs. 11.5%, p < 0.001), MAEs (51.8% vs. 17.4%, p < 0.001), cardiovascular death, major bleeding, and tended to have higher thromboembolic risk. After adjusting for confounding factors, high big ET-1 level was an independent predictor of all-cause mortality (hazard ratio (HR) 2.11, 95% confidence interval (CI) 1.46-3.05; p < 0.001), MAEs (HR 2.05, 95% CI 1.50-2.80; p = 0.001), and cardiovascular death (HR 2.44, 95% CI 1.52-3.93; p < 0.001). NRI analysis showed that big ET-1 allowed a significant improvement of 0.32 in the accuracy of predicting the risk of both all-cause mortality and MAEs. Elevated big ET-1 levels is an independent predictor of long-term all-cause mortality, MAEs, and cardiovascular death in patients with AF. Copyright © 2018 Elsevier B.V. All rights reserved.

The Data Science Landscape

NASA Astrophysics Data System (ADS)

Mentzel, C.

2017-12-01

Modern scientific data continue to increase in volume, variety, and velocity, and though the hype of big data has subsided, its usefulness for scientific discovery has only just begun. Harnessing these data for new insights, more efficient decision making, and other mission critical uses requires a combination of skills and expertise, often labeled data science. Data science can be thought of as a combination of statistics, computation and the domain from which the data relate, and so is a true interdisciplinary pursuit. Though it has reaped large benefits in companies able to afford the high cost of the severely limited talent pool, it suffers from lack of support in mission driven organizations. Not purely in any one historical field, data science is proving difficult to find a home in traditional university academic departments and other research organizations. The landscape of data science efforts, from academia, industry and government, can be characterized as nascent, enthusiastic, uneven, and highly competitive. Part of the challenge in documenting these trends is the lack of agreement about what data science is, and who is a data scientist. Defining these terms too closely and too early runs the risk of cutting off a tremendous amount of productive creativity, but waiting too long leaves many people without a sustainable career, and many organizations without the necessary skills to gain value from their data. This talk will explore the landscape of data science efforts in the US, including how organizations are building and sustaining data science teams.

Controversies in the Hydrosphere: an iBook exploring current global water issues for middle school classrooms

NASA Astrophysics Data System (ADS)

Dufoe, A.; Guertin, L. A.

2012-12-01

This project looks to help teachers utilize iPad technology in their classrooms as an instructional tool for Earth system science and connections to the Big Ideas in Earth Science. The project is part of Penn State University's National Science Foundation (NSF) Targeted Math Science Partnership grant, with one goal of the grant to help current middle school teachers across Pennsylvania engage students with significant and complex questions of Earth science. The free Apple software iBooks Author was used to create an electronic book for the iPad, focusing on a variety of controversial issues impacting the hydrosphere. The iBook includes image slideshows, embedded videos, interactive images and quizzes, and critical thinking questions along Bloom's Taxonomic Scale of Learning Objectives. Outlined in the introductory iBook chapters are the Big Ideas of Earth System Science and an overview of Earth's spheres. Since the book targets the hydrosphere, each subsequent chapter focuses on specific water issues, including glacial melts, aquifer depletion, coastal oil pollution, marine debris, and fresh-water chemical contamination. Each chapter is presented in a case study format that highlights the history of the issue, the development and current status of the issue, and some solutions that have been generated. The next section includes critical thinking questions in an open-ended discussion format that focus on the Big Ideas, proposing solutions for rectifying the situation, and/or assignments specifically targeting an idea presented in the case study chapter. Short, comprehensive multiple-choice quizzes are also in each chapter. Throughout the iBook, students are free to watch videos, explore the content and form their own opinions. As a result, this iBook fulfills the grant objective by engaging teachers and students with an innovative technological presentation that incorporates Earth system science with current case studies regarding global water issues.

Air Toxics Under the Big Sky: Examining the Effectiveness of Authentic Scientific Research on High School Students' Science Skills and Interest.

PubMed

Ward, Tony J; Delaloye, Naomi; Adams, Earle Raymond; Ware, Desirae; Vanek, Diana; Knuth, Randy; Hester, Carolyn Laurie; Marra, Nancy Noel; Holian, Andrij

2016-01-01

Air Toxics Under the Big Sky is an environmental science outreach/education program that incorporates the Next Generation Science Standards (NGSS) 8 Practices with the goal of promoting knowledge and understanding of authentic scientific research in high school classrooms through air quality research. A quasi-experimental design was used in order to understand: 1) how the program affects student understanding of scientific inquiry and research and 2) how the open inquiry learning opportunities provided by the program increase student interest in science as a career path . Treatment students received instruction related to air pollution (airborne particulate matter), associated health concerns, and training on how to operate air quality testing equipment. They then participated in a yearlong scientific research project in which they developed and tested hypotheses through research of their own design regarding the sources and concentrations of air pollution in their homes and communities. Results from an external evaluation revealed that treatment students developed a deeper understanding of scientific research than did comparison students, as measured by their ability to generate good hypotheses and research designs, and equally expressed an increased interest in pursuing a career in science. These results emphasize the value of and need for authentic science learning opportunities in the modern science classroom.

Air Toxics Under the Big Sky: examining the effectiveness of authentic scientific research on high school students' science skills and interest

NASA Astrophysics Data System (ADS)

Ward, Tony J.; Delaloye, Naomi; Adams, Earle Raymond; Ware, Desirae; Vanek, Diana; Knuth, Randy; Hester, Carolyn Laurie; Marra, Nancy Noel; Holian, Andrij

2016-04-01

Air Toxics Under the Big Sky is an environmental science outreach/education program that incorporates the Next Generation Science Standards (NGSS) 8 Practices with the goal of promoting knowledge and understanding of authentic scientific research in high school classrooms through air quality research. This research explored: (1) how the program affects student understanding of scientific inquiry and research and (2) how the open-inquiry learning opportunities provided by the program increase student interest in science as a career path. Treatment students received instruction related to air pollution (airborne particulate matter), associated health concerns, and training on how to operate air quality testing equipment. They then participated in a yearlong scientific research project in which they developed and tested hypotheses through research of their own design regarding the sources and concentrations of air pollution in their homes and communities. Results from an external evaluation revealed that treatment students developed a deeper understanding of scientific research than did comparison students, as measured by their ability to generate good hypotheses and research designs, and equally expressed an increased interest in pursuing a career in science. These results emphasize the value of and need for authentic science learning opportunities in the modern science classroom.

Air Toxics Under the Big Sky: Examining the Effectiveness of Authentic Scientific Research on High School Students’ Science Skills and Interest

PubMed Central

Delaloye, Naomi; Adams, Earle Raymond; Ware, Desirae; Vanek, Diana; Knuth, Randy; Hester, Carolyn Laurie; Marra, Nancy Noel; Holian, Andrij

2016-01-01

Air Toxics Under the Big Sky is an environmental science outreach/education program that incorporates the Next Generation Science Standards (NGSS) 8 Practices with the goal of promoting knowledge and understanding of authentic scientific research in high school classrooms through air quality research. A quasi-experimental design was used in order to understand: 1) how the program affects student understanding of scientific inquiry and research and 2) how the open inquiry learning opportunities provided by the program increase student interest in science as a career path. Treatment students received instruction related to air pollution (airborne particulate matter), associated health concerns, and training on how to operate air quality testing equipment. They then participated in a yearlong scientific research project in which they developed and tested hypotheses through research of their own design regarding the sources and concentrations of air pollution in their homes and communities. Results from an external evaluation revealed that treatment students developed a deeper understanding of scientific research than did comparison students, as measured by their ability to generate good hypotheses and research designs, and equally expressed an increased interest in pursuing a career in science. These results emphasize the value of and need for authentic science learning opportunities in the modern science classroom. PMID:28286375

Unlocking the Power of Big Data at the National Institutes of Health.

PubMed

Coakley, Meghan F; Leerkes, Maarten R; Barnett, Jason; Gabrielian, Andrei E; Noble, Karlynn; Weber, M Nick; Huyen, Yentram

2013-09-01

The era of "big data" presents immense opportunities for scientific discovery and technological progress, with the potential to have enormous impact on research and development in the public sector. In order to capitalize on these benefits, there are significant challenges to overcome in data analytics. The National Institute of Allergy and Infectious Diseases held a symposium entitled "Data Science: Unlocking the Power of Big Data" to create a forum for big data experts to present and share some of the creative and innovative methods to gleaning valuable knowledge from an overwhelming flood of biological data. A significant investment in infrastructure and tool development, along with more and better-trained data scientists, may facilitate methods for assimilation of data and machine learning, to overcome obstacles such as data security, data cleaning, and data integration.

Unlocking the Power of Big Data at the National Institutes of Health

PubMed Central

Coakley, Meghan F.; Leerkes, Maarten R.; Barnett, Jason; Gabrielian, Andrei E.; Noble, Karlynn; Weber, M. Nick

2013-01-01

Abstract The era of “big data” presents immense opportunities for scientific discovery and technological progress, with the potential to have enormous impact on research and development in the public sector. In order to capitalize on these benefits, there are significant challenges to overcome in data analytics. The National Institute of Allergy and Infectious Diseases held a symposium entitled “Data Science: Unlocking the Power of Big Data” to create a forum for big data experts to present and share some of the creative and innovative methods to gleaning valuable knowledge from an overwhelming flood of biological data. A significant investment in infrastructure and tool development, along with more and better-trained data scientists, may facilitate methods for assimilation of data and machine learning, to overcome obstacles such as data security, data cleaning, and data integration. PMID:27442200

Big Questions: Missing Antimatter

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

Lincoln, Don

2013-08-27

Einstein's equation E = mc2 is often said to mean that energy can be converted into matter. More accurately, energy can be converted to matter and antimatter. During the first moments of the Big Bang, the universe was smaller, hotter and energy was everywhere. As the universe expanded and cooled, the energy converted into matter and antimatter. According to our best understanding, these two substances should have been created in equal quantities. However when we look out into the cosmos we see only matter and no antimatter. The absence of antimatter is one of the Big Mysteries of modern physics.more » In this video, Fermilab's Dr. Don Lincoln explains the problem, although doesn't answer it. The answer, as in all Big Mysteries, is still unknown and one of the leading research topics of contemporary science.« less

Sneak Preview of Berkeley Lab's Science at the Theatre on June 6th, 2011

ScienceCinema

Sanii, Babak

2017-12-11

Babak Sanii provides a sneak preview of Berkeley Lab's next Science at the Theater Event: Big Thinking: The Power of Nanoscience. Berkeley Lab scientists reveal how nanoscience will bring us cleaner energy, faster computers, and improved medicine. Berkeley Repertory Theatre on June 6th, 2011.

Collective Awareness and the New Institution Science

NASA Astrophysics Data System (ADS)

Pitt, Jeremy; Nowak, Andrzej

The following sections are included: * Introduction * Challenges for Institutions * Collective Awareness * A New Science of Institutions * Complex social ensembles * Interoceptive collective awareness * Planned emergence * Self-organising electronic institutions * Transformative Impact on Society * Social attitudes and processes * Innovative service creation and social innovation * Scientific impact * Big data * Self-regulation * Summary and Conclusions

Evolution: Don't Debate, Educate.

ERIC Educational Resources Information Center

Bybee, Rodger W.

2000-01-01

Discusses controversy over the teaching of biological evolution and other scientific ideas such as Big Bang theory. Recommends that teachers avoid debating creationists, help students develop a greater understanding and appreciation for science as a way of explaining the natural world, and emphasize inquiry and the nature of science. (Contains 19…

How Cosmology Became a Science.

ERIC Educational Resources Information Center

Brush, Stephen G.

1992-01-01

Describes the origin of the science of cosmology and the competing theories to explain the beginning of the universe. The big bang theory for the creation of the universe is contrasted with the steady state theory. The author details discoveries that led to the demise of the steady state theory. (PR)

2013 Student Science Jeopardy Tournament a Big Success | Poster

Cancer.gov

By Robin Meckley, Contributing Writer The category was “General Science,” and the clue read: “Named for an Italian scientist, it is the scientific number of molecules in 1 gram mole of any substance.” Everything depended on knowing the correct response and wagering enough points.  

WHK Interns Win Big at Frederick County Science Fair | Poster

Cancer.gov

Three Werner H. Kirsten student interns claimed awards at the 35th Annual Frederick County Science and Engineering Fair—and got a shot at the national competition—for imaginative projects that reached out to the rings of Saturn and down to the details of advanced cancer diagnostics.

Sneak Preview of Berkeley Lab's Science at the Theatre on June 6th, 2011

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

Sanii, Babak

Babak Sanii provides a sneak preview of Berkeley Lab's next Science at the Theater Event: Big Thinking: The Power of Nanoscience. Berkeley Lab scientists reveal how nanoscience will bring us cleaner energy, faster computers, and improved medicine. Berkeley Repertory Theatre on June 6th, 2011.

Science's Big Picture

ERIC Educational Resources Information Center

Sapp, Gregg

2007-01-01

The state of science is a moving target, and its ever-shifting horizons can best be gleaned by the contents of scientific journals. However, the bigger picture of the scientific enterprise, which also encompasses its past, its future, and its overarching philosophies, can often be better represented through the more reflective pace of popular…

Waves in Nature, Lasers to Tsumanis and Beyond

ScienceCinema

LLNL - University of California Television

2017-12-09

Waves are everywhere. Microwaves, laser beams, music, tsunamis. Electromagnetic waves emanating from the Big Bang fill the universe. Learn about the similarities and difference in all of these wavy phenomena with Ed Moses and Rick Sawicki, Lawrence Livermore National Laboratory scientists Series: Science on Saturday [10/2006] [Science] [Show ID: 11541

Science 101: What, Exactly, Is the Heisenberg Uncertainty Principle?

ERIC Educational Resources Information Center

Robertson, Bill

2016-01-01

Bill Robertson is the author of the NSTA Press book series, "Stop Faking It! Finally Understanding Science So You Can Teach It." In this month's issue, Robertson describes and explains the Heisenberg Uncertainty Principle. The Heisenberg Uncertainty Principle was discussed on "The Big Bang Theory," the lead character in…

Waves in Nature, Lasers to Tsumanis and Beyond

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

LLNL - University of California Television

2008-05-01

Waves are everywhere. Microwaves, laser beams, music, tsunamis. Electromagnetic waves emanating from the Big Bang fill the universe. Learn about the similarities and difference in all of these wavy phenomena with Ed Moses and Rick Sawicki, Lawrence Livermore National Laboratory scientists Series: Science on Saturday [10/2006] [Science] [Show ID: 11541

Development of a Computational Framework for Big Data-Driven Prediction of Long-Term Bridge Performance and Traffic Flow

DOT National Transportation Integrated Search

2018-04-01

Consistent efforts with dense sensor deployment and data gathering processes for bridge big data have accumulated profound information regarding bridge performance, associated environments, and traffic flows. However, direct applications of bridge bi...

Application and Prospect of Big Data in Water Resources

NASA Astrophysics Data System (ADS)

Xi, Danchi; Xu, Xinyi

2017-04-01

Because of developed information technology and affordable data storage, we h ave entered the era of data explosion. The term "Big Data" and technology relate s to it has been created and commonly applied in many fields. However, academic studies just got attention on Big Data application in water resources recently. As a result, water resource Big Data technology has not been fully developed. This paper introduces the concept of Big Data and its key technologies, including the Hadoop system and MapReduce. In addition, this paper focuses on the significance of applying the big data in water resources and summarizing prior researches by others. Most studies in this field only set up theoretical frame, but we define the "Water Big Data" and explain its tridimensional properties which are time dimension, spatial dimension and intelligent dimension. Based on HBase, the classification system of Water Big Data is introduced: hydrology data, ecology data and socio-economic data. Then after analyzing the challenges in water resources management, a series of solutions using Big Data technologies such as data mining and web crawler, are proposed. Finally, the prospect of applying big data in water resources is discussed, it can be predicted that as Big Data technology keeps developing, "3D" (Data Driven Decision) will be utilized more in water resources management in the future.

ARC-2009-ACD09-0261-013

NASA Image and Video Library

2009-12-10

Korean High Level Delegation Visit Ames Certer Director and various Senior staff: Dan Andrews give presentation about LCROSS/LRO to Seorium Lee, Senior Researcher, International Relations Korea Aerospace Research Institute, Soon-Duk Bae, Deputy Director, Big Science Policy Division, Ministry of Educaiton, Science Technology, Young-Mok Hyun, Deputy Director, Space Development Division, Ministry of Educaiton, Science Technology, Seorium Lee, Senior Researcher, International Relations Korea Aerospace Research Institute.

Using 'big data' to validate claims made in the pharmaceutical approval process.

PubMed

Wasser, Thomas; Haynes, Kevin; Barron, John; Cziraky, Mark

2015-01-01

Big Data in the healthcare setting refers to the storage, assimilation, and analysis of large quantities of information regarding patient care. These data can be collected and stored in a wide variety of ways including electronic medical records collected at the patient bedside, or through medical records that are coded and passed to insurance companies for reimbursement. When these data are processed it is possible to validate claims as a part of the regulatory review process regarding the anticipated performance of medications and devices. In order to analyze properly claims by manufacturers and others, there is a need to express claims in terms that are testable in a timeframe that is useful and meaningful to formulary committees. Claims for the comparative benefits and costs, including budget impact, of products and devices need to be expressed in measurable terms, ideally in the context of submission or validation protocols. Claims should be either consistent with accessible Big Data or able to support observational studies where Big Data identifies target populations. Protocols should identify, in disaggregated terms, key variables that would lead to direct or proxy validation. Once these variables are identified, Big Data can be used to query massive quantities of data in the validation process. Research can be passive or active in nature. Passive, where the data are collected retrospectively; active where the researcher is prospectively looking for indicators of co-morbid conditions, side-effects or adverse events, testing these indicators to determine if claims are within desired ranges set forth by the manufacturer. Additionally, Big Data can be used to assess the effectiveness of therapy through health insurance records. This, for example, could indicate that disease or co-morbid conditions cease to be treated. Understanding the basic strengths and weaknesses of Big Data in the claim validation process provides a glimpse of the value that this research can provide to industry. Big Data can support a research agenda that focuses on the process of claims validation to support formulary submissions as well as inputs to ongoing disease area and therapeutic class reviews.

The EarthServer project: Exploiting Identity Federations, Science Gateways and Social and Mobile Clients for Big Earth Data Analysis

NASA Astrophysics Data System (ADS)

Barbera, Roberto; Bruno, Riccardo; Calanducci, Antonio; Messina, Antonio; Pappalardo, Marco; Passaro, Gianluca

2013-04-01

The EarthServer project (www.earthserver.eu), funded by the European Commission under its Seventh Framework Program, aims at establishing open access and ad-hoc analytics on extreme-size Earth Science data, based on and extending leading-edge Array Database technology. The core idea is to use database query languages as client/server interface to achieve barrier-free "mix & match" access to multi-source, any-size, multi-dimensional space-time data -- in short: "Big Earth Data Analytics" - based on the open standards of the Open Geospatial Consortium Web Coverage Processing Service (OGC WCPS) and the W3C XQuery. EarthServer combines both, thereby achieving a tight data/metadata integration. Further, the rasdaman Array Database System (www.rasdaman.com) is extended with further space-time coverage data types. On server side, highly effective optimizations - such as parallel and distributed query processing - ensure scalability to Exabyte volumes. Six Lighthouse Applications are being established in EarthServer, each of which poses distinct challenges on Earth Data Analytics: Cryospheric Science, Airborne Science, Atmospheric Science, Geology, Oceanography, and Planetary Science. Altogether, they cover all Earth Science domains; the Planetary Science use case has been added to challenge concepts and standards in non-standard environments. In addition, EarthLook (maintained by Jacobs University) showcases use of OGC standards in 1D through 5D use cases. In this contribution we will report on the first applications integrated in the EarthServer Science Gateway and on the clients for mobile appliances developed to access them. We will also show how federated and social identity services can allow Big Earth Data Providers to expose their data in a distributed environment keeping a strict and fine-grained control on user authentication and authorisation. The degree of fulfilment of the EarthServer implementation with the recommendations made in the recent TERENA Study on AAA Platforms For Scientific Resources in Europe (https://confluence.terena.org/display/aaastudy/AAA+Study+Home+Page) will also be assessed.

Complexity Science Framework for Big Data: Data-enabled Science

NASA Astrophysics Data System (ADS)

Surjalal Sharma, A.

2016-07-01

The ubiquity of Big Data has stimulated the development of analytic tools to harness the potential for timely and improved modeling and prediction. While much of the data is available near-real time and can be compiled to specify the current state of the system, the capability to make predictions is lacking. The main reason is the basic nature of Big Data - the traditional techniques are challenged in their ability to cope with its velocity, volume and variability to make optimum use of the available information. Another aspect is the absence of an effective description of the time evolution or dynamics of the specific system, derived from the data. Once such dynamical models are developed predictions can be made readily. This approach of " letting the data speak for itself " is distinct from the first-principles models based on the understanding of the fundamentals of the system. The predictive capability comes from the data-derived dynamical model, with no modeling assumptions, and can address many issues such as causality and correlation. This approach provides a framework for addressing the challenges in Big Data, especially in the case of spatio-temporal time series data. The reconstruction of dynamics from time series data is based on recognition that in most systems the different variables or degrees of freedom are coupled nonlinearly and in the presence of dissipation the state space contracts, effectively reducing the number of variables, thus enabling a description of its dynamical evolution and consequently prediction of future states. The predictability is analysed from the intrinsic characteristics of the distribution functions, such as Hurst exponents and Hill estimators. In most systems the distributions have heavy tails, which imply higher likelihood for extreme events. The characterization of the probabilities of extreme events are critical in many cases e. g., natural hazards, for proper assessment of risk and mitigation strategies. Big Data with such new analytics can yield improved risk estimates. The challenges of scientific inference from complex and massive data are addressed by data-enabled science, also referred as the Fourth paradigm, after experiment, theory and simulation. An example of this approach is the modelling of dynamical and statistical features of natural systems, without assumptions of specific processes. An effective use of the techniques of complexity science to yield the inherent features of a system from extensive data from observations and large scale numerical simulations is evident in the case of Earth's magnetosphere. The multiscale nature of the magnetosphere makes the numerical simulations a challenge, requiring very large computing resources. The reconstruction of dynamics from observational data can however yield the inherent characteristics using typical desktop computers. Such studies for other systems are in progress. Data-enabled approach using the framework of complexity science provides new techniques for modelling and prediction using Big Data. The studies of Earth's magnetosphere, provide an example of the potential for a new approach to the development of quantitative analytic tools.

The concept lens diagram: a new mechanism for presenting biochemistry content in terms of "big ideas".

PubMed

Rowland, Susan L; Smith, Christopher A; Gillam, Elizabeth M A; Wright, Tony

2011-07-01

A strong, recent movement in tertiary education is the development of conceptual, or "big idea" teaching. The emphasis in course design is now on promoting key understandings, core competencies, and an understanding of connections between different fields. In biochemistry teaching, this radical shift from the content-based tradition is being driven by the "omics" information explosion; we can no longer teach all the information we have available. Biochemistry is a core, enabling discipline for much of modern scientific research, and biochemistry teaching is in urgent need of a method for delivery of conceptual frameworks. In this project, we aimed to define the key concepts in biochemistry. We find that the key concepts we defined map well onto the core science concepts recommended by the Vision and Change project. We developed a new method to present biochemistry through the lenses of these concepts. This new method challenged the way we thought about biochemistry as teachers. It also stimulated the majority of the students to think more deeply about biochemistry and to make links between biochemistry and material in other courses. This method is applicable to the full spectrum of content usually taught in biochemistry. Copyright © 2011 Wiley Periodicals, Inc.

Clinical Research Informatics for Big Data and Precision Medicine.

PubMed

Weng, C; Kahn, M G

2016-11-10

To reflect on the notable events and significant developments in Clinical Research Informatics (CRI) in the year of 2015 and discuss near-term trends impacting CRI. We selected key publications that highlight not only important recent advances in CRI but also notable events likely to have significant impact on CRI activities over the next few years or longer, and consulted the discussions in relevant scientific communities and an online living textbook for modern clinical trials. We also related the new concepts with old problems to improve the continuity of CRI research. The highlights in CRI in 2015 include the growing adoption of electronic health records (EHR), the rapid development of regional, national, and global clinical data research networks for using EHR data to integrate scalable clinical research with clinical care and generate robust medical evidence. Data quality, integration, and fusion, data access by researchers, study transparency, results reproducibility, and infrastructure sustainability are persistent challenges. The advances in Big Data Analytics and Internet technologies together with the engagement of citizens in sciences are shaping the global clinical research enterprise, which is getting more open and increasingly stakeholder-centered, where stakeholders include patients, clinicians, researchers, and sponsors.

Clinical Research Informatics for Big Data and Precision Medicine

PubMed Central

Kahn, M. G.

2016-01-01

Summary Objectives To reflect on the notable events and significant developments in Clinical Research Informatics (CRI) in the year of 2015 and discuss near-term trends impacting CRI. Methods We selected key publications that highlight not only important recent advances in CRI but also notable events likely to have significant impact on CRI activities over the next few years or longer, and consulted the discussions in relevant scientific communities and an online living textbook for modern clinical trials. We also related the new concepts with old problems to improve the continuity of CRI research. Results The highlights in CRI in 2015 include the growing adoption of electronic health records (EHR), the rapid development of regional, national, and global clinical data research networks for using EHR data to integrate scalable clinical research with clinical care and generate robust medical evidence. Data quality, integration, and fusion, data access by researchers, study transparency, results reproducibility, and infrastructure sustainability are persistent challenges. Conclusion The advances in Big Data Analytics and Internet technologies together with the engagement of citizens in sciences are shaping the global clinical research enterprise, which is getting more open and increasingly stakeholder-centered, where stakeholders include patients, clinicians, researchers, and sponsors. PMID:27830253

Cattle grazing and vegetation succession on burned sagebrush steppe

USDA-ARS?s Scientific Manuscript database

There is limited information on the effects of cattle grazing to longer-term plant community composition and productivity following fire in big sagebrush steppe. This study evaluated vegetation response to cattle grazing over seven years (2007-2013) on burned Wyoming big sagebrush (Artemisia triden...

Big data, smart cities and city planning

PubMed Central

2013-01-01

I define big data with respect to its size but pay particular attention to the fact that the data I am referring to is urban data, that is, data for cities that are invariably tagged to space and time. I argue that this sort of data are largely being streamed from sensors, and this represents a sea change in the kinds of data that we have about what happens where and when in cities. I describe how the growth of big data is shifting the emphasis from longer term strategic planning to short-term thinking about how cities function and can be managed, although with the possibility that over much longer periods of time, this kind of big data will become a source for information about every time horizon. By way of conclusion, I illustrate the need for new theory and analysis with respect to 6 months of smart travel card data of individual trips on Greater London’s public transport systems. PMID:29472982

Big data, smart cities and city planning.

PubMed

Batty, Michael

2013-11-01

I define big data with respect to its size but pay particular attention to the fact that the data I am referring to is urban data, that is, data for cities that are invariably tagged to space and time. I argue that this sort of data are largely being streamed from sensors, and this represents a sea change in the kinds of data that we have about what happens where and when in cities. I describe how the growth of big data is shifting the emphasis from longer term strategic planning to short-term thinking about how cities function and can be managed, although with the possibility that over much longer periods of time, this kind of big data will become a source for information about every time horizon. By way of conclusion, I illustrate the need for new theory and analysis with respect to 6 months of smart travel card data of individual trips on Greater London's public transport systems.

Small Bodies, Big Concepts: Engaging Teachers and Their Students in Visual Analysis of Comets and Asteroids

NASA Astrophysics Data System (ADS)

Cobb, W. H.; Buxner, S.; Lebofsky, L. A.; Ristvey, J.; Weeks, S.; Zolensky, M.

2011-12-01

Small Bodies, Big Concepts is a multi-disciplinary, professional development project that engages 5th - 8th grade teachers in high end planetary science using a research-based pedagogical framework, Designing Effective Science Instruction (DESI). In addition to developing sound background knowledge with a focus on visual analysis, teachers' awareness of the process of learning new content is heightened, and they use that experience to deepen their science teaching practice. Culling from NASA E/PO educational materials, activities are sequenced to enhance conceptual understanding of big ideas in space science: what do we know, how do we know it, why do we care? Helping teachers develop a picture of the history and evolution of our understanding of the solar system, and honing in on the place of comets and asteroids in helping us answer old questions and discover new ones, teachers see the power and excitement underlying planetary science as human endeavor. Research indicates that science inquiry is powerful in the classroom and mission scientists are real-life models of science inquiry in action. Using guest scientist facilitators from the Planetary Science Institute, NASA Johnson Space Center, Lockheed Martin, and NASA E/PO professionals from McREL and NASA AESP, teachers practice framing scientific questions, using current visual data, and adapting NASA E/PO activities related to current exploration of asteroids and comets in our Solar System. Cross-curricular elements included examining research-based strategies for enhancing English language learners' ability to engage in higher order questions and a professional astronomy artist's insight into how visual analysis requires not just our eyes engaged, but our brains: comparing, synthesizing, questioning, evaluating, and wondering. This summer we pilot tested the SBBC curriculum with thirteen 5th- 10th grade teachers modeling a variety of instructional approaches over eight days. Each teacher developed lesson plans that incorporate DESI strategies with new space science content to implement during the coming year in their classroom. Initial evaluation of the workshop showed that teachers left with an increased understanding of small bodies in the solar system, current exploration, and ways to integrate this exploration into their current curriculum. We will reconvene the teachers in the spring of 2012 to share their implementation experiences. The professional development is a year-long effort, supported both online and through future face-to-face workshops. Next summer there will be a field test of the project will be implemented after evaluation data informs best steps for improvement. The result of the project will be a model for implementing professional development that integrates research-based instructional strategies and science findings from NASA missions to improve teacher practice. Small Bodies, BIG Concepts is based upon work supported by the National Aeronautics and Space Administration (NASA) under Grant/Contract/Agreement No. 09-EPOESS09-0044 issued through the Science Mission Directorate.

Tissue Engineering and Regenerative Medicine 2017: A Year in Review.

PubMed

Park, Kyung Min; Shin, Young Min; Kim, Kyobum; Shin, Heungsoo

2018-04-26

In 2017, a new paradigm change caused by artificial intelligence and big data analysis resulted in innovation in each field of science and technology, and also significantly influenced progress in tissue engineering and regenerative medicine (TERM). TERM has continued to make technological advances based on interdisciplinary approaches and has contributed to the overall field of biomedical technology, including cancer biology, personalized medicine, development biology, and cell-based therapeutics. While researchers are aware that there is still a long way to go until TERM reaches the ultimate goal of patient treatment through clinical translation, the rapid progress in convergence studies led by technological improvements in TERM has been encouraging. In this review, we highlighted the significant advances made in TERM in 2017 (with an overlap of 5 months in 2016). We identified major progress in TERM in a manner similar to previous reviews published in the last few years. In addition, we carefully considered all four previous reviews during the selection process and chose main themes that minimize the duplication of the topics. Therefore, we have identified three areas that have been the focus of most journal publications in the TERM community in 2017: (i) advanced biomaterials and three-dimensional (3D) cell printing, (ii) exosomes as bioactive agents for regenerative medicine, and (iii) 3D culture in regenerative medicine.

A Guided Inquiry on Hubble Plots and the Big Bang

NASA Astrophysics Data System (ADS)

Forringer, Ted

2014-04-01

In our science for non-science majors course "21st Century Physics," we investigate modern "Hubble plots" (plots of velocity versus distance for deep space objects) in order to discuss the Big Bang, dark matter, and dark energy. There are two potential challenges that our students face when encountering these topics for the first time. The first challenge is in understanding and interpreting Hubble plots. The second is that some of our students have religious or cultural objections to the concept of a "Big Bang" or a universe that is billions of years old. This paper presents a guided inquiry exercise that was created with the goal of introducing students to Hubble plots and giving them the opportunity to discover for themselves why we believe our universe started with an explosion billions of years ago. The exercise is designed to be completed before the topics are discussed in the classroom. We did the exercise during a one hour and 45 minute "lab" time and it was done in groups of three or four students, but it would also work as an individual take-home assignment.

Envisioning the future of 'big data' biomedicine.

PubMed

Bui, Alex A T; Van Horn, John Darrell

2017-05-01

Through the increasing availability of more efficient data collection procedures, biomedical scientists are now confronting ever larger sets of data, often finding themselves struggling to process and interpret what they have gathered. This, while still more data continues to accumulate. This torrent of biomedical information necessitates creative thinking about how the data are being generated, how they might be best managed, analyzed, and eventually how they can be transformed into further scientific understanding for improving patient care. Recognizing this as a major challenge, the National Institutes of Health (NIH) has spearheaded the "Big Data to Knowledge" (BD2K) program - the agency's most ambitious biomedical informatics effort ever undertaken to date. In this commentary, we describe how the NIH has taken on "big data" science head-on, how a consortium of leading research centers are developing the means for handling large-scale data, and how such activities are being marshalled for the training of a new generation of biomedical data scientists. All in all, the NIH BD2K program seeks to position data science at the heart of 21 st Century biomedical research. Copyright © 2017 Elsevier Inc. All rights reserved.

Models of the Universe: Children's Experiences and Evidence from the History of Science

NASA Astrophysics Data System (ADS)

Spiliotopoulou-Papantoniou, Vasiliki

2007-08-01

This study focuses on children’s experiences and the creation of “the big picture”, the Universe. It draws data from an age range 6 16 and is based on 270 children’s drawings of how they imagine the Universe to be, and on their answers to a number of short questions about it. Results are discussed using as a base a specially developed systemic network, which is considered to be a formulation broad enough to cover the different ways of experiencing the Universe. The categories of descriptions which have been developed are exemplified by children’s characteristic drawings, and analogies with historical conceptions are discussed. They have also been tested with groups of teachers’ and student teachers’ descriptions. Moreover, dominant images held during the history of Science, have been explored in terms of their relevance to the categories of the systemic network. It appears that, although there is no analogical evolution of the ideas between these two fields, some historical instances resemble some of the children’s models.

Discovery informatics in biological and biomedical sciences: research challenges and opportunities.

PubMed

Honavar, Vasant

2015-01-01

New discoveries in biological, biomedical and health sciences are increasingly being driven by our ability to acquire, share, integrate and analyze, and construct and simulate predictive models of biological systems. While much attention has focused on automating routine aspects of management and analysis of "big data", realizing the full potential of "big data" to accelerate discovery calls for automating many other aspects of the scientific process that have so far largely resisted automation: identifying gaps in the current state of knowledge; generating and prioritizing questions; designing studies; designing, prioritizing, planning, and executing experiments; interpreting results; forming hypotheses; drawing conclusions; replicating studies; validating claims; documenting studies; communicating results; reviewing results; and integrating results into the larger body of knowledge in a discipline. Against this background, the PSB workshop on Discovery Informatics in Biological and Biomedical Sciences explores the opportunities and challenges of automating discovery or assisting humans in discovery through advances (i) Understanding, formalization, and information processing accounts of, the entire scientific process; (ii) Design, development, and evaluation of the computational artifacts (representations, processes) that embody such understanding; and (iii) Application of the resulting artifacts and systems to advance science (by augmenting individual or collective human efforts, or by fully automating science).

Infectious Disease Surveillance in the Big Data Era: Towards Faster and Locally Relevant Systems

PubMed Central

Simonsen, Lone; Gog, Julia R.; Olson, Don; Viboud, Cécile

2016-01-01

While big data have proven immensely useful in fields such as marketing and earth sciences, public health is still relying on more traditional surveillance systems and awaiting the fruits of a big data revolution. A new generation of big data surveillance systems is needed to achieve rapid, flexible, and local tracking of infectious diseases, especially for emerging pathogens. In this opinion piece, we reflect on the long and distinguished history of disease surveillance and discuss recent developments related to use of big data. We start with a brief review of traditional systems relying on clinical and laboratory reports. We then examine how large-volume medical claims data can, with great spatiotemporal resolution, help elucidate local disease patterns. Finally, we review efforts to develop surveillance systems based on digital and social data streams, including the recent rise and fall of Google Flu Trends. We conclude by advocating for increased use of hybrid systems combining information from traditional surveillance and big data sources, which seems the most promising option moving forward. Throughout the article, we use influenza as an exemplar of an emerging and reemerging infection which has traditionally been considered a model system for surveillance and modeling. PMID:28830112

Short-term regeneration dynamics of Wyoming big sagebrush at two sites in northern Utah

USDA-ARS?s Scientific Manuscript database

The herbicide tebuthiuron has been used historically to control cover of Wyoming big sagebrush (Artemisia tridentata ssp. wyomingensis - complete taxonomic designation), a widespread shrub across the western United States, with the intent of increasing herbaceous plant cover. Although the tebuthiur...

Small Things Draw Big Interest

ERIC Educational Resources Information Center

Green, Susan; Smith III, Julian

2005-01-01

Although the microscope is a basic tool in both physical and biological sciences, it is notably absent from most elementary school science programs. One reason teachers find it challenging to introduce microscopy at the elementary level is because children can have a hard time connecting the image of an object seen through a microscope with what…

The Negative Effect of School-Average Ability on Science Self-Concept in the UK, the UK Countries and the World: The Big-Fish-Little-Pond-Effect for PISA 2006

ERIC Educational Resources Information Center

Nagengast, Benjamin; Marsh, Herbert W.

2011-01-01

Research on the relation between students' achievement (ACH) and their academic self-concept (ASC) has consistently shown a Big-Fish-Little-Pond-Effect (BFLPE); ASC is positively affected by individual ACH, but negatively affected by school-average ACH. Surprisingly, however, there are few good UK studies of the BFLPE and few anywhere in the world…

Using big data to map the network organization of the brain.

PubMed

Swain, James E; Sripada, Chandra; Swain, John D

2014-02-01

The past few years have shown a major rise in network analysis of "big data" sets in the social sciences, revealing non-obvious patterns of organization and dynamic principles. We speculate that the dependency dimension - individuality versus sociality - might offer important insights into the dynamics of neurons and neuronal ensembles. Connectomic neural analyses, informed by social network theory, may be helpful in understanding underlying fundamental principles of brain organization.

Using big data to map the network organization of the brain

PubMed Central

Swain, James E.; Sripada, Chandra; Swain, John D.

2015-01-01

The past few years have shown a major rise in network analysis of “big data” sets in the social sciences, revealing non-obvious patterns of organization and dynamic principles. We speculate that the dependency dimension – individuality versus sociality – might offer important insights into the dynamics of neurons and neuronal ensembles. Connectomic neural analyses, informed by social network theory, may be helpful in understanding underlying fundamental principles of brain organization. PMID:24572243

Talking About Your Science Is Just Like Talking About Yourself (Warning: You May Be Difficult to Explain)

NASA Astrophysics Data System (ADS)

Bitter, C.

2016-12-01

Talking about your science is just like talking about yourself (although you may be difficult to explain). You are not alone, and even the most famous scientists and engineers struggle because parts of our work are hard to explain. We'll explore the BIG stuff like the best ways to tackle the Scale of the Universe for the public, REALLY big numbers for little kids, and crowd favorites like Deep Time and Climate Change. We'll sweat the small stuff too like subatomic particles, and the unseeables but knowables like exoplanets, ground water and dark matter. Through case studies spanning over a decade of working with and observing scientists and engineers in public programming, education, outreach, and working groups for communicating science through museum exhibits, discover why the best science communicators are straightforward, curious, great storytellers and use everyday objects, humor, excitement and fun to share concepts. We'll examine a few epic fails too, and how to recover, as well as helping your audience feel truly accomplished after communicating with you.

A Physicist's Odyssey in the Public Schools

NASA Astrophysics Data System (ADS)

Blatt, S. Leslie

2004-03-01

My colleagues and I developed our "Discovering Physics" course a dozen years ago based on the best available research on (predominantly pre-college) student learning in the sciences. The hands-on small-group approach we subsequently adopted works quite nicely in the university environment, as well. As a major side benefit, we began consulting with and eventually working closely with teachers in the Worcester Public Schools. Over the years, we developed a regular collaborative cycle: 1.) A curriculum team of Clark faculty and K-12 teachers meets during the academic year for discussions and to design activities built around a "big idea" in the sciences; 2.) A summer institute is offered, for a larger group of teachers, based on the work of the curriculum team; 3.) A "Ways of Knowing in the Sciences" course is offered in the fall for Education Department students, centered on the previously-tested science content coupled with a variety of pedagogical approaches, as well as observations in the schools; and 4.) The cycle resumes with a new team and a different "big idea." The experience continues to be both rewarding and eye-opening.

Reviews Book: Nucleus Book: The Wonderful World of Relativity Book: Head Shot Book: Cosmos Close-Up Places to Visit: Physics DemoLab Book: Quarks, Leptons and the Big Bang EBook: Shooting Stars Equipment: Victor 70C USB Digital Multimeter Web Watch

NASA Astrophysics Data System (ADS)

2012-09-01

WE RECOMMEND Nucleus: A Trip into the Heart of Matter A coffee-table book for everyone to dip into and learn from The Wonderful World of Relativity A charming, stand-out introduction to relativity The Physics DemoLab, National University of Singapore A treasure trove of physics for hands-on science experiences Quarks, Leptons and the Big Bang Perfect to polish up on particle physics for older students Victor 70C USB Digital Multimeter Equipment impresses for usability and value WORTH A LOOK Cosmos Close-Up Weighty tour of the galaxy that would make a good display Shooting Stars Encourage students to try astrophotography with this ebook HANDLE WITH CARE Head Shot: The Science Behind the JKF Assassination Exploration of the science behind the crime fails to impress WEB WATCH App-lied science for education: a selection of free Android apps are reviewed and iPhone app options are listed

Systems biology for nursing in the era of big data and precision health.

PubMed

Founds, Sandra

2017-12-02

The systems biology framework was previously synthesized with the person-environment-health-nursing metaparadigm. The purpose of this paper is to present a nursing discipline-specific perspective of the association of systems biology with big data and precision health. The fields of systems biology, big data, and precision health are now overviewed, from origins through expansions, with examples of what is being done by nurses in each area of science. Technological advances continue to expand omics and other varieties of big data that inform the person's phenotype and health outcomes for precision care. Meanwhile, millions of participants in the United States are being recruited for health-care research initiatives aimed at building the information commons of digital health data. Implications and opportunities abound via conceptualizing the integration of these fields through the nursing metaparadigm. Copyright © 2017 Elsevier Inc. All rights reserved.

Data Science and its Relationship to Big Data and Data-Driven Decision Making.

PubMed

Provost, Foster; Fawcett, Tom

2013-03-01

Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot-even "sexy"-career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.

ARC-2009-ACD09-0261-014

NASA Image and Video Library

2009-12-10

Korean High Level Delegation Visit Ames Certer Director and various Senior staff: John Hines, Ames Center Chief Technologist (middel left) explains PharmaSat (small Satellites) to Soon-Duk Bae, Deputy Director, Big Science Policy Division, Ministry of Educaiton, Science Technology, Young-Mok Hyun, Deputy Director, Space Development Division, Ministry of Educaiton, Science Technology, Seorium Lee, Senior Researcher, International Relations Korea Aerospace Research Institute. Unkonw person at the end of table.

Adolescent personality factors in self-ratings and peer nominations and their prediction of peer acceptance and peer rejection.

PubMed

Scholte, R H; van Aken, M A; van Lieshout, C F

1997-12-01

In this study, the robustness of the Big Five personality factors in adolescents' self-ratings and peer nominations was investigated. Data were obtained on 2,001 adolescents attending secondary school (885 girls; 1,116 boys; M age = 14.5 years). Exploratory and confirmatory factor analyses on the self-ratings confirmed the Big Five personality factors. In contrast, exploratory analysis on the peer nominations revealed five different factors: Aggression-Inattentiveness, Achievement-Withdrawal, Self-Confidence, Sociability, and Emotionality-Nervousness. It is suggested that peers evaluate group members not in terms of their personality but in terms of their group reputation. Peer evaluations contributed substantially to the prediction of peer acceptance and rejection; the Big Five personality factors based on self-ratings did not.

Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats

NASA Astrophysics Data System (ADS)

Ghiringhelli, Luca M.; Carbogno, Christian; Levchenko, Sergey; Mohamed, Fawzi; Huhs, Georg; Lüders, Martin; Oliveira, Micael; Scheffler, Matthias

2017-11-01

With big-data driven materials research, the new paradigm of materials science, sharing and wide accessibility of data are becoming crucial aspects. Obviously, a prerequisite for data exchange and big-data analytics is standardization, which means using consistent and unique conventions for, e.g., units, zero base lines, and file formats. There are two main strategies to achieve this goal. One accepts the heterogeneous nature of the community, which comprises scientists from physics, chemistry, bio-physics, and materials science, by complying with the diverse ecosystem of computer codes and thus develops "converters" for the input and output files of all important codes. These converters then translate the data of each code into a standardized, code-independent format. The other strategy is to provide standardized open libraries that code developers can adopt for shaping their inputs, outputs, and restart files, directly into the same code-independent format. In this perspective paper, we present both strategies and argue that they can and should be regarded as complementary, if not even synergetic. The represented appropriate format and conventions were agreed upon by two teams, the Electronic Structure Library (ESL) of the European Center for Atomic and Molecular Computations (CECAM) and the NOvel MAterials Discovery (NOMAD) Laboratory, a European Centre of Excellence (CoE). A key element of this work is the definition of hierarchical metadata describing state-of-the-art electronic-structure calculations.

Water issues and agriculture - the view from 30,000 feet

USDA-ARS?s Scientific Manuscript database

There are different perspectives on the big picture of water issues especially as they relate to water use, and watershed planning considerations. The big picture of water issues can be couched in terms that the general public can understand, rather than an academic, statistically laden presentation...

ARC-2009-ACD09-0261-006

NASA Image and Video Library

2009-12-10

Korean High Level Delegation Visit Ames Certer Director and variou Senior staff: from left to right; Gary Martin, Director of New Ventures and Communication, NASA. Ames, Chris Giulietti, NASA Headquarters, Soon-Duk Bae, Deputy Director, Big Science Policy Division, Ministry of Educaiton, Science Technology, Young-Mok Hyun, Deputy Director, Space Development Division, Ministry of Educaiton, Science Technology, Yvonne Pendleton, Director of Lunar Science Institute, Terry Pagaduan, Ames Government Relations/Legislative Affairs Office, Seorium Lee, Senior Researcher, International Relations Korea Aerospace Research Institute

Using Reactive Transport Modeling to Understand Formation of the Stimson Sedimentary Unit and Altered Fracture Zones at Gale Crater, Mars

NASA Technical Reports Server (NTRS)

Hausrath, E. M.; Ming, D. W.; Peretyazhko, T.; Rampe, E. B.

2017-01-01

Water flowing through sediments at Gale Crater, Mars created environments that were likely habitable, and sampled basin-wide hydrological systems. However, many questions remain about these environments and the fluids that generated them. Measurements taken by the Mars Science Laboratory Curiosity of multiple fracture zones can help constrain the environments that formed them because they can be compared to nearby associated parent material (Figure 1). For example, measurements of altered fracture zones from the target Greenhorn in the Stimson sandstone can be compared to parent material measured in the nearby Big Sky target, allowing constraints to be placed on the alteration conditions that formed the Greenhorn target from the Big Sky target. Similarly, CheMin measurements of the powdered < 150 micron fraction from the drillhole at Big Sky and sample from the Rocknest eolian deposit indicate that the mineralogies are strikingly similar. The main differences are the presence of olivine in the Rocknest eolian deposit, which is absent in the Big Sky target, and the presence of far more abundant Fe oxides in the Big Sky target. Quantifying the changes between the Big Sky target and the Rocknest eolian deposit can therefore help us understand the diagenetic changes that occurred forming the Stimson sedimentary unit. In order to interpret these aqueous changes, we performed reactive transport modeling of 1) the formation of the Big Sky target from a Rocknest eolian deposit-like parent material, and 2) the formation of the Greenhorn target from the Big Sky target. This work allows us to test the relationships between the targets and the characteristics of the aqueous conditions that formed the Greenhorn target from the Big Sky target, and the Big Sky target from a Rocknest eolian deposit-like parent material.

Enabling a new Paradigm to Address Big Data and Open Science Challenges

NASA Astrophysics Data System (ADS)

Ramamurthy, Mohan; Fisher, Ward

2017-04-01

Data are not only the lifeblood of the geosciences but they have become the currency of the modern world in science and society. Rapid advances in computing, communi¬cations, and observational technologies — along with concomitant advances in high-resolution modeling, ensemble and coupled-systems predictions of the Earth system — are revolutionizing nearly every aspect of our field. Modern data volumes from high-resolution ensemble prediction/projection/simulation systems and next-generation remote-sensing systems like hyper-spectral satellite sensors and phased-array radars are staggering. For example, CMIP efforts alone will generate many petabytes of climate projection data for use in assessments of climate change. And NOAA's National Climatic Data Center projects that it will archive over 350 petabytes by 2030. For researchers and educators, this deluge and the increasing complexity of data brings challenges along with the opportunities for discovery and scientific breakthroughs. The potential for big data to transform the geosciences is enormous, but realizing the next frontier depends on effectively managing, analyzing, and exploiting these heterogeneous data sources, extracting knowledge and useful information from heterogeneous data sources in ways that were previously impossible, to enable discoveries and gain new insights. At the same time, there is a growing focus on the area of "Reproducibility or Replicability in Science" that has implications for Open Science. The advent of cloud computing has opened new avenues for not only addressing both big data and Open Science challenges to accelerate scientific discoveries. However, to successfully leverage the enormous potential of cloud technologies, it will require the data providers and the scientific communities to develop new paradigms to enable next-generation workflows and transform the conduct of science. Making data readily available is a necessary but not a sufficient condition. Data providers also need to give scientists an ecosystem that includes data, tools, workflows and other services needed to perform analytics, integration, interpretation, and synthesis - all in the same environment or platform. Instead of moving data to processing systems near users, as is the tradition, the cloud permits one to bring processing, computing, analysis and visualization to data - so called data proximate workbench capabilities, also known as server-side processing. In this talk, I will present the ongoing work at Unidata to facilitate a new paradigm for doing science by offering a suite of tools, resources, and platforms to leverage cloud technologies for addressing both big data and Open Science/reproducibility challenges. That work includes the development and deployment of new protocols for data access and server-side operations and Docker container images of key applications, JupyterHub Python notebook tools, and cloud-based analysis and visualization capability via the CloudIDV tool to enable reproducible workflows and effectively use the accessed data.

D1-3: Marshfield Dictionary of Clinical and Translational Science (MD-CTS): An Online Reference for Clinical and Translational Science Terminology

PubMed Central

Finamore, Joe; Ray, William; Kadolph, Chris; Rastegar-Mojarad, Majid; Ye, Zhan; Jacqueline, Bohne; Tachinardi, Umberto; Mendonça, Eneida; Finnegan, Brian; Bartkowiak, Barbara; Weichelt, Bryan; Lin, Simon

2014-01-01

Background/Aims New terms are rapidly appearing in the literature and practice of clinical medicine and translational research. To catalog real-world usage of medical terms, we report the first construction of an online dictionary of clinical and translational medicinal terms, which are computationally generated in near real-time using a big data approach. This project is NIH CTSA-funded and developed by the Marshfield Clinic Research Foundation in conjunction with University of Wisconsin - Madison. Currently titled Marshfield Dictionary of Clinical and Translational Science (MD-CTS), this application is a Google-like word search tool. By entering a term into the search bar, MD-CTS will display that term’s definition, usage examples, contextual terms, related images, and ontological information. A prototype is available for public viewing at http://spellchecker.mfldclin.edu/. Methods We programmatically derived the lexicon for MD-CTS from scholarly communications by parsing through 15,156,745 MEDLINE abstracts and extracting all of the unique words found therein. We then ran this list through several filters in order to remove words that were not relevant for searching, such as common English words and numeric expressions. We then loaded the resulting 1,795,769 terms into SQL tables. Each term is cross-referenced with every occurrence in all abstracts in which it was found. Additional information is aggregated from Wiktionary, Bioportal, and Wikipedia in real-time and displayed on-screen. From this lexicon we created a supplemental dictionary resource (updated quarterly) to be used in Microsoft Office® products. Results We evaluated the utility of MD-CTS by creating a list of 100 words derived from recent clinical and translational medicine publications in the week of July 22, 2013. We then performed comparative searches for each term with Taber’s Cyclopedic Medical Dictionary, Stedman’s Medical Dictionary, Dorland’s Illustrated Medical Dictionary, Medical Subject Headings (MeSH), and MD-CTS. We compared our supplemental dictionary resource to OpenMedSpell for effectiveness in accuracy of term recognition. Conclusions In summary, we developed an online mobile and desktop reference, which comprehensively integrates Wiktionary (term information), Bioportal (ontological information), Wikipedia (related images), and Medline abstract information (term usage) for scientists and clinicians to browse in real-time. We also created a supplemental dictionary resource to be used in Microsoft Office® products.

Addressing the Big-Earth-Data Variety Challenge with the Hierarchical Triangular Mesh

NASA Technical Reports Server (NTRS)

Rilee, Michael L.; Kuo, Kwo-Sen; Clune, Thomas; Oloso, Amidu; Brown, Paul G.; Yu, Honfeng

2016-01-01

We have implemented an updated Hierarchical Triangular Mesh (HTM) as the basis for a unified data model and an indexing scheme for geoscience data to address the variety challenge of Big Earth Data. We observe that, in the absence of variety, the volume challenge of Big Data is relatively easily addressable with parallel processing. The more important challenge in achieving optimal value with a Big Data solution for Earth Science (ES) data analysis, however, is being able to achieve good scalability with variety. With HTM unifying at least the three popular data models, i.e. Grid, Swath, and Point, used by current ES data products, data preparation time for integrative analysis of diverse datasets can be drastically reduced and better variety scaling can be achieved. In addition, since HTM is also an indexing scheme, when it is used to index all ES datasets, data placement alignment (or co-location) on the shared nothing architecture, which most Big Data systems are based on, is guaranteed and better performance is ensured. Moreover, our updated HTM encoding turns most geospatial set operations into integer interval operations, gaining further performance advantages.

Analysis of Big Data in Gait Biomechanics: Current Trends and Future Directions.

PubMed

Phinyomark, Angkoon; Petri, Giovanni; Ibáñez-Marcelo, Esther; Osis, Sean T; Ferber, Reed

2018-01-01

The increasing amount of data in biomechanics research has greatly increased the importance of developing advanced multivariate analysis and machine learning techniques, which are better able to handle "big data". Consequently, advances in data science methods will expand the knowledge for testing new hypotheses about biomechanical risk factors associated with walking and running gait-related musculoskeletal injury. This paper begins with a brief introduction to an automated three-dimensional (3D) biomechanical gait data collection system: 3D GAIT, followed by how the studies in the field of gait biomechanics fit the quantities in the 5 V's definition of big data: volume, velocity, variety, veracity, and value. Next, we provide a review of recent research and development in multivariate and machine learning methods-based gait analysis that can be applied to big data analytics. These modern biomechanical gait analysis methods include several main modules such as initial input features, dimensionality reduction (feature selection and extraction), and learning algorithms (classification and clustering). Finally, a promising big data exploration tool called "topological data analysis" and directions for future research are outlined and discussed.

Integrating the Apache Big Data Stack with HPC for Big Data

NASA Astrophysics Data System (ADS)

Fox, G. C.; Qiu, J.; Jha, S.

2014-12-01

There is perhaps a broad consensus as to important issues in practical parallel computing as applied to large scale simulations; this is reflected in supercomputer architectures, algorithms, libraries, languages, compilers and best practice for application development. However, the same is not so true for data intensive computing, even though commercially clouds devote much more resources to data analytics than supercomputers devote to simulations. We look at a sample of over 50 big data applications to identify characteristics of data intensive applications and to deduce needed runtime and architectures. We suggest a big data version of the famous Berkeley dwarfs and NAS parallel benchmarks and use these to identify a few key classes of hardware/software architectures. Our analysis builds on combining HPC and ABDS the Apache big data software stack that is well used in modern cloud computing. Initial results on clouds and HPC systems are encouraging. We propose the development of SPIDAL - Scalable Parallel Interoperable Data Analytics Library -- built on system aand data abstractions suggested by the HPC-ABDS architecture. We discuss how it can be used in several application areas including Polar Science.

Potential Solution of a Hardware-Software System V-Cluster for Big Data Analysis

NASA Astrophysics Data System (ADS)

Morra, G.; Tufo, H.; Yuen, D. A.; Brown, J.; Zihao, S.

2017-12-01

Today it cannot be denied that the Big Data revolution is taking place and is replacing HPC and numerical simulation as the main driver in society. Outside the immediate scientific arena, the Big Data market encompass much more than the AGU. There are many sectors in society that Big Data can ably serve, such as governments finances, hospitals, tourism, and, last by not least, scientific and engineering problems. In many countries, education has not kept pace with the demands from students outside computer science to get into Big Data science. Ultimate Vision (UV) in Beijing attempts to address this need in China by focusing part of our energy on education and training outside the immediate university environment. UV plans a strategy to maximize profits in our beginning. Therefore, we will focus on growing markets such as provincial governments, medical sectors, mass media, and education. And will not address issues such as performance for scientific collaboration, such as seismic networks, where the market share and profits are small by comparison. We have developed a software-hardware system, called V-Cluster, built with the latest NVIDIA GPUs and Intel CPUs with ample amounts of RAM (over couple of Tbytes) and local storage. We have put in an internal network with high bandwidth (over 100 Gbits/sec) and each node of V-Cluster can run at around 40 Tflops. Our system can scale linearly with the number of codes. Our main strength in data analytics is the use of graph-computing paradigm for optimizing the transfer rate in collaborative efforts. We focus in training and education with our clients in order to gain experience in learning about new applications. We will present the philosophy of this second generation of our Data Analytic system, whose costs fall far below those offered elsewhere.

A Competence-Based Science Learning Framework Illustrated Through the Study of Natural Hazards and Disaster Risk Reduction

NASA Astrophysics Data System (ADS)

Oyao, Sheila G.; Holbrook, Jack; Rannikmäe, Miia; Pagunsan, Marmon M.

2015-09-01

This article proposes a competence-based learning framework for science teaching, applied to the study of 'big ideas', in this case to the study of natural hazards and disaster risk reduction (NH&DRR). The framework focuses on new visions of competence, placing emphasis on nurturing connectedness and behavioral actions toward resilience and sustainability. The framework draws together competences familiarly expressed as cognitive knowledge and skills, plus dispositions and adds connectedness and action-related behaviors, and applies this by means of a progression shift associated with NH&DRR from abilities to capabilities. The target is enhanced scientific literacy approached through an education through science focus, amplified through the study of a big idea, promotion of sustained resilience in the face of disaster and the taking of responsibilities for behavioral actions. The framework is applied to a learning progression for each interrelated education dimension, thus serving as a guide for both the development of abilities and as a platform for stimulating student capabilities within instruction and assessment.

Big Data Challenges in Climate Science: Improving the Next-Generation Cyberinfrastructure

NASA Technical Reports Server (NTRS)

Schnase, John L.; Lee, Tsengdar J.; Mattmann, Chris A.; Lynnes, Christopher S.; Cinquini, Luca; Ramirez, Paul M.; Hart, Andre F.; Williams, Dean N.; Waliser, Duane; Rinsland, Pamela;

Balancing Benefits and Risks of Immortal Data: Participants' Views of Open Consent in the Personal Genome Project.

PubMed

Zarate, Oscar A; Brody, Julia Green; Brown, Phil; Ramirez-Andreotta, Mónica D; Perovich, Laura; Matz, Jacob

2016-01-01

An individual's health, genetic, or environmental-exposure data, placed in an online repository, creates a valuable shared resource that can accelerate biomedical research and even open opportunities for crowd-sourcing discoveries by members of the public. But these data become "immortalized" in ways that may create lasting risk as well as benefit. Once shared on the Internet, the data are difficult or impossible to redact, and identities may be revealed by a process called data linkage, in which online data sets are matched to each other. Reidentification (re-ID), the process of associating an individual's name with data that were considered deidentified, poses risks such as insurance or employment discrimination, social stigma, and breach of the promises often made in informed-consent documents. At the same time, re-ID poses risks to researchers and indeed to the future of science, should re-ID end up undermining the trust and participation of potential research participants. The ethical challenges of online data sharing are heightened as so-called big data becomes an increasingly important research tool and driver of new research structures. Big data is shifting research to include large numbers of researchers and institutions as well as large numbers of participants providing diverse types of data, so the participants' consent relationship is no longer with a person or even a research institution. In addition, consent is further transformed because big data analysis often begins with descriptive inquiry and generation of a hypothesis, and the research questions cannot be clearly defined at the outset and may be unforeseeable over the long term. In this article, we consider how expanded data sharing poses new challenges, illustrated by genomics and the transition to new models of consent. We draw on the experiences of participants in an open data platform-the Personal Genome Project-to allow study participants to contribute their voices to inform ethical consent practices and protocol reviews for big-data research. © 2015 The Hastings Center.

Thinking in early modernity and the separation process between philosophy and psychology.

PubMed

Klempe, Sven Hroar

2015-03-01

One of the big questions in psychology is when and how psychology disentangled from philosophy. Usually it is referred to the laboratory Wundt established in Leipzig in 1879 as the birth for psychology as an independent science. However this separation process can also be traced in other ways, like by focusing on how the two sciences approach and understand thinking. Although thinking and language were not included in the research in this laboratory, Wundt (1897) regarded thinking as the core of psychology. As a commentary to Papanicolaou (Integr Psychol Behav Sci doi:10.1007/s12124-014-9273-3, 2014), this paper investigates the differences in how psychology and philosophy conceptualized thinking in early Western modernity. Thus one of the findings is that the separation process between the two was more or less initiated by Immanuel Kant. By defining thinking in terms of the pure reason he excluded the psychological understanding of thinking because psychology basically defined thinking in terms of ideas derived from qualia and sensation. Another finding is that psychology itself has not completely realized the differences between the philosophical and the psychological understanding of thinking by having been influenced by Kant's ideal of the pure reason. This may also explain some of the crises psychology went through during the twentieth century.

The Big Bang Theory--Coping with Multi-Religious Beliefs in the Super-Diverse Science Classroom

ERIC Educational Resources Information Center

De Carvalho, Roussel

2013-01-01

Large urban schools have to cope with a "super-diverse" population with a multireligious background in their classrooms. The job of the science teacher within this environment requires an ultra-sensitive pedagogical approach, and a deeper understanding of students' backgrounds and of scientific epistemology. Teachers must create a safe…

A Competence-Based Science Learning Framework Illustrated through the Study of Natural Hazards and Disaster Risk Reduction

ERIC Educational Resources Information Center

Oyao, Sheila G.; Holbrook, Jack; Rannikmäe, Miia; Pagunsan, Marmon M.

2015-01-01

This article proposes a competence-based learning framework for science teaching, applied to the study of "big ideas", in this case to the study of natural hazards and disaster risk reduction (NH&DRR). The framework focuses on new visions of competence, placing emphasis on nurturing connectedness and behavioral actions toward…

Thinking Through Computational Exposure as an Evolving Paradign Shift for Exposure Science: Development and Application of Predictive Models from Big Data

EPA Science Inventory

Symposium Abstract: Exposure science has evolved from a time when the primary focus was on measurements of environmental and biological media and the development of enabling field and laboratory methods. The Total Exposure Assessment Method (TEAM) studies of the 1980s were class...

Space Technology and Earth System Science

NASA Technical Reports Server (NTRS)

Habib, Shahid

2011-01-01

Science must continue to drive the technology development. Partnering and Data Sharing among nations is very important to maximize the cost benefits of such investments Climate changes and adaptability will be a big challenge for the next several decades (1) Natural disasters frequency and locations (2) Economic and social impact can be global and (3) Water resources and management.

Implementing "Big Ideas" to Advance the Teaching and Learning of Science, Technology, Engineering, and Mathematics (STEM)

ERIC Educational Resources Information Center

Chalmers, Christina; Carter, Merilyn; Cooper, Tom; Nason, Rod

2017-01-01

Although education experts are increasingly advocating the incorporation of integrated Science, Technology, Engineering, and Mathematics (STEM) curriculum units to address limitations in much current STEM teaching and learning, a review of the literature reveals that more often than not such curriculum units are not mediating the construction of…

The big science of stockpile stewardship

NASA Astrophysics Data System (ADS)

Reis, Victor; Hanrahan, Robert; Levedahl, Kirk

2017-11-01

In the quarter century since the US last exploded a nuclear weapon, an extensive research enterprise has maintained the resources and know-how needed to preserve confidence in the country's stockpile.

The Truth about Wolves.

ERIC Educational Resources Information Center

Mannesto, Jean

2002-01-01

Reports on a project by a reading teacher that combines reading and science while debunking the myth of the big, bad wolf. Related activities include making animal tracks, writing, and measurement activities. (DDR)

Medicinal chemistry in drug discovery in big pharma: past, present and future.

PubMed

Campbell, Ian B; Macdonald, Simon J F; Procopiou, Panayiotis A

2018-02-01

The changes in synthetic and medicinal chemistry and related drug discovery science as practiced in big pharma over the past few decades are described. These have been predominantly driven by wider changes in society namely the computer, internet and globalisation. Thoughts about the future of medicinal chemistry are also discussed including sharing the risks and costs of drug discovery and the future of outsourcing. The continuing impact of access to substantial computing power and big data, the use of algorithms in data analysis and drug design are also presented. The next generation of medicinal chemists will communicate in ways that reflect social media and the results of constantly being connected to each other and data. Copyright © 2017. Published by Elsevier Ltd.

Population-based imaging biobanks as source of big data.

PubMed

Gatidis, Sergios; Heber, Sophia D; Storz, Corinna; Bamberg, Fabian

2017-06-01

Advances of computational sciences over the last decades have enabled the introduction of novel methodological approaches in biomedical research. Acquiring extensive and comprehensive data about a research subject and subsequently extracting significant information has opened new possibilities in gaining insight into biological and medical processes. This so-called big data approach has recently found entrance into medical imaging and numerous epidemiological studies have been implementing advanced imaging to identify imaging biomarkers that provide information about physiological processes, including normal development and aging but also on the development of pathological disease states. The purpose of this article is to present existing epidemiological imaging studies and to discuss opportunities, methodological and organizational aspects, and challenges that population imaging poses to the field of big data research.

Historical Trauma, Substance Use, and Indigenous Peoples: Seven Generations of Harm From a "Big Event".

PubMed

Nutton, Jennifer; Fast, Elizabeth

2015-01-01

Indigenous peoples the world over have and continue to experience the devastating effects of colonialism including loss of life, land, language, culture, and identity. Indigenous peoples suffer disproportionately across many health risk factors including an increased risk of substance use. We use the term "Big Event" to describe the historical trauma attributed to colonial policies as a potential pathway to explain the disparity in rates of substance use among many Indigenous populations. We present "Big Solutions" that have the potential to buffer the negative effects of the Big Event, including: (1) decolonizing strategies, (2) identity development, and (3) culturally adapted interventions. Study limitations are noted and future needed research is suggested.

The Problem with Big Data: Operating on Smaller Datasets to Bridge the Implementation Gap.

PubMed

Mann, Richard P; Mushtaq, Faisal; White, Alan D; Mata-Cervantes, Gabriel; Pike, Tom; Coker, Dalton; Murdoch, Stuart; Hiles, Tim; Smith, Clare; Berridge, David; Hinchliffe, Suzanne; Hall, Geoff; Smye, Stephen; Wilkie, Richard M; Lodge, J Peter A; Mon-Williams, Mark

2016-01-01

Big datasets have the potential to revolutionize public health. However, there is a mismatch between the political and scientific optimism surrounding big data and the public's perception of its benefit. We suggest a systematic and concerted emphasis on developing models derived from smaller datasets to illustrate to the public how big data can produce tangible benefits in the long term. In order to highlight the immediate value of a small data approach, we produced a proof-of-concept model predicting hospital length of stay. The results demonstrate that existing small datasets can be used to create models that generate a reasonable prediction, facilitating health-care delivery. We propose that greater attention (and funding) needs to be directed toward the utilization of existing information resources in parallel with current efforts to create and exploit "big data."

The phytotronist and the phenotype: plant physiology, Big Science, and a Cold War biology of the whole plant.

PubMed

Munns, David P D

2015-04-01

This paper describes how, from the early twentieth century, and especially in the early Cold War era, the plant physiologists considered their discipline ideally suited among all the plant sciences to study and explain biological functions and processes, and ranked their discipline among the dominant forms of the biological sciences. At their apex in the late-1960s, the plant physiologists laid claim to having discovered nothing less than the "basic laws of physiology." This paper unwraps that claim, showing that it emerged from the construction of monumental big science laboratories known as phytotrons that gave control over the growing environment. Control meant that plant physiologists claimed to be able to produce a standard phenotype valid for experimental biology. Invoking the standards of the physical sciences, the plant physiologists heralded basic biological science from the phytotronic produced phenotype. In the context of the Cold War era, the ability to pursue basic science represented the highest pinnacle of standing within the scientific community. More broadly, I suggest that by recovering the history of an underappreciated discipline, plant physiology, and by establishing the centrality of the story of the plant sciences in the history of biology can historians understand the massive changes wrought to biology by the conceptual emergence of the molecular understanding of life, the dominance of the discipline of molecular biology, and the rise of biotechnology in the 1980s. Copyright © 2015 Elsevier Ltd. All rights reserved.

Solution structure of the Big domain from Streptococcus pneumoniae reveals a novel Ca2+-binding module

PubMed Central

Wang, Tao; Zhang, Jiahai; Zhang, Xuecheng; Xu, Chao; Tu, Xiaoming

2013-01-01

Streptococcus pneumoniae is a pathogen causing acute respiratory infection, otitis media and some other severe diseases in human. In this study, the solution structure of a bacterial immunoglobulin-like (Big) domain from a putative S. pneumoniae surface protein SP0498 was determined by NMR spectroscopy. SP0498 Big domain adopts an eight-β-strand barrel-like fold, which is different in some aspects from the two-sheet sandwich-like fold of the canonical Ig-like domains. Intriguingly, we identified that the SP0498 Big domain was a Ca2+ binding domain. The structure of the Big domain is different from those of the well known Ca2+ binding domains, therefore revealing a novel Ca2+-binding module. Furthermore, we identified the critical residues responsible for the binding to Ca2+. We are the first to report the interactions between the Big domain and Ca2+ in terms of structure, suggesting an important role of the Big domain in many essential calcium-dependent cellular processes such as pathogenesis. PMID:23326635

Integrating continental-scale ecological data into university courses: Developing NEON's Online Learning Portal

NASA Astrophysics Data System (ADS)

Wasser, L. A.; Gram, W.; Lunch, C. K.; Petroy, S. B.; Elmendorf, S.

2013-12-01

'Big Data' are becoming increasingly common in many fields. The National Ecological Observatory Network (NEON) will be collecting data over the 30 years, using consistent, standardized methods across the United States. Similar efforts are underway in other parts of the globe (e.g. Australia's Terrestrial Ecosystem Research Network, TERN). These freely available new data provide an opportunity for increased understanding of continental- and global scale processes such as changes in vegetation structure and condition, biodiversity and landuse. However, while 'big data' are becoming more accessible and available, integrating big data into the university courses is challenging. New and potentially unfamiliar data types and associated processing methods, required to work with a growing diversity of available data, may warrant time and resources that present a barrier to classroom integration. Analysis of these big datasets may further present a challenge given large file sizes, and uncertainty regarding best methods to properly statistically summarize and analyze results. Finally, teaching resources, in the form of demonstrative illustrations, and other supporting media that might help teach key data concepts, take time to find and more time to develop. Available resources are often spread widely across multi-online spaces. This presentation will overview the development of NEON's collaborative University-focused online education portal. Portal content will include 1) interactive, online multi-media content that explains key concepts related to NEON's data products including collection methods, key metadata to consider and consideration of potential error and uncertainty surrounding data analysis; and 2) packaged 'lab' activities that include supporting data to be used in an ecology, biology or earth science classroom. To facilitate broad use in classrooms, lab activities will take advantage of freely and commonly available processing tools, techniques and scripts. All NEON materials are being developed in collaboration with labs and organizations across the globe. Integrating data analysis and processing techniques, early in student's careers will support and facilitate student advancement in the sciences - contributing to a larger body of knowledge and understanding of continental and global scale issues. Facilitating understanding of data use and empowering young ecologists with the tools required to process the data, is thus as integral to the observatory as the data itself. In this presentation, we discuss the integral role of freely available education materials that demonstrate the use of big data to address ecological questions and concepts. We also review gaps in existing educational resources related to big data and associated tools. Further, we address the great potential for big data inclusion into both an existing ecological, physical and environmental science courses and self-paced learning model through engaging and interactive multi-media presentation. Finally, we present beta-versions of the interactive, multi-media modules and results from feedback following early piloting and review.

Vocational Interests and Big Five Traits as Predictors of Job Instability

ERIC Educational Resources Information Center

Wille, Bart; De Fruyt, Filip; Feys, Marjolein

2010-01-01

Although empirical research on this topic is scarce, personality traits and vocational interests have repeatedly been named as potential individual level predictors of job change. Using a long-term cohort study (N = 291), we examined RIASEC interest profiles and Big Five personality scores at the beginning of the professional career as predictors…

A decentralized training algorithm for Echo State Networks in distributed big data applications.

PubMed

Scardapane, Simone; Wang, Dianhui; Panella, Massimo

2016-06-01

The current big data deluge requires innovative solutions for performing efficient inference on large, heterogeneous amounts of information. Apart from the known challenges deriving from high volume and velocity, real-world big data applications may impose additional technological constraints, including the need for a fully decentralized training architecture. While several alternatives exist for training feed-forward neural networks in such a distributed setting, less attention has been devoted to the case of decentralized training of recurrent neural networks (RNNs). In this paper, we propose such an algorithm for a class of RNNs known as Echo State Networks. The algorithm is based on the well-known Alternating Direction Method of Multipliers optimization procedure. It is formulated only in terms of local exchanges between neighboring agents, without reliance on a coordinating node. Additionally, it does not require the communication of training patterns, which is a crucial component in realistic big data implementations. Experimental results on large scale artificial datasets show that it compares favorably with a fully centralized implementation, in terms of speed, efficiency and generalization accuracy. Copyright © 2015 Elsevier Ltd. All rights reserved.

A primer on theory-driven web scraping: Automatic extraction of big data from the Internet for use in psychological research.

PubMed

Landers, Richard N; Brusso, Robert C; Cavanaugh, Katelyn J; Collmus, Andrew B

2016-12-01

The term big data encompasses a wide range of approaches of collecting and analyzing data in ways that were not possible before the era of modern personal computing. One approach to big data of great potential to psychologists is web scraping, which involves the automated collection of information from webpages. Although web scraping can create massive big datasets with tens of thousands of variables, it can also be used to create modestly sized, more manageable datasets with tens of variables but hundreds of thousands of cases, well within the skillset of most psychologists to analyze, in a matter of hours. In this article, we demystify web scraping methods as currently used to examine research questions of interest to psychologists. First, we introduce an approach called theory-driven web scraping in which the choice to use web-based big data must follow substantive theory. Second, we introduce data source theories , a term used to describe the assumptions a researcher must make about a prospective big data source in order to meaningfully scrape data from it. Critically, researchers must derive specific hypotheses to be tested based upon their data source theory, and if these hypotheses are not empirically supported, plans to use that data source should be changed or eliminated. Third, we provide a case study and sample code in Python demonstrating how web scraping can be conducted to collect big data along with links to a web tutorial designed for psychologists. Fourth, we describe a 4-step process to be followed in web scraping projects. Fifth and finally, we discuss legal, practical and ethical concerns faced when conducting web scraping projects. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

The Big Science of stockpile stewardship

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

Reis, Victor H.; Hanrahan, Robert J.; Levedahl, W. Kirk

2016-08-15

In the quarter century since the US last exploded a nuclear weapon, an extensive research enterprise has maintained the resources and know-how needed to preserve confidence in the country’s stockpile.

Big Oil Polluter Pays Act

THOMAS, 111th Congress

Sen. Whitehouse, Sheldon [D-RI

2010-05-11

Senate - 05/11/2010 Read twice and referred to the Committee on Commerce, Science, and Transportation. (All Actions) Tracker: This bill has the status IntroducedHere are the steps for Status of Legislation:

Assessing Teachers' Comprehension of What Matters in Earth Science

NASA Astrophysics Data System (ADS)

Penuel, W. R.; Kreikemeier, P.; Venezky, D.; Blank, J. G.; Davatzes, A.; Davatzes, N.

2006-12-01

Curricular standards developed for individual U.S. States tell teachers what they should teach. Most sets of standards are too numerous to be taught in a single year, forcing teachers to make decisions about what to emphasize in their curriculum. Ideally, such decisions would be based on what matters most in Earth science, namely, the big ideas that anchor scientific inquiry in the field. A measure of teachers' ability to associate curriculum standards with fundamental concepts in Earth science would help K-12 program and curriculum developers to bridge gaps in teachers' knowledge in order to help teachers make better decisions about what is most important to teach and communicate big ideas to students. This paper presents preliminary results of an attempt to create and validate a measure of teachers' comprehension of what matters in three sub-disciplines of Earth science. This measure was created as part of an experimental study of teacher professional development in Earth science. It is a task that requires teachers to take their state's curriculum standards and identify which standards are necessary or supplemental to developing students' understanding of fundamental concepts in the target sub-disciplines. To develop the task, a team of assessment experts and educational researchers asked a panel of four Earth scientists to identify key concepts embedded within middle school standards for the state of Florida. The Earth science panel reached a consensus on which standards needed to be taught in order to develop understanding of those concepts; this was used as a basis for comparison with teacher responses. Preliminary analysis of the responses of 44 teachers who participated in a pilot validation study identified differences between teachers' and scientists' maps of standards to big ideas in the sub-disciplines. On average, teachers identified just under one-third of the connections seen by expert Earth scientists between the concepts and their state standards. Teachers with higher levels of agreement also had a higher percentage of standards identified that were "off-grade," meaning that they saw connections to standards that they were not themselves required to teach but that nonetheless were relevant to developing student understanding of a particular concept. This result is consistent with the premise that to make good decisions about what to teach, teachers need to be able to identify relevant standards from other grade levels that are connected to the big ideas of a discipline (Shulman, 1986, Educ. Res. 15:4-14).

Innovations in glaucoma surgery from Dr. Rajendra Prasad Centre for Ophthalmic Sciences.

PubMed

Dada, Tanuj; Midha, Neha; Shah, Pooja; Sidhu, Talvir; Angmo, Dewang; Sihota, Ramanjit

2017-02-01

Trabeculectomy surgery is the current standard of care in glaucoma for achieving a low target intraocular pressure if medical therapy is not adequate. Augmentation of trabeculectomy with antimetabolites brought a revolutionary change in the long-term success rates of trabeculectomy, but along with it came a plethora of complications. There still is a big window for therapeutic innovations on this subject. The foremost target for these innovations is to modulate the wound healing response after glaucoma drainage surgery. Achieving the desired balance between long-term success of filtering blebs versus early failure due to scarring of blebs and hypotony due to dysfunctional filtering blebs poses a unique challenge to the ophthalmologists. Alternatives to trabeculectomy such as glaucoma drainage devices and minimally invasive glaucoma surgeries cannot solve the problem of glaucoma blindness in our country, mainly due to their unpredictable results and unfavorable cost-benefit ratio. In this article, we present a summary of our innovations in glaucoma surgery to advance patient care by making it more effective, safer, and economical.

Towards Big Earth Data Analytics: The EarthServer Approach

NASA Astrophysics Data System (ADS)

Baumann, Peter

2013-04-01

Big Data in the Earth sciences, the Tera- to Exabyte archives, mostly are made up from coverage data whereby the term "coverage", according to ISO and OGC, is defined as the digital representation of some space-time varying phenomenon. Common examples include 1-D sensor timeseries, 2-D remote sensing imagery, 3D x/y/t image timeseries and x/y/z geology data, and 4-D x/y/z/t atmosphere and ocean data. Analytics on such data requires on-demand processing of sometimes significant complexity, such as getting the Fourier transform of satellite images. As network bandwidth limits prohibit transfer of such Big Data it is indispensable to devise protocols allowing clients to task flexible and fast processing on the server. The EarthServer initiative, funded by EU FP7 eInfrastructures, unites 11 partners from computer and earth sciences to establish Big Earth Data Analytics. One key ingredient is flexibility for users to ask what they want, not impeded and complicated by system internals. The EarthServer answer to this is to use high-level query languages; these have proven tremendously successful on tabular and XML data, and we extend them with a central geo data structure, multi-dimensional arrays. A second key ingredient is scalability. Without any doubt, scalability ultimately can only be achieved through parallelization. In the past, parallelizing code has been done at compile time and usually with manual intervention. The EarthServer approach is to perform a samentic-based dynamic distribution of queries fragments based on networks optimization and further criteria. The EarthServer platform is comprised by rasdaman, an Array DBMS enabling efficient storage and retrieval of any-size, any-type multi-dimensional raster data. In the project, rasdaman is being extended with several functionality and scalability features, including: support for irregular grids and general meshes; in-situ retrieval (evaluation of database queries on existing archive structures, avoiding data import and, hence, duplication); the aforementioned distributed query processing. Additionally, Web clients for multi-dimensional data visualization are being established. Client/server interfaces are strictly based on OGC and W3C standards, in particular the Web Coverage Processing Service (WCPS) which defines a high-level raster query language. We present the EarthServer project with its vision and approaches, relate it to the current state of standardization, and demonstrate it by way of large-scale data centers and their services using rasdaman.

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

Shankar, Arjun

Computer scientist Arjun Shankar is director of the Compute and Data Environment for Science (CADES), ORNL’s multidisciplinary big data computing center. CADES offers computing, networking and data analytics to facilitate workflows for both ORNL and external research projects.

Science and Technology Review, January-February 1997

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

NONE

Table of contents: accelerators at Livermore; the B-Factory and the Big Bang; assessing exposure to radiation; next generation of computer storage; and a powerful new tool to detect clandestine nuclear tests.

How quantum is the big bang?

PubMed

Bojowald, Martin

2008-06-06

When quantum gravity is used to discuss the big bang singularity, the most important, though rarely addressed, question is what role genuine quantum degrees of freedom play. Here, complete effective equations are derived for isotropic models with an interacting scalar to all orders in the expansions involved. The resulting coupling terms show that quantum fluctuations do not affect the bounce much. Quantum correlations, however, do have an important role and could even eliminate the bounce. How quantum gravity regularizes the big bang depends crucially on properties of the quantum state.

The Widening Gulf between Genomics Data Generation and Consumption: A Practical Guide to Big Data Transfer Technology.

PubMed

Feltus, Frank A; Breen, Joseph R; Deng, Juan; Izard, Ryan S; Konger, Christopher A; Ligon, Walter B; Preuss, Don; Wang, Kuang-Ching

2015-01-01

In the last decade, high-throughput DNA sequencing has become a disruptive technology and pushed the life sciences into a distributed ecosystem of sequence data producers and consumers. Given the power of genomics and declining sequencing costs, biology is an emerging "Big Data" discipline that will soon enter the exabyte data range when all subdisciplines are combined. These datasets must be transferred across commercial and research networks in creative ways since sending data without thought can have serious consequences on data processing time frames. Thus, it is imperative that biologists, bioinformaticians, and information technology engineers recalibrate data processing paradigms to fit this emerging reality. This review attempts to provide a snapshot of Big Data transfer across networks, which is often overlooked by many biologists. Specifically, we discuss four key areas: 1) data transfer networks, protocols, and applications; 2) data transfer security including encryption, access, firewalls, and the Science DMZ; 3) data flow control with software-defined networking; and 4) data storage, staging, archiving and access. A primary intention of this article is to orient the biologist in key aspects of the data transfer process in order to frame their genomics-oriented needs to enterprise IT professionals.

``All that Matter ... in One Big Bang ...'', &Other Cosmological Singularities

NASA Astrophysics Data System (ADS)

Elizalde, Emilio

2018-02-01

The first part of this paper contains a brief description of the beginnings of modern cosmology, which, the author will argue, was most likely born in the Year 1912. Some of the pieces of evidence presented here have emerged from recent research in the history of science, and are not usually shared with the general audiences in popular science books. In special, the issue of the correct formulation of the original Big Bang concept, according to the precise words of Fred Hoyle, is discussed. Too often, this point is very deficiently explained (when not just misleadingly) in most of the available generalist literature. Other frequent uses of the same words, Big Bang, as to name the initial singularity of the cosmos, and also whole cosmological models, are then addressed, as evolutions of its original meaning. Quantum and inflationary additions to the celebrated singularity theorems by Penrose, Geroch, Hawking and others led to subsequent results by Borde, Guth and Vilenkin. And corresponding corrections to the Einstein field equations have originated, in particular, $R^2$, $f(R)$, and scalar-tensor gravities, giving rise to a plethora of new singularities. For completeness, an updated table with a classification of the same is given.

How Big Science Came to Long Island: The Birth of Brookhaven Laboratory (429th Brookhaven Lecture)

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

Crease, Robert P.

Robert P. Crease, historian for the U.S. Department of Energy's Brookhaven National Laboratory and Chair of the Philosophy Department at Stony Brook University, will give two talks on the Laboratory's history on October 31 and December 12. Crease's October 31 talk, titled "How Big Science Came to Long Island: The Birth of Brookhaven Lab," will cover the founding of the Laboratory soon after World War II as a peacetime facility to construct and maintain basic research facilities, such as nuclear reactors and particle accelerators, that were too large for single institutions to build and operate. He will discuss the keymore » figures involved in starting the Laboratory, including Nobel laureates I.I. Rabi and Norman Ramsey, as well as Donald Dexter Van Slyke, one of the most renowned medical researchers in American history. Crease also will focus on the many problems that had to be overcome in creating the Laboratory and designing its first big machines, as well as the evolving relations of the Laboratory with the surrounding Long Island community and news media. Throughout his talk, Crease will tell fascinating stories about Brookhaven's scientists and their research.« less

Trend of laser research developments in global level

NASA Astrophysics Data System (ADS)

Golnabi, H.; Mahdieh, M. H.

2006-03-01

An up-to-date progress of the international laser research and development is given in this article. The number of scientific publications and filed patents are considered as a figure of merit and based on these numbers the growth pace and important aspects are investigated. We have used the Science Finder Scholar search engine, which indexes more than 4000 journals, in different languages, and represents most significant published materials in laser science and engineering. The growth of the laser and related fields are described in terms of resulting scientific publications for the period of 1990-2003. The share of top nations in scientific publications, and in particular laser publications in terms of their gross domestic product (GDP) is presented. It is noted that the four countries including the USA, Japan, Germany and China have a laser publication contribution of 58.9% while the rest of the world including 189 countries contribute 41.1%. However, for the case of patent, which is a more important factor, these four countries hold a share of 90.1% while the remaining nations have a small share of 9.9%. The USA heads all the nations in the number of scientific publications, citations, and laser publications, however, in terms of accepted laser patents Japan shows a big lead. Scientific scopes of the laser systems are presented and some requirements to be met in each field are described. The key points in this field of research, which might be helpful in the future development of the laser technology are discussed.

ARC-2009-ACD09-0261-012

NASA Image and Video Library

2009-12-10

Korean High Level Delegation Visit Ames Certer Director and various Senior staff: Dan Andrews give presentation about LCROSS/LRO to Seorium Lee, Senior Researcher, International Relations Korea Aerospace Research Institute, Soon-Duk Bae, Deputy Director, Big Science Policy Division, Ministry of Educaiton, Science Technology, Young-Mok Hyun, Deputy Director, Space Development Division, Ministry of Educaiton, Science Technology. Also at table are Chris Giulietti, NASA HQ, John Hines, Ames Center Chief Technologist, Unknow person and Terry Pagaduan, Government Relations/Legislative Affairs office.

Live Blogging Science News: The Rosetta Mission

NASA Astrophysics Data System (ADS)

Clark, S.

2016-03-01

When one of the world's most popular online news websites decides to cover a space science event live, you know that something big is brewing. Stuart Clark reports on how live blogging can be used for science reporting and how an idea that was triggered by his observations during the Rosetta flyby of the asteroid Lutetia and the landing of the Curiosity rover on Mars led to him live blogging two of Rosetta's most memorable occasions for The Guardian newspaper.

ESO Science Outreach Network in Poland during 2011-2013

NASA Astrophysics Data System (ADS)

Czart, Krzysztof

2014-12-01

ESON Poland works since 2010. One of the main tasks of the ESO Science Outreach Network (ESON) is translation of various materials at ESO website, as well as contacts with journalists. We support also science festivals, conferences, contests, exhibitions, astronomy camps and workshops and other educational and outreach activities. During 2011-2013 we supported events like ESO Astronomy Camp 2013, ESO Industry Days in Warsaw, Warsaw Science Festival, Torun Festival of Science and Art, international astronomy olympiad held in Poland and many others. Among big tasks there was also translation of over 60 ESOcast movies.

Managing Data and Facilitating Science: A spectrum of activities in the Centre for Environmental Data Archival. (Invited)

NASA Astrophysics Data System (ADS)

Lawrence, B.; Bennett, V.; Callaghan, S.; Juckes, M. N.; Pepler, S.

2013-12-01

The UK Centre for Environmental Data Archival (CEDA) hosts a number of formal data centres, including the British Atmospheric Data Centre (BADC), and is a partner in a range of national and international data federations, including the InfraStructure for the European Network for Earth system Simulation, the Earth System Grid Federation, and the distributed IPCC Data Distribution Centres. The mission of CEDA is to formally curate data from, and facilitate the doing of, environmental science. The twin aims are symbiotic: data curation helps facilitate science, and facilitating science helps with data curation. Here we cover how CEDA delivers this strategy by established internal processes supplemented by short-term projects, supported by staff with a range of roles. We show how CEDA adds value to data in the curated archive, and how it supports science, and show examples of the aforementioned symbiosis. We begin by discussing curation: CEDA has the formal responsibility for curating the data products of atmospheric science and earth observation research funded by the UK Natural Environment Research Council (NERC). However, curation is not just about the provider community, the consumer communities matter too, and the consumers of these data cross the boundaries of science, including engineers, medics, as well as the gamut of the environmental sciences. There is a small, and growing cohort of non-science users. For both producers and consumers of data, information about data is crucial, and a range of CEDA staff have long worked on tools and techniques for creating, managing, and delivering metadata (as well as data). CEDA "science support" staff work with scientists to help them prepare and document data for curation. As one of a spectrum of activities, CEDA has worked on data Publication as a method of both adding value to some data, and rewarding the effort put into the production of quality datasets. As such, we see this activity as both a curation and a facilitation activity. A range of more focused facilitation activities are carried out, from providing a computing platform suitable for big-data analytics (the Joint Analysis System, JASMIN), to working on distributed data analysis (EXARCH), and the acquisition of third party data to support science and impact (e.g. in the context of the facility for Climate and Environmental Monitoring from Space, CEMS). We conclude by confronting the view of Parsons and Fox (2013) that metaphors such as Data Publication, Big Iron, Science Support etc are limiting, and suggest the CEDA experience is that these sorts of activities can and do co-exist, much as they conclude they should. However, we also believe that within co-existing metaphors, production systems need to be limited in their scope, even if they are on a road to a more joined up infrastructure. We shouldn't confuse what we can do now with what we might want to do in the future.

Enhancing Teachers' Awareness About Relations Between Science and Religion. The Debate Between Steady State and Big Bang Theories

NASA Astrophysics Data System (ADS)

Bagdonas, Alexandre; Silva, Cibelle Celestino

2015-11-01

Educators advocate that science education can help the development of more responsible worldviews when students learn not only scientific concepts, but also about science, or "nature of science". Cosmology can help the formation of worldviews because this topic is embedded in socio-cultural and religious issues. Indeed, during the Cold War period, the cosmological controversy between Big Bang and Steady State theory was tied up with political and religious arguments. The present paper discusses a didactic sequence developed for and applied in a pre-service science teacher-training course on history of science. After studying the historical case, pre-service science teachers discussed how to deal with possible conflicts between scientific views and students' personal worldviews related to religion. The course focused on the study of primary and secondary sources about cosmology and religion written by cosmologists such as Georges Lemaître, Fred Hoyle and the Pope Pius XII. We used didactic strategies such as short seminars given by groups of pre-service teachers, videos, computer simulations, role-play, debates and preparation of written essays. Along the course, most pre-service teachers emphasized differences between science and religion and pointed out that they do not feel prepared to conduct classroom discussions about this topic. Discussing the relations between science and religion using the history of cosmology turned into an effective way to teach not only science concepts but also to stimulate reflections about nature of science. This topic may contribute to increasing students' critical stance on controversial issues, without the need to explicitly defend certain positions, or disapprove students' cultural traditions. Moreover, pre-service teachers practiced didactic strategies to deal with this kind of unusual content.

How to Talk About Science: Lessons from a Middle School Science Classroom

NASA Astrophysics Data System (ADS)

Cushman-Patz, B. J.

2010-12-01

Middle school students are curious, energetic, and impatient. A middle school science teacher is always challenged to find ways to relate the content she’d like to convey to the students’ everyday lives, working to both satiate and foster their natural curiosity. She must communicate science in language appropriate for her audience, teaching new vocabulary words the first time she uses them, and reviewing them often. A thriving middle school science classroom is noisy, messy, and fun. Understanding what makes this classroom dynamic work can lead to better communication about science to any audience. 1) Know your bottom-line message, and keep it simple. Research science is complicated and nuanced. Your audience may be interested in some of these details, but start with the big picture first, and fill in the details as appropriate. 2) Avoid jargon. Use language that you would use to explain science to your 13-year-old neighbor or your 85-year old grandmother. They know what a volcano is, but they may not know the difference between a crater and a caldera. They definitely don’t know what a phreatomagmatic eruption is. As you introduce necessary jargon into your discussion, define it clearly in terms of something you are sure they do know and understand. 3) Engage the audience. Use pictures; use your hands; use common-reference points. Whenever possible, get the audience members to use their hands to mimic your motion. Encourage them to try to reframe what you say in terms that they’re comfortable with. Make it a two-way conversation 4) Pause. New concepts take time to absorb. Take a breath; give your audience a moment to absorb what you just explained and to formulate questions they may have. 5) Pay attention to cues. Middle school students make it obvious when they’re bored; adults tend to be more subtle. When eyes wander or eyelids droop, ask a question that engages your audience, even if it’s just, “do you follow?” or, “where did I lose you?” Communicating about science requires us to remember what it was like before we became experts in our disciplines. Middle school students think science is fun; let’s use the lessons from a successful middle school classroom to model our communication about science in general. We can work together towards a more science-literate society.

Analyzing Earth Science Research Networking through Visualizations

NASA Astrophysics Data System (ADS)

Hasnain, S.; Stephan, R.; Narock, T.

2017-12-01

Using D3.js we visualize collaboration amongst several geophysical science organizations, such as the American Geophysical Union (AGU) and the Federation of Earth Science Information Partners (ESIP). We look at historical trends in Earth Science research topics, cross-domain collaboration, and topics of interest to the general population. The visualization techniques used provide an effective way for non-experts to easily explore distributed and heterogeneous Big Data. Analysis of these visualizations provides stakeholders with insights into optimizing meetings, performing impact evaluation, structuring outreach efforts, and identifying new opportunities for collaboration.

Surface transport processes in charged porous media

DOE PAGES

Gabitto, Jorge; Tsouris, Costas

2017-03-03

Surface transport processes are important in chemistry, colloidal sciences, engineering, biology, and geophysics. Natural or externally produced charges on surfaces create electrical double layers (EDLs) at the solid-liquid interface. The existence of the EDLs produces several complex processes including bulk and surface transport of ions. In this work, a model is presented to simulate bulk and transport processes in homogeneous porous media comprising big pores. It is based on a theory for capacitive charging by ideally polarizable porous electrodes without Faradaic reactions or specific adsorption of ions. A volume averaging technique is used to derive the averaged transport equations inmore » the limit of thin electrical double layers. Description of the EDL between the electrolyte solution and the charged wall is accomplished using the Gouy-Chapman-Stern (GCS) model. The surface transport terms enter into the average equations due to the use of boundary conditions for diffuse interfaces. Two extra surface transports terms appear in the closed average equations. One is a surface diffusion term equivalent to the transport process in non-charged porous media. The second surface transport term is a migration term unique to charged porous media. The effective bulk and transport parameters for isotropic porous media are calculated solving the corresponding closure problems.« less

Surface transport processes in charged porous media

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

Gabitto, Jorge; Tsouris, Costas

Surface transport processes are important in chemistry, colloidal sciences, engineering, biology, and geophysics. Natural or externally produced charges on surfaces create electrical double layers (EDLs) at the solid-liquid interface. The existence of the EDLs produces several complex processes including bulk and surface transport of ions. In this work, a model is presented to simulate bulk and transport processes in homogeneous porous media comprising big pores. It is based on a theory for capacitive charging by ideally polarizable porous electrodes without Faradaic reactions or specific adsorption of ions. A volume averaging technique is used to derive the averaged transport equations inmore » the limit of thin electrical double layers. Description of the EDL between the electrolyte solution and the charged wall is accomplished using the Gouy-Chapman-Stern (GCS) model. The surface transport terms enter into the average equations due to the use of boundary conditions for diffuse interfaces. Two extra surface transports terms appear in the closed average equations. One is a surface diffusion term equivalent to the transport process in non-charged porous media. The second surface transport term is a migration term unique to charged porous media. The effective bulk and transport parameters for isotropic porous media are calculated solving the corresponding closure problems.« less

Monitoring surface water quality using social media in the context of citizen science

NASA Astrophysics Data System (ADS)

Zheng, Hang; Hong, Yang; Long, Di; Jing, Hua

2017-02-01

Surface water quality monitoring (SWQM) provides essential information for water environmental protection. However, SWQM is costly and limited in terms of equipment and sites. The global popularity of social media and intelligent mobile devices with GPS and photography functions allows citizens to monitor surface water quality. This study aims to propose a method for SWQM using social media platforms. Specifically, a WeChat-based application platform is built to collect water quality reports from volunteers, which have been proven valuable for water quality monitoring. The methods for data screening and volunteer recruitment are discussed based on the collected reports. The proposed methods provide a framework for collecting water quality data from citizens and offer a primary foundation for big data analysis in future research.

What problem are you working on?

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

None

2013-11-21

Superconductors, supercomputers, new materials, clean energy, big science - ORNL researchers' work is multidisciplinary and world-leading. Hear them explain it in their own words in this video first shown at UT-Battelle's 2013 Awards Night.

Energy Efficient Supercomputing

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

Anypas, Katie

2014-10-17

Katie Anypas, Head of NERSC's Services Department discusses the Lab's research into developing increasingly powerful and energy efficient supercomputers at our '8 Big Ideas' Science at the Theater event on October 8th, 2014, in Oakland, California.

What problem are you working on?

ScienceCinema

None

2018-05-07

Superconductors, supercomputers, new materials, clean energy, big science - ORNL researchers' work is multidisciplinary and world-leading. Hear them explain it in their own words in this video first shown at UT-Battelle's 2013 Awards Night.

Energy Efficient Supercomputing

ScienceCinema

Anypas, Katie

2018-05-07

Katie Anypas, Head of NERSC's Services Department discusses the Lab's research into developing increasingly powerful and energy efficient supercomputers at our '8 Big Ideas' Science at the Theater event on October 8th, 2014, in Oakland, California.

NASA at the Space & Science Festival

NASA Image and Video Library

2017-08-05

Former NASA astronaut Mike Massimino participates in a panel discussion titled "The Big Picture", Saturday, Aug. 5, 2017 at the Intrepid Sea, Air & Space Museum in New York City. Photo Credit: (NASA/Bill Ingalls)

Big Science, Nano Science?: Mapping the Evolution and Socio-Cognitive Structure of Nanoscience/Nanotechnology Using Mixed Methods

ERIC Educational Resources Information Center

Milojevic, Stasa

2009-01-01

This study examines the development of nanoscience/nanotechnology over a 35 year period (1970-2004) by mapping its social and cognitive structures using social network analysis, bibliometrics and document analysis, and following their changes in time. Mapping is performed based on 580,000 journal articles, 240,000 patents and 53,000 research…

Concept-Focused Teaching: Using Big Ideas to Guide Instruction in Science

ERIC Educational Resources Information Center

Olson, Joanne K.

2008-01-01

One of the main problems we face in science teaching is that students are learning isolated facts and missing central concepts. For instance, consider what you know about life cycles. Chances are that you remember something about butterflies and stages, such as egg, larva, pupa, adult. But what's the take-home idea that we should have learned…

What if English Is Not My Students' Mother Tongue?

ERIC Educational Resources Information Center

Ross, Keith; Gardenier, Alison

2017-01-01

Teaching science in an English-medium school where your students have a different mother tongue brings various issues to light. Our ultimate goal in teaching science is to help students understand the big ideas; however, poor language skills may make this hard and can lead to a heavy emphasis on "passing the exam at all costs" using rote…

Starting Small, Thinking Big - Continuum Magazine | NREL

Science.gov Websites

field of urban science by preparing cities with world-class decision support. -Written by Kevin Eber How generation of clean energy decision science. Photo of two long cabins with a view of one roof, which is solar power systems to the grid in the U.S. Virgin Islands have led to a significant growth in installed

Teaching the "Big Ideas" of Electricity at Primary Level

ERIC Educational Resources Information Center

Chapman, Steven

2014-01-01

Electricity can be a fun topic in a primary school class. It includes many practical experiments and links to real life contexts. However, teachers can feel daunted by the subject as they think they do not know enough about the science behind it to answer off-topic questions. The reason for the difficulty is that much of the science takes place…

The Meta-Analytic Big Bang

ERIC Educational Resources Information Center

Shadish, William R.; Lecy, Jesse D.

2015-01-01

This article looks at the impact of meta-analysis and then explores why meta-analysis was developed at the time and by the scholars it did in the social sciences in the 1970s. For the first problem, impact, it examines the impact of meta-analysis using citation network analysis. The impact is seen in the sciences, arts and humanities, and on such…

The Big Crunch: A Hybrid Solution to Earth and Space Science Instruction for Elementary Education Majors

ERIC Educational Resources Information Center

Cervato, Cinzia; Kerton, Charles; Peer, Andrea; Hassall, Lesya; Schmidt, Allan

2013-01-01

We describe the rationale and process for the development of a new hybrid Earth and Space Science course for elementary education majors. A five-step course design model, applicable to both online and traditional courses, is presented. Assessment of the course outcomes after two semesters indicates that the intensive time invested in the…

Scientists vs. Engineers

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

Wiley, H. S.

In the past, I have heard there was conflict between the “two cultures” of science and the humanities. I don’t see a lot of evidence for that type of conflict today, mostly because my scientific friends all are big fans of the arts and literature. However, the two cultures that I do see a great deal of conflict between are those of science and engineering.

Colonizing Bodies: Corporate Power and Biotechnology in Young Adult Science Fiction

ERIC Educational Resources Information Center

Guerra, Stephanie

2009-01-01

The American cultural and political landscape has seen changes on the level of seismic shifts in the past four decades, thanks in part to the two very diverse fields of big business and biotechnology. Linking the two arenas together in the literary landscape is a growing body of young adult science fiction that envisions a future shaped profoundly…

Developing a Mobile Learning Management System for Outdoors Nature Science Activities Based on 5E Learning Cycle

ERIC Educational Resources Information Center

Lai, Ah-Fur; Lai, Horng-Yih; Chuang, Wei-Hsiang; Wu, Zih-Heng

2015-01-01

Traditional outdoor learning activities such as inquiry-based learning in nature science encounter many dilemmas. Due to prompt development of mobile computing and widespread of mobile devices, mobile learning becomes a big trend on education. The main purpose of this study is to develop a mobile-learning management system for overcoming the…

Earth science big data at users' fingertips: the EarthServer Science Gateway Mobile

NASA Astrophysics Data System (ADS)

Barbera, Roberto; Bruno, Riccardo; Calanducci, Antonio; Fargetta, Marco; Pappalardo, Marco; Rundo, Francesco

2014-05-01

The EarthServer project (www.earthserver.eu), funded by the European Commission under its Seventh Framework Program, aims at establishing open access and ad-hoc analytics on extreme-size Earth Science data, based on and extending leading-edge Array Database technology. The core idea is to use database query languages as client/server interface to achieve barrier-free "mix & match" access to multi-source, any-size, multi-dimensional space-time data -- in short: "Big Earth Data Analytics" - based on the open standards of the Open Geospatial Consortium Web Coverage Processing Service (OGC WCPS) and the W3C XQuery. EarthServer combines both, thereby achieving a tight data/metadata integration. Further, the rasdaman Array Database System (www.rasdaman.com) is extended with further space-time coverage data types. On server side, highly effective optimizations - such as parallel and distributed query processing - ensure scalability to Exabyte volumes. In this contribution we will report on the EarthServer Science Gateway Mobile, an app for both iOS and Android-based devices that allows users to seamlessly access some of the EarthServer applications using SAML-based federated authentication and fine-grained authorisation mechanisms.

The Big Sky Model: A Regional Collaboration for Participatory Research on Environmental Health in the Rural West

PubMed Central

Ward, Tony J.; Vanek, Diana; Marra, Nancy; Holian, Andrij; Adams, Earle; Jones, David; Knuth, Randy

2010-01-01

The case for inquiry-based, hands-on, meaningful science education continues to gain credence as an effective and appropriate pedagogical approach (Karukstis 2005; NSF 2000). An innovative community-based framework for science learning, hereinafter referred to as the Big Sky Model, successfully addresses these educational aims, guiding high school and tribal college students from rural areas of Montana and Idaho in their understanding of chemical, physical, and environmental health concepts. Students participate in classroom lessons and continue with systematic inquiry through actual field research to investigate a pressing, real-world issue: understanding the complex links between poor air quality and respiratory health outcomes. This article provides background information, outlines the procedure for implementing the model, and discusses its effectiveness as demonstrated through various evaluation tools. PMID:20428505

Research funding. Big names or big ideas: do peer-review panels select the best science proposals?

PubMed

Li, Danielle; Agha, Leila

2015-04-24

This paper examines the success of peer-review panels in predicting the future quality of proposed research. We construct new data to track publication, citation, and patenting outcomes associated with more than 130,000 research project (R01) grants funded by the U.S. National Institutes of Health from 1980 to 2008. We find that better peer-review scores are consistently associated with better research outcomes and that this relationship persists even when we include detailed controls for an investigator's publication history, grant history, institutional affiliations, career stage, and degree types. A one-standard deviation worse peer-review score among awarded grants is associated with 15% fewer citations, 7% fewer publications, 19% fewer high-impact publications, and 14% fewer follow-on patents. Copyright © 2015, American Association for the Advancement of Science.

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge

PubMed Central

May, Jody C.; McLean, John A.

2017-01-01

Hybrid analytical instrumentation constructed around mass spectrometry (MS) are becoming preferred techniques for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements which are used to infer systems-level information. In this review, we describe multidimensional MS configurations as technologies which are big data drivers and discuss some new and emerging strategies for mining information from large-scale datasets. A discussion is included on the information content which can be obtained from individual dimensions, as well as the unique information which can be derived by comparing different levels of data. Finally, we discuss some emerging data visualization strategies which seek to make highly dimensional datasets both accessible and comprehensible. PMID:27306312

Through the Eyes of NASA: NASA's 2017 Eclipse Education Progam

NASA Astrophysics Data System (ADS)

Mayo, L.

2017-12-01

Over the last three years, NASA has been developing plans to bring the August 21st total solar eclipse to the nation, "as only NASA can", leveraging its considerable space assets, technology, scientists, and its unmatched commitment to science education. The eclipse, long anticipated by many groups, represents the largest Big Event education program that NASA has ever undertaken. It is the latest in a long string of successful Big Event international celebrations going back two decades including both transits of Venus, three solar eclipses, solar maximum, and mission events such as the MSL/Curiosity landing on Mars, and the launch of the Lunar Reconnaissance Orbiter (LRO) to name a few. This talk will detail NASA's program development methods, strategic partnerships, and strategies for using this celestial event to engage the nation and improve overall science literacy.

Managing the Big Data Avalanche in Astronomy - Data Mining the Galaxy Zoo Classification Database

NASA Astrophysics Data System (ADS)

Borne, Kirk D.

2014-01-01

We will summarize a variety of data mining experiments that have been applied to the Galaxy Zoo database of galaxy classifications, which were provided by the volunteer citizen scientists. The goal of these exercises is to learn new and improved classification rules for diverse populations of galaxies, which can then be applied to much larger sky surveys of the future, such as the LSST (Large Synoptic Sky Survey), which is proposed to obtain detailed photometric data for approximately 20 billion galaxies. The massive Big Data that astronomy projects will generate in the future demand greater application of data mining and data science algorithms, as well as greater training of astronomy students in the skills of data mining and data science. The project described here has involved several graduate and undergraduate research assistants at George Mason University.

Lowering the barriers for accessing distributed geospatial big data to advance spatial data science: the PolarHub solution

NASA Astrophysics Data System (ADS)

Li, W.

2017-12-01

Data is the crux of science. The widespread availability of big data today is of particular importance for fostering new forms of geospatial innovation. This paper reports a state-of-the-art solution that addresses a key cyberinfrastructure research problem—providing ready access to big, distributed geospatial data resources on the Web. We first formulate this data-access problem and introduce its indispensable elements, including identifying the cyber-location, space and time coverage, theme, and quality of the dataset. We then propose strategies to tackle each data-access issue and make the data more discoverable and usable for geospatial data users and decision makers. Among these strategies is large-scale web crawling as a key technique to support automatic collection of online geospatial data that are highly distributed, intrinsically heterogeneous, and known to be dynamic. To better understand the content and scientific meanings of the data, methods including space-time filtering, ontology-based thematic classification, and service quality evaluation are incorporated. To serve a broad scientific user community, these techniques are integrated into an operational data crawling system, PolarHub, which is also an important cyberinfrastructure building block to support effective data discovery. A series of experiments were conducted to demonstrate the outstanding performance of the PolarHub system. We expect this work to contribute significantly in building the theoretical and methodological foundation for data-driven geography and the emerging spatial data science.

On Darwin's science and its contexts.

PubMed

Hodge, M J S

2014-01-01

The notions of 'the Darwinian revolution' and of 'the scientific Revolution' are no longer unproblematic; so this paper does not construe its task as relating these two items to each other. There can be big-picture and long-run history even when that task is declined. Such history has to be done pluralistically. Relating Darwin's science to Newton's science is one kind of historiographical challenge; relating Darwin's science to seventeenth-century finance capitalism is another kind. Relating Darwin's science to long-run traditions and transitions is a different kind of task from relating his science to the immediate short-run contexts. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modeling in Big Data Environments

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

Endert, Alexander; Szymczak, Samantha; Gunning, Dave

Human-Centered Big Data Research (HCBDR) is an area of work that focuses on the methodologies and research areas focused on understanding how humans interact with “big data”. In the context of this paper, we refer to “big data” in a holistic sense, including most (if not all) the dimensions defining the term, such as complexity, variety, velocity, veracity, etc. Simply put, big data requires us as researchers of to question and reconsider existing approaches, with the opportunity to illuminate new kinds of insights that were traditionally out of reach to humans. The purpose of this article is to summarize themore » discussions and ideas about the role of models in HCBDR at a recent workshop. Models, within the context of this paper, include both computational and conceptual mental models. As such, the discussions summarized in this article seek to understand the connection between these two categories of models.« less

Trends in Soil Science education: moving from teacher's questioning to student's questioning

NASA Astrophysics Data System (ADS)

Roca, Núria

2017-04-01

Soil science has suffered from communication problems within its own discipline, with other disciplines (except perhaps agronomy) and with the general public. Prof. Dennis Greenland wrote the following in the early 1990s: "…soil scientists have also been frustrated as their advice has gone apparently unheeded. This may be because the advice is couched in terms more easily understood by other soil scientists than by politicians and economists who control the disposition of land. If soil science is to serve society fully it is essential that its arguments are presented in terms readily understood by all and with both scientific and economic rigor so that they are not easily refuted". Soil is a 3-dimensional body with properties that reflect the impact of climate, vegetation, fauna, man and topography on the soil's parent material over a variable time span. Therefore, soil sciences must integrate different knowledge of many disciplines. How should one go about the teaching and learning of a subject like soil science? This is an ever present question resident in the mind of a soil science teacher who knows that students will find soil science an inherently difficult subject to understand. Therefore, Soil Science cannot be taught in the same way. This paper proposes a mural construction that allows to understand soil formation, soil evolution and soil distribution. This experience has been realized with secondary teachers to offer tools for active learning methodologies. Therefore, this teaching project starts with a box and a global soil map distribution in a wall mural. The box contains many cards with soil properties, soil factors, soil process, soils orders and different natural soil photos as the pieces of a big puzzle. All these pieces will be arranged in the wall mural. These environments imply a new perspective of teaching: moving from a teacher-centered teaching to a student-centered teaching. In contrast to learning-before-doing— the model of most educational settings. The main functions such as encouraging students to think, arousing interest and curiosity, developing students' reflection and stimulate students to ask questions of their own will be developed with the construction of the mural.

The Concept Lens Diagram: A New Mechanism for Presenting Biochemistry Content in Terms of "Big Ideas"

ERIC Educational Resources Information Center

Rowland, Susan L.; Smith, Christopher A.; Gillam, Elizabeth M. A.; Wright, Tony

2011-01-01

A strong, recent movement in tertiary education is the development of conceptual, or "big idea" teaching. The emphasis in course design is now on promoting key understandings, core competencies, and an understanding of connections between different fields. In biochemistry teaching, this radical shift from the content-based tradition is…

Precision Teaching a Foundational Motor Skill to a Child with Autism

ERIC Educational Resources Information Center

Fabrizio, Michael A.; Schirmer, Kristin; King, Amy; Diakite, Ami; Stovel, Leah

2007-01-01

Since the early work of Anne Desjardin (1980) and others, Precision Teachers have developed Big 6+6 skills in their students' repertoires when needed. In this article, the authors present the Standard Celeration Chart (SCC) which documents how they analyzed the Big 6+6 skill of "squeeze" in terms of arranging sequences of instruction. The SCC…

Next Generation Workload Management and Analysis System for Big Data

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

De, Kaushik

We report on the activities and accomplishments of a four-year project (a three-year grant followed by a one-year no cost extension) to develop a next generation workload management system for Big Data. The new system is based on the highly successful PanDA software developed for High Energy Physics (HEP) in 2005. PanDA is used by the ATLAS experiment at the Large Hadron Collider (LHC), and the AMS experiment at the space station. The program of work described here was carried out by two teams of developers working collaboratively at Brookhaven National Laboratory (BNL) and the University of Texas at Arlingtonmore » (UTA). These teams worked closely with the original PanDA team – for the sake of clarity the work of the next generation team will be referred to as the BigPanDA project. Their work has led to the adoption of BigPanDA by the COMPASS experiment at CERN, and many other experiments and science projects worldwide.« less

Adapting bioinformatics curricula for big data.

PubMed

Greene, Anna C; Giffin, Kristine A; Greene, Casey S; Moore, Jason H

2016-01-01

Modern technologies are capable of generating enormous amounts of data that measure complex biological systems. Computational biologists and bioinformatics scientists are increasingly being asked to use these data to reveal key systems-level properties. We review the extent to which curricula are changing in the era of big data. We identify key competencies that scientists dealing with big data are expected to possess across fields, and we use this information to propose courses to meet these growing needs. While bioinformatics programs have traditionally trained students in data-intensive science, we identify areas of particular biological, computational and statistical emphasis important for this era that can be incorporated into existing curricula. For each area, we propose a course structured around these topics, which can be adapted in whole or in parts into existing curricula. In summary, specific challenges associated with big data provide an important opportunity to update existing curricula, but we do not foresee a wholesale redesign of bioinformatics training programs. © The Author 2015. Published by Oxford University Press.

Adapting bioinformatics curricula for big data

PubMed Central

Greene, Anna C.; Giffin, Kristine A.; Greene, Casey S.

2016-01-01

Modern technologies are capable of generating enormous amounts of data that measure complex biological systems. Computational biologists and bioinformatics scientists are increasingly being asked to use these data to reveal key systems-level properties. We review the extent to which curricula are changing in the era of big data. We identify key competencies that scientists dealing with big data are expected to possess across fields, and we use this information to propose courses to meet these growing needs. While bioinformatics programs have traditionally trained students in data-intensive science, we identify areas of particular biological, computational and statistical emphasis important for this era that can be incorporated into existing curricula. For each area, we propose a course structured around these topics, which can be adapted in whole or in parts into existing curricula. In summary, specific challenges associated with big data provide an important opportunity to update existing curricula, but we do not foresee a wholesale redesign of bioinformatics training programs. PMID:25829469

Landscape ecology: a concept for protecting park resources

USGS Publications Warehouse

Allen, Craig D.; Lissoway, John; Yarborough, Keith

1990-01-01

The Southwest Region has been supporting Resource Basic Inventory (RBI) efforts to establish baseline data for comparisons with long-term monitoring results to be conducted in the future. This “pulse taking” is a part of the Servicewide initiative being fostered so that resource managers, scientists, and park managers will be able to track the health of park resources by determining changes and trends. The RBI work is being linked with the development of Geographic Information Systems (GIS) at Bandelier, Big Thicket, Big Bend, Padre Island, and Guadalupe Mountains. Many of the parks in the southwest Region have only partially completed RBIs. This informational shortcoming is a pervasive threat to the parks because without detailed knowledge of the parks’ respective resources the Service cannot protect them adequately. To overcome this deficiency, the SWRO’s Division of Natural Resources Management and Science has fostered at Bandelier a pilot research effort, which started in FY ’87 and utilizes a landscape ecology paradigm. This concept links the RBI, GIS, and research activities in a park to present an overall picture of the park in its regional ecosystem setting. The flowchart diagrams this project’s concept. The results have been encouraging. A final report was recently completed (Allen 1989). This concept may now be applied to other Southwest Region parks.

Geologic map of Big Bend National Park, Texas

USGS Publications Warehouse

Turner, Kenzie J.; Berry, Margaret E.; Page, William R.; Lehman, Thomas M.; Bohannon, Robert G.; Scott, Robert B.; Miggins, Daniel P.; Budahn, James R.; Cooper, Roger W.; Drenth, Benjamin J.; Anderson, Eric D.; Williams, Van S.

2011-01-01

The purpose of this map is to provide the National Park Service and the public with an updated digital geologic map of Big Bend National Park (BBNP). The geologic map report of Maxwell and others (1967) provides a fully comprehensive account of the important volcanic, structural, geomorphological, and paleontological features that define BBNP. However, the map is on a geographically distorted planimetric base and lacks topography, which has caused difficulty in conducting GIS-based data analyses and georeferencing the many geologic features investigated and depicted on the map. In addition, the map is outdated, excluding significant data from numerous studies that have been carried out since its publication more than 40 years ago. This report includes a modern digital geologic map that can be utilized with standard GIS applications to aid BBNP researchers in geologic data analysis, natural resource and ecosystem management, monitoring, assessment, inventory activities, and educational and recreational uses. The digital map incorporates new data, many revisions, and greater detail than the original map. Although some geologic issues remain unresolved for BBNP, the updated map serves as a foundation for addressing those issues. Funding for the Big Bend National Park geologic map was provided by the United States Geological Survey (USGS) National Cooperative Geologic Mapping Program and the National Park Service. The Big Bend mapping project was administered by staff in the USGS Geology and Environmental Change Science Center, Denver, Colo. Members of the USGS Mineral and Environmental Resources Science Center completed investigations in parallel with the geologic mapping project. Results of these investigations addressed some significant current issues in BBNP and the U.S.-Mexico border region, including contaminants and human health, ecosystems, and water resources. Funding for the high-resolution aeromagnetic survey in BBNP, and associated data analyses and interpretation, was from the USGS Crustal Geophysics and Geochemistry Science Center. Mapping contributed from university professors and students was mostly funded by independent sources, including academic institutions, private industry, and other agencies.

Abraham Pais Prize for History of Physics Lecture: Big, Bigger, Too Big? From Los Alamos to Fermilab and the SSC

NASA Astrophysics Data System (ADS)

Hoddeson, Lillian

2012-03-01

The modern era of big science emerged during World War II. Oppenheimer's Los Alamos laboratory offered the quintessential model of a government-funded, mission-oriented facility directed by a strong charismatic leader. The postwar beneficiaries of this model included the increasingly ambitious large laboratories that participated in particle physics--in particular, Brookhaven, SLAC, and Fermilab. They carried the big science they practiced into a new realm where experiments eventually became as large and costly as entire laboratories had been. Meanwhile the available funding grew more limited causing the physics research to be concentrated into fewer and bigger experiments that appeared never to end. The next phase in American high-energy physics was the Superconducting Super Collider, the most costly pure physics project ever attempted. The SSC's termination was a tragedy for American science, but for historians it offers an opportunity to understand what made the success of earlier large high-energy physics laboratories possible, and what made the continuation of the SSC impossible. The most obvious reason for the SSC's failure was its enormous and escalating budget, which Congress would no longer support. Other factors need to be recognized however: no leader could be found with directing skills as strong as those of Wilson, Panofsky, Lederman, or Richter; the scale of the project subjected it to uncomfortable public and Congressional scrutiny; and the DOE's enforcement of management procedures of the military-industrial complex that clashed with those typical of the scientific community led to the alienation and withdrawal of many of the most creative scientists, and to the perception and the reality of poor management. These factors, exacerbated by negative pressure from scientists in other fields and a post-Cold War climate in which physicists had little of their earlier cultural prestige, discouraged efforts to gain international support. They made the SSC crucially different from its predecessors and sealed its doom.

Has the time come for big science in wildlife health?

USGS Publications Warehouse

Sleeman, Jonathan M.

2013-01-01

The consequences of wildlife emerging diseases are global and profound with increased burden on the public health system, negative impacts on the global economy, declines and extinctions of wildlife species, and subsequent loss of ecological integrity. Examples of health threats to wildlife include Batrachochytrium dendrobatidis, which causes a cutaneous fungal infection of amphibians and is linked to declines of amphibians globally; and the recently discovered Pseudogymnoascus (Geomyces) destructans, the etiologic agent of white nose syndrome which has caused precipitous declines of North American bat species. Of particular concern are the novel pathogens that have emerged as they are particularly devastating and challenging to manage. A big science approach to wildlife health research is needed if we are to make significant and enduring progress in managing these diseases. The advent of new analytical models and bench assays will provide us with the mathematical and molecular tools to identify and anticipate threats to wildlife, and understand the ecology and epidemiology of these diseases. Specifically, new molecular diagnostic techniques have opened up avenues for pathogen discovery, and the application of spatially referenced databases allows for risk assessments that can assist in targeting surveillance. Long-term, systematic collection of data for wildlife health and integration with other datasets is also essential. Multidisciplinary research programs should be expanded to increase our understanding of the drivers of emerging diseases and allow for the development of better disease prevention and management tools, such as vaccines. Finally, we need to create a National Fish and Wildlife Health Network that provides the operational framework (governance, policies, procedures, etc.) by which entities with a stake in wildlife health cooperate and collaborate to achieve optimal outcomes for human, animal, and ecosystem health.

Gathering the forgotten voices: an oral history of the CFHT's early years

NASA Astrophysics Data System (ADS)

Laychak, Mary Beth; Bryson, Liz

2011-06-01

They came to the Big Island from as far away as Murrumbeena, Australia, and as near by as Hilo, Hawaii. They were progeny of Scottish coal miners, French physicists, Chicago truck drivers, Japanese samurai and Big Island cane workers. Together, these men and women would build and commission one of the most dynamic and productive 3.6 meter telescopes in the world that remains in the forefront of science and technology. The CFHT oral history DVD preserves the stories of the first decade and a half of the observatory.

Power to the People: Addressing Big Data Challenges in Neuroscience by Creating a New Cadre of Citizen Neuroscientists.

PubMed

Roskams, Jane; Popović, Zoran

2016-11-02

Global neuroscience projects are producing big data at an unprecedented rate that informatic and artificial intelligence (AI) analytics simply cannot handle. Online games, like Foldit, Eterna, and Eyewire-and now a new neuroscience game, Mozak-are fueling a people-powered research science (PPRS) revolution, creating a global community of "new experts" that over time synergize with computational efforts to accelerate scientific progress, empowering us to use our collective cerebral talents to drive our understanding of our brain. Copyright © 2016 Elsevier Inc. All rights reserved.

Prey species as possible sources of PBDE exposures for peregrine falcons (Falco peregrinus) nesting in major California cities.

PubMed

Park, June-Soo; Fong, Alison; Chu, Vivian; Holden, Arthur; Linthicum, Janet; Hooper, Kim

2011-04-01

Our earlier findings indicate that (1) peregrine falcons (Falco peregrinus anatum Bonaparte) nesting in major California cities have among the highest polybrominated diphenyl ether (PBDE) levels in the world (max ∑PBDEs=100 ppm), and (2) Big City peregrines have higher levels and proportions of the higher-brominated congeners (hepta- to deca-BDEs) than do their Coastal counterparts. In this study we classified the prey species (n =185) from the remains of prey (feathers) at 38 peregrine nest sites over 25 years (1974-1998). We grouped the prey species into 15 categories based on diet and found distinctly different prey patterns for Big City vs. Coastal peregrines. Big City peregrines had a higher (almost three times) weight percentage intake of food waste-eating birds (e.g., rock pigeons, Columba livia) than Coastal peregrines. These differing prey patterns suggest diet as a potential source of the unusually high levels and proportions of higher-brominated PBDEs in Big City peregrines. The relative contributions of diet and dust (e.g., preening) exposure to PBDE patterns in Big City peregrines will be explored in future investigations. © Springer Science+Business Media, LLC 2010

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges.

PubMed

Yin, Zekun; Lan, Haidong; Tan, Guangming; Lu, Mian; Vasilakos, Athanasios V; Liu, Weiguo

2017-01-01

The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics.

Earth Science Data Analytics: Preparing for Extracting Knowledge from Information

NASA Technical Reports Server (NTRS)

Kempler, Steven; Barbieri, Lindsay

2016-01-01

Data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Data analytics is a broad term that includes data analysis, as well as an understanding of the cognitive processes an analyst uses to understand problems and explore data in meaningful ways. Analytics also include data extraction, transformation, and reduction, utilizing specific tools, techniques, and methods. Turning to data science, definitions of data science sound very similar to those of data analytics (which leads to a lot of the confusion between the two). But the skills needed for both, co-analyzing large amounts of heterogeneous data, understanding and utilizing relevant tools and techniques, and subject matter expertise, although similar, serve different purposes. Data Analytics takes on a practitioners approach to applying expertise and skills to solve issues and gain subject knowledge. Data Science, is more theoretical (research in itself) in nature, providing strategic actionable insights and new innovative methodologies. Earth Science Data Analytics (ESDA) is the process of examining, preparing, reducing, and analyzing large amounts of spatial (multi-dimensional), temporal, or spectral data using a variety of data types to uncover patterns, correlations and other information, to better understand our Earth. The large variety of datasets (temporal spatial differences, data types, formats, etc.) invite the need for data analytics skills that understand the science domain, and data preparation, reduction, and analysis techniques, from a practitioners point of view. The application of these skills to ESDA is the focus of this presentation. The Earth Science Information Partners (ESIP) Federation Earth Science Data Analytics (ESDA) Cluster was created in recognition of the practical need to facilitate the co-analysis of large amounts of data and information for Earth science. Thus, from a to advance science point of view: On the continuum of ever evolving data management systems, we need to understand and develop ways that allow for the variety of data relationships to be examined, and information to be manipulated, such that knowledge can be enhanced, to facilitate science. Recognizing the importance and potential impacts of the unlimited ways to co-analyze heterogeneous datasets, now and especially in the future, one of the objectives of the ESDA cluster is to facilitate the preparation of individuals to understand and apply needed skills to Earth science data analytics. Pinpointing and communicating the needed skills and expertise is new, and not easy. Information technology is just beginning to provide the tools for advancing the analysis of heterogeneous datasets in a big way, thus, providing opportunity to discover unobvious scientific relationships, previously invisible to the science eye. And it is not easy It takes individuals, or teams of individuals, with just the right combination of skills to understand the data and develop the methods to glean knowledge out of data and information. In addition, whereas definitions of data science and big data are (more or less) available (summarized in Reference 5), Earth science data analytics is virtually ignored in the literature, (barring a few excellent sources).

ScienceCast 105: Big Weather on Hot Jupiters

NASA Image and Video Library

2013-05-24

Astronomers using NASA's Spitzer Space Telescope are making weather maps of an exotic class of exoplanets called "hot Jupiters." What they're finding is wilder than anything we experience here in our own solar system.

NASA at the Space & Science Festival

NASA Image and Video Library

2017-08-05

Honeybee Robotics co-founder and chairman Stephen Gorevan participates in a panel discussion titled "The Big Picture", Saturday, Aug. 5, 2017 at the Intrepid Sea, Air & Space Museum in New York City. Photo Credit: (NASA/Bill Ingalls)

Wideband linear-to-circular polarization conversion realized by a transmissive anisotropic metasurface

NASA Astrophysics Data System (ADS)

Lin, Bao-Qin; Guo, Jian-Xin; Huang, Bai-Gang; Fang, Lin-Bo; Chu, Peng; Liu, Xiang-Wen

2018-05-01

Not Available Project supported by the National Natural Science Foundation of China (Grant No. 61471387) and the Research Center for Internet of Things and Big Data Technology of Xijing University, China.

Big Crater as Viewed by Pathfinder Lander

NASA Technical Reports Server (NTRS)

1997-01-01

The 'Big Crater' is actually a relatively small Martian crater to the southeast of the Mars Pathfinder landing site. It is 1500 meters (4900 feet) in diameter, or about the same size as Meteor Crater in Arizona. Superimposed on the rim of Big Crater (the central part of the rim as seen here) is a smaller crater nicknamed 'Rimshot Crater.' The distance to this smaller crater, and the nearest portion of the rim of Big Crater, is 2200 meters (7200 feet). To the right of Big Crater, south from the spacecraft, almost lost in the atmospheric dust 'haze,' is the large streamlined mountain nicknamed 'Far Knob.' This mountain is over 450 meters (1480 feet) tall, and is over 30 kilometers (19 miles) from the spacecraft. Another, smaller and closer knob, nicknamed 'Southeast Knob' can be seen as a triangular peak to the left of the flanks of the Big Crater rim. This knob is 21 kilometers (13 miles) southeast from the spacecraft.

The larger features visible in this scene - Big Crater, Far Knob, and Southeast Knob - were discovered on the first panoramas taken by the IMP camera on the 4th of July, 1997, and subsequently identified in Viking Orbiter images taken over 20 years ago. The scene includes rocky ridges and swales or 'hummocks' of flood debris that range from a few tens of meters away from the lander to the distance of South Twin Peak. The largest rock in the nearfield, just left of center in the foreground, nicknamed 'Otter', is about 1.5 meters (4.9 feet) long and 10 meters (33 feet) from the spacecraft.

This view of Big Crater was produced by combining 6 individual 'Superpan' scenes from the left and right eyes of the IMP camera. Each frame consists of 8 individual frames (left eye) and 7 frames (right eye) taken with different color filters that were enlarged by 500% and then co-added using Adobe Photoshop to produce, in effect, a super-resolution panchromatic frame that is sharper than an individual frame would be.

Mars Pathfinder is the second in NASA's Discovery program of low-cost spacecraft with highly focused science goals. The Jet Propulsion Laboratory, Pasadena, CA, developed and manages the Mars Pathfinder mission for NASA's Office of Space Science, Washington, D.C. JPL is a division of the California Institute of Technology (Caltech). The IMP was developed by the University of Arizona Lunar and Planetary Laboratory under contract to JPL. Peter Smith is the Principal Investigator.

Concurrence of big data analytics and healthcare: A systematic review.

PubMed

Mehta, Nishita; Pandit, Anil

2018-06-01

The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care. This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges. A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered. Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected. Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare. A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare. This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries. Copyright © 2018 Elsevier B.V. All rights reserved.

Big Geo Data Management: AN Exploration with Social Media and Telecommunications Open Data

NASA Astrophysics Data System (ADS)

Arias Munoz, C.; Brovelli, M. A.; Corti, S.; Zamboni, G.

2016-06-01

The term Big Data has been recently used to define big, highly varied, complex data sets, which are created and updated at a high speed and require faster processing, namely, a reduced time to filter and analyse relevant data. These data is also increasingly becoming Open Data (data that can be freely distributed) made public by the government, agencies, private enterprises and among others. There are at least two issues that can obstruct the availability and use of Open Big Datasets: Firstly, the gathering and geoprocessing of these datasets are very computationally intensive; hence, it is necessary to integrate high-performance solutions, preferably internet based, to achieve the goals. Secondly, the problems of heterogeneity and inconsistency in geospatial data are well known and affect the data integration process, but is particularly problematic for Big Geo Data. Therefore, Big Geo Data integration will be one of the most challenging issues to solve. With these applications, we demonstrate that is possible to provide processed Big Geo Data to common users, using open geospatial standards and technologies. NoSQL databases like MongoDB and frameworks like RASDAMAN could offer different functionalities that facilitate working with larger volumes and more heterogeneous geospatial data sources.

USA Science and Engineering Festival 2014

NASA Image and Video Library

2014-04-25

An attendee of the USA Science and Engineering Festival examines how glass blocks some heat, altering the infrared image of himself. The James Webb Space Telescope will be a large infrared telescope with a 6.5 meter primary mirror and will study every phase in the history of our Universe ranging from the Big Bang to the formation of our Solar System. The USA Science and Engineering Festival took place at the Washington Convention Center in Washington, DC on April 26 and 27, 2014. Photo Credit: (NASA/Aubrey Gemignani)

USA Science and Engineering Festival 2014

NASA Image and Video Library

2014-04-25

Attendees of the USA Science and Engineering Festival observe their infrared images as a NASA Staff member describes the James Webb Space Telescope. It will be a large infrared telescope with a 6.5 meter primary mirror and will study every phase in the history of our Universe ranging from the Big Bang to the formation of our Solar System. The USA Science and Engineering Festival took place at the Washington Convention Center in Washington, DC on April 26 and 27, 2014. Photo Credit: (NASA/Aubrey Gemignani)

Toward a Big Data Science: A challenge of "Science Cloud"

NASA Astrophysics Data System (ADS)

Murata, Ken T.; Watanabe, Hidenobu

2013-04-01

During these 50 years, along with appearance and development of high-performance computers (and super-computers), numerical simulation is considered to be a third methodology for science, following theoretical (first) and experimental and/or observational (second) approaches. The variety of data yielded by the second approaches has been getting more and more. It is due to the progress of technologies of experiments and observations. The amount of the data generated by the third methodologies has been getting larger and larger. It is because of tremendous development and programming techniques of super computers. Most of the data files created by both experiments/observations and numerical simulations are saved in digital formats and analyzed on computers. The researchers (domain experts) are interested in not only how to make experiments and/or observations or perform numerical simulations, but what information (new findings) to extract from the data. However, data does not usually tell anything about the science; sciences are implicitly hidden in the data. Researchers have to extract information to find new sciences from the data files. This is a basic concept of data intensive (data oriented) science for Big Data. As the scales of experiments and/or observations and numerical simulations get larger, new techniques and facilities are required to extract information from a large amount of data files. The technique is called as informatics as a fourth methodology for new sciences. Any methodologies must work on their facilities: for example, space environment are observed via spacecraft and numerical simulations are performed on super-computers, respectively in space science. The facility of the informatics, which deals with large-scale data, is a computational cloud system for science. This paper is to propose a cloud system for informatics, which has been developed at NICT (National Institute of Information and Communications Technology), Japan. The NICT science cloud, we named as OneSpaceNet (OSN), is the first open cloud system for scientists who are going to carry out their informatics for their own science. The science cloud is not for simple uses. Many functions are expected to the science cloud; such as data standardization, data collection and crawling, large and distributed data storage system, security and reliability, database and meta-database, data stewardship, long-term data preservation, data rescue and preservation, data mining, parallel processing, data publication and provision, semantic web, 3D and 4D visualization, out-reach and in-reach, and capacity buildings. Figure (not shown here) is a schematic picture of the NICT science cloud. Both types of data from observation and simulation are stored in the storage system in the science cloud. It should be noted that there are two types of data in observation. One is from archive site out of the cloud: this is a data to be downloaded through the Internet to the cloud. The other one is data from the equipment directly connected to the science cloud. They are often called as sensor clouds. In the present talk, we first introduce the NICT science cloud. We next demonstrate the efficiency of the science cloud, showing several scientific results which we achieved with this cloud system. Through the discussions and demonstrations, the potential performance of sciences cloud will be revealed for any research fields.

Plant genetic resources for food and agriculture: opportunities and challenges emerging from the science and information technology revolution.

PubMed

Halewood, Michael; Chiurugwi, Tinashe; Sackville Hamilton, Ruaraidh; Kurtz, Brad; Marden, Emily; Welch, Eric; Michiels, Frank; Mozafari, Javad; Sabran, Muhamad; Patron, Nicola; Kersey, Paul; Bastow, Ruth; Dorius, Shawn; Dias, Sonia; McCouch, Susan; Powell, Wayne

2018-03-01

Contents Summary 1407 I. Introduction 1408 II. Technological advances and their utility for gene banks and breeding, and longer-term contributions to SDGs 1408 III. The challenges that must be overcome to realise emerging R&D opportunities 1410 IV. Renewed governance structures for PGR (and related big data) 1413 V. Access and benefit sharing and big data 1416 VI. Conclusion 1417 Acknowledgements 1417 ORCID 1417 References 1417 SUMMARY: Over the last decade, there has been an ongoing revolution in the exploration, manipulation and synthesis of biological systems, through the development of new technologies that generate, analyse and exploit big data. Users of Plant Genetic Resources (PGR) can potentially leverage these capacities to significantly increase the efficiency and effectiveness of their efforts to conserve, discover and utilise novel qualities in PGR, and help achieve the Sustainable Development Goals (SDGs). This review advances the discussion on these emerging opportunities and discusses how taking advantage of them will require data integration and synthesis across disciplinary, organisational and international boundaries, and the formation of multi-disciplinary, international partnerships. We explore some of the institutional and policy challenges that these efforts will face, particularly how these new technologies may influence the structure and role of research for sustainable development, ownership of resources, and access and benefit sharing. We discuss potential responses to political and institutional challenges, ranging from options for enhanced structure and governance of research discovery platforms to internationally brokered benefit-sharing agreements, and identify a set of broad principles that could guide the global community as it seeks or considers solutions. © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.

SETI as a part of Big History

NASA Astrophysics Data System (ADS)

Maccone, Claudio

2014-08-01

Big History is an emerging academic discipline which examines history scientifically from the Big Bang to the present. It uses a multidisciplinary approach based on combining numerous disciplines from science and the humanities, and explores human existence in the context of this bigger picture. It is taught at some universities. In a series of recent papers ([11] through [15] and [17] through [18]) and in a book [16], we developed a new mathematical model embracing Darwinian Evolution (RNA to Humans, see, in particular, [17] and Human History (Aztecs to USA, see [16]) and then we extrapolated even that into the future up to ten million years (see 18), the minimum time requested for a civilization to expand to the whole Milky Way (Fermi paradox). In this paper, we further extend that model in the past so as to let it start at the Big Bang (13.8 billion years ago) thus merging Big History, Evolution on Earth and SETI (the modern Search for ExtraTerrestrial Intelligence) into a single body of knowledge of a statistical type. Our idea is that the Geometric Brownian Motion (GBM), so far used as the key stochastic process of financial mathematics (Black-Sholes models and related 1997 Nobel Prize in Economics!) may be successfully applied to the whole of Big History. In particular, in this paper we derive

Research groups: How big should they be?

PubMed

Cook, Isabelle; Grange, Sam; Eyre-Walker, Adam

2015-01-01

Understanding the relationship between scientific productivity and research group size is important for deciding how science should be funded. We have investigated the relationship between these variables in the life sciences in the United Kingdom using data from 398 principle investigators (PIs). We show that three measures of productivity, the number of publications, the impact factor of the journals in which papers are published and the number of citations, are all positively correlated to group size, although they all show a pattern of diminishing returns-doubling group size leads to less than a doubling in productivity. The relationships for the impact factor and the number of citations are extremely weak. Our analyses suggest that an increase in productivity will be achieved by funding more PIs with small research groups, unless the cost of employing post-docs and PhD students is less than 20% the cost of a PI. We also provide evidence that post-docs are more productive than PhD students both in terms of the number of papers they produce and where those papers are published.

Using agent-based modeling to study multiple risk factors and multiple health outcomes at multiple levels.

PubMed

Yang, Yong

2017-11-01

Most health studies focus on one health outcome and examine the influence of one or multiple risk factors. However, in reality, various pathways, interactions, and associations exist not only between risk factors and health outcomes but also among the risk factors and among health outcomes. The advance of system science methods, Big Data, and accumulated knowledge allows us to examine how multiple risk factors influence multiple health outcomes at multiple levels (termed a 3M study). Using the study of neighborhood environment and health as an example, I elaborate on the significance of 3M studies. 3M studies may lead to a significantly deeper understanding of the dynamic interactions among risk factors and outcomes and could help us design better interventions that may be of particular relevance for upstream interventions. Agent-based modeling (ABM) is a promising method in the 3M study, although its potentials are far from being fully explored. Future challenges include the gap of epidemiologic knowledge and evidence, lack of empirical data sources, and the technical challenges of ABM. © 2017 New York Academy of Sciences.

New Technologies for 21st Century Plant Science

PubMed Central

Ehrhardt, David W.; Frommer, Wolf B.

2012-01-01

Plants are one of the most fascinating and important groups of organisms living on Earth. They serve as the conduit of energy into the biosphere, provide food, and shape our environment. If we want to make headway in understanding how these essential organisms function and build the foundation for a more sustainable future, then we need to apply the most advanced technologies available to the study of plant life. In 2009, a committee of the National Academy highlighted the “understanding of plant growth” as one of the big challenges for society and part of a new era which they termed “new biology.” The aim of this article is to identify how new technologies can and will transform plant science to address the challenges of new biology. We assess where we stand today regarding current technologies, with an emphasis on molecular and imaging technologies, and we try to address questions about where we may go in the future and whether we can get an idea of what is at and beyond the horizon. PMID:22366161

Scientometric indicators for Brazilian research on High Energy Physics, 1983-2013.

PubMed

Alvarez, Gonzalo R; Vanz, Samile A S; Barbosa, Marcia C

2017-01-01

This article presents an analysis of Brazilian research on High Energy Physics (HEP) indexed by Web of Science (WoS) from 1983 to 2013. Scientometric indicators for output, collaboration and impact were used to characterize the field under study. The results show that the Brazilian articles account for 3% of total HEP research worldwide and that the sharp rise in the scientific activity between 2009 and 2013 may have resulted from the consolidation of graduate programs, the increase of the funding and of the international collaboration as well as the implementation of the Rede Nacional de Física de Altas Energias (RENAFAE) in 2008. Our results also indicate that the collaboration patterns in terms of the authors, the institutions and the countries confirm the presence of Brazil in multinational Big Science experiments, which may also explain the prevalence of foreign citing documents (all types), emphasizing the international prestige and visibility of the output of Brazilian scientists. We concluded that the scientometric indicators suggested scientific maturity in the Brazilian HEP community due to its long history of experimental research.

Earth Science Capability Demonstration Project

NASA Technical Reports Server (NTRS)

Cobleigh, Brent

2006-01-01

A viewgraph presentation reviewing the Earth Science Capability Demonstration Project is shown. The contents include: 1) ESCD Project; 2) Available Flight Assets; 3) Ikhana Procurement; 4) GCS Layout; 5) Baseline Predator B Architecture; 6) Ikhana Architecture; 7) UAV Capability Assessment; 8) The Big Picture; 9) NASA/NOAA UAV Demo (5/05 to 9/05); 10) NASA/USFS Western States Fire Mission (8/06); and 11) Suborbital Telepresence.

"But Aren't Diesel Engines Just for Big, Smelly Trucks?" An Interdisciplinary Curriculum Project for High School Chemistry Students

ERIC Educational Resources Information Center

Zoellner, Brian P.; Chant, Richard H.; Wood, Kelly

2014-01-01

In a collaboration between the University of North Florida College of Education and Human Services and Sandalwood High School in Duval County, Florida, social studies and science education professors and a science teacher worked together to develop student understanding about the limited use of diesel-fueled cars in the United States when compared…

Developing Low-Cost Solutions to Improve Public Policy: The Work of MDRC's Center for Applied Behavioral Science. Issue Focus

ERIC Educational Resources Information Center

MDRC, 2016

2016-01-01

Many social policy and education programs start from the assumption that people act in their best interest. But behavioral science shows that people often weigh intuition over reason, make inconsistent choices, and put off big decisions. The individuals and families who need services and the staff who provide them are no exception. From city…

Using More than 10% of Our Brains: Examining Belief in Science-Related Myths from an Individual Differences Perspective

ERIC Educational Resources Information Center

Swami, Viren; Stieger, Stefan; Pietschnig, Jakob; Nader, Ingo W.; Voracek, Martin

2012-01-01

There currently exists a dearth of research on the transmission and assimilation of myths. To overcome this limitation, we developed a novel scale that measures belief in science-related myths. A total of 363 participants completed this new scale along with measures of personality (the Big Five factors), anti-scientific attitudes, and New Age…

[Big Data- challenges and risks].

PubMed

Krauß, Manuela; Tóth, Tamás; Hanika, Heinrich; Kozlovszky, Miklós; Dinya, Elek

2015-12-06

The term "Big Data" is commonly used to describe the growing mass of information being created recently. New conclusions can be drawn and new services can be developed by the connection, processing and analysis of these information. This affects all aspects of life, including health and medicine. The authors review the application areas of Big Data, and present examples from health and other areas. However, there are several preconditions of the effective use of the opportunities: proper infrastructure, well defined regulatory environment with particular emphasis on data protection and privacy. These issues and the current actions for solution are also presented.

Sources of Evidence-of-Learning: Learning and Assessment in the Era of Big Data

ERIC Educational Resources Information Center

Cope, Bill; Kalantzis, Mary

2015-01-01

This article sets out to explore a shift in the sources of evidence-of-learning in the era of networked computing. One of the key features of recent developments has been popularly characterized as "big data". We begin by examining, in general terms, the frame of reference of contemporary debates on machine intelligence and the role of…

Non-Cognitive Traits That Impact Female Success in BigLaw

ERIC Educational Resources Information Center

Hogan, Milana Lauren

2013-01-01

In spite of the fact that women account for nearly half of the lawyers entering BigLaw, there are significantly fewer women occupying the most prestigious, powerful, and best-paid positions within today's law firms. This paper focuses on the non-cognitive traits known as grit--defined as perseverance and passion for long-term goals--and a growth…

Harnessing big data for health care and research: are urologists ready?

PubMed

Ghani, Khurshid R; Zheng, Kai; Wei, John T; Friedman, Charles P

2014-12-01

We are at a crossroads in terms of the data we collect and how they are analyzed. In health care, big data analytics may uncover associations, patterns, and trends with the potential to advance patient care and lower costs. To adapt to this approach, urologists will have to ask the right questions. Published by Elsevier B.V.

Long-term effects of traumatic experience: Comparison study in the adolescent IDPs in Serbia.

PubMed

Matsunaga, Chieko; Ristic, Dragana; Niregi, Mitsuki

2006-12-01

The purpose of this study is to examine the long term psychological effects of war stress regarded as traumatic experience. The subjects are Serbian internally displaced people (IDP) of adolescent population from Kosovo. It is a very big concern whether the adolescents would overcome the social and psychological difficulties caused by the war stress in order to reconstruct the better society. The result came out that the long-term effects still exist in PTSD, depression and hopelessness, which affects self-esteem and the attitude in purpose in life that are important factors for personality development. This paper also examines the difference between IDPs with war stress and the adolescent sufferers of the big earthquake in Japan.

Making big sense from big data in toxicology by read-across.

PubMed

Hartung, Thomas

2016-01-01

Modern information technologies have made big data available in safety sciences, i.e., extremely large data sets that may be analyzed only computationally to reveal patterns, trends and associations. This happens by (1) compilation of large sets of existing data, e.g., as a result of the European REACH regulation, (2) the use of omics technologies and (3) systematic robotized testing in a high-throughput manner. All three approaches and some other high-content technologies leave us with big data--the challenge is now to make big sense of these data. Read-across, i.e., the local similarity-based intrapolation of properties, is gaining momentum with increasing data availability and consensus on how to process and report it. It is predominantly applied to in vivo test data as a gap-filling approach, but can similarly complement other incomplete datasets. Big data are first of all repositories for finding similar substances and ensure that the available data is fully exploited. High-content and high-throughput approaches similarly require focusing on clusters, in this case formed by underlying mechanisms such as pathways of toxicity. The closely connected properties, i.e., structural and biological similarity, create the confidence needed for predictions of toxic properties. Here, a new web-based tool under development called REACH-across, which aims to support and automate structure-based read-across, is presented among others.

Infectious Disease Surveillance in the Big Data Era: Towards Faster and Locally Relevant Systems.

PubMed

Simonsen, Lone; Gog, Julia R; Olson, Don; Viboud, Cécile

2016-12-01

While big data have proven immensely useful in fields such as marketing and earth sciences, public health is still relying on more traditional surveillance systems and awaiting the fruits of a big data revolution. A new generation of big data surveillance systems is needed to achieve rapid, flexible, and local tracking of infectious diseases, especially for emerging pathogens. In this opinion piece, we reflect on the long and distinguished history of disease surveillance and discuss recent developments related to use of big data. We start with a brief review of traditional systems relying on clinical and laboratory reports. We then examine how large-volume medical claims data can, with great spatiotemporal resolution, help elucidate local disease patterns. Finally, we review efforts to develop surveillance systems based on digital and social data streams, including the recent rise and fall of Google Flu Trends. We conclude by advocating for increased use of hybrid systems combining information from traditional surveillance and big data sources, which seems the most promising option moving forward. Throughout the article, we use influenza as an exemplar of an emerging and reemerging infection which has traditionally been considered a model system for surveillance and modeling. Published by Oxford University Press for the Infectious Diseases Society of America 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

[Big data approaches in psychiatry: examples in depression research].

PubMed

Bzdok, D; Karrer, T M; Habel, U; Schneider, F

2017-11-29

The exploration and therapy of depression is aggravated by heterogeneous etiological mechanisms and various comorbidities. With the growing trend towards big data in psychiatry, research and therapy can increasingly target the individual patient. This novel objective requires special methods of analysis. The possibilities and challenges of the application of big data approaches in depression are examined in closer detail. Examples are given to illustrate the possibilities of big data approaches in depression research. Modern machine learning methods are compared to traditional statistical methods in terms of their potential in applications to depression. Big data approaches are particularly suited to the analysis of detailed observational data, the prediction of single data points or several clinical variables and the identification of endophenotypes. A current challenge lies in the transfer of results into the clinical treatment of patients with depression. Big data approaches enable biological subtypes in depression to be identified and predictions in individual patients to be made. They have enormous potential for prevention, early diagnosis, treatment choice and prognosis of depression as well as for treatment development.

[Fingers, toes and thumbs, correct digital nomenclature based on early Hebrew texts].

PubMed

Leibner, Efraim D; London, Eli; Elishoov, Ofer

2005-08-01

There is some dissonance as to the correct Hebrew terms for the digits of the extremities. Terms in common use include 'Etzba, 'Bohen' and 'Agudal'. While most agree that 'Etzba' in the singular represents the index finger, there is debate about the plural (Etzba'ot), whether it represents 'fingers' (upper extremity only) or 'digits' (upper and lower). The meaning of 'Bohen' is disputed as well, with proponents existing for it to represent: 'Toe', 'Big Toe' or 'Big Digit'. 'Agudal' is in the same predicament, with uses as 'Thumb' or 'Big Digit'. We undertook a computerized search of the Bible for these words and their derivatives in order to establish their correct use. The term 'Etzba' and its derivatives appeared numerous times in the scriptures both in singular and in plural. 'Bohen' appeared somewhat less, however, all appearances were in conjunction, viz" 'Bohen' of the hand" or " 'Bohen' of the foot". 'Agudal' was not found in our computerized search. According to the early Hebrew texts 'Etzba' in singular usually represents the index finger. However, the plural form 'Etzba'ot', corresponds to the term 'digits' and may be used both for fingers and toes. 'Bohen' is a term representing the large digit of all extremities, i.e. both 'Thumb' and 'Hallux'. Likewise, the term 'Agudal', while not appearing in the scriptures, appears in later contexts in early Hebrew texts, and also represents both the thumb and the hallux.