Generation of a precise DEM by interactive synthesis of multi-temporal elevation datasets: a case study of Schirmacher Oasis, East Antarctica

NASA Astrophysics Data System (ADS)

Jawak, Shridhar D.; Luis, Alvarinho J.

2016-05-01

Digital elevation model (DEM) is indispensable for analysis such as topographic feature extraction, ice sheet melting, slope stability analysis, landscape analysis and so on. Such analysis requires a highly accurate DEM. Available DEMs of Antarctic region compiled by using radar altimetry and the Antarctic digital database indicate elevation variations of up to hundreds of meters, which necessitates the generation of local improved DEM. An improved DEM of the Schirmacher Oasis, East Antarctica has been generated by synergistically fusing satellite-derived laser altimetry data from Geoscience Laser Altimetry System (GLAS), Radarsat Antarctic Mapping Project (RAMP) elevation data and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global elevation data (GDEM). This is a characteristic attempt to generate a DEM of any part of Antarctica by fusing multiple elevation datasets, which is essential to model the ice elevation change and address the ice mass balance. We analyzed a suite of interpolation techniques for constructing a DEM from GLAS, RAMP and ASTER DEM-based point elevation datasets, in order to determine the level of confidence with which the interpolation techniques can generate a better interpolated continuous surface, and eventually improve the elevation accuracy of DEM from synergistically fused RAMP, GLAS and ASTER point elevation datasets. The DEM presented in this work has a vertical accuracy (≈ 23 m) better than RAMP DEM (≈ 57 m) and ASTER DEM (≈ 64 m) individually. The RAMP DEM and ASTER DEM elevations were corrected using differential GPS elevations as ground reference data, and the accuracy obtained after fusing multitemporal datasets is found to be improved than that of existing DEMs constructed by using RAMP or ASTER alone. This is our second attempt of fusing multitemporal, multisensory and multisource elevation data to generate a DEM of Antarctica, in order to address the ice elevation change and address the ice mass

Evaluation of catchment delineation methods for the medium-resolution National Hydrography Dataset

USGS Publications Warehouse

Johnston, Craig M.; Dewald, Thomas G.; Bondelid, Timothy R.; Worstell, Bruce B.; McKay, Lucinda D.; Rea, Alan; Moore, Richard B.; Goodall, Jonathan L.

2009-01-01

Different methods for determining catchments (incremental drainage areas) for stream segments of the medium-resolution (1:100,000-scale) National Hydrography Dataset (NHD) were evaluated by the U.S. Geological Survey (USGS), in cooperation with the U.S. Environmental Protection Agency (USEPA). The NHD is a comprehensive set of digital spatial data that contains information about surface-water features (such as lakes, ponds, streams, and rivers) of the United States. The need for NHD catchments was driven primarily by the goal to estimate NHD streamflow and velocity to support water-quality modeling. The application of catchments for this purpose also demonstrates the broader value of NHD catchments for supporting landscape characterization and analysis. Five catchment delineation methods were evaluated. Four of the methods use topographic information for the delineation of the NHD catchments. These methods include the Raster Seeding Method; two variants of a method first used in a USGS New England study-one used the Watershed Boundary Dataset (WBD) and the other did not-termed the 'New England Methods'; and the Outlet Matching Method. For these topographically based methods, the elevation data source was the 30-meter (m) resolution National Elevation Dataset (NED), as this was the highest resolution available for the conterminous United States and Hawaii. The fifth method evaluated, the Thiessen Polygon Method, uses distance to the nearest NHD stream segments to determine catchment boundaries. Catchments were generated using each method for NHD stream segments within six hydrologically and geographically distinct Subbasins to evaluate the applicability of the method across the United States. The five methods were evaluated by comparing the resulting catchments with the boundaries and the computed area measurements available from several verification datasets that were developed independently using manual methods. The results of the evaluation indicated that the two

National Hydropower Plant Dataset, Version 2 (FY18Q3)

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

Samu, Nicole; Kao, Shih-Chieh; O'Connor, Patrick

The National Hydropower Plant Dataset, Version 2 (FY18Q3) is a geospatially comprehensive point-level dataset containing locations and key characteristics of U.S. hydropower plants that are currently either in the hydropower development pipeline (pre-operational), operational, withdrawn, or retired. These data are provided in GIS and tabular formats with corresponding metadata for each. In addition, we include access to download 2 versions of the National Hydropower Map, which was produced with these data (i.e. Map 1 displays the geospatial distribution and characteristics of all operational hydropower plants; Map 2 displays the geospatial distribution and characteristics of operational hydropower plants with pumped storagemore » and mixed capabilities only). This dataset is a subset of ORNL's Existing Hydropower Assets data series, updated quarterly as part of ORNL's National Hydropower Asset Assessment Program.« less

National Enhanced Elevation Assessment at a glance

USGS Publications Warehouse

Snyder, Gregory I.

2012-01-01

Elevation data are essential for hazards mitigation, conservation, infrastructure development, national security, and many other applications. Under the leadership of the U.S. Geological Survey and the member States of the National Digital Elevation Program (NDEP), Federal agencies, State agencies, and others work together to acquire high-quality elevation data for the United States and its territories. New elevation data are acquired using modern technology to replace elevation data that are, on average, more than 30 years old. Through the efforts of the NDEP, a project-by-project data acquisition approach resulted in improved, publicly available data for 28 percent of the conterminous United States and 15 percent of Alaska over the past 15 years. Although the program operates efficiently, the rate of data collection and the typical project specifications are currently insufficient to address the needs of government, the private sector, and other organizations. The National Enhanced Elevation Assessment was conducted to (1) document national-level requirements for improved elevation data, (2) estimate the benefits and costs of meeting those requirements, and (3) evaluate multiple national-level program-implementation scenarios. The assessment was sponsored by the NDEP's member agencies. The study participants came from 34 Federal agencies, agencies from all 50 States, selected local government and Tribal offices, and private and not-for-profit organizations. A total of 602 mission-critical activities were identified that need significantly more accurate data than are currently available. The results of the assessment indicate that a national-level enhanced-elevation-data program has the potential to generate from $1.2 billion to $13 billion in new benefits annually.

The Wind Integration National Dataset (WIND) toolkit (Presentation)

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

Caroline Draxl: NREL

2014-01-01

Regional wind integration studies require detailed wind power output data at many locations to perform simulations of how the power system will operate under high penetration scenarios. The wind datasets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as being time synchronized with available load profiles.As described in this presentation, the WIND Toolkit fulfills these requirements by providing a state-of-the-art national (US) wind resource, power production and forecast dataset.

The LANDFIRE Refresh strategy: updating the national dataset

USGS Publications Warehouse

Nelson, Kurtis J.; Connot, Joel A.; Peterson, Birgit E.; Martin, Charley

2013-01-01

The LANDFIRE Program provides comprehensive vegetation and fuel datasets for the entire United States. As with many large-scale ecological datasets, vegetation and landscape conditions must be updated periodically to account for disturbances, growth, and natural succession. The LANDFIRE Refresh effort was the first attempt to consistently update these products nationwide. It incorporated a combination of specific systematic improvements to the original LANDFIRE National data, remote sensing based disturbance detection methods, field collected disturbance information, vegetation growth and succession modeling, and vegetation transition processes. This resulted in the creation of two complete datasets for all 50 states: LANDFIRE Refresh 2001, which includes the systematic improvements, and LANDFIRE Refresh 2008, which includes the disturbance and succession updates to the vegetation and fuel data. The new datasets are comparable for studying landscape changes in vegetation type and structure over a decadal period, and provide the most recent characterization of fuel conditions across the country. The applicability of the new layers is discussed and the effects of using the new fuel datasets are demonstrated through a fire behavior modeling exercise using the 2011 Wallow Fire in eastern Arizona as an example.

LANDFIRE 2010—Updates to the national dataset to support improved fire and natural resource management

USGS Publications Warehouse

Nelson, Kurtis J.; Long, Donald G.; Connot, Joel A.

2016-02-29

The Landscape Fire and Resource Management Planning Tools (LANDFIRE) 2010 data release provides updated and enhanced vegetation, fuel, and fire regime layers consistently across the United States. The data represent landscape conditions from approximately 2010 and are the latest release in a series of planned updates to maintain currency of LANDFIRE data products. Enhancements to the data products included refinement of urban areas by incorporating the National Land Cover Database 2006 land cover product, refinement of agricultural lands by integrating the National Agriculture Statistics Service 2011 cropland data layer, and improved wetlands delineations using the National Land Cover Database 2006 land cover and the U.S. Fish and Wildlife Service National Wetlands Inventory data. Disturbance layers were generated for years 2008 through 2010 using remotely sensed imagery, polygons representing disturbance events submitted by local organizations, and fire mapping program data such as the Monitoring Trends in Burn Severity perimeters produced by the U.S. Geological Survey and the U.S. Forest Service. Existing vegetation data were updated to account for transitions in disturbed areas and to account for vegetation growth and succession in undisturbed areas. Surface and canopy fuel data were computed from the updated vegetation type, cover, and height and occasionally from potential vegetation. Historical fire frequency and succession classes were also updated. Revised topographic layers were created based on updated elevation data from the National Elevation Dataset. The LANDFIRE program also released a new Web site offering updated content, enhanced usability, and more efficient navigation.

Wind Integration National Dataset (WIND) Toolkit; NREL (National Renewable Energy Laboratory)

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

Draxl, Caroline; Hodge, Bri-Mathias

A webinar about the Wind Integration National Dataset (WIND) Toolkit was presented by Bri-Mathias Hodge and Caroline Draxl on July 14, 2015. It was hosted by the Southern Alliance for Clean Energy. The toolkit is a grid integration data set that contains meteorological and power data at a 5-minute resolution across the continental United States for 7 years and hourly power forecasts.

Crowdsourced Contributions to the Nation's Geodetic Elevation Infrastructure

NASA Astrophysics Data System (ADS)

Stone, W. A.

2014-12-01

NOAA's National Geodetic Survey (NGS), a United States Department of Commerce agency, is engaged in providing the nation's fundamental positioning infrastructure - the National Spatial Reference System (NSRS) - which includes the framework for latitude, longitude, and elevation determination as well as various geodetic models, tools, and data. Capitalizing on Global Navigation Satellite System (GNSS) technology for improved access to the nation's precise geodetic elevation infrastructure requires use of a geoid model, which relates GNSS-derived heights (ellipsoid heights) with traditional elevations (orthometric heights). NGS is facilitating the use of crowdsourced GNSS observations collected at published elevation control stations by the professional surveying, geospatial, and scientific communities to help improve NGS' geoid modeling capability. This collocation of published elevation data and newly collected GNSS data integrates together the two height systems. This effort in turn supports enhanced access to accurate elevation information across the nation, thereby benefiting all users of geospatial data. By partnering with the public in this collaborative effort, NGS is not only helping facilitate improvements to the elevation infrastructure for all users but also empowering users of NSRS with the capability to do their own high-accuracy positioning. The educational outreach facet of this effort helps inform the public, including the scientific community, about the utility of various NGS tools, including the widely used Online Positioning User Service (OPUS). OPUS plays a key role in providing user-friendly and high accuracy access to NSRS, with optional sharing of results with NGS and the public. All who are interested in helping evolve and improve the nationwide elevation determination capability are invited to participate in this nationwide partnership and to learn more about the geodetic infrastructure which is a vital component of viable spatial data for

The benefits of improved national elevation data

USGS Publications Warehouse

Snyder, Gregory I.

2013-01-01

This article describes how the National Enhanced Elevation Assessment (NEEA) has identified substantial benefits that could come about if improved elevation data were publicly available for current and emerging applications and business uses such as renewable energy, precision agriculture, and intelligent vehicle navigation and safety. In order to support these diverse needs, new national elevation data with higher resolution and accuracy are needed. The 3D Elevation Program (3DEP) initiative was developed to meet the majority of these needs and it is expected that 3DEP will result in new, unimagined information services that would result in job growth and the transformation of the geospatial community. Private-sector data collection companies are continuously evolving sensors and positioning technologies that are needed to collect improved elevation data. An initiative of this scope might also provide an opportunity for companies to improve their capabilities and produce even higher data quality and consistency at a pace that might not have otherwise occurred.

The Landcover Impact on the Aspect/Slope Accuracy Dependence of the SRTM-1 Elevation Data for the Humboldt Range.

PubMed

Miliaresis, George C

2008-05-15

The U.S. National Landcover Dataset (NLCD) and the U.S National Elevation Dataset (NED) (bare earth elevations) were used in an attempt to assess to what extent the directional and slope dependency of the Shuttle Radar Topography Mission (SRTM) finished digital elevation model is affected by landcover. Four landcover classes: forest, shrubs, grass and snow cover, were included in the study area (Humboldt Range in NW portion of Nevada, USA). Statistics, rose diagrams, and frequency distributions of the elevation differences (NED-SRTM) per landcover class per geographic direction were used. The decomposition of elevation differences on the basis of aspect and slope terrain classes identifies a) over-estimation of elevation by the SRTM instrument along E, NE and N directions (negative elevation difference that decreases linearly with slope) while b) underestimation is evident towards W, SW and S directions (positive elevation difference increasing with slope). The aspect/slope/landcover elevation differences modelling overcome the systematic errors evident in the SRTM dataset and revealed vegetation height information and the snow penetration capability of the SRTM instrument. The linear regression lines per landcover class might provide means of correcting the systematic error (aspect/slope dependency) evident in SRTM dataset.

High-resolution digital elevation dataset for Crater Lake National Park and vicinity, Oregon, based on LiDAR survey of August-September 2010 and bathymetric survey of July 2000

USGS Publications Warehouse

Robinson, Joel E.

2012-01-01

Crater Lake partially fills the caldera that formed approximately 7,700 years ago during the eruption of a 12,000-foot volcano known as Mount Mazama. The caldera-forming or climactic eruption of Mount Mazama devastated the surrounding landscape, left a thick deposit of pumice and ash in adjacent valleys, and spread a blanket of volcanic ash as far away as southern Canada. Because the Crater Lake region is potentially volcanically active, knowledge of past events is important to understanding hazards from future eruptions. Similarly, because the area is seismically active, documenting and evaluating geologic faults is critical to assessing hazards from earthquakes. As part of the American Recovery and Reinvestment Act (ARRA) of 2009, the U.S. Geological Survey was awarded funding for high-precision airborne LiDAR (Light Detection And Ranging) data collection at several volcanoes in the Cascade Range through the Oregon LiDAR Consortium, administered by the Oregon Department of Geology and Mineral Industries (DOGAMI). The Oregon LiDAR Consortium contracted with Watershed Sciences, Inc., to conduct the data collection surveys. Collaborating agencies participating with the Oregon LiDAR Consortium for data collection in the Crater Lake region include Crater Lake National Park (National Park Service) and the Federal Highway Administration. In the immediate vicinity of Crater Lake National Park, 798 square kilometers of LiDAR data were collected, providing a digital elevation dataset of the ground surface beneath forest cover with an average resolution of 1.6 laser returns/m2 and both vertical and horizontal accuracies of ±5 cm. The LiDAR data were mosaicked in this report with bathymetry of the lake floor of Crater Lake, collected in 2000 using high-resolution multibeam sonar in a collaborative effort between the U.S. Geological Survey, Crater Lake National Park, and the Center for Coastal and Ocean Mapping at the University of New Hampshire. The bathymetric survey

The 3D Elevation Program national indexing scheme

USGS Publications Warehouse

Thatcher, Cindy A.; Heidemann, Hans Karl; Stoker, Jason M.; Eldridge, Diane F.

2017-11-02

The 3D Elevation Program (3DEP) of the U.S. Geological Survey (USGS) acquires high-resolution elevation data for the Nation. This program has been operating under an opportunity-oriented approach, acquiring light detection and ranging (lidar) projects of varying sizes scattered across the United States. As a result, the national 3DEP elevation layer is subject to data gaps or unnecessary overlap between adjacent collections. To mitigate this problem, 3DEP is adopting a strategic, systematic approach to national data acquisition that will create efficiencies in efforts to achieve nationwide elevation data coverage and help capture additional Federal and non-Federal investments resulting from advance awareness of proposed acquisitions and partnership opportunities. The 3DEP Working Group, an interagency group managed by the USGS, has agreed that all future 3DEP collections within the lower 48 States should be coordinated by using a 1-kilometer by 1-kilometer tiling scheme for the conterminous United States. Fiscal Year 2018 is being considered a transition year, and in Fiscal Year 2019 the national indexing scheme will be fully implemented, so that all 3DEP-supported projects will be acquired and delivered in the national indexing scheme and projected into the Albers Equal Area projection. 

The Landcover Impact on the Aspect/Slope Accuracy Dependence of the SRTM-1 Elevation Data for the Humboldt Range

PubMed Central

Miliaresis, George C.

2008-01-01

The U.S. National Landcover Dataset (NLCD) and the U.S National Elevation Dataset (NED) (bare earth elevations) were used in an attempt to assess to what extent the directional and slope dependency of the Shuttle Radar Topography Mission (SRTM) finished digital elevation model is affected by landcover. Four landcover classes: forest, shrubs, grass and snow cover, were included in the study area (Humboldt Range in NW portion of Nevada, USA). Statistics, rose diagrams, and frequency distributions of the elevation differences (NED-SRTM) per landcover class per geographic direction were used. The decomposition of elevation differences on the basis of aspect and slope terrain classes identifies a) over-estimation of elevation by the SRTM instrument along E, NE and N directions (negative elevation difference that decreases linearly with slope) while b) underestimation is evident towards W, SW and S directions (positive elevation difference increasing with slope). The aspect/slope/landcover elevation differences modelling overcome the systematic errors evident in the SRTM dataset and revealed vegetation height information and the snow penetration capability of the SRTM instrument. The linear regression lines per landcover class might provide means of correcting the systematic error (aspect/slope dependency) evident in SRTM dataset. PMID:27879870

A method for mapping corn using the US Geological Survey 1992 National Land Cover Dataset

USGS Publications Warehouse

Maxwell, S.K.; Nuckols, J.R.; Ward, M.H.

2006-01-01

Long-term exposure to elevated nitrate levels in community drinking water supplies has been associated with an elevated risk of several cancers including non-Hodgkin's lymphoma, colon cancer, and bladder cancer. To estimate human exposure to nitrate, specific crop type information is needed as fertilizer application rates vary widely by crop type. Corn requires the highest application of nitrogen fertilizer of crops grown in the Midwest US. We developed a method to refine the US Geological Survey National Land Cover Dataset (NLCD) (including map and original Landsat images) to distinguish corn from other crops. Overall average agreement between the resulting corn and other row crops class and ground reference data was 0.79 kappa coefficient with individual Landsat images ranging from 0.46 to 0.93 kappa. The highest accuracies occurred in Regions where corn was the single dominant crop (greater than 80.0%) and the crop vegetation conditions at the time of image acquisition were optimum for separation of corn from all other crops. Factors that resulted in lower accuracies included the accuracy of the NLCD map, accuracy of corn areal estimates, crop mixture, crop condition at the time of Landsat overpass, and Landsat scene anomalies.

National Hydropower Plant Dataset, Version 1 (Update FY18Q2)

DOE Data Explorer

Samu, Nicole; Kao, Shih-Chieh; O'Connor, Patrick; Johnson, Megan; Uria-Martinez, Rocio; McManamay, Ryan

2016-09-30

The National Hydropower Plant Dataset, Version 1, Update FY18Q2, includes geospatial point-level locations and key characteristics of existing hydropower plants in the United States that are currently online. These data are a subset extracted from NHAAP’s Existing Hydropower Assets (EHA) dataset, which is a cornerstone of NHAAP’s EHA effort that has supported multiple U.S. hydropower R&D research initiatives related to market acceleration, environmental impact reduction, technology-to-market activities, and climate change impact assessment.

Can a national dataset generate a nomogram for necrotizing enterocolitis onset?

PubMed

Gordon, P V; Clark, R; Swanson, J R; Spitzer, A

2014-10-01

Mother's own milk and donor human milk use is increasing as a means of necrotizing enterocolitis (NEC) prevention. Early onset of enteral feeding has been associated with improvement of many outcomes but has not been shown to reduce the incidence of NEC. Better definition of the window of risk for NEC by gestational strata should improve resource management with respect to donor human milk and enhance our understanding of NEC timing and pathogenesis. Our objective was to establish a NEC dataset of sufficient size and quality, then build a generalizable model of NEC onset from the dataset across gestational strata. We used de-identified data from the Pediatrix national dataset and filtered out all diagnostic confounders that could be identified by either specific diagnoses or logical exclusions (example dual diagnoses), with a specific focus on NEC and spontaneous intestinal perforation (SIP) as the outcomes of interest. The median day of onset was plotted against the gestational age for each of these diagnoses and analyzed for similarities and differences in the day of diagnosis. Onset time of medical NEC was inversely proportional to gestation in a linear relationship across all gestational ages. We found the medical NEC dataset displayed characteristics most consistent with a homogeneous disease entity, whereas there was a skew towards early presentation in the youngest gestation groups of surgical NEC (suggesting probable SIP contamination). Our national dataset demonstrates that NEC onset occurs in an inverse stereotypic, linear relationship with gestational age at birth. Medical NEC is the most reliable sub-cohort for the purpose of determining the temporal window of NEC risk.

The French Muséum national d'histoire naturelle vascular plant herbarium collection dataset

NASA Astrophysics Data System (ADS)

Le Bras, Gwenaël; Pignal, Marc; Jeanson, Marc L.; Muller, Serge; Aupic, Cécile; Carré, Benoît; Flament, Grégoire; Gaudeul, Myriam; Gonçalves, Claudia; Invernón, Vanessa R.; Jabbour, Florian; Lerat, Elodie; Lowry, Porter P.; Offroy, Bérangère; Pimparé, Eva Pérez; Poncy, Odile; Rouhan, Germinal; Haevermans, Thomas

2017-02-01

We provide a quantitative description of the French national herbarium vascular plants collection dataset. Held at the Muséum national d'histoire naturelle, Paris, it currently comprises records for 5,400,000 specimens, representing 90% of the estimated total of specimens. Ninety nine percent of the specimen entries are linked to one or more images and 16% have field-collecting information available. This major botanical collection represents the results of over three centuries of exploration and study. The sources of the collection are global, with a strong representation for France, including overseas territories, and former French colonies. The compilation of this dataset was made possible through numerous national and international projects, the most important of which was linked to the renovation of the herbarium building. The vascular plant collection is actively expanding today, hence the continuous growth exhibited by the dataset, which can be fully accessed through the GBIF portal or the MNHN database portal (available at: https://science.mnhn.fr/institution/mnhn/collection/p/item/search/form). This dataset is a major source of data for systematics, global plants macroecological studies or conservation assessments.

The French Muséum national d'histoire naturelle vascular plant herbarium collection dataset.

PubMed

Le Bras, Gwenaël; Pignal, Marc; Jeanson, Marc L; Muller, Serge; Aupic, Cécile; Carré, Benoît; Flament, Grégoire; Gaudeul, Myriam; Gonçalves, Claudia; Invernón, Vanessa R; Jabbour, Florian; Lerat, Elodie; Lowry, Porter P; Offroy, Bérangère; Pimparé, Eva Pérez; Poncy, Odile; Rouhan, Germinal; Haevermans, Thomas

2017-02-14

We provide a quantitative description of the French national herbarium vascular plants collection dataset. Held at the Muséum national d'histoire naturelle, Paris, it currently comprises records for 5,400,000 specimens, representing 90% of the estimated total of specimens. Ninety nine percent of the specimen entries are linked to one or more images and 16% have field-collecting information available. This major botanical collection represents the results of over three centuries of exploration and study. The sources of the collection are global, with a strong representation for France, including overseas territories, and former French colonies. The compilation of this dataset was made possible through numerous national and international projects, the most important of which was linked to the renovation of the herbarium building. The vascular plant collection is actively expanding today, hence the continuous growth exhibited by the dataset, which can be fully accessed through the GBIF portal or the MNHN database portal (available at: https://science.mnhn.fr/institution/mnhn/collection/p/item/search/form). This dataset is a major source of data for systematics, global plants macroecological studies or conservation assessments.

The French Muséum national d’histoire naturelle vascular plant herbarium collection dataset

PubMed Central

Le Bras, Gwenaël; Pignal, Marc; Jeanson, Marc L.; Muller, Serge; Aupic, Cécile; Carré, Benoît; Flament, Grégoire; Gaudeul, Myriam; Gonçalves, Claudia; Invernón, Vanessa R.; Jabbour, Florian; Lerat, Elodie; Lowry, Porter P.; Offroy, Bérangère; Pimparé, Eva Pérez; Poncy, Odile; Rouhan, Germinal; Haevermans, Thomas

2017-01-01

We provide a quantitative description of the French national herbarium vascular plants collection dataset. Held at the Muséum national d’histoire naturelle, Paris, it currently comprises records for 5,400,000 specimens, representing 90% of the estimated total of specimens. Ninety nine percent of the specimen entries are linked to one or more images and 16% have field-collecting information available. This major botanical collection represents the results of over three centuries of exploration and study. The sources of the collection are global, with a strong representation for France, including overseas territories, and former French colonies. The compilation of this dataset was made possible through numerous national and international projects, the most important of which was linked to the renovation of the herbarium building. The vascular plant collection is actively expanding today, hence the continuous growth exhibited by the dataset, which can be fully accessed through the GBIF portal or the MNHN database portal (available at: https://science.mnhn.fr/institution/mnhn/collection/p/item/search/form). This dataset is a major source of data for systematics, global plants macroecological studies or conservation assessments. PMID:28195585

Development of large scale riverine terrain-bathymetry dataset by integrating NHDPlus HR with NED,CoNED and HAND data

NASA Astrophysics Data System (ADS)

Li, Z.; Clark, E. P.

2017-12-01

Large scale and fine resolution riverine bathymetry data is critical for flood inundation modelingbut not available over the continental United States (CONUS). Previously we implementedbankfull hydraulic geometry based approaches to simulate bathymetry for individual riversusing NHDPlus v2.1 data and 10 m National Elevation Dataset (NED). USGS has recentlydeveloped High Resolution NHD data (NHDPlus HR Beta) (USGS, 2017), and thisenhanced dataset has a significant improvement on its spatial correspondence with 10 m DEM.In this study, we used this high resolution data, specifically NHDFlowline and NHDArea,to create bathymetry/terrain for CONUS river channels and floodplains. A software packageNHDPlus Inundation Modeler v5.0 Beta was developed for this project as an Esri ArcGIShydrological analysis extension. With the updated tools, raw 10 m DEM was first hydrologicallytreated to remove artificial blockages (e.g., overpasses, bridges and eve roadways, etc.) usinglow pass moving window filters. Cross sections were then automatically constructed along eachflowline to extract elevation from the hydrologically treated DEM. In this study, river channelshapes were approximated using quadratic curves to reduce uncertainties from commonly usedtrapezoids. We calculated underneath water channel elevation at each cross section samplingpoint using bankfull channel dimensions that were estimated from physiographicprovince/division based regression equations (Bieger et al. 2015). These elevation points werethen interpolated to generate bathymetry raster. The simulated bathymetry raster wasintegrated with USGS NED and Coastal National Elevation Database (CoNED) (whereveravailable) to make seamless terrain-bathymetry dataset. Channel bathymetry was alsointegrated to the HAND (Height above Nearest Drainage) dataset to improve large scaleinundation modeling. The generated terrain-bathymetry was processed at WatershedBoundary Dataset Hydrologic Unit 4 (WBDHU4) level.

Topogrid Derived 10 Meter Resolution Digital Elevation Model of the Shenandoah National Park and Surrounding Region, Virginia

USGS Publications Warehouse

Chirico, Peter G.; Tanner, Seth D.

2004-01-01

Explanation The purpose of developing a new 10m resolution DEM of the Shenandoah National Park Region was to more accurately depict geologic structure, surfical geology, and landforms of the Shenandoah National Park Region in preparation for automated landform classification. Previously, only a 30m resolution DEM was available through the National Elevation Dataset (NED). During production of the Shenandoah10m DEM of the Park the Geography Discipline of the USGS completed a revised 10m DEM to be included into the NED. However, different methodologies were used to produce the two similar DEMs. The ANUDEM algorithm was used to develop the Shenadoah DEM data. This algorithm allows for the inclusion of contours, streams, rivers, lake and water body polygons as well as spot height data to control the elevation model. A statistical analysis using over 800 National Geodetic Survey (NGS) first and second order vertical control points reveals that the Shenandoah10m DEM, produced as a part of the Appalachian Blue Ridge Landscape project, has a vertical accuracy of ?4.87 meters. The metadata for the 10m NED data reports a vertical accuracy of ?7m. A table listing the NGS control points, the elevation comparison, and the RMSE for the Shenandoah10m DEM is provided. The process of automated terrain classification involves developing statistical signatures from the DEM for each type of surficial deposit and landform type. The signature will be a measure of several characteristics derived from the elevation data including slope, aspect, planform curvature, and profile curvature. The quality of the DEM is of critical importance when extracting terrain signatures. The highest possible horizontal and vertical accuracy is required. The more accurate Shenandoah 10m DEM can now be analyzed and integrated with the geologic observations to yield statistical correlations between the two in the development of landform and surface geology mapping projects.

USGS Mineral Resources Program; national maps and datasets for research and land planning

USGS Publications Warehouse

Nicholson, S.W.; Stoeser, D.B.; Ludington, S.D.; Wilson, Frederic H.

2001-01-01

The U.S. Geological Survey, the Nation’s leader in producing and maintaining earth science data, serves as an advisor to Congress, the Department of the Interior, and many other Federal and State agencies. Nationwide datasets that are easily available and of high quality are critical for addressing a wide range of land-planning, resource, and environmental issues. Four types of digital databases (geological, geophysical, geochemical, and mineral occurrence) are being compiled and upgraded by the Mineral Resources Program on regional and national scales to meet these needs. Where existing data are incomplete, new data are being collected to ensure national coverage. Maps and analyses produced from these databases provide basic information essential for mineral resource assessments and environmental studies, as well as fundamental information for regional and national land-use studies. Maps and analyses produced from the databases are instrumental to ongoing basic research, such as the identification of mineral deposit origins, determination of regional background values of chemical elements with known environmental impact, and study of the relationships between toxic elements or mining practices to human health. As datasets are completed or revised, the information is made available through a variety of media, including the Internet. Much of the available information is the result of cooperative activities with State and other Federal agencies. The upgraded Mineral Resources Program datasets make geologic, geophysical, geochemical, and mineral occurrence information at the state, regional, and national scales available to members of Congress, State and Federal government agencies, researchers in academia, and the general public. The status of the Mineral Resources Program datasets is outlined below.

Comparison of Surface Flow Features from Lidar-Derived Digital Elevation Models with Historical Elevation and Hydrography Data for Minnehaha County, South Dakota

USGS Publications Warehouse

Poppenga, Sandra K.; Worstell, Bruce B.; Stoker, Jason M.; Greenlee, Susan K.

2009-01-01

The U.S. Geological Survey (USGS) has taken the lead in the creation of a valuable remote sensing product by incorporating digital elevation models (DEMs) derived from Light Detection and Ranging (lidar) into the National Elevation Dataset (NED), the elevation layer of 'The National Map'. High-resolution lidar-derived DEMs provide the accuracy needed to systematically quantify and fully integrate surface flow including flow direction, flow accumulation, sinks, slope, and a dense drainage network. In 2008, 1-meter resolution lidar data were acquired in Minnehaha County, South Dakota. The acquisition was a collaborative effort between Minnehaha County, the city of Sioux Falls, and the USGS Earth Resources Observation and Science (EROS) Center. With the newly acquired lidar data, USGS scientists generated high-resolution DEMs and surface flow features. This report compares lidar-derived surface flow features in Minnehaha County to 30- and 10-meter elevation data previously incorporated in the NED and ancillary hydrography datasets. Surface flow features generated from lidar-derived DEMs are consistently integrated with elevation and are important in understanding surface-water movement to better detect surface-water runoff, flood inundation, and erosion. Many topographic and hydrologic applications will benefit from the increased availability of accurate, high-quality, and high-resolution surface-water data. The remotely sensed data provide topographic information and data integration capabilities needed for meeting current and future human and environmental needs.

Production of a national 1:1,000,000-scale hydrography dataset for the United States: feature selection, simplification, and refinement

USGS Publications Warehouse

Gary, Robin H.; Wilson, Zachary D.; Archuleta, Christy-Ann M.; Thompson, Florence E.; Vrabel, Joseph

2009-01-01

During 2006-09, the U.S. Geological Survey, in cooperation with the National Atlas of the United States, produced a 1:1,000,000-scale (1:1M) hydrography dataset comprising streams and waterbodies for the entire United States, including Puerto Rico and the U.S. Virgin Islands, for inclusion in the recompiled National Atlas. This report documents the methods used to select, simplify, and refine features in the 1:100,000-scale (1:100K) (1:63,360-scale in Alaska) National Hydrography Dataset to create the national 1:1M hydrography dataset. Custom tools and semi-automated processes were created to facilitate generalization of the 1:100K National Hydrography Dataset (1:63,360-scale in Alaska) to 1:1M on the basis of existing small-scale hydrography datasets. The first step in creating the new 1:1M dataset was to address feature selection and optimal data density in the streams network. Several existing methods were evaluated. The production method that was established for selecting features for inclusion in the 1:1M dataset uses a combination of the existing attributes and network in the National Hydrography Dataset and several of the concepts from the methods evaluated. The process for creating the 1:1M waterbodies dataset required a similar approach to that used for the streams dataset. Geometric simplification of features was the next step. Stream reaches and waterbodies indicated in the feature selection process were exported as new feature classes and then simplified using a geographic information system tool. The final step was refinement of the 1:1M streams and waterbodies. Refinement was done through the use of additional geographic information system tools.

The need for a national LIDAR dataset

USGS Publications Warehouse

Stoker, Jason M.; Harding, David; Parrish, Jay

2008-01-01

On May 21st and 22nd 2008, the U.S. Geological Survey (USGS), the National Aeronautics and Space Administration (NASA), and the Association of American State Geologists (AASG) hosted the Second National Light Detection and Ranging (Lidar) Initiative Strategy Meeting at USGS Headquarters in Reston, Virginia. The USGS is taking the lead in cooperation with many partners to design and implement a future high-resolution National Lidar Dataset. Initial work is focused on determining viability, developing requirements and specifi cations, establishing what types of information contained in a lidar signal are most important, and identifying key stakeholders and their respective roles. In February 2007, USGS hosted the fi rst National Lidar Initiative Strategy Meeting at USGS Headquarters in Virginia. The presentations and a published summary report from the fi rst meeting can be found on the Center for Lidar Information Coordination and Knowledge (CLICK) Website: http://lidar.cr.usgs.gov. The fi rst meeting demonstrated the public need for consistent lidar data at the national scale. The goals of the second meeting were to further expand on the ideas and information developed in the fi rst meeting, to bring more stakeholders together, to both refi ne and expand on the requirements and capabilities needed, and to discuss an organizational and funding approach for an initiative of this magnitude. The approximately 200 participants represented Federal, State, local, commercial and academic interests. The second meeting included a public solicitation for presentations and posters to better democratize the workshop. All of the oral presentation abstracts that were submitted were accepted, and the 25 poster submissions augmented and expanded upon the oral presentations. The presentations from this second meeting, including audio, can be found on CLICK at http://lidar.cr.usgs.gov/national_lidar_2008.php. Based on the presentations and the discussion sessions, the following

A comparison of U.S. geological survey seamless elevation models with shuttle radar topography mission data

USGS Publications Warehouse

Gesch, D.; Williams, J.; Miller, W.

2001-01-01

Elevation models produced from Shuttle Radar Topography Mission (SRTM) data will be the most comprehensive, consistently processed, highest resolution topographic dataset ever produced for the Earth's land surface. Many applications that currently use elevation data will benefit from the increased availability of data with higher accuracy, quality, and resolution, especially in poorly mapped areas of the globe. SRTM data will be produced as seamless data, thereby avoiding many of the problems inherent in existing multi-source topographic databases. Serving as precursors to SRTM datasets, the U.S. Geological Survey (USGS) has produced and is distributing seamless elevation datasets that facilitate scientific use of elevation data over large areas. GTOPO30 is a global elevation model with a 30 arc-second resolution (approximately 1-kilometer). The National Elevation Dataset (NED) covers the United States at a resolution of 1 arc-second (approximately 30-meters). Due to their seamless format and broad area coverage, both GTOPO30 and NED represent an advance in the usability of elevation data, but each still includes artifacts from the highly variable source data used to produce them. The consistent source data and processing approach for SRTM data will result in elevation products that will be a significant addition to the current availability of seamless datasets, specifically for many areas outside the U.S. One application that demonstrates some advantages that may be realized with SRTM data is delineation of land surface drainage features (watersheds and stream channels). Seamless distribution of elevation data in which a user interactively specifies the area of interest and order parameters via a map server is already being successfully demonstrated with existing USGS datasets. Such an approach for distributing SRTM data is ideal for a dataset that undoubtedly will be of very high interest to the spatial data user community.

NHDPlusHR: A national geospatial framework for surface-water information

USGS Publications Warehouse

Viger, Roland; Rea, Alan H.; Simley, Jeffrey D.; Hanson, Karen M.

2016-01-01

The U.S. Geological Survey is developing a new geospatial hydrographic framework for the United States, called the National Hydrography Dataset Plus High Resolution (NHDPlusHR), that integrates a diversity of the best-available information, robustly supports ongoing dataset improvements, enables hydrographic generalization to derive alternate representations of the network while maintaining feature identity, and supports modern scientific computing and Internet accessibility needs. This framework is based on the High Resolution National Hydrography Dataset, the Watershed Boundaries Dataset, and elevation from the 3-D Elevation Program, and will provide an authoritative, high precision, and attribute-rich geospatial framework for surface-water information for the United States. Using this common geospatial framework will provide a consistent basis for indexing water information in the United States, eliminate redundancy, and harmonize access to, and exchange of water information.

The National Map seamless digital elevation model specifications

USGS Publications Warehouse

Archuleta, Christy-Ann M.; Constance, Eric W.; Arundel, Samantha T.; Lowe, Amanda J.; Mantey, Kimberly S.; Phillips, Lori A.

2017-08-02

This specification documents the requirements and standards used to produce the seamless elevation layers for The National Map of the United States. Seamless elevation data are available for the conterminous United States, Hawaii, Alaska, and the U.S. territories, in three different resolutions—1/3-arc-second, 1-arc-second, and 2-arc-second. These specifications include requirements and standards information about source data requirements, spatial reference system, distribution tiling schemes, horizontal resolution, vertical accuracy, digital elevation model surface treatment, georeferencing, data source and tile dates, distribution and supporting file formats, void areas, metadata, spatial metadata, and quality assurance and control.

The 3D Elevation Program: summary for Wisconsin

USGS Publications Warehouse

Carswell, William J.

2013-01-01

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Wisconsin, elevation data are critical for agriculture and precision farming, natural resources conservation, flood risk management, infrastructure and construction management, water supply and quality, and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

Development of a seamless multisource topographic/bathymetric elevation model of Tampa Bay

USGS Publications Warehouse

Gesch, D.; Wilson, R.

2001-01-01

Many applications of geospatial data in coastal environments require knowledge of the nearshore topography and bathymetry. However, because existing topographic and bathymetric data have been collected independently for different purposes, it has been difficult to use them together at the land/water interface owing to differences in format, projection, resolution, accuracy, and datums. As a first step toward solving the problems of integrating diverse coastal datasets, the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA) are collaborating on a joint demonstration project to merge their data for the Tampa Bay region of Florida. The best available topographic and bathymetric data were extracted from the USGS National Elevation Dataset and the NOAA hydrographic survey database, respectively. Before being merged, the topographic and bathymetric datasets were processed with standard geographic information system tools to place them in a common horizontal reference frame. Also, a key part of the preprocessing was transformation to a common vertical reference through the use of VDatum, a new tool created by NOAA's National Geodetic Survey for vertical datum conversions. The final merged product is a seamless topographic/bathymetric model covering the Tampa Bay region at a grid spacing of 1 arc-second. Topographic LIDAR data were processed and merged with the bathymetry to demonstrate the incorporation of recent third party data sources for several test areas. A primary application of a merged topographic/bathymetric elevation model is for user-defined shoreline delineation, in which the user decides on the tidal condition (for example, low or high water) to be superimposed on the elevation data to determine the spatial position of the water line. Such a use of merged topographic/bathymetric data could lead to the development of a shoreline zone, which could reduce redundant mapping efforts by federal, state, and local agencies

Overview and Meteorological Validation of the Wind Integration National Dataset toolkit

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

Draxl, C.; Hodge, B. M.; Clifton, A.

2015-04-13

The Wind Integration National Dataset (WIND) Toolkit described in this report fulfills these requirements, and constitutes a state-of-the-art national wind resource data set covering the contiguous United States from 2007 to 2013 for use in a variety of next-generation wind integration analyses and wind power planning. The toolkit is a wind resource data set, wind forecast data set, and wind power production and forecast data set derived from the Weather Research and Forecasting (WRF) numerical weather prediction model. WIND Toolkit data are available online for over 116,000 land-based and 10,000 offshore sites representing existing and potential wind facilities.

NHDPlus (National Hydrography Dataset Plus)

EPA Pesticide Factsheets

NHDPlus is a geospatial, hydrologic framework dataset that is intended for use by geospatial analysts and modelers to support water resources related applications. NHDPlus was developed by the USEPA in partnership with the US Geologic Survey

Elevated Suicide Rates at High Altitude: Sociodemographic and Health Issues May Be to Blame

ERIC Educational Resources Information Center

Betz, Marian E.; Valley, Morgan A.; Lowenstein, Steven R.; Hedegaard, Holly; Thomas, Deborah; Stallones, Lorann; Honigman, Benjamin

2011-01-01

Suicide rates are higher at high altitudes; some hypothesize that hypoxia is the cause. We examined 8,871 suicides recorded in 2006 in 15 states by the National Violent Death Reporting System, with the victim's home county altitude determined from the National Elevation Dataset through FIPS code matching. We grouped cases by altitude (low less…

The 3D Elevation Program: summary for Texas

USGS Publications Warehouse

Carswell, William J.

2013-01-01

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Texas, elevation data are critical for natural resources conservation; wildfire management, planning, and response; flood risk management; agriculture and precision farming; infrastructure and construction management; water supply and quality; and other business uses. Today, high-quality light detection and ranging (lidar) data are the source for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Minnesota

USGS Publications Warehouse

Carswell, William J.

2013-01-01

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Minnesota, elevation data are critical for agriculture and precision farming, natural resources conservation, flood risk management, infrastructure and construction management, water supply and quality, coastal zone management, and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for California

USGS Publications Warehouse

Carswell, William J.

2013-01-01

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of California, elevation data are critical for infrastructure and construction management; natural resources conservation; flood risk management; wildfire management, planning, and response; agriculture and precision farming; geologic resource assessment and hazard mitigation; and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Virginia

USGS Publications Warehouse

Carswell, William J.

2013-01-01

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the Commonwealth of Virginia, elevation data are critical for urban and regional planning, natural resources conservation, flood risk management, agriculture and precision farming, resource mining, infrastructure and construction management, and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

Elevation of north facades of #156158 (triple wards) National ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Elevation of north facades of #156-158 (triple wards) - National Home for Disabled Volunteer Soldiers, Pacific Branch, Mental Health Buildings, 11301 Wilshire Boulevard, West Los Angeles, Los Angeles County, CA

12. ELEVATOR DOORS AND CAB. Hot Springs National Park, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

12. ELEVATOR DOORS AND CAB. - Hot Springs National Park, Bathhouse Row, Fordyce Bathhouse: Mechanical & Piping Systems, State Highway 7, 1 mile north of U.S. Highway 70, Hot Springs, Garland County, AR

High-resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset

Treesearch

Christopher Daly; Melissa E. Slater; Joshua A. Roberti; Stephanie H. Laseter; Lloyd W. Swift

2017-01-01

A 69-station, densely spaced rain gauge network was maintained over the period 1951–1958 in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA. This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and...

The 3D Elevation Program: summary for Rhode Island

USGS Publications Warehouse

Carswell, William J.

2013-01-01

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Rhode Island, elevation data are critical for flood risk management, natural resources conservation, coastal zone management, sea level rise and subsidence, agriculture and precision farming, and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative (Snyder, 2012a,b), managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

A dataset on the species composition of amphipods (Crustacea) in a Mexican marine national park: Alacranes Reef, Yucatan

PubMed Central

Simões, Nuno; Pech, Daniel

2018-01-01

Abstract Background Alacranes Reef was declared as a National Marine Park in 1994. Since then, many efforts have been made to inventory its biodiversity. However, groups such as amphipods have been underestimated or not considered when benthic invertebrates were inventoried. Here we present a dataset that contributes to the knowledge of benthic amphipods (Crustacea, Peracarida) from the inner lagoon habitats from the Alacranes Reef National Park, the largest coral reef ecosystem in the Gulf of Mexico. The dataset contains information on records collected from 2009 to 2011. Data are available through Global Biodiversity Information Facility (GBIF). New information A total of 110 amphipod species distributed in 93 nominal species and 17 generic species, belonging to 71 genera, 33 families and three suborders are presented here. This information represents the first online dataset of amphipods from the Alacranes Reef National Park. The biological material is currently deposited in the crustacean collection from the regional unit of the National Autonomous University of Mexico located at Sisal, Yucatan, Mexico (UAS-Sisal). The biological material includes 588 data records with a total abundance of 6,551 organisms. The species inventory represents, until now, the richest fauna of benthic amphipods registered from any discrete coral reef ecosystem in Mexico. PMID:29416428

A dataset on the species composition of amphipods (Crustacea) in a Mexican marine national park: Alacranes Reef, Yucatan.

PubMed

Paz-Ríos, Carlos E; Simões, Nuno; Pech, Daniel

2018-01-01

Alacranes Reef was declared as a National Marine Park in 1994. Since then, many efforts have been made to inventory its biodiversity. However, groups such as amphipods have been underestimated or not considered when benthic invertebrates were inventoried. Here we present a dataset that contributes to the knowledge of benthic amphipods (Crustacea, Peracarida) from the inner lagoon habitats from the Alacranes Reef National Park, the largest coral reef ecosystem in the Gulf of Mexico. The dataset contains information on records collected from 2009 to 2011. Data are available through Global Biodiversity Information Facility (GBIF). A total of 110 amphipod species distributed in 93 nominal species and 17 generic species, belonging to 71 genera, 33 families and three suborders are presented here. This information represents the first online dataset of amphipods from the Alacranes Reef National Park. The biological material is currently deposited in the crustacean collection from the regional unit of the National Autonomous University of Mexico located at Sisal, Yucatan, Mexico (UAS-Sisal). The biological material includes 588 data records with a total abundance of 6,551 organisms. The species inventory represents, until now, the richest fauna of benthic amphipods registered from any discrete coral reef ecosystem in Mexico.

USDA National Nutrient Database for Standard Reference Dataset for What We Eat in America, NHANES (Survey-SR) 2013-2014

USDA-ARS?s Scientific Manuscript database

USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES (Survey-SR) provides the nutrient data for assessing dietary intakes from the national survey What We Eat In America, National Health and Nutrition Examination Survey (WWEIA, NHANES). The current versi...

National Stream Quality Accounting Network and National Monitoring Network Basin Boundary Geospatial Dataset, 2008–13

USGS Publications Warehouse

Baker, Nancy T.

2011-01-01

This report and the accompanying geospatial data were created to assist in analysis and interpretation of water-quality data provided by the U.S. Geological Survey's National Stream Quality Accounting Network (NASQAN) and by the U.S. Coastal Waters and Tributaries National Monitoring Network (NMN), which is a cooperative monitoring program of Federal, regional, and State agencies. The report describes the methods used to develop the geospatial data, which was primarily derived from the National Watershed Boundary Dataset. The geospatial data contains polygon shapefiles of basin boundaries for 33 NASQAN and 5 NMN streamflow and water-quality monitoring stations. In addition, 30 polygon shapefiles of the closed and noncontributing basins contained within the NASQAN or NMN boundaries are included. Also included is a point shapefile of the NASQAN and NMN monitoring stations and associated basin and station attributes. Geospatial data for basin delineations, associated closed and noncontributing basins, and monitoring station locations are available at http://water.usgs.gov/GIS/metadata/usgswrd/XML/ds641_nasqan_wbd12.xml.

The 3D Elevation Program: summary for Idaho

USGS Publications Warehouse

Carswell, William J.

2013-01-01

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Idaho, elevation data are critical for agriculture and precision farming, natural resources conservation, infrastructure and construction management, geologic resource assessment and hazard mitigation, flood risk management, forest resources management, and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features. The Idaho LiDAR Consortium provides statewide collaboration and data sharing mechanisms that can be used as a resource by State and Federal partners implementing the 3DEP initiative.

The Schema.org Datasets Schema: Experiences at the National Snow and Ice Data Center

NASA Astrophysics Data System (ADS)

Duerr, R.; Billingsley, B. W.; Harper, D.; Kovarik, J.

2014-12-01

Data discovery, is still a major challenge for many users. Relevant data may be located anywhere. There are currently no existing universal data registries. Often users start with a simple query through their web browser. But how do you get your data to actually show up near the top of the results? One relatively new way to accomplish this is to use schema.org dataset markup in your data pages. Theoretically this provides web crawlers the additional information needed so that a query for data will preferentially return those pages that were marked up accordingly. The National Snow and Ice Data Center recently implemented an initial set of markup in the data set pages returned by its catalog. The Datasets data model, our process, challenges encountered and results will be described.

Landscape unit based digital elevation model development for the freshwater wetlands within the Arthur C. Marshall Loxahatchee National Wildlife Refuge, Southeastern Florida

USGS Publications Warehouse

Xie, Zhixiao; Liu, Zhongwei; Jones, John W.; Higer, Aaron L.; Telis, Pamela A.

2011-01-01

The hydrologic regime is a critical limiting factor in the delicate ecosystem of the greater Everglades freshwater wetlands in south Florida that has been severely altered by management activities in the past several decades. "Getting the water right" is regarded as the key to successful restoration of this unique wetland ecosystem. An essential component to represent and model its hydrologic regime, specifically water depth, is an accurate ground Digital Elevation Model (DEM). The Everglades Depth Estimation Network (EDEN) supplies important hydrologic data, and its products (including a ground DEM) have been well received by scientists and resource managers involved in Everglades restoration. This study improves the EDEN DEMs of the Loxahatchee National Wildlife Refuge, also known as Water Conservation Area 1 (WCA1), by adopting a landscape unit (LU) based interpolation approach. The study first filtered the input elevation data based on newly available vegetation data, and then created a separate geostatistical model (universal kriging) for each LU. The resultant DEMs have encouraging cross-validation and validation results, especially since the validation is based on an independent elevation dataset (derived by subtracting water depth measurements from EDEN water surface elevations). The DEM product of this study will directly benefit hydrologic and ecological studies as well as restoration efforts. The study will also be valuable for a broad range of wetland studies.

Elevation-derived watershed basins and characteristics for major rivers of the conterminous United States

USGS Publications Warehouse

Poppenga, S.K.; Worstell, B.B.

2008-01-01

The U.S. Geological Survey Earth Resources Observation and Science Center Topographic Science Project has developed elevation-derived watershed basins and characteristics for major rivers of the conterminous United States. Watershed basins are delineated upstream from the mouth of major rivers by using the hydrologic connectivity of the Elevation Derivatives for National Applications (EDNA) seamless database. Watershed characteristics are quantified by integrating ancillary geospatial datasets, including land cover, population, slope, and topography, with elevation-derived watershed boundaries. The results are published in an online EDNA Watershed Atlas at http://edna.usgs.gov/watersheds. The atlas serves as a framework for evaluating and analyzing the physical, biological, and anthropogenic status of watersheds.

An assessment of differences in gridded precipitation datasets in complex terrain

NASA Astrophysics Data System (ADS)

Henn, Brian; Newman, Andrew J.; Livneh, Ben; Daly, Christopher; Lundquist, Jessica D.

2018-01-01

Hydrologic modeling and other geophysical applications are sensitive to precipitation forcing data quality, and there are known challenges in spatially distributing gauge-based precipitation over complex terrain. We conduct a comparison of six high-resolution, daily and monthly gridded precipitation datasets over the Western United States. We compare the long-term average spatial patterns, and interannual variability of water-year total precipitation, as well as multi-year trends in precipitation across the datasets. We find that the greatest absolute differences among datasets occur in high-elevation areas and in the maritime mountain ranges of the Western United States, while the greatest percent differences among datasets relative to annual total precipitation occur in arid and rain-shadowed areas. Differences between datasets in some high-elevation areas exceed 200 mm yr-1 on average, and relative differences range from 5 to 60% across the Western United States. In areas of high topographic relief, true uncertainties and biases are likely higher than the differences among the datasets; we present evidence of this based on streamflow observations. Precipitation trends in the datasets differ in magnitude and sign at smaller scales, and are sensitive to how temporal inhomogeneities in the underlying precipitation gauge data are handled.

Data-driven decision support for radiologists: re-using the National Lung Screening Trial dataset for pulmonary nodule management.

PubMed

Morrison, James J; Hostetter, Jason; Wang, Kenneth; Siegel, Eliot L

2015-02-01

Real-time mining of large research trial datasets enables development of case-based clinical decision support tools. Several applicable research datasets exist including the National Lung Screening Trial (NLST), a dataset unparalleled in size and scope for studying population-based lung cancer screening. Using these data, a clinical decision support tool was developed which matches patient demographics and lung nodule characteristics to a cohort of similar patients. The NLST dataset was converted into Structured Query Language (SQL) tables hosted on a web server, and a web-based JavaScript application was developed which performs real-time queries. JavaScript is used for both the server-side and client-side language, allowing for rapid development of a robust client interface and server-side data layer. Real-time data mining of user-specified patient cohorts achieved a rapid return of cohort cancer statistics and lung nodule distribution information. This system demonstrates the potential of individualized real-time data mining using large high-quality clinical trial datasets to drive evidence-based clinical decision-making.

Inter-comparison of multiple statistically downscaled climate datasets for the Pacific Northwest, USA

PubMed Central

Jiang, Yueyang; Kim, John B.; Still, Christopher J.; Kerns, Becky K.; Kline, Jeffrey D.; Cunningham, Patrick G.

2018-01-01

Statistically downscaled climate data have been widely used to explore possible impacts of climate change in various fields of study. Although many studies have focused on characterizing differences in the downscaling methods, few studies have evaluated actual downscaled datasets being distributed publicly. Spatially focusing on the Pacific Northwest, we compare five statistically downscaled climate datasets distributed publicly in the US: ClimateNA, NASA NEX-DCP30, MACAv2-METDATA, MACAv2-LIVNEH and WorldClim. We compare the downscaled projections of climate change, and the associated observational data used as training data for downscaling. We map and quantify the variability among the datasets and characterize the spatio-temporal patterns of agreement and disagreement among the datasets. Pair-wise comparisons of datasets identify the coast and high-elevation areas as areas of disagreement for temperature. For precipitation, high-elevation areas, rainshadows and the dry, eastern portion of the study area have high dissimilarity among the datasets. By spatially aggregating the variability measures into watersheds, we develop guidance for selecting datasets within the Pacific Northwest climate change impact studies. PMID:29461513

Inter-comparison of multiple statistically downscaled climate datasets for the Pacific Northwest, USA.

PubMed

Jiang, Yueyang; Kim, John B; Still, Christopher J; Kerns, Becky K; Kline, Jeffrey D; Cunningham, Patrick G

2018-02-20

Statistically downscaled climate data have been widely used to explore possible impacts of climate change in various fields of study. Although many studies have focused on characterizing differences in the downscaling methods, few studies have evaluated actual downscaled datasets being distributed publicly. Spatially focusing on the Pacific Northwest, we compare five statistically downscaled climate datasets distributed publicly in the US: ClimateNA, NASA NEX-DCP30, MACAv2-METDATA, MACAv2-LIVNEH and WorldClim. We compare the downscaled projections of climate change, and the associated observational data used as training data for downscaling. We map and quantify the variability among the datasets and characterize the spatio-temporal patterns of agreement and disagreement among the datasets. Pair-wise comparisons of datasets identify the coast and high-elevation areas as areas of disagreement for temperature. For precipitation, high-elevation areas, rainshadows and the dry, eastern portion of the study area have high dissimilarity among the datasets. By spatially aggregating the variability measures into watersheds, we develop guidance for selecting datasets within the Pacific Northwest climate change impact studies.

Advancements in Wind Integration Study Data Modeling: The Wind Integration National Dataset (WIND) Toolkit; Preprint

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

Draxl, C.; Hodge, B. M.; Orwig, K.

2013-10-01

Regional wind integration studies in the United States require detailed wind power output data at many locations to perform simulations of how the power system will operate under high-penetration scenarios. The wind data sets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as be time synchronized with available load profiles. The Wind Integration National Dataset (WIND) Toolkit described in this paper fulfills these requirements. A wind resource dataset, wind power production time series, and simulated forecasts from a numerical weather predictionmore » model run on a nationwide 2-km grid at 5-min resolution will be made publicly available for more than 110,000 onshore and offshore wind power production sites.« less

The effects of wavelet compression on Digital Elevation Models (DEMs)

USGS Publications Warehouse

Oimoen, M.J.

2004-01-01

This paper investigates the effects of lossy compression on floating-point digital elevation models using the discrete wavelet transform. The compression of elevation data poses a different set of problems and concerns than does the compression of images. Most notably, the usefulness of DEMs depends largely in the quality of their derivatives, such as slope and aspect. Three areas extracted from the U.S. Geological Survey's National Elevation Dataset were transformed to the wavelet domain using the third order filters of the Daubechies family (DAUB6), and were made sparse by setting 95 percent of the smallest wavelet coefficients to zero. The resulting raster is compressible to a corresponding degree. The effects of the nulled coefficients on the reconstructed DEM are noted as residuals in elevation, derived slope and aspect, and delineation of drainage basins and streamlines. A simple masking technique also is presented, that maintains the integrity and flatness of water bodies in the reconstructed DEM.

Integrating disparate lidar datasets for a regional storm tide inundation analysis of Hurricane Katrina

USGS Publications Warehouse

Stoker, Jason M.; Tyler, Dean J.; Turnipseed, D. Phil; Van Wilson, K.; Oimoen, Michael J.

2009-01-01

Hurricane Katrina was one of the largest natural disasters in U.S. history. Due to the sheer size of the affected areas, an unprecedented regional analysis at very high resolution and accuracy was needed to properly quantify and understand the effects of the hurricane and the storm tide. Many disparate sources of lidar data were acquired and processed for varying environmental reasons by pre- and post-Katrina projects. The datasets were in several formats and projections and were processed to varying phases of completion, and as a result the task of producing a seamless digital elevation dataset required a high level of coordination, research, and revision. To create a seamless digital elevation dataset, many technical issues had to be resolved before producing the desired 1/9-arc-second (3meter) grid needed as the map base for projecting the Katrina peak storm tide throughout the affected coastal region. This report presents the methodology that was developed to construct seamless digital elevation datasets from multipurpose, multi-use, and disparate lidar datasets, and describes an easily accessible Web application for viewing the maximum storm tide caused by Hurricane Katrina in southeastern Louisiana, Mississippi, and Alabama.

How robust is the pre-1931 National Climatic Data Center—climate divisional dataset? Examples from Georgia and Louisiana

NASA Astrophysics Data System (ADS)

Allard, Jason; Thompson, Clint; Keim, Barry D.

2015-04-01

The National Climatic Data Center's climate divisional dataset (CDD) is commonly used in climate change analyses. This dataset is a spatially continuous dataset for the conterminous USA from 1895 to the present. The CDD since 1931 is computed by averaging all available representative cooperative weather station data into a single monthly value for each of the 344 climate divisions of the conterminous USA, while pre-1931 data for climate divisions are derived from statewide averages using regression equations. This study examines the veracity of these pre-1931 data. All available Cooperative Observer Program (COOP) stations within each climate division in Georgia and Louisiana were averaged into a single monthly value for each month and each climate division from 1897 to 1930 to generate a divisional dataset (COOP DD), using similar methods to those used by the National Climatic Data Center to generate the post-1931 CDD. The reliability of the official CDD—derived from statewide averages—to produce temperature and precipitation means and trends prior to 1931 are then evaluated by comparing that dataset with the COOP DD with difference-of-means tests, correlations, and linear regression techniques. The CDD and the COOP DD are also compared to a divisional dataset derived from the United States Historical Climatology Network (USHCN) data (USHCN DD), with difference of means and correlation techniques, to demonstrate potential impacts of inhomogeneities within the CDD and the COOP DD. The statistical results, taken as a whole, not only indicate broad similarities between the CDD and COOP DD but also show that the CDD does not adequately portray pre-1931 temperature and precipitation in certain climate divisions within Georgia and Louisiana. In comparison with the USHCN DD, both the CDD and the COOP DD appear to be subject to biases that probably result from changing stations within climate divisions. As such, the CDD should be used judiciously for long-term studies

Geocoding and stereo display of tropical forest multisensor datasets

NASA Technical Reports Server (NTRS)

Welch, R.; Jordan, T. R.; Luvall, J. C.

1990-01-01

Concern about the future of tropical forests has led to a demand for geocoded multisensor databases that can be used to assess forest structure, deforestation, thermal response, evapotranspiration, and other parameters linked to climate change. In response to studies being conducted at the Braulino Carrillo National Park, Costa Rica, digital satellite and aircraft images recorded by Landsat TM, SPOT HRV, Thermal Infrared Multispectral Scanner, and Calibrated Airborne Multispectral Scanner sensors were placed in register using the Landsat TM image as the reference map. Despite problems caused by relief, multitemporal datasets, and geometric distortions in the aircraft images, registration was accomplished to within + or - 20 m (+ or - 1 data pixel). A digital elevation model constructed from a multisensor Landsat TM/SPOT stereopair proved useful for generating perspective views of the rugged, forested terrain.

Evaluation of Uncertainty in Precipitation Datasets for New Mexico, USA

NASA Astrophysics Data System (ADS)

Besha, A. A.; Steele, C. M.; Fernald, A.

2014-12-01

Climate change, population growth and other factors are endangering water availability and sustainability in semiarid/arid areas particularly in the southwestern United States. Wide coverage of spatial and temporal measurements of precipitation are key for regional water budget analysis and hydrological operations which themselves are valuable tool for water resource planning and management. Rain gauge measurements are usually reliable and accurate at a point. They measure rainfall continuously, but spatial sampling is limited. Ground based radar and satellite remotely sensed precipitation have wide spatial and temporal coverage. However, these measurements are indirect and subject to errors because of equipment, meteorological variability, the heterogeneity of the land surface itself and lack of regular recording. This study seeks to understand precipitation uncertainty and in doing so, lessen uncertainty propagation into hydrological applications and operations. We reviewed, compared and evaluated the TRMM (Tropical Rainfall Measuring Mission) precipitation products, NOAA's (National Oceanic and Atmospheric Administration) Global Precipitation Climatology Centre (GPCC) monthly precipitation dataset, PRISM (Parameter elevation Regression on Independent Slopes Model) data and data from individual climate stations including Cooperative Observer Program (COOP), Remote Automated Weather Stations (RAWS), Soil Climate Analysis Network (SCAN) and Snowpack Telemetry (SNOTEL) stations. Though not yet finalized, this study finds that the uncertainty within precipitation estimates datasets is influenced by regional topography, season, climate and precipitation rate. Ongoing work aims to further evaluate precipitation datasets based on the relative influence of these phenomena so that we can identify the optimum datasets for input to statewide water budget analysis.

The 3D Elevation Program: summary for Hawaii

USGS Publications Warehouse

Carswell, William J.

2016-01-01

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Hawaii, elevation data are critical for infrastructure and construction management, flood risk management, geologic resource assessment and hazard mitigation, natural resources conservation, coastal zone management, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, U.S. territorial, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data.The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States, Hawaii, and selected U.S. territories, and quality level 5 interferometric synthetic aperture radar (IfSAR) data for Alaska, all with a 6- to 10-year acquisition cycle, provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other three-dimensional (3D) representations of the Nation’s natural and constructed features.

Developing a new global network of river reaches from merged satellite-derived datasets

NASA Astrophysics Data System (ADS)

Lion, C.; Allen, G. H.; Beighley, E.; Pavelsky, T.

2015-12-01

In 2020, the Surface Water and Ocean Topography satellite (SWOT), a joint mission of NASA/CNES/CSA/UK will be launched. One of its major products will be the measurements of continental water extent, including the width, height, and slope of rivers and the surface area and elevations of lakes. The mission will improve the monitoring of continental water and also our understanding of the interactions between different hydrologic reservoirs. For rivers, SWOT measurements of slope must be carried out over predefined river reaches. As such, an a priori dataset for rivers is needed in order to facilitate analysis of the raw SWOT data. The information required to produce this dataset includes measurements of river width, elevation, slope, planform, river network topology, and flow accumulation. To produce this product, we have linked two existing global datasets: the Global River Widths from Landsat (GRWL) database, which contains river centerline locations, widths, and a braiding index derived from Landsat imagery, and a modified version of the HydroSHEDS hydrologically corrected digital elevation product, which contains heights and flow accumulation measurements for streams at 3 arcsecond spatial resolution. Merging these two datasets requires considerable care. The difficulties, among others, lie in the difference of resolution: 30m versus 3 arseconds, and the age of the datasets: 2000 versus ~2010 (some rivers have moved, the braided sections are different). As such, we have developed custom software to merge the two datasets, taking into account the spatial proximity of river channels in the two datasets and ensuring that flow accumulation in the final dataset always increases downstream. Here, we present our preliminary results for a portion of South America and demonstrate the strengths and weaknesses of the method.

Adapting generalization tools to physiographic diversity for the united states national hydrography dataset

USGS Publications Warehouse

Buttenfield, B.P.; Stanislawski, L.V.; Brewer, C.A.

2011-01-01

This paper reports on generalization and data modeling to create reduced scale versions of the National Hydrographic Dataset (NHD) for dissemination through The National Map, the primary data delivery portal for USGS. Our approach distinguishes local differences in physiographic factors, to demonstrate that knowledge about varying terrain (mountainous, hilly or flat) and varying climate (dry or humid) can support decisions about algorithms, parameters, and processing sequences to create generalized, smaller scale data versions which preserve distinct hydrographic patterns in these regions. We work with multiple subbasins of the NHD that provide a range of terrain and climate characteristics. Specifically tailored generalization sequences are used to create simplified versions of the high resolution data, which was compiled for 1:24,000 scale mapping. Results are evaluated cartographically and metrically against a medium resolution benchmark version compiled for 1:100,000, developing coefficients of linear and areal correspondence.

Characterization of ASTER GDEM Elevation Data over Vegetated Area Compared with Lidar Data

NASA Technical Reports Server (NTRS)

Ni, Wenjian; Sun, Guoqing; Ranson, Kenneth J.

2013-01-01

Current researches based on areal or spaceborne stereo images with very high resolutions (less than 1 meter) have demonstrated that it is possible to derive vegetation height from stereo images. The second version of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is a state-of-the-art global elevation data-set developed by stereo images. However, the resolution of ASTER stereo images (15 meters) is much coarser than areal stereo images, and the ASTER GDEM is compiled products from stereo images acquired over 10 years. The forest disturbances as well as forest growth are inevitable in 10 years time span. In this study, the features of ASTER GDEM over vegetated areas under both flat and mountainous conditions were investigated by comparisons with lidar data. The factors possibly affecting the extraction of vegetation canopy height considered include (1) co-registration of DEMs; (2) spatial resolution of digital elevation models (DEMs); (3) spatial vegetation structure; and (4) terrain slope. The results show that accurate co-registration between ASTER GDEM and the National Elevation Dataset (NED) is necessary over mountainous areas. The correlation between ASTER GDEM minus NED and vegetation canopy height is improved from 0.328 to 0.43 by degrading resolutions from 1 arc-second to 5 arc-seconds and further improved to 0.6 if only homogenous vegetated areas were considered.

Benchmark Dataset for Whole Genome Sequence Compression.

PubMed

C L, Biji; S Nair, Achuthsankar

2017-01-01

The research in DNA data compression lacks a standard dataset to test out compression tools specific to DNA. This paper argues that the current state of achievement in DNA compression is unable to be benchmarked in the absence of such scientifically compiled whole genome sequence dataset and proposes a benchmark dataset using multistage sampling procedure. Considering the genome sequence of organisms available in the National Centre for Biotechnology and Information (NCBI) as the universe, the proposed dataset selects 1,105 prokaryotes, 200 plasmids, 164 viruses, and 65 eukaryotes. This paper reports the results of using three established tools on the newly compiled dataset and show that their strength and weakness are evident only with a comparison based on the scientifically compiled benchmark dataset. The sample dataset and the respective links are available @ https://sourceforge.net/projects/benchmarkdnacompressiondataset/.

[German national consensus on wound documentation of leg ulcer : Part 1: Routine care - standard dataset and minimum dataset].

PubMed

Heyer, K; Herberger, K; Protz, K; Mayer, A; Dissemond, J; Debus, S; Augustin, M

2017-09-01

Standards for basic documentation and the course of treatment increase quality assurance and efficiency in health care. To date, no standards for the treatment of patients with leg ulcers are available in Germany. The aim of the study was to develop standards under routine conditions in the documentation of patients with leg ulcers. This article shows the recommended variables of a "standard dataset" and a "minimum dataset". Consensus building among experts from 38 scientific societies, professional associations, insurance and supply networks (n = 68 experts) took place. After conducting a systematic international literature research, available standards were reviewed and supplemented with our own considerations of the expert group. From 2012-2015 standards for documentation were defined in multistage online visits and personal meetings. A consensus was achieved for 18 variables for the minimum dataset and 48 variables for the standard dataset in a total of seven meetings and nine online Delphi visits. The datasets involve patient baseline data, data on the general health status, wound characteristics, diagnostic and therapeutic interventions, patient reported outcomes, nutrition, and education status. Based on a multistage continuous decision-making process, a standard in the measurement of events in routine care in patients with a leg ulcer was developed.

Status report for the 3D Elevation Program, 2013-2014

USGS Publications Warehouse

Lukas, Vicki; Eldridge, Diane F.; Jason, Allyson L.; Saghy, David L.; Steigerwald, Pamela R.; Stoker, Jason M.; Sugarbaker, Larry J.; Thunen, Diana R.

2015-09-25

The 3D Elevation Program (3DEP) goal is to acquire, manage, and distribute enhanced three-dimensional elevation data for the Nation and U.S. territories by 2023. This status report covers implementation activities during 2013–2014 to include meeting funding objectives, developing a management structure, modernizing systems, and collecting and producing initial 3DEP data and products. The Nation will not have complete coverage of 3DEP quality data until 2023 assuming that sufficient funding is available. In spite of the overall condition of government budgets, the 3DEP initiative has gained widespread support and had incremental budget success to include supplemental funding resulting from natural disasters. The 3DEP Executive Forum and a wide range of professional organizations are actively working to maintain support for the program. The systems that have been developed to support increasing acquisition and processing levels are largely in place. The first 3DEP quality datasets were released to the public in late 2014. In addition, light detection and ranging (lidar), interferometric synthetic aperture radar (ifsar), and digital elevation models (DEMs) acquired before 2014 are all supported within the new infrastructure and available for download. Research is ongoing to expand the suite of products and services, and to increase overall throughput and data management efficiency. Emerging technologies may result in lower acquisition costs in the future. Elevation data acquired by 3DEP partnerships will be available through The National Map representing one of the largest and most comprehensive databases publicly available for the United States. 

State of Texas - Highlighting low-lying areas derived from USGS Digital Elevation Data

USGS Publications Warehouse

Kosovich, John J.

2008-01-01

In support of U.S. Geological Survey (USGS) disaster preparedness efforts, this map depicts a color shaded relief representation of Texas and a grayscale relief of the surrounding areas. The first 30 feet of relief above mean sea level are displayed as brightly colored 5-foot elevation bands, which highlight low-elevation areas at a coarse spatial resolution. Standard USGS National Elevation Dataset (NED) 1 arc-second (nominally 30-meter) digital elevation model (DEM) data are the basis for the map, which is designed to be used at a broad scale and for informational purposes only. The NED data were derived from the original 1:24,000-scale USGS topographic map bare-earth contours, which were converted into gridded quadrangle-based DEM tiles at a constant post spacing (grid cell size) of either 30 meters (data before the mid-1990s) or 10 meters (mid-1990s and later data). These individual-quadrangle DEMs were then converted to spherical coordinates (latitude/longitude decimal degrees) and edge-matched to ensure seamlessness. The NED source data for this map consists of a mixture of 30-meter- and 10-meter-resolution DEMs. State and county boundary, hydrography, city, and road layers were modified from USGS National Atlas data downloaded in 2003. The NED data were downloaded in 2002. Shaded relief over Mexico was obtained from the USGS National Atlas.

The 3D Elevation Program: summary for Puerto Rico

USGS Publications Warehouse

Carswell, William J.

2016-02-03

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, scientific research, national security, recreation, and many others. For the Commonwealth of Puerto Rico, elevation data are critical for flood risk management, landslide mitigation, natural resources conservation, sea level rise and subsidence, coastal zone management, infrastructure and construction management, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, U.S. territorial, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data.The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States, Hawaii, and selected U.S. territories, and quality level 5 interferometric synthetic aperture radar (IfSAR) data for Alaska, all with a 6- to 10-year acquisition cycle, provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A‒16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other three-dimensional (3D) representations of the Nation’s natural and constructed features.

Geometric correction and digital elevation extraction using multiple MTI datasets

USGS Publications Warehouse

Mercier, Jeffrey A.; Schowengerdt, Robert A.; Storey, James C.; Smith, Jody L.

2007-01-01

Digital Elevation Models (DEMs) are traditionally acquired from a stereo pair of aerial photographs sequentially captured by an airborne metric camera. Standard DEM extraction techniques can be naturally extended to satellite imagery, but the particular characteristics of satellite imaging can cause difficulties. The spacecraft ephemeris with respect to the ground site during image collects is the most important factor in the elevation extraction process. When the angle of separation between the stereo images is small, the extraction process typically produces measurements with low accuracy, while a large angle of separation can cause an excessive number of erroneous points in the DEM from occlusion of ground areas. The use of three or more images registered to the same ground area can potentially reduce these problems and improve the accuracy of the extracted DEM. The pointing capability of some sensors, such as the Multispectral Thermal Imager (MTI), allows for multiple collects of the same area from different perspectives. This functionality of MTI makes it a good candidate for the implementation of a DEM extraction algorithm using multiple images for improved accuracy. Evaluation of this capability and development of algorithms to geometrically model the MTI sensor and extract DEMs from multi-look MTI imagery are described in this paper. An RMS elevation error of 6.3-meters is achieved using 11 ground test points, while the MTI band has a 5-meter ground sample distance.

On the Utility of National Datasets and Resource Cost Models for Estimating Faculty Instructional Costs in Higher Education

ERIC Educational Resources Information Center

Morphew, Christopher; Baker, Bruce

2007-01-01

In this article, the authors present the results of a research study in which they used two national datasets to construct and examine a model that estimates relative faculty instructional costs for specific undergraduate degree programs and also identifies differences in these costs by region and institutional type. They conducted this research…

Analysis of the uncertainty associated with national fossil fuel CO2 emissions datasets for use in the global Fossil Fuel Data Assimilation System (FFDAS) and carbon budgets

NASA Astrophysics Data System (ADS)

Song, Y.; Gurney, K. R.; Rayner, P. J.; Asefi-Najafabady, S.

2012-12-01

High resolution quantification of global fossil fuel CO2 emissions has become essential in research aimed at understanding the global carbon cycle and supporting the verification of international agreements on greenhouse gas emission reductions. The Fossil Fuel Data Assimilation System (FFDAS) was used to estimate global fossil fuel carbon emissions at 0.25 degree from 1992 to 2010. FFDAS quantifies CO2 emissions based on areal population density, per capita economic activity, energy intensity and carbon intensity. A critical constraint to this system is the estimation of national-scale fossil fuel CO2 emissions disaggregated into economic sectors. Furthermore, prior uncertainty estimation is an important aspect of the FFDAS. Objective techniques to quantify uncertainty for the national emissions are essential. There are several institutional datasets that quantify national carbon emissions, including British Petroleum (BP), the International Energy Agency (IEA), the Energy Information Administration (EIA), and the Carbon Dioxide Information and Analysis Center (CDIAC). These four datasets have been "harmonized" by Jordan Macknick for inter-comparison purposes (Macknick, Carbon Management, 2011). The harmonization attempted to generate consistency among the different institutional datasets via a variety of techniques such as reclassifying into consistent emitting categories, recalculating based on consistent emission factors, and converting into consistent units. These harmonized data form the basis of our uncertainty estimation. We summarized the maximum, minimum and mean national carbon emissions for all the datasets from 1992 to 2010. We calculated key statistics highlighting the remaining differences among the harmonized datasets. We combine the span (max - min) of datasets for each country and year with the standard deviation of the national spans over time. We utilize the economic sectoral definitions from IEA to disaggregate the national total emission into

National hydrography dataset--linear referencing

USGS Publications Warehouse

Simley, Jeffrey; Doumbouya, Ariel

2012-01-01

Geospatial data normally have a certain set of standard attributes, such as an identification number, the type of feature, and name of the feature. These standard attributes are typically embedded into the default attribute table, which is directly linked to the geospatial features. However, it is impractical to embed too much information because it can create a complex, inflexible, and hard to maintain geospatial dataset. Many scientists prefer to create a modular, or relational, data design where the information about the features is stored and maintained separately, then linked to the geospatial data. For example, information about the water chemistry of a lake can be maintained in a separate file and linked to the lake. A Geographic Information System (GIS) can then relate the water chemistry to the lake and analyze it as one piece of information. For example, the GIS can select all lakes more than 50 acres, with turbidity greater than 1.5 milligrams per liter.

Genomic Datasets for Cancer Research

Cancer.gov

A variety of datasets from genome-wide association studies of cancer and other genotype-phenotype studies, including sequencing and molecular diagnostic assays, are available to approved investigators through the Extramural National Cancer Institute Data Access Committee.

National Project III, Elevating the Importance of Teaching. Fund Associate's Final Report.

ERIC Educational Resources Information Center

Seibert, Warren F.

Purdue University's participation as a fund associate in National Project III (NP III) for elevating the importance of teaching has its origins in a flexible and diagnostic instructional evaluation system called "CAFETERIA." CAFETERIA services include test development, scoring, and analysis; social surveys on topics of importance in…

A User's Guide to the Tsunami Datasets at NOAA's National Data Buoy Center

NASA Astrophysics Data System (ADS)

Bouchard, R. H.; O'Neil, K.; Grissom, K.; Garcia, M.; Bernard, L. J.; Kern, K. J.

2013-12-01

The National Data Buoy Center (NDBC) has maintained and operated the National Oceanic and Atmospheric Administration's (NOAA) tsunameter network since 2003. The tsunameters employ the NOAA-developed Deep-ocean Assessment and Reporting of Tsunamis (DART) technology. The technology measures the pressure and temperature every 15 seconds on the ocean floor and transforms them into equivalent water-column height observations. A complex series of subsampled observations are transmitted acoustically in real-time to a moored buoy or marine autonomous vehicle (MAV) at the ocean surface. The surface platform uses its satellite communications to relay the observations to NDBC. NDBC places the observations onto the Global Telecommunication System (GTS) for relay to NOAA's Tsunami Warning Centers (TWC) in Hawai'i and Alaska and to the international community. It takes less than three minutes to speed the observations from the ocean floor to the TWCs. NDBC can retrieve limited amounts of the 15-s measurements from the instrumentation on the ocean floor using the technology's two-way communications. NDBC recovers the full resolution 15-s measurements about every 2 years and forwards the datasets and metadata to the National Geophysical Data Center for permanent archive. Meanwhile, NDBC retains the real-time observations on its website. The type of real-time observation depends on the operating mode of the tsunameter. NDBC provides the observations in a variety of traditional and innovative methods and formats that include descriptors of the operating mode. Datasets, organized by station, are available from the NDBC website as text files and from the NDBC THREDDS server in netCDF format. The website provides alerts and lists of events that allow users to focus on the information relevant for tsunami hazard analysis. In addition, NDBC developed a basic web service to query station information and observations to support the Short-term Inundation Forecasting for Tsunamis (SIFT

The 3D Elevation Program initiative: a call for action

USGS Publications Warehouse

Sugarbaker, Larry J.; Constance, Eric W.; Heidemann, Hans Karl; Jason, Allyson L.; Lukas, Vicki; Saghy, David L.; Stoker, Jason M.

2014-01-01

The 3D Elevation Program (3DEP) initiative is accelerating the rate of three-dimensional (3D) elevation data collection in response to a call for action to address a wide range of urgent needs nationwide. It began in 2012 with the recommendation to collect (1) high-quality light detection and ranging (lidar) data for the conterminous United States (CONUS), Hawaii, and the U.S. territories and (2) interferometric synthetic aperture radar (ifsar) data for Alaska. Specifications were created for collecting 3D elevation data, and the data management and delivery systems are being modernized. The National Elevation Dataset (NED) will be completely refreshed with new elevation data products and services. The call for action requires broad support from a large partnership community committed to the achievement of national 3D elevation data coverage. The initiative is being led by the U.S. Geological Survey (USGS) and includes many partners—Federal agencies and State, Tribal, and local governments—who will work together to build on existing programs to complete the national collection of 3D elevation data in 8 years. Private sector firms, under contract to the Government, will continue to collect the data and provide essential technology solutions for the Government to manage and deliver these data and services. The 3DEP governance structure includes (1) an executive forum established in May 2013 to have oversight functions and (2) a multiagency coordinating committee based upon the committee structure already in place under the National Digital Elevation Program (NDEP). The 3DEP initiative is based on the results of the National Enhanced Elevation Assessment (NEEA) that was funded by NDEP agencies and completed in 2011. The study, led by the USGS, identified more than 600 requirements for enhanced (3D) elevation data to address mission-critical information requirements of 34 Federal agencies, all 50 States, and a sample of private sector companies and Tribal and local

SIFlore, a dataset of geographical distribution of vascular plants covering five centuries of knowledge in France: Results of a collaborative project coordinated by the Federation of the National Botanical Conservatories.

PubMed

Just, Anaïs; Gourvil, Johan; Millet, Jérôme; Boullet, Vincent; Milon, Thomas; Mandon, Isabelle; Dutrève, Bruno

2015-01-01

More than 20 years ago, the French Muséum National d'Histoire Naturelle (MNHN, Secretariat of the Fauna and Flora) published the first part of an atlas of the flora of France at a 20km spatial resolution, accounting for 645 taxa (Dupont 1990). Since then, at the national level, there has not been any work on this scale relating to flora distribution, despite the obvious need for a better understanding. In 2011, in response to this need, the Federation des Conservatoires Botaniques Nationaux (FCBN, http://www.fcbn.fr) launched an ambitious collaborative project involving eleven national botanical conservatories of France. The project aims to establish a formal procedure and standardized system for data hosting, aggregation and publication for four areas: flora, fungi, vegetation and habitats. In 2014, the first phase of the project led to the development of the national flora dataset: SIFlore. As it includes about 21 million records of flora occurrences, this is currently the most comprehensive dataset on the distribution of vascular plants (Tracheophyta) in the French territory. SIFlore contains information for about 15'454 plant taxa occurrences (indigenous and alien taxa) in metropolitan France and Reunion Island, from 1545 until 2014. The data records were originally collated from inventories, checklists, literature and herbarium records. SIFlore was developed by assembling flora datasets from the regional to the national level. At the regional level, source records are managed by the national botanical conservatories that are responsible for flora data collection and validation. In order to present our results, a geoportal was developed by the Fédération des conservatoires botaniques nationaux that allows the SIFlore dataset to be publically viewed. This portal is available at: http://siflore.fcbn.fr. As the FCBN belongs to the Information System for Nature and Landscapes' (SINP), a governmental program, the dataset is also accessible through the websites of

SIFlore, a dataset of geographical distribution of vascular plants covering five centuries of knowledge in France: Results of a collaborative project coordinated by the Federation of the National Botanical Conservatories

PubMed Central

Just, Anaïs; Gourvil, Johan; Millet, Jérôme; Boullet, Vincent; Milon, Thomas; Mandon, Isabelle; Dutrève, Bruno

2015-01-01

Abstract More than 20 years ago, the French Muséum National d’Histoire Naturelle1 (MNHN, Secretariat of the Fauna and Flora) published the first part of an atlas of the flora of France at a 20km spatial resolution, accounting for 645 taxa (Dupont 1990). Since then, at the national level, there has not been any work on this scale relating to flora distribution, despite the obvious need for a better understanding. In 2011, in response to this need, the Federation des Conservatoires Botaniques Nationaux2 (FCBN, http://www.fcbn.fr) launched an ambitious collaborative project involving eleven national botanical conservatories of France. The project aims to establish a formal procedure and standardized system for data hosting, aggregation and publication for four areas: flora, fungi, vegetation and habitats. In 2014, the first phase of the project led to the development of the national flora dataset: SIFlore. As it includes about 21 million records of flora occurrences, this is currently the most comprehensive dataset on the distribution of vascular plants (Tracheophyta) in the French territory. SIFlore contains information for about 15'454 plant taxa occurrences (indigenous and alien taxa) in metropolitan France and Reunion Island, from 1545 until 2014. The data records were originally collated from inventories, checklists, literature and herbarium records. SIFlore was developed by assembling flora datasets from the regional to the national level. At the regional level, source records are managed by the national botanical conservatories that are responsible for flora data collection and validation. In order to present our results, a geoportal was developed by the Fédération des conservatoires botaniques nationaux that allows the SIFlore dataset to be publically viewed. This portal is available at: http://siflore.fcbn.fr. As the FCBN belongs to the Information System for Nature and Landscapes’ (SINP), a governmental program, the dataset is also accessible through

Study of elevation changes along a profile crossing the Greenland Ice Sheet

NASA Astrophysics Data System (ADS)

Hvidegaard, S. M.; Sandberg, L.

2009-04-01

In recent years much research has focused on determining how the Greenland Ice Sheet is responding to the observed climate changes. There is wide agreement on the fact that the Ice Sheet is currently loosing mass, and studies have shown that the mass loss is found near the ice edge and that no significant changes are found in the central part of the Ice Sheet. As a part of European Space Agency's CryoSat Validation Experiment (CryoVEx) running from 2004 to 2008, the National Space Institute (DTU Space) measured the elevations along a profile crossing the Greenland Ice Sheet. The elevation observations were carried out in 2004, 2006 and 2008 using airborne laser altimetry from a Twin Otter aircraft. The observed profile follows the old EGIG line (Expédition Glaciologique au Groenland, measured in the 1950's) situated between 69-71N, heading nearly east-west. This unique dataset gives the opportunity to study elevation changes along the profile crossing the ice sheet. With this work, we outline the observed elevation changes from the different zones of the ice sheet. We furthermore compare elevation changes based on coincident ICESat and airborne laser altimeter data.

Personalizing lung cancer risk prediction and imaging follow-up recommendations using the National Lung Screening Trial dataset.

PubMed

Hostetter, Jason M; Morrison, James J; Morris, Michael; Jeudy, Jean; Wang, Kenneth C; Siegel, Eliot

2017-11-01

To demonstrate a data-driven method for personalizing lung cancer risk prediction using a large clinical dataset. An algorithm was used to categorize nodules found in the first screening year of the National Lung Screening Trial as malignant or nonmalignant. Risk of malignancy for nodules was calculated based on size criteria according to the Fleischner Society recommendations from 2005, along with the additional discriminators of pack-years smoking history, sex, and nodule location. Imaging follow-up recommendations were assigned according to Fleischner size category malignancy risk. Nodule size correlated with malignancy risk as predicted by the Fleischner Society recommendations. With the additional discriminators of smoking history, sex, and nodule location, significant risk stratification was observed. For example, men with ≥60 pack-years smoking history and upper lobe nodules measuring >4 and ≤6 mm demonstrated significantly increased risk of malignancy at 12.4% compared to the mean of 3.81% for similarly sized nodules (P < .0001). Based on personalized malignancy risk, 54% of nodules >4 and ≤6 mm were reclassified to longer-term follow-up than recommended by Fleischner. Twenty-seven percent of nodules ≤4 mm were reclassified to shorter-term follow-up. Using available clinical datasets such as the National Lung Screening Trial in conjunction with locally collected datasets can help clinicians provide more personalized malignancy risk predictions and follow-up recommendations. By incorporating 3 demographic data points, the risk of lung nodule malignancy within the Fleischner categories can be considerably stratified and more personalized follow-up recommendations can be made. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Federal standards and procedures for the National Watershed Boundary Dataset (WBD)

USGS Publications Warehouse

,; ,; ,

2013-01-01

The Watershed Boundary Dataset (WBD) is a comprehensive aggregated collection of hydrologic unit data consistent with the national criteria for delineation and resolution. This document establishes Federal standards and procedures for creating the WBD as seamless and hierarchical hydrologic unit data, based on topographic and hydrologic features at a 1:24,000 scale in the United States, except for Alaska at 1:63,360 scale, and 1:25,000 scale in the Caribbean. The data within the WBD have been reviewed for certification through the 12-digit hydrologic unit for compliance with the criteria outlined in this document. Any edits to certified data will be reviewed against this standard prior to inclusion. Although not required as part of the framework WBD, the guidelines contain details for compiling and delineating the boundaries of two additional levels, the 14- and 16-digit hydrologic units, as well as the use of higher resolution base information to improve delineations. The guidelines presented herein are designed to enable local, regional, and national partners to delineate hydrologic units consistently and accurately. Such consistency improves watershed management through efficient sharing of information and resources and by ensuring that digital geographic data are usable with other related Geographic Information System (GIS) data.Terminology, definitions, and procedural information are provided to ensure uniformity in hydrologic unit boundaries, names, and numerical codes. Detailed standards and specifications for data are included. The document also includes discussion of objectives, communications required for revising the data resolution in the United States and the Caribbean, as well as final review and data-quality criteria. Instances of unusual landforms or artificial features that affect the hydrologic units are described with metadata standards. Up-to-date information and availability of the hydrologic units are listed at http:// www.nrcs.usda.gov/wps/portal/nrcs/detail/national

Federal standards and procedures for the National Watershed Boundary Dataset (WBD)

USGS Publications Warehouse

U.S. Geological Survey and U.S. Department of Agriculture, Natural Resources Conservation Service

2012-01-01

The Watershed Boundary Dataset (WBD) is a comprehensive aggregated collection of hydrologic unit data consistent with the national criteria for delineation and resolution. This document establishes Federal standards and procedures for creating the WBD as seamless and hierarchical hydrologic unit data, based on topographic and hydrologic features at a 1:24,000 scale in the United States, except for Alaska at 1:63,360 scale, and 1:25,000 scale in the Caribbean. The data within the WBD have been reviewed for certification through the 12-digit hydrologic unit for compliance with the criteria outlined in this document. Any edits to certified data will be reviewed against this standard prior to inclusion. Although not required as part of the framework WBD, the guidelines contain details for compiling and delineating the boundaries of two additional levels, the 14- and 16-digit hydrologic units, as well as the use of higher resolution base information to improve delineations. The guidelines presented herein are designed to enable local, regional, and national partners to delineate hydrologic units consistently and accurately. Such consistency improves watershed management through efficient sharing of information and resources and by ensuring that digital geographic data are usable with other related Geographic Information System (GIS) data. Terminology, definitions, and procedural information are provided to ensure uniformity in hydrologic unit boundaries, names, and numerical codes. Detailed standards and specifications for data are included. The document also includes discussion of objectives, communications required for revising the data resolution in the United States and the Caribbean, as well as final review and data-quality criteria. Instances of unusual landforms or artificial features that affect the hydrologic units are described with metadata standards. Up-to-date information and availability of the hydrologic units are listed at http://www.nrcs.usda.gov/wps/portal/nrcs/detail/national

4. South Elevation Columbia Island Abutment Four; South Elevation ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

4. South Elevation - Columbia Island Abutment Four; South Elevation - Washington Abutment One - Arlington Memorial Bridge, Spanning Potomac River between Lincoln Memorial & Arlington National Cemetery, Washington, District of Columbia, DC

Neighborhood Screening in Communities Throughout the Nation for Children with Elevated Blood Lead Levels

PubMed Central

Anderson, Dudley G.; Clark, John L.

1974-01-01

From the spring of 1971 to September 1973, neighborhood surveys were conducted in 58 communities throughout the nation to determine whether children with confirmed elevated blood lead levels could be identified. Another purpose of these screenings was to assist communities in identifying children with elevated blood lead levels and thereby demonstrate to community officials that such children do exist in communities screened. The children screened were not a random sample. In those communities where the initial elevated blood levels were confirmed all but seven had one or more children requiring followup and/or treatment. Of those children screened, black children had an elevated rate about three times as great as nonblack children. With few exceptions, the homes in the neighborhoods had at least one interior surface with sufficient quantities of lead paint to be dangerous if the paint were ingested. PMID:4831146

Google Earth elevation data extraction and accuracy assessment for transportation applications.

PubMed

Wang, Yinsong; Zou, Yajie; Henrickson, Kristian; Wang, Yinhai; Tang, Jinjun; Park, Byung-Jung

2017-01-01

Roadway elevation data is critical for a variety of transportation analyses. However, it has been challenging to obtain such data and most roadway GIS databases do not have them. This paper intends to address this need by proposing a method to extract roadway elevation data from Google Earth (GE) for transportation applications. A comprehensive accuracy assessment of the GE-extracted elevation data is conducted for the area of conterminous USA. The GE elevation data was compared with the ground truth data from nationwide GPS benchmarks and roadway monuments from six states in the conterminous USA. This study also compares the GE elevation data with the elevation raster data from the U.S. Geological Survey National Elevation Dataset (USGS NED), which is a widely used data source for extracting roadway elevation. Mean absolute error (MAE) and root mean squared error (RMSE) are used to assess the accuracy and the test results show MAE, RMSE and standard deviation of GE roadway elevation error are 1.32 meters, 2.27 meters and 2.27 meters, respectively. Finally, the proposed extraction method was implemented and validated for the following three scenarios: (1) extracting roadway elevation differentiating by directions, (2) multi-layered roadway recognition in freeway segment and (3) slope segmentation and grade calculation in freeway segment. The methodology validation results indicate that the proposed extraction method can locate the extracting route accurately, recognize multi-layered roadway section, and segment the extracted route by grade automatically. Overall, it is found that the high accuracy elevation data available from GE provide a reliable data source for various transportation applications.

Lake Michigan Diversion Accounting land cover change estimation by use of the National Land Cover Dataset and raingage network partitioning analysis

USGS Publications Warehouse

Sharpe, Jennifer B.; Soong, David T.

2015-01-01

This study used the National Land Cover Dataset (NLCD) and developed an automated process for determining the area of the three land cover types, thereby allowing faster updating of future models, and for evaluating land cover changes by use of historical NLCD datasets. The study also carried out a raingage partitioning analysis so that the segmentation of land cover and rainfall in each modeled unit is directly applicable to the HSPF modeling. Historical and existing impervious, grass, and forest land acreages partitioned by percentages covered by two sets of raingages for the Lake Michigan diversion SCAs, gaged basins, and ungaged basins are presented.

Final Report on the Creation of the Wind Integration National Dataset (WIND) Toolkit and API: October 1, 2013 - September 30, 2015

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

Hodge, Bri-Mathias

2016-04-08

The primary objective of this work was to create a state-of-the-art national wind resource data set and to provide detailed wind plant output data for specific sites based on that data set. Corresponding retrospective wind forecasts were also included at all selected locations. The combined information from these activities was used to create the Wind Integration National Dataset (WIND), and an extraction tool was developed to allow web-based data access.

Spatio-Temporal Data Model for Integrating Evolving Nation-Level Datasets

NASA Astrophysics Data System (ADS)

Sorokine, A.; Stewart, R. N.

2017-10-01

Ability to easily combine the data from diverse sources in a single analytical workflow is one of the greatest promises of the Big Data technologies. However, such integration is often challenging as datasets originate from different vendors, governments, and research communities that results in multiple incompatibilities including data representations, formats, and semantics. Semantics differences are hardest to handle: different communities often use different attribute definitions and associate the records with different sets of evolving geographic entities. Analysis of global socioeconomic variables across multiple datasets over prolonged time is often complicated by the difference in how boundaries and histories of countries or other geographic entities are represented. Here we propose an event-based data model for depicting and tracking histories of evolving geographic units (countries, provinces, etc.) and their representations in disparate data. The model addresses the semantic challenge of preserving identity of geographic entities over time by defining criteria for the entity existence, a set of events that may affect its existence, and rules for mapping between different representations (datasets). Proposed model is used for maintaining an evolving compound database of global socioeconomic and environmental data harvested from multiple sources. Practical implementation of our model is demonstrated using PostgreSQL object-relational database with the use of temporal, geospatial, and NoSQL database extensions.

A Gridded Daily Min/Max Temperature Dataset With 0.1° Resolution for the Yangtze River Valley and its Error Estimation

NASA Astrophysics Data System (ADS)

Xiong, Qiufen; Hu, Jianglin

2013-05-01

The minimum/maximum (Min/Max) temperature in the Yangtze River valley is decomposed into the climatic mean and anomaly component. A spatial interpolation is developed which combines the 3D thin-plate spline scheme for climatological mean and the 2D Barnes scheme for the anomaly component to create a daily Min/Max temperature dataset. The climatic mean field is obtained by the 3D thin-plate spline scheme because the relationship between the decreases in Min/Max temperature with elevation is robust and reliable on a long time-scale. The characteristics of the anomaly field tend to be related to elevation variation weakly, and the anomaly component is adequately analyzed by the 2D Barnes procedure, which is computationally efficient and readily tunable. With this hybridized interpolation method, a daily Min/Max temperature dataset that covers the domain from 99°E to 123°E and from 24°N to 36°N with 0.1° longitudinal and latitudinal resolution is obtained by utilizing daily Min/Max temperature data from three kinds of station observations, which are national reference climatological stations, the basic meteorological observing stations and the ordinary meteorological observing stations in 15 provinces and municipalities in the Yangtze River valley from 1971 to 2005. The error estimation of the gridded dataset is assessed by examining cross-validation statistics. The results show that the statistics of daily Min/Max temperature interpolation not only have high correlation coefficient (0.99) and interpolation efficiency (0.98), but also the mean bias error is 0.00 °C. For the maximum temperature, the root mean square error is 1.1 °C and the mean absolute error is 0.85 °C. For the minimum temperature, the root mean square error is 0.89 °C and the mean absolute error is 0.67 °C. Thus, the new dataset provides the distribution of Min/Max temperature over the Yangtze River valley with realistic, successive gridded data with 0.1° × 0.1° spatial resolution and

Google Earth elevation data extraction and accuracy assessment for transportation applications

PubMed Central

Wang, Yinsong; Zou, Yajie; Henrickson, Kristian; Wang, Yinhai; Tang, Jinjun; Park, Byung-Jung

2017-01-01

Roadway elevation data is critical for a variety of transportation analyses. However, it has been challenging to obtain such data and most roadway GIS databases do not have them. This paper intends to address this need by proposing a method to extract roadway elevation data from Google Earth (GE) for transportation applications. A comprehensive accuracy assessment of the GE-extracted elevation data is conducted for the area of conterminous USA. The GE elevation data was compared with the ground truth data from nationwide GPS benchmarks and roadway monuments from six states in the conterminous USA. This study also compares the GE elevation data with the elevation raster data from the U.S. Geological Survey National Elevation Dataset (USGS NED), which is a widely used data source for extracting roadway elevation. Mean absolute error (MAE) and root mean squared error (RMSE) are used to assess the accuracy and the test results show MAE, RMSE and standard deviation of GE roadway elevation error are 1.32 meters, 2.27 meters and 2.27 meters, respectively. Finally, the proposed extraction method was implemented and validated for the following three scenarios: (1) extracting roadway elevation differentiating by directions, (2) multi-layered roadway recognition in freeway segment and (3) slope segmentation and grade calculation in freeway segment. The methodology validation results indicate that the proposed extraction method can locate the extracting route accurately, recognize multi-layered roadway section, and segment the extracted route by grade automatically. Overall, it is found that the high accuracy elevation data available from GE provide a reliable data source for various transportation applications. PMID:28445480

Development of a global historic monthly mean precipitation dataset

NASA Astrophysics Data System (ADS)

Yang, Su; Xu, Wenhui; Xu, Yan; Li, Qingxiang

2016-04-01

Global historic precipitation dataset is the base for climate and water cycle research. There have been several global historic land surface precipitation datasets developed by international data centers such as the US National Climatic Data Center (NCDC), European Climate Assessment & Dataset project team, Met Office, etc., but so far there are no such datasets developed by any research institute in China. In addition, each dataset has its own focus of study region, and the existing global precipitation datasets only contain sparse observational stations over China, which may result in uncertainties in East Asian precipitation studies. In order to take into account comprehensive historic information, users might need to employ two or more datasets. However, the non-uniform data formats, data units, station IDs, and so on add extra difficulties for users to exploit these datasets. For this reason, a complete historic precipitation dataset that takes advantages of various datasets has been developed and produced in the National Meteorological Information Center of China. Precipitation observations from 12 sources are aggregated, and the data formats, data units, and station IDs are unified. Duplicated stations with the same ID are identified, with duplicated observations removed. Consistency test, correlation coefficient test, significance t-test at the 95% confidence level, and significance F-test at the 95% confidence level are conducted first to ensure the data reliability. Only those datasets that satisfy all the above four criteria are integrated to produce the China Meteorological Administration global precipitation (CGP) historic precipitation dataset version 1.0. It contains observations at 31 thousand stations with 1.87 × 107 data records, among which 4152 time series of precipitation are longer than 100 yr. This dataset plays a critical role in climate research due to its advantages in large data volume and high density of station network, compared to

Back to the Future: Have Remotely Sensed Digital Elevation Models Improved Hydrological Parameter Extraction?

NASA Astrophysics Data System (ADS)

Jarihani, B.

2015-12-01

Digital Elevation Models (DEMs) that accurately replicate both landscape form and processes are critical to support modeling of environmental processes. Pre-processing analysis of DEMs and extracting characteristics of the watershed (e.g., stream networks, catchment delineation, surface and subsurface flow paths) is essential for hydrological and geomorphic analysis and sediment transport. This study investigates the status of the current remotely-sensed DEMs in providing advanced morphometric information of drainage basins particularly in data sparse regions. Here we assess the accuracy of three available DEMs: (i) hydrologically corrected "H-DEM" of Geoscience Australia derived from the Shuttle Radar Topography Mission (SRTM) data; (ii) the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) version2 1-arc-second (~30 m) data; and (iii) the 9-arc-second national GEODATA DEM-9S ver3 from Geoscience Australia and the Australian National University. We used ESRI's geospatial data model, Arc Hydro and HEC-GeoHMS, designed for building hydrologic information systems to synthesize geospatial and temporal water resources data that support hydrologic modeling and analysis. A coastal catchment in northeast Australia was selected as the study site where very high resolution LiDAR data are available for parts of the area as reference data to assess the accuracy of other lower resolution datasets. This study provides morphometric information for drainage basins as part of the broad research on sediment flux from coastal basins to Great Barrier Reef, Australia. After applying geo-referencing and elevation corrections, stream and sub basins were delineated for each DEM. Then physical characteristics for streams (i.e., length, upstream and downstream elevation, and slope) and sub-basins (i.e., longest flow lengths, area, relief and slopes) were extracted and compared with reference datasets from LiDAR. Results showed that

A downscaled 1 km dataset of daily Greenland ice sheet surface mass balance components (1958-2014)

NASA Astrophysics Data System (ADS)

Noel, B.; Van De Berg, W. J.; Fettweis, X.; Machguth, H.; Howat, I. M.; van den Broeke, M. R.

2015-12-01

The current spatial resolution in regional climate models (RCMs), typically around 5 to 20 km, remains too coarse to accurately reproduce the spatial variability in surface mass balance (SMB) components over the narrow ablation zones, marginal outlet glaciers and neighbouring ice caps of the Greenland ice sheet (GrIS). In these topographically rough terrains, the SMB components are highly dependent on local variations in topography. However, the relatively low-resolution elevation and ice mask prescribed in RCMs contribute to significantly underestimate melt and runoff in these regions due to unresolved valley glaciers and fjords. Therefore, near-km resolution topography is essential to better capture SMB variability in these spatially restricted regions. We present a 1 km resolution dataset of daily GrIS SMB covering the period 1958-2014, which is statistically downscaled from data of the polar regional climate model RACMO2.3 at 11 km, using an elevation dependence. The dataset includes all individual SMB components projected on the elevation and ice mask from the GIMP DEM, down-sampled to 1 km. Daily runoff and sublimation are interpolated to the 1 km topography using a local regression to elevation valid for each day specifically; daily precipitation is bi-linearly downscaled without elevation corrections. The daily SMB dataset is then reconstructed by summing downscaled precipitation, sublimation and runoff. High-resolution elevation and ice mask allow for properly resolving the narrow ablation zones and valley glaciers at the GrIS margins, leading to significant increase in runoff estimate. In these regions, and especially over narrow glaciers tongues, the downscaled products improve on the original RACMO2.3 outputs by better representing local SMB patterns through a gradual ablation increase towards the GrIS margins. We discuss the impact of downscaling on the SMB components in a case study for a spatially restricted region, where large elevation

Glacier-specific elevation changes in western Alaska

NASA Astrophysics Data System (ADS)

Paul, Frank; Le Bris, Raymond

2013-04-01

Deriving glacier-specific elevation changes from DEM differencing and digital glacier outlines is rather straight-forward if the required datasets are available. Calculating such changes over large regions and including glaciers selected for mass balance measurements in the field, provides a possibility to determine the representativeness of the changes observed at these glaciers for the entire region. The related comparison of DEM-derived values for these glaciers with the overall mean avoids the rather error-prone conversion of volume to mass changes (e.g. due to unknown densities) and gives unit-less correction factors for upscaling the field measurements to a larger region. However, several issues have to be carefully considered, such as proper co-registration of the two DEMs, date and accuracy of the datasets compared, as well as source data used for DEM creation and potential artefacts (e.g. voids). In this contribution we present an assessment of the representativeness of the two mass balance glaciers Gulkana and Wolverine for the overall changes of nearly 3200 glaciers in western Alaska over a ca. 50-year time period. We use an elevation change dataset from a study by Berthier et al. (2010) that was derived from the USGS DEM of the 1960s (NED) and a more recent DEM derived from SPOT5 data for the SPIRIT project. Additionally, the ASTER GDEM was used as a more recent DEM. Historic glacier outlines were taken from the USGS digital line graph (DLG) dataset, corrected with the digital raster graph (DRG) maps from USGS. Mean glacier specific elevation changes were derived based on drainage divides from a recently created inventory. Land-terminating, lake-calving and tidewater glaciers were marked in the attribute table to determine their changes separately. We also investigated the impact of handling potential DEM artifacts in three different ways and compared elevation changes with altitude. The mean elevation changes of Gulkana and Wolverine glaciers (about -0

Final Report: Archiving Data to Support Data Synthesis of DOE Sponsored Elevated CO 2 Experiments

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

Megonigal, James; Lu, Meng

Over the last three decades DOE made a large investment in field-scale experiments in order to understand the role of terrestrial ecosystems in the global carbon cycle, and forecast how carbon cycling will change over the next century. The Smithsonian Environmental Research Center received one of the first awards in this program and managed two long-term studies (25 years and 10 years) with a total of approximately $10 million of support from DOE, and many more millions leveraged from the Smithsonian Institution and agencies such as NSF. The present DOE grant was based on the premise that such a largemore » investment demands a proper synthesis effort so that the full potential of these experiments are realized through data analysis and modeling. The goal of the this grant was to archive legacy data from two major elevated carbon dioxide experiments in DOE databases, and to engage in synthesis activities using these data. Both goals were met. All datasets deemed a high priority for data synthesis and modeling were prepared for archiving and analysis. Many of these datasets were deposited in DOE’s CDIAC, while others are being held at the Oak Ridge National Lab and the Smithsonian Institution until they can be received by DOE’s new ESS-DIVE system at Berkeley Lab. Most of the effort was invested in researching and re-constituting high-quality data sets from a 30-year elevated CO 2 experiment. Using these data, the grant produced products that are already benefiting climate change science, including the publication of new coastal wetland allometry equations based on 9,771 observations, public posting of dozens of datasets, metadata and supporting codes from long-term experiments at the Global Change Research Wetland, and publication of two synthetic data papers on scrub oak forest responses to elevated CO 2. In addition, three papers are in review or nearing submission reporting unexpected long-term patterns in ecosystem responses to elevated CO 2 and nitrogen

The Southampton-York Natural Scenes (SYNS) dataset: Statistics of surface attitude

PubMed Central

Adams, Wendy J.; Elder, James H.; Graf, Erich W.; Leyland, Julian; Lugtigheid, Arthur J.; Muryy, Alexander

2016-01-01

Recovering 3D scenes from 2D images is an under-constrained task; optimal estimation depends upon knowledge of the underlying scene statistics. Here we introduce the Southampton-York Natural Scenes dataset (SYNS: https://syns.soton.ac.uk), which provides comprehensive scene statistics useful for understanding biological vision and for improving machine vision systems. In order to capture the diversity of environments that humans encounter, scenes were surveyed at random locations within 25 indoor and outdoor categories. Each survey includes (i) spherical LiDAR range data (ii) high-dynamic range spherical imagery and (iii) a panorama of stereo image pairs. We envisage many uses for the dataset and present one example: an analysis of surface attitude statistics, conditioned on scene category and viewing elevation. Surface normals were estimated using a novel adaptive scale selection algorithm. Across categories, surface attitude below the horizon is dominated by the ground plane (0° tilt). Near the horizon, probability density is elevated at 90°/270° tilt due to vertical surfaces (trees, walls). Above the horizon, probability density is elevated near 0° slant due to overhead structure such as ceilings and leaf canopies. These structural regularities represent potentially useful prior assumptions for human and machine observers, and may predict human biases in perceived surface attitude. PMID:27782103

Finding the Maine Story in Hugh Cumbersome National Monitoring Datasets

EPA Science Inventory

What’s a manager, analyst, or concerned citizen to do with the complex datasets generated by State and Federal monitoring efforts? Is it possible to use such information to address Maine’s environmental issues without having a degree in informatics and statistics? This presentati...

Topographic and hydrographic GIS dataset for the Afghanistan Geological Survey and U.S. Geological Survey 2010 Minerals Project

USGS Publications Warehouse

Chirico, P.G.; Moran, T.W.

2011-01-01

This dataset contains a collection of 24 folders, each representing a specific U.S. Geological Survey area of interest (AOI; fig. 1), as well as datasets for AOI subsets. Each folder includes the extent, contours, Digital Elevation Model (DEM), and hydrography of the corresponding AOI, which are organized into feature vector and raster datasets. The dataset comprises a geographic information system (GIS), which is available upon request from the USGS Afghanistan programs Web site (http://afghanistan.cr.usgs.gov/minerals.php), and the maps of the 24 areas of interest of the USGS AOIs.

Food habits of introduced rodents in high-elevation shrubland of Haleakala National Park, Maui, Hawai'i

USGS Publications Warehouse

Cole, F. Russell; Loope, Lloyd L.; Medeiros, Arthur C.; Howe, Cameron E.; Anderson, Laurel J.

2000-01-01

Mus musculus and Rattus rattus are ubiquitous consumers in the high-elevation shrubland of Haleakala National Park. Food habits of these two rodent species were determined from stomach samples obtained by snaptrapping along transects located at four different elevations during November 1984 and February, May, and August 1985. Mus musculus fed primarily on fruits, grass seeds, and arthropods. Rattus rattus ate various fruits, dicot leaves, and arthropods. Arthropods, many of which are endemic, were taken frequently by Mus musculus throughout the year at the highest elevation where plant food resources were scarce. Araneida, Lepidoptera (primarily larvae), Coleoptera, and Homoptera were the main arthropod taxa taken. These rodents, particularly Mus musculus, exert strong predation pressure on populations of arthropod species, including locally endemic species on upper Haleakala Volcano.

Development of a global slope dataset for estimation of landslide occurrence resulting from earthquakes

USGS Publications Warehouse

Verdin, Kristine L.; Godt, Jonathan W.; Funk, Christopher C.; Pedreros, Diego; Worstell, Bruce; Verdin, James

2007-01-01

Landslides resulting from earthquakes can cause widespread loss of life and damage to critical infrastructure. The U.S. Geological Survey (USGS) has developed an alarm system, PAGER (Prompt Assessment of Global Earthquakes for Response), that aims to provide timely information to emergency relief organizations on the impact of earthquakes. Landslides are responsible for many of the damaging effects following large earthquakes in mountainous regions, and thus data defining the topographic relief and slope are critical to the PAGER system. A new global topographic dataset was developed to aid in rapidly estimating landslide potential following large earthquakes. We used the remotely-sensed elevation data collected as part of the Shuttle Radar Topography Mission (SRTM) to generate a slope dataset with nearly global coverage. Slopes from the SRTM data, computed at 3-arc-second resolution, were summarized at 30-arc-second resolution, along with statistics developed to describe the distribution of slope within each 30-arc-second pixel. Because there are many small areas lacking SRTM data and the northern limit of the SRTM mission was lat 60?N., statistical methods referencing other elevation data were used to fill the voids within the dataset and to extrapolate the data north of 60?. The dataset will be used in the PAGER system to rapidly assess the susceptibility of areas to landsliding following large earthquakes.

On sample size and different interpretations of snow stability datasets

NASA Astrophysics Data System (ADS)

Schirmer, M.; Mitterer, C.; Schweizer, J.

2009-04-01

Interpretations of snow stability variations need an assessment of the stability itself, independent of the scale investigated in the study. Studies on stability variations at a regional scale have often chosen stability tests such as the Rutschblock test or combinations of various tests in order to detect differences in aspect and elevation. The question arose: ‘how capable are such stability interpretations in drawing conclusions'. There are at least three possible errors sources: (i) the variance of the stability test itself; (ii) the stability variance at an underlying slope scale, and (iii) that the stability interpretation might not be directly related to the probability of skier triggering. Various stability interpretations have been proposed in the past that provide partly different results. We compared a subjective one based on expert knowledge with a more objective one based on a measure derived from comparing skier-triggered slopes vs. slopes that have been skied but not triggered. In this study, the uncertainties are discussed and their effects on regional scale stability variations will be quantified in a pragmatic way. An existing dataset with very large sample sizes was revisited. This dataset contained the variance of stability at a regional scale for several situations. The stability in this dataset was determined using the subjective interpretation scheme based on expert knowledge. The question to be answered was how many measurements were needed to obtain similar results (mainly stability differences in aspect or elevation) as with the complete dataset. The optimal sample size was obtained in several ways: (i) assuming a nominal data scale the sample size was determined with a given test, significance level and power, and by calculating the mean and standard deviation of the complete dataset. With this method it can also be determined if the complete dataset consists of an appropriate sample size. (ii) Smaller subsets were created with similar

A Systematic Review of Global Drivers of Ant Elevational Diversity

PubMed Central

Szewczyk, Tim; McCain, Christy M.

2016-01-01

Ant diversity shows a variety of patterns across elevational gradients, though the patterns and drivers have not been evaluated comprehensively. In this systematic review and reanalysis, we use published data on ant elevational diversity to detail the observed patterns and to test the predictions and interactions of four major diversity hypotheses: thermal energy, the mid-domain effect, area, and the elevational climate model. Of sixty-seven published datasets from the literature, only those with standardized, comprehensive sampling were used. Datasets included both local and regional ant diversity and spanned 80° in latitude across six biogeographical provinces. We used a combination of simulations, linear regressions, and non-parametric statistics to test multiple quantitative predictions of each hypothesis. We used an environmentally and geometrically constrained model as well as multiple regression to test their interactions. Ant diversity showed three distinct patterns across elevations: most common were hump-shaped mid-elevation peaks in diversity, followed by low-elevation plateaus and monotonic decreases in the number of ant species. The elevational climate model, which proposes that temperature and precipitation jointly drive diversity, and area were partially supported as independent drivers. Thermal energy and the mid-domain effect were not supported as primary drivers of ant diversity globally. The interaction models supported the influence of multiple drivers, though not a consistent set. In contrast to many vertebrate taxa, global ant elevational diversity patterns appear more complex, with the best environmental model contingent on precipitation levels. Differences in ecology and natural history among taxa may be crucial to the processes influencing broad-scale diversity patterns. PMID:27175999

elevatr: Access Elevation Data from Various APIs | Science ...

EPA Pesticide Factsheets

Several web services are available that provide access to elevation data. This package provides access to several of those services and returns elevation data either as a SpatialPointsDataFrame from point elevation services or as a raster object from raster elevation services. Currently, the package supports access to the Mapzen Elevation Service, Mapzen Terrain Service, and the USGS Elevation Point Query Service. The R language for statistical computing is increasingly used for spatial data analysis . This R package, elevatr, is in response to this and provides access to elevation data from various sources directly in R. The impact of `elevatr` is that it will 1) facilitate spatial analysis in R by providing access to foundational dataset for many types of analyses (e.g. hydrology, limnology) 2) open up a new set of users and uses for APIs widely used outside of R, and 3) provide an excellent example federal open source development as promoted by the Federal Source Code Policy (https://sourcecode.cio.gov/).

Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades.

PubMed

Orchard, Garrick; Jayawant, Ajinkya; Cohen, Gregory K; Thakor, Nitish

2015-01-01

Creating datasets for Neuromorphic Vision is a challenging task. A lack of available recordings from Neuromorphic Vision sensors means that data must typically be recorded specifically for dataset creation rather than collecting and labeling existing data. The task is further complicated by a desire to simultaneously provide traditional frame-based recordings to allow for direct comparison with traditional Computer Vision algorithms. Here we propose a method for converting existing Computer Vision static image datasets into Neuromorphic Vision datasets using an actuated pan-tilt camera platform. Moving the sensor rather than the scene or image is a more biologically realistic approach to sensing and eliminates timing artifacts introduced by monitor updates when simulating motion on a computer monitor. We present conversion of two popular image datasets (MNIST and Caltech101) which have played important roles in the development of Computer Vision, and we provide performance metrics on these datasets using spike-based recognition algorithms. This work contributes datasets for future use in the field, as well as results from spike-based algorithms against which future works can compare. Furthermore, by converting datasets already popular in Computer Vision, we enable more direct comparison with frame-based approaches.

Long-term monitoring of high-elevation white pine communities in Pacific West Region National Parks

Treesearch

Shawn T. McKinney; Tom Rodhouse; Les Chow; Penelope Latham; Daniel Sarr; Lisa Garrett; Linda Mutch

2011-01-01

National Park Service Inventory and Monitoring (I&M) networks conduct long-term monitoring to provide park managers information on the status and trends in key biological and environmental attributes (Vital Signs). Here we present an overview of a collaborative approach to long-term monitoring of high-elevation white pine forest dynamics among three Pacific West...

State of Louisiana - Highlighting low-lying areas derived from USGS Digital Elevation Data

USGS Publications Warehouse

Kosovich, John J.

2008-01-01

In support of U.S. Geological Survey (USGS) disaster preparedness efforts, this map depicts a color shaded relief representation highlighting the State of Louisiana and depicts the surrounding areas using muted elevation colors. The first 30 feet of relief above mean sea level are displayed as brightly colored 5-foot elevation bands, which highlight low-elevation areas at a coarse spatial resolution. Areas below sea level typically are surrounded by levees or some other type of flood-control structures. Standard USGS National Elevation Dataset (NED) 1 arc-second (nominally 30-meter) digital elevation model (DEM) data are the basis for the map, which is designed to be used at a broad scale and for informational purposes only. The NED data are a mixture of data and were derived from the original 1:24,000-scale USGS topographic map bare-earth contours, which were converted into gridded quadrangle-based DEM tiles at a constant post spacing (grid cell size) of either 30 meters (data before the mid-1990s) or 10 meters (mid-1990s and later data). These individual-quadrangle DEMs were then converted to spherical coordinates (latitude/longitude decimal degrees) and edge-matched to ensure seamlessness. Approximately one-half of the area shown on this map has DEM source data at a 30-meter resolution, with the remaining half consisting of mostly 10-meter contour-derived DEM data and some small areas of higher-resolution LIght Detection And Ranging (LIDAR) data along parts of the coastline. Areas below sea level typically are surrounded by levees or some other type of flood-control structures. State and parish boundary, hydrography, city, and road layers were modified from USGS National Atlas data downloaded in 2003. The NED data were downloaded in 2007.

Gulf of Mexico region - Highlighting low-lying areas derived from USGS Digital Elevation Data

USGS Publications Warehouse

Kosovich, John J.

2008-01-01

In support of U.S. Geological Survey (USGS) disaster preparedness efforts, this map depicts a color shaded relief representation of the area surrounding the Gulf of Mexico. The first 30 feet of relief above mean sea level are displayed as brightly colored 5-foot elevation bands, which highlight low-elevation areas at a coarse spatial resolution. Standard USGS National Elevation Dataset (NED) 1 arc-second (nominally 30-meter) digital elevation model (DEM) data are the basis for the map, which is designed to be used at a broad scale and for informational purposes only. The NED data were derived from the original 1:24,000-scale USGS topographic map bare-earth contours, which were converted into gridded quadrangle-based DEM tiles at a constant post spacing (grid cell size) of either 30 meters (data before the mid-1990s data) or 10 meters (mid-1990s and later data). These individual-quadrangle DEMs were then converted to spherical coordinates (latitude/longitude decimal degrees) and edge-matched to ensure seamlessness. Approximately one-half of the area shown on this map has DEM source data at a 30-meter resolution, with the remaining half consisting of 10-meter contour-derived DEM data or higher-resolution LIDAR data. Areas below sea level typically are surrounded by levees or some other type of flood-control structures. State and county boundary, hydrography, city, and road layers were modified from USGS National Atlas data downloaded in 2003. The NED data were downloaded in 2005.

An Intercomparison of Large-Extent Tree Canopy Cover Geospatial Datasets

NASA Astrophysics Data System (ADS)

Bender, S.; Liknes, G.; Ruefenacht, B.; Reynolds, J.; Miller, W. P.

2017-12-01

As a member of the Multi-Resolution Land Characteristics Consortium (MRLC), the U.S. Forest Service (USFS) is responsible for producing and maintaining the tree canopy cover (TCC) component of the National Land Cover Database (NLCD). The NLCD-TCC data are available for the conterminous United States (CONUS), coastal Alaska, Hawai'i, Puerto Rico, and the U.S. Virgin Islands. The most recent official version of the NLCD-TCC data is based primarily on reference data from 2010-2011 and is part of the multi-component 2011 version of the NLCD. NLCD data are updated on a five-year cycle. The USFS is currently producing the next official version (2016) of the NLCD-TCC data for the United States, and it will be made publicly-available in early 2018. In this presentation, we describe the model inputs, modeling methods, and tools used to produce the 30-m NLCD-TCC data. Several tree cover datasets at 30-m, as well as datasets at finer resolution, have become available in recent years due to advancements in earth observation data and their availability, computing, and sensors. We compare multiple tree cover datasets that have similar resolution to the NLCD-TCC data. We also aggregate the tree class from fine-resolution land cover datasets to a percent canopy value on a 30-m pixel, in order to compare the fine-resolution datasets to the datasets created directly from 30-m Landsat data. The extent of the tree canopy cover datasets included in the study ranges from global and national to the state level. Preliminary investigation of multiple tree cover datasets over the CONUS indicates a high amount of spatial variability. For example, in a comparison of the NLCD-TCC and the Global Land Cover Facility's Landsat Tree Cover Continuous Fields (2010) data by MRLC mapping zones, the zone-level root mean-square deviation ranges from 2% to 39% (mean=17%, median=15%). The analysis outcomes are expected to inform USFS decisions with regard to the next cycle (2021) of NLCD-TCC production.

Topogrid Derived 10 Meter Resolution Digital Elevation Model of Charleston, and Parts of Berkeley, Colleton, Dorchester and Georgetown Counties, South Carolina

USGS Publications Warehouse

Chirico, Peter G.

2005-01-01

EXPLANATION The purpose of developing a new 10m resolution digital elevation model (DEM) of the Charleston Region was to more accurately depict geologic structure, surfical geology, and landforms of the Charleston County Region. Previously, many areas northeast and southwest of Charleston were originally mapped with a 20 foot contour interval. As a result, large areas within the National Elevation Dataset (NED) depict flat terraced topography where there was a lack of higher resolution elevation data. To overcome these data voids, the new DEM is supplemented with additional elevation data and break-lines derived from aerial photography and topographic maps. The resultant DEM is stored as a raster grid at uniform 10m horizontal resolution. The elevation model contained in this publication was prodcued utilizing the ANUDEM algorthim. ANUDEM allows for the inclusion of contours, streams, rivers, lake and water body polygons as well as spot height data to control the development of the elevation model. A preliminary statistical analysis using over 788 vertical elevation check points, primarily located in the northeastern part of the study area, derived from USGS 7.5 Minute Topographic maps reveals that the final DEM, has a vertical accuracy of ?3.27 meters. A table listing the elevation comparison between the elevation check points and the final DEM is provided.

Development of a SPARK Training Dataset

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

Sayre, Amanda M.; Olson, Jarrod R.

2015-03-01

In its first five years, the National Nuclear Security Administration’s (NNSA) Next Generation Safeguards Initiative (NGSI) sponsored more than 400 undergraduate, graduate, and post-doctoral students in internships and research positions (Wyse 2012). In the past seven years, the NGSI program has, and continues to produce a large body of scientific, technical, and policy work in targeted core safeguards capabilities and human capital development activities. Not only does the NGSI program carry out activities across multiple disciplines, but also across all U.S. Department of Energy (DOE)/NNSA locations in the United States. However, products are not readily shared among disciplines and acrossmore » locations, nor are they archived in a comprehensive library. Rather, knowledge of NGSI-produced literature is localized to the researchers, clients, and internal laboratory/facility publication systems such as the Electronic Records and Information Capture Architecture (ERICA) at the Pacific Northwest National Laboratory (PNNL). There is also no incorporated way of analyzing existing NGSI literature to determine whether the larger NGSI program is achieving its core safeguards capabilities and activities. A complete library of NGSI literature could prove beneficial to a cohesive, sustainable, and more economical NGSI program. The Safeguards Platform for Automated Retrieval of Knowledge (SPARK) has been developed to be a knowledge storage, retrieval, and analysis capability to capture safeguards knowledge to exist beyond the lifespan of NGSI. During the development process, it was necessary to build a SPARK training dataset (a corpus of documents) for initial entry into the system and for demonstration purposes. We manipulated these data to gain new information about the breadth of NGSI publications, and they evaluated the science-policy interface at PNNL as a practical demonstration of SPARK’s intended analysis capability. The analysis demonstration sought to answer

Comparison of CORA and EN4 in-situ datasets validation methods, toward a better quality merged dataset.

NASA Astrophysics Data System (ADS)

Szekely, Tanguy; Killick, Rachel; Gourrion, Jerome; Reverdin, Gilles

2017-04-01

CORA and EN4 are both global delayed time mode validated in-situ ocean temperature and salinity datasets distributed by the Met Office (http://www.metoffice.gov.uk/) and Copernicus (www.marine.copernicus.eu). A large part of the profiles distributed by CORA and EN4 in recent years are Argo profiles from the ARGO DAC, but profiles are also extracted from the World Ocean Database and TESAC profiles from GTSPP. In the case of CORA, data coming from the EUROGOOS Regional operationnal oserving system( ROOS) operated by European institutes no managed by National Data Centres and other datasets of profiles povided by scientific sources can also be found (Sea mammals profiles from MEOP, XBT datasets from cruises ...). (EN4 also takes data from the ASBO dataset to supplement observations in the Arctic). First advantage of this new merge product is to enhance the space and time coverage at global and european scales for the period covering 1950 till a year before the current year. This product is updated once a year and T&S gridded fields are alos generated for the period 1990-year n-1. The enhancement compared to the revious CORA product will be presented Despite the fact that the profiles distributed by both datasets are mostly the same, the quality control procedures developed by the Met Office and Copernicus teams differ, sometimes leading to different quality control flags for the same profile. Started in 2016 a new study started that aims to compare both validation procedures to move towards a Copernicus Marine Service dataset with the best features of CORA and EN4 validation.A reference data set composed of the full set of in-situ temperature and salinity measurements collected by Coriolis during 2015 is used. These measurements have been made thanks to wide range of instruments (XBTs, CTDs, Argo floats, Instrumented sea mammals,...), covering the global ocean. The reference dataset has been validated simultaneously by both teams.An exhaustive comparison of the

A nonparametric statistical technique for combining global precipitation datasets: development and hydrological evaluation over the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Abul Ehsan Bhuiyan, Md; Nikolopoulos, Efthymios I.; Anagnostou, Emmanouil N.; Quintana-Seguí, Pere; Barella-Ortiz, Anaïs

2018-02-01

This study investigates the use of a nonparametric, tree-based model, quantile regression forests (QRF), for combining multiple global precipitation datasets and characterizing the uncertainty of the combined product. We used the Iberian Peninsula as the study area, with a study period spanning 11 years (2000-2010). Inputs to the QRF model included three satellite precipitation products, CMORPH, PERSIANN, and 3B42 (V7); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived near-surface daily soil moisture data; and a terrain elevation dataset. We calibrated the QRF model for two seasons and two terrain elevation categories and used it to generate ensemble for these conditions. Evaluation of the combined product was based on a high-resolution, ground-reference precipitation dataset (SAFRAN) available at 5 km 1 h-1 resolution. Furthermore, to evaluate relative improvements and the overall impact of the combined product in hydrological response, we used the generated ensemble to force a distributed hydrological model (the SURFEX land surface model and the RAPID river routing scheme) and compared its streamflow simulation results with the corresponding simulations from the individual global precipitation and reference datasets. We concluded that the proposed technique could generate realizations that successfully encapsulate the reference precipitation and provide significant improvement in streamflow simulations, with reduction in systematic and random error on the order of 20-99 and 44-88 %, respectively, when considering the ensemble mean.

Elevated Maternal C-Reactive Protein and Autism in a National Birth Cohort

PubMed Central

Brown, Alan S.; Sourander, Andre; Hinkka-Yli-Salomäki, Susanna; McKeague, Ian W.; Sundvall, Jouko; Surcel, Helja-Marja

2012-01-01

Autism is a complex neuropsychiatric syndrome with a largely unknown etiology. Inflammation during pregnancy may represent a common pathway by which infections and other insults increase risk for the disorder. Hence, we investigated the association between early gestational C-reactive protein (CRP), an established inflammatory biomarker, prospectively assayed in maternal sera, and childhood autism in a large national birth cohort with an extensive serum biobank. Other strengths of the cohort included nearly complete ascertainment of pregnancies in Finland (N=1.2 million) over the study period and national psychiatric registries consisting of virtually all treated autism cases in the population. Increasing maternal CRP levels, classified as a continuous variable, were significantly associated with autism in offspring. For maternal CRP levels in the highest quintile, compared to the lowest quintile, there was a significant, 43% elevated risk. This finding suggests that maternal inflammation may play a significant role in autism, with possible implications for identifying preventive strategies and pathogenic mechanisms in autism and other neurodevelopmental disorders. PMID:23337946

Wide-Open: Accelerating public data release by automating detection of overdue datasets

PubMed Central

Poon, Hoifung; Howe, Bill

2017-01-01

Open data is a vital pillar of open science and a key enabler for reproducibility, data reuse, and novel discoveries. Enforcement of open-data policies, however, largely relies on manual efforts, which invariably lag behind the increasingly automated generation of biological data. To address this problem, we developed a general approach to automatically identify datasets overdue for public release by applying text mining to identify dataset references in published articles and parse query results from repositories to determine if the datasets remain private. We demonstrate the effectiveness of this approach on 2 popular National Center for Biotechnology Information (NCBI) repositories: Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA). Our Wide-Open system identified a large number of overdue datasets, which spurred administrators to respond directly by releasing 400 datasets in one week. PMID:28594819

Wide-Open: Accelerating public data release by automating detection of overdue datasets.

PubMed

Grechkin, Maxim; Poon, Hoifung; Howe, Bill

2017-06-01

Open data is a vital pillar of open science and a key enabler for reproducibility, data reuse, and novel discoveries. Enforcement of open-data policies, however, largely relies on manual efforts, which invariably lag behind the increasingly automated generation of biological data. To address this problem, we developed a general approach to automatically identify datasets overdue for public release by applying text mining to identify dataset references in published articles and parse query results from repositories to determine if the datasets remain private. We demonstrate the effectiveness of this approach on 2 popular National Center for Biotechnology Information (NCBI) repositories: Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA). Our Wide-Open system identified a large number of overdue datasets, which spurred administrators to respond directly by releasing 400 datasets in one week.

A comparison of two global datasets of extreme sea levels and resulting flood exposure

NASA Astrophysics Data System (ADS)

Muis, Sanne; Verlaan, Martin; Nicholls, Robert J.; Brown, Sally; Hinkel, Jochen; Lincke, Daniel; Vafeidis, Athanasios T.; Scussolini, Paolo; Winsemius, Hessel C.; Ward, Philip J.

2017-04-01

Estimating the current risk of coastal flooding requires adequate information on extreme sea levels. For over a decade, the only global data available was the DINAS-COAST Extreme Sea Levels (DCESL) dataset, which applies a static approximation to estimate extreme sea levels. Recently, a dynamically derived dataset was developed: the Global Tide and Surge Reanalysis (GTSR) dataset. Here, we compare the two datasets. The differences between DCESL and GTSR are generally larger than the confidence intervals of GTSR. Compared to observed extremes, DCESL generally overestimates extremes with a mean bias of 0.6 m. With a mean bias of -0.2 m GTSR generally underestimates extremes, particularly in the tropics. The Dynamic Interactive Vulnerability Assessment model is applied to calculate the present-day flood exposure in terms of the land area and the population below the 1 in 100-year sea levels. Global exposed population is 28% lower when based on GTSR instead of DCESL. Considering the limited data available at the time, DCESL provides a good estimate of the spatial variation in extremes around the world. However, GTSR allows for an improved assessment of the impacts of coastal floods, including confidence bounds. We further improve the assessment of coastal impacts by correcting for the conflicting vertical datum of sea-level extremes and land elevation, which has not been accounted for in previous global assessments. Converting the extreme sea levels to the same vertical reference used for the elevation data is shown to be a critical step resulting in 39-59% higher estimate of population exposure.

Evaluation, Calibration and Comparison of the Precipitation-Runoff Modeling System (PRMS) National Hydrologic Model (NHM) Using Moderate Resolution Imaging Spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) Gridded Datasets

NASA Astrophysics Data System (ADS)

Norton, P. A., II; Haj, A. E., Jr.

2014-12-01

The United States Geological Survey is currently developing a National Hydrologic Model (NHM) to support and facilitate coordinated and consistent hydrologic modeling efforts at the scale of the continental United States. As part of this effort, the Geospatial Fabric (GF) for the NHM was created. The GF is a database that contains parameters derived from datasets that characterize the physical features of watersheds. The GF was used to aggregate catchments and flowlines defined in the National Hydrography Dataset Plus dataset for more than 100,000 hydrologic response units (HRUs), and to establish initial parameter values for input to the Precipitation-Runoff Modeling System (PRMS). Many parameter values are adjusted in PRMS using an automated calibration process. Using these adjusted parameter values, the PRMS model estimated variables such as evapotranspiration (ET), potential evapotranspiration (PET), snow-covered area (SCA), and snow water equivalent (SWE). In order to evaluate the effectiveness of parameter calibration, and model performance in general, several satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) gridded datasets including ET, PET, SCA, and SWE were compared to PRMS-simulated values. The MODIS and SNODAS data were spatially averaged for each HRU, and compared to PRMS-simulated ET, PET, SCA, and SWE values for each HRU in the Upper Missouri River watershed. Default initial GF parameter values and PRMS calibration ranges were evaluated. Evaluation results, and the use of MODIS and SNODAS datasets to update GF parameter values and PRMS calibration ranges, are presented and discussed.

Statistical Reference Datasets

National Institute of Standards and Technology Data Gateway

Statistical Reference Datasets (Web, free access)   The Statistical Reference Datasets is also supported by the Standard Reference Data Program. The purpose of this project is to improve the accuracy of statistical software by providing reference datasets with certified computational results that enable the objective evaluation of statistical software.

A gridded hourly rainfall dataset for the UK applied to a national physically-based modelling system

NASA Astrophysics Data System (ADS)

Lewis, Elizabeth; Blenkinsop, Stephen; Quinn, Niall; Freer, Jim; Coxon, Gemma; Woods, Ross; Bates, Paul; Fowler, Hayley

2016-04-01

An hourly gridded rainfall product has great potential for use in many hydrological applications that require high temporal resolution meteorological data. One important example of this is flood risk management, with flooding in the UK highly dependent on sub-daily rainfall intensities amongst other factors. Knowledge of sub-daily rainfall intensities is therefore critical to designing hydraulic structures or flood defences to appropriate levels of service. Sub-daily rainfall rates are also essential inputs for flood forecasting, allowing for estimates of peak flows and stage for flood warning and response. In addition, an hourly gridded rainfall dataset has significant potential for practical applications such as better representation of extremes and pluvial flash flooding, validation of high resolution climate models and improving the representation of sub-daily rainfall in weather generators. A new 1km gridded hourly rainfall dataset for the UK has been created by disaggregating the daily Gridded Estimates of Areal Rainfall (CEH-GEAR) dataset using comprehensively quality-controlled hourly rain gauge data from over 1300 observation stations across the country. Quality control measures include identification of frequent tips, daily accumulations and dry spells, comparison of daily totals against the CEH-GEAR daily dataset, and nearest neighbour checks. The quality control procedure was validated against historic extreme rainfall events and the UKCP09 5km daily rainfall dataset. General use of the dataset has been demonstrated by testing the sensitivity of a physically-based hydrological modelling system for Great Britain to the distribution and rates of rainfall and potential evapotranspiration. Of the sensitivity tests undertaken, the largest improvements in model performance were seen when an hourly gridded rainfall dataset was combined with potential evapotranspiration disaggregated to hourly intervals, with 61% of catchments showing an increase in NSE between

Dataset Lifecycle Policy

NASA Technical Reports Server (NTRS)

Armstrong, Edward; Tauer, Eric

2013-01-01

The presentation focused on describing a new dataset lifecycle policy that the NASA Physical Oceanography DAAC (PO.DAAC) has implemented for its new and current datasets to foster improved stewardship and consistency across its archive. The overarching goal is to implement this dataset lifecycle policy for all new GHRSST GDS2 datasets and bridge the mission statements from the GHRSST Project Office and PO.DAAC to provide the best quality SST data in a cost-effective, efficient manner, preserving its integrity so that it will be available and usable to a wide audience.

The 3D Elevation Program: summary of program direction

USGS Publications Warehouse

Snyder, Gregory I.

2012-01-01

The 3D Elevation Program (3DEP) initiative responds to a growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation's natural and constructed features. The National Enhanced Elevation Assessment (NEEA), which was completed in 2011, clearly documented this need within government and industry sectors. The results of the NEEA indicated that enhanced elevation data have the potential to generate $13 billion in new benefits annually. The benefits apply to food risk management, agriculture, water supply, homeland security, renewable energy, aviation safety, and other areas. The 3DEP initiative was recommended by the National Digital Elevation Program and its 12 Federal member agencies and was endorsed by the National States Geographic Information Council (NSGIC) and the National Geospatial Advisory Committee (NGAC).

Dataset on spatial distribution and location of universities in Nigeria.

PubMed

Adeyemi, G A; Edeki, S O

2018-06-01

Access to quality educational system, and the location of educational institutions are of great importance for future prospect of youth in any nation. These in return, have great effects on the economy growth and development of any country. Thus, the dataset contained in this article examines and explains the spatial distribution of universities in the Nigeria system of education. Data from the university commission, Nigeria, as at December 2017 are used. These include all the 40 federal universities, 44 states universities, and 69 private universities making a total of 153 universities in the Nigerian system of education. The data analysis is via the Geographic Information System (GIS) software. The dataset contained in this article will be of immense assistance to the national educational policy makers, parents, and potential students as regards smart and reliable decision making academically.

Datasets2Tools, repository and search engine for bioinformatics datasets, tools and canned analyses

PubMed Central

Torre, Denis; Krawczuk, Patrycja; Jagodnik, Kathleen M.; Lachmann, Alexander; Wang, Zichen; Wang, Lily; Kuleshov, Maxim V.; Ma’ayan, Avi

2018-01-01

Biomedical data repositories such as the Gene Expression Omnibus (GEO) enable the search and discovery of relevant biomedical digital data objects. Similarly, resources such as OMICtools, index bioinformatics tools that can extract knowledge from these digital data objects. However, systematic access to pre-generated ‘canned’ analyses applied by bioinformatics tools to biomedical digital data objects is currently not available. Datasets2Tools is a repository indexing 31,473 canned bioinformatics analyses applied to 6,431 datasets. The Datasets2Tools repository also contains the indexing of 4,901 published bioinformatics software tools, and all the analyzed datasets. Datasets2Tools enables users to rapidly find datasets, tools, and canned analyses through an intuitive web interface, a Google Chrome extension, and an API. Furthermore, Datasets2Tools provides a platform for contributing canned analyses, datasets, and tools, as well as evaluating these digital objects according to their compliance with the findable, accessible, interoperable, and reusable (FAIR) principles. By incorporating community engagement, Datasets2Tools promotes sharing of digital resources to stimulate the extraction of knowledge from biomedical research data. Datasets2Tools is freely available from: http://amp.pharm.mssm.edu/datasets2tools. PMID:29485625

Datasets2Tools, repository and search engine for bioinformatics datasets, tools and canned analyses.

PubMed

Torre, Denis; Krawczuk, Patrycja; Jagodnik, Kathleen M; Lachmann, Alexander; Wang, Zichen; Wang, Lily; Kuleshov, Maxim V; Ma'ayan, Avi

2018-02-27

Biomedical data repositories such as the Gene Expression Omnibus (GEO) enable the search and discovery of relevant biomedical digital data objects. Similarly, resources such as OMICtools, index bioinformatics tools that can extract knowledge from these digital data objects. However, systematic access to pre-generated 'canned' analyses applied by bioinformatics tools to biomedical digital data objects is currently not available. Datasets2Tools is a repository indexing 31,473 canned bioinformatics analyses applied to 6,431 datasets. The Datasets2Tools repository also contains the indexing of 4,901 published bioinformatics software tools, and all the analyzed datasets. Datasets2Tools enables users to rapidly find datasets, tools, and canned analyses through an intuitive web interface, a Google Chrome extension, and an API. Furthermore, Datasets2Tools provides a platform for contributing canned analyses, datasets, and tools, as well as evaluating these digital objects according to their compliance with the findable, accessible, interoperable, and reusable (FAIR) principles. By incorporating community engagement, Datasets2Tools promotes sharing of digital resources to stimulate the extraction of knowledge from biomedical research data. Datasets2Tools is freely available from: http://amp.pharm.mssm.edu/datasets2tools.

Providing Access to a Diverse Set of Global Reanalysis Dataset Collections

NASA Astrophysics Data System (ADS)

Schuster, D.; Worley, S. J.

2015-12-01

The National Center for Atmospheric Research (NCAR) Research Data Archive (RDA, http://rda.ucar.edu) provides open access to a variety of global reanalysis dataset collections to support atmospheric and related sciences research worldwide. These include products from the European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), National Centers for Environmental Prediction (NCEP), National Oceanic and Atmospheric Administration (NOAA), and NCAR.All RDA hosted reanalysis collections are freely accessible to registered users through a variety of methods. Standard access methods include traditional browser and scripted HTTP file download. Enhanced downloads are available through the Globus GridFTP "fire and forget" data transfer service, which provides an efficient, reliable, and preferred alternative to traditional HTTP-based methods. For those that favor interoperable access using compatible tools, the Unidata THREDDS Data server provides remote access to complete reanalysis collections by virtual dataset aggregation "files". Finally, users can request data subsets and format conversions to be prepared for them through web interface form requests or web service API batch requests. This approach uses NCAR HPC and central file systems to effectively prepare products from the high-resolution and very large reanalyses archives. The presentation will include a detailed inventory of all RDA reanalysis dataset collection holdings, and highlight access capabilities to these collections through use case examples.

Learning to recognize rat social behavior: Novel dataset and cross-dataset application.

PubMed

Lorbach, Malte; Kyriakou, Elisavet I; Poppe, Ronald; van Dam, Elsbeth A; Noldus, Lucas P J J; Veltkamp, Remco C

2018-04-15

Social behavior is an important aspect of rodent models. Automated measuring tools that make use of video analysis and machine learning are an increasingly attractive alternative to manual annotation. Because machine learning-based methods need to be trained, it is important that they are validated using data from different experiment settings. To develop and validate automated measuring tools, there is a need for annotated rodent interaction datasets. Currently, the availability of such datasets is limited to two mouse datasets. We introduce the first, publicly available rat social interaction dataset, RatSI. We demonstrate the practical value of the novel dataset by using it as the training set for a rat interaction recognition method. We show that behavior variations induced by the experiment setting can lead to reduced performance, which illustrates the importance of cross-dataset validation. Consequently, we add a simple adaptation step to our method and improve the recognition performance. Most existing methods are trained and evaluated in one experimental setting, which limits the predictive power of the evaluation to that particular setting. We demonstrate that cross-dataset experiments provide more insight in the performance of classifiers. With our novel, public dataset we encourage the development and validation of automated recognition methods. We are convinced that cross-dataset validation enhances our understanding of rodent interactions and facilitates the development of more sophisticated recognition methods. Combining them with adaptation techniques may enable us to apply automated recognition methods to a variety of animals and experiment settings. Copyright © 2017 Elsevier B.V. All rights reserved.

Channel mapping river miles 29–62 of the Colorado River in Grand Canyon National Park, Arizona, May 2009

USGS Publications Warehouse

Kaplinski, Matt; Hazel, Joseph E.; Grams, Paul E.; Kohl, Keith; Buscombe, Daniel D.; Tusso, Robert B.

2017-03-23

Bathymetric, topographic, and grain-size data were collected in May 2009 along a 33-mi reach of the Colorado River in Grand Canyon National Park, Arizona. The study reach is located from river miles 29 to 62 at the confluence of the Colorado and Little Colorado Rivers. Channel bathymetry was mapped using multibeam and singlebeam echosounders, subaerial topography was mapped using ground-based total-stations, and bed-sediment grain-size data were collected using an underwater digital microscope system. These data were combined to produce digital elevation models, spatially variable estimates of digital elevation model uncertainty, georeferenced grain-size data, and bed-sediment distribution maps. This project is a component of a larger effort to monitor the status and trends of sand storage along the Colorado River in Grand Canyon National Park. This report documents the survey methods and post-processing procedures, digital elevation model production and uncertainty assessment, and procedures for bed-sediment classification, and presents the datasets resulting from this study.

Utilizing the Antarctic Master Directory to find orphan datasets

NASA Astrophysics Data System (ADS)

Bonczkowski, J.; Carbotte, S. M.; Arko, R. A.; Grebas, S. K.

2011-12-01

While most Antarctic data are housed at an established disciplinary-specific data repository, there are data types for which no suitable repository exists. In some cases, these "orphan" data, without an appropriate national archive, are served from local servers by the principal investigators who produced the data. There are many pitfalls with data served privately, including the frequent lack of adequate documentation to ensure the data can be understood by others for re-use and the impermanence of personal web sites. For example, if an investigator leaves an institution and the data moves, the link published is no longer accessible. To ensure continued availability of data, submission to long-term national data repositories is needed. As stated in the National Science Foundation Office of Polar Programs (NSF/OPP) Guidelines and Award Conditions for Scientific Data, investigators are obligated to submit their data for curation and long-term preservation; this includes the registration of a dataset description into the Antarctic Master Directory (AMD), http://gcmd.nasa.gov/Data/portals/amd/. The AMD is a Web-based, searchable directory of thousands of dataset descriptions, known as DIF records, submitted by scientists from over 20 countries. It serves as a node of the International Directory Network/Global Change Master Directory (IDN/GCMD). The US Antarctic Program Data Coordination Center (USAP-DCC), http://www.usap-data.org/, funded through NSF/OPP, was established in 2007 to help streamline the process of data submission and DIF record creation. When data does not quite fit within any existing disciplinary repository, it can be registered within the USAP-DCC as the fallback data repository. Within the scope of the USAP-DCC we undertook the challenge of discovering and "rescuing" orphan datasets currently registered within the AMD. In order to find which DIF records led to data served privately, all records relating to US data within the AMD were parsed. After

Influence of Elevation Data Source on 2D Hydraulic Modelling

NASA Astrophysics Data System (ADS)

Bakuła, Krzysztof; StĘpnik, Mateusz; Kurczyński, Zdzisław

2016-08-01

The aim of this paper is to analyse the influence of the source of various elevation data on hydraulic modelling in open channels. In the research, digital terrain models from different datasets were evaluated and used in two-dimensional hydraulic models. The following aerial and satellite elevation data were used to create the representation of terrain-digital terrain model: airborne laser scanning, image matching, elevation data collected in the LPIS, EuroDEM, and ASTER GDEM. From the results of five 2D hydrodynamic models with different input elevation data, the maximum depth and flow velocity of water were derived and compared with the results of the most accurate ALS data. For such an analysis a statistical evaluation and differences between hydraulic modelling results were prepared. The presented research proved the importance of the quality of elevation data in hydraulic modelling and showed that only ALS and photogrammetric data can be the most reliable elevation data source in accurate 2D hydraulic modelling.

seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day

NASA Astrophysics Data System (ADS)

Lussana, Cristian; Saloranta, Tuomo; Skaugen, Thomas; Magnusson, Jan; Tveito, Ole Einar; Andersen, Jess

2018-02-01

The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successive-correction schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall-runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2

Sodankylä ionospheric tomography dataset 2003-2014

NASA Astrophysics Data System (ADS)

Norberg, J.; Roininen, L.; Kero, A.; Raita, T.; Ulich, T.; Markkanen, M.; Juusola, L.; Kauristie, K.

2015-12-01

Sodankylä Geophysical Observatory has been operating a tomographic receiver network and collecting the produced data since 2003. The collected dataset consists of phase difference curves measured from Russian COSMOS dual-frequency (150/400 MHz) low-Earth-orbit satellite signals, and tomographic electron density reconstructions obtained from these measurements. In this study vertical total electron content (VTEC) values are integrated from the reconstructed electron densities to make a qualitative and quantitative analysis to validate the long-term performance of the tomographic system. During the observation period, 2003-2014, there were three-to-five operational stations at the Fenno-Scandinavian sector. Altogether the analysis consists of around 66 000 overflights, but to ensure the quality of the reconstructions, the examination is limited to cases with descending (north to south) overflights and maximum elevation over 60°. These constraints limit the number of overflights to around 10 000. Based on this dataset, one solar cycle of ionospheric vertical total electron content estimates is constructed. The measurements are compared against International Reference Ionosphere IRI-2012 model, F10.7 solar flux index and sunspot number data. Qualitatively the tomographic VTEC estimate corresponds to reference data very well, but the IRI-2012 model are on average 40 % higher of that of the tomographic results.

DATS, the data tag suite to enable discoverability of datasets.

PubMed

Sansone, Susanna-Assunta; Gonzalez-Beltran, Alejandra; Rocca-Serra, Philippe; Alter, George; Grethe, Jeffrey S; Xu, Hua; Fore, Ian M; Lyle, Jared; Gururaj, Anupama E; Chen, Xiaoling; Kim, Hyeon-Eui; Zong, Nansu; Li, Yueling; Liu, Ruiling; Ozyurt, I Burak; Ohno-Machado, Lucila

2017-06-06

Today's science increasingly requires effective ways to find and access existing datasets that are distributed across a range of repositories. For researchers in the life sciences, discoverability of datasets may soon become as essential as identifying the latest publications via PubMed. Through an international collaborative effort funded by the National Institutes of Health (NIH)'s Big Data to Knowledge (BD2K) initiative, we have designed and implemented the DAta Tag Suite (DATS) model to support the DataMed data discovery index. DataMed's goal is to be for data what PubMed has been for the scientific literature. Akin to the Journal Article Tag Suite (JATS) used in PubMed, the DATS model enables submission of metadata on datasets to DataMed. DATS has a core set of elements, which are generic and applicable to any type of dataset, and an extended set that can accommodate more specialized data types. DATS is a platform-independent model also available as an annotated serialization in schema.org, which in turn is widely used by major search engines like Google, Microsoft, Yahoo and Yandex.

National Water Model: Providing the Nation with Actionable Water Intelligence

NASA Astrophysics Data System (ADS)

Aggett, G. R.; Bates, B.

2017-12-01

The National Water Model (NWM) provides national, street-level detail of water movement through time and space. Operating hourly, this flood of information offers enormous benefits in the form of water resource management, natural disaster preparedness, and the protection of life and property. The Geo-Intelligence Division at the NOAA National Water Center supplies forecasters and decision-makers with timely, actionable water intelligence through the processing of billions of NWM data points every hour. These datasets include current streamflow estimates, short and medium range streamflow forecasts, and many other ancillary datasets. The sheer amount of NWM data produced yields a dataset too large to allow for direct human comprehension. As such, it is necessary to undergo model data post-processing, filtering, and data ingestion by visualization web apps that make use of cartographic techniques to bring attention to the areas of highest urgency. This poster illustrates NWM output post-processing and cartographic visualization techniques being developed and employed by the Geo-Intelligence Division at the NOAA National Water Center to provide national actionable water intelligence.

Chemical elements in the environment: multi-element geochemical datasets from continental to national scale surveys on four continents

USGS Publications Warehouse

Caritat, Patrice de; Reimann, Clemens; Smith, David; Wang, Xueqiu

2017-01-01

During the last 10-20 years, Geological Surveys around the world have undertaken a major effort towards delivering fully harmonized and tightly quality-controlled low-density multi-element soil geochemical maps and datasets of vast regions including up to whole continents. Concentrations of between 45 and 60 elements commonly have been determined in a variety of different regolith types (e.g., sediment, soil). The multi-element datasets are published as complete geochemical atlases and made available to the general public. Several other geochemical datasets covering smaller areas but generally at a higher spatial density are also available. These datasets may, however, not be found by superficial internet-based searches because the elements are not mentioned individually either in the title or in the keyword lists of the original references. This publication attempts to increase the visibility and discoverability of these fundamental background datasets covering large areas up to whole continents.

NASADEM Global Elevation Model of Earth: Methods for the Refinement and Merger of SRTM and ASTER GDEM

NASA Astrophysics Data System (ADS)

Crippen, R. E.; Buckley, S.; Agram, P. S.; Belz, J. E.; Gurrola, E. M.; Hensley, S.; Kobrick, M.; Lavalle, M.; Martin, J. M.; Neumann, M.; Nguyen, Q.; Rosen, P. A.; Shimada, J.; Simard, M.; Tung, W.

2016-12-01

NASADEM is a near-global elevation model that is being produced primarily by completely reprocessing the Shuttle Radar Topography Mission (SRTM) radar data and then merging it with refined ASTER GDEM elevations. The new and improved SRTM elevations in NASADEM result from better vertical control of each SRTM data swath via reference to ICESat elevations and from SRTM void reductions using advanced interferometric unwrapping algorithms. Errors in SRTM (due to incorrect interferometric unwrapping) are rare but can be found and removed via a detector that relies upon pattern analysis within synergistic comparisons of SRTM and GDEM. Remnant voids in SRTM are filled primarily by GDEM3, but with removal of GDEM glitches that are mostly related to clouds. GDEM glitch removal uses a measure of curvature and then spatial filtering to detect, isolate, and delete anomalous spikes and pits that are uncharacteristic of natural topography. Water masking uses the original SRTM Water Body Dataset (SWBD), but with errors corrected via a new ASTER Water Body Database. The improved SRTM, GDEM, and water body databases will be made available individually in addition to our merged product, which is particularly important for the SRTM dataset, which stands as a February 2000 baseline for many topographic change studies. New and forthcoming freely available elevation data (at reduced resolutions) from the ALOS PRISM World 3D and TanDEM-X projects will contribute to the critical but not yet reached goal of a complete, high-quality elevation model of Earth, and they are expected to provide additional validation for NASADEM. Indeed, cross validation among all of these datasets is a vital part of reaching that goal. The value of elevation data is difficult to overstate. These data are used in nearly all types of geophysical study conducted at or near Earth's surface.

Scalable persistent identifier systems for dynamic datasets

NASA Astrophysics Data System (ADS)

Golodoniuc, P.; Cox, S. J. D.; Klump, J. F.

2016-12-01

Reliable and persistent identification of objects, whether tangible or not, is essential in information management. Many Internet-based systems have been developed to identify digital data objects, e.g., PURL, LSID, Handle, ARK. These were largely designed for identification of static digital objects. The amount of data made available online has grown exponentially over the last two decades and fine-grained identification of dynamically generated data objects within large datasets using conventional systems (e.g., PURL) has become impractical. We have compared capabilities of various technological solutions to enable resolvability of data objects in dynamic datasets, and developed a dataset-centric approach to resolution of identifiers. This is particularly important in Semantic Linked Data environments where dynamic frequently changing data is delivered live via web services, so registration of individual data objects to obtain identifiers is impractical. We use identifier patterns and pattern hierarchies for identification of data objects, which allows relationships between identifiers to be expressed, and also provides means for resolving a single identifier into multiple forms (i.e. views or representations of an object). The latter can be implemented through (a) HTTP content negotiation, or (b) use of URI querystring parameters. The pattern and hierarchy approach has been implemented in the Linked Data API supporting the United Nations Spatial Data Infrastructure (UNSDI) initiative and later in the implementation of geoscientific data delivery for the Capricorn Distal Footprints project using International Geo Sample Numbers (IGSN). This enables flexible resolution of multi-view persistent identifiers and provides a scalable solution for large heterogeneous datasets.

Soil Erosion map of Europe based on high resolution input datasets

NASA Astrophysics Data System (ADS)

Panagos, Panos; Borrelli, Pasquale; Meusburger, Katrin; Ballabio, Cristiano; Alewell, Christine

2015-04-01

Modelling soil erosion in European Union is of major importance for agro-environmental policies. Soil erosion estimates are important inputs for the Common Agricultural Policy (CAP) and the implementation of the Soil Thematic Strategy. Using the findings of a recent pan-European data collection through the EIONET network, it was concluded that most Member States are applying the empirical Revised Universal Soil Loss Equation (RUSLE) for the modelling soil erosion at National level. This model was chosen for the pan-European soil erosion risk assessment and it is based on 6 input factors. Compared to past approaches, each of the factors is modelled using the latest pan-European datasets, expertise and data from Member states and high resolution remote sensing data. The soil erodibility (K-factor) is modelled using the recently published LUCAS topsoil database with 20,000 point measurements and incorporating the surface stone cover which can reduce K-factor by 15%. The rainfall erosivity dataset (R-factor) has been implemented using high temporal resolution rainfall data from more than 1,500 precipitation stations well distributed in Europe. The cover-management (C-factor) incorporates crop statistics and management practices such as cover crops, tillage practices and plant residuals. The slope length and steepness (combined LS-factor) is based on the first ever 25m Digital Elevation Model (DEM) of Europe. Finally, the support practices (P-factor) is modelled for first time at this scale taking into account the 270,000 LUCAS earth observations and the Good Agricultural and Environmental Condition (GAEC) that farmers have to follow in Europe. The high resolution input layers produce the final soil erosion risk map at 100m resolution and allow policy makers to run future land use, management and climate change scenarios.

Segmentation of Unstructured Datasets

NASA Technical Reports Server (NTRS)

Bhat, Smitha

1996-01-01

Datasets generated by computer simulations and experiments in Computational Fluid Dynamics tend to be extremely large and complex. It is difficult to visualize these datasets using standard techniques like Volume Rendering and Ray Casting. Object Segmentation provides a technique to extract and quantify regions of interest within these massive datasets. This thesis explores basic algorithms to extract coherent amorphous regions from two-dimensional and three-dimensional scalar unstructured grids. The techniques are applied to datasets from Computational Fluid Dynamics and from Finite Element Analysis.

Standardising trauma monitoring: the development of a minimum dataset for trauma registries in Australia and New Zealand.

PubMed

Palmer, Cameron S; Davey, Tamzyn M; Mok, Meng Tuck; McClure, Rod J; Farrow, Nathan C; Gruen, Russell L; Pollard, Cliff W

2013-06-01

Trauma registries are central to the implementation of effective trauma systems. However, differences between trauma registry datasets make comparisons between trauma systems difficult. In 2005, the collaborative Australian and New Zealand National Trauma Registry Consortium began a process to develop a bi-national minimum dataset (BMDS) for use in Australasian trauma registries. This study aims to describe the steps taken in the development and preliminary evaluation of the BMDS. A working party comprising sixteen representatives from across Australasia identified and discussed the collectability and utility of potential BMDS fields. This included evaluating existing national and international trauma registry datasets, as well as reviewing all quality indicators and audit filters in use in Australasian trauma centres. After the working party activities concluded, this process was continued by a number of interested individuals, with broader feedback sought from the Australasian trauma community on a number of occasions. Once the BMDS had reached a suitable stage of development, an email survey was conducted across Australasian trauma centres to assess whether BMDS fields met an ideal minimum standard of field collectability. The BMDS was also compared with three prominent international datasets to assess the extent of dataset overlap. Following this, the BMDS was encapsulated in a data dictionary, which was introduced in late 2010. The finalised BMDS contained 67 data fields. Forty-seven of these fields met a previously published criterion of 80% collectability across respondent trauma institutions; the majority of the remaining fields either could be collected without any change in resources, or could be calculated from other data fields in the BMDS. However, comparability with international registry datasets was poor. Only nine BMDS fields had corresponding, directly comparable fields in all the national and international-level registry datasets evaluated. A

The National Map - Elevation

USGS Publications Warehouse

,

2002-01-01

Governments depend on a common set of base geographic information as a tool for economic and community development, land and natural resource management, and health and safety services. Emergency management and homeland security applications rely on this information. Private industry, nongovernmental organizations, and individual citizens use the same geographic data. Geographic information underpins an increasingly large part of the Nation's economy.

44 CFR 67.4 - Proposed flood elevation determination.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 44 Emergency Management and Assistance 1 2010-10-01 2010-10-01 false Proposed flood elevation..., DEPARTMENT OF HOMELAND SECURITY INSURANCE AND HAZARD MITIGATION National Flood Insurance Program APPEALS FROM PROPOSED FLOOD ELEVATION DETERMINATIONS § 67.4 Proposed flood elevation determination. The Federal...

44 CFR 67.4 - Proposed flood elevation determination.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., DEPARTMENT OF HOMELAND SECURITY INSURANCE AND HAZARD MITIGATION National Flood Insurance Program APPEALS FROM PROPOSED FLOOD ELEVATION DETERMINATIONS § 67.4 Proposed flood elevation determination. The Federal... 44 Emergency Management and Assistance 1 2012-10-01 2011-10-01 true Proposed flood elevation...

44 CFR 67.4 - Proposed flood elevation determination.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., DEPARTMENT OF HOMELAND SECURITY INSURANCE AND HAZARD MITIGATION National Flood Insurance Program APPEALS FROM PROPOSED FLOOD ELEVATION DETERMINATIONS § 67.4 Proposed flood elevation determination. The Federal... 44 Emergency Management and Assistance 1 2014-10-01 2014-10-01 false Proposed flood elevation...

A geospatial framework for improving the vertical accuracy of elevation models in Florida's coastal Everglades

NASA Astrophysics Data System (ADS)

Cooper, H.; Zhang, C.; Sirianni, M.

2016-12-01

South Florida relies upon the health of the Everglades, the largest subtropical wetland in North America, as a vital source of water. Since the late 1800's, this imperiled ecosystem has been highly engineered to meet human needs of flood control and water use. The Comprehensive Everglades Restoration Plan (CERP) was initiated in 2000 to restore original water flows to the Everglades and improve overall ecosystem health, while also aiming to achieve balance with human water usage. Due to subtle changes in the Everglades terrain, better vertical accuracy elevation data are needed to model groundwater and surface water levels that are integral to monitoring the effects of restoration under impacts such as sea-level rise. The current best available elevation datasets for the coastal Everglades include High Accuracy Elevation Data (HAED) and Florida Department of Emergency Management (FDEM) Light Detection and Ranging (LiDAR). However, the horizontal resolution of the HAED data is too coarse ( 400 m) for fine scale mapping, and the LiDAR data does not contain an accuracy assessment for coastal Everglades' vegetation communities. The purpose of this study is to develop a framework for generating better vertical accuracy and horizontal resolution Digital Elevation Models in the Flamingo District of Everglades National Park. In the framework, field work is conducted to collect RTK GPS and total station elevation measurements for mangrove swamp, coastal prairies, and freshwater marsh, and the proposed accuracy assessment and elevation modeling methodology is integrated with a Geographical Information System (GIS). It is anticipated that this study will provide more accurate models of the soil substrate elevation that can be used by restoration planners to better predict the future state of the Everglades ecosystem.

1-Meter Digital Elevation Model specification

USGS Publications Warehouse

Arundel, Samantha T.; Archuleta, Christy-Ann M.; Phillips, Lori A.; Roche, Brittany L.; Constance, Eric W.

2015-10-21

In January 2015, the U.S. Geological Survey National Geospatial Technical Operations Center began producing the 1-Meter Digital Elevation Model data product. This new product was developed to provide high resolution bare-earth digital elevation models from light detection and ranging (lidar) elevation data and other elevation data collected over the conterminous United States (lower 48 States), Hawaii, and potentially Alaska and the U.S. territories. The 1-Meter Digital Elevation Model consists of hydroflattened, topographic bare-earth raster digital elevation models, with a 1-meter x 1-meter cell size, and is available in 10,000-meter x 10,000-meter square blocks with a 6-meter overlap. This report details the specifications required for the production of the 1-Meter Digital Elevation Model.

A semi-automated tool for reducing the creation of false closed depressions from a filled LIDAR-derived digital elevation model

USGS Publications Warehouse

Waller, John S.; Doctor, Daniel H.; Terziotti, Silvia

2015-01-01

Closed depressions on the land surface can be identified by ‘filling’ a digital elevation model (DEM) and subtracting the filled model from the original DEM. However, automated methods suffer from artificial ‘dams’ where surface streams cross under bridges and through culverts. Removal of these false depressions from an elevation model is difficult due to the lack of bridge and culvert inventories; thus, another method is needed to breach these artificial dams. Here, we present a semi-automated workflow and toolbox to remove falsely detected closed depressions created by artificial dams in a DEM. The approach finds the intersections between transportation routes (e.g., roads) and streams, and then lowers the elevation surface across the roads to stream level allowing flow to be routed under the road. Once the surface is corrected to match the approximate location of the National Hydrologic Dataset stream lines, the procedure is repeated with sequentially smaller flow accumulation thresholds in order to generate stream lines with less contributing area within the watershed. Through multiple iterations, artificial depressions that may arise due to ephemeral flow paths can also be removed. Preliminary results reveal that this new technique provides significant improvements for flow routing across a DEM and minimizes artifacts within the elevation surface. Slight changes in the stream flow lines generally improve the quality of flow routes; however some artificial dams may persist. Problematic areas include extensive road ditches, particularly along divided highways, and where surface flow crosses beneath road intersections. Limitations do exist, and the results partially depend on the quality of data being input. Of 166 manually identified culverts from a previous study by Doctor and Young in 2013, 125 are within 25 m of culverts identified by this tool. After three iterations, 1,735 culverts were identified and cataloged. The result is a reconditioned

ASTER-Derived 30-Meter-Resolution Digital Elevation Models of Afghanistan

USGS Publications Warehouse

Chirico, Peter G.; Warner, Michael B.

2007-01-01

INTRODUCTION The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is an imaging instrument aboard the Terra satellite, launched on December 19, 1999, as part of the National Aeronautics and Space Administration's (NASA) Earth Observing System (EOS). The ASTER sensor consists of three subsystems: the visible and near infrared (VNIR), the shortwave infrared (SWIR), and the thermal infrared (TIR), each with a different spatial resolution (VNIR, 15 meters; SWIR, 30 meters, TIR 90 meters). The VNIR system has the capability to generate along-track stereo images that can be used to create digital elevation models (DEMs) at 30-meter resolution. Currently, the only available DEM dataset for Afghanistan is the 90-meter-resolution Shuttle Radar Topography Mission (SRTM) data. This dataset is appropriate for macroscale DEM analysis and mapping. However, ASTER provides a low cost opportunity to generate higher resolution data. For this publication, study areas were identified around populated areas and areas where higher resolution elevation data were desired to assist in natural resource assessments. The higher resolution fidelity of these DEMs can also be used for other terrain analysis including landform classification and geologic structure analysis. For this publication, ASTER scenes were processed and mosaicked to generate 36 DEMs which were created and extracted using PCI Geomatics' OrthoEngine 3D Stereo software. The ASTER images were geographically registered to Landsat data with at least 15 accurate and well distributed ground control points with a root mean square error (RMSE) of less that one pixel (15 meters). An elevation value was then assigned to each ground control point by extracting the elevation from the 90-meter SRTM data. The 36 derived DEMs demonstrate that the software correlated on nearly flat surfaces and smooth slopes accurately. Larger errors occur in cloudy and snow-covered areas, lakes, areas with steep slopes, and

One-meter topobathymetric digital elevation model for Majuro Atoll, Republic of the Marshall Islands, 1944 to 2016

USGS Publications Warehouse

Palaseanu-Lovejoy, Monica; Poppenga, Sandra K.; Danielson, Jeffrey J.; Tyler, Dean J.; Gesch, Dean B.; Kottermair, Maria; Jalandoni, Andrea; Carlson, Edward; Thatcher, Cindy A.; Barbee, Matthew M.

2018-03-30

Atoll and island coastal communities are highly exposed to sea-level rise, tsunamis, storm surges, rogue waves, king tides, and the occasional combination of multiple factors, such as high regional sea levels, extreme high local tides, and unusually strong wave set-up. The elevation of most of these atolls averages just under 3 meters (m), with many areas roughly at sea level. The lack of high-resolution topographic data has been identified as a critical data gap for hazard vulnerability and adaptation efforts and for high-resolution inundation modeling for atoll nations. Modern topographic survey equipment and airborne lidar surveys can be very difficult and costly to deploy. Therefore, unmanned aircraft systems (UAS) were investigated for collecting overlapping imagery to generate topographic digital elevation models (DEMs). Medium- and high-resolution satellite imagery (Landsat 8 and WorldView-3) was investigated to derive nearshore bathymetry.The Republic of the Marshall Islands is associated with the United States through a Compact of Free Association, and Majuro Atoll is home to the capital city of Majuro and the largest population of the Republic of the Marshall Islands. The only elevation datasets currently available for the entire Majuro Atoll are the Shuttle Radar Topography Mission and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version 2 elevation data, which have a 30-m grid-cell spacing and a 8-m vertical root mean square error (RMSE). Both these datasets have inadequate spatial resolution and vertical accuracy for inundation modeling.The final topobathymetric DEM (TBDEM) developed for Majuro Atoll is derived from various data sources including charts, soundings, acoustic sonar, and UAS and satellite imagery spanning over 70 years of data collection (1944 to 2016) on different sections of the atoll. The RMSE of the TBDEM over the land area is 0.197 m using over 70,000 Global Navigation Satellite

International Digital Elevation Model Service (IDEMS): A Revived IAG Service

NASA Astrophysics Data System (ADS)

Kelly, K. M.; Hirt, C., , Dr; Kuhn, M.; Barzaghi, R.

2017-12-01

A newly developed International Digital Elevation Model Service (IDEMS) is now available under the umbrella of the International Gravity Field Service of the International Association of Geodesy. Hosted and operated by Environmental Systems Research Institute (Esri) (http://www.esri.com/), the new IDEMS website is available at: https://idems.maps.arcgis.com/home/index.html. IDEMS provides a focus for distribution of data and information about various digital elevation models, including spherical-harmonic models of Earth's global topography and lunar and planetary DEM. Related datasets, such as representation of inland water within DEMs, and relevant software which are available in the public domain are also provided. Currently, IDEMS serves as repository of links to providers of global terrain and bathymetry, terrain related Earth models and datasets such as digital elevation data services managed and maintained by Esri (Terrain and TopoBathy), Bedmap2-Ice thickness and subglacial topographic model of Antarctica and Ice, Cloud, and Land Elevation ICESat/GLAS Data, as well as planetary terrain data provided by PDS Geosciences Node at Washington University, St. Louis. These services provide online access to a collection of multi-resolution and multi-source elevation and bathymetry data, including metadata and source information. In addition to IDEMS current holdings of terrestrial and planetary DEMs, some topography related products IDEMS may include in future are: dynamic ocean topography, 3D crustal density models, Earth's dynamic topography, etc. IDEMS may also consider terrain related products such as quality assessments, global terrain corrections, global height anomaly-to-geoid height corrections and other geodesy-relevant studies and products. IDEMS encourages contributions to the site from the geodetic community in any of the product types listed above. Please contact the authors if you would like to contribute or recommend content you think appropriate for

A Compilation of Spatial Datasets to Support a Preliminary Assessment of Pesticides and Pesticide Use on Tribal Lands in Oklahoma

USGS Publications Warehouse

Mashburn, Shana L.; Winton, Kimberly T.

2010-01-01

This CD-ROM contains spatial datasets that describe natural and anthropogenic features and county-level estimates of agricultural pesticide use and pesticide data for surface-water, groundwater, and biological specimens in the state of Oklahoma. County-level estimates of pesticide use were compiled from the Pesticide National Synthesis Project of the U.S. Geological Survey, National Water-Quality Assessment Program. Pesticide data for surface water, groundwater, and biological specimens were compiled from U.S. Geological Survey National Water Information System database. These spatial datasets that describe natural and manmade features were compiled from several agencies and contain information collected by the U.S. Geological Survey. The U.S. Geological Survey datasets were not collected specifically for this compilation, but were previously collected for projects with various objectives. The spatial datasets were created by different agencies from sources with varied quality. As a result, features common to multiple layers may not overlay exactly. Users should check the metadata to determine proper use of these spatial datasets. These data were not checked for accuracy or completeness. If a question of accuracy or completeness arise, the user should contact the originator cited in the metadata.

EAARL Topography - George Washington Birthplace National Monument 2008

USGS Publications Warehouse

Brock, John C.; Nayegandhi, Amar; Wright, C. Wayne; Stevens, Sara; Yates, Xan

2009-01-01

These remotely sensed, geographically referenced elevation measurements of Lidar-derived bare earth (BE) and first surface (FS) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the George Washington Birthplace National Monument in Virginia, acquired on March 26, 2008. The datasets are made available for use as a management tool to research scientists and natural resource managers. An innovative airborne Lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL) was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) Lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive Lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multi-spectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for submeter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a Lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL

An integrated pan-tropical biomass map using multiple reference datasets.

PubMed

Avitabile, Valerio; Herold, Martin; Heuvelink, Gerard B M; Lewis, Simon L; Phillips, Oliver L; Asner, Gregory P; Armston, John; Ashton, Peter S; Banin, Lindsay; Bayol, Nicolas; Berry, Nicholas J; Boeckx, Pascal; de Jong, Bernardus H J; DeVries, Ben; Girardin, Cecile A J; Kearsley, Elizabeth; Lindsell, Jeremy A; Lopez-Gonzalez, Gabriela; Lucas, Richard; Malhi, Yadvinder; Morel, Alexandra; Mitchard, Edward T A; Nagy, Laszlo; Qie, Lan; Quinones, Marcela J; Ryan, Casey M; Ferry, Slik J W; Sunderland, Terry; Laurin, Gaia Vaglio; Gatti, Roberto Cazzolla; Valentini, Riccardo; Verbeeck, Hans; Wijaya, Arief; Willcock, Simon

2016-04-01

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha(-1) vs. 21 and 28 Mg ha(-1) for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets. © 2015 John Wiley & Sons Ltd.

Northern Hemisphere winter storm track trends since 1959 derived from multiple reanalysis datasets

NASA Astrophysics Data System (ADS)

Chang, Edmund K. M.; Yau, Albert M. W.

2016-09-01

In this study, a comprehensive comparison of Northern Hemisphere winter storm track trend since 1959 derived from multiple reanalysis datasets and rawinsonde observations has been conducted. In addition, trends in terms of variance and cyclone track statistics have been compared. Previous studies, based largely on the National Center for Environmental Prediction-National Center for Atmospheric Research Reanalysis (NNR), have suggested that both the Pacific and Atlantic storm tracks have significantly intensified between the 1950s and 1990s. Comparison with trends derived from rawinsonde observations suggest that the trends derived from NNR are significantly biased high, while those from the European Center for Medium Range Weather Forecasts 40-year Reanalysis and the Japanese 55-year Reanalysis are much less biased but still too high. Those from the two twentieth century reanalysis datasets are most consistent with observations but may exhibit slight biases of opposite signs. Between 1959 and 2010, Pacific storm track activity has likely increased by 10 % or more, while Atlantic storm track activity has likely increased by <10 %. Our analysis suggests that trends in Pacific and Atlantic basin wide storm track activity prior to the 1950s derived from the two twentieth century reanalysis datasets are unlikely to be reliable due to changes in density of surface observations. Nevertheless, these datasets may provide useful information on interannual variability, especially over the Atlantic.

Validation of the Hospital Episode Statistics Outpatient Dataset in England.

PubMed

Thorn, Joanna C; Turner, Emma; Hounsome, Luke; Walsh, Eleanor; Donovan, Jenny L; Verne, Julia; Neal, David E; Hamdy, Freddie C; Martin, Richard M; Noble, Sian M

2016-02-01

The Hospital Episode Statistics (HES) dataset is a source of administrative 'big data' with potential for costing purposes in economic evaluations alongside clinical trials. This study assesses the validity of coverage in the HES outpatient dataset. Men who died of, or with, prostate cancer were selected from a prostate-cancer screening trial (CAP, Cluster randomised triAl of PSA testing for Prostate cancer). Details of visits that took place after 1/4/2003 to hospital outpatient departments for conditions related to prostate cancer were extracted from medical records (MR); these appointments were sought in the HES outpatient dataset based on date. The matching procedure was repeated for periods before and after 1/4/2008, when the HES outpatient dataset was accredited as a national statistic. 4922 outpatient appointments were extracted from MR for 370 men. 4088 appointments recorded in MR were identified in the HES outpatient dataset (83.1%; 95% confidence interval [CI] 82.0-84.1). For appointments occurring prior to 1/4/2008, 2195/2755 (79.7%; 95% CI 78.2-81.2) matches were observed, while 1893/2167 (87.4%; 95% CI 86.0-88.9) appointments occurring after 1/4/2008 were identified (p for difference <0.001). 215/370 men (58.1%) had at least one appointment in the MR review that was unmatched in HES, 155 men (41.9%) had all their appointments identified, and 20 men (5.4%) had no appointments identified in HES. The HES outpatient dataset appears reasonably valid for research, particularly following accreditation. The dataset may be a suitable alternative to collecting MR data from hospital notes within a trial, although caution should be exercised with data collected prior to accreditation.

WIND Toolkit Offshore Summary Dataset

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

Draxl, Caroline; Musial, Walt; Scott, George

This dataset contains summary statistics for offshore wind resources for the continental United States derived from the Wind Integration National Datatset (WIND) Toolkit. These data are available in two formats: GDB - Compressed geodatabases containing statistical summaries aligned with lease blocks (aliquots) stored in a GIS format. These data are partitioned into Pacific, Atlantic, and Gulf resource regions. HDF5 - Statistical summaries of all points in the offshore Pacific, Atlantic, and Gulf offshore regions. These data are located on the original WIND Toolkit grid and have not been reassigned or downsampled to lease blocks. These data were developed under contractmore » by NREL for the Bureau of Oceanic Energy Management (BOEM).« less

EAARL Coastal Topography-Cape Hatteras National Seashore, North Carolina, Post-Nor'Ida, 2009: Bare Earth

USGS Publications Warehouse

Bonisteel-Cormier, J.M.; Nayegandhi, Amar; Fredericks, Xan; Brock, J.C.; Wright, C.W.; Nagle, D.B.; Stevens, Sara

2011-01-01

These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the National Park Service Southeast Coast Network's Cape Hatteras National Seashore in North Carolina, acquired post-Nor'Ida (November 2009 nor'easter) on November 27 and 29 and December 1, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the

An elevational gradient in snowpack chemical loading at Glacier National Park, Montana: implications for ecosystem processes

USGS Publications Warehouse

Fagre, Daniel; Tonnessen, Kathy; Morris, Kristi; Ingersoll, George; McKeon, Lisa; Holzer, Karen

2000-01-01

The accumulation and melting of mountain snowpacks are major drivers of ecosystem processes in the Rocky Mountains. These include the influence of snow water equivalent (SWE) timing and amount of release on soil moisture for annual tree growth, and alpine stream discharge and temperature that control aquatic biota life histories. Snowfall also brings with it atmospheric deposition. Snowpacks will hold as much as 8 months of atmospheric deposition for release into mountain ecosystems during the spring melt. These pulses of chemicals influence soil microbiota and biogeochemical processes affecting mountain vegetation growth. Increased atmospheric nitrogen inputs recently have been documented in remote parts of Colorado's mountain systems but no baseline data exist for the Northern Rockies. We examined patterns of SWE and snow chemistry in an elevational gradient stretching from west to east over the continental divide in Glacier National Park in March 1999 and 2000. Sites ranged from 1080m to 2192m at Swiftcurrent Pass. At each site, two vertically-integrated columns of snow were sampled from snowpits up to 600cm deep and analyzed for major cations and anions. Minor differences in snow chemistry, on a volumetric basis, existed over the elvational gradient. Snowpack chemical loading estimates were calculated for NH4, SO4 and NO3 and closely followed elevational increases in SWE. NO3 (in microequivalents/square meter) ranged from 1,000 ueq/m2 at low elevation sites to 8,000+ ueq/m2 for high elevation sites. Western slopes received greater amounts of SWE and chemical loads for all tested compounds.

Topographic and Hydrographic GIS Datasets for the Afghanistan Geological Survey and U.S. Geological Survey 2014 Mineral Areas of Interest

USGS Publications Warehouse

DeWitt, Jessica D.; Chirico, Peter G.; Malpeli, Katherine C.

2015-11-18

This work represents the fourth installment of the series, and publishes a dataset of eight new AOIs and one subarea within Afghanistan. These areas include Dasht-e-Nawar, Farah, North Ghazni, South Ghazni, Chakhansur, Godzareh East, Godzareh West, and Namaksar-e-Herat AOIs and the Central Bamyan subarea of the South Bamyan AOI (datasets for South Bamyan were published previously in Casey and Chirico, 2013). For each AOI and subarea, this dataset collection consists of the areal extent boundaries, elevation contours at 25-, 50-, and 100-m intervals, and an enhanced DEM. Hydrographic datasets covering the extent of four AOIs and one subarea are also included in the collection. The resulting raster and vector layers are intended for use by government agencies, developmental organizations, and private companies in Afghanistan to support mineral assessments, monitoring, management, and investment.

Fixing Dataset Search

NASA Technical Reports Server (NTRS)

Lynnes, Chris

2014-01-01

Three current search engines are queried for ozone data at the GES DISC. The results range from sub-optimal to counter-intuitive. We propose a method to fix dataset search by implementing a robust relevancy ranking scheme. The relevancy ranking scheme is based on several heuristics culled from more than 20 years of helping users select datasets.

78 FR 22222 - Proposed Flood Elevation Determinations

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-15

... of referenced elevations, effective and modified elevations, and communities affected for Mercer...) and modified BFEs for communities participating in the National Flood Insurance Program (NFIP), in... CFR 60.3, are minimum requirements. They should not be construed to mean that the community must...

Object-oriented classification of drumlins from digital elevation models

NASA Astrophysics Data System (ADS)

Saha, Kakoli

Drumlins are common elements of glaciated landscapes which are easily identified by their distinct morphometric characteristics including shape, length/width ratio, elongation ratio, and uniform direction. To date, most researchers have mapped drumlins by tracing contours on maps, or through on-screen digitization directly on top of hillshaded digital elevation models (DEMs). This paper seeks to utilize the unique morphometric characteristics of drumlins and investigates automated extraction of the landforms as objects from DEMs by Definiens Developer software (V.7), using the 30 m United States Geological Survey National Elevation Dataset DEM as input. The Chautauqua drumlin field in Pennsylvania and upstate New York, USA was chosen as a study area. As the study area is huge (approximately covers 2500 sq.km. of area), small test areas were selected for initial testing of the method. Individual polygons representing the drumlins were extracted from the elevation data set by automated recognition, using Definiens' Multiresolution Segmentation tool, followed by rule-based classification. Subsequently parameters such as length, width and length-width ratio, perimeter and area were measured automatically. To test the accuracy of the method, a second base map was produced by manual on-screen digitization of drumlins from topographic maps and the same morphometric parameters were extracted from the mapped landforms using Definiens Developer. Statistical comparison showed a high agreement between the two methods confirming that object-oriented classification for extraction of drumlins can be used for mapping these landforms. The proposed method represents an attempt to solve the problem by providing a generalized rule-set for mass extraction of drumlins. To check that the automated extraction process was next applied to a larger area. Results showed that the proposed method is as successful for the bigger area as it was for the smaller test areas.

ASSESSING THE ACCURACY OF NATIONAL LAND COVER DATASET AREA ESTIMATES AT MULTIPLE SPATIAL EXTENTS

EPA Science Inventory

Site specific accuracy assessments provide fine-scale evaluation of the thematic accuracy of land use/land cover (LULC) datasets; however, they provide little insight into LULC accuracy across varying spatial extents. Additionally, LULC data are typically used to describe lands...

2016 National Census of Ferry Operators [supporting datasets

DOT National Transportation Integrated Search

2018-03-06

The Bureau of Transportation Statistics (BTS) conducted the National Census of Ferry Operators (NCFO) from April through November 2016, collecting the operational characteristics of the 2015 calendar year ferry operations. : The supporting zip file c...

NCAR's Research Data Archive: OPeNDAP Access for Complex Datasets

NASA Astrophysics Data System (ADS)

Dattore, R.; Worley, S. J.

2014-12-01

Many datasets have complex structures including hundreds of parameters and numerous vertical levels, grid resolutions, and temporal products. Making these data accessible is a challenge for a data provider. OPeNDAP is powerful protocol for delivering in real-time multi-file datasets that can be ingested by many analysis and visualization tools, but for these datasets there are too many choices about how to aggregate. Simple aggregation schemes can fail to support, or at least make it very challenging, for many potential studies based on complex datasets. We address this issue by using a rich file content metadata collection to create a real-time customized OPeNDAP service to match the full suite of access possibilities for complex datasets. The Climate Forecast System Reanalysis (CFSR) and it's extension, the Climate Forecast System Version 2 (CFSv2) datasets produced by the National Centers for Environmental Prediction (NCEP) and hosted by the Research Data Archive (RDA) at the Computational and Information Systems Laboratory (CISL) at NCAR are examples of complex datasets that are difficult to aggregate with existing data server software. CFSR and CFSv2 contain 141 distinct parameters on 152 vertical levels, six grid resolutions and 36 products (analyses, n-hour forecasts, multi-hour averages, etc.) where not all parameter/level combinations are available at all grid resolution/product combinations. These data are archived in the RDA with the data structure provided by the producer; no additional re-organization or aggregation have been applied. Since 2011, users have been able to request customized subsets (e.g. - temporal, parameter, spatial) from the CFSR/CFSv2, which are processed in delayed-mode and then downloaded to a user's system. Until now, the complexity has made it difficult to provide real-time OPeNDAP access to the data. We have developed a service that leverages the already-existing subsetting interface and allows users to create a virtual dataset

Aster Global dem Version 3, and New Aster Water Body Dataset

NASA Astrophysics Data System (ADS)

Abrams, M.

2016-06-01

In 2016, the US/Japan ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) project released Version 3 of the Global DEM (GDEM). This 30 m DEM covers the earth's surface from 82N to 82S, and improves on two earlier versions by correcting some artefacts and filling in areas of missing DEMs by the acquisition of additional data. The GDEM was produced by stereocorrelation of 2 million ASTER scenes and operation on a pixel-by-pixel basis: cloud screening; stacking data from overlapping scenes; removing outlier values, and averaging elevation values. As previously, the GDEM is packaged in ~ 23,000 1 x 1 degree tiles. Each tile has a DEM file, and a NUM file reporting the number of scenes used for each pixel, and identifying the source for fill-in data (where persistent clouds prevented computation of an elevation value). An additional data set was concurrently produced and released: the ASTER Water Body Dataset (AWBD). This is a 30 m raster product, which encodes every pixel as either lake, river, or ocean; thus providing a global inland and shore-line water body mask. Water was identified through spectral analysis algorithms and manual editing. This product was evaluated against the Shuttle Water Body Dataset (SWBD), and the Landsat-based Global Inland Water (GIW) product. The SWBD only covers the earth between about 60 degrees north and south, so it is not a global product. The GIW only delineates inland water bodies, and does not deal with ocean coastlines. All products are at 30 m postings.

78 FR 27 - Final Flood Elevation Determinations

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-02

...-2012-0003] Final Flood Elevation Determinations AGENCY: Federal Emergency Management Agency, DHS. ACTION: Final rule. SUMMARY: Base (1% annual-chance) Flood Elevations (BFEs) and modified BFEs are made... effect in order to qualify or remain qualified for participation in the National Flood Insurance Program...

75 FR 78926 - Final Flood Elevation Determinations

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-17

...-2010-0003] Final Flood Elevation Determinations AGENCY: Federal Emergency Management Agency, DHS. ACTION: Final rule. SUMMARY: Base (1% annual-chance) Flood Elevations (BFEs) and modified BFEs are made... effect in order to qualify or remain qualified for participation in the National Flood Insurance Program...

77 FR 74610 - Final Flood Elevation Determinations

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-17

...-2012-0003] Final Flood Elevation Determinations AGENCY: Federal Emergency Management Agency, DHS. ACTION: Final rule. SUMMARY: Base (1% annual-chance) Flood Elevations (BFEs) and modified BFEs are made... effect in order to qualify or remain qualified for participation in the National Flood Insurance Program...

The 3D Elevation Program: summary for Michigan

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation's natural and constructed features. The Michigan Statewide Authoritative Imagery and Lidar (MiSAIL) program provides statewide lidar coordination with local, State, and national groups in support of 3DEP for Michigan.

EAARL Topography - Vicksburg National Military Park 2008: Bare Earth

USGS Publications Warehouse

Nayegandhi, Amar; Brock, John C.; Wright, C. Wayne; Segura, Martha; Yates, Xan

2008-01-01

These remotely sensed, geographically referenced elevation measurements of Lidar-derived bare earth (BE) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the National Park Service (NPS), Gulf Coast Network, Lafayette, LA; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the Vicksburg National Military Park in Mississippi, acquired on March 6, 2008. The datasets are made available for use as a management tool to research scientists and natural resource managers. An innovative airborne Lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) Lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive Lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multi-spectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for submeter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a Lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed

EAARL Topography-Vicksburg National Military Park 2007: First Surface

USGS Publications Warehouse

Nayegandhi, Amar; Brock, John C.; Wright, C. Wayne; Segura, Martha; Yates, Xan

2009-01-01

These remotely sensed, geographically referenced elevation measurements of Lidar-derived first-surface (FS) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the National Park Service (NPS), Gulf Coast Network, Lafayette, LA; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the Vicksburg National Military Park in Mississippi, acquired on September 12, 2007. The datasets are made available for use as a management tool to research scientists and natural resource managers. An innovative airborne Lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) Lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive Lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multi-spectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for submeter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a Lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then

National Transportation Atlas Databases : 2013

DOT National Transportation Integrated Search

2013-01-01

The National Transportation Atlas Databases 2013 (NTAD2013) is a set of nationwide geographic datasets of transportation facilities, transportation networks, associated infrastructure, and other political and administrative entities. These datasets i...

77 FR 74142 - Proposed Flood Elevation Determinations

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-13

...-2011-0002; Internal Agency Docket No. FEMA-B-1100 and FEMA-B-1222] Proposed Flood Elevation... Base (1% annual-chance) Flood Elevations (BFEs) and modified BFEs for communities participating in the National Flood Insurance Program (NFIP), in accordance with section 110 of the Flood Disaster Protection...

Comparison of Four Precipitation Forcing Datasets in Land Information System Simulations over the Continental U.S.

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Kumar, Sujay V.; Kuligowski, Robert J.; Langston, Carrie

2013-01-01

The NASA Short ]term Prediction Research and Transition (SPoRT) Center in Huntsville, AL is running a real ]time configuration of the NASA Land Information System (LIS) with the Noah land surface model (LSM). Output from the SPoRT ]LIS run is used to initialize land surface variables for local modeling applications at select National Weather Service (NWS) partner offices, and can be displayed in decision support systems for situational awareness and drought monitoring. The SPoRT ]LIS is run over a domain covering the southern and eastern United States, fully nested within the National Centers for Environmental Prediction Stage IV precipitation analysis grid, which provides precipitation forcing to the offline LIS ]Noah runs. The SPoRT Center seeks to expand the real ]time LIS domain to the entire Continental U.S. (CONUS); however, geographical limitations with the Stage IV analysis product have inhibited this expansion. Therefore, a goal of this study is to test alternative precipitation forcing datasets that can enable the LIS expansion by improving upon the current geographical limitations of the Stage IV product. The four precipitation forcing datasets that are inter ]compared on a 4 ]km resolution CONUS domain include the Stage IV, an experimental GOES quantitative precipitation estimate (QPE) from NESDIS/STAR, the National Mosaic and QPE (NMQ) product from the National Severe Storms Laboratory, and the North American Land Data Assimilation System phase 2 (NLDAS ]2) analyses. The NLDAS ]2 dataset is used as the control run, with each of the other three datasets considered experimental runs compared against the control. The regional strengths, weaknesses, and biases of each precipitation analysis are identified relative to the NLDAS ]2 control in terms of accumulated precipitation pattern and amount, and the impacts on the subsequent LSM spin ]up simulations. The ultimate goal is to identify an alternative precipitation forcing dataset that can best support an

FLUXNET2015 Dataset: Batteries included

NASA Astrophysics Data System (ADS)

Pastorello, G.; Papale, D.; Agarwal, D.; Trotta, C.; Chu, H.; Canfora, E.; Torn, M. S.; Baldocchi, D. D.

2016-12-01

The synthesis datasets have become one of the signature products of the FLUXNET global network. They are composed from contributions of individual site teams to regional networks, being then compiled into uniform data products - now used in a wide variety of research efforts: from plant-scale microbiology to global-scale climate change. The FLUXNET Marconi Dataset in 2000 was the first in the series, followed by the FLUXNET LaThuile Dataset in 2007, with significant additions of data products and coverage, solidifying the adoption of the datasets as a research tool. The FLUXNET2015 Dataset counts with another round of substantial improvements, including extended quality control processes and checks, use of downscaled reanalysis data for filling long gaps in micrometeorological variables, multiple methods for USTAR threshold estimation and flux partitioning, and uncertainty estimates - all of which accompanied by auxiliary flags. This "batteries included" approach provides a lot of information for someone who wants to explore the data (and the processing methods) in detail. This inevitably leads to a large number of data variables. Although dealing with all these variables might seem overwhelming at first, especially to someone looking at eddy covariance data for the first time, there is method to our madness. In this work we describe the data products and variables that are part of the FLUXNET2015 Dataset, and the rationale behind the organization of the dataset, covering the simplified version (labeled SUBSET), the complete version (labeled FULLSET), and the auxiliary products in the dataset.

Isfahan MISP Dataset

PubMed Central

Kashefpur, Masoud; Kafieh, Rahele; Jorjandi, Sahar; Golmohammadi, Hadis; Khodabande, Zahra; Abbasi, Mohammadreza; Teifuri, Nilufar; Fakharzadeh, Ali Akbar; Kashefpoor, Maryam; Rabbani, Hossein

2017-01-01

An online depository was introduced to share clinical ground truth with the public and provide open access for researchers to evaluate their computer-aided algorithms. PHP was used for web programming and MySQL for database managing. The website was entitled “biosigdata.com.” It was a fast, secure, and easy-to-use online database for medical signals and images. Freely registered users could download the datasets and could also share their own supplementary materials while maintaining their privacies (citation and fee). Commenting was also available for all datasets, and automatic sitemap and semi-automatic SEO indexing have been set for the site. A comprehensive list of available websites for medical datasets is also presented as a Supplementary (http://journalonweb.com/tempaccess/4800.584.JMSS_55_16I3253.pdf). PMID:28487832

Isfahan MISP Dataset.

PubMed

Kashefpur, Masoud; Kafieh, Rahele; Jorjandi, Sahar; Golmohammadi, Hadis; Khodabande, Zahra; Abbasi, Mohammadreza; Teifuri, Nilufar; Fakharzadeh, Ali Akbar; Kashefpoor, Maryam; Rabbani, Hossein

2017-01-01

An online depository was introduced to share clinical ground truth with the public and provide open access for researchers to evaluate their computer-aided algorithms. PHP was used for web programming and MySQL for database managing. The website was entitled "biosigdata.com." It was a fast, secure, and easy-to-use online database for medical signals and images. Freely registered users could download the datasets and could also share their own supplementary materials while maintaining their privacies (citation and fee). Commenting was also available for all datasets, and automatic sitemap and semi-automatic SEO indexing have been set for the site. A comprehensive list of available websites for medical datasets is also presented as a Supplementary (http://journalonweb.com/tempaccess/4800.584.JMSS_55_16I3253.pdf).

Preprocessed Consortium for Neuropsychiatric Phenomics dataset.

PubMed

Gorgolewski, Krzysztof J; Durnez, Joke; Poldrack, Russell A

2017-01-01

Here we present preprocessed MRI data of 265 participants from the Consortium for Neuropsychiatric Phenomics (CNP) dataset. The preprocessed dataset includes minimally preprocessed data in the native, MNI and surface spaces accompanied with potential confound regressors, tissue probability masks, brain masks and transformations. In addition the preprocessed dataset includes unthresholded group level and single subject statistical maps from all tasks included in the original dataset. We hope that availability of this dataset will greatly accelerate research.

77 FR 20999 - Final Flood Elevation Determinations

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-09

... set forth below: * Elevation in feet (NGVD) + Elevation in feet (NAVD) Depth in feet Flooding source(s..., and Incorporated Areas Docket No.: FEMA-B-1100 Mississippi River Approximately 11.2 miles +585 City of.... Approximately 12.8 miles +594 upstream of State Highway 136. * National Geodetic Vertical Datum. + North...

The 3D Elevation Program: summary for Ohio

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation's natural and constructed features.

The 3D Elevation Program: summary for Indiana

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation's natural and constructed features.

Overcoming boundaries of worldwide joint arthroplasty registers: the European Arthroplasty Register minimal dataset.

PubMed

Sadoghi, Patrick; Leithner, Andreas; Labek, Gerold

2013-09-01

Worldwide joint arthroplasty registers are instrumental to screen for complications or implant failures. In order to achieve comparable results a similar classification dataset is essential. The authors therefore present the European Federation of National Associations of Orthopaedics and Traumatology (EFORT) European Arthroplasty Register (EAR) minimal dataset for primary and revision joint arthroplasty. Main parameters include the following: date of operation, country, hospital ID-code, patient's name and prename, birthday, identification code of the implant, gender, diagnosis, preoperations, type of prosthesis (partial, total), side, cementation technique, use of antibiotics in the cement, surgical approach, and others specifically related to the affected joint. The authors believe that using this minimal dataset will improve the chance for a worldwide comparison of arthroplasty registers and ask future countries for implementation. Copyright © 2013 Elsevier Inc. All rights reserved.

Preliminary AirMSPI Datasets

Atmospheric Science Data Center

2018-02-26

... Datasets   The data files available through this web page and ftp links are preliminary AIrMSPI datasets from recent campaigns. ... and geometric corrections. Caution should be used for science analysis. At a later date, more qualified versions will be made public. ...

Integrative Exploratory Analysis of Two or More Genomic Datasets.

PubMed

Meng, Chen; Culhane, Aedin

2016-01-01

Exploratory analysis is an essential step in the analysis of high throughput data. Multivariate approaches such as correspondence analysis (CA), principal component analysis, and multidimensional scaling are widely used in the exploratory analysis of single dataset. Modern biological studies often assay multiple types of biological molecules (e.g., mRNA, protein, phosphoproteins) on a same set of biological samples, thereby creating multiple different types of omics data or multiassay data. Integrative exploratory analysis of these multiple omics data is required to leverage the potential of multiple omics studies. In this chapter, we describe the application of co-inertia analysis (CIA; for analyzing two datasets) and multiple co-inertia analysis (MCIA; for three or more datasets) to address this problem. These methods are powerful yet simple multivariate approaches that represent samples using a lower number of variables, allowing a more easily identification of the correlated structure in and between multiple high dimensional datasets. Graphical representations can be employed to this purpose. In addition, the methods simultaneously project samples and variables (genes, proteins) onto the same lower dimensional space, so the most variant variables from each dataset can be selected and associated with samples, which can be further used to facilitate biological interpretation and pathway analysis. We applied CIA to explore the concordance between mRNA and protein expression in a panel of 60 tumor cell lines from the National Cancer Institute. In the same 60 cell lines, we used MCIA to perform a cross-platform comparison of mRNA gene expression profiles obtained on four different microarray platforms. Last, as an example of integrative analysis of multiassay or multi-omics data we analyzed transcriptomic, proteomic, and phosphoproteomic data from pluripotent (iPS) and embryonic stem (ES) cell lines.

Status and Preliminary Evaluation for Chinese Re-Analysis Datasets

NASA Astrophysics Data System (ADS)

bin, zhao; chunxiang, shi; tianbao, zhao; dong, si; jingwei, liu

2016-04-01

Based on operational T639L60 spectral model, combined with Hybird_GSI assimilation system by using meteorological observations including radiosondes, buoyes, satellites el al., a set of Chinese Re-Analysis (CRA) datasets is developing by Chinese National Meteorological Information Center (NMIC) of Chinese Meteorological Administration (CMA). The datasets are run at 30km (0.28°latitude / longitude) resolution which holds higher resolution than most of the existing reanalysis dataset. The reanalysis is done in an effort to enhance the accuracy of historical synoptic analysis and aid to find out detailed investigation of various weather and climate systems. The current status of reanalysis is in a stage of preliminary experimental analysis. One-year forecast data during Jun 2013 and May 2014 has been simulated and used in synoptic and climate evaluation. We first examine the model prediction ability with the new assimilation system, and find out that it represents significant improvement in Northern and Southern hemisphere, due to addition of new satellite data, compared with operational T639L60 model, the effect of upper-level prediction is improved obviously and overall prediction stability is enhanced. In climatological analysis, compared with ERA-40, NCEP/NCAR and NCEP/DOE reanalyses, the results show that surface temperature simulates a bit lower in land and higher over ocean, 850-hPa specific humidity reflects weakened anomaly and the zonal wind value anomaly is focus on equatorial tropics. Meanwhile, the reanalysis dataset shows good ability for various climate index, such as subtropical high index, ESMI (East-Asia subtropical Summer Monsoon Index) et al., especially for the Indian and western North Pacific monsoon index. Latter we will further improve the assimilation system and dynamical simulating performance, and obtain 40-years (1979-2018) reanalysis datasets. It will provide a more comprehensive analysis for synoptic and climate diagnosis.

Development of National Map ontologies for organization and orchestration of hydrologic observations

NASA Astrophysics Data System (ADS)

Lieberman, J. E.

2014-12-01

Feature layers in the National Map program (TNM) are a fundamental context for much of the data collection and analysis conducted by the USGS and other governmental and nongovernmental organizations. Their computational usefulness, though, has been constrained by the lack of formal relationships besides superposition between TNM layers, as well as limited means of representing how TNM datasets relate to additional attributes, datasets, and activities. In the field of Geospatial Information Science, there has been a growing recognition of the value of semantic representation and technology for addressing these limitations, particularly in the face of burgeoning information volume and heterogeneity. Fundamental to this approach is the development of formal ontologies for concepts related to that information that can be processed computationally to enhance creation and discovery of new geospatial knowledge. They offer a means of making much of the presently innate knowledge about relationships in and between TNM features accessible for machine processing and distributed computation.A full and comprehensive ontology of all knowledge represented by TNM features is still impractical. The work reported here involves elaboration and integration of a number of small ontology design patterns (ODP's) that represent limited, discrete, but commonly accepted and broadly applicable physical theories for the behavior of TNM features representing surface water bodies and landscape surfaces and the connections between them. These ontology components are validated through use in applications for discovery and aggregation of water science observational data associated with National Hydrography Data features, features from the National Elevation Dataset (NED) and Water Boundary Dataset (WBD) that constrain water occurrence in the continental US. These applications emphasize workflows which are difficult or impossible to automate using existing data structures. Evaluation of the

The surface elevation table and marker horizon technique: A protocol for monitoring wetland elevation dynamics

USGS Publications Warehouse

James C. Lynch,; Phillippe Hensel,; Cahoon, Donald R.

2015-01-01

The National Park Service, in response to the growing evidence and awareness of the effects of climate change on federal lands, determined that monitoring wetland elevation change is a top priority in North Atlantic Coastal parks (Stevens et al, 2010). As a result, the NPS Northeast Coastal and Barrier Network (NCBN) in collaboration with colleagues from the U.S. Geological Survey (USGS) and The National Oceanic and Atmospheric Administration (NOAA) have developed a protocol for monitoring wetland elevation change and other processes important for determining the viability of wetland communities. Although focused on North Atlantic Coastal parks, this document is applicable to all coastal and inland wetland regions. Wetlands exist within a narrow range of elevation which is influenced by local hydrologic conditions. For coastal wetlands in particular, local hydrologic conditions may be changing as sea levels continue to rise. As sea level rises, coastal wetland systems may respond by building elevation to maintain favorable hydrologic conditions for their survival. This protocol provides the reader with instructions and guidelines on designing a monitoring plan or study to: A) Quantify elevation change in wetlands with the Surface Elevation Table (SET). B) Understand the processes that influence elevation change, including vertical accretion (SET and Marker Horizon methods). C) Survey the wetland surface and SET mark to a common reference datum to allow for comparing sample stations to each other and to local tidal datums. D) Survey the SET mark to monitor its relative stability. This document is divided into two parts; the main body that presents an overview of all aspects of monitoring wetland elevation dynamics, and a collection of Standard Operating Procedures (SOP) that describes in detail how to perform or execute each step of the methodology. Detailed instruction on the installation, data collection, data management and analysis are provided in this report

Open University Learning Analytics dataset.

PubMed

Kuzilek, Jakub; Hlosta, Martin; Zdrahal, Zdenek

2017-11-28

Learning Analytics focuses on the collection and analysis of learners' data to improve their learning experience by providing informed guidance and to optimise learning materials. To support the research in this area we have developed a dataset, containing data from courses presented at the Open University (OU). What makes the dataset unique is the fact that it contains demographic data together with aggregated clickstream data of students' interactions in the Virtual Learning Environment (VLE). This enables the analysis of student behaviour, represented by their actions. The dataset contains the information about 22 courses, 32,593 students, their assessment results, and logs of their interactions with the VLE represented by daily summaries of student clicks (10,655,280 entries). The dataset is freely available at https://analyse.kmi.open.ac.uk/open_dataset under a CC-BY 4.0 license.

Open University Learning Analytics dataset

PubMed Central

Kuzilek, Jakub; Hlosta, Martin; Zdrahal, Zdenek

2017-01-01

Learning Analytics focuses on the collection and analysis of learners’ data to improve their learning experience by providing informed guidance and to optimise learning materials. To support the research in this area we have developed a dataset, containing data from courses presented at the Open University (OU). What makes the dataset unique is the fact that it contains demographic data together with aggregated clickstream data of students’ interactions in the Virtual Learning Environment (VLE). This enables the analysis of student behaviour, represented by their actions. The dataset contains the information about 22 courses, 32,593 students, their assessment results, and logs of their interactions with the VLE represented by daily summaries of student clicks (10,655,280 entries). The dataset is freely available at https://analyse.kmi.open.ac.uk/open_dataset under a CC-BY 4.0 license. PMID:29182599

Development of South Australian-Victorian Prostate Cancer Health Outcomes Research Dataset.

PubMed

Ruseckaite, Rasa; Beckmann, Kerri; O'Callaghan, Michael; Roder, David; Moretti, Kim; Zalcberg, John; Millar, Jeremy; Evans, Sue

2016-01-22

Prostate cancer is the most commonly diagnosed and prevalent malignancy reported to Australian cancer registries, with numerous studies from single institutions summarizing patient outcomes at individual hospitals or States. In order to provide an overview of patterns of care of men with prostate cancer across multiple institutions in Australia, a specialized dataset was developed. This dataset, containing amalgamated data from South Australian and Victorian prostate cancer registries, is called the South Australian-Victorian Prostate Cancer Health Outcomes Research Dataset (SA-VIC PCHORD). A total of 13,598 de-identified records of men with prostate cancer diagnosed and consented between 2008 and 2013 in South Australia and Victoria were merged into the SA-VIC PCHORD. SA-VIC PCHORD contains detailed information about socio-demographic, diagnostic and treatment characteristics of patients with prostate cancer in South Australia and Victoria. Data from individual registries are available to researchers and can be accessed under individual data access policies in each State. The SA-VIC PCHORD will be used for numerous studies summarizing trends in diagnostic characteristics, survival and patterns of care in men with prostate cancer in Victoria and South Australia. It is expected that in the future the SA-VIC PCHORD will become a principal component of the recently developed bi-national Australian and New Zealand Prostate Cancer Outcomes Registry to collect and report patterns of care and standardised patient reported outcome measures of men nation-wide in Australia and New Zealand.

Framework for National Flood Risk Assessment for Canada

NASA Astrophysics Data System (ADS)

Elshorbagy, A. A.; Raja, B.; Lakhanpal, A.; Razavi, S.; Ceola, S.; Montanari, A.

2016-12-01

Worldwide, floods have been identified as a standout amongst the most widely recognized catastrophic events, resulting in the loss of life and property. These natural hazards cannot be avoided, but their consequences can certainly be reduced by having prior knowledge of their occurrence and impact. In the context of floods, the terms occurrence and impact are substituted by flood hazard and flood vulnerability, respectively, which collectively define the flood risk. There is a high need for identifying the flood-prone areas and to quantify the risk associated with them. The present study aims at delivering flood risk maps, which prioritize the potential flood risk areas in Canada. The methodology adopted in this study involves integrating various available spatial datasets such as nightlights satellite imagery, land use, population and the digital elevation model, to build a flexible framework for national flood risk assessment for Canada. The flood risk framework assists in identifying the flood-prone areas and evaluating the associated risk. All these spatial datasets were brought to a common GIS platform for flood risk analysis. The spatial datasets deliver the socioeconomic and topographical information that is required for evaluating the flood vulnerability and flood hazard, respectively. Nightlights have been investigated as a tool to be used as a proxy for the human activities to identify areas with regard to economic investment. However, other datasets, including existing flood protection measures, we added to identify a realistic flood assessment framework. Furthermore, the city of Calgary was used as an example to investigate the effect of using Digital Elevation Models (DEMs) of varying resolutions on risk maps. Along with this, the risk map for the city was further enhanced by including the population data to give a social dimension to the risk map. Flood protection measures play a major role by significantly reducing the flood risk of events with a

EAARL Topography - Jean Lafitte National Historical Park and Preserve 2006

USGS Publications Warehouse

Nayegandhi, Amar; Brock, John C.; Wright, C. Wayne; Segura, Martha; Yates, Xan

2008-01-01

These remotely sensed, geographically referenced elevation measurements of Lidar-derived first surface (FS) and bare earth (BE) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the National Park Service (NPS), Gulf Coast Network, Lafayette, LA; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the Jean Lafitte National Historical Park and Preserve in Louisiana, acquired on September 22, 2006. The datasets are made available for use as a management tool to research scientists and natural resource managers. An innovative airborne Lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) Lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive Lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multi-spectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for submeter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a Lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system

EAARL Coastal Topography-Assateague Island National Seashore, 2008: Bare Earth

USGS Publications Warehouse

Bonisteel, Jamie M.; Nayegandhi, Amar; Brock, John C.; Wright, C. Wayne; Stevens, Sara; Yates, Xan; Klipp, Emily S.

2009-01-01

These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the Assateague Island National Seashore in Maryland and Virginia, acquired March 24-25, 2008. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL) was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multi-spectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the

EAARL Coastal Topography-Assateague Island National Seashore, 2008: First Surface

USGS Publications Warehouse

Bonisteel, Jamie M.; Nayegandhi, Amar; Brock, John C.; Wright, C. Wayne; Stevens, Sara; Yates, Xan; Klipp, Emily S.

2009-01-01

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the Assateague Island National Seashore in Maryland and Virginia, acquired March 24-25, 2008. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multi-spectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the

A unified high-resolution wind and solar dataset from a rapidly updating numerical weather prediction model

DOE PAGES

James, Eric P.; Benjamin, Stanley G.; Marquis, Melinda

2016-10-28

A new gridded dataset for wind and solar resource estimation over the contiguous United States has been derived from hourly updated 1-h forecasts from the National Oceanic and Atmospheric Administration High-Resolution Rapid Refresh (HRRR) 3-km model composited over a three-year period (approximately 22 000 forecast model runs). The unique dataset features hourly data assimilation, and provides physically consistent wind and solar estimates for the renewable energy industry. The wind resource dataset shows strong similarity to that previously provided by a Department of Energy-funded study, and it includes estimates in southern Canada and northern Mexico. The solar resource dataset represents anmore » initial step towards application-specific fields such as global horizontal and direct normal irradiance. This combined dataset will continue to be augmented with new forecast data from the advanced HRRR atmospheric/land-surface model.« less

EPA Office of Water (OW): 2002 Impaired Waters Baseline NHDPlus Indexed Dataset

EPA Pesticide Factsheets

This dataset consists of geospatial and attribute data identifying the spatial extent of state-reported impaired waters (EPA's Integrated Reporting categories 4a, 4b, 4c and 5)* available in EPA's Reach Address Database (RAD) at the time of extraction. For the 2002 baseline reporting year, EPA compiled state-submitted GIS data to create a seamless and nationally consistent picture of the Nation's impaired waters for measuring progress. EPA's Assessment and TMDL Tracking and Implementation System (ATTAINS) is a national compilation of states' 303(d) listings and TMDL development information, spanning several years of tracking over 40,000 impaired waters.

TAPIR--Finnish national geochemical baseline database.

PubMed

Jarva, Jaana; Tarvainen, Timo; Reinikainen, Jussi; Eklund, Mikael

2010-09-15

In Finland, a Government Decree on the Assessment of Soil Contamination and Remediation Needs has generated a need for reliable and readily accessible data on geochemical baseline concentrations in Finnish soils. According to the Decree, baseline concentrations, referring both to the natural geological background concentrations and the diffuse anthropogenic input of substances, shall be taken into account in the soil contamination assessment process. This baseline information is provided in a national geochemical baseline database, TAPIR, that is publicly available via the Internet. Geochemical provinces with elevated baseline concentrations were delineated to provide regional geochemical baseline values. The nationwide geochemical datasets were used to divide Finland into geochemical provinces. Several metals (Co, Cr, Cu, Ni, V, and Zn) showed anomalous concentrations in seven regions that were defined as metal provinces. Arsenic did not follow a similar distribution to any other elements, and four arsenic provinces were separately determined. Nationwide geochemical datasets were not available for some other important elements such as Cd and Pb. Although these elements are included in the TAPIR system, their distribution does not necessarily follow the ones pre-defined for metal and arsenic provinces. Regional geochemical baseline values, presented as upper limit of geochemical variation within the region, can be used as trigger values to assess potential soil contamination. Baseline values have also been used to determine upper and lower guideline values that must be taken into account as a tool in basic risk assessment. If regional geochemical baseline values are available, the national guideline values prescribed in the Decree based on ecological risks can be modified accordingly. The national geochemical baseline database provides scientifically sound, easily accessible and generally accepted information on the baseline values, and it can be used in various

Large Scale Flood Risk Analysis using a New Hyper-resolution Population Dataset

NASA Astrophysics Data System (ADS)

Smith, A.; Neal, J. C.; Bates, P. D.; Quinn, N.; Wing, O.

2017-12-01

Here we present the first national scale flood risk analyses, using high resolution Facebook Connectivity Lab population data and data from a hyper resolution flood hazard model. In recent years the field of large scale hydraulic modelling has been transformed by new remotely sensed datasets, improved process representation, highly efficient flow algorithms and increases in computational power. These developments have allowed flood risk analysis to be undertaken in previously unmodeled territories and from continental to global scales. Flood risk analyses are typically conducted via the integration of modelled water depths with an exposure dataset. Over large scales and in data poor areas, these exposure data typically take the form of a gridded population dataset, estimating population density using remotely sensed data and/or locally available census data. The local nature of flooding dictates that for robust flood risk analysis to be undertaken both hazard and exposure data should sufficiently resolve local scale features. Global flood frameworks are enabling flood hazard data to produced at 90m resolution, resulting in a mis-match with available population datasets which are typically more coarsely resolved. Moreover, these exposure data are typically focused on urban areas and struggle to represent rural populations. In this study we integrate a new population dataset with a global flood hazard model. The population dataset was produced by the Connectivity Lab at Facebook, providing gridded population data at 5m resolution, representing a resolution increase over previous countrywide data sets of multiple orders of magnitude. Flood risk analysis undertaken over a number of developing countries are presented, along with a comparison of flood risk analyses undertaken using pre-existing population datasets.

Evaluation of reanalysis datasets against observational soil temperature data over China

NASA Astrophysics Data System (ADS)

Yang, Kai; Zhang, Jingyong

2018-01-01

Soil temperature is a key land surface variable, and is a potential predictor for seasonal climate anomalies and extremes. Using observational soil temperature data in China for 1981-2005, we evaluate four reanalysis datasets, the land surface reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA-Interim/Land), the second modern-era retrospective analysis for research and applications (MERRA-2), the National Center for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR), and version 2 of the Global Land Data Assimilation System (GLDAS-2.0), with a focus on 40 cm soil layer. The results show that reanalysis data can mainly reproduce the spatial distributions of soil temperature in summer and winter, especially over the east of China, but generally underestimate their magnitudes. Owing to the influence of precipitation on soil temperature, the four datasets perform better in winter than in summer. The ERA-Interim/Land and GLDAS-2.0 produce spatial characteristics of the climatological mean that are similar to observations. The interannual variability of soil temperature is well reproduced by the ERA-Interim/Land dataset in summer and by the CFSR dataset in winter. The linear trend of soil temperature in summer is well rebuilt by reanalysis datasets. We demonstrate that soil heat fluxes in April-June and in winter are highly correlated with the soil temperature in summer and winter, respectively. Different estimations of surface energy balance components can contribute to different behaviors in reanalysis products in terms of estimating soil temperature. In addition, reanalysis datasets can mainly rebuild the northwest-southeast gradient of soil temperature memory over China.

The National Map - geographic names

USGS Publications Warehouse

Yost, Lou; Carswell, William J.

2009-01-01

The Geographic Names Information System (GNIS), developed by the U.S. Geological Survey (USGS) in cooperation with the U.S. Board on Geographic Names (BGN), contains information about the official names for places, features, and areas in the 50 States, the District of Columbia, the territories and outlying areas of the United States, including Antarctica. It is the geographic names component of The National Map. The BGN maintains working relationships with State names authorities to cooperate in achieving the standardization of geographic names. The GNIS contains records on more than 2 million geographic names in the United States - from populated places, schools, reservoirs, and parks to streams, valleys, springs, ridges, and every feature type except roads and highways. Entries include information such as the federally-recognized name and variant names and spellings for the feature; former names; the status of the name as determined by the BGN; county or counties in which each named feature is located; geographic coordinates that locate the approximate center of an aerial feature or the mouth and source of a linear feature, such as a stream; name of the cell of the USGS topographic map or maps on which the feature may appear; elevation figures derived from the National Elevation Dataset; bibliographic code for the source of the name; BGN decision dates and historical information are available for some features. Data from the GNIS are used for emergency preparedness, mapmaking, local and regional planning, service delivery routing, marketing, site selection, environmental analysis, genealogical research, and other applications.

Evaluation of the U.S. Geological Survey standard elevation products in a two-dimensional hydraulic modeling application for a low relief coastal floodplain

USGS Publications Warehouse

Witt, Emitt C.

2015-01-01

Growing use of two-dimensional (2-D) hydraulic models has created a need for high resolution data to support flood volume estimates, floodplain specific engineering data, and accurate flood inundation scenarios. Elevation data are a critical input to these models that guide the flood-wave across the landscape allowing the computation of valuable engineering specific data that provides a better understanding of flooding impacts on structures, debris movement, bed scour, and direction. High resolution elevation data are becoming publicly available that can benefit the 2-D flood modeling community. Comparison of these newly available data with legacy data suggests that better modeling outcomes are achieved by using 3D Elevation Program (3DEP) lidar point data and the derived 1 m Digital Elevation Model (DEM) product relative to the legacy 3 m, 10 m, or 30 m products currently available in the U.S. Geological Survey (USGS) National Elevation Dataset. Within the low topographic relief of a coastal floodplain, the newer 3DEP data better resolved elevations within the forested and swampy areas achieving simulations that compared well with a historic flooding event. Results show that the 1 m DEM derived from 3DEP lidar source provides a more conservative estimate of specific energy, static pressure, and impact pressure for grid elements at maximum flow relative to the legacy DEM data. Better flood simulations are critically important in coastal floodplains where climate change driven storm frequency and sea level rise will contribute to more frequent flooding events.

A Seamless, High-Resolution, Coastal Digital Elevation Model (DEM) for Southern California

USGS Publications Warehouse

Barnard, Patrick L.; Hoover, Daniel

2010-01-01

A seamless, 3-meter digital elevation model (DEM) was constructed for the entire Southern California coastal zone, extending 473 km from Point Conception to the Mexican border. The goal was to integrate the most recent, high-resolution datasets available (for example, Light Detection and Ranging (Lidar) topography, multibeam and single beam sonar bathymetry, and Interferometric Synthetic Aperture Radar (IfSAR) topography) into a continuous surface from at least the 20-m isobath to the 20-m elevation contour. This dataset was produced to provide critical boundary conditions (bathymetry and topography) for a modeling effort designed to predict the impacts of severe winter storms on the Southern California coast (Barnard and others, 2009). The hazards model, run in real-time or with prescribed scenarios, incorporates atmospheric information (wind and pressure fields) with a suite of state-of-the-art physical process models (tide, surge, and wave) to enable detailed prediction of water levels, run-up, wave heights, and currents. Research-grade predictions of coastal flooding, inundation, erosion, and cliff failure are also included. The DEM was constructed to define the general shape of nearshore, beach and cliff surfaces as accurately as possible, with less emphasis on the detailed variations in elevation inland of the coast and on bathymetry inside harbors. As a result this DEM should not be used for navigation purposes.

EAARL Coastal Topography - Fire Island National Seashore 2007

USGS Publications Warehouse

Nayegandhi, Amar; Brock, John C.; Wright, C. Wayne; Stevens, Sara; Yates, Xan; Bonisteel, Jamie M.

2008-01-01

These remotely sensed, geographically referenced elevation measurements of Lidar-derived first surface (FS) and bare earth (BE) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of Fire Island National Seashore in New York, acquired on April 29-30 and May 15-16, 2007. The datasets are made available for use as a management tool to research scientists and natural resource managers. An innovative airborne Lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL) was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) Lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive Lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multi-spectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers and an integrated miniature digital inertial measurement unit, which provide for submeter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a Lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system

NASA's Earth Science Use of Commercially Availiable Remote Sensing Datasets: Cover Image

NASA Technical Reports Server (NTRS)

Underwood, Lauren W.; Goward, Samuel N.; Fearon, Matthew G.; Fletcher, Rose; Garvin, Jim; Hurtt, George

2008-01-01

The cover image incorporates high resolution stereo pairs acquired from the DigitalGlobe(R) QuickBird sensor. It shows a digital elevation model of Meteor Crater, Arizona at approximately 1.3 meter point-spacing. Image analysts used the Leica Photogrammetry Suite to produce the DEM. The outside portion was computed from two QuickBird panchromatic scenes acquired October 2006, while an Optech laser scan dataset was used for the crater s interior elevations. The crater s terrain model and image drape were created in a NASA Constellation Program project focused on simulating lunar surface environments for prototyping and testing lunar surface mission analysis and planning tools. This work exemplifies NASA s Scientific Data Purchase legacy and commercial high resolution imagery applications, as scientists use commercial high resolution data to examine lunar analog Earth landscapes for advanced planning and trade studies for future lunar surface activities. Other applications include landscape dynamics related to volcanism, hydrologic events, climate change, and ice movement.

Extraction of lidar-based dune-crest elevations for use in examining the vulnerability of beaches to inundation during hurricanes

USGS Publications Warehouse

Stockdon, H.F.; Doran, K.S.; Sallenger, A.H.

2009-01-01

The morphology of coastal sand dunes plays an important role in determining how a beach will respond to a hurricane. Accurate measurements of dune height and position are essential for assessing the vulnerability of beaches to extreme coastal change during future landfalls. Lidar topographic surveys provide rapid, accurate, high-resolution datasets for identifying the location, position, and morphology of coastal sand dunes over large stretches of coast. An algorithm has been developed for identification of the crest of the most seaward sand dune that defines the landward limit of the beach system. Based on changes in beach slope along cross-shore transects of lidar data, dune elevation and location can automatically be extracted every few meters along the coastline. Dune elevations in conjunction with storm-induced water levels can be used to predict the type of coastal response (e.g., beach erosion, dune erosion, overwash, or inundation) that may be expected during hurricane landfall. The vulnerability of the beach system at Fire Island National Seashore in New York to the most extreme of these changes, inundation, is assessed by comparing lidar-derived dune elevations to modeled wave setup and storm surge height. The vulnerability of the beach system to inundation during landfall of a Category 3 hurricane is shown to be spatially variable because of longshore variations in dune height (mean elevation 5.44 m, standard deviation 1.32 m). Hurricane-induced mean water levels exceed dune elevations along 70 of the coastal park, making these locations more vulnerable to inundation during a Category 3 storm. ?? 2009 Coastal Education and Research Foundation.

EAARL coastal topography-Cape Hatteras National Seashore, North Carolina, post-Nor'Ida, 2009: first surface

USGS Publications Warehouse

Bonisteel-Cormier, J.M.; Nayegandhi, Amar; Brock, J.C.; Wright, C.W.; Nagle, D.B.; Fredericks, Xan; Stevens, Sara

2010-01-01

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the National Park Service Southeast Coast Network's Cape Hatteras National Seashore in North Carolina, acquired post-Nor'Ida (November 2009 nor'easter) on November 27 and 29 and December 1, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and

EAARL Coastal Topography and Imagery-Naval Live Oaks Area, Gulf Islands National Seashore, Florida, 2007

USGS Publications Warehouse

Nagle, David B.; Nayegandhi, Amar; Yates, Xan; Brock, John C.; Wright, C. Wayne; Bonisteel, Jamie M.; Klipp, Emily S.; Segura, Martha

2010-01-01

These remotely sensed, geographically referenced color-infrared (CIR) imagery and elevation measurements of lidar-derived bare-earth (BE) topography, first-surface (FS) topography, and canopy-height (CH) datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Science Center, St. Petersburg, FL; the National Park Service (NPS), Gulf Coast Network, Lafayette, LA; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the Naval Live Oaks Area in Florida's Gulf Islands National Seashore, acquired June 30, 2007. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral CIR camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area

Paleolimnological records of nitrogen deposition in shallow, high-elevation lakes of Grand Teton National Park, Wyoming, USA

USGS Publications Warehouse

Spaulding, Sarah A.; Otu, Megan K.; Wolfe, Alexander P.; Baron, Jill S.

2015-01-01

Reactive nitrogen (Nr) from anthropogenic sources has been altering ecosystem function in lakes of the Rocky Mountains, other regions of western North America, and the Arctic over recent decades. The response of biota in shallow lakes to atmospheric deposition of Nr, however, has not been considered. Benthic algae are dominant in shallow, high-elevation lakes and are less sensitive to nutrient inputs than planktonic algae. Because the benthos is typically more nutrient rich than the water column, shallow lakes are not expected to show evidence of anthropogenic Nr. In this study, we assessed sedimentary evidence for regional Nr deposition, sediment chronology, and the nature of algal community response in five shallow, high-elevation lakes in Grand Teton National Park (GRTE). Over 140 diatom taxa were identified from the sediments, with a relatively high species richness of taxa characteristic of oligotrophic conditions. The diatom assemblages were dominated by benthic taxa, especially motile taxa. The GRTE lakes demonstrate assemblage-wide shifts in diatoms, including 1) synchronous and significant assemblage changes centered on ~1960 AD; 2) pre-1960 assemblages differed significantly from post-1960 assemblages; 3) pre-1960 diatom assemblages fluctuated randomly, whereas post- 1960 assemblages showed directional change; 4) changes in δ15N signatures were correlated with diatom community composition. These results demonstrate recent changes in shallow high18 elevation lakes that are most correlated with anthropogenic Nr. It is also possible, however, that the combined effect of Nr deposition and warming is accelerating species shifts in benthic diatoms. While uncertainties remain about the potential synergy of Nr deposition and warming, this study adds shallow lakes to the growing list of impacted high-elevation localities in western North America.

Commentary: A cautionary tale regarding use of the National Land Cover Dataset 1992

USGS Publications Warehouse

Thogmartin, Wayne E.; Gallant, Alisa L.; Knutson, Melinda G.; Fox, Timothy J.; Suarez, Manuel J.

2004-01-01

Digital land-cover data are among the most popular data sources used in ecological research and natural resource management. However, processes for accurate land-cover classification over large regions are still evolving. We identified inconsistencies in the National Land Cover Dataset 1992, the most current and available representation of land cover for the conterminous United States. We also report means to address these inconsistencies in a bird-habitat model. We used a Geographic Information System (GIS) to position a regular grid (or lattice) over the upper midwestern United States and summarized the proportion of individual land covers in each cell within the lattice. These proportions were then mapped back onto the lattice, and the resultant lattice was compared to satellite paths, state borders, and regional map classification units. We observed mapping inconsistencies at the borders between mapping regions, states, and Thematic Mapper (TM) mapping paths in the upper midwestern United States, particularly related to grass I and-herbaceous, emergent-herbaceous wetland, and small-grain land covers. We attributed these discrepancies to differences in image dates between mapping regions, suboptimal image dates for distinguishing certain land-cover types, lack of suitable ancillary data for improving discrimination for rare land covers, and possibly differences among image interpreters. To overcome these inconsistencies for the purpose of modeling regional populations of birds, we combined grassland-herbaceous and pasture-hay land-cover classes and excluded the use of emergent-herbaceous and small-grain land covers. We recommend that users of digital land-cover data conduct similar assessments for other regions before using these data for habitat evaluation. Further, caution is advised in using these data in the analysis of regional land-cover change because it is not likely that future digital land-cover maps will repeat the same problems, thus resulting in

Comparing methods of analysing datasets with small clusters: case studies using four paediatric datasets.

PubMed

Marston, Louise; Peacock, Janet L; Yu, Keming; Brocklehurst, Peter; Calvert, Sandra A; Greenough, Anne; Marlow, Neil

2009-07-01

Studies of prematurely born infants contain a relatively large percentage of multiple births, so the resulting data have a hierarchical structure with small clusters of size 1, 2 or 3. Ignoring the clustering may lead to incorrect inferences. The aim of this study was to compare statistical methods which can be used to analyse such data: generalised estimating equations, multilevel models, multiple linear regression and logistic regression. Four datasets which differed in total size and in percentage of multiple births (n = 254, multiple 18%; n = 176, multiple 9%; n = 10 098, multiple 3%; n = 1585, multiple 8%) were analysed. With the continuous outcome, two-level models produced similar results in the larger dataset, while generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) produced divergent estimates using the smaller dataset. For the dichotomous outcome, most methods, except generalised least squares multilevel modelling (ML GH 'xtlogit' in Stata) gave similar odds ratios and 95% confidence intervals within datasets. For the continuous outcome, our results suggest using multilevel modelling. We conclude that generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) should be used with caution when the dataset is small. Where the outcome is dichotomous and there is a relatively large percentage of non-independent data, it is recommended that these are accounted for in analyses using logistic regression with adjusted standard errors or multilevel modelling. If, however, the dataset has a small percentage of clusters greater than size 1 (e.g. a population dataset of children where there are few multiples) there appears to be less need to adjust for clustering.

Comparison and validation of gridded precipitation datasets for Spain

NASA Astrophysics Data System (ADS)

Quintana-Seguí, Pere; Turco, Marco; Míguez-Macho, Gonzalo

2016-04-01

In this study, two gridded precipitation datasets are compared and validated in Spain: the recently developed SAFRAN dataset and the Spain02 dataset. These are validated using rain gauges and they are also compared to the low resolution ERA-Interim reanalysis. The SAFRAN precipitation dataset has been recently produced, using the SAFRAN meteorological analysis, which is extensively used in France (Durand et al. 1993, 1999; Quintana-Seguí et al. 2008; Vidal et al., 2010) and which has recently been applied to Spain (Quintana-Seguí et al., 2015). SAFRAN uses an optimal interpolation (OI) algorithm and uses all available rain gauges from the Spanish State Meteorological Agency (Agencia Estatal de Meteorología, AEMET). The product has a spatial resolution of 5 km and it spans from September 1979 to August 2014. This dataset has been produced mainly to be used in large scale hydrological applications. Spain02 (Herrera et al. 2012, 2015) is another high quality precipitation dataset for Spain based on a dense network of quality-controlled stations and it has different versions at different resolutions. In this study we used the version with a resolution of 0.11°. The product spans from 1971 to 2010. Spain02 is well tested and widely used, mainly, but not exclusively, for RCM model validation and statistical downscliang. ERA-Interim is a well known global reanalysis with a spatial resolution of ˜79 km. It has been included in the comparison because it is a widely used product for continental and global scale studies and also in smaller scale studies in data poor countries. Thus, its comparison with higher resolution products of a data rich country, such as Spain, allows us to quantify the errors made when using such datasets for national scale studies, in line with some of the objectives of the EU-FP7 eartH2Observe project. The comparison shows that SAFRAN and Spain02 perform similarly, even though their underlying principles are different. Both products are largely

Background qualitative analysis of the European Reference Life Cycle Database (ELCD) energy datasets - part I: fuel datasets.

PubMed

Garraín, Daniel; Fazio, Simone; de la Rúa, Cristina; Recchioni, Marco; Lechón, Yolanda; Mathieux, Fabrice

2015-01-01

The aim of this study is to identify areas of potential improvement of the European Reference Life Cycle Database (ELCD) fuel datasets. The revision is based on the data quality indicators described by the ILCD Handbook, applied on sectorial basis. These indicators evaluate the technological, geographical and time-related representativeness of the dataset and the appropriateness in terms of completeness, precision and methodology. Results show that ELCD fuel datasets have a very good quality in general terms, nevertheless some findings and recommendations in order to improve the quality of Life-Cycle Inventories have been derived. Moreover, these results ensure the quality of the fuel-related datasets to any LCA practitioner, and provide insights related to the limitations and assumptions underlying in the datasets modelling. Giving this information, the LCA practitioner will be able to decide whether the use of the ELCD fuel datasets is appropriate based on the goal and scope of the analysis to be conducted. The methodological approach would be also useful for dataset developers and reviewers, in order to improve the overall DQR of databases.

Online, On Demand Access to Coastal Digital Elevation Models

NASA Astrophysics Data System (ADS)

Long, J.; Bristol, S.; Long, D.; Thompson, S.

2014-12-01

Process-based numerical models for coastal waves, water levels, and sediment transport are initialized with digital elevation models (DEM) constructed by interpolating and merging bathymetric and topographic elevation data. These gridded surfaces must seamlessly span the land-water interface and may cover large regions where the individual raw data sources are collected at widely different spatial and temporal resolutions. In addition, the datasets are collected from different instrument platforms with varying accuracy and may or may not overlap in coverage. The lack of available tools and difficulties in constructing these DEMs lead scientists to 1) rely on previously merged, outdated, or over-smoothed DEMs; 2) discard more recent data that covers only a portion of the DEM domain; and 3) use inconsistent methodologies to generate DEMs. The objective of this work is to address the immediate need of integrating land and water-based elevation data sources and streamline the generation of a seamless data surface that spans the terrestrial-marine boundary. To achieve this, the U.S. Geological Survey (USGS) is developing a web processing service to format and initialize geoprocessing tasks designed to create coastal DEMs. The web processing service is maintained within the USGS ScienceBase data management system and has an associated user interface. Through the map-based interface, users define a geographic region that identifies the bounds of the desired DEM and a time period of interest. This initiates a query for elevation datasets within federal science agency data repositories. A geoprocessing service is then triggered to interpolate, merge, and smooth the data sources creating a DEM based on user-defined configuration parameters. Uncertainty and error estimates for the DEM are also returned by the geoprocessing service. Upon completion, the information management platform provides access to the final gridded data derivative and saves the configuration parameters

EPA GHG Certification of Medium- and Heavy-Duty Vehicles: Development of Road Grade Profiles Representative of US Controlled Access Highways

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

Wood, Eric; Duran, Adam; Burton, Evan

This report includes a detailed comparison of the TomTom national road grade database relative to a local road grade dataset generated by Southwest Research Institute and a national elevation dataset publically available from the U.S. Geological Survey. This analysis concluded that the TomTom national road grade database was a suitable source of road grade data for purposes of this study.

Open-Source Digital Elevation Model (DEMs) Evaluation with GPS and LiDAR Data

NASA Astrophysics Data System (ADS)

Khalid, N. F.; Din, A. H. M.; Omar, K. M.; Khanan, M. F. A.; Omar, A. H.; Hamid, A. I. A.; Pa'suya, M. F.

2016-09-01

Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Shuttle Radar Topography Mission (SRTM), and Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) are freely available Digital Elevation Model (DEM) datasets for environmental modeling and studies. The quality of spatial resolution and vertical accuracy of the DEM data source has a great influence particularly on the accuracy specifically for inundation mapping. Most of the coastal inundation risk studies used the publicly available DEM to estimated the coastal inundation and associated damaged especially to human population based on the increment of sea level. In this study, the comparison between ground truth data from Global Positioning System (GPS) observation and DEM is done to evaluate the accuracy of each DEM. The vertical accuracy of SRTM shows better result against ASTER and GMTED10 with an RMSE of 6.054 m. On top of the accuracy, the correlation of DEM is identified with the high determination of coefficient of 0.912 for SRTM. For coastal zone area, DEMs based on airborne light detection and ranging (LiDAR) dataset was used as ground truth data relating to terrain height. In this case, the LiDAR DEM is compared against the new SRTM DEM after applying the scale factor. From the findings, the accuracy of the new DEM model from SRTM can be improved by applying scale factor. The result clearly shows that the value of RMSE exhibit slightly different when it reached 0.503 m. Hence, this new model is the most suitable and meets the accuracy requirement for coastal inundation risk assessment using open source data. The suitability of these datasets for further analysis on coastal management studies is vital to assess the potentially vulnerable areas caused by coastal inundation.

Design of an audio advertisement dataset

NASA Astrophysics Data System (ADS)

Fu, Yutao; Liu, Jihong; Zhang, Qi; Geng, Yuting

2015-12-01

Since more and more advertisements swarm into radios, it is necessary to establish an audio advertising dataset which could be used to analyze and classify the advertisement. A method of how to establish a complete audio advertising dataset is presented in this paper. The dataset is divided into four different kinds of advertisements. Each advertisement's sample is given in *.wav file format, and annotated with a txt file which contains its file name, sampling frequency, channel number, broadcasting time and its class. The classifying rationality of the advertisements in this dataset is proved by clustering the different advertisements based on Principal Component Analysis (PCA). The experimental results show that this audio advertisement dataset offers a reliable set of samples for correlative audio advertisement experimental studies.

Topographic and hydrographic GIS datasets for the Afghan Geological Survey and U.S. Geological Survey 2013 mineral areas of interest

USGS Publications Warehouse

Casey, Brittany N.; Chirico, Peter G.

2013-01-01

Afghanistan is endowed with a vast amount of mineral resources, and it is believed that the current economic state of the country could be greatly improved through investment in the extraction and production of these resources. In 2007, the “Preliminary Non-Fuel Resource Assessment of Afghanistan 2007” was completed by members of the U.S. Geological Survey and Afghan Geological Survey (Peters and others, 2007). The assessment delineated 20 mineralized areas for further study using a geologic-based methodology. In 2011, a follow-on data product, “Summaries and Data Packages of Important Areas for Mineral Investment and Production Opportunities of Nonfuel Minerals in Afghanistan,” was released (Peters and others, 2011). As part of this more recent work, geologic, geohydrologic, and hyperspectral studies were carried out in the areas of interest (AOIs) to assess the location and characteristics of the mineral resources. The 2011 publication included a dataset of 24 identified AOIs containing subareas, a corresponding digital elevation model (DEM), elevation contours, areal extent, and hydrography for each AOI. In 2012, project scientists identified five new AOIs and two subareas in Afghanistan. These new areas are Ahankashan, Kandahar, Parwan, North Bamyan, and South Bamyan. The two identified subareas include Obatu-Shela and Sekhab-ZamtoKalay, both located within the larger Kandahar AOI. In addition, an extended Kandahar AOI is included in the project for water resource modeling purposes. The dataset presented in this publication consists of the areal extent of the five new AOIs, two subareas, and the extended Kandahar AOI, elevation contours at 100-, 50-, and 25-meter intervals, an enhanced DEM, and a hydrographic dataset covering the extent of the new study area. The resulting raster and vector layers are intended for use by government agencies, developmental organizations, and private companies in Afghanistan to assist with mineral assessments, monitoring

Creation of the Naturalistic Engagement in Secondary Tasks (NEST) distracted driving dataset.

PubMed

Owens, Justin M; Angell, Linda; Hankey, Jonathan M; Foley, James; Ebe, Kazutoshi

2015-09-01

Distracted driving has become a topic of critical importance to driving safety research over the past several decades. Naturalistic driving data offer a unique opportunity to study how drivers engage with secondary tasks in real-world driving; however, the complexities involved with identifying and coding relevant epochs of naturalistic data have limited its accessibility to the general research community. This project was developed to help address this problem by creating an accessible dataset of driver behavior and situational factors observed during distraction-related safety-critical events and baseline driving epochs, using the Strategic Highway Research Program 2 (SHRP2) naturalistic dataset. The new NEST (Naturalistic Engagement in Secondary Tasks) dataset was created using crashes and near-crashes from the SHRP2 dataset that were identified as including secondary task engagement as a potential contributing factor. Data coding included frame-by-frame video analysis of secondary task and hands-on-wheel activity, as well as summary event information. In addition, information about each secondary task engagement within the trip prior to the crash/near-crash was coded at a higher level. Data were also coded for four baseline epochs and trips per safety-critical event. 1,180 events and baseline epochs were coded, and a dataset was constructed. The project team is currently working to determine the most useful way to allow broad public access to the dataset. We anticipate that the NEST dataset will be extraordinarily useful in allowing qualified researchers access to timely, real-world data concerning how drivers interact with secondary tasks during safety-critical events and baseline driving. The coded dataset developed for this project will allow future researchers to have access to detailed data on driver secondary task engagement in the real world. It will be useful for standalone research, as well as for integration with additional SHRP2 data to enable the

Background qualitative analysis of the European reference life cycle database (ELCD) energy datasets - part II: electricity datasets.

PubMed

Garraín, Daniel; Fazio, Simone; de la Rúa, Cristina; Recchioni, Marco; Lechón, Yolanda; Mathieux, Fabrice

2015-01-01

The aim of this paper is to identify areas of potential improvement of the European Reference Life Cycle Database (ELCD) electricity datasets. The revision is based on the data quality indicators described by the International Life Cycle Data system (ILCD) Handbook, applied on sectorial basis. These indicators evaluate the technological, geographical and time-related representativeness of the dataset and the appropriateness in terms of completeness, precision and methodology. Results show that ELCD electricity datasets have a very good quality in general terms, nevertheless some findings and recommendations in order to improve the quality of Life-Cycle Inventories have been derived. Moreover, these results ensure the quality of the electricity-related datasets to any LCA practitioner, and provide insights related to the limitations and assumptions underlying in the datasets modelling. Giving this information, the LCA practitioner will be able to decide whether the use of the ELCD electricity datasets is appropriate based on the goal and scope of the analysis to be conducted. The methodological approach would be also useful for dataset developers and reviewers, in order to improve the overall Data Quality Requirements of databases.

Analysis of lidar elevation data for improved identification and delineation of lands vulnerable to sea-level rise

USGS Publications Warehouse

Gesch, Dean B.

2009-01-01

The importance of sea-level rise in shaping coastal landscapes is well recognized within the earth science community, but as with many natural hazards, communicating the risks associated with sea-level rise remains a challenge. Topography is a key parameter that influences many of the processes involved in coastal change, and thus, up-to-date, high-resolution, high-accuracy elevation data are required to model the coastal environment. Maps of areas subject to potential inundation have great utility to planners and managers concerned with the effects of sea-level rise. However, most of the maps produced to date are simplistic representations derived from older, coarse elevation data. In the last several years, vast amounts of high quality elevation data derived from lidar have become available. Because of their high vertical accuracy and spatial resolution, these lidar data are an excellent source of up-to-date information from which to improve identification and delineation of vulnerable lands. Four elevation datasets of varying resolution and accuracy were processed to demonstrate that the improved quality of lidar data leads to more precise delineation of coastal lands vulnerable to inundation. A key component of the comparison was to calculate and account for the vertical uncertainty of the elevation datasets. This comparison shows that lidar allows for a much more detailed delineation of the potential inundation zone when compared to other types of elevation models. It also shows how the certainty of the delineation of lands vulnerable to a given sea-level rise scenario is much improved when derived from higher resolution lidar data.

Validation of the Aster Global Digital Elevation Model Version 3 Over the Conterminous United States

NASA Astrophysics Data System (ADS)

Gesch, D.; Oimoen, M.; Danielson, J.; Meyer, D.

2016-06-01

The ASTER Global Digital Elevation Model Version 3 (GDEM v3) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009 and GDEM Version 2 (v2) in 2011. The absolute vertical accuracy of GDEM v3 was calculated by comparison with more than 23,000 independent reference geodetic ground control points from the U.S. National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v3 is 8.52 meters. This compares with the RMSE of 8.68 meters for GDEM v2. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v3 mean error of -1.20 meters reflects an overall negative bias in GDEM v3. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover type to provide insight into how GDEM v3 performs in various land surface conditions. While the RMSE varies little across cover types (6.92 to 9.25 meters), the mean error (bias) does appear to be affected by land cover type, ranging from -2.99 to +4.16 meters across 14 land cover classes. These results indicate that in areas where built or natural aboveground features are present, GDEM v3 is measuring elevations above the ground level, a condition noted in assessments of previous GDEM versions (v1 and v2) and an expected condition given the type of stereo-optical image data collected by ASTER. GDEM v3 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v3 has elevations that are higher in the canopy than SRTM. The overall validation effort also included an evaluation of the GDEM v3 water mask. In general, the number of distinct water polygons in GDEM v3 is much lower than the number in a reference land cover dataset, but the total areas compare much more closely.

Validation of the ASTER Global Digital Elevation Model version 3 over the conterminous United States

USGS Publications Warehouse

Gesch, Dean B.; Oimoen, Michael J.; Danielson, Jeffrey J.; Meyer, David; Halounova, L; Šafář, V.; Jiang, J.; Olešovská, H.; Dvořáček, P.; Holland, D.; Seredovich, V.A.; Muller, J.P.; Pattabhi Rama Rao, E.; Veenendaal, B.; Mu, L.; Zlatanova, S.; Oberst, J.; Yang, C.P.; Ban, Y.; Stylianidis, S.; Voženílek, V.; Vondráková, A.; Gartner, G.; Remondino, F.; Doytsher, Y.; Percivall, George; Schreier, G.; Dowman, I.; Streilein, A.; Ernst, J.

2016-01-01

The ASTER Global Digital Elevation Model Version 3 (GDEM v3) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009 and GDEM Version 2 (v2) in 2011. The absolute vertical accuracy of GDEM v3 was calculated by comparison with more than 23,000 independent reference geodetic ground control points from the U.S. National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v3 is 8.52 meters. This compares with the RMSE of 8.68 meters for GDEM v2. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v3 mean error of −1.20 meters reflects an overall negative bias in GDEM v3. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover type to provide insight into how GDEM v3 performs in various land surface conditions. While the RMSE varies little across cover types (6.92 to 9.25 meters), the mean error (bias) does appear to be affected by land cover type, ranging from −2.99 to +4.16 meters across 14 land cover classes. These results indicate that in areas where built or natural aboveground features are present, GDEM v3 is measuring elevations above the ground level, a condition noted in assessments of previous GDEM versions (v1 and v2) and an expected condition given the type of stereo-optical image data collected by ASTER. GDEM v3 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v3 has elevations that are higher in the canopy than SRTM. The overall validation effort also included an evaluation of the GDEM v3 water mask. In general, the number of distinct water polygons in GDEM v3 is much lower than the number in a reference land cover dataset, but the total areas compare much more closely.

Subsampling for dataset optimisation

NASA Astrophysics Data System (ADS)

Ließ, Mareike

2017-04-01

Soil-landscapes have formed by the interaction of soil-forming factors and pedogenic processes. In modelling these landscapes in their pedodiversity and the underlying processes, a representative unbiased dataset is required. This concerns model input as well as output data. However, very often big datasets are available which are highly heterogeneous and were gathered for various purposes, but not to model a particular process or data space. As a first step, the overall data space and/or landscape section to be modelled needs to be identified including considerations regarding scale and resolution. Then the available dataset needs to be optimised via subsampling to well represent this n-dimensional data space. A couple of well-known sampling designs may be adapted to suit this purpose. The overall approach follows three main strategies: (1) the data space may be condensed and de-correlated by a factor analysis to facilitate the subsampling process. (2) Different methods of pattern recognition serve to structure the n-dimensional data space to be modelled into units which then form the basis for the optimisation of an existing dataset through a sensible selection of samples. Along the way, data units for which there is currently insufficient soil data available may be identified. And (3) random samples from the n-dimensional data space may be replaced by similar samples from the available dataset. While being a presupposition to develop data-driven statistical models, this approach may also help to develop universal process models and identify limitations in existing models.

Front Elevation and Floor Plan in 1893, 1894, and 1909; ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Front Elevation and Floor Plan in 1893, 1894, and 1909; Office End Elevation, Waiting End Elevation, Section A (1894), Section B (1894), Signage (ca. 1908-1911), Map of Rail Lines & Depots on Soldiers' Home - National Home for Disabled Volunteer Soldiers, Pacific Branch, Streetcar Depot, Corner of Pershing & Dewey Avenues, Los Angeles, Los Angeles County, CA

Decibel: The Relational Dataset Branching System

PubMed Central

Maddox, Michael; Goehring, David; Elmore, Aaron J.; Madden, Samuel; Parameswaran, Aditya; Deshpande, Amol

2017-01-01

As scientific endeavors and data analysis become increasingly collaborative, there is a need for data management systems that natively support the versioning or branching of datasets to enable concurrent analysis, cleaning, integration, manipulation, or curation of data across teams of individuals. Common practice for sharing and collaborating on datasets involves creating or storing multiple copies of the dataset, one for each stage of analysis, with no provenance information tracking the relationships between these datasets. This results not only in wasted storage, but also makes it challenging to track and integrate modifications made by different users to the same dataset. In this paper, we introduce the Relational Dataset Branching System, Decibel, a new relational storage system with built-in version control designed to address these shortcomings. We present our initial design for Decibel and provide a thorough evaluation of three versioned storage engine designs that focus on efficient query processing with minimal storage overhead. We also develop an exhaustive benchmark to enable the rigorous testing of these and future versioned storage engine designs. PMID:28149668

State of Florida 1:24,000- and 1:100,000-scale quadrangle index map - Highlighting low-lying areas derived from USGS Digital Elevation Models

USGS Publications Warehouse

Kosovich, John J.

2008-01-01

In support of U.S. Geological Survey (USGS) disaster preparedness efforts, this map depicts 1:24,000- and 1:100,000-scale quadrangle footprints over a color shaded relief representation of the State of Florida. The first 30 feet of relief above mean sea level are displayed as brightly colored 5-foot elevation bands, which highlight low-elevation areas at a coarse spatial resolution. Standard USGS National Elevation Dataset (NED) 1 arc-second (nominally 30-meter) digital elevation model (DEM) data are the basis for the map, which is designed to be used at a broad scale and for informational purposes only. The NED source data for this map consists of a mixture of 30-meter- and 10-meter-resolution DEMs. The NED data were derived from the original 1:24,000-scale USGS topographic map bare-earth contours, which were converted into gridded quadrangle-based DEM tiles at a constant post spacing (grid cell size) of either 30 meters (data before the mid-1990s) or 10 meters (mid-1990s and later data). These individual-quadrangle DEMs were then converted to spherical coordinates (latitude/longitude decimal degrees) and edge-matched to ensure seamlessness. Figure 1 shows a similar representation for the entire U.S. Gulf Coast, using coarsened 30-meter NED data. Areas below sea level typically are surrounded by levees or some other type of flood-control structures. State and county boundary, hydrography, city, and road layers were modified from USGS National Atlas data downloaded in 2003. Quadrangle names, dated April, 2006, were obtained from the Federal Geographic Names Information System. The NED data were downloaded in 2004.

Global Mapping Project - Applications and Development of Version 2 Dataset

NASA Astrophysics Data System (ADS)

Ubukawa, T.; Nakamura, T.; Otsuka, T.; Iimura, T.; Kishimoto, N.; Nakaminami, K.; Motojima, Y.; Suga, M.; Yatabe, Y.; Koarai, M.; Okatani, T.

2012-07-01

The Global Mapping Project aims to develop basic geospatial information of the whole land area of the globe, named Global Map, through the cooperation of National Mapping Organizations (NMOs) around the world. The Global Map data can be a base of global geospatial infrastructure and is composed of eight layers: Boundaries, Drainage, Transportation, Population Centers, Elevation, Land Use, Land Cover and Vegetation. The Global Map Version 1 was released in 2008, and the Version 2 will be released in 2013 as the data are to be updated every five years. In 2009, the International Steering Committee for Global Mapping (ISCGM) adopted new Specifications to develop the Global Map Version 2 with a change of its format so that it is compatible with the international standards, namely ISO 19136 and ISO 19115. With the support of the secretariat of ISCGM, the project participating countries are accelerating their data development toward the completion of the global coverage in 2013, while some countries have already released their Global Map version 2 datasets since 2010. Global Map data are available from the Internet free of charge for non-commercial purposes, which can be used to predict, assess, prepare for and cope with global issues by combining with other spatial data. There are a lot of Global Map applications in various fields, and further utilization of Global Map is expected. This paper summarises the activities toward the development of the Global Map Version 2 as well as some examples of the Global Map applications in various fields.

Comparing Goldstone Solar System Radar Earth-based Observations of Mars with Orbital Datasets

NASA Technical Reports Server (NTRS)

Haldemann, A. F. C.; Larsen, K. W.; Jurgens, R. F.; Slade, M. A.

2005-01-01

The Goldstone Solar System Radar (GSSR) has collected a self-consistent set of delay-Doppler near-nadir radar echo data from Mars since 1988. Prior to the Mars Global Surveyor (MGS) Mars Orbiter Laser Altimeter (MOLA) global topography for Mars, these radar data provided local elevation information, along with radar scattering information with global coverage. Two kinds of GSSR Mars delay-Doppler data exist: low 5 km x 150 km resolution and, more recently, high (5 to 10 km) spatial resolution. Radar data, and non-imaging delay-Doppler data in particular, requires significant data processing to extract elevation, reflectivity and roughness of the reflecting surface. Interpretation of these parameters, while limited by the complexities of electromagnetic scattering, provide information directly relevant to geophysical and geomorphic analyses of Mars. In this presentation we want to demonstrate how to compare GSSR delay-Doppler data to other Mars datasets, including some idiosyncracies of the radar data. Additional information is included in the original extended abstract.

ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers.

PubMed

Teodoro, Douglas; Sundvall, Erik; João Junior, Mario; Ruch, Patrick; Miranda Freire, Sergio

2018-01-01

The openEHR specifications are designed to support implementation of flexible and interoperable Electronic Health Record (EHR) systems. Despite the increasing number of solutions based on the openEHR specifications, it is difficult to find publicly available healthcare datasets in the openEHR format that can be used to test, compare and validate different data persistence mechanisms for openEHR. To foster research on openEHR servers, we present the openEHR Benchmark Dataset, ORBDA, a very large healthcare benchmark dataset encoded using the openEHR formalism. To construct ORBDA, we extracted and cleaned a de-identified dataset from the Brazilian National Healthcare System (SUS) containing hospitalisation and high complexity procedures information and formalised it using a set of openEHR archetypes and templates. Then, we implemented a tool to enrich the raw relational data and convert it into the openEHR model using the openEHR Java reference model library. The ORBDA dataset is available in composition, versioned composition and EHR openEHR representations in XML and JSON formats. In total, the dataset contains more than 150 million composition records. We describe the dataset and provide means to access it. Additionally, we demonstrate the usage of ORBDA for evaluating inserting throughput and query latency performances of some NoSQL database management systems. We believe that ORBDA is a valuable asset for assessing storage models for openEHR-based information systems during the software engineering process. It may also be a suitable component in future standardised benchmarking of available openEHR storage platforms.

ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers

PubMed Central

Sundvall, Erik; João Junior, Mario; Ruch, Patrick; Miranda Freire, Sergio

2018-01-01

The openEHR specifications are designed to support implementation of flexible and interoperable Electronic Health Record (EHR) systems. Despite the increasing number of solutions based on the openEHR specifications, it is difficult to find publicly available healthcare datasets in the openEHR format that can be used to test, compare and validate different data persistence mechanisms for openEHR. To foster research on openEHR servers, we present the openEHR Benchmark Dataset, ORBDA, a very large healthcare benchmark dataset encoded using the openEHR formalism. To construct ORBDA, we extracted and cleaned a de-identified dataset from the Brazilian National Healthcare System (SUS) containing hospitalisation and high complexity procedures information and formalised it using a set of openEHR archetypes and templates. Then, we implemented a tool to enrich the raw relational data and convert it into the openEHR model using the openEHR Java reference model library. The ORBDA dataset is available in composition, versioned composition and EHR openEHR representations in XML and JSON formats. In total, the dataset contains more than 150 million composition records. We describe the dataset and provide means to access it. Additionally, we demonstrate the usage of ORBDA for evaluating inserting throughput and query latency performances of some NoSQL database management systems. We believe that ORBDA is a valuable asset for assessing storage models for openEHR-based information systems during the software engineering process. It may also be a suitable component in future standardised benchmarking of available openEHR storage platforms. PMID:29293556

A global distributed basin morphometric dataset

NASA Astrophysics Data System (ADS)

Shen, Xinyi; Anagnostou, Emmanouil N.; Mei, Yiwen; Hong, Yang

2017-01-01

Basin morphometry is vital information for relating storms to hydrologic hazards, such as landslides and floods. In this paper we present the first comprehensive global dataset of distributed basin morphometry at 30 arc seconds resolution. The dataset includes nine prime morphometric variables; in addition we present formulas for generating twenty-one additional morphometric variables based on combination of the prime variables. The dataset can aid different applications including studies of land-atmosphere interaction, and modelling of floods and droughts for sustainable water management. The validity of the dataset has been consolidated by successfully repeating the Hack's law.

EAARL coastal topography and imagery-Fire Island National Seashore, New York, 2009

USGS Publications Warehouse

Vivekanandan, Saisudha; Klipp, E.S.; Nayegandhi, Amar; Bonisteel-Cormier, J.M.; Brock, J.C.; Wright, C.W.; Nagle, D.B.; Fredericks, Xan; Stevens, Sara

2010-01-01

These remotely sensed, geographically referenced color-infrared (CIR) imagery and elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the Fire Island National Seashore in New York, acquired on July 9 and August 3, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral CIR camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar

Soil chemistry in lithologically diverse datasets: the quartz dilution effect

USGS Publications Warehouse

Bern, Carleton R.

2009-01-01

National- and continental-scale soil geochemical datasets are likely to move our understanding of broad soil geochemistry patterns forward significantly. Patterns of chemistry and mineralogy delineated from these datasets are strongly influenced by the composition of the soil parent material, which itself is largely a function of lithology and particle size sorting. Such controls present a challenge by obscuring subtler patterns arising from subsequent pedogenic processes. Here the effect of quartz concentration is examined in moist-climate soils from a pilot dataset of the North American Soil Geochemical Landscapes Project. Due to variable and high quartz contents (6.2–81.7 wt.%), and its residual and inert nature in soil, quartz is demonstrated to influence broad patterns in soil chemistry. A dilution effect is observed whereby concentrations of various elements are significantly and strongly negatively correlated with quartz. Quartz content drives artificial positive correlations between concentrations of some elements and obscures negative correlations between others. Unadjusted soil data show the highly mobile base cations Ca, Mg, and Na to be often strongly positively correlated with intermediately mobile Al or Fe, and generally uncorrelated with the relatively immobile high-field-strength elements (HFS) Ti and Nb. Both patterns are contrary to broad expectations for soils being weathered and leached. After transforming bulk soil chemistry to a quartz-free basis, the base cations are generally uncorrelated with Al and Fe, and negative correlations generally emerge with the HFS elements. Quartz-free element data may be a useful tool for elucidating patterns of weathering or parent-material chemistry in large soil datasets.

Integrated remotely sensed datasets for disaster management

NASA Astrophysics Data System (ADS)

McCarthy, Timothy; Farrell, Ronan; Curtis, Andrew; Fotheringham, A. Stewart

2008-10-01

Video imagery can be acquired from aerial, terrestrial and marine based platforms and has been exploited for a range of remote sensing applications over the past two decades. Examples include coastal surveys using aerial video, routecorridor infrastructures surveys using vehicle mounted video cameras, aerial surveys over forestry and agriculture, underwater habitat mapping and disaster management. Many of these video systems are based on interlaced, television standards such as North America's NTSC and European SECAM and PAL television systems that are then recorded using various video formats. This technology has recently being employed as a front-line, remote sensing technology for damage assessment post-disaster. This paper traces the development of spatial video as a remote sensing tool from the early 1980s to the present day. The background to a new spatial-video research initiative based at National University of Ireland, Maynooth, (NUIM) is described. New improvements are proposed and include; low-cost encoders, easy to use software decoders, timing issues and interoperability. These developments will enable specialists and non-specialists collect, process and integrate these datasets within minimal support. This integrated approach will enable decision makers to access relevant remotely sensed datasets quickly and so, carry out rapid damage assessment during and post-disaster.

School Attendance Problems and Youth Psychopathology: Structural Cross-Lagged Regression Models in Three Longitudinal Datasets

PubMed Central

Wood, Jeffrey J.; Lynne, Sarah D.; Langer, David A.; Wood, Patricia A.; Clark, Shaunna L.; Eddy, J. Mark; Ialongo, Nicholas

2011-01-01

This study tests a model of reciprocal influences between absenteeism and youth psychopathology using three longitudinal datasets (Ns= 20745, 2311, and 671). Participants in 1st through 12th grades were interviewed annually or bi-annually. Measures of psychopathology include self-, parent-, and teacher-report questionnaires. Structural cross-lagged regression models were tested. In a nationally representative dataset (Add Health), middle school students with relatively greater absenteeism at study year 1 tended towards increased depression and conduct problems in study year 2, over and above the effects of autoregressive associations and demographic covariates. The opposite direction of effects was found for both middle and high school students. Analyses with two regionally representative datasets were also partially supportive. Longitudinal links were more evident in adolescence than in childhood. PMID:22188462

Avulsion research using flume experiments and highly accurate and temporal-rich SfM datasets

NASA Astrophysics Data System (ADS)

Javernick, L.; Bertoldi, W.; Vitti, A.

2017-12-01

SfM's ability to produce high-quality, large-scale digital elevation models (DEMs) of complicated and rapidly evolving systems has made it a valuable technique for low-budget researchers and practitioners. While SfM has provided valuable datasets that capture single-flood event DEMs, there is an increasing scientific need to capture higher temporal resolution datasets that can quantify the evolutionary processes instead of pre- and post-flood snapshots. However, flood events' dangerous field conditions and image matching challenges (e.g. wind, rain) prevent quality SfM-image acquisition. Conversely, flume experiments offer opportunities to document flood events, but achieving consistent and accurate DEMs to detect subtle changes in dry and inundated areas remains a challenge for SfM (e.g. parabolic error signatures).This research aimed at investigating the impact of naturally occurring and manipulated avulsions on braided river morphology and on the encroachment of floodplain vegetation, using laboratory experiments. This required DEMs with millimeter accuracy and precision and at a temporal resolution to capture the processes. SfM was chosen as it offered the most practical method. Through redundant local network design and a meticulous ground control point (GCP) survey with a Leica Total Station in red laser configuration (reported 2 mm accuracy), the SfM residual errors compared to separate ground truthing data produced mean errors of 1.5 mm (accuracy) and standard deviations of 1.4 mm (precision) without parabolic error signatures. Lighting conditions in the flume were limited to uniform, oblique, and filtered LED strips, which removed glint and thus improved bed elevation mean errors to 4 mm, but errors were further reduced by means of an open source software for refraction correction. The obtained datasets have provided the ability to quantify how small flood events with avulsion can have similar morphologic and vegetation impacts as large flood events

Microbial community and nitrogen cycling shift with snowmelt in high-elevation barren soils of Mount Rainier National Park

NASA Astrophysics Data System (ADS)

Simpson, A.; Zabowski, D.

2015-12-01

Climate change and nutrient deposition have the potential to accelerate soil formation in high-elevation sediments recently exposed by glacier or snow melt. This process has implications not only for ecosystem formation on Earth but for the formation of Earth-like ecosystems on other planets and icy moons. Research into microbial communities shifting from subnival to mesotrophic conditions has mainly focused on changes on respiration and biomass, and is generally limited to one or two well-studied geographical locations. In particular, more information is needed on microbial shifts in snow-covered volcanic sediments, which may prove the closest analog to the most 'habitable' non-terrestrial environments for Earth microorganisms. We sampled in volcanic soil and sediment along gradients of elevation and snowmelt - dry soil, moist soil next to snowpack, and soil underneath snowpack - at the Muir Snowfields at Mount Rainier National Park, in order to investigate changes in carbon and nitrogen compounds, microbial diversity and gene expression. Initial results show a decrease in available ammonium and increase in microbial biomass carbon in exposed sediment with increasing soil moisture, and a sharp decrease in microbial C:N ratios after snowmelt and drying. Available/labile organic carbon and organic nitrogen decrease strongly with elevation, while microbial biomass carbon and nitrogen and mineral nitrogen compounds show no change with elevation. Though gene expression data is needed for confirmation, we hypothesize that these snowfields receive strong wind-borne deposits of carbon and nitrogen but that chemoautotrophic communities under semi-permanent snowpack do not shift to more mesotrophic communities until after exposed sediment has already begun to desiccate, limiting soil formation.

Bayesian correlated clustering to integrate multiple datasets

PubMed Central

Kirk, Paul; Griffin, Jim E.; Savage, Richard S.; Ghahramani, Zoubin; Wild, David L.

2012-01-01

Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct—but often complementary—information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets. Results: Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI’s performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation–chip and protein–protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques—as well as to non-integrative approaches—demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods. Availability: A Matlab implementation of MDI is available from http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software/. Contact: D.L.Wild@warwick.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID

Accelerated warming at high elevations: a review of the current evidence and proposals for future research (Invited)

NASA Astrophysics Data System (ADS)

Pepin, N. C.

2013-12-01

Arctic amplification, whereby enhanced warming is evident at high latitudes, is well accepted amongst the scientific community. Increased warming at high elevations is more controversial and is often given the more vague term 'elevational dependency'. The way in which different approaches (mountain surface data, radiosondes, satellite data and models) often yield different results is discussed, along with the differences between these approaches. Analyses of surface data differ in the stations chosen for comparison, the time period, elevational range, and methods of trend identification. An analysis of global datasets using over a thousand stations (GHCN, CRU) and defining change by the most common method of calculating the linear gradient of a best fit line (linear regression) shows no simple relationship between warming rate and elevation. There are however feedback mechanisms in the mountain environment (e.g. cryospheric change, water vapor and treelines) which, although they may enhance warming at certain elevations, are fairly poorly understood. Warming rates are also shown to be influenced by factors in the mountain environment other than elevation, including topography (aspect, slope, topographic exposure) as well as mean annual temperature, but the relative influences of such controls have yet to be disentangled from those that show a more simple elevationally-dependent signal. Mountain summits and exposed ridge sites are shown to show least variability in warming rates, rising up above a sea of noise. Radiosondes and satellite data are further removed from changes on the ground (surface temperatures) and studies using such data tend to be rather divorced from the mountain environment and need calibration/comparison with surface datasets. Reanalyses such as NCEP/NCAR and ERA, although having good spatial coverage, tend to suffer from the same problems. Following a discussion of differences between all these approaches, a plan to develop an integrated global

A seamless, high-resolution digital elevation model (DEM) of the north-central California coast

USGS Publications Warehouse

Foxgrover, Amy C.; Barnard, Patrick L.

2012-01-01

A seamless, 2-meter resolution digital elevation model (DEM) of the north-central California coast has been created from the most recent high-resolution bathymetric and topographic datasets available. The DEM extends approximately 150 kilometers along the California coastline, from Half Moon Bay north to Bodega Head. Coverage extends inland to an elevation of +20 meters and offshore to at least the 3 nautical mile limit of state waters. This report describes the procedures of DEM construction, details the input data sources, and provides the DEM for download in both ESRI Arc ASCII and GeoTIFF file formats with accompanying metadata.

Developing a regional retrospective ensemble precipitation dataset for watershed hydrology modeling, Idaho, USA

NASA Astrophysics Data System (ADS)

Flores, A. N.; Smith, K.; LaPorte, P.

2011-12-01

Applications like flood forecasting, military trafficability assessment, and slope stability analysis necessitate the use of models capable of resolving hydrologic states and fluxes at spatial scales of hillslopes (e.g., 10s to 100s m). These models typically require precipitation forcings at spatial scales of kilometers or better and time intervals of hours. Yet in especially rugged terrain that typifies much of the Western US and throughout much of the developing world, precipitation data at these spatiotemporal resolutions is difficult to come by. Ground-based weather radars have significant problems in high-relief settings and are sparsely located, leaving significant gaps in coverage and high uncertainties. Precipitation gages provide accurate data at points but are very sparsely located and their placement is often not representative, yielding significant coverage gaps in a spatial and physiographic sense. Numerical weather prediction efforts have made precipitation data, including critically important information on precipitation phase, available globally and in near real-time. However, these datasets present watershed modelers with two problems: (1) spatial scales of many of these datasets are tens of kilometers or coarser, (2) numerical weather models used to generate these datasets include a land surface parameterization that in some circumstances can significantly affect precipitation predictions. We report on the development of a regional precipitation dataset for Idaho that leverages: (1) a dataset derived from a numerical weather prediction model, (2) gages within Idaho that report hourly precipitation data, and (3) a long-term precipitation climatology dataset. Hourly precipitation estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA) are stochastically downscaled using a hybrid orographic and statistical model from their native resolution (1/2 x 2/3 degrees) to a resolution of approximately 1 km. Downscaled

CPTAC Releases Largest-Ever Ovarian Cancer Proteome Dataset from Previously Genome Characterized Tumors | Office of Cancer Clinical Proteomics Research

Cancer.gov

National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) scientists have just released a comprehensive dataset of the proteomic analysis of high grade serous ovarian tumor samples, previously genomically analyzed by The Cancer Genome Atlas (TCGA).  This is one of the largest public datasets covering the proteome, phosphoproteome and glycoproteome with complementary deep genomic sequencing data on the same tumor.

ACCURACY OF THE 1992 NATIONAL LAND COVER DATASET AREA ESTIMATES: AN ANALYSIS AT MULTIPLE SPATIAL EXTENTS

EPA Science Inventory

Abstract for poster presentation:

Site-specific accuracy assessments evaluate fine-scale accuracy of land-use/land-cover(LULC) datasets but provide little insight into accuracy of area estimates of LULC

classes derived from sampling units of varying size. Additiona...

A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900

NASA Astrophysics Data System (ADS)

Xu, Wenhui; Li, Qingxiang; Jones, Phil; Wang, Xiaolan L.; Trewin, Blair; Yang, Su; Zhu, Chen; Zhai, Panmao; Wang, Jinfeng; Vincent, Lucie; Dai, Aiguo; Gao, Yun; Ding, Yihui

2018-04-01

A new dataset of integrated and homogenized monthly surface air temperature over global land for the period since 1900 [China Meteorological Administration global Land Surface Air Temperature (CMA-LSAT)] is developed. In total, 14 sources have been collected and integrated into the newly developed dataset, including three global (CRUTEM4, GHCN, and BEST), three regional and eight national sources. Duplicate stations are identified, and those with the higher priority are chosen or spliced. Then, a consistency test and a climate outlier test are conducted to ensure that each station series is quality controlled. Next, two steps are adopted to assure the homogeneity of the station series: (1) homogenized station series in existing national datasets (by National Meteorological Services) are directly integrated into the dataset without any changes (50% of all stations), and (2) the inhomogeneities are detected and adjusted for in the remaining data series using a penalized maximal t test (50% of all stations). Based on the dataset, we re-assess the temperature changes in global and regional areas compared with GHCN-V3 and CRUTEM4, as well as the temperature changes during the three periods of 1900-2014, 1979-2014 and 1998-2014. The best estimates of warming trends and there 95% confidence ranges for 1900-2014 are approximately 0.102 ± 0.006 °C/decade for the whole year, and 0.104 ± 0.009, 0.112 ± 0.007, 0.090 ± 0.006, and 0.092 ± 0.007 °C/decade for the DJF (December, January, February), MAM, JJA, and SON seasons, respectively. MAM saw the most significant warming trend in both 1900-2014 and 1979-2014. For an even shorter and more recent period (1998-2014), MAM, JJA and SON show similar warming trends, while DJF shows opposite trends. The results show that the ability of CMA-LAST for describing the global temperature changes is similar with other existing products, while there are some differences when describing regional temperature changes.

Developing a Resource for Implementing ArcSWAT Using Global Datasets

NASA Astrophysics Data System (ADS)

Taggart, M.; Caraballo Álvarez, I. O.; Mueller, C.; Palacios, S. L.; Schmidt, C.; Milesi, C.; Palmer-Moloney, L. J.

2015-12-01

This project developed a comprehensive user manual outlining methods for adapting and implementing global datasets for use within ArcSWAT for international and worldwide applications. The Soil and Water Assessment Tool (SWAT) is a hydrologic model that looks at a number of hydrologic variables including runoff and the chemical makeup of water at a given location on the Earth's surface using Digital Elevation Models (DEM), land cover, soil, and weather data. However, the application of ArcSWAT for projects outside of the United States is challenging as there is no standard framework for inputting global datasets into ArcSWAT. This project aims to remove this obstacle by outlining methods for adapting and implementing these global datasets via the user manual. The manual takes the user through the processes of data conditioning while providing solutions and suggestions for common errors. The efficacy of the manual was explored using examples from watersheds located in Puerto Rico, Mexico and Western Africa. Each run explored the various options for setting up a ArcSWAT project as well as a range of satellite data products and soil databases. Future work will incorporate in-situ data for validation and calibration of the model and outline additional resources to assist future users in efficiently implementing the model for worldwide applications. The capacity to manage and monitor freshwater availability is of critical importance in both developed and developing countries. As populations grow and climate changes, both the quality and quantity of freshwater are affected resulting in negative impacts on the health of the surrounding population. The use of hydrologic models such as ArcSWAT can help stakeholders and decision makers understand the future impacts of these changes enabling informed and substantiated decisions.

Digging Deep: how the convergence of national-scale and field-based soil core data shines a light on sustainability of wetland carbon sequestration

NASA Astrophysics Data System (ADS)

Windham-Myers, L.; Holmquist, J. R.; Sundquist, E. T.; Drexler, J. Z.; Bliss, N.

2016-12-01

Wetland soils have long been recognized as conditional archives of past environments, including vegetation structure, nutrient status, sediment supply and the variability in those factors. Both sedimentary processes and organic accretion processes form the soil matrix that identifies wetland soils as "hydric" while also providing archival insights. As repositories of information on net biogeochemical processes, their down-core and across-site structure can show both consistency and distinction. Through several related studies, we have been exploring the use of component-level U.S. Natural Resources Conservation Service (NRCS) Soil Survey data (SSURGO) to map carbon density to 1m depth across wetlands of the US, with an emphasis on coastal wetlands. To assess the accuracy of mapped carbon data from SSURGO, several field-generated datasets (public or compiled for the NASA-funded Blue Carbon Monitoring Project) have been extracted for key metrics such as dry bulk density (g/cc), organic carbon content (%C by combustion) and the combination, soil carbon density (g C /cc) with depth. These profiles indicate ecogeomorphic feedbacks of elevation, vegetation structure and biogeochemical processes through millennia, illustrating both resilience and shifts in behavior that constrain wetland extent as well as wetland function. National datasets such as SSURGO and validation datasets such as the EPA's National Wetland Condition Assessment (NWCA) and Louisiana's Coastwide Reference Monitoring System (CRMS) are publically available and have been underutilized for predicting and/or validating changes in wetland carbon dynamics. We have explored their use for interpretating and understanding changing carbon accretion rates, changing wetland extents through elevation gain or loss, and changing methane emissions. This talk will focus on insights for wetland carbon sequestration functions as determined by soil core structure, both for coastal settings and potentially for inland

Handwritten mathematical symbols dataset.

PubMed

Chajri, Yassine; Bouikhalene, Belaid

2016-06-01

Due to the technological advances in recent years, paper scientific documents are used less and less. Thus, the trend in the scientific community to use digital documents has increased considerably. Among these documents, there are scientific documents and more specifically mathematics documents. In this context, we present our own dataset of handwritten mathematical symbols composed of 10,379 images. This dataset gathers Arabic characters, Latin characters, Arabic numerals, Latin numerals, arithmetic operators, set-symbols, comparison symbols, delimiters, etc.

The effects of spatial population dataset choice on estimates of population at risk of disease

PubMed Central

2011-01-01

Background The spatial modeling of infectious disease distributions and dynamics is increasingly being undertaken for health services planning and disease control monitoring, implementation, and evaluation. Where risks are heterogeneous in space or dependent on person-to-person transmission, spatial data on human population distributions are required to estimate infectious disease risks, burdens, and dynamics. Several different modeled human population distribution datasets are available and widely used, but the disparities among them and the implications for enumerating disease burdens and populations at risk have not been considered systematically. Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example. Methods The recent construction of a global map of P. falciparum malaria endemicity enabled the testing of different gridded population datasets for providing estimates of PAR by endemicity class. The estimated population numbers within each class were calculated for each country using four different global gridded human population datasets: GRUMP (~1 km spatial resolution), LandScan (~1 km), UNEP Global Population Databases (~5 km), and GPW3 (~5 km). More detailed assessments of PAR variation and accuracy were conducted for three African countries where census data were available at a higher administrative-unit level than used by any of the four gridded population datasets. Results The estimates of PAR based on the datasets varied by more than 10 million people for some countries, even accounting for the fact that estimates of population totals made by different agencies are used to correct national totals in these datasets and can vary by more than 5% for many low-income countries. In many cases, these variations in PAR estimates comprised more than 10% of the total national population. The detailed country-level assessments suggested that none of the datasets was consistently more

77 FR 15052 - Dataset Workshop-U.S. Billion Dollar Disasters Dataset (1980-2011): Assessing Dataset Strengths...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-14

... and related methodology. Emphasis will be placed on dataset accuracy and time-dependent biases. Pathways to overcome accuracy and bias issues will be an important focus. Participants will consider...] Guidance for improving these methods. [cir] Recommendations for rectifying any known time-dependent biases...

Indirectly Estimating International Net Migration Flows by Age and Gender: The Community Demographic Model International Migration (CDM-IM) Dataset

PubMed Central

Nawrotzki, Raphael J.; Jiang, Leiwen

2015-01-01

Although data for the total number of international migrant flows is now available, no global dataset concerning demographic characteristics, such as the age and gender composition of migrant flows exists. This paper reports on the methods used to generate the CDM-IM dataset of age and gender specific profiles of bilateral net (not gross) migrant flows. We employ raw data from the United Nations Global Migration Database and estimate net migrant flows by age and gender between two time points around the year 2000, accounting for various demographic processes (fertility, mortality). The dataset contains information on 3,713 net migrant flows. Validation analyses against existing data sets and the historical, geopolitical context demonstrate that the CDM-IM dataset is of reasonably high quality. PMID:26692590

Analysis of phylogenomic datasets reveals conflict, concordance, and gene duplications with examples from animals and plants.

PubMed

Smith, Stephen A; Moore, Michael J; Brown, Joseph W; Yang, Ya

2015-08-05

The use of transcriptomic and genomic datasets for phylogenetic reconstruction has become increasingly common as researchers attempt to resolve recalcitrant nodes with increasing amounts of data. The large size and complexity of these datasets introduce significant phylogenetic noise and conflict into subsequent analyses. The sources of conflict may include hybridization, incomplete lineage sorting, or horizontal gene transfer, and may vary across the phylogeny. For phylogenetic analysis, this noise and conflict has been accommodated in one of several ways: by binning gene regions into subsets to isolate consistent phylogenetic signal; by using gene-tree methods for reconstruction, where conflict is presumed to be explained by incomplete lineage sorting (ILS); or through concatenation, where noise is presumed to be the dominant source of conflict. The results provided herein emphasize that analysis of individual homologous gene regions can greatly improve our understanding of the underlying conflict within these datasets. Here we examined two published transcriptomic datasets, the angiosperm group Caryophyllales and the aculeate Hymenoptera, for the presence of conflict, concordance, and gene duplications in individual homologs across the phylogeny. We found significant conflict throughout the phylogeny in both datasets and in particular along the backbone. While some nodes in each phylogeny showed patterns of conflict similar to what might be expected with ILS alone, the backbone nodes also exhibited low levels of phylogenetic signal. In addition, certain nodes, especially in the Caryophyllales, had highly elevated levels of strongly supported conflict that cannot be explained by ILS alone. This study demonstrates that phylogenetic signal is highly variable in phylogenomic data sampled across related species and poses challenges when conducting species tree analyses on large genomic and transcriptomic datasets. Further insight into the conflict and processes

Differential in surface elevation change across mangrove forests in the intertidal zone

NASA Astrophysics Data System (ADS)

Fu, Haifeng; Wang, Wenqing; Ma, Wei; Wang, Mao

2018-07-01

A better understanding of surface elevation changes in different mangrove forests would improve our predictions of sea-level rise impacts, not only upon mangrove species distributions in the intertidal zone, but also on the functioning of these wetlands. Here, a two-year (2015-2017) dataset derived from 18 RSET-MH (rod surface elevation table-marker horizon) stations at Dongzhaigang Bay, Hainan, China, was analyzed to investigate how surface elevation changes differed across mangrove species zones. The current SET data indicated a rather high rate (9.6 mm y-1, on average) of surface elevation gain that was mostly consistent with that (8.1 mm y-1, on average) inferred from either the 137Cs or 210Pb dating of sediment cores. In addition, these surface elevation changes were sensitive to elevation in the intertidal zone and differed significantly between the two study sites (Sanjiang and Houpai). Mangrove species inhabiting the lower intertidal zone tended to experience greater surface elevation change at Sanjiang, which agrees with the general view that sedimentation and elevation gains are driven by elevation in the intertidal zone (i.e., greater when positioned lower in the intertidal profile). However, at Houpai, both surface elevation change and surface accretion showed the opposite trend (i.e., greater when positioned higher in the intertidal profile). This study's results indicate that the pattern of surface elevation changes across the intertidal profile maybe inconsistent due to intricate biophysical controls. Therefore, instead of using a constant rate, models should presume a topography that evolves at differing rates of surface elevation change in different species zones across the intertidal profile when predicting the impacts of sea-level rise on mangrove distributions.

EAARL Coastal Topography-Fire Island National Seashore, New York, Post-Nor'Ida, 2009

USGS Publications Warehouse

Nayegandhi, Amar; Vivekanandan, Saisudha; Brock, J.C.; Wright, C.W.; Nagle, D.B.; Bonisteel-Cormier, J.M.; Fredericks, Xan; Stevens, Sara

2010-01-01

These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the Fire Island National Seashore in New York, acquired post-Nor'Ida (November 2009 nor'easter) on December 4, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne

The Similarity and Appropriate Usage of Three Honey Bee (Hymenoptera: Apidae) Datasets for Longitudinal Studies.

PubMed

Highland, Steven; James, R R

2016-04-01

Honey bee (Apis mellifera L., Hymenoptera: Apidae) colonies have experienced profound fluctuations, especially declines, in the past few decades. Long-term datasets on honey bees are needed to identify the most important environmental and cultural factors associated with these changes. While a few such datasets exist, scientists have been hesitant to use some of these due to perceived shortcomings in the data. We compared data and trends for three datasets. Two come from the US Department of Agriculture's National Agricultural Statistics Service (NASS), Agricultural Statistics Board: one is the annual survey of honey-producing colonies from the Annual Bee and Honey program (ABH), and the other is colony counts from the Census of Agriculture conducted every five years. The third dataset we developed from the number of colonies registered annually by some states. We compared the long-term patterns of change in colony numbers among the datasets on a state-by-state basis. The three datasets often showed similar hive numbers and trends varied by state, with differences between datasets being greatest for those states receiving a large number of migratory colonies. Dataset comparisons provide a method to estimate the number of colonies in a state used for pollination versus honey production. Some states also had separate data for local and migratory colonies, allowing one to determine whether the migratory colonies were typically used for pollination or honey production. The Census of Agriculture should provide the most accurate long-term data on colony numbers, but only every five years. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Establishing the science foundation to sustain high-elevation five-needle pine forests threatened by novel interacting stresses in four western National Parks

Treesearch

A. W. Schoettle; J. Connor; J. Mack; P. Pineda Bovin; J. Beck; G. M. Baker; R. A. Sniezko; K. S. Burns

2013-01-01

High-elevation, five-needle white pines are among the most picturesque trees in many national parks as well as other federal, state, and private lands in western North America. These trees often live to a great age; the trees' gnarled trunks give testimony to fierce winds that buffet them on exposed rocky sites. Ancient limber pines (Pinus flexilis) in Rocky...

DataMed - an open source discovery index for finding biomedical datasets.

PubMed

Chen, Xiaoling; Gururaj, Anupama E; Ozyurt, Burak; Liu, Ruiling; Soysal, Ergin; Cohen, Trevor; Tiryaki, Firat; Li, Yueling; Zong, Nansu; Jiang, Min; Rogith, Deevakar; Salimi, Mandana; Kim, Hyeon-Eui; Rocca-Serra, Philippe; Gonzalez-Beltran, Alejandra; Farcas, Claudiu; Johnson, Todd; Margolis, Ron; Alter, George; Sansone, Susanna-Assunta; Fore, Ian M; Ohno-Machado, Lucila; Grethe, Jeffrey S; Xu, Hua

2018-01-13

Finding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain. DataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health-funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium. It consists of 2 main components: (1) a data ingestion pipeline that collects and transforms original metadata information to a unified metadata model, called DatA Tag Suite (DATS), and (2) a search engine that finds relevant datasets based on user-entered queries. In addition to describing its architecture and techniques, we evaluated individual components within DataMed, including the accuracy of the ingestion pipeline, the prevalence of the DATS model across repositories, and the overall performance of the dataset retrieval engine. Our manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services. Currently, we have made the DataMed system publically available as an open source package for the biomedical community. © The Author 2018. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

A global gridded dataset of daily precipitation going back to 1950, ideal for analysing precipitation extremes

NASA Astrophysics Data System (ADS)

Contractor, S.; Donat, M.; Alexander, L. V.

2017-12-01

Reliable observations of precipitation are necessary to determine past changes in precipitation and validate models, allowing for reliable future projections. Existing gauge based gridded datasets of daily precipitation and satellite based observations contain artefacts and have a short length of record, making them unsuitable to analyse precipitation extremes. The largest limiting factor for the gauge based datasets is a dense and reliable station network. Currently, there are two major data archives of global in situ daily rainfall data, first is Global Historical Station Network (GHCN-Daily) hosted by National Oceanic and Atmospheric Administration (NOAA) and the other by Global Precipitation Climatology Centre (GPCC) part of the Deutsche Wetterdienst (DWD). We combine the two data archives and use automated quality control techniques to create a reliable long term network of raw station data, which we then interpolate using block kriging to create a global gridded dataset of daily precipitation going back to 1950. We compare our interpolated dataset with existing global gridded data of daily precipitation: NOAA Climate Prediction Centre (CPC) Global V1.0 and GPCC Full Data Daily Version 1.0, as well as various regional datasets. We find that our raw station density is much higher than other datasets. To avoid artefacts due to station network variability, we provide multiple versions of our dataset based on various completeness criteria, as well as provide the standard deviation, kriging error and number of stations for each grid cell and timestep to encourage responsible use of our dataset. Despite our efforts to increase the raw data density, the in situ station network remains sparse in India after the 1960s and in Africa throughout the timespan of the dataset. Our dataset would allow for more reliable global analyses of rainfall including its extremes and pave the way for better global precipitation observations with lower and more transparent uncertainties.

NP-PAH Interaction Dataset

EPA Pesticide Factsheets

Dataset presents concentrations of organic pollutants, such as polyaromatic hydrocarbon compounds, in water samples. Water samples of known volume and concentration were allowed to equilibrate with known mass of nanoparticles. The mixture was then ultracentrifuged and sampled for analysis. This dataset is associated with the following publication:Sahle-Demessie, E., A. Zhao, C. Han, B. Hann, and H. Grecsek. Interaction of engineered nanomaterials with hydrophobic organic pollutants.. Journal of Nanotechnology. Hindawi Publishing Corporation, New York, NY, USA, 27(28): 284003, (2016).

Handwritten mathematical symbols dataset

PubMed Central

Chajri, Yassine; Bouikhalene, Belaid

2016-01-01

Due to the technological advances in recent years, paper scientific documents are used less and less. Thus, the trend in the scientific community to use digital documents has increased considerably. Among these documents, there are scientific documents and more specifically mathematics documents. In this context, we present our own dataset of handwritten mathematical symbols composed of 10,379 images. This dataset gathers Arabic characters, Latin characters, Arabic numerals, Latin numerals, arithmetic operators, set-symbols, comparison symbols, delimiters, etc. PMID:27006975

The Montage architecture for grid-enabled science processing of large, distributed datasets

NASA Technical Reports Server (NTRS)

Jacob, Joseph C.; Katz, Daniel S .; Prince, Thomas; Berriman, Bruce G.; Good, John C.; Laity, Anastasia C.; Deelman, Ewa; Singh, Gurmeet; Su, Mei-Hui

2004-01-01

Montage is an Earth Science Technology Office (ESTO) Computational Technologies (CT) Round III Grand Challenge investigation to deploy a portable, compute-intensive, custom astronomical image mosaicking service for the National Virtual Observatory (NVO). Although Montage is developing a compute- and data-intensive service for the astronomy community, we are also helping to address a problem that spans both Earth and Space science, namely how to efficiently access and process multi-terabyte, distributed datasets. In both communities, the datasets are massive, and are stored in distributed archives that are, in most cases, remote from the available Computational resources. Therefore, state of the art computational grid technologies are a key element of the Montage portal architecture. This paper describes the aspects of the Montage design that are applicable to both the Earth and Space science communities.

Development of a consensus core dataset in juvenile dermatomyositis for clinical use to inform research

PubMed Central

McCann, Liza J; Pilkington, Clarissa A; Huber, Adam M; Ravelli, Angelo; Appelbe, Duncan; Kirkham, Jamie J; Williamson, Paula R; Aggarwal, Amita; Christopher-Stine, Lisa; Constantin, Tamas; Feldman, Brian M; Lundberg, Ingrid; Maillard, Sue; Mathiesen, Pernille; Murphy, Ruth; Pachman, Lauren M; Reed, Ann M; Rider, Lisa G; van Royen-Kerkof, Annet; Russo, Ricardo; Spinty, Stefan; Wedderburn, Lucy R

2018-01-01

Objectives This study aimed to develop consensus on an internationally agreed dataset for juvenile dermatomyositis (JDM), designed for clinical use, to enhance collaborative research and allow integration of data between centres. Methods A prototype dataset was developed through a formal process that included analysing items within existing databases of patients with idiopathic inflammatory myopathies. This template was used to aid a structured multistage consensus process. Exploiting Delphi methodology, two web-based questionnaires were distributed to healthcare professionals caring for patients with JDM identified through email distribution lists of international paediatric rheumatology and myositis research groups. A separate questionnaire was sent to parents of children with JDM and patients with JDM, identified through established research networks and patient support groups. The results of these parallel processes informed a face-to-face nominal group consensus meeting of international myositis experts, tasked with defining the content of the dataset. This developed dataset was tested in routine clinical practice before review and finalisation. Results A dataset containing 123 items was formulated with an accompanying glossary. Demographic and diagnostic data are contained within form A collected at baseline visit only, disease activity measures are included within form B collected at every visit and disease damage items within form C collected at baseline and annual visits thereafter. Conclusions Through a robust international process, a consensus dataset for JDM has been formulated that can capture disease activity and damage over time. This dataset can be incorporated into national and international collaborative efforts, including existing clinical research databases. PMID:29084729

Contribution of Road Grade to the Energy Use of Modern Automobiles Across Large Datasets of Real-World Drive Cycles: Preprint

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

Wood, E.; Burton, E.; Duran, A.

Understanding the real-world power demand of modern automobiles is of critical importance to engineers using modeling and simulation to inform the intelligent design of increasingly efficient powertrains. Increased use of global positioning system (GPS) devices has made large scale data collection of vehicle speed (and associated power demand) a reality. While the availability of real-world GPS data has improved the industry's understanding of in-use vehicle power demand, relatively little attention has been paid to the incremental power requirements imposed by road grade. This analysis quantifies the incremental efficiency impacts of real-world road grade by appending high fidelity elevation profiles tomore » GPS speed traces and performing a large simulation study. Employing a large real-world dataset from the National Renewable Energy Laboratory's Transportation Secure Data Center, vehicle powertrain simulations are performed with and without road grade under five vehicle models. Aggregate results of this study suggest that road grade could be responsible for 1% to 3% of fuel use in light-duty automobiles.« less

Identifying Populace Susceptible to Flooding Using ArcGIS and Remote Sensing Datasets

NASA Astrophysics Data System (ADS)

Fernandez, Sim Joseph; Milano, Alan

2016-07-01

Remote sensing technologies are growing vastly as with its various applications. The Department of Science and Technology (DOST), Republic of the Philippines, has made projects exploiting LiDAR datasets from remote sensing technologies. The Phil-LiDAR 1 project of DOST is a flood hazard mapping project. Among the project's objectives is the identification of building features which can be associated to the flood-exposed population. The extraction of building features from the LiDAR dataset is arduous as it requires manual identification of building features on an elevation map. The mapping of building footprints is made meticulous in order to compensate the accuracy between building floor area and building height both of which are crucial in flood decision making. A building identification method was developed to generate a LiDAR derivative which will serve as a guide in mapping building footprints. The method utilizes several tools of a Geographic Information System (GIS) software called ArcGIS which can operate on physical attributes of buildings such as roofing curvature, slope and blueprint area in order to obtain the LiDAR derivative from LiDAR dataset. The method also uses an intermediary process called building removal process wherein buildings and other features lying below the defined minimum building height - 2 meters in the case of Phil-LiDAR 1 project - are removed. The building identification method was developed in the hope to hasten the identification of building features especially when orthophotographs and/or satellite imageries are not made available.

Clusternomics: Integrative context-dependent clustering for heterogeneous datasets

PubMed Central

Wernisch, Lorenz

2017-01-01

Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm. PMID:29036190

Clusternomics: Integrative context-dependent clustering for heterogeneous datasets.

PubMed

Gabasova, Evelina; Reid, John; Wernisch, Lorenz

2017-10-01

Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm.

Topographic Science

USGS Publications Warehouse

Poppenga, Sandra K.; Evans, Gayla; Gesch, Dean; Stoker, Jason M.; Queija, Vivian R.; Worstell, Bruce; Tyler, Dean J.; Danielson, Jeff; Bliss, Norman; Greenlee, Susan

2010-01-01

The mission of U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center Topographic Science is to establish partnerships and conduct research and applications that facilitate the development and use of integrated national and global topographic datasets. Topographic Science includes a wide range of research and applications that result in improved seamless topographic datasets, advanced elevation technology, data integration and terrain visualization, new and improved elevation derivatives, and development of Web-based tools. In cooperation with our partners, Topographic Science is developing integrated-science applications for mapping, national natural resource initiatives, hazards, and global change science. http://topotools.cr.usgs.gov/.

TRI Preliminary Dataset

EPA Pesticide Factsheets

The TRI preliminary dataset includes the most current TRI data available and reflects toxic chemical releases and pollution prevention activities that occurred at TRI facilities during the each calendar year.

Establishing the science foundation to sustain high-elevation five-needle pine forests threatened by novel interacting stresses in four western National Parks [Proceedings

Treesearch

A. W. Schoettle; Jeff Connor; John Mack; Phyllis Pineda Bovin; Jen Beck; Gretchen Baker; R. A. Sniezko; K. S. Burns

2014-01-01

High-elevation five-needle white pines are among the most picturesque trees in many national parks, as well as other federal, state, and private lands in western North America. These trees often live to great ages; the trees' gnarled trunks give testimony to fierce winds that buffet them on exposed rocky sites. Ancient limber pines (Pinus flexilis) in Rocky...

Comparison of recent SnIa datasets

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

Sanchez, J.C. Bueno; Perivolaropoulos, L.; Nesseris, S., E-mail: jbueno@cc.uoi.gr, E-mail: nesseris@nbi.ku.dk, E-mail: leandros@uoi.gr

2009-11-01

We rank the six latest Type Ia supernova (SnIa) datasets (Constitution (C), Union (U), ESSENCE (Davis) (E), Gold06 (G), SNLS 1yr (S) and SDSS-II (D)) in the context of the Chevalier-Polarski-Linder (CPL) parametrization w(a) = w{sub 0}+w{sub 1}(1−a), according to their Figure of Merit (FoM), their consistency with the cosmological constant (ΛCDM), their consistency with standard rulers (Cosmic Microwave Background (CMB) and Baryon Acoustic Oscillations (BAO)) and their mutual consistency. We find a significant improvement of the FoM (defined as the inverse area of the 95.4% parameter contour) with the number of SnIa of these datasets ((C) highest FoM, (U),more » (G), (D), (E), (S) lowest FoM). Standard rulers (CMB+BAO) have a better FoM by about a factor of 3, compared to the highest FoM SnIa dataset (C). We also find that the ranking sequence based on consistency with ΛCDM is identical with the corresponding ranking based on consistency with standard rulers ((S) most consistent, (D), (C), (E), (U), (G) least consistent). The ranking sequence of the datasets however changes when we consider the consistency with an expansion history corresponding to evolving dark energy (w{sub 0},w{sub 1}) = (−1.4,2) crossing the phantom divide line w = −1 (it is practically reversed to (G), (U), (E), (S), (D), (C)). The SALT2 and MLCS2k2 fitters are also compared and some peculiar features of the SDSS-II dataset when standardized with the MLCS2k2 fitter are pointed out. Finally, we construct a statistic to estimate the internal consistency of a collection of SnIa datasets. We find that even though there is good consistency among most samples taken from the above datasets, this consistency decreases significantly when the Gold06 (G) dataset is included in the sample.« less

PERSIANN-CDR Daily Precipitation Dataset for Hydrologic Applications and Climate Studies.

NASA Astrophysics Data System (ADS)

Sorooshian, S.; Hsu, K. L.; Ashouri, H.; Braithwaite, D.; Nguyen, P.; Thorstensen, A. R.

2015-12-01

Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Climate Data Record (PERSIANN-CDR) is a newly developed and released dataset which covers more than 3 decades (01/01/1983 - 03/31/2015 to date) of daily precipitation estimations at 0.25° resolution for 60°S-60°N latitude band. PERSIANN-CDR is processed using the archive of the Gridded Satellite IRWIN CDR (GridSat-B1) from the International Satellite Cloud Climatology Project (ISCCP), and the Global Precipitation Climatology Project (GPCP) 2.5° monthly product for bias correction. The dataset has been released and made available for public access through NOAA's National Centers for Environmental Information (NCEI) (http://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/PERSIANN/Overview.pdf). PERSIANN-CDR has already shown its usefulness for a wide range of applications, including climate variability and change monitoring, hydrologic applications, and water resources system planning and management. This precipitation CDR data has also been used in studying the behavior of historical extreme precipitation events. Demonstration of PERSIANN-CDR data in detecting trends and variability of precipitation over the past 30 years, the potential usefulness of the dataset for evaluating climate model performance relevant to precipitation in retrospective mode, will be presented.

The 3D Elevation Program—Flood risk management

USGS Publications Warehouse

Carswell, William J.; Lukas, Vicki

2018-01-25

Flood-damage reduction in the United States has been a longstanding but elusive societal goal. The national strategy for reducing flood damage has shifted over recent decades from a focus on construction of flood-control dams and levee systems to a three-pronged strategy to (1) improve the design and operation of such structures, (2) provide more accurate and accessible flood forecasting, and (3) shift the Federal Emergency Management Agency (FEMA) National Flood Insurance Program to a more balanced, less costly flood-insurance paradigm. Expanding the availability and use of high-quality, three-dimensional (3D) elevation information derived from modern light detection and ranging (lidar) technologies to provide essential terrain data poses a singular opportunity to dramatically enhance the effectiveness of all three components of this strategy. Additionally, FEMA, the National Weather Service, and the U.S. Geological Survey (USGS) have developed tools and joint program activities to support the national strategy.The USGS 3D Elevation Program (3DEP) has the programmatic infrastructure to produce and provide essential terrain data. This infrastructure includes (1) data acquisition partnerships that leverage funding and reduce duplicative efforts, (2) contracts with experienced private mapping firms that ensure acquisition of consistent, low-cost 3D elevation data, and (3) the technical expertise, standards, and specifications required for consistent, edge-to-edge utility across multiple collection platforms and public access unfettered by individual database designs and limitations.High-quality elevation data, like that collected through 3DEP, are invaluable for assessing and documenting flood risk and communicating detailed information to both responders and planners alike. Multiple flood-mapping programs make use of USGS streamflow and 3DEP data. Flood insurance rate maps, flood documentation studies, and flood-inundation map libraries are products of these

Early Invasive Strategy and In-Hospital Survival Among Diabetics With Non-ST-Elevation Acute Coronary Syndromes: A Contemporary National Insight.

PubMed

Mahmoud, Ahmed N; Elgendy, Islam Y; Mansoor, Hend; Wen, Xuerong; Mojadidi, Mohammad K; Bavry, Anthony A; Anderson, R David

2017-03-18

There are limited data on the merits of an early invasive strategy in diabetics with non-ST-elevation acute coronary syndrome, with unclear influence of this strategy on survival. The aim of this study was to evaluate the in-hospital survival of diabetics with non-ST-elevation acute coronary syndrome treated with an early invasive strategy compared with an initial conservative strategy. The National Inpatient Sample database, years 2012-2013, was queried for diabetics with a primary diagnosis of non-ST-elevation acute coronary syndrome defined as either non-ST-elevation myocardial infarction or unstable angina (unstable angina). An early invasive strategy was defined as coronary angiography±revascularization within 48 hours of admission. Propensity scores were used to assemble a cohort managed with either an early invasive or initial conservative strategy balanced on >50 baseline characteristics and hospital presentations. Incidence of in-hospital mortality was compared in both groups. In a cohort of 363 500 diabetics with non-ST-elevation acute coronary syndrome, 164 740 (45.3%) were treated with an early invasive strategy. Propensity scoring matched 21 681 diabetics in both arms. Incidence of in-hospital mortality was lower with an early invasive strategy in both the unadjusted (2.0% vs 4.8%; odds ratio [OR], 0.41; 95% CI, 0.39-0.42; P <0.0001) and propensity-matched models (2.2% vs 3.8%; OR, 0.57; 95% CI, 0.50-0.63; P <0.0001). The benefit was observed across various subgroups, except for patients with unstable angina ( P interaction =0.02). An early invasive strategy may be associated with a lower incidence of in-hospital mortality in patients with diabetes. The benefit of this strategy appears to be superior in patients presenting with non-ST-elevation myocardial infarction compared with unstable angina. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

1. VIEW OF ARVFS BUNKER TAKEN FROM GROUND ELEVATION. CAMERA ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

1. VIEW OF ARVFS BUNKER TAKEN FROM GROUND ELEVATION. CAMERA FACING NORTH. VIEW SHOWS PROFILE OF BUNKER IN RELATION TO NATURAL GROUND ELEVATION. TOP OF BUNKER HAS APPROXIMATELY THREE FEET OF EARTH COVER. - Idaho National Engineering Laboratory, Advanced Reentry Vehicle Fusing System, Scoville, Butte County, ID

A publicly available benchmark for biomedical dataset retrieval: the reference standard for the 2016 bioCADDIE dataset retrieval challenge

PubMed Central

Gururaj, Anupama E.; Chen, Xiaoling; Pournejati, Saeid; Alter, George; Hersh, William R.; Demner-Fushman, Dina; Ohno-Machado, Lucila

2017-01-01

Abstract The rapid proliferation of publicly available biomedical datasets has provided abundant resources that are potentially of value as a means to reproduce prior experiments, and to generate and explore novel hypotheses. However, there are a number of barriers to the re-use of such datasets, which are distributed across a broad array of dataset repositories, focusing on different data types and indexed using different terminologies. New methods are needed to enable biomedical researchers to locate datasets of interest within this rapidly expanding information ecosystem, and new resources are needed for the formal evaluation of these methods as they emerge. In this paper, we describe the design and generation of a benchmark for information retrieval of biomedical datasets, which was developed and used for the 2016 bioCADDIE Dataset Retrieval Challenge. In the tradition of the seminal Cranfield experiments, and as exemplified by the Text Retrieval Conference (TREC), this benchmark includes a corpus (biomedical datasets), a set of queries, and relevance judgments relating these queries to elements of the corpus. This paper describes the process through which each of these elements was derived, with a focus on those aspects that distinguish this benchmark from typical information retrieval reference sets. Specifically, we discuss the origin of our queries in the context of a larger collaborative effort, the biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium, and the distinguishing features of biomedical dataset retrieval as a task. The resulting benchmark set has been made publicly available to advance research in the area of biomedical dataset retrieval. Database URL: https://biocaddie.org/benchmark-data PMID:29220453

Feature pruning by upstream drainage area to support automated generalization of the United States National Hydrography Dataset

USGS Publications Warehouse

Stanislawski, L.V.

2009-01-01

The United States Geological Survey has been researching generalization approaches to enable multiple-scale display and delivery of geographic data. This paper presents automated methods to prune network and polygon features of the United States high-resolution National Hydrography Dataset (NHD) to lower resolutions. Feature-pruning rules, data enrichment, and partitioning are derived from knowledge of surface water, the NHD model, and associated feature specification standards. Relative prominence of network features is estimated from upstream drainage area (UDA). Network and polygon features are pruned by UDA and NHD reach code to achieve a drainage density appropriate for any less detailed map scale. Data partitioning maintains local drainage density variations that characterize the terrain. For demonstration, a 48 subbasin area of 1:24 000-scale NHD was pruned to 1:100 000-scale (100 K) and compared to a benchmark, the 100 K NHD. The coefficient of line correspondence (CLC) is used to evaluate how well pruned network features match the benchmark network. CLC values of 0.82 and 0.77 result from pruning with and without partitioning, respectively. The number of polygons that remain after pruning is about seven times that of the benchmark, but the area covered by the polygons that remain after pruning is only about 10% greater than the area covered by benchmark polygons. ?? 2009.

[Spatial domain display for interference image dataset].

PubMed

Wang, Cai-Ling; Li, Yu-Shan; Liu, Xue-Bin; Hu, Bing-Liang; Jing, Juan-Juan; Wen, Jia

2011-11-01

The requirements of imaging interferometer visualization is imminent for the user of image interpretation and information extraction. However, the conventional researches on visualization only focus on the spectral image dataset in spectral domain. Hence, the quick show of interference spectral image dataset display is one of the nodes in interference image processing. The conventional visualization of interference dataset chooses classical spectral image dataset display method after Fourier transformation. In the present paper, the problem of quick view of interferometer imager in image domain is addressed and the algorithm is proposed which simplifies the matter. The Fourier transformation is an obstacle since its computation time is very large and the complexion would be even deteriorated with the size of dataset increasing. The algorithm proposed, named interference weighted envelopes, makes the dataset divorced from transformation. The authors choose three interference weighted envelopes respectively based on the Fourier transformation, features of interference data and human visual system. After comparing the proposed with the conventional methods, the results show the huge difference in display time.

SPERTI Control Building (PER601). Elevations. Note cable inlet on west ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

SPERT-I Control Building (PER-601). Elevations. Note cable inlet on west elevation. Idaho Operations Office. Date: February 1955. INEEL index no. 760-0601-00-396-109143 - Idaho National Engineering Laboratory, SPERT-I & Power Burst Facility Area, Scoville, Butte County, ID

TRMM-3B43 Bias Correction over the High Elevations of the Contiguous United States

NASA Astrophysics Data System (ADS)

Hashemi, H.; Nordin, K. M.; Lakshmi, V.; Knight, R. J.

2016-12-01

Precipitation can be quantified using a rain gauge network, or a remotely sensed precipitation product. Ultimately, the choice of dataset depends on the particular application, the catchment size, climate and the time period of study. In a region with a long record and a dense rain gauge network, the elevation-modified ground-based precipitation product, PRISM, has been found to work well. However, in poorly gauged regions the use of remotely sensed precipitation products is an absolute necessity. The Tropical Rainfall Measuring Mission (TRMM) has provided valuable precipitation datasets for hydrometeorological studies over the past two decades (1998-2015). One concern regarding the usage of TRMM data is the accuracy of the precipitation estimates, when compared to those obtained using PRISM. The reason for this concern is that TRMM and PRISM do not always agree and, typically, TRMM underestimates PRISM over the mountainous regions of the United States. In this study, we develop a correction function to improve the accuracy of the TRMM monthly product (TRMM-3B43) by estimating and removing the bias in the satellite data using the ground-based precipitation product, PRISM. We observe a strong relationship between the bias and land surface elevation; TRMM-3B43 tends to underestimate the PRISM product at altitudes greater than 1500 m above mean sea level (m.amsl) in the contiguous United States. A relationship is developed between TRMM-PRISM bias and elevation. The correction function is used to adjust the TRMM monthly precipitation using PRISM and elevation data. The model is calibrated using 25% of the available time period and the remaining 75% of the time period is used for validation. The corrected TRMM-3B43 product is verified for the high elevations over the contiguous United States and two local regions in the mountainous areas of the western United States. The results show a significant improvement in the accuracy of the TRMM product in the high elevations of

Establishment and analysis of High-Resolution Assimilation Dataset of water-energy cycle over China

NASA Astrophysics Data System (ADS)

Wen, Xiaohang; Liao, Xiaohan; Dong, Wenjie; Yuan, Wenping

2015-04-01

For better prediction and understanding of water-energy exchange process and land-atmospheric interaction, the in-situ observed meteorological data which were acquired from China Meteorological Administration (CMA) were assimilated in the Weather Research and Forecasting (WRF) model and the monthly Green Vegetation Coverage (GVF) data, which was calculated by the Normalized Difference Vegetation Index (NDVI) of Earth Observing System Moderate-Resolution Imaging Spectroradiometer (EOS-MODIS), Digital Elevation Model (DEM) data of the Shuttle Radar Topography Mission (SRTM) system were also integrated in the WRF model over China. Further, the High-Resolution Assimilation Dataset of water-energy cycle over China (HRADC) was produced by WRF model. This dataset include 25 km horizontal resolution near surface meteorological data such as air temperature, humidity, ground temperature, and pressure at 19 levels, soil temperature and soil moisture at 4 levels, green vegetation coverage, latent heat flux, sensible heat flux, and ground heat flux for 3 hours. In this study, we 1) briefly introduce the cycling 3D-Var assimilation method; 2) Compare results of meteorological elements such as 2 m temperature, precipitation and ground temperature generated by the HRADC with the gridded observation data from CMA, and Global Land Data Assimilation System (GLDAS) output data from National Aeronautics and Space Administration (NASA). It is found that the results of 2 m temperature were improved compared with the control simulation and has effectively reproduced the observed patterns, and the simulated results of ground temperature, 0-10 cm soil temperature and specific humidity were as much closer to GLDAS outputs. Root mean square errors are reduced in assimilation run than control run, and the assimilation run of ground temperature, 0-10 cm soil temperature, radiation and surface fluxes were agreed well with the GLDAS outputs over China. The HRADC could be used in further research

Patterns of oribatid mite species diversity: testing the effects of elevation, area and sampling effort.

PubMed

Mumladze, Levan; Murvanidze, Maka; Maraun, Mark

2017-07-01

Elevational gradients in species diversity and species area relationships are two well established patterns that are not mutually exclusive in space and time. Elevation and area are both considered as good proxies to detect and characterize the patterns of species diversity distribution. However, such studies are hampered by the incomplete biodiversity data available for ecologists, which may affect the pattern perceptions. Using the large dataset of oribatid mite communities sampled in Georgia, we tested the effects of altitude and area on species distribution using various approaches, while explicitly considering the biases from sampling effort. Our results showed that elevation and area are strongly correlated (with increasing absolute elevation, land area decreases) and both have strong linear effects on species diversity distribution when studied separately. Approaches based on multiple regression and direct removal of co-varied factors, indicated that the effect of area can actually override the effect of elevation in describing the oribatid species diversity distribution along with elevation. On the other hand, the bias of sampling proved significant in perception of elevational species richness pattern with less effect on elevational species area relationship. We suggest that the sampling alone may be responsible for patterns observed and thus should be considered in ecological studies when eligible.

Internationally coordinated glacier monitoring: strategy and datasets

NASA Astrophysics Data System (ADS)

Hoelzle, Martin; Armstrong, Richard; Fetterer, Florence; Gärtner-Roer, Isabelle; Haeberli, Wilfried; Kääb, Andreas; Kargel, Jeff; Nussbaumer, Samuel; Paul, Frank; Raup, Bruce; Zemp, Michael

2014-05-01

Internationally coordinated monitoring of long-term glacier changes provide key indicator data about global climate change and began in the year 1894 as an internationally coordinated effort to establish standardized observations. Today, world-wide monitoring of glaciers and ice caps is embedded within the Global Climate Observing System (GCOS) in support of the United Nations Framework Convention on Climate Change (UNFCCC) as an important Essential Climate Variable (ECV). The Global Terrestrial Network for Glaciers (GTN-G) was established in 1999 with the task of coordinating measurements and to ensure the continuous development and adaptation of the international strategies to the long-term needs of users in science and policy. The basic monitoring principles must be relevant, feasible, comprehensive and understandable to a wider scientific community as well as to policy makers and the general public. Data access has to be free and unrestricted, the quality of the standardized and calibrated data must be high and a combination of detailed process studies at selected field sites with global coverage by satellite remote sensing is envisaged. Recently a GTN-G Steering Committee was established to guide and advise the operational bodies responsible for the international glacier monitoring, which are the World Glacier Monitoring Service (WGMS), the US National Snow and Ice Data Center (NSIDC), and the Global Land Ice Measurements from Space (GLIMS) initiative. Several online databases containing a wealth of diverse data types having different levels of detail and global coverage provide fast access to continuously updated information on glacier fluctuation and inventory data. For world-wide inventories, data are now available through (a) the World Glacier Inventory containing tabular information of about 130,000 glaciers covering an area of around 240,000 km2, (b) the GLIMS-database containing digital outlines of around 118,000 glaciers with different time stamps and

Validation of the ASTER Global Digital Elevation Model Version 2 over the conterminous United States

USGS Publications Warehouse

Gesch, Dean B.; Oimoen, Michael J.; Zhang, Zheng; Meyer, David J.; Danielson, Jeffrey J.

2012-01-01

The ASTER Global Digital Elevation Model Version 2 (GDEM v2) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009. The absolute vertical accuracy of GDEM v2 was calculated by comparison with more than 18,000 independent reference geodetic ground control points from the National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v2 is 8.68 meters. This compares with the RMSE of 9.34 meters for GDEM v1. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v2 mean error of -0.20 meters is a significant improvement over the GDEM v1 mean error of -3.69 meters. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover to examine the effects of cover types on measured errors. The GDEM v2 mean errors by land cover class verify that the presence of aboveground features (tree canopies and built structures) cause a positive elevation bias, as would be expected for an imaging system like ASTER. In open ground classes (little or no vegetation with significant aboveground height), GDEM v2 exhibits a negative bias on the order of 1 meter. GDEM v2 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v2 has elevations that are higher in the canopy than SRTM.

Development of a consensus core dataset in juvenile dermatomyositis for clinical use to inform research.

PubMed

McCann, Liza J; Pilkington, Clarissa A; Huber, Adam M; Ravelli, Angelo; Appelbe, Duncan; Kirkham, Jamie J; Williamson, Paula R; Aggarwal, Amita; Christopher-Stine, Lisa; Constantin, Tamas; Feldman, Brian M; Lundberg, Ingrid; Maillard, Sue; Mathiesen, Pernille; Murphy, Ruth; Pachman, Lauren M; Reed, Ann M; Rider, Lisa G; van Royen-Kerkof, Annet; Russo, Ricardo; Spinty, Stefan; Wedderburn, Lucy R; Beresford, Michael W

2018-02-01

This study aimed to develop consensus on an internationally agreed dataset for juvenile dermatomyositis (JDM), designed for clinical use, to enhance collaborative research and allow integration of data between centres. A prototype dataset was developed through a formal process that included analysing items within existing databases of patients with idiopathic inflammatory myopathies. This template was used to aid a structured multistage consensus process. Exploiting Delphi methodology, two web-based questionnaires were distributed to healthcare professionals caring for patients with JDM identified through email distribution lists of international paediatric rheumatology and myositis research groups. A separate questionnaire was sent to parents of children with JDM and patients with JDM, identified through established research networks and patient support groups. The results of these parallel processes informed a face-to-face nominal group consensus meeting of international myositis experts, tasked with defining the content of the dataset. This developed dataset was tested in routine clinical practice before review and finalisation. A dataset containing 123 items was formulated with an accompanying glossary. Demographic and diagnostic data are contained within form A collected at baseline visit only, disease activity measures are included within form B collected at every visit and disease damage items within form C collected at baseline and annual visits thereafter. Through a robust international process, a consensus dataset for JDM has been formulated that can capture disease activity and damage over time. This dataset can be incorporated into national and international collaborative efforts, including existing clinical research databases. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

05/04/2018: Articles citing Ag Data Commons datasets | National

Science.gov Websites

Agricultural Library Skip to main content Home National Agricultural Library United States trademark of Dries Buytaert. NAL Home | USDA.gov | Agricultural Research Service | Plain Language | FOIA

Temporal and spatial variation of atmospherically deposited organic contaminants at high elevation in Yosemite National Park, California, USA.

PubMed

Bradford, David F; Stanley, Kerri A; Tallent, Nita G; Sparling, Donald W; Nash, Maliha S; Knapp, Roland A; McConnell, Laura L; Massey Simonich, Staci L

2013-03-01

Contaminants used at low elevation, such as pesticides on crops, can be transported tens of kilometers and deposited in adjacent mountains in many parts of the world. Atmospherically deposited organic contaminants in the Sierra Nevada Mountains of California, USA, have exceeded some thresholds of concern, but the spatial and temporal distributions of contaminants in the mountains are not well known. The authors sampled shallow-water sediment and tadpoles (Pseudacris sierra) for pesticides, polycyclic aromatic hydrocarbons (PAHs), and polychlorinated biphenyls in four high-elevation sites in Yosemite National Park in the central Sierra Nevada twice during the summers of 2006, 2007, and 2008. Both historic- and current-use pesticides showed a striking pattern of lower concentrations in both sediment and tadpoles in Yosemite than was observed previously in Sequoia-Kings Canyon National Parks in the southern Sierra Nevada. By contrast, PAH concentrations in sediment were generally greater in Yosemite than in Sequoia-Kings Canyon. The authors suggest that pesticide concentrations tend to be greater in Sequoia-Kings Canyon because of a longer air flow path over agricultural lands for this park along with greater pesticide use near this park. Concentrations for DDT-related compounds in some sediment samples exceeded guidelines or critical thresholds in both parks. A general pattern of difference between Yosemite and Sequoia-Kings Canyon was not evident for total tadpole cholinesterase activity, an indicator of harmful exposure to organophosphorus and carbamate pesticides. Variability of chemical concentrations among sites, between sampling periods within each year, and among years, contributed significantly to total variation, although the relative contributions differed between sediment and tadpoles. Copyright © 2013 SETAC.

Topography- and nightlight-based national flood risk assessment in Canada

NASA Astrophysics Data System (ADS)

Elshorbagy, Amin; Bharath, Raja; Lakhanpal, Anchit; Ceola, Serena; Montanari, Alberto; Lindenschmidt, Karl-Erich

2017-04-01

In Canada, flood analysis and water resource management, in general, are tasks conducted at the provincial level; therefore, unified national-scale approaches to water-related problems are uncommon. In this study, a national-scale flood risk assessment approach is proposed and developed. The study focuses on using global and national datasets available with various resolutions to create flood risk maps. First, a flood hazard map of Canada is developed using topography-based parameters derived from digital elevation models, namely, elevation above nearest drainage (EAND) and distance from nearest drainage (DFND). This flood hazard mapping method is tested on a smaller area around the city of Calgary, Alberta, against a flood inundation map produced by the city using hydraulic modelling. Second, a flood exposure map of Canada is developed using a land-use map and the satellite-based nightlight luminosity data as two exposure parameters. Third, an economic flood risk map is produced, and subsequently overlaid with population density information to produce a socioeconomic flood risk map for Canada. All three maps of hazard, exposure, and risk are classified into five classes, ranging from very low to severe. A simple way to include flood protection measures in hazard estimation is also demonstrated using the example of the city of Winnipeg, Manitoba. This could be done for the entire country if information on flood protection across Canada were available. The evaluation of the flood hazard map shows that the topography-based method adopted in this study is both practical and reliable for large-scale analysis. Sensitivity analysis regarding the resolution of the digital elevation model is needed to identify the resolution that is fine enough for reliable hazard mapping, but coarse enough for computational tractability. The nightlight data are found to be useful for exposure and risk mapping in Canada; however, uncertainty analysis should be conducted to investigate the

Simulation of water-surface elevations for a hypothetical 100-year peak flow in Birch Creek at the Idaho National Engineering and Environmental Laboratory, Idaho

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

Berenbrock, C.; Kjelstrom, L.C.

1997-10-01

Delineation of areas at the Idaho National Engineering and Environmental Laboratory that would be inundated by a 100-year peak flow in Birch Creek is needed by the US Department of Energy to fulfill flood-plain regulatory requirements. Birch Creek flows southward about 40 miles through an alluvium-filled valley onto the northern part of the Idaho National Engineering and Environmental laboratory site on the eastern Snake River Plain. The lower 10-mile reach of Birch Creek that ends in Birch Creek Playa near several Idaho National Engineering and Environmental Laboratory facilities is of particular concern. Twenty-six channel cross sections were surveyed to developmore » and apply a hydraulic model to simulate water-surface elevations for a hypothetical 100-year peak flow in Birch Creek. Model simulation of the 100-year peak flow (700 cubic feet per second) in reaches upstream from State Highway 22 indicated that flow was confined within channels even when all flow was routed to one channel. Where the highway crosses Birch Creek, about 315 cubic feet per second of water was estimated to move downstream--115 cubic feet per second through a culvert and 200 cubic feet per second over the highway. Simulated water-surface elevation at this crossing was 0.8 foot higher than the elevation of the highway. The remaining 385 cubic feet per second flowed southwestward in a trench along the north side of the highway. Flow also was simulated with the culvert removed. The exact location of flood boundaries on Birch Creek could not be determined because of the highly braided channel and the many anthropogenic features (such as the trench, highway, and diversion channels) in the study area that affect flood hydraulics and flow. Because flood boundaries could not be located exactly, only a generalized flood-prone map was developed.« less

Seasonal evaluation of evapotranspiration fluxes from MODIS satellite and mesoscale model downscaled global reanalysis datasets

NASA Astrophysics Data System (ADS)

Srivastava, Prashant K.; Han, Dawei; Islam, Tanvir; Petropoulos, George P.; Gupta, Manika; Dai, Qiang

2016-04-01

Reference evapotranspiration (ETo) is an important variable in hydrological modeling, which is not always available, especially for ungauged catchments. Satellite data, such as those available from the MODerate Resolution Imaging Spectroradiometer (MODIS), and global datasets via the European Centre for Medium Range Weather Forecasts (ECMWF) reanalysis (ERA) interim and National Centers for Environmental Prediction (NCEP) reanalysis are important sources of information for ETo. This study explored the seasonal performances of MODIS (MOD16) and Weather Research and Forecasting (WRF) model downscaled global reanalysis datasets, such as ERA interim and NCEP-derived ETo, against ground-based datasets. Overall, on the basis of the statistical metrics computed, ETo derived from ERA interim and MODIS were more accurate in comparison to the estimates from NCEP for all the seasons. The pooled datasets also revealed a similar performance to the seasonal assessment with higher agreement for the ERA interim (r = 0.96, RMSE = 2.76 mm/8 days; bias = 0.24 mm/8 days), followed by MODIS (r = 0.95, RMSE = 7.66 mm/8 days; bias = -7.17 mm/8 days) and NCEP (r = 0.76, RMSE = 11.81 mm/8 days; bias = -10.20 mm/8 days). The only limitation with downscaling ERA interim reanalysis datasets using WRF is that it is time-consuming in contrast to the readily available MODIS operational product for use in mesoscale studies and practical applications.

A modern pollen-climate dataset from the Darjeeling area, eastern Himalaya: Assessing its potential for past climate reconstruction

NASA Astrophysics Data System (ADS)

Ghosh, Ruby; Bruch, Angela A.; Portmann, Felix; Bera, Subir; Paruya, Dipak Kumar; Morthekai, P.; Ali, Sheikh Nawaz

2017-10-01

Relying on the ability of pollen assemblages to differentiate among elevationally stratified vegetation zones, we assess the potential of a modern pollen-climate dataset from the Darjeeling area, eastern Himalaya, in past climate reconstructions. The dataset includes 73 surface samples from 25 sites collected from a c. 130-3600 m a.s.l. elevation gradient along a horizontal distance of c. 150 km and 124 terrestrial pollen taxa, which are analysed with respect to various climatic and environmental variables such as mean annual temperature (MAT), mean annual precipitation (MAP), mean temperature of coldest quarter (MTCQ), mean temperature of warmest quarter (MTWQ), mean precipitation of driest quarter (MPDQ), mean precipitation of wettest quarter (MPWQ), AET (actual evapotranspiration) and MI (moisture index). To check the reliability of the modern pollen-climate relationships different ordination methods are employed and subsequently tested with Huisman-Olff-Fresco (HOF) models. A series of pollen-climate parameter transfer functions using weighted-averaging regression and calibration partial least squares (WA-PLS) models are developed to reconstruct past climate changes from modern pollen data, and have been cross-validated. Results indicate that three of the environmental variables i.e., MTCQ, MPDQ and MI have strong potential for past climate reconstruction based on the available surface pollen dataset. The potential of the present modern pollen-climate relationship for regional quantitative paleoclimate reconstruction is further tested on a Late Quaternary fossil pollen profile from the Darjeeling foothill region with previously reconstructed and quantified climate. The good agreement with existing data allows for new insights in the hydroclimatic conditions during the Last glacial maxima (LGM) with (winter) temperature being the dominant controlling factor for glacial changes during the LGM in the eastern Himalaya.

Digital Elevation Models

USGS Publications Warehouse

,

1993-01-01

The Earth Science Information Center (ESIC) distributes digital cartographic/geographic data files produced by the U.S. Geological Survey (USGS) as part of the National Mapping Program. Digital cartographic data files may be grouped into four basic types. The first of these, called a Digital Line Graph (DLG), is the line map information in digital form. These data files include information on base data categories, such as transportation, hypsography, hydrography, and boundaries. The second type, called a Digital Elevation Model (DEM), consists of a sampled array of elevations for a number of ground positions at regularly spaced intervals. The third type is Land Use and Land Cover digital data which provides information on nine major classes of land use such as urban, agricultural, or forest as well as associated map data such as political units and Federal land ownership. The fourth type, the Geographic Names Information System, provides primary information for all known places, features, and areas in the United States identified by a proper name.

Initial Everglades Depth Estimation Network (EDEN) Digital Elevation Model Research and Development

USGS Publications Warehouse

Jones, John W.; Price, Susan D.

2007-01-01

Introduction The Everglades Depth Estimation Network (EDEN) offers a consistent and documented dataset that can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, and to support biological and ecological assessments that measure ecosystem responses to the Comprehensive Everglades Restoration Plan (Telis, 2006). To produce historic and near-real time maps of water depths, the EDEN requires a system-wide digital elevation model (DEM) of the ground surface. Accurate Everglades wetland ground surface elevation data were non-existent before the U.S. Geological Survey (USGS) undertook the collection of highly accurate surface elevations at the regional scale. These form the foundation for EDEN DEM development. This development process is iterative as additional high accuracy elevation data (HAED) are collected, water surfacing algorithms improve, and additional ground-based ancillary data become available. Models are tested using withheld HAED and independently measured water depth data, and by using DEM data in EDEN adaptive management applications. Here the collection of HAED is briefly described before the approach to DEM development and the current EDEN DEM are detailed. Finally future research directions for continued model development, testing, and refinement are provided.

U.S. Datasets

Cancer.gov

Datasets for U.S. mortality, U.S. populations, standard populations, county attributes, and expected survival. Plus SEER-linked databases (SEER-Medicare, SEER-Medicare Health Outcomes Survey [SEER-MHOS], SEER-Consumer Assessment of Healthcare Providers and Systems [SEER-CAHPS]).

3D Elevation Program: summary for Vermont

USGS Publications Warehouse

Carswell, William J.

2015-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

3D Elevation Program: summary for Nebraska

USGS Publications Warehouse

Carswell, William J.

2015-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

Dataset of Scientific Inquiry Learning Environment

ERIC Educational Resources Information Center

Ting, Choo-Yee; Ho, Chiung Ching

2015-01-01

This paper presents the dataset collected from student interactions with INQPRO, a computer-based scientific inquiry learning environment. The dataset contains records of 100 students and is divided into two portions. The first portion comprises (1) "raw log data", capturing the student's name, interfaces visited, the interface…

Simulation of Smart Home Activity Datasets

PubMed Central

Synnott, Jonathan; Nugent, Chris; Jeffers, Paul

2015-01-01

A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. Access to sensor datasets is necessary for the development of novel activity monitoring and recognition approaches. Access to such datasets is limited due to issues such as sensor cost, availability and deployment time. The use of simulated environments and sensors may address these issues and facilitate the generation of comprehensive datasets. This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars. The paper also provides recommendation for future work in intelligent environment simulation. PMID:26087371

Simulation of Smart Home Activity Datasets.

PubMed

Synnott, Jonathan; Nugent, Chris; Jeffers, Paul

2015-06-16

A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. Access to sensor datasets is necessary for the development of novel activity monitoring and recognition approaches. Access to such datasets is limited due to issues such as sensor cost, availability and deployment time. The use of simulated environments and sensors may address these issues and facilitate the generation of comprehensive datasets. This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars. The paper also provides recommendation for future work in intelligent environment simulation.

44 CFR 67.3 - Establishment and maintenance of a flood elevation determination docket (FEDD).

Code of Federal Regulations, 2014 CFR

2014-10-01

... MITIGATION National Flood Insurance Program APPEALS FROM PROPOSED FLOOD ELEVATION DETERMINATIONS § 67.3 Establishment and maintenance of a flood elevation determination docket (FEDD). The Federal Insurance... of a flood elevation determination docket (FEDD). 67.3 Section 67.3 Emergency Management and...

44 CFR 67.3 - Establishment and maintenance of a flood elevation determination docket (FEDD).

Code of Federal Regulations, 2012 CFR

2012-10-01

... MITIGATION National Flood Insurance Program APPEALS FROM PROPOSED FLOOD ELEVATION DETERMINATIONS § 67.3 Establishment and maintenance of a flood elevation determination docket (FEDD). The Federal Insurance... of a flood elevation determination docket (FEDD). 67.3 Section 67.3 Emergency Management and...

Estimating Coastal Digital Elevation Model (DEM) Uncertainty

NASA Astrophysics Data System (ADS)

Amante, C.; Mesick, S.

2017-12-01

Integrated bathymetric-topographic digital elevation models (DEMs) are representations of the Earth's solid surface and are fundamental to the modeling of coastal processes, including tsunami, storm surge, and sea-level rise inundation. Deviations in elevation values from the actual seabed or land surface constitute errors in DEMs, which originate from numerous sources, including: (i) the source elevation measurements (e.g., multibeam sonar, lidar), (ii) the interpolative gridding technique (e.g., spline, kriging) used to estimate elevations in areas unconstrained by source measurements, and (iii) the datum transformation used to convert bathymetric and topographic data to common vertical reference systems. The magnitude and spatial distribution of the errors from these sources are typically unknown, and the lack of knowledge regarding these errors represents the vertical uncertainty in the DEM. The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) has developed DEMs for more than 200 coastal communities. This study presents a methodology developed at NOAA NCEI to derive accompanying uncertainty surfaces that estimate DEM errors at the individual cell-level. The development of high-resolution (1/9th arc-second), integrated bathymetric-topographic DEMs along the southwest coast of Florida serves as the case study for deriving uncertainty surfaces. The estimated uncertainty can then be propagated into the modeling of coastal processes that utilize DEMs. Incorporating the uncertainty produces more reliable modeling results, and in turn, better-informed coastal management decisions.

Providing Geographic Datasets as Linked Data in Sdi

NASA Astrophysics Data System (ADS)

Hietanen, E.; Lehto, L.; Latvala, P.

2016-06-01

In this study, a prototype service to provide data from Web Feature Service (WFS) as linked data is implemented. At first, persistent and unique Uniform Resource Identifiers (URI) are created to all spatial objects in the dataset. The objects are available from those URIs in Resource Description Framework (RDF) data format. Next, a Web Ontology Language (OWL) ontology is created to describe the dataset information content using the Open Geospatial Consortium's (OGC) GeoSPARQL vocabulary. The existing data model is modified in order to take into account the linked data principles. The implemented service produces an HTTP response dynamically. The data for the response is first fetched from existing WFS. Then the Geographic Markup Language (GML) format output of the WFS is transformed on-the-fly to the RDF format. Content Negotiation is used to serve the data in different RDF serialization formats. This solution facilitates the use of a dataset in different applications without replicating the whole dataset. In addition, individual spatial objects in the dataset can be referred with URIs. Furthermore, the needed information content of the objects can be easily extracted from the RDF serializations available from those URIs. A solution for linking data objects to the dataset URI is also introduced by using the Vocabulary of Interlinked Datasets (VoID). The dataset is divided to the subsets and each subset is given its persistent and unique URI. This enables the whole dataset to be explored with a web browser and all individual objects to be indexed by search engines.

Do the psychosocial risks associated with television viewing increase mortality? Evidence from the 2008 General Social Survey-National Death Index dataset.

PubMed

Muennig, Peter; Rosen, Zohn; Johnson, Gretchen

2013-06-01

Television viewing is associated with an increased risk of mortality, which could be caused by a sedentary lifestyle, the content of television programming (e.g., cigarette product placement or stress-inducing content), or both. We examined the relationship between self-reported hours of television viewing and mortality risk over 30 years in a representative sample of the American adult population using the 2008 General Social Survey-National Death Index dataset. We also explored the intervening variable effect of various emotional states (e.g., happiness) and beliefs (e.g., trust in government) of the relationship between television viewing and mortality. We find that, for each additional hour of viewing, mortality risks increased 4%. Given the mean duration of television viewing in our sample, this amounted to about 1.2 years of life expectancy in the United States. This association was tempered by a number of potential psychosocial mediators, including self-reported measures of happiness, social capital, or confidence in institutions. Although none of these were clinically significant, the combined mediation power was statistically significant (P < .001). Television viewing among healthy adults is correlated with premature mortality in a nationally representative sample of U.S. adults, and this association may be partially mediated by programming content related to beliefs or affective states. However, this mediation effect is the result of many small changes in psychosocial states rather than large effects from a few factors. Copyright © 2013 Elsevier Inc. All rights reserved.

Do the psychosocial risks associated with television viewing increase mortality? Evidence from the 2008 General Social Survey-National Death Index Dataset

PubMed Central

Rosen, Zohn; Johnson, Gretchen

2013-01-01

Background Television viewing is associated with an increased risk of mortality, which could be caused by a sedentary lifestyle, the content of television programming (e.g., cigarette product placement or stress-inducing content), or both. Methods We examined the relationship between self-reported hours of television viewing and mortality risk over 30 years in a representative sample of the American adult population using the 2008 General Social Survey-National Death Index dataset. We also explored the intervening variable effect of various emotional states (e.g., happiness) and beliefs (e.g., trust in government) of the relationship between television viewing and mortality. Results We find that for each additional hour of viewing, mortality risks increased 4%. Given the mean duration of television viewing in our sample, this amounted to about 1.2 years of life expectancy in the US. This association was tempered by a number of potential psychosocial mediators, including self-reported measures of happiness, social capital, or confidence in institutions. While none of these were clinically significant, the combined mediation power was statistically significant (p < 0.001). Conclusions Television viewing among healthy adults is correlated with premature mortality in a nationally-representative sample of US adults, and this association may be partially mediated by programming content related to beliefs or affective states. However, this mediation effect is the result of many small changes in psychosocial states rather than large effects from a few factors. PMID:23683712

Hydrologic connectivity: Quantitative assessments of hydrologic-enforced drainage structures in an elevation model

USGS Publications Warehouse

Poppenga, Sandra K.; Worstell, Bruce B.

2016-01-01

Elevation data derived from light detection and ranging present challenges for hydrologic modeling as the elevation surface includes bridge decks and elevated road features overlaying culvert drainage structures. In reality, water is carried through these structures; however, in the elevation surface these features impede modeled overland surface flow. Thus, a hydrologically-enforced elevation surface is needed for hydrodynamic modeling. In the Delaware River Basin, hydrologic-enforcement techniques were used to modify elevations to simulate how constructed drainage structures allow overland surface flow. By calculating residuals between unfilled and filled elevation surfaces, artificially pooled depressions that formed upstream of constructed drainage structure features were defined, and elevation values were adjusted by generating transects at the location of the drainage structures. An assessment of each hydrologically-enforced drainage structure was conducted using field-surveyed culvert and bridge coordinates obtained from numerous public agencies, but it was discovered the disparate drainage structure datasets were not comprehensive enough to assess all remotely located depressions in need of hydrologic-enforcement. Alternatively, orthoimagery was interpreted to define drainage structures near each depression, and these locations were used as reference points for a quantitative hydrologic-enforcement assessment. The orthoimagery-interpreted reference points resulted in a larger corresponding sample size than the assessment between hydrologic-enforced transects and field-surveyed data. This assessment demonstrates the viability of rules-based hydrologic-enforcement that is needed to achieve hydrologic connectivity, which is valuable for hydrodynamic models in sensitive coastal regions. Hydrologic-enforced elevation data are also essential for merging with topographic/bathymetric elevation data that extend over vulnerable urbanized areas and dynamic coastal

EAARL Coastal Topography and Imagery-Assateague Island National Seashore, Maryland and Virginia, Post-Nor'Ida, 2009

USGS Publications Warehouse

Bonisteel-Cormier, J.M.; Nayegandhi, Amar; Brock, J.C.; Wright, C.W.; Nagle, D.B.; Klipp, E.S.; Vivekanandan, Saisudha; Fredericks, Xan; Stevens, Sara

2010-01-01

These remotely sensed, geographically referenced color-infrared (CIR) imagery and elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the Assateague Island National Seashore in Maryland and Virginia, acquired post-Nor'Ida (November 2009 nor'easter) on November 28 and 30, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar(EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and

EAARL Coastal Topography-Sandy Hook Unit, Gateway National Recreation Area, New Jersey, Post-Nor'Ida, 2009

USGS Publications Warehouse

Nayegandhi, Amar; Vivekanandan, Saisudha; Brock, J.C.; Wright, C.W.; Bonisteel-Cormier, J.M.; Nagle, D.B.; Klipp, E.S.; Stevens, Sara

2010-01-01

These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the Sandy Hook Unit of Gateway National Recreation Area in New Jersey, acquired post-Nor'Ida (November 2009 nor'easter) on December 4, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then

The Optimum Dataset method - examples of the application

NASA Astrophysics Data System (ADS)

Błaszczak-Bąk, Wioleta; Sobieraj-Żłobińska, Anna; Wieczorek, Beata

2018-01-01

Data reduction is a procedure to decrease the dataset in order to make their analysis more effective and easier. Reduction of the dataset is an issue that requires proper planning, so after reduction it meets all the user's expectations. Evidently, it is better if the result is an optimal solution in terms of adopted criteria. Within reduction methods, which provide the optimal solution there is the Optimum Dataset method (OptD) proposed by Błaszczak-Bąk (2016). The paper presents the application of this method for different datasets from LiDAR and the possibility of using the method for various purposes of the study. The following reduced datasets were presented: (a) measurement of Sielska street in Olsztyn (Airbrone Laser Scanning data - ALS data), (b) measurement of the bas-relief that is on the building in Gdańsk (Terrestrial Laser Scanning data - TLS data), (c) dataset from Biebrza river measurment (TLS data).

The ISLSCP initiative I global datasets: Surface boundary conditions and atmospheric forcings for land-atmosphere studies

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

Sellers, P.J.; Collatz, J.; Koster, R.

1996-09-01

A comprehensive series of global datasets for land-atmosphere models has been collected, formatted to a common grid, and released on a set of CD-ROMs. This paper describes the motivation for and the contents of the dataset. In June of 1992, an interdisciplinary earth science workshop was convened in Columbia, Maryland, to assess progress in land-atmosphere research, specifically in the areas of models, satellite data algorithms, and field experiments. At the workshop, representatives of the land-atmosphere modeling community defined a need for global datasets to prescribe boundary conditions, initialize state variables, and provide near-surface meteorological and radiative forcings for their models.more » The International Satellite Land Surface Climatology Project (ISLSCP), a part of the Global Energy and Water Cycle Experiment, worked with the Distributed Active Archive Center of the National Aeronautics and Space Administration Goddard Space Flight Center to bring the required datasets together in a usable format. The data have since been released on a collection of CD-ROMs. The datasets on the CD-ROMs are grouped under the following headings: vegetation; hydrology and soils; snow, ice, and oceans; radiation and clouds; and near-surface meteorology. All datasets cover the period 1987-88, and all but a few are spatially continuous over the earth`s land surface. All have been mapped to a common 1{degree} x 1{degree} equal-angle grid. The temporal frequency for most of the datasets is monthly. A few of the near-surface meteorological parameters are available both as six-hourly values and as monthly means. 26 refs., 8 figs., 2 tabs.« less

36. ARCHITECTURAL AND STRUCTURAL DETAILS OF ELEVATOR HOUSING, NaK HEATER ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

36. ARCHITECTURAL AND STRUCTURAL DETAILS OF ELEVATOR HOUSING, NaK HEATER STACK ROOF FLASHING, HOOD ELEVATION DETAIL. INCLUDES PARTIAL 'BILL OF MATERIAL.' INEEL DRAWING NUMBER 200-0633-00-287-106361. FLUOR NUMBER 5775-CPP-633-A-11. - Idaho National Engineering Laboratory, Old Waste Calcining Facility, Scoville, Butte County, ID

The Transition of NASA EOS Datasets to WFO Operations: A Model for Future Technology Transfer

NASA Technical Reports Server (NTRS)

Darden, C.; Burks, J.; Jedlovec, G.; Haines, S.

2007-01-01

The collocation of a National Weather Service (NWS) Forecast Office with atmospheric scientists from NASA/Marshall Space Flight Center (MSFC) in Huntsville, Alabama has afforded a unique opportunity for science sharing and technology transfer. Specifically, the NWS office in Huntsville has interacted closely with research scientists within the SPORT (Short-term Prediction and Research and Transition) Center at MSFC. One significant technology transfer that has reaped dividends is the transition of unique NASA EOS polar orbiting datasets into NWS field operations. NWS forecasters primarily rely on the AWIPS (Advanced Weather Information and Processing System) decision support system for their day to day forecast and warning decision making. Unfortunately, the transition of data from operational polar orbiters or low inclination orbiting satellites into AWIPS has been relatively slow due to a variety of reasons. The ability to integrate these high resolution NASA datasets into operations has yielded several benefits. The MODIS (MODerate-resolution Imaging Spectrometer ) instrument flying on the Aqua and Terra satellites provides a broad spectrum of multispectral observations at resolutions as fine as 250m. Forecasters routinely utilize these datasets to locate fine lines, boundaries, smoke plumes, locations of fog or haze fields, and other mesoscale features. In addition, these important datasets have been transitioned to other WFOs for a variety of local uses. For instance, WFO Great Falls Montana utilizes the MODIS snow cover product for hydrologic planning purposes while several coastal offices utilize the output from the MODIS and AMSR-E instruments to supplement observations in the data sparse regions of the Gulf of Mexico and western Atlantic. In the short term, these datasets have benefited local WFOs in a variety of ways. In the longer term, the process by which these unique datasets were successfully transitioned to operations will benefit the planning and

Elevation Difference and Bouguer Anomaly Analysis Tool (EDBAAT) User's Guide

USGS Publications Warehouse

Smittle, Aaron M.; Shoberg, Thomas G.

2017-06-16

This report describes a software tool that imports gravity anomaly point data from the Gravity Database of the United States (GDUS) of the National Geospatial-Intelligence Agency and University of Texas at El Paso along with elevation data from The National Map (TNM) of the U.S. Geological Survey that lie within a user-specified geographic area of interest. Further, the tool integrates these two sets of data spatially and analyzes the consistency of the elevation of each gravity station from the GDUS with TNM elevation data; it also evaluates the consistency of gravity anomaly data within the GDUS data repository. The tool bins the GDUS data based on user-defined criteria of elevation misfit between the GDUS and TNM elevation data. It also provides users with a list of points from the GDUS data, which have Bouguer anomaly values that are considered outliers (two standard deviations or greater) with respect to other nearby GDUS anomaly data. “Nearby” can be defined by the user at time of execution. These outputs should allow users to quickly and efficiently choose which points from the GDUS would be most useful in reconnaissance studies or in augmenting and extending the range of individual gravity studies.

Relevancy Ranking of Satellite Dataset Search Results

NASA Technical Reports Server (NTRS)

Lynnes, Christopher; Quinn, Patrick; Norton, James

2017-01-01

As the Variety of Earth science datasets increases, science researchers find it more challenging to discover and select the datasets that best fit their needs. The most common way of search providers to address this problem is to rank the datasets returned for a query by their likely relevance to the user. Large web page search engines typically use text matching supplemented with reverse link counts, semantic annotations and user intent modeling. However, this produces uneven results when applied to dataset metadata records simply externalized as a web page. Fortunately, data and search provides have decades of experience in serving data user communities, allowing them to form heuristics that leverage the structure in the metadata together with knowledge about the user community. Some of these heuristics include specific ways of matching the user input to the essential measurements in the dataset and determining overlaps of time range and spatial areas. Heuristics based on the novelty of the datasets can prioritize later, better versions of data over similar predecessors. And knowledge of how different user types and communities use data can be brought to bear in cases where characteristics of the user (discipline, expertise) or their intent (applications, research) can be divined. The Earth Observing System Data and Information System has begun implementing some of these heuristics in the relevancy algorithm of its Common Metadata Repository search engine.

Using LiDAR datasets to improve HSPF water quality modeling in the Red River of the North Basin

NASA Astrophysics Data System (ADS)

Burke, M. P.; Foreman, C. S.

2013-12-01

The Red River of the North Basin (RRB), located in the lakebed of ancient glacial Lake Agassiz, comprises one of the flattest landscapes in North America. The topography of the basin, coupled with the Red River's direction of flow from south to north results in a system that is highly susceptible to flooding. The magnitude and frequency of flood events in the RRB has prompted several multijurisdictional projects and mitigation efforts. In response to the devastating 1997 flood, an International Joint Commission sponsored task force established the need for accurate elevation data to help improve flood forecasting and better understand risks. This led to the International Water Institute's Red River Basin Mapping Initiative, and the acquisition LiDAR Data for the entire US portion of the RRB. The resulting 1 meter bare earth digital elevation models have been used to improve hydraulic and hydrologic modeling within the RRB, with focus on flood prediction and mitigation. More recently, these LiDAR datasets have been incorporated into Hydrological Simulation Program-FORTRAN (HSPF) model applications to improve water quality predictions in the MN portion of the RRB. RESPEC is currently building HSPF model applications for five of MN's 8-digit HUC watersheds draining to the Red River, including: the Red Lake River, Clearwater River, Sandhill River, Two Rivers, and Tamarac River watersheds. This work is being conducted for the Minnesota Pollution Control Agency (MPCA) as part of MN's statewide watershed approach to restoring and protecting water. The HSPF model applications simulate hydrology (discharge, stage), as well as a number of water quality constituents (sediment, temperature, organic and inorganic nitrogen, total ammonia, organic and inorganic phosphorus, dissolved oxygen and biochemical oxygen demand, and algae) continuously for the period 1995-2009 and are formulated to provide predictions at points of interest within the watersheds, such as observation gages

Datasets collected in general practice: an international comparison using the example of obesity.

PubMed

Sturgiss, Elizabeth; van Boven, Kees

2018-06-04

International datasets from general practice enable the comparison of how conditions are managed within consultations in different primary healthcare settings. The Australian Bettering the Evaluation and Care of Health (BEACH) and TransHIS from the Netherlands collect in-consultation general practice data that have been used extensively to inform local policy and practice. Obesity is a global health issue with different countries applying varying approaches to management. The objective of the present paper is to compare the primary care management of obesity in Australia and the Netherlands using data collected from consultations. Despite the different prevalence in obesity in the two countries, the number of patients per 1000 patient-years seen with obesity is similar. Patients in Australia with obesity are referred to allied health practitioners more often than Dutch patients. Without quality general practice data, primary care researchers will not have data about the management of conditions within consultations. We use obesity to highlight the strengths of these general practice data sources and to compare their differences. What is known about the topic? Australia had one of the longest-running consecutive datasets about general practice activity in the world, but it has recently lost government funding. The Netherlands has a longitudinal general practice dataset of information collected within consultations since 1985. What does this paper add? We discuss the benefits of general practice-collected data in two countries. Using obesity as a case example, we compare management in general practice between Australia and the Netherlands. This type of analysis should start all international collaborations of primary care management of any health condition. Having a national general practice dataset allows international comparisons of the management of conditions with primary care. Without a current, quality general practice dataset, primary care researchers will not

The 3D Elevation Program: summary for Missouri

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Montana

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The new 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Louisiana

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Tennessee

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Maryland

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Maine

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Kentucky

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Oregon

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Florida

USGS Publications Warehouse

Carswell, William J.

2013-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios.The new 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the OMB Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Alabama

USGS Publications Warehouse

Carswell, William J.

2013-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The new 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A-16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

Host-pathogen metapopulation dynamics suggest high elevation refugia for boreal toads

USGS Publications Warehouse

Mosher, Brittany A.; Bailey, Larissa L.; Muths, Erin L.; Huyvaert, Kathryn P

2018-01-01

Emerging infectious diseases are an increasingly common threat to wildlife. Chytridiomycosis, caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd), is an emerging infectious disease that has been linked to amphibian declines around the world. Few studies exist that explore amphibian-Bd dynamics at the landscape scale, limiting our ability to identify which factors are associated with variation in population susceptibility and to develop effective in situdisease management. Declines of boreal toads (Anaxyrus boreas boreas) in the Southern Rocky Mountains are largely attributed to chytridiomycosis but variation exists in local extinction of boreal toads across this metapopulation. Using a large-scale historic dataset, we explored several potential factors influencing disease dynamics in the boreal toad-Bd system: geographic isolation of populations, amphibian community richness, elevational differences, and habitat permanence. We found evidence that boreal toad extinction risk was lowest at high elevations where temperatures may be sub-optimal for Bd growth and where small boreal toad populations may be below the threshold needed for efficient pathogen transmission. In addition, boreal toads were more likely to recolonize high elevation sites after local extinction, again suggesting that high elevations may provide refuge from disease for boreal toads. We illustrate a modeling framework that will be useful to natural resource managers striving to make decisions in amphibian-Bdsystems. Our data suggest that in the southern Rocky Mountains high elevation sites should be prioritized for conservation initiatives like reintroductions.

A dataset on tail risk of commodities markets.

PubMed

Powell, Robert J; Vo, Duc H; Pham, Thach N; Singh, Abhay K

2017-12-01

This article contains the datasets related to the research article "The long and short of commodity tails and their relationship to Asian equity markets"(Powell et al., 2017) [1]. The datasets contain the daily prices (and price movements) of 24 different commodities decomposed from the S&P GSCI index and the daily prices (and price movements) of three share market indices including World, Asia, and South East Asia for the period 2004-2015. Then, the dataset is divided into annual periods, showing the worst 5% of price movements for each year. The datasets are convenient to examine the tail risk of different commodities as measured by Conditional Value at Risk (CVaR) as well as their changes over periods. The datasets can also be used to investigate the association between commodity markets and share markets.

Basin Characteristics for Selected Streamflow-Gaging Stations In and Near West Virginia

USGS Publications Warehouse

Paybins, Katherine S.

2008-01-01

Basin characteristics have long been used to develop equations describing streamflow. In the past, flow equations used in West Virginia were based on a few hand-calculated basin characteristics. More recently, the use of a Geographic Information System (GIS) to generate basin characteristics from existing datasets has refined the process for developing equations to describe flow values in the Mountain State. These basin characteristics are described in this document for streamflow-gaging stations in and near West Virginia. The GIS program developed in ArcGIS Workstation by Environmental Systems Research Institute (ESRI?) used data that included National Elevation Dataset (NED) at 1:24,000 scale, climate data from the National Oceanic and Atmospheric Agency (NOAA), streamlines from the National Hydrologic Dataset (NHD), and LandSat-based land-cover data (NLCD) for the period 1999-2003. Full automation of data generation was not achieved due to some inaccuracies in the elevation dataset, as well as inaccuracies in the streamflow-gage locations retrieved from the National Water Information System (NWIS). A Pearson?s correlation examination of the data indicates that several of the basin characteristics are correlated with drainage area. However, the GIS-generated data provide a consistent and documented set of basin characteristics for resource managers and researchers to use.

Multiresolution comparison of precipitation datasets for large-scale models

NASA Astrophysics Data System (ADS)

Chun, K. P.; Sapriza Azuri, G.; Davison, B.; DeBeer, C. M.; Wheater, H. S.

2014-12-01

Gridded precipitation datasets are crucial for driving large-scale models which are related to weather forecast and climate research. However, the quality of precipitation products is usually validated individually. Comparisons between gridded precipitation products along with ground observations provide another avenue for investigating how the precipitation uncertainty would affect the performance of large-scale models. In this study, using data from a set of precipitation gauges over British Columbia and Alberta, we evaluate several widely used North America gridded products including the Canadian Gridded Precipitation Anomalies (CANGRD), the National Center for Environmental Prediction (NCEP) reanalysis, the Water and Global Change (WATCH) project, the thin plate spline smoothing algorithms (ANUSPLIN) and Canadian Precipitation Analysis (CaPA). Based on verification criteria for various temporal and spatial scales, results provide an assessment of possible applications for various precipitation datasets. For long-term climate variation studies (~100 years), CANGRD, NCEP, WATCH and ANUSPLIN have different comparative advantages in terms of their resolution and accuracy. For synoptic and mesoscale precipitation patterns, CaPA provides appealing performance of spatial coherence. In addition to the products comparison, various downscaling methods are also surveyed to explore new verification and bias-reduction methods for improving gridded precipitation outputs for large-scale models.

Social voting advice applications-definitions, challenges, datasets and evaluation.

PubMed

Katakis, Ioannis; Tsapatsoulis, Nicolas; Mendez, Fernando; Triga, Vasiliki; Djouvas, Constantinos

2014-07-01

Voting advice applications (VAAs) are online tools that have become increasingly popular and purportedly aid users in deciding which party/candidate to vote for during an election. In this paper we present an innovation to current VAA design which is based on the introduction of a social network element. We refer to this new type of online tool as a social voting advice application (SVAA). SVAAs extend VAAs by providing (a) community-based recommendations, (b) comparison of users' political opinions, and (c) a channel of user communication. In addition, SVAAs enriched with data mining modules, can operate as citizen sensors recording the sentiment of the electorate on issues and candidates. Drawing on VAA datasets generated by the Preference Matcher research consortium, we evaluate the results of the first VAA-Choose4Greece-which incorporated social voting features and was launched during the landmark Greek national elections of 2012. We demonstrate how an SVAA can provide community based features and, at the same time, serve as a citizen sensor. Evaluation of the proposed techniques is realized on a series of datasets collected from various VAAs, including Choose4Greece. The collection is made available online in order to promote research in the field.

North & south wall elevation of the east entrance loggia; ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

North & south wall elevation of the east entrance loggia; detail of pilaster base and capital - National Zoological Park, Elephant House, 3001 Connecticut Avenue NW, Washington, District of Columbia, DC

Multi-temporal AirSWOT elevations on the Willamette river: error characterization and algorithm testing

NASA Astrophysics Data System (ADS)

Tuozzolo, S.; Frasson, R. P. M.; Durand, M. T.

2017-12-01

We analyze a multi-temporal dataset of in-situ and airborne water surface measurements from the March 2015 AirSWOT field campaign on the Willamette River in Western Oregon, which included six days of AirSWOT flights over a 75km stretch of the river. We examine systematic errors associated with dark water and layover effects in the AirSWOT dataset, and test the efficacies of different filtering and spatial averaging techniques at reconstructing the water surface profile. Finally, we generate a spatially-averaged time-series of water surface elevation and water surface slope. These AirSWOT-derived reach-averaged values are ingested in a prospective SWOT discharge algorithm to assess its performance on SWOT-like data collected from a borderline SWOT-measurable river (mean width = 90m).

Control Measure Dataset

EPA Pesticide Factsheets

The EPA Control Measure Dataset is a collection of documents describing air pollution control available to regulated facilities for the control and abatement of air pollution emissions from a range of regulated source types, whether directly through the use of technical measures, or indirectly through economic or other measures.

Reconstruction of a Three Hourly 1-km Land Surface Air Temperature Dataset in the Qinghai-Tibet Plateau

NASA Astrophysics Data System (ADS)

Zhou, J.; Ding, L.

2017-12-01

Land surface air temperature (SAT) is an important parameter in the modeling of radiation balance and energy budget of the earth surface. Generally, SAT is measured at ground meteorological stations; then SAT mapping is possible though a spatial interpolation process. The interpolated SAT map relies on the spatial distribution of ground stations, the terrain, and many other factors; thus, it has great uncertainties in regions with complicated terrain. Instead, SAT map can also be obtained through physical modeling of interactions between the land surface and the atmosphere. Such dataset generally has coarse spatial resolution (e.g. coarser than 0.1°) and cannot satisfy the applications at fine scales, e.g. 1 km. This presentation reports the reconstruction of a three hourly 1-km SAT dataset from 2001 to 2015 over the Qinghai-Tibet Plateau. The terrain in the Qinghai-Tibet Plateau, especially in the eastern part, is extremely complicated. Two SAT datasets with good qualities are used in this study. The first one is from the 3h China Meteorological Forcing Dataset with a 0.1° resolution released by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (Yang et al., 2010); the second one is from the ERA-Interim product with the same temporal resolution and a 0.125° resolution. A statistical approach is developed to downscale the spatial resolution of the derived SAT to 1-km. The elevation and the normalized difference vegetation index (NDVI) are selected as two scaling factors in the downscaling approach. Results demonstrate there is significantly negative correlation between the SAT and elevation in all seasons; there is also significantly negative correlation between the SAT and NDVI in the vegetation growth seasons, while the correlation decreases in the other seasons. Therefore, a temporally dynamic downscaling approach is feasible to enhance the spatial resolution of the SAT. Compared with the SAT at the 0.1° or 0.125°, the reconstructed 1

Developing a provisional, international minimal dataset for Juvenile Dermatomyositis: for use in clinical practice to inform research.

PubMed

McCann, Liza J; Arnold, Katie; Pilkington, Clarissa A; Huber, Adam M; Ravelli, Angelo; Beard, Laura; Beresford, Michael W; Wedderburn, Lucy R

2014-01-01

Juvenile dermatomyositis (JDM) is a rare but severe autoimmune inflammatory myositis of childhood. International collaboration is essential in order to undertake clinical trials, understand the disease and improve long-term outcome. The aim of this study was to propose from existing collaborative initiatives a preliminary minimal dataset for JDM. This will form the basis of the future development of an international consensus-approved minimum core dataset to be used both in clinical care and inform research, allowing integration of data between centres. A working group of internationally-representative JDM experts was formed to develop a provisional minimal dataset. Clinical and laboratory variables contained within current national and international collaborative databases of patients with idiopathic inflammatory myopathies were scrutinised. Judgements were informed by published literature and a more detailed analysis of the Juvenile Dermatomyositis Cohort Biomarker Study and Repository, UK and Ireland. A provisional minimal JDM dataset has been produced, with an associated glossary of definitions. The provisional minimal dataset will request information at time of patient diagnosis and during on-going prospective follow up. At time of patient diagnosis, information will be requested on patient demographics, diagnostic criteria and treatments given prior to diagnosis. During on-going prospective follow-up, variables will include the presence of active muscle or skin disease, major organ involvement or constitutional symptoms, investigations, treatment, physician global assessments and patient reported outcome measures. An internationally agreed minimal dataset has the potential to significantly enhance collaboration, allow effective communication between groups, provide a minimal standard of care and enable analysis of the largest possible number of JDM patients to provide a greater understanding of this disease. This preliminary dataset can now be developed into

Developing a provisional, international Minimal Dataset for Juvenile Dermatomyositis: for use in clinical practice to inform research

PubMed Central

2014-01-01

Background Juvenile dermatomyositis (JDM) is a rare but severe autoimmune inflammatory myositis of childhood. International collaboration is essential in order to undertake clinical trials, understand the disease and improve long-term outcome. The aim of this study was to propose from existing collaborative initiatives a preliminary minimal dataset for JDM. This will form the basis of the future development of an international consensus-approved minimum core dataset to be used both in clinical care and inform research, allowing integration of data between centres. Methods A working group of internationally-representative JDM experts was formed to develop a provisional minimal dataset. Clinical and laboratory variables contained within current national and international collaborative databases of patients with idiopathic inflammatory myopathies were scrutinised. Judgements were informed by published literature and a more detailed analysis of the Juvenile Dermatomyositis Cohort Biomarker Study and Repository, UK and Ireland. Results A provisional minimal JDM dataset has been produced, with an associated glossary of definitions. The provisional minimal dataset will request information at time of patient diagnosis and during on-going prospective follow up. At time of patient diagnosis, information will be requested on patient demographics, diagnostic criteria and treatments given prior to diagnosis. During on-going prospective follow-up, variables will include the presence of active muscle or skin disease, major organ involvement or constitutional symptoms, investigations, treatment, physician global assessments and patient reported outcome measures. Conclusions An internationally agreed minimal dataset has the potential to significantly enhance collaboration, allow effective communication between groups, provide a minimal standard of care and enable analysis of the largest possible number of JDM patients to provide a greater understanding of this disease. This

Comparison of Shallow Survey 2012 Multibeam Datasets

NASA Astrophysics Data System (ADS)

Ramirez, T. M.

2012-12-01

The purpose of the Shallow Survey common dataset is a comparison of the different technologies utilized for data acquisition in the shallow survey marine environment. The common dataset consists of a series of surveys conducted over a common area of seabed using a variety of systems. It provides equipment manufacturers the opportunity to showcase their latest systems while giving hydrographic researchers and scientists a chance to test their latest algorithms on the dataset so that rigorous comparisons can be made. Five companies collected data for the Common Dataset in the Wellington Harbor area in New Zealand between May 2010 and May 2011; including Kongsberg, Reson, R2Sonic, GeoAcoustics, and Applied Acoustics. The Wellington harbor and surrounding coastal area was selected since it has a number of well-defined features, including the HMNZS South Seas and HMNZS Wellington wrecks, an armored seawall constructed of Tetrapods and Akmons, aquifers, wharves and marinas. The seabed inside the harbor basin is largely fine-grained sediment, with gravel and reefs around the coast. The area outside the harbor on the southern coast is an active environment, with moving sand and exposed reefs. A marine reserve is also in this area. For consistency between datasets, the coastal research vessel R/V Ikatere and crew were used for all surveys conducted for the common dataset. Using Triton's Perspective processing software multibeam datasets collected for the Shallow Survey were processed for detail analysis. Datasets from each sonar manufacturer were processed using the CUBE algorithm developed by the Center for Coastal and Ocean Mapping/Joint Hydrographic Center (CCOM/JHC). Each dataset was gridded at 0.5 and 1.0 meter resolutions for cross comparison and compliance with International Hydrographic Organization (IHO) requirements. Detailed comparisons were made of equipment specifications (transmit frequency, number of beams, beam width), data density, total uncertainty, and

Marsh collapse thresholds for coastal Louisiana estimated using elevation and vegetation index data

USGS Publications Warehouse

Couvillion, Brady R.; Beck, Holly

2013-01-01

Forecasting marsh collapse in coastal Louisiana as a result of changes in sea-level rise, subsidence, and accretion deficits necessitates an understanding of thresholds beyond which inundation stress impedes marsh survival. The variability in thresholds at which different marsh types cease to occur (i.e., marsh collapse) is not well understood. We utilized remotely sensed imagery, field data, and elevation data to help gain insight into the relationships between vegetation health and inundation. A Normalized Difference Vegetation Index (NDVI) dataset was calculated using remotely sensed data at peak biomass (August) and used as a proxy for vegetation health and productivity. Statistics were calculated for NDVI values by marsh type for intermediate, brackish, and saline marsh in coastal Louisiana. Marsh-type specific NDVI values of 1.5 and 2 standard deviations below the mean were used as upper and lower limits to identify conditions indicative of collapse. As marshes seldom occur beyond these values, they are believed to represent a range within which marsh collapse is likely to occur. Inundation depth was selected as the primary candidate for evaluation of marsh collapse thresholds. Elevation relative to mean water level (MWL) was calculated by subtracting MWL from an elevation dataset compiled from multiple data types including light detection and ranging (lidar) and bathymetry. A polynomial cubic regression was used to examine a random subset of pixels to determine the relationship between elevation (relative to MWL) and NDVI. The marsh collapse uncertainty range values were found by locating the intercept of the regression line with the 1.5 and 2 standard deviations below the mean NDVI value for each marsh type. Results indicate marsh collapse uncertainty ranges of 30.7–35.8 cm below MWL for intermediate marsh, 20–25.6 cm below MWL for brackish marsh, and 16.9–23.5 cm below MWL for saline marsh. These values are thought to represent the ranges of

Developing a data dictionary for the irish nursing minimum dataset.

PubMed

Henry, Pamela; Mac Neela, Pádraig; Clinton, Gerard; Scott, Anne; Treacy, Pearl; Butler, Michelle; Hyde, Abbey; Morris, Roisin; Irving, Kate; Byrne, Anne

2006-01-01

One of the challenges in health care in Ireland is the relatively slow acceptance of standardised clinical information systems. Yet the national Irish health reform programme indicates that an Electronic Health Care Record (EHCR) will be implemented on a phased basis. [3-5]. While nursing has a key role in ensuring the quality and comparability of health information, the so- called 'invisibility' of some nursing activities makes this a challenging aim to achieve [3-5]. Any integrated health care system requires the adoption of uniform standards for electronic data exchange [1-2]. One of the pre-requisites for uniform standards is the composition of a data dictionary. Inadequate definition of data elements in a particular dataset hinders the development of an integrated data depository or electronic health care record (EHCR). This paper outlines how work on the data dictionary for the Irish Nursing Minimum Dataset (INMDS) has addressed this issue. Data set elements were devised on the basis of a large scale empirical research programme. ISO 18104, the reference terminology for nursing [6], was used to cross-map the data set elements with semantic domains, categories and links and data set items were dissected.

Abandoned Uranium Mine (AUM) Points, Navajo Nation, 2016, US EPA Region 9

EPA Pesticide Factsheets

This GIS dataset contains point features of all Abandoned Uranium Mines (AUMs) on or within one mile of the Navajo Nation. Points are centroids developed from the Navajo Nation production mines polygon dataset that comprise of productive or unproductive Abandoned Uranium Mines. Attributes include mine names, aliases, links to AUM reports, indicators whether an AUM was mined above or below ground, indicators whether an AUM was mined above or below the local water table, and the region in which an AUM is located. This dataset contains 608 features.

Two ultraviolet radiation datasets that cover China

NASA Astrophysics Data System (ADS)

Liu, Hui; Hu, Bo; Wang, Yuesi; Liu, Guangren; Tang, Liqin; Ji, Dongsheng; Bai, Yongfei; Bao, Weikai; Chen, Xin; Chen, Yunming; Ding, Weixin; Han, Xiaozeng; He, Fei; Huang, Hui; Huang, Zhenying; Li, Xinrong; Li, Yan; Liu, Wenzhao; Lin, Luxiang; Ouyang, Zhu; Qin, Boqiang; Shen, Weijun; Shen, Yanjun; Su, Hongxin; Song, Changchun; Sun, Bo; Sun, Song; Wang, Anzhi; Wang, Genxu; Wang, Huimin; Wang, Silong; Wang, Youshao; Wei, Wenxue; Xie, Ping; Xie, Zongqiang; Yan, Xiaoyuan; Zeng, Fanjiang; Zhang, Fawei; Zhang, Yangjian; Zhang, Yiping; Zhao, Chengyi; Zhao, Wenzhi; Zhao, Xueyong; Zhou, Guoyi; Zhu, Bo

2017-07-01

Ultraviolet (UV) radiation has significant effects on ecosystems, environments, and human health, as well as atmospheric processes and climate change. Two ultraviolet radiation datasets are described in this paper. One contains hourly observations of UV radiation measured at 40 Chinese Ecosystem Research Network stations from 2005 to 2015. CUV3 broadband radiometers were used to observe the UV radiation, with an accuracy of 5%, which meets the World Meteorology Organization's measurement standards. The extremum method was used to control the quality of the measured datasets. The other dataset contains daily cumulative UV radiation estimates that were calculated using an all-sky estimation model combined with a hybrid model. The reconstructed daily UV radiation data span from 1961 to 2014. The mean absolute bias error and root-mean-square error are smaller than 30% at most stations, and most of the mean bias error values are negative, which indicates underestimation of the UV radiation intensity. These datasets can improve our basic knowledge of the spatial and temporal variations in UV radiation. Additionally, these datasets can be used in studies of potential ozone formation and atmospheric oxidation, as well as simulations of ecological processes.

76 FR 43968 - Proposed Flood Elevation Determinations

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-22

... qualify or remain qualified for participation in the National Flood Insurance Program (NFIP). In addition, these elevations, once finalized, will be used by insurance agents and others to calculate appropriate flood insurance premium rates for new buildings and the contents in those buildings. DATES: Comments are...

The Harvard organic photovoltaic dataset

DOE PAGES

Lopez, Steven A.; Pyzer-Knapp, Edward O.; Simm, Gregor N.; ...

2016-09-27

Presented in this work is the Harvard Organic Photovoltaic Dataset (HOPV15), a collation of experimental photovoltaic data from the literature, and corresponding quantum-chemical calculations performed over a range of conformers, each with quantum chemical results using a variety of density functionals and basis sets. It is anticipated that this dataset will be of use in both relating electronic structure calculations to experimental observations through the generation of calibration schemes, as well as for the creation of new semi-empirical methods and the benchmarking of current and future model chemistries for organic electronic applications.

The Harvard organic photovoltaic dataset.

PubMed

Lopez, Steven A; Pyzer-Knapp, Edward O; Simm, Gregor N; Lutzow, Trevor; Li, Kewei; Seress, Laszlo R; Hachmann, Johannes; Aspuru-Guzik, Alán

2016-09-27

The Harvard Organic Photovoltaic Dataset (HOPV15) presented in this work is a collation of experimental photovoltaic data from the literature, and corresponding quantum-chemical calculations performed over a range of conformers, each with quantum chemical results using a variety of density functionals and basis sets. It is anticipated that this dataset will be of use in both relating electronic structure calculations to experimental observations through the generation of calibration schemes, as well as for the creation of new semi-empirical methods and the benchmarking of current and future model chemistries for organic electronic applications.

Evaluation of Elevation, Slope and Stream Network Quality of SPOT Dems

NASA Astrophysics Data System (ADS)

El Hage, M.; Simonetto, E.; Faour, G.; Polidori, L.

2012-07-01

Digital elevation models are considered the most useful data for dealing with geomorphology. The quality of these models is an important issue for users. This quality concerns position and shape. Vertical accuracy is the most assessed in many studies and shape quality is often neglected. However, both of them have an impact on the quality of the final results for a particular application. For instance, the elevation accuracy is required for orthorectification and the shape quality for geomorphology and hydrology. In this study, we deal with photogrammetric DEMs and show the importance of the quality assessment of both elevation and shape. For this purpose, we produce several SPOT HRV DEMs with the same dataset but with different template size, that is one of the production parameters from optical images. Then, we evaluate both elevation and shape quality. The shape quality is assessed with in situ measurements and analysis of slopes as an elementary shape and stream networks as a complex shape. We use the fractal dimension and sinuosity to evaluate the stream network shape. The results show that the elevation accuracy as well as the slope accuracy are affected by the template size. Indeed, an improvement of 1 m in the elevation accuracy and of 5 degrees in the slope accuracy has been obtained while changing this parameter. The elevation RMSE ranges from 7.6 to 8.6 m, which is smaller than the pixel size (10 m). For slope, the RMSE depends on the sampling distance. With a distance of 10 m, the minimum slope RMSE is 11.4 degrees. The stream networks extracted from these DEMs present a higher fractal dimension than the reference river. Moreover, the fractal dimension of the extracted networks has a negligible change according to the template size. Finally, the sinuosity of the stream networks is slightly affected by the change of the template size.

The 3D Elevation Program: summary for Connecticut

USGS Publications Warehouse

Carswell, William J.

2015-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Mississippi

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios.The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Georgia

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios.The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Iowa

USGS Publications Warehouse

Carswell, William J.

2015-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Oklahoma

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment (NEEA; Dewberry, 2011) evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Kansas

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Nevada

USGS Publications Warehouse

Carswell, William J.

2015-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Illinois

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Colorado

USGS Publications Warehouse

Carswell, William J.

2013-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Utah

USGS Publications Warehouse

Carswell, William J.

2015-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Delaware

USGS Publications Warehouse

Carswell, William J.

2015-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Massachusetts

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Washington

USGS Publications Warehouse

Carswell, William J.

2013-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Wyoming

USGS Publications Warehouse

Carswell, William J.

2015-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios.The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Arizona

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Pennsylvania

USGS Publications Warehouse

Carswell, William J.

2015-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Arkansas

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios.The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

SPERTI Instrument Cell Building (PER606) elevation; plan of Guard House ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

SPERT-I Instrument Cell Building (PER-606) elevation; plan of Guard House (PER-607); elevations for Pit Building (PER-605) southwest, southeast, and northeast sides. Earthen shield is mounded between back wall of Instrument Cell Building and the southwest elevation of Pit Building. Detail of filtered louver in door of Instrument Cell Building. Idaho Operations Office PER-605-IDO-3. INEEL index no. 761-0605-00-396-109183 - Idaho National Engineering Laboratory, SPERT-I & Power Burst Facility Area, Scoville, Butte County, ID

Coastal Digital Elevation Models (DEMs) for tsunami hazard assessment on the French coasts

NASA Astrophysics Data System (ADS)

Maspataud, Aurélie; Biscara, Laurie; Hébert, Hélène; Schmitt, Thierry; Créach, Ronan

2015-04-01

Building precise and up-to-date coastal DEMs is a prerequisite for accurate modeling and forecasting of hydrodynamic processes at local scale. Marine flooding, originating from tsunamis, storm surges or waves, is one of them. Some high resolution DEMs are being generated for multiple coast configurations (gulf, embayment, strait, estuary, harbor approaches, low-lying areas…) along French Atlantic and Channel coasts. This work is undertaken within the framework of the TANDEM project (Tsunamis in the Atlantic and the English ChaNnel: Definition of the Effects through numerical Modeling) (2014-2017). DEMs boundaries were defined considering the vicinity of French civil nuclear facilities, site effects considerations and potential tsunamigenic sources. Those were identified from available historical observations. Seamless integrated topographic and bathymetric coastal DEMs will be used by institutions taking part in the study to simulate expected wave height at regional and local scale on the French coasts, for a set of defined scenarii. The main tasks were (1) the development of a new capacity of production of DEM, (2) aiming at the release of high resolution and precision digital field models referred to vertical reference frameworks, that require (3) horizontal and vertical datum conversions (all source elevation data need to be transformed to a common datum), on the basis of (4) the building of (national and/or local) conversion grids of datum relationships based on known measurements. Challenges in coastal DEMs development deal with good practices throughout model development that can help minimizing uncertainties. This is particularly true as scattered elevation data with variable density, from multiple sources (national hydrographic services, state and local government agencies, research organizations and private engineering companies) and from many different types (paper fieldsheets to be digitized, single beam echo sounder, multibeam sonar, airborne laser

A new global 1-km dataset of percentage tree cover derived from remote sensing

USGS Publications Warehouse

DeFries, R.S.; Hansen, M.C.; Townshend, J.R.G.; Janetos, A.C.; Loveland, Thomas R.

2000-01-01

Accurate assessment of the spatial extent of forest cover is a crucial requirement for quantifying the sources and sinks of carbon from the terrestrial biosphere. In the more immediate context of the United Nations Framework Convention on Climate Change, implementation of the Kyoto Protocol calls for estimates of carbon stocks for a baseline year as well as for subsequent years. Data sources from country level statistics and other ground-based information are based on varying definitions of 'forest' and are consequently problematic for obtaining spatially and temporally consistent carbon stock estimates. By combining two datasets previously derived from the Advanced Very High Resolution Radiometer (AVHRR) at 1 km spatial resolution, we have generated a prototype global map depicting percentage tree cover and associated proportions of trees with different leaf longevity (evergreen and deciduous) and leaf type (broadleaf and needleleaf). The product is intended for use in terrestrial carbon cycle models, in conjunction with other spatial datasets such as climate and soil type, to obtain more consistent and reliable estimates of carbon stocks. The percentage tree cover dataset is available through the Global Land Cover Facility at the University of Maryland at http://glcf.umiacs.umd.edu.

National Transportation Atlas Databases : 2002

DOT National Transportation Integrated Search

2002-01-01

The National Transportation Atlas Databases 2002 (NTAD2002) is a set of nationwide geographic databases of transportation facilities, transportation networks, and associated infrastructure. These datasets include spatial information for transportatio...

National Transportation Atlas Databases : 2010

DOT National Transportation Integrated Search

2010-01-01

The National Transportation Atlas Databases 2010 (NTAD2010) is a set of nationwide geographic databases of transportation facilities, transportation networks, and associated infrastructure. These datasets include spatial information for transportatio...

National Transportation Atlas Databases : 2006

DOT National Transportation Integrated Search

2006-01-01

The National Transportation Atlas Databases 2006 (NTAD2006) is a set of nationwide geographic databases of transportation facilities, transportation networks, and associated infrastructure. These datasets include spatial information for transportatio...

National Transportation Atlas Databases : 2005

DOT National Transportation Integrated Search

2005-01-01

The National Transportation Atlas Databases 2005 (NTAD2005) is a set of nationwide geographic databases of transportation facilities, transportation networks, and associated infrastructure. These datasets include spatial information for transportatio...

National Transportation Atlas Databases : 2008

DOT National Transportation Integrated Search

2008-01-01

The National Transportation Atlas Databases 2008 (NTAD2008) is a set of nationwide geographic databases of transportation facilities, transportation networks, and associated infrastructure. These datasets include spatial information for transportatio...

National Transportation Atlas Databases : 2003

DOT National Transportation Integrated Search

2003-01-01

The National Transportation Atlas Databases 2003 (NTAD2003) is a set of nationwide geographic databases of transportation facilities, transportation networks, and associated infrastructure. These datasets include spatial information for transportatio...

National Transportation Atlas Databases : 2014

DOT National Transportation Integrated Search

2014-01-01

The National Transportation Atlas Databases 2014 : (NTAD2014) is a set of nationwide geographic datasets of : transportation facilities, transportation networks, associated : infrastructure, and other political and administrative entities. : These da...

National Transportation Atlas Databases : 2004

DOT National Transportation Integrated Search

2004-01-01

The National Transportation Atlas Databases 2004 (NTAD2004) is a set of nationwide geographic databases of transportation facilities, transportation networks, and associated infrastructure. These datasets include spatial information for transportatio...

National Transportation Atlas Databases : 2009

DOT National Transportation Integrated Search

2009-01-01

The National Transportation Atlas Databases 2009 (NTAD2009) is a set of nationwide geographic databases of transportation facilities, transportation networks, and associated infrastructure. These datasets include spatial information for transportatio...

National Transportation Atlas Databases : 2007

DOT National Transportation Integrated Search

2007-01-01

The National Transportation Atlas Databases 2007 (NTAD2007) is a set of nationwide geographic databases of transportation facilities, transportation networks, and associated infrastructure. These datasets include spatial information for transportatio...

National Transportation Atlas Databases : 2012

DOT National Transportation Integrated Search

2012-01-01

The National Transportation Atlas Databases 2012 (NTAD2012) is a set of nationwide geographic databases of transportation facilities, transportation networks, and associated infrastructure. These datasets include spatial information for transportatio...

National Transportation Atlas Databases : 2015

DOT National Transportation Integrated Search

2015-01-01

The National Transportation Atlas Databases 2015 : (NTAD2015) is a set of nationwide geographic datasets of : transportation facilities, transportation networks, associated : infrastructure, and other political and administrative entities. : These da...

National Transportation Atlas Databases : 2011

DOT National Transportation Integrated Search

2011-01-01

The National Transportation Atlas Databases 2011 (NTAD2011) is a set of nationwide geographic databases of transportation facilities, transportation networks, and associated infrastructure. These datasets include spatial information for transportatio...

Assimilation of nontraditional datasets to improve atmospheric compensation

NASA Astrophysics Data System (ADS)

Kelly, Michael A.; Osei-Wusu, Kwame; Spisz, Thomas S.; Strong, Shadrian; Setters, Nathan; Gibson, David M.

2012-06-01

Detection and characterization of space objects require the capability to derive physical properties such as brightness temperature and reflectance. These quantities, together with trajectory and position, are often used to correlate an object from a catalogue of known characteristics. However, retrieval of these physical quantities can be hampered by the radiative obscuration of the atmosphere. Atmospheric compensation must therefore be applied to remove the radiative signature of the atmosphere from electro-optical (EO) collections and enable object characterization. The JHU/APL Atmospheric Compensation System (ACS) was designed to perform atmospheric compensation for long, slant-range paths at wavelengths from the visible to infrared. Atmospheric compensation is critically important for airand ground-based sensors collecting at low elevations near the Earth's limb. It can be demonstrated that undetected thin, sub-visual cirrus clouds in the line of sight (LOS) can significantly alter retrieved target properties (temperature, irradiance). The ACS algorithm employs non-traditional cirrus datasets and slant-range atmospheric profiles to estimate and remove atmospheric radiative effects from EO/IR collections. Results are presented for a NASA-sponsored collection in the near-IR (NIR) during hypersonic reentry of the Space Shuttle during STS-132.

Developing a 1 km resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States

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

Li, Xiaoma; Zhou, Yuyu; Asrar, Ghassem R.

High spatiotemporal resolution air temperature (Ta) datasets are increasingly needed for assessing the impact of temperature change on people, ecosystems, and energy system, especially in the urban domains. However, such datasets are not widely available because of the large spatiotemporal heterogeneity of Ta caused by complex biophysical and socioeconomic factors such as built infrastructure and human activities. In this study, we developed a 1-km gridded dataset of daily minimum Ta (Tmin) and maximum Ta (Tmax), and the associated uncertainties, in urban and surrounding areas in the conterminous U.S. for the 2003–2016 period. Daily geographically weighted regression (GWR) models were developedmore » and used to interpolate Ta using 1 km daily land surface temperature and elevation as explanatory variables. The leave-one-out cross-validation approach indicates that our method performs reasonably well, with root mean square errors of 2.1 °C and 1.9 °C, mean absolute errors of 1.5 °C and 1.3 °C, and R 2 of 0.95 and 0.97, for Tmin and Tmax, respectively. The resulting dataset captures reasonably the spatial heterogeneity of Ta in the urban areas, and also captures effectively the urban heat island (UHI) phenomenon that Ta rises with the increase of urban development (i.e., impervious surface area). The new dataset is valuable for studying environmental impacts of urbanization such as UHI and other related effects (e.g., on building energy consumption and human health). The proposed methodology also shows a potential to build a long-term record of Ta worldwide, to fill the data gap that currently exists for studies of urban systems.« less

Improving clinical models based on knowledge extracted from current datasets: a new approach.

PubMed

Mendes, D; Paredes, S; Rocha, T; Carvalho, P; Henriques, J; Morais, J

2016-08-01

The Cardiovascular Diseases (CVD) are the leading cause of death in the world, being prevention recognized to be a key intervention able to contradict this reality. In this context, although there are several models and scores currently used in clinical practice to assess the risk of a new cardiovascular event, they present some limitations. The goal of this paper is to improve the CVD risk prediction taking into account the current models as well as information extracted from real and recent datasets. This approach is based on a decision tree scheme in order to assure the clinical interpretability of the model. An innovative optimization strategy is developed in order to adjust the decision tree thresholds (rule structure is fixed) based on recent clinical datasets. A real dataset collected in the ambit of the National Registry on Acute Coronary Syndromes, Portuguese Society of Cardiology is applied to validate this work. In order to assess the performance of the new approach, the metrics sensitivity, specificity and accuracy are used. This new approach achieves sensitivity, a specificity and an accuracy values of, 80.52%, 74.19% and 77.27% respectively, which represents an improvement of about 26% in relation to the accuracy of the original score.

Genomics dataset of unidentified disclosed isolates.

PubMed

Rekadwad, Bhagwan N

2016-09-01

Analysis of DNA sequences is necessary for higher hierarchical classification of the organisms. It gives clues about the characteristics of organisms and their taxonomic position. This dataset is chosen to find complexities in the unidentified DNA in the disclosed patents. A total of 17 unidentified DNA sequences were thoroughly analyzed. The quick response codes were generated. AT/GC content of the DNA sequences analysis was carried out. The QR is helpful for quick identification of isolates. AT/GC content is helpful for studying their stability at different temperatures. Additionally, a dataset on cleavage code and enzyme code studied under the restriction digestion study, which helpful for performing studies using short DNA sequences was reported. The dataset disclosed here is the new revelatory data for exploration of unique DNA sequences for evaluation, identification, comparison and analysis.

Application of Huang-Hilbert Transforms to Geophysical Datasets

NASA Technical Reports Server (NTRS)

Duffy, Dean G.

2003-01-01

The Huang-Hilbert transform is a promising new method for analyzing nonstationary and nonlinear datasets. In this talk I will apply this technique to several important geophysical datasets. To understand the strengths and weaknesses of this method, multi- year, hourly datasets of the sea level heights and solar radiation will be analyzed. Then we will apply this transform to the analysis of gravity waves observed in a mesoscale observational net.

Investigation of potential sea level rise impact on the Nile Delta, Egypt using digital elevation models.

PubMed

Hasan, Emad; Khan, Sadiq Ibrahim; Hong, Yang

2015-10-01

In this study, the future impact of Sea Level Rise (SLR) on the Nile Delta region in Egypt is assessed by evaluating the elevations of two freely available Digital Elevation Models (DEMs): the SRTM and the ASTER-GDEM-V2. The SLR is a significant worldwide dilemma that has been triggered by recent climatic changes. In Egypt, the Nile Delta is projected to face SLR of 1 m by the end of the 21th century. In order to provide a more accurate assessment of the future SLR impact on Nile Delta's land and population, this study corrected the DEM's elevations by using linear regression model with ground elevations from GPS survey. The information for the land cover types and future population numbers were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover and the Gridded Population of the Worlds (GPWv3) datasets respectively. The DEM's vertical accuracies were assessed using GPS measurements and the uncertainty analysis revealed that the SRTM-DEM has positive bias of 2.5 m, while the ASTER-GDEM-V2 showed a positive bias of 0.8 m. The future inundated land cover areas and the affected population were illustrated based on two SLR scenarios of 0.5 m and 1 m. The SRTM DEM data indicated that 1 m SLR will affect about 3900 km(2) of cropland, 1280 km(2) of vegetation, 205 km(2) of wetland, 146 km(2) of urban areas and cause more than 6 million people to lose their houses. The overall vulnerability assessment using ASTER-GDEM-V2 indicated that the influence of SLR will be intense and confined along the coastal areas. For instance, the data indicated that 1 m SLR will inundate about 580 Km(2) (6%) of the total land cover areas and approximately 887 thousand people will be relocated. Accordingly, the uncertainty analysis of the DEM's elevations revealed that the ASTER-GDEM-V2 dataset product was considered the best to determine the future impact of SLR on the Nile Delta region.

Technical note: Space-time analysis of rainfall extremes in Italy: clues from a reconciled dataset

NASA Astrophysics Data System (ADS)

Libertino, Andrea; Ganora, Daniele; Claps, Pierluigi

2018-05-01

Like other Mediterranean areas, Italy is prone to the development of events with significant rainfall intensity, lasting for several hours. The main triggering mechanisms of these events are quite well known, but the aim of developing rainstorm hazard maps compatible with their actual probability of occurrence is still far from being reached. A systematic frequency analysis of these occasional highly intense events would require a complete countrywide dataset of sub-daily rainfall records, but this kind of information was still lacking for the Italian territory. In this work several sources of data are gathered, for assembling the first comprehensive and updated dataset of extreme rainfall of short duration in Italy. The resulting dataset, referred to as the Italian Rainfall Extreme Dataset (I-RED), includes the annual maximum rainfalls recorded in 1 to 24 consecutive hours from more than 4500 stations across the country, spanning the period between 1916 and 2014. A detailed description of the spatial and temporal coverage of the I-RED is presented, together with an exploratory statistical analysis aimed at providing preliminary information on the climatology of extreme rainfall at the national scale. Due to some legal restrictions, the database can be provided only under certain conditions. Taking into account the potentialities emerging from the analysis, a description of the ongoing and planned future work activities on the database is provided.

4. TOP OF EAST ELEVATION WALL SHOWING BUTTRESS CAPS LOOKING ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

4. TOP OF EAST ELEVATION WALL SHOWING BUTTRESS CAPS LOOKING NORTHEAST. - Original Airport Entrance Overpass, Spanning original Airport Entrance Road at National Airport, Arlington, Arlington County, VA

The Harvard organic photovoltaic dataset

PubMed Central

Lopez, Steven A.; Pyzer-Knapp, Edward O.; Simm, Gregor N.; Lutzow, Trevor; Li, Kewei; Seress, Laszlo R.; Hachmann, Johannes; Aspuru-Guzik, Alán

2016-01-01

The Harvard Organic Photovoltaic Dataset (HOPV15) presented in this work is a collation of experimental photovoltaic data from the literature, and corresponding quantum-chemical calculations performed over a range of conformers, each with quantum chemical results using a variety of density functionals and basis sets. It is anticipated that this dataset will be of use in both relating electronic structure calculations to experimental observations through the generation of calibration schemes, as well as for the creation of new semi-empirical methods and the benchmarking of current and future model chemistries for organic electronic applications. PMID:27676312

LPT. Elevations of low power test building (TAN640 and 641). ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

LPT. Elevations of low power test building (TAN-640 and -641). West and south elevations show stepped shield wall. South and east elevations show pumice block passageway on south side. Reactor cell walls are concrete. One-story parts are pumice block. Metal rollup doors. Ralph M. Parsons 1229-12 ANP/GE-7-640-A-2. November 1956. Approved by INEEL Classification Office for public release. INEEL index code no. 038-0640-00-693-107275 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

4. DETAIL OF ELEVATOR DRUM AND DRIVE. Hot Springs ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

4. DETAIL OF ELEVATOR DRUM AND DRIVE. - Hot Springs National Park, Bathhouse Row, Fordyce Bathhouse: Mechanical & Piping Systems, State Highway 7, 1 mile north of U.S. Highway 70, Hot Springs, Garland County, AR

13. DETAIL OF INTERIOR OF ELEVATOR SHAFT. Hot Springs ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

13. DETAIL OF INTERIOR OF ELEVATOR SHAFT. - Hot Springs National Park, Bathhouse Row, Fordyce Bathhouse: Mechanical & Piping Systems, State Highway 7, 1 mile north of U.S. Highway 70, Hot Springs, Garland County, AR

2. ELEVATOR DRIVE, CABLE MOTOR, CIRCUIT BOX, Hot Springs ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

2. ELEVATOR DRIVE, CABLE MOTOR, CIRCUIT BOX, - Hot Springs National Park, Bathhouse Row, Fordyce Bathhouse: Mechanical & Piping Systems, State Highway 7, 1 mile north of U.S. Highway 70, Hot Springs, Garland County, AR

Interpolation of diffusion weighted imaging datasets.

PubMed

Dyrby, Tim B; Lundell, Henrik; Burke, Mark W; Reislev, Nina L; Paulson, Olaf B; Ptito, Maurice; Siebner, Hartwig R

2014-12-01

Diffusion weighted imaging (DWI) is used to study white-matter fibre organisation, orientation and structural connectivity by means of fibre reconstruction algorithms and tractography. For clinical settings, limited scan time compromises the possibilities to achieve high image resolution for finer anatomical details and signal-to-noise-ratio for reliable fibre reconstruction. We assessed the potential benefits of interpolating DWI datasets to a higher image resolution before fibre reconstruction using a diffusion tensor model. Simulations of straight and curved crossing tracts smaller than or equal to the voxel size showed that conventional higher-order interpolation methods improved the geometrical representation of white-matter tracts with reduced partial-volume-effect (PVE), except at tract boundaries. Simulations and interpolation of ex-vivo monkey brain DWI datasets revealed that conventional interpolation methods fail to disentangle fine anatomical details if PVE is too pronounced in the original data. As for validation we used ex-vivo DWI datasets acquired at various image resolutions as well as Nissl-stained sections. Increasing the image resolution by a factor of eight yielded finer geometrical resolution and more anatomical details in complex regions such as tract boundaries and cortical layers, which are normally only visualized at higher image resolutions. Similar results were found with typical clinical human DWI dataset. However, a possible bias in quantitative values imposed by the interpolation method used should be considered. The results indicate that conventional interpolation methods can be successfully applied to DWI datasets for mining anatomical details that are normally seen only at higher resolutions, which will aid in tractography and microstructural mapping of tissue compartments. Copyright © 2014. Published by Elsevier Inc.

A multitemporal (1979-2009) land-use/land-cover dataset of the binational Santa Cruz Watershed

USGS Publications Warehouse

2011-01-01

Trends derived from multitemporal land-cover data can be used to make informed land management decisions and to help managers model future change scenarios. We developed a multitemporal land-use/land-cover dataset for the binational Santa Cruz watershed of southern Arizona, United States, and northern Sonora, Mexico by creating a series of land-cover maps at decadal intervals (1979, 1989, 1999, and 2009) using Landsat Multispectral Scanner and Thematic Mapper data and a classification and regression tree classifier. The classification model exploited phenological changes of different land-cover spectral signatures through the use of biseasonal imagery collected during the (dry) early summer and (wet) late summer following rains from the North American monsoon. Landsat images were corrected to remove atmospheric influences, and the data were converted from raw digital numbers to surface reflectance values. The 14-class land-cover classification scheme is based on the 2001 National Land Cover Database with a focus on "Developed" land-use classes and riverine "Forest" and "Wetlands" cover classes required for specific watershed models. The classification procedure included the creation of several image-derived and topographic variables, including digital elevation model derivatives, image variance, and multitemporal Kauth-Thomas transformations. The accuracy of the land-cover maps was assessed using a random-stratified sampling design, reference aerial photography, and digital imagery. This showed high accuracy results, with kappa values (the statistical measure of agreement between map and reference data) ranging from 0.80 to 0.85.

CPTAC Releases Largest-Ever Breast Cancer Proteome Dataset from Previously Genome Characterized Tumors | Office of Cancer Clinical Proteomics Research

Cancer.gov

National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) scientists have released a dataset of proteins and  phosphopeptides identified through deep proteomic and phosphoproteomic analysis of breast tumor samples, previously genomically analyzed by The Cancer Genome Atlas (TCGA).

Does using different modern climate datasets impact pollen-based paleoclimate reconstructions in North America during the past 2,000 years

NASA Astrophysics Data System (ADS)

Ladd, Matthew; Viau, Andre

2013-04-01

Paleoclimate reconstructions rely on the accuracy of modern climate datasets for calibration of fossil records under the assumption of climate normality through time, which means that the modern climate operates in a similar manner as over the past 2,000 years. In this study, we show how using different modern climate datasets have an impact on a pollen-based reconstruction of mean temperature of the warmest month (MTWA) during the past 2,000 years for North America. The modern climate datasets used to explore this research question include the: Whitmore et al., (2005) modern climate dataset; North American Regional Reanalysis (NARR); National Center For Environmental Prediction (NCEP); European Center for Medium Range Weather Forecasting (ECMWF) ERA-40 reanalysis; WorldClim, Global Historical Climate Network (GHCN) and New et al., which is derived from the CRU dataset. Results show that some caution is advised in using the reanalysis data on large-scale reconstructions. Station data appears to dampen out the variability of the reconstruction produced using station based datasets. The reanalysis or model-based datasets are not recommended for paleoclimate large-scale North American reconstructions as they appear to lack some of the dynamics observed in station datasets (CRU) which resulted in warm-biased reconstructions as compared to the station-based reconstructions. The Whitmore et al. (2005) modern climate dataset appears to be a compromise between CRU-based datasets and model-based datasets except for the ERA-40. In addition, an ultra-high resolution gridded climate dataset such as WorldClim may only be useful if the pollen calibration sites in North America have at least the same spatial precision. We reconstruct the MTWA to within +/-0.01°C by using an average of all curves derived from the different modern climate datasets, demonstrating the robustness of the procedure used. It may be that the use of an average of different modern datasets may reduce the

EEG datasets for motor imagery brain-computer interface.

PubMed

Cho, Hohyun; Ahn, Minkyu; Ahn, Sangtae; Kwon, Moonyoung; Jun, Sung Chan

2017-07-01

Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states. We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states. © The Authors 2017. Published by Oxford University Press.

15. FAN HOUSE ON TOP OF ELEVATOR SHAFT. Hot ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

15. FAN HOUSE ON TOP OF ELEVATOR SHAFT. - Hot Springs National Park, Bathhouse Row, Fordyce Bathhouse: Mechanical & Piping Systems, State Highway 7, 1 mile north of U.S. Highway 70, Hot Springs, Garland County, AR

The 3D Elevation Program: summary for New York

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for North Dakota

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios.The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

ADM. Fuel Pump House (TAN611). Elevations, floor plan. Drawing includes ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

ADM. Fuel Pump House (TAN-611). Elevations, floor plan. Drawing includes elevation and plans for "H.M." structures (Hose Storage?). Ralph M. Parsons 902-2-ANP-611-A 78 Date: December 1952. Approved by INEEL Classification Office for public release. INEEL index code no. 035-0611-00-693-106741 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

A high-resolution European dataset for hydrologic modeling

NASA Astrophysics Data System (ADS)

Ntegeka, Victor; Salamon, Peter; Gomes, Goncalo; Sint, Hadewij; Lorini, Valerio; Thielen, Jutta

2013-04-01

There is an increasing demand for large scale hydrological models not only in the field of modeling the impact of climate change on water resources but also for disaster risk assessments and flood or drought early warning systems. These large scale models need to be calibrated and verified against large amounts of observations in order to judge their capabilities to predict the future. However, the creation of large scale datasets is challenging for it requires collection, harmonization, and quality checking of large amounts of observations. For this reason, only a limited number of such datasets exist. In this work, we present a pan European, high-resolution gridded dataset of meteorological observations (EFAS-Meteo) which was designed with the aim to drive a large scale hydrological model. Similar European and global gridded datasets already exist, such as the HadGHCND (Caesar et al., 2006), the JRC MARS-STAT database (van der Goot and Orlandi, 2003) and the E-OBS gridded dataset (Haylock et al., 2008). However, none of those provide similarly high spatial resolution and/or a complete set of variables to force a hydrologic model. EFAS-Meteo contains daily maps of precipitation, surface temperature (mean, minimum and maximum), wind speed and vapour pressure at a spatial grid resolution of 5 x 5 km for the time period 1 January 1990 - 31 December 2011. It furthermore contains calculated radiation, which is calculated by using a staggered approach depending on the availability of sunshine duration, cloud cover and minimum and maximum temperature, and evapotranspiration (potential evapotranspiration, bare soil and open water evapotranspiration). The potential evapotranspiration was calculated using the Penman-Monteith equation with the above-mentioned meteorological variables. The dataset was created as part of the development of the European Flood Awareness System (EFAS) and has been continuously updated throughout the last years. The dataset variables are used as

UAS-SfM for coastal research: Geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery

USGS Publications Warehouse

Sturdivant, Emily; Lentz, Erika; Thieler, E. Robert; Farris, Amy; Weber, Kathryn; Remsen, David P.; Miner, Simon; Henderson, Rachel

2017-01-01

The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.

EAARL coastal topography-Assategue Island National Seashore, Maryland and Virginia, 2010

USGS Publications Warehouse

Bonisteel-Cormier, J.M.; Nayegandhi, Amar; Wright, C.W.; Brock, J.C.; Nagle, D.B.; Vivekanandan, Saisudha; Klipp, E.S.; Fredericks, Xan; Stevens, Sara

2011-01-01

This DVD contains lidar-derived bare-earth (BE) and first-surface (FS) topography GIS datasets of a portion of the Assateague Island National Seashore in Maryland and Virginia. These datasets were acquired on March 19 and 24, 2010.

ASSISTments Dataset from Multiple Randomized Controlled Experiments

ERIC Educational Resources Information Center

Selent, Douglas; Patikorn, Thanaporn; Heffernan, Neil

2016-01-01

In this paper, we present a dataset consisting of data generated from 22 previously and currently running randomized controlled experiments inside the ASSISTments online learning platform. This dataset provides data mining opportunities for researchers to analyze ASSISTments data in a convenient format across multiple experiments at the same time.…

Free internet datasets for streamflow modelling using SWAT in the Johor river basin, Malaysia

NASA Astrophysics Data System (ADS)

Tan, M. L.

2014-02-01

Streamflow modelling is a mathematical computational approach that represents terrestrial hydrology cycle digitally and is used for water resources assessment. However, such modelling endeavours require a large amount of data. Generally, governmental departments produce and maintain these data sets which make it difficult to obtain this data due to bureaucratic constraints. In some countries, the availability and quality of geospatial and climate datasets remain a critical issue due to many factors such as lacking of ground station, expertise, technology, financial support and war time. To overcome this problem, this research used public domain datasets from the Internet as "input" to a streamflow model. The intention is simulate daily and monthly streamflow of the Johor River Basin in Malaysia. The model used is the Soil and Water Assessment Tool (SWAT). As input free data including a digital elevation model (DEM), land use information, soil and climate data were used. The model was validated by in-situ streamflow information obtained from Rantau Panjang station for the year 2006. The coefficient of determination and Nash-Sutcliffe efficiency were 0.35/0.02 for daily simulated streamflow and 0.92/0.21 for monthly simulated streamflow, respectively. The results show that free data can provide a better simulation at a monthly scale compared to a daily basis in a tropical region. A sensitivity analysis and calibration procedure should be conducted in order to maximize the "goodness-of-fit" between simulated and observed streamflow. The application of Internet datasets promises an acceptable performance of streamflow modelling. This research demonstrates that public domain data is suitable for streamflow modelling in a tropical river basin within acceptable accuracy.

Extension of research data repository system to support direct compute access to biomedical datasets: enhancing Dataverse to support large datasets.

PubMed

McKinney, Bill; Meyer, Peter A; Crosas, Mercè; Sliz, Piotr

2017-01-01

Access to experimental X-ray diffraction image data is important for validation and reproduction of macromolecular models and indispensable for the development of structural biology processing methods. In response to the evolving needs of the structural biology community, we recently established a diffraction data publication system, the Structural Biology Data Grid (SBDG, data.sbgrid.org), to preserve primary experimental datasets supporting scientific publications. All datasets published through the SBDG are freely available to the research community under a public domain dedication license, with metadata compliant with the DataCite Schema (schema.datacite.org). A proof-of-concept study demonstrated community interest and utility. Publication of large datasets is a challenge shared by several fields, and the SBDG has begun collaborating with the Institute for Quantitative Social Science at Harvard University to extend the Dataverse (dataverse.org) open-source data repository system to structural biology datasets. Several extensions are necessary to support the size and metadata requirements for structural biology datasets. In this paper, we describe one such extension-functionality supporting preservation of file system structure within Dataverse-which is essential for both in-place computation and supporting non-HTTP data transfers. © 2016 New York Academy of Sciences.

Extension of research data repository system to support direct compute access to biomedical datasets: enhancing Dataverse to support large datasets

PubMed Central

McKinney, Bill; Meyer, Peter A.; Crosas, Mercè; Sliz, Piotr

2016-01-01

Access to experimental X-ray diffraction image data is important for validation and reproduction of macromolecular models and indispensable for the development of structural biology processing methods. In response to the evolving needs of the structural biology community, we recently established a diffraction data publication system, the Structural Biology Data Grid (SBDG, data.sbgrid.org), to preserve primary experimental datasets supporting scientific publications. All datasets published through the SBDG are freely available to the research community under a public domain dedication license, with metadata compliant with the DataCite Schema (schema.datacite.org). A proof-of-concept study demonstrated community interest and utility. Publication of large datasets is a challenge shared by several fields, and the SBDG has begun collaborating with the Institute for Quantitative Social Science at Harvard University to extend the Dataverse (dataverse.org) open-source data repository system to structural biology datasets. Several extensions are necessary to support the size and metadata requirements for structural biology datasets. In this paper, we describe one such extension—functionality supporting preservation of filesystem structure within Dataverse—which is essential for both in-place computation and supporting non-http data transfers. PMID:27862010

Estimating parameters for probabilistic linkage of privacy-preserved datasets.

PubMed

Brown, Adrian P; Randall, Sean M; Ferrante, Anna M; Semmens, James B; Boyd, James H

2017-07-10

Probabilistic record linkage is a process used to bring together person-based records from within the same dataset (de-duplication) or from disparate datasets using pairwise comparisons and matching probabilities. The linkage strategy and associated match probabilities are often estimated through investigations into data quality and manual inspection. However, as privacy-preserved datasets comprise encrypted data, such methods are not possible. In this paper, we present a method for estimating the probabilities and threshold values for probabilistic privacy-preserved record linkage using Bloom filters. Our method was tested through a simulation study using synthetic data, followed by an application using real-world administrative data. Synthetic datasets were generated with error rates from zero to 20% error. Our method was used to estimate parameters (probabilities and thresholds) for de-duplication linkages. Linkage quality was determined by F-measure. Each dataset was privacy-preserved using separate Bloom filters for each field. Match probabilities were estimated using the expectation-maximisation (EM) algorithm on the privacy-preserved data. Threshold cut-off values were determined by an extension to the EM algorithm allowing linkage quality to be estimated for each possible threshold. De-duplication linkages of each privacy-preserved dataset were performed using both estimated and calculated probabilities. Linkage quality using the F-measure at the estimated threshold values was also compared to the highest F-measure. Three large administrative datasets were used to demonstrate the applicability of the probability and threshold estimation technique on real-world data. Linkage of the synthetic datasets using the estimated probabilities produced an F-measure that was comparable to the F-measure using calculated probabilities, even with up to 20% error. Linkage of the administrative datasets using estimated probabilities produced an F-measure that was higher

Viking Seismometer PDS Archive Dataset

NASA Astrophysics Data System (ADS)

Lorenz, R. D.

2016-12-01

The Viking Lander 2 seismometer operated successfully for over 500 Sols on the Martian surface, recording at least one likely candidate Marsquake. The Viking mission, in an era when data handling hardware (both on board and on the ground) was limited in capability, predated modern planetary data archiving, and ad-hoc repositories of the data, and the very low-level record at NSSDC, were neither convenient to process nor well-known. In an effort supported by the NASA Mars Data Analysis Program, we have converted the bulk of the Viking dataset (namely the 49,000 and 270,000 records made in High- and Event- modes at 20 and 1 Hz respectively) into a simple ASCII table format. Additionally, since wind-generated lander motion is a major component of the signal, contemporaneous meteorological data are included in summary records to facilitate correlation. These datasets are being archived at the PDS Geosciences Node. In addition to brief instrument and dataset descriptions, the archive includes code snippets in the freely-available language 'R' to demonstrate plotting and analysis. Further, we present examples of lander-generated noise, associated with the sampler arm, instrument dumps and other mechanical operations.

76 FR 58409 - Changes in Flood Elevation Determinations

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-21

... changes in BFEs are in accordance with 44 CFR 65.4. National Environmental Policy Act. This interim rule is categorically excluded from the requirements of 44 CFR part 10, Environmental Consideration. An environmental impact assessment has not been prepared. Regulatory Flexibility Act. As flood elevation...

SpecTIR and SEBASS analysis of the National Mining District, Humboldt County, Nevada

NASA Astrophysics Data System (ADS)

Morken, Todd O.

The purpose of this study was to evaluate the minerals and materials that could be uniquely identified and mapped from measurements made with airborne hyperspectral SpecTIR VNIR/SWIR and SEBASS TIR sensors over areas in the National Mining District. SpecTIR Corporation and Aerospace Corporation acquired Hyperspectral measurements on June 26, 2008 using their ProSpecTIR and SEBASS sensors respectively. In addition the effects of vegetation, elevation, the atmosphere on spectral measurements were evaluated to determine their impact upon the data analysis and target identification. The National Mining District is located approximately 75 miles northeast of Winnemucca, Nevada at the northern end of the Santa Rosa Mountains. Precious metal mining has been dormant in this area since the 1940's, however with increased metal prices over the last decade economic interest in the region has increased substantially. Buckskin Mountain has a preserved alteration assemblage that is exposed in topographically steep terrain, ideal for exploring what hydrothermal alteration products can be identified and mapped in these datasets. These Visible Near Infrared (VNIR), Short Wave Infrared (SWIR), and Long Wave Infrared (LWIR) hyperspectral datasets were used to identify and map kaolinite, alunite, quartz, opal, and illite/muscovite, all of which are useful exploration target identifiers and can indicate regions of alteration. These mapping results were then combined with and compared to other geospatial data in a geographic information systems (GIS) database. The TIR hyperspectral data provided significant additional information that can benefit geologic exploration and demonstrated its usefulness as an additional tool for geological exploration.

A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces.

PubMed

Wang, Yijun; Chen, Xiaogang; Gao, Xiaorong; Gao, Shangkai

2017-10-01

This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset acquired with a 40-target brain- computer interface (BCI) speller. The dataset consists of 64-channel Electroencephalogram (EEG) data from 35 healthy subjects (8 experienced and 27 naïve) while they performed a cue-guided target selecting task. The virtual keyboard of the speller was composed of 40 visual flickers, which were coded using a joint frequency and phase modulation (JFPM) approach. The stimulation frequencies ranged from 8 Hz to 15.8 Hz with an interval of 0.2 Hz. The phase difference between two adjacent frequencies was . For each subject, the data included six blocks of 40 trials corresponding to all 40 flickers indicated by a visual cue in a random order. The stimulation duration in each trial was five seconds. The dataset can be used as a benchmark dataset to compare the methods for stimulus coding and target identification in SSVEP-based BCIs. Through offline simulation, the dataset can be used to design new system diagrams and evaluate their BCI performance without collecting any new data. The dataset also provides high-quality data for computational modeling of SSVEPs. The dataset is freely available fromhttp://bci.med.tsinghua.edu.cn/download.html.

Dataset-Driven Research to Support Learning and Knowledge Analytics

ERIC Educational Resources Information Center

Verbert, Katrien; Manouselis, Nikos; Drachsler, Hendrik; Duval, Erik

2012-01-01

In various research areas, the availability of open datasets is considered as key for research and application purposes. These datasets are used as benchmarks to develop new algorithms and to compare them to other algorithms in given settings. Finding such available datasets for experimentation can be a challenging task in technology enhanced…

5. SHAFT HOUSE, EAST ELEVATION; VIEW TO THE NORTHWEST; (WASTE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

5. SHAFT HOUSE, EAST ELEVATION; VIEW TO THE NORTHWEST; (WASTE ROCK/SPOIL PILE IN THE FOREGROUND). - Joker Mine, Shafthouse, Medicine Bow National Forest, Northwest of Keystone, Keystone, Albany County, WY

1. SHAFT HOUSE, NORTH ELEVATION; (HEADFRAME INCORPORATED INTO THE STRUCTURE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

1. SHAFT HOUSE, NORTH ELEVATION; (HEADFRAME INCORPORATED INTO THE STRUCTURE IS VISIBLE AS THE PROJECTING SUPERSTRUCTURE). - Joker Mine, Shafthouse, Medicine Bow National Forest, Northwest of Keystone, Keystone, Albany County, WY

Development and validation of a national data registry for midwife-led births: the Midwives Alliance of North America Statistics Project 2.0 dataset.

PubMed

Cheyney, Melissa; Bovbjerg, Marit; Everson, Courtney; Gordon, Wendy; Hannibal, Darcy; Vedam, Saraswathi

2014-01-01

In 2004, the Midwives Alliance of North America's (MANA's) Division of Research developed a Web-based data collection system to gather information on the practices and outcomes associated with midwife-led births in the United States. This system, called the MANA Statistics Project (MANA Stats), grew out of a widely acknowledged need for more reliable data on outcomes by intended place of birth. This article describes the history and development of the MANA Stats birth registry and provides an analysis of the 2.0 dataset's content, strengths, and limitations. Data collection and review procedures for the MANA Stats 2.0 dataset are described, along with methods for the assessment of data accuracy. We calculated descriptive statistics for client demographics and contributing midwife credentials, and assessed the quality of data by calculating point estimates, 95% confidence intervals, and kappa statistics for key outcomes on pre- and postreview samples of records. The MANA Stats 2.0 dataset (2004-2009) contains 24,848 courses of care, 20,893 of which are for women who planned a home or birth center birth at the onset of labor. The majority of these records were planned home births (81%). Births were attended primarily by certified professional midwives (73%), and clients were largely white (92%), married (87%), and college-educated (49%). Data quality analyses of 9932 records revealed no differences between pre- and postreviewed samples for 7 key benchmarking variables (kappa, 0.98-1.00). The MANA Stats 2.0 data were accurately entered by participants; any errors in this dataset are likely random and not systematic. The primary limitation of the 2.0 dataset is that the sample was captured through voluntary participation; thus, it may not accurately reflect population-based outcomes. The dataset's primary strength is that it will allow for the examination of research questions on normal physiologic birth and midwife-led birth outcomes by intended place of birth. �

Data layer integration for the national map of the united states

USGS Publications Warehouse

Usery, E.L.; Finn, M.P.; Starbuck, M.

2009-01-01

The integration of geographic data layers in multiple raster and vector formats, from many different organizations and at a variety of resolutions and scales, is a significant problem for The National Map of the United States being developed by the U.S. Geological Survey. Our research has examined data integration from a layer-based approach for five of The National Map data layers: digital orthoimages, elevation, land cover, hydrography, and transportation. An empirical approach has included visual assessment by a set of respondents with statistical analysis to establish the meaning of various types of integration. A separate theoretical approach with established hypotheses tested against actual data sets has resulted in an automated procedure for integration of specific layers and is being tested. The empirical analysis has established resolution bounds on meanings of integration with raster datasets and distance bounds for vector data. The theoretical approach has used a combination of theories on cartographic transformation and generalization, such as T??pfer's radical law, and additional research concerning optimum viewing scales for digital images to establish a set of guiding principles for integrating data of different resolutions.

Method of generating features optimal to a dataset and classifier

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

Bruillard, Paul J.; Gosink, Luke J.; Jarman, Kenneth D.

A method of generating features optimal to a particular dataset and classifier is disclosed. A dataset of messages is inputted and a classifier is selected. An algebra of features is encoded. Computable features that are capable of describing the dataset from the algebra of features are selected. Irredundant features that are optimal for the classifier and the dataset are selected.

Building a high resolution national elevation model from SRTM: The Australian experience

NASA Astrophysics Data System (ADS)

Gallant, J. C.; Read, A.; Dowling, T. I.

2011-12-01

The global SRTM DEM is a valuable global data set that, for many countries including Australia, provides the best basis for a fine resolution national DEM. But the SRTM data suffers from a variety of artefacts and errors that prevent its routine application with familiar terrain analysis tools. The most important of these are stripes, voids, random noise and offsets due to trees. The tree offsets are particularly disruptive in riparian areas where they make rivers appear as ridge lines. This paper describes how a suite of tools was applied to the 1 second SRTM data for Australia to treat each of these artefacts. An FFT-based tool was developed to detect and remove regular striping. Voids were filled using a modification of the delta surface fill method. Offsets due to trees were modelled and removed using a vegetation mask derived from remotely sensed imagery and a statistical estimate of the offset at vegetation patch boundaries. Random noise was removed using an adaptive smoothing method that responds to variations in both local relief and noise magnitude. Finally, mapped channel networks were imposed using a modified version of the ANUDEM software to enforce hydrological connectivity. The resulting products are being distributed by Geoscience Australia and the smoothed and drainage enforced products in particular are suitable for use in routine terrain analysis tasks. With some adaptation, the same processes could be applied to the global SRTM to derive a product that, in combination with an improved ASTER G-DEM, would provide a high quality comprehensive global elevation model suitable for most purposes.

Querying Patterns in High-Dimensional Heterogenous Datasets

ERIC Educational Resources Information Center

Singh, Vishwakarma

2012-01-01

The recent technological advancements have led to the availability of a plethora of heterogenous datasets, e.g., images tagged with geo-location and descriptive keywords. An object in these datasets is described by a set of high-dimensional feature vectors. For example, a keyword-tagged image is represented by a color-histogram and a…

Development of an internationally agreed minimal dataset for juvenile dermatomyositis (JDM) for clinical and research use.

PubMed

McCann, Liza J; Kirkham, Jamie J; Wedderburn, Lucy R; Pilkington, Clarissa; Huber, Adam M; Ravelli, Angelo; Appelbe, Duncan; Williamson, Paula R; Beresford, Michael W

2015-06-12

Juvenile dermatomyositis (JDM) is a rare autoimmune inflammatory disorder associated with significant morbidity and mortality. International collaboration is necessary to better understand the pathogenesis of the disease, response to treatment and long-term outcome. To aid international collaboration, it is essential to have a core set of data that all researchers and clinicians collect in a standardised way for clinical purposes and for research. This should include demographic details, diagnostic data and measures of disease activity, investigations and treatment. Variables in existing clinical registries have been compared to produce a provisional data set for JDM. We now aim to develop this into a consensus-approved minimum core dataset, tested in a wider setting, with the objective of achieving international agreement. A two-stage bespoke Delphi-process will engage the opinion of a large number of key stakeholders through Email distribution via established international paediatric rheumatology and myositis organisations. This, together with a formalised patient/parent participation process will help inform a consensus meeting of international experts that will utilise a nominal group technique (NGT). The resulting proposed minimal dataset will be tested for feasibility within existing database infrastructures. The developed minimal dataset will be sent to all internationally representative collaborators for final comment. The participants of the expert consensus group will be asked to draw together these comments, ratify and 'sign off' the final minimal dataset. An internationally agreed minimal dataset has the potential to significantly enhance collaboration, allow effective communication between groups, provide a minimal standard of care and enable analysis of the largest possible number of JDM patients to provide a greater understanding of this disease. The final approved minimum core dataset could be rapidly incorporated into national and international

Detail view of east northeast elevation to show steps and ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Detail view of east northeast elevation to show steps and lanterns; note causeway to Swiss Chalet in background - National Park Seminary, Japanese Pagoda, 2805 Linden Lane, Silver Spring, Montgomery County, MD

A global digital elevation model - GTOP030

USGS Publications Warehouse