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In this data release, the eight videos were reanalyzed using IVyTools, which applies Space-Time Image Velocimetry (STIV) techniques to estimate surface water velocities and compute discharge.\nThe release includes: (1) per-site discharge station tables summarizing the midsection discharge computation at each measurement station; (2) per-site STIV velocity results tables containing the pixel-level velocity vectors computed by the STIV algorithm; (3) per-site STI (Space-Time Image) review tables documenting the STIV streak angles, auto-detected and manually corrected velocity magnitudes, and operator comments for each grid node; (4) summary comparison tables presenting IVyTools discharge estimates alongside VGC2020 reference values and uncertainty metrics (corresponding to Tables 2 and 3 in Engel and others, 2026); and (5) uncertainty contribution analysis data indicating the fractional contribution of each uncertainty component to total discharge variance for all eight sites (corresponding to Figure 6 in Engel and others, 2026).\nThis dataset supports reproducibility and transparency in image-based streamflow measurement and contributes to ongoing efforts to evaluate operator and software effects in video-based hydrometry. Data are organized into child items based on data type and purpose:\n&lt;ul&gt;\n&lt;li&gt;&lt;strong&gt;Ancillary Scripts:&lt;/strong&gt; Python scripts used to extract uncertainty data from IVyTools project files and to generate the uncertainty contribution figure.&lt;/li&gt;\n&lt;li&gt;&lt;strong&gt;IVyTools Results:&lt;/strong&gt; Per-site discharge station tables, STIV velocity results, and STI review tables extracted from IVyTools project files for each of the eight VGC2020 videos.&lt;/li&gt;\n&lt;li&gt;&lt;strong&gt;Summary Tables:&lt;/strong&gt; Tabular summaries comparing IVyTools discharge and uncertainty results to VGC2020 reference values.&lt;/li&gt;\n&lt;li&gt;&lt;strong&gt;Uncertainty Analysis:&lt;/strong&gt; Extracted uncertainty component data.&lt;/li&gt;\n&lt;/ul&gt;\nEach site in the VGC2020 dataset is identified by the following abbreviations used throughout this data release:\n&lt;ul&gt;\n&lt;li&gt;GDH1: Video Globe Challenge 2020, Video 1 (Vence, France)&lt;/li&gt;\n&lt;li&gt;GDH2: Video Globe Challenge 2020, Video 2 (Roxton Falls, Canada)&lt;/li&gt;\n&lt;li&gt;GDH3: Video Globe Challenge 2020, Video 3 (Tana, Norway)&lt;/li&gt;\n&lt;li&gt;GDH4: Video Globe Challenge 2020, Video 4 (\u00c5seral, Norway)&lt;/li&gt;\n&lt;li&gt;GDH5: Video Globe Challenge 2020, Video 5 (Le Gier, France)&lt;/li&gt;\n&lt;li&gt;GDH6: Video Globe Challenge 2020, Video 6 (Tana, Norway)&lt;/li&gt;\n&lt;li&gt;GDH7: Video Globe Challenge 2020, Video 7 (Brisbane, Australia)&lt;/li&gt;\n&lt;li&gt;GDH8: Video Globe Challenge 2020, Video 8 (Vence, France)&lt;/li&gt;\n&lt;/ul&gt;","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13RF37Q","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.689ca156d4be02580cd2759a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_689ca156d4be02580cd2759a","keyword":["Australia","Canada","France","IVyTools","Norway","STIV","USGS:689ca156d4be02580cd2759a","VGC2020","Video Globe Challenge 2020","discharge","discharge uncertainty","environment","image velocimetry","inlandWaters","remote sensing","space-time image velocimetry","streamflow","streamflow measurement","surface water (non-coverage type)","video monitoring"],"modified":"2026-08-19T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-180.0000, -90.0000, 180.0000, 90.0000","theme":["geospatial"],"title":"IVyTools discharge and uncertainty results for eight benchmark image velocimetry videos"},"description":"This data release contains image velocimetry results derived from the publicly available Video Globe Challenge 2020 (VGC2020) dataset, processed using the U.S. Geological Survey's IVyTools software application (version 1.4.0.0). 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The water surface elevations therein can be used to describe historical environmental conditions, contextualize contemporary conditions, project future conditions, conduct scientific research on aquatic and floodplain organisms and processes, assess existing and future without-project conditions as required for Upper Mississippi River Restoration Program restoration project, and many other applications. 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The water surface elevations therein can be used to describe historical environmental conditions, contextualize contemporary conditions, project future conditions, conduct scientific research on aquatic and floodplain organisms and processes, assess existing and future without-project conditions as required for Upper Mississippi River Restoration Program restoration project, and many other applications. 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The surface was generated from points collected by \nthe echosounder and real-time kinematic (RTK) GPS on cross-sections in the downstream project reach \nsurrounding the construction area at River Mile 770 below Gavins Point Dam on the Missouri River in \nSouth Dakota before and after construction of the sandbar.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ofr07-1056_up_diff_tin","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.bab471f5-254a-4619-a308-17af1ab39dd0.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_bab471f5-254a-4619-a308-17af1ab39dd0","keyword":["Hydrographic Survey","USGS:bab471f5-254a-4619-a308-17af1ab39dd0","environment","geoscientificInformation","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-96.876334, 42.712792, -96.851104, 42.732505","theme":["geospatial"],"title":"Difference betweem postconstruction and preconstruction land surface elevation tins on the Missouri River Downstream from Gavins Point Dam near River Mile 769.8."},"description":"This geospatial data set contains an interpolated 3-D surface or, triangulated-irregular network (TIN), of \nthe change in elevation, in feet, of the substrate between cross-sections 22 and 35 following construction \nof Emergent Sandbar Habitat near River Mile 770.  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The attributes for each polygon shapefile describe the characteristics of all source datasets used to generate the topobathymetric dataset.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/4d9d021d-2a49-42a2-ba26-43c181706cd4","harvest_record_raw":"https://catalog.data.gov/harvest_record/4d9d021d-2a49-42a2-ba26-43c181706cd4/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a15bbc7b66b012f9f081d89","keyword":["3D Elevation Program","3DEP","Acoustic Sonar","Antelope Island State Park","Ash Meadows National Wildlife Refuge","Bear River Migratory Bird Refuge","Box Elder County","Burns Paiute Indian Colony","California","Carson Lake","Carson Lake Pasture","Carson Sink","Churchill County","DEM","David County","Eagle Lake","Fallon National Wildlife Refuge","Fallon Paiute-Shoshone Reservation","Fish Springs National Wildlife Refuge","Flood Inundation Modeling","Franklin Lake","Fremont\u2013Winema National Forest","Goose Lake","Harney County","Harney Lake","Honey Lake","Honey Lake Wildlife Area","Idaho","Inland Bathymetry","Inyo National Forest","Lake Abert","Lake County","Lassen County","Lassen National Forest","Light Detection and Ranging","Lower Chewaucan Marsh","Malheur Lake","Malheur National Wildlife Refuge","Millard County","Mineral County","Modoc National Forest","Mono County","Mono Lake","Mono Lake Tufa State Natural Reserve","Mud Lake","Nevada","Oregon","Pershing County","Pyramid Lake","Pyramid Lake Paiute Reservation","Reservoir Storage Capacity","Ruby Lake","Ruby Lake National Wildlife Refuge","SLEIWAAs","Saline Lakes Ecosystems Integrated Water Availability Assessment","Salt Lake County","Sevier Lake","Silver Lake","Stillwater National Wildlife Refuge","Summer Lake","Summer Lake Wildlife Area","Susanville Indian Rancheria","TBDEM","Tooele County","U.S. Geological Survey","USGS","USGS:6a15bbc7b66b012f9f081d89","Upper Chewaucan Marsh","Utah","Utah Water Science Center","Walker Lake","Walker River Reservation","Washoe County","Weber County","Winnemucca Lake","Wyoming","XL Ranch Rancheria","aquatic ecosystems","bathymetry","benthic ecosystems","biota","birds","climatologyMeteorologyAtmosphere","digital elevation models","dissolved solids","earth sciences","economy","ecosystem management","ecosystem monitoring","elevation","environment","environmental assessment","geography","geomorphology","geoscientificInformation","geospatial analysis","geospatial datasets","habitat distribution","hydrology","inlandWaters","lake elevation","lidar","limnology","natural resource assessment","salinity","salt budget","salt cycling","shorebird habitat","society","storage capacity","surface area","surface-water level","topobathymetric digital elevation model","topobathymetry","topography","volume","water budget","water depth","water quality","water resource management","water surface elevation","water use","watershed management","wetland ecosystems"],"last_harvested_date":"2026-08-20T00:48:13.485733","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"popularity":0,"publisher":"U.S. Geological Survey","slug":"spatial-metadata-in-topobathymetric-elevation-models-and-elevation-area-volume-relationshi","spatial_centroid":{"lat":39.148219999999995,"lon":-117.33627999999999},"spatial_shape":{"coordinates":[[[-120.966,36.2283],[-120.966,43.5281],[-111.8917,43.5281],[-111.8917,36.2283],[-120.966,36.2283]]],"type":"Polygon"},"theme":["geospatial"],"title":"Spatial Metadata, in Topobathymetric Elevation Models and Elevation-Area-Volume Relationships for Selected Lakes in Closed Basins of the Great Basin States","type":"dataset"},{"_score":6.6944494,"_sort":[1787186541991,6.6944494,5,"ecff340e-3458-4575-be26-e3f864fadaec"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"U.S. Geological Survey, Alaska Science Center","hasEmail":"mailto:gs-ak_asc_datamanagers@usgs.gov"},"description":"This dataset contains fatty acid (FA) data expressed as mass percent of total FA for bearded seals, ringed seals and walrus. This is one of many datasets used in Bromaghin et al. 2016 (https://doi.org/10.1111/2041-210X.12456). These supplemental data were used in computer simulations to compare the bias of several quantitative fatty acid signature analysis (QFASA) estimators and develop recommendations regarding estimator selection.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7PR7T2W","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.ASC31.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_ASC31","keyword":["Alaska","Animals/Vertebrates","Arctic","Bearded seal","Bears","Biological informatics","Biota","Carnivores","Chukchi Sea","Coastal ecosystems","Diet composition","Diet estimation","Diets","Environment","Erignathus barbatus","Fatty Acids","Mammals","Marine ecosystems","Marine mammals","Pelagic habitat","Pinniped","Polar bear","Predator-prey","Pusa hispida","QFASA","Quantitative fatty acid signature analysis","Ringed seal","Seals/Sea lions/Walruses","USGS:ASC31","Ursus maritimus","Wildlife"],"modified":"2024-11-30T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-173.0, 65.0, -155.0, 72.0","theme":["geospatial"],"title":"Assessing the Robustness of Quantitative Fatty Acid Signature Analysis to Assumption Violations (Supplementary Data)"},"description":"This dataset contains fatty acid (FA) data expressed as mass percent of total FA for bearded seals, ringed seals and walrus. This is one of many datasets used in Bromaghin et al. 2016 (https://doi.org/10.1111/2041-210X.12456). These supplemental data were used in computer simulations to compare the bias of several quantitative fatty acid signature analysis (QFASA) estimators and develop recommendations regarding estimator selection.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/23e130f3-54b4-457c-a618-2e1eed20bbb2","harvest_record_raw":"https://catalog.data.gov/harvest_record/23e130f3-54b4-457c-a618-2e1eed20bbb2/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_ASC31","keyword":["Alaska","Animals/Vertebrates","Arctic","Bearded seal","Bears","Biological informatics","Biota","Carnivores","Chukchi Sea","Coastal ecosystems","Diet composition","Diet estimation","Diets","Environment","Erignathus barbatus","Fatty Acids","Mammals","Marine ecosystems","Marine mammals","Pelagic habitat","Pinniped","Polar bear","Predator-prey","Pusa hispida","QFASA","Quantitative fatty acid signature analysis","Ringed seal","Seals/Sea lions/Walruses","USGS:ASC31","Ursus maritimus","Wildlife"],"last_harvested_date":"2026-08-20T00:42:21.991995","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"popularity":5,"publisher":"U.S. Geological Survey","slug":"assessing-the-robustness-of-quantitative-fatty-acid-signature-analysis-to-assumption-viola","spatial_centroid":{"lat":67.8,"lon":-165.8},"spatial_shape":{"coordinates":[[[-173.0,65.0],[-173.0,72.0],[-155.0,72.0],[-155.0,65.0],[-173.0,65.0]]],"type":"Polygon"},"theme":["geospatial"],"title":"Assessing the Robustness of Quantitative Fatty Acid Signature Analysis to Assumption Violations (Supplementary Data)","type":"dataset"},{"_score":6.883606,"_sort":[1787186382075,6.883606,0,"954c6e47-a280-4b9c-8818-314c467c197b"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Marc Pons","hasEmail":"mailto:info@ceg.group"},"description":"This single-band float32 GeoTIFF raster represents a spatially smoothed vulnerability\nindex for sagebrush (Artemisia spp.) ecosystems at the northern extent of the sagebrush\nbiome, spanning the US\u2013Canada border region from north-central Montana into\nsoutheastern Alberta and southwestern Saskatchewan, with smaller portions of Wyoming,\nNorth Dakota, and South Dakota (approximately 46.0\u00b0N to 51.1\u00b0N latitude, 102.0\u00b0W to\n113.3\u00b0W longitude). The area falls primarily within the Northwestern Glaciated Plains\nand Northwestern Great Plains ecoregions (EPA Level III / CEC ecoregion classification),\nwith its western edge reaching the Rocky Mountain Front and Canadian Rockies foothills.\nThis region is the northernmost range of the Greater Sage-Grouse (Centrocercus\nurophasianus), centered on the Milk River and Frenchman River basins, and is sometimes\nreferred to informally as the greater northern sagebrush biome. Vulnerability values\nare continuous and range from 1 (lowest vulnerability) to 8 (highest vulnerability),\nreflecting a composite assessment of exposure, sensitivity, and adaptive capacity of\nsagebrush ecosystems to stressors including climate change, invasive species (e.g.,\ncheatgrass, Bromus tectorum), wildfire, and land conversion. A focal mean filter with\na window size of 10 pixels was applied to the source vulnerability raster. At the\nnative 100 m (nominal, projected-CRS) cell resolution, this window corresponds to a\nnominal 1,000 m ground footprint \u2014 see Positional Accuracy below for a caveat on true\nground distance under this projection. This smoothing reduces local pixel-level noise\nand emphasizes landscape-scale vulnerability patterns. The raster is projected in\nWGS 84 / Pseudo-Mercator (EPSG:3857) \u2014 selected for compatibility with ArcGIS Online\npublishing \u2014 with a datum of WGS84 (not NAD83). Cells with no data are assigned IEEE 754\nNaN (float32).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P14KPFAL","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6a3ece3e1ba49b7c2e2634db.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a3ece3e1ba49b7c2e2634db","keyword":["Alberta","Artemisia","EPSG:3857","GeoTIFF","Greater Sage-Grouse","Montana","North Dakota","Northern Great Plains","Northern Rocky Mountains","Northwestern Glaciated Plains","Northwestern Great Plains","Saskatchewan","South Dakota","USGS:6a3ece3e1ba49b7c2e2634db","WGS84","Wyoming","biota","cheatgrass","climate change","conservation planning","environment","focal mean smoothing","geoscientificInformation","invasive species","raster","sagebrush","sagebrush steppe","shrubland","vulnerability index","wildfire"],"modified":"2026-08-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-113.3097, 45.9977, -101.9756, 51.1180","theme":["geospatial"],"title":"Greater Northern Sagebrush Vulnerability Map"},"description":"This single-band float32 GeoTIFF raster represents a spatially smoothed vulnerability\nindex for sagebrush (Artemisia spp.) ecosystems at the northern extent of the sagebrush\nbiome, spanning the US\u2013Canada border region from north-central Montana into\nsoutheastern Alberta and southwestern Saskatchewan, with smaller portions of Wyoming,\nNorth Dakota, and South Dakota (approximately 46.0\u00b0N to 51.1\u00b0N latitude, 102.0\u00b0W to\n113.3\u00b0W longitude). The area falls primarily within the Northwestern Glaciated Plains\nand Northwestern Great Plains ecoregions (EPA Level III / CEC ecoregion classification),\nwith its western edge reaching the Rocky Mountain Front and Canadian Rockies foothills.\nThis region is the northernmost range of the Greater Sage-Grouse (Centrocercus\nurophasianus), centered on the Milk River and Frenchman River basins, and is sometimes\nreferred to informally as the greater northern sagebrush biome. Vulnerability values\nare continuous and range from 1 (lowest vulnerability) to 8 (highest vulnerability),\nreflecting a composite assessment of exposure, sensitivity, and adaptive capacity of\nsagebrush ecosystems to stressors including climate change, invasive species (e.g.,\ncheatgrass, Bromus tectorum), wildfire, and land conversion. A focal mean filter with\na window size of 10 pixels was applied to the source vulnerability raster. At the\nnative 100 m (nominal, projected-CRS) cell resolution, this window corresponds to a\nnominal 1,000 m ground footprint \u2014 see Positional Accuracy below for a caveat on true\nground distance under this projection. This smoothing reduces local pixel-level noise\nand emphasizes landscape-scale vulnerability patterns. The raster is projected in\nWGS 84 / Pseudo-Mercator (EPSG:3857) \u2014 selected for compatibility with ArcGIS Online\npublishing \u2014 with a datum of WGS84 (not NAD83). Cells with no data are assigned IEEE 754\nNaN (float32).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/860d182f-b7a2-4d91-9094-ed84f52e0c83","harvest_record_raw":"https://catalog.data.gov/harvest_record/860d182f-b7a2-4d91-9094-ed84f52e0c83/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a3ece3e1ba49b7c2e2634db","keyword":["Alberta","Artemisia","EPSG:3857","GeoTIFF","Greater Sage-Grouse","Montana","North Dakota","Northern Great Plains","Northern Rocky Mountains","Northwestern Glaciated Plains","Northwestern Great Plains","Saskatchewan","South Dakota","USGS:6a3ece3e1ba49b7c2e2634db","WGS84","Wyoming","biota","cheatgrass","climate change","conservation planning","environment","focal mean smoothing","geoscientificInformation","invasive species","raster","sagebrush","sagebrush steppe","shrubland","vulnerability index","wildfire"],"last_harvested_date":"2026-08-20T00:39:42.075012","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"popularity":0,"publisher":"U.S. Geological Survey","slug":"greater-northern-sagebrush-vulnerability-map","spatial_centroid":{"lat":48.045820000000006,"lon":-108.77606},"spatial_shape":{"coordinates":[[[-113.3097,45.9977],[-113.3097,51.118],[-101.9756,51.118],[-101.9756,45.9977],[-113.3097,45.9977]]],"type":"Polygon"},"theme":["geospatial"],"title":"Greater Northern Sagebrush Vulnerability Map","type":"dataset"},{"_score":2.3915186,"_sort":[1787186351761,2.3915186,0,"5a57a7f7-45a1-48bf-b782-053b9679c038"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jonathan Casey Root","hasEmail":"mailto:jroot@usgs.gov"},"description":"Terminal and saline lakes across the western United States serve as critical hydrologic, ecologic, and geomorphic resources. This data release provides assimilated topobathymetric elevation models and elevation area volume (EAV) relationships for selected lakes within these terminal basins. The datasets integrate the best available topographic lidar and bathymetric information, including recent high resolution lidar digital elevation models (DEMs) and historical or modern bathymetric surveys, to produce continuous elevation surfaces for each lake. Bathymetric sources include interpolated DEMs derived from historical contour maps as well as more recent echosounder based or lidar supported lakebed surveys. All elevation data were transformed to the North American Vertical Datum of 1988 (NAVD88) and merged at the highest available spatial resolution for each lake domain.\nTopobathymetric rasters were generated in ArcGIS Pro (v. 3.5.5) using consistent horizontal projections within the Universal Transverse Mercator system and processed to ensure seamless topographic transitions between dry and submerged surfaces. In cases where bathymetric coverage did not overlap with lidar, elevation gaps were interpolated using hydrologically consistent void filling models to create continuous topobathymetry. Elevation area volume relationships were computed at 0.1 meter intervals across each modeled lake using the ESRI Storage Capacity tool, with hydrologically conditioned processing for lakes in which natural or manmade barriers form multiple basins that connect only at specific elevations. The uppermost elevation in each EAV table is equal to or above the highest recorded water-surface elevation observed in historical records. These EAV curves provide a quantitative basis for hydrologic modeling, water budget analyses, and ecological assessment within each closed basin.\nThis data release delivers standardized, high\u2011quality elevation datasets and EAV metrics for lakes including Eagle Lake, Goose Lake, Honey Lake, and Mono Lake in California, Carson Lake, Carson Sink, Franklin Lake, Pyramid Lake, Ruby Lake, Winnemucca Lake, and Walker Lake in Nevada, Lake Abert, Harney Lake, Malheur Lake, Mud Lake, Silver Lake, and Summer Lake in Oregon, and Sevier Lake in Utah. Together, these products support improved understanding of lake dynamics, ecosystem management, and hydrogeomorphic change across terminal lake systems of the western United States.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P147WRTT","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6a107437b66b01c1459c95e0.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a107437b66b01c1459c95e0","keyword":["3D Elevation Program","3DEP","Acoustic Sonar","Antelope Island State Park","Ash Meadows National Wildlife Refuge","Bear River Migratory Bird Refuge","Box Elder County","Burns Paiute Indian Colony","California","Carson Lake","Carson Lake Pasture","Carson Sink","Churchill County","DEM","David County","Eagle Lake","Fallon National Wildlife Refuge","Fallon Paiute-Shoshone Reservation","Fish Springs National Wildlife Refuge","Flood Inundation Modeling","Franklin Lake","Fremont\u2013Winema National Forest","Goose Lake","Harney County","Harney Lake","Honey Lake","Honey Lake Wildlife Area","Idaho","Inland Bathymetry","Inyo National Forest","Lake Abert","Lake County","Lassen County","Lassen National Forest","Light Detection and Ranging","Lower Chewaucan Marsh","Malheur Lake","Malheur National Wildlife Refuge","Millard County","Mineral County","Modoc National Forest","Mono County","Mono Lake","Mono Lake Tufa State Natural Reserve","Mud Lake","Nevada","Oregon","Pershing County","Pyramid Lake","Pyramid Lake Paiute Reservation","Reservoir Storage Capacity","Ruby Lake","Ruby Lake National Wildlife Refuge","SLEIWAAs","Saline Lakes Ecosystems Integrated Water Availability Assessment","Salt Lake County","Sevier Lake","Silver Lake","Stillwater National Wildlife Refuge","Summer Lake","Summer Lake Wildlife Area","Susanville Indian Rancheria","TBDEM","Tooele County","U.S. Geological Survey","USGS","USGS:6a107437b66b01c1459c95e0","Upper Chewaucan Marsh","Utah","Utah Water Science Center","Walker Lake","Walker River Reservation","Washoe County","Weber County","Winnemucca Lake","Wyoming","XL Ranch Rancheria","aquatic ecosystems","bathymetry","benthic ecosystems","biota","birds","climatologyMeteorologyAtmosphere","digital elevation models","dissolved solids","earth sciences","economy","ecosystem management","ecosystem monitoring","elevation","environment","environmental assessment","geography","geomorphology","geoscientificInformation","geospatial analysis","geospatial datasets","habitat distribution","hydrology","inlandWaters","lake elevation","lidar","limnology","natural resource assessment","salinity","salt budget","salt cycling","shorebird habitat","society","storage capacity","surface area","surface-water level","topobathymetric digital elevation model","topobathymetry","topography","volume","water budget","water depth","water quality","water resource management","water surface elevation","water use","watershed management","wetland ecosystems"],"modified":"2026-08-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-120.9660, 36.2283, -111.8917, 43.5281","theme":["geospatial"],"title":"Topobathymetric Elevation Models and Elevation-Area-Volume Relationships for Selected Lakes in Closed Basins of the Great Basin States"},"description":"Terminal and saline lakes across the western United States serve as critical hydrologic, ecologic, and geomorphic resources. This data release provides assimilated topobathymetric elevation models and elevation area volume (EAV) relationships for selected lakes within these terminal basins. The datasets integrate the best available topographic lidar and bathymetric information, including recent high resolution lidar digital elevation models (DEMs) and historical or modern bathymetric surveys, to produce continuous elevation surfaces for each lake. Bathymetric sources include interpolated DEMs derived from historical contour maps as well as more recent echosounder based or lidar supported lakebed surveys. All elevation data were transformed to the North American Vertical Datum of 1988 (NAVD88) and merged at the highest available spatial resolution for each lake domain.\nTopobathymetric rasters were generated in ArcGIS Pro (v. 3.5.5) using consistent horizontal projections within the Universal Transverse Mercator system and processed to ensure seamless topographic transitions between dry and submerged surfaces. In cases where bathymetric coverage did not overlap with lidar, elevation gaps were interpolated using hydrologically consistent void filling models to create continuous topobathymetry. Elevation area volume relationships were computed at 0.1 meter intervals across each modeled lake using the ESRI Storage Capacity tool, with hydrologically conditioned processing for lakes in which natural or manmade barriers form multiple basins that connect only at specific elevations. The uppermost elevation in each EAV table is equal to or above the highest recorded water-surface elevation observed in historical records. These EAV curves provide a quantitative basis for hydrologic modeling, water budget analyses, and ecological assessment within each closed basin.\nThis data release delivers standardized, high\u2011quality elevation datasets and EAV metrics for lakes including Eagle Lake, Goose Lake, Honey Lake, and Mono Lake in California, Carson Lake, Carson Sink, Franklin Lake, Pyramid Lake, Ruby Lake, Winnemucca Lake, and Walker Lake in Nevada, Lake Abert, Harney Lake, Malheur Lake, Mud Lake, Silver Lake, and Summer Lake in Oregon, and Sevier Lake in Utah. 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Results from these analyses demonstrated that, in many instances, drinking water is an exposure pathway for multiple contaminants of concern (exposures greater than the federal and state drinking water health-based guidelines) in private, public, and bottled water sources. In addition to targeted chemical analyses, in vitro bioactivity analyses were performed to help characterize the potential biological effects from multiple contaminants.\nFrom 2016-2020, 265 water-quality samples, including 21 quality-assurance field blanks, which had previously been extracted from 1-liter samples were sent to Attagene, Inc., Morrisville, North Carolina for in vitro bioactivity screening. These extracts were analyzed for 48 biological endpoints using the cis-factorial assay described in Romanov and others (2008). Detailed method information and further analysis can be found in the associated report Bradley and others (2026).\nReferences--\nBradley, P.M., Romanok, K.M., Smalling, K.L., Gordon, S.E., Huffman, B.J., Friedman, K.P., Villeneuve, D.L., Blackwell, B.R., Fitzpatrick, S.C., Focazio, M.J., Medlock-Kakaley, E., Meppelink, S.M., Navas-Acien, A., Nigra, A.E., and Schreiner, M.L., 2025, Private, public, and bottled drinking water: Shared contaminant-mixture exposures and effects challenge: Environmental International, v. 195, 18 p., accessed on April 29, 2020 2026, at https://doi.org/10.1016/j.envint.2024.109220.\nRomanov, S., Medvedev, A., Gambarian, M., Poltoratskaya, N., Moeser, M., Medvedeva, L., Gambarian, M., Diatchenko, L., and Makarov, S., 2008, Homogeneous reporter system enables quantitative functional assessment of multiple transcription factors: Nature Methods, v. 5, p. 253-60, accessed on April 28, 2026 at https://doi.org/10.1038/nmeth.1186.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13HST8C","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.69f2398cb66b010e8bec5c39.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69f2398cb66b010e8bec5c39","keyword":["Attagene","USGS:69f2398cb66b010e8bec5c39","biota","bottled water","cis-Factorial endpoints","dissolved contaminants","drinking water","environment","environmental health (human)","geoscientificInformation","health","in vitro bioassay","inlandWaters","private wells","public supply","tapwater"],"modified":"2026-08-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-126.3867, 17.4764, -64.6875, 49.3824","theme":["geospatial"],"title":"In vitro bioactivity results analyzed in private, public, and bottled drinking-water samples, 2016-20."},"description":"Beginning in 2016, the U.S. Geological Survey, Environmental Health Program, Drinking Water and Wastewater Infrastructure Integrated Science Team, in collaboration with other federal, non-governmental and Tribal partners, began collecting and analyzing tapwater samples from across the Nation for a large suite of inorganic, organic, and biological contaminants (Bradley and others, 2025). Results from these analyses demonstrated that, in many instances, drinking water is an exposure pathway for multiple contaminants of concern (exposures greater than the federal and state drinking water health-based guidelines) in private, public, and bottled water sources. In addition to targeted chemical analyses, in vitro bioactivity analyses were performed to help characterize the potential biological effects from multiple contaminants.\nFrom 2016-2020, 265 water-quality samples, including 21 quality-assurance field blanks, which had previously been extracted from 1-liter samples were sent to Attagene, Inc., Morrisville, North Carolina for in vitro bioactivity screening. These extracts were analyzed for 48 biological endpoints using the cis-factorial assay described in Romanov and others (2008). Detailed method information and further analysis can be found in the associated report Bradley and others (2026).\nReferences--\nBradley, P.M., Romanok, K.M., Smalling, K.L., Gordon, S.E., Huffman, B.J., Friedman, K.P., Villeneuve, D.L., Blackwell, B.R., Fitzpatrick, S.C., Focazio, M.J., Medlock-Kakaley, E., Meppelink, S.M., Navas-Acien, A., Nigra, A.E., and Schreiner, M.L., 2025, Private, public, and bottled drinking water: Shared contaminant-mixture exposures and effects challenge: Environmental International, v. 195, 18 p., accessed on April 29, 2020 2026, at https://doi.org/10.1016/j.envint.2024.109220.\nRomanov, S., Medvedev, A., Gambarian, M., Poltoratskaya, N., Moeser, M., Medvedeva, L., Gambarian, M., Diatchenko, L., and Makarov, S., 2008, Homogeneous reporter system enables quantitative functional assessment of multiple transcription factors: Nature Methods, v. 5, p. 253-60, accessed on April 28, 2026 at https://doi.org/10.1038/nmeth.1186.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/9cb7b4f2-47e6-4bf6-aeaa-eaffadd54825","harvest_record_raw":"https://catalog.data.gov/harvest_record/9cb7b4f2-47e6-4bf6-aeaa-eaffadd54825/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69f2398cb66b010e8bec5c39","keyword":["Attagene","USGS:69f2398cb66b010e8bec5c39","biota","bottled water","cis-Factorial endpoints","dissolved contaminants","drinking water","environment","environmental health (human)","geoscientificInformation","health","in vitro bioassay","inlandWaters","private wells","public supply","tapwater"],"last_harvested_date":"2026-08-20T00:35:34.524528","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"popularity":0,"publisher":"U.S. Geological Survey","slug":"in-vitro-bioactivity-results-analyzed-in-private-public-and-bottled-drinking-water-2016-20","spatial_centroid":{"lat":30.238799999999998,"lon":-101.70702},"spatial_shape":{"coordinates":[[[-126.3867,17.4764],[-126.3867,49.3824],[-64.6875,49.3824],[-64.6875,17.4764],[-126.3867,17.4764]]],"type":"Polygon"},"theme":["geospatial"],"title":"In vitro bioactivity results analyzed in private, public, and bottled drinking-water samples, 2016-20.","type":"dataset"},{"_score":2.4128208,"_sort":[1787186000579,2.4128208,0,"7d3cc5ec-c983-4eb2-bb28-ffaef006eec4"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jonathan Casey Root","hasEmail":"mailto:jroot@usgs.gov"},"description":"Terminal and saline lakes across the western United States serve as critical hydrologic, ecologic, and geomorphic resources. 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All elevation data were transformed to the North American Vertical Datum of 1988 (NAVD88) and merged at the highest available spatial resolution for each lake domain.\nTopobathymetric rasters were generated in ArcGIS Pro (v. 3.5.5) using consistent horizontal projections within the Universal Transverse Mercator system and processed to ensure seamless topographic transitions between dry and submerged surfaces. In cases where bathymetric coverage did not overlap with lidar, elevation gaps were interpolated using hydrologically consistent void filling models to create continuous topobathymetry. Elevation area volume relationships were computed at 0.1 meter intervals across each modeled lake using the ESRI Storage Capacity tool, with hydrologically conditioned processing for lakes in which natural or manmade barriers form multiple basins that connect only at specific elevations. The uppermost elevation in each EAV table is equal to or above the highest recorded water-surface elevation observed in historical records. These EAV curves provide a quantitative basis for hydrologic modeling, water budget analyses, and ecological assessment within each closed basin.\nThis data release delivers standardized, high\u2011quality elevation datasets and EAV metrics for lakes including Eagle Lake, Goose Lake, Honey Lake, and Mono Lake in California, Carson Lake, Carson Sink, Franklin Lake, Pyramid Lake, Ruby Lake, Winnemucca Lake, and Walker Lake in Nevada, Lake Abert, Harney Lake, Malheur Lake, Mud Lake, Silver Lake, and Summer Lake in Oregon, and Sevier Lake in Utah. Together, these products support improved understanding of lake dynamics, ecosystem management, and hydrogeomorphic change across terminal lake systems of the western United States.\nThis section of the data release includes tables in the format of comma-separated value (CSV) files with elevation-area-volume relationships for selected lakes in closed basins of the Great Basin States. 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The uppermost elevation in each EAV table is equal to or above the highest recorded water-surface elevation observed in historical records. These EAV curves provide a quantitative basis for hydrologic modeling, water budget analyses, and ecological assessment within each closed basin.\nThis data release delivers standardized, high\u2011quality elevation datasets and EAV metrics for lakes including Eagle Lake, Goose Lake, Honey Lake, and Mono Lake in California, Carson Lake, Carson Sink, Franklin Lake, Pyramid Lake, Ruby Lake, Winnemucca Lake, and Walker Lake in Nevada, Lake Abert, Harney Lake, Malheur Lake, Mud Lake, Silver Lake, and Summer Lake in Oregon, and Sevier Lake in Utah. Together, these products support improved understanding of lake dynamics, ecosystem management, and hydrogeomorphic change across terminal lake systems of the western United States.\nThis section of the data release includes tables in the format of comma-separated value (CSV) files with elevation-area-volume relationships for selected lakes in closed basins of the Great Basin States. 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We developed and calibrated WARMER-Coast, an intermediate-complexity modeling framework, to simulate tidal wetland evolution, assess potential upslope migration pathways, and evaluate management scenarios developed in consultation with local stakeholders. WARMER-Coast couples the 1-D soil cohort model WARMER v3 with a simplified 2-D hydrodynamic framework to simulate inundation, sediment transport, surface and edge erosion, plant growth, organic matter accumulation, and carbon storage. Model calibration leveraged extensive field observations, including water-level monitoring, elevation and vegetation surveys, surface elevation table measurements, soil cores, feldspar marker horizons, and short-term sediment deposition measurements. We applied the calibrated model to project marsh and seagrass habitat trajectories across five SLR scenarios and three sediment regimes. 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These data are from a replicated BACI (before-after-control-impact) designed study, at two marshes with differing hydrology and geomorphology. Data were collected using HOBO Water Level Data Loggers (U20L-04) deployed below the marsh surface between May and October (full duration of deployment varies by year). Data were collected every 15-minutes and filtered to generate a minimum daily water level. 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This analysis involved several steps. First, time-series climate data were compiled and plotted to identify drought periods. Similarly, time-series data representing insect outbreaks were compiled and plotted to identify trends in insect mortality. The study area was classified into forest canopy types using existing modeled estimates of tree basal area by tree species. Using remotely-sensed Normalized Difference Moisture Index (NDMI) from the Landsat archive, NDMI anomalies from reference conditions were calculated in 2001 and 2009. Based on these anomalies, and using the forest classification map, refugia from drought (in 2001) and combined drought-MPB effects (in 2009) were identified as NDMI anomalies greater than the 90\\u003Csup\\u003Eth\\u003C/sup\\u003E-percentile value within each forest type. Once refugia had been identified, landscape variables (topographic, soil, and forest stand characteristics) were compiled and used to model the landscape controls on refugia locations using Boosted Regression Tree (BRT) modeling, a machine-learning algorithm. For detailed descriptions of data-release components, please consult the appropriate metadata documents that accompany the processing scripts and data products.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F74Q7SWX","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.59637fdbe4b0d1f9f059d824.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_59637fdbe4b0d1f9f059d824","keyword":["USGS:59637fdbe4b0d1f9f059d824","biota","elevation","environment","geoscientificInformation"],"modified":"2026-08-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-120.908504664726, 42.46096497075, -120.768520709553, 42.5920986015813","theme":["geospatial"],"title":"Analysis of remotely-sensed vegetation conditions during droughts and a mountain pine beetle outbreak, Gearhart Mountain Wilderness, Oregon"},"description":"This data release includes data-processing scripts, data products, and associated metadata for a remote-sensing based approach to characterize vegetation conditions within a dry, mixed conifer forest study area in southern Oregon in 2001 (a single year drought without any widespread insect mortality) and 2009 (during a multi-year drought that coincided with a severe outbreak of mountain pine beetle; MPB). This analysis involved several steps. First, time-series climate data were compiled and plotted to identify drought periods. Similarly, time-series data representing insect outbreaks were compiled and plotted to identify trends in insect mortality. The study area was classified into forest canopy types using existing modeled estimates of tree basal area by tree species. Using remotely-sensed Normalized Difference Moisture Index (NDMI) from the Landsat archive, NDMI anomalies from reference conditions were calculated in 2001 and 2009. Based on these anomalies, and using the forest classification map, refugia from drought (in 2001) and combined drought-MPB effects (in 2009) were identified as NDMI anomalies greater than the 90\\u003Csup\\u003Eth\\u003C/sup\\u003E-percentile value within each forest type. Once refugia had been identified, landscape variables (topographic, soil, and forest stand characteristics) were compiled and used to model the landscape controls on refugia locations using Boosted Regression Tree (BRT) modeling, a machine-learning algorithm. 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All elevation data were transformed to the North American Vertical Datum of 1988 (NAVD88) and merged at the highest available spatial resolution for each lake domain.\nTopobathymetric rasters were generated in ArcGIS Pro (v. 3.5.5) using consistent horizontal projections within the Universal Transverse Mercator system and processed to ensure seamless topographic transitions between dry and submerged surfaces. In cases where bathymetric coverage did not overlap with lidar, elevation gaps were interpolated using hydrologically consistent void filling models to create continuous topobathymetry. Elevation area volume relationships were computed at 0.1 meter intervals across each modeled lake using the ESRI Storage Capacity tool, with hydrologically conditioned processing for lakes in which natural or manmade barriers form multiple basins that connect only at specific elevations. The uppermost elevation in each EAV table is equal to or above the highest recorded water-surface elevation observed in historical records. These EAV curves provide a quantitative basis for hydrologic modeling, water budget analyses, and ecological assessment within each closed basin.\nThis data release delivers standardized, high\u2011quality elevation datasets and EAV metrics for lakes including Eagle Lake, Goose Lake, Honey Lake, and Mono Lake in California, Carson Lake, Carson Sink, Franklin Lake, Pyramid Lake, Ruby Lake, Winnemucca Lake, and Walker Lake in Nevada, Lake Abert, Harney Lake, Malheur Lake, Mud Lake, Silver Lake, and Summer Lake in Oregon, and Sevier Lake in Utah. Together, these products support improved understanding of lake dynamics, ecosystem management, and hydrogeomorphic change across terminal lake systems of the western United States.\nThis section of the data releases includes high-resolution topobathymetric elevation data for selected lakes in closed basins of the Great Basin States. 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All elevation data were transformed to the North American Vertical Datum of 1988 (NAVD88) and merged at the highest available spatial resolution for each lake domain.\nTopobathymetric rasters were generated in ArcGIS Pro (v. 3.5.5) using consistent horizontal projections within the Universal Transverse Mercator system and processed to ensure seamless topographic transitions between dry and submerged surfaces. In cases where bathymetric coverage did not overlap with lidar, elevation gaps were interpolated using hydrologically consistent void filling models to create continuous topobathymetry. Elevation area volume relationships were computed at 0.1 meter intervals across each modeled lake using the ESRI Storage Capacity tool, with hydrologically conditioned processing for lakes in which natural or manmade barriers form multiple basins that connect only at specific elevations. 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We compared them against mice living in standard conditions (Vivarium Ground Control) and mice living in an environment matched to ISS conditions (Habitat Ground Control). 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Non-coding RNAs like miRNAs are key to regulating this landscape. We thus analyzed 686 small RNA samples of mice from 13 solid organs at 3 and 8 months of age, after at least 3 weeks on the ISS and compared them to earth-bound controls. We observed significant spaceflight effects in systemic tissue remodeling pathways along the Fat-Liver-Pancreas axis and in heart, brain, spleen and thymus. The MIR-17/92 and MIR-1/133 families drive distinct molecular changes through specific gene targeting. Age-dependent changes, smaller in magnitude compared to age-independent changes, primarily involved tissue remodeling through MIR-8, MIR-154 and MIR-15 families in MAT, pancreas, and diaphragm. Our findings provide evidence on how spaceflight regulates mammalian gene expression in preparation for interplanetary spaceflight. We sequenced 686 samples across 13 organs of young (3 months) and middle-aged (8 months) mice that were sent to the ISS (Flight). We compared them against mice living in standard conditions (Vivarium Ground Control) and mice living in an environment matched to ISS conditions (Habitat Ground Control). We euthanized mice at two time-points (matching timelines for controls and flight mice), one before returning to earth (TERM) and one after (LAR) in order to distinguish spaceflight-induced effects from the reentry-induced stress.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/f95ce988-ba95-443b-bb71-17ea1261ebf4","harvest_record_raw":"https://catalog.data.gov/harvest_record/f95ce988-ba95-443b-bb71-17ea1261ebf4/raw","has_download":true,"has_spatial":false,"identifier":"10.26030/bnfb-5953","keyword":["biological-and-physical-sciences","genelab","nasa"],"last_harvested_date":"2026-08-19T00:47:39.869294","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"popularity":0,"publisher":"Open Science Data Repository","slug":"micrornas-shape-mouse-age-independent-tissue-adaptation-to-spaceflight-via-ecm-and-develop-5a902","spatial_centroid":null,"spatial_shape":null,"theme":["Biological and Physical Sciences"],"title":"MicroRNAs shape mouse age-independent tissue adaptation to spaceflight via ECM and developmental pathways - Pancreas data","type":"dataset"},{"_score":14.970541,"_sort":[1787100454737,14.970541,2,"f540ded7-c45d-4af2-b606-89069f27ff50"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Open Science Data Repository Help Desk","hasEmail":"mailto:arc-dl-osdr-help@mail.nasa.gov"},"description":"Translating fundamental biological discoveries from NASA Space Biology program into health risk from space flights has been an ongoing challenge. We propose to use NASA GeneLab database to gain new knowledge on potential systemic responses to space. Unbiased systems biology analysis of transcriptomic data from seven different rodent datasets reveals for the first time the existence of potential 'master regulators' coordinating a systemic response to microgravity and/or space radiation with TGF-\u03b21 being the most common regulator. We hypothesized the space environment leads to the release of biomolecules circulating inside the blood stream. Through datamining we identified 13 candidate microRNAs (miRNA) which are common in all studies and directly interact with TGF-\u03b21 that can be potential circulating factors impacting space biology. This study exemplifies the utility of the GeneLab data repository to aid in the process of performing novel hypothesis-based research.","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://purl.bioontology.org/ontology/NCBITAXON/10116","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://lsda.jsc.nasa.gov/Experiment/exper/30","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/missions/STS-40","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/studies/OSD-422","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.26030/jq04-0n51","keyword":["biological-and-physical-sciences","genelab","nasa"],"license":"https://www.usa.gov/government-works","modified":"2026-08-10","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Open Science Data Repository"},"theme":["Biological and Physical Sciences"],"title":"Rodent Research-1 (RR1) NASA Validation Flight: Mouse liver transcriptomic, proteomic, epigenomic and histology data"},"description":"Translating fundamental biological discoveries from NASA Space Biology program into health risk from space flights has been an ongoing challenge. We propose to use NASA GeneLab database to gain new knowledge on potential systemic responses to space. Unbiased systems biology analysis of transcriptomic data from seven different rodent datasets reveals for the first time the existence of potential 'master regulators' coordinating a systemic response to microgravity and/or space radiation with TGF-\u03b21 being the most common regulator. We hypothesized the space environment leads to the release of biomolecules circulating inside the blood stream. Through datamining we identified 13 candidate microRNAs (miRNA) which are common in all studies and directly interact with TGF-\u03b21 that can be potential circulating factors impacting space biology. This study exemplifies the utility of the GeneLab data repository to aid in the process of performing novel hypothesis-based research.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/01123c39-0983-4dd0-a053-e5c88fce877e","harvest_record_raw":"https://catalog.data.gov/harvest_record/01123c39-0983-4dd0-a053-e5c88fce877e/raw","has_download":true,"has_spatial":false,"identifier":"10.26030/jq04-0n51","keyword":["biological-and-physical-sciences","genelab","nasa"],"last_harvested_date":"2026-08-19T00:47:34.737955","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"popularity":2,"publisher":"Open Science Data Repository","slug":"rodent-research-1-rr1-nasa-validation-flight-mouse-liver-transcriptomic-proteomic-epigenom","spatial_centroid":null,"spatial_shape":null,"theme":["Biological and Physical Sciences"],"title":"Rodent Research-1 (RR1) NASA Validation Flight: Mouse liver transcriptomic, proteomic, epigenomic and histology data","type":"dataset"},{"_score":14.161049,"_sort":[1787100443301,14.161049,4,"61da5e7b-f47a-469e-8a2f-575ff8764053"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Open Science Data Repository Help Desk","hasEmail":"mailto:arc-dl-osdr-help@mail.nasa.gov"},"description":"Suborbital spaceflights now enable human-tended research investigating short-term gravitational effects in biological systems, eliminating the need for complex automation. Here, we discuss a method utilizing KSC Fixation Tubes (KFTs) to both carry biology to suborbital space as well as fix that biology at certain stages of flight. Plants on support media were inserted into the sample side of KFTs preloaded with RNAlater in the fixation chamber. The KFTs were activated at various stages of a simulated flight to fix the plants. RNA-seq analysis conducted on tissue samples housed in KFTs, showed that plants behaved consistently in KFTs when compared to petri-plates. Over the time course, roots adjusted to hypoxia and leaves adjusted to changes in photosynthesis. These responses were due in part to the environment imposed by the encased triple containment of the KFTs, which is a requirement for flight in human spacecraft. While plants exhibited expected reproducible transcriptomic alteration over time in the KFTs, responses to clinorotation during the simulated flight suggest that transcriptomic responses to suborbital spaceflight can be examined using this approach.","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://purl.bioontology.org/ontology/NCBITAXON/162425","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://lsda.jsc.nasa.gov/Experiment/exper/13671#data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://lsda.jsc.nasa.gov/Mission/miss/1391","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/studies/OSD-233","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.ncbi.nlm.nih.gov/bioproject/PRJNA486827","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.26030/3he5-md28","keyword":["biological-and-physical-sciences","genelab","nasa"],"license":"https://www.usa.gov/government-works","modified":"2026-08-10","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Open Science Data Repository"},"theme":["Biological and Physical Sciences"],"title":"Utilizing the KSC Fixation Tube to Conduct Human-Tended Plant Biology Experiments on a Suborbital Spaceflight"},"description":"Suborbital spaceflights now enable human-tended research investigating short-term gravitational effects in biological systems, eliminating the need for complex automation. Here, we discuss a method utilizing KSC Fixation Tubes (KFTs) to both carry biology to suborbital space as well as fix that biology at certain stages of flight. Plants on support media were inserted into the sample side of KFTs preloaded with RNAlater in the fixation chamber. The KFTs were activated at various stages of a simulated flight to fix the plants. RNA-seq analysis conducted on tissue samples housed in KFTs, showed that plants behaved consistently in KFTs when compared to petri-plates. Over the time course, roots adjusted to hypoxia and leaves adjusted to changes in photosynthesis. These responses were due in part to the environment imposed by the encased triple containment of the KFTs, which is a requirement for flight in human spacecraft. While plants exhibited expected reproducible transcriptomic alteration over time in the KFTs, responses to clinorotation during the simulated flight suggest that transcriptomic responses to suborbital spaceflight can be examined using this approach.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/7f0f1e66-d301-49a6-9084-8f874376004d","harvest_record_raw":"https://catalog.data.gov/harvest_record/7f0f1e66-d301-49a6-9084-8f874376004d/raw","has_download":true,"has_spatial":false,"identifier":"10.26030/3he5-md28","keyword":["biological-and-physical-sciences","genelab","nasa"],"last_harvested_date":"2026-08-19T00:47:23.301196","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"popularity":4,"publisher":"Open Science Data Repository","slug":"utilizing-the-ksc-fixation-tube-to-conduct-human-tended-plant-biology-experiments-on-a-sub","spatial_centroid":null,"spatial_shape":null,"theme":["Biological and Physical Sciences"],"title":"Utilizing the KSC Fixation Tube to Conduct Human-Tended Plant Biology Experiments on a Suborbital Spaceflight","type":"dataset"},{"_score":8.414444,"_sort":[1787100438120,8.414444,4,"50f50334-586e-4572-826a-42f520d9b2a0"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Open Science Data Repository Help Desk","hasEmail":"mailto:arc-dl-osdr-help@mail.nasa.gov"},"description":"The wild type WS plants and the Sku5 mutant plants in the WS background were germinated on ISS or on the ground and the gene expression profiles in roots at 4 days or 8 days were established. The Sku5 gene (AT4G12420) codes for a multi-copper oxidase-like protein SKU5 protein that is involved in directed root tip growth. The Sku5 protein is glycosylated, GPI-anchored and localizes to the plasma membrane and the cell wall. The Sku5 gene is expressed most strongly in expanding tissues. The development of WS and Sku5 plants on orbit differs from that on the ground as demonstrated by the comparison of the gene expression profiles between 4 days old to 8 days old plant in the two environments. However the development of Sku5 mutant plants also differs from WS at either age and in either environment, suggesting the role of the genetic background in these developmental decisions. The 4 days old Sku5 roots in orbit engaged substantially more genes than the 4 days old WS roots in orbit but also more than the 4 days old Sku5 roots on the ground. Overall the 4 days old roots differentially expressed more genes in spaceflight relative to ground than the 8 days old roots of either genotype. Finally, the 4 days old Sku5 roots in orbit differ in 862 genes from the 8 days Sku5 roots in orbit while on the ground there is merely half of the number of genes differentially expressed between the 4 days and 8 days developmental stages of the Sku5 roots. APEX03-2 (Advanced Plant Experiment 03-2) also identified as TAGES-Isa (Transgenic Arabidopsis Gene Expression System\u2014Intracellular Signaling Architecture) was launched on SpaceX mission CRS-5 on 10 January 2015. Dry, sterilized Arabidopsis seeds were planted aseptically on the surface of 10-cm2 solid media plates and remained dormant until removed from cold stowage and exposed to light at the initiation of the experiment on the ISS (International Space Station). The plates were grown in the Vegetable Production System (VPS/Veggie) hardware on the Columbus Module of the ISS with the overhead LED lighting of the VPS. At four or for the second experimental set at eight days, seedlings were harvested by an astronaut into KFT (Kennedy Fixation Tube) containing RNAlater solutions. Upon return to Earth, the harvested material was used to compare the transcriptomes of each genotype and each age using RNAseq technology. The patterns of gene expression was compared between treatments (spaceflight versus ground control) within each genotype of same age or between two ages within same genotype and within same treatment. For 4 day old roots 5-8 roots were combined to serve as one biological replica. For 8 day old roots 2-3 roots were used to form one biological replica. In each case the four biological replicas were used for the transcriptomic analysis.","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://purl.bioontology.org/ontology/NCBITAXON/3702","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/missions/SpaceX-5","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/studies/OSD-281","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95620","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.26030/j07k-nm86","keyword":["biological-and-physical-sciences","genelab","nasa"],"license":"https://www.usa.gov/government-works","modified":"2026-08-10","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Open Science Data Repository"},"theme":["Biological and Physical Sciences"],"title":"During development, the Sku5 mutant roots engage different genes than wild type WS roots, either on the ground or in spaceflight."},"description":"The wild type WS plants and the Sku5 mutant plants in the WS background were germinated on ISS or on the ground and the gene expression profiles in roots at 4 days or 8 days were established. The Sku5 gene (AT4G12420) codes for a multi-copper oxidase-like protein SKU5 protein that is involved in directed root tip growth. The Sku5 protein is glycosylated, GPI-anchored and localizes to the plasma membrane and the cell wall. The Sku5 gene is expressed most strongly in expanding tissues. The development of WS and Sku5 plants on orbit differs from that on the ground as demonstrated by the comparison of the gene expression profiles between 4 days old to 8 days old plant in the two environments. However the development of Sku5 mutant plants also differs from WS at either age and in either environment, suggesting the role of the genetic background in these developmental decisions. The 4 days old Sku5 roots in orbit engaged substantially more genes than the 4 days old WS roots in orbit but also more than the 4 days old Sku5 roots on the ground. Overall the 4 days old roots differentially expressed more genes in spaceflight relative to ground than the 8 days old roots of either genotype. Finally, the 4 days old Sku5 roots in orbit differ in 862 genes from the 8 days Sku5 roots in orbit while on the ground there is merely half of the number of genes differentially expressed between the 4 days and 8 days developmental stages of the Sku5 roots. APEX03-2 (Advanced Plant Experiment 03-2) also identified as TAGES-Isa (Transgenic Arabidopsis Gene Expression System\u2014Intracellular Signaling Architecture) was launched on SpaceX mission CRS-5 on 10 January 2015. Dry, sterilized Arabidopsis seeds were planted aseptically on the surface of 10-cm2 solid media plates and remained dormant until removed from cold stowage and exposed to light at the initiation of the experiment on the ISS (International Space Station). The plates were grown in the Vegetable Production System (VPS/Veggie) hardware on the Columbus Module of the ISS with the overhead LED lighting of the VPS. At four or for the second experimental set at eight days, seedlings were harvested by an astronaut into KFT (Kennedy Fixation Tube) containing RNAlater solutions. Upon return to Earth, the harvested material was used to compare the transcriptomes of each genotype and each age using RNAseq technology. The patterns of gene expression was compared between treatments (spaceflight versus ground control) within each genotype of same age or between two ages within same genotype and within same treatment. For 4 day old roots 5-8 roots were combined to serve as one biological replica. For 8 day old roots 2-3 roots were used to form one biological replica. 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The objective of our spaceflight experiment \u201cPlant Signaling in Microgravity\u201d (carried out on the International Space Station, ISS), was to compare transcript profiles of wild type and transgenic InsP 5-ptase plants with compromised InsP3 signaling. The transgenic Arabidopsis plants constitutively express the mammalian type I inositol polyphosphate 5-phosphatase (InsP 5-ptase), an enzyme that specifically hydrolyzes the lipid-derived second messenger inositol 1,4,5-trisphosphate (InsP3). These transgenic plants exhibit normal growth and morphology; however, their responses to environmental stimuli including gravity and drought are altered. Seedlings were grown for 5 days under continuous light in experimental containers placed in the European Modular Cultivation system (EMCS) onboard the ISS. The EMCS consists of two rotors within a controlled chamber, allowing for a \u201c1g\u201d control in space. After sample retrieval from the ISS, RNA was isolated from shoot and root tissue and subjected to RNA sequencing. Two-way comparisons of micro g versus \u201c1\u201dg have uncovered regulatory mechanisms that are both conserved and altered between the wild type and transgenic seedlings.","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://purl.bioontology.org/ontology/NCBITAXON/3702","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://nlsp.nasa.gov/view/lsdapub/lsda_experiment/8bb8cb8a-d1ee-5806-bac3-a8c28d3e7fad","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/missions/STS-135","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/studies/OSD-223","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.26030/wdtz-v612","keyword":["biological-and-physical-sciences","genelab","nasa"],"license":"https://www.usa.gov/government-works","modified":"2026-08-10","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Open Science Data Repository"},"theme":["Biological and Physical Sciences"],"title":"The effect of spaceflight on transgenic Arabidopsis plants with compromised signaling"},"description":"Understanding the molecular mechanisms by which plants sense and adapt to changes in the space environment is essential for generating plants that are better adapted to withstand space flight, microgravity, and other adverse conditions encountered in space. 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