{"after":"WzE3ODkwODA1NjIwODMsNy45Nzg1MDA0LDUsIjFiNzk2NzU3LTQxOTAtNDMxZC1iOWM4LTc1MDQyNDJlNzU4ZSJd","results":[{"_score":5.9376144,"_sort":[1789080654962,5.9376144,1,"61cc9477-ec05-48bd-89e7-f4bd16490a01"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"PCMSC Science Data Coordinator","hasEmail":"mailto:pcmsc_data@usgs.gov"},"description":"This part of DS 781 presents fault data for the geologic and geomorphic map of the Monterey Canyon and Vicinity map area, California. The vector data file is included in \"Faults_MontereyCanyon.zip,\" which is accessible from http://pubs.usgs.gov/ds/781/MontereyCanyon/data_catalog_MontereyCanyon.html. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series\u2014Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016\u20131072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072.\nFaults in the Monterey Canyon and Vicinity map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data from USGS field activities S\u2013N1\u201309\u2013MB and S\u20136\u201311\u2013MB. 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Cochran, eds.), 2016, California State Waters Map Series\u2014Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016\u20131072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072.\nFaults in the Monterey Canyon and Vicinity map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data from USGS field activities S\u2013N1\u201309\u2013MB and S\u20136\u201311\u2013MB. 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The model was calibrated using parameter estimation (PEST) by \nfitting estimated drawdowns to simulated drawdowns from 16 multiple-well aquifer tests. Water-level models \nwere used to estimate drawdowns from continuous water-level data collected during multiple-well aquifer \ntesting. This USGS data release contains all of the input and output files for the simulations described \nin the associated model documentation report (http://doi.org/10.3133/sir20165151). This data release \nalso includes (1) preprocessing Microsoft Excel macros, FORTRAN executables, and associated input \ndata files for creating the groundwater-flow models; and (2) post-processing FORTRAN executables for \ngenerating model output files.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F76H4FJQ","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.bb08a70e-38dd-4b9c-aed4-352879cd5ecb.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_bb08a70e-38dd-4b9c-aed4-352879cd5ecb","keyword":["Groundwater","Groundwater Model","MODFLOW","MODFLOW-2005","Nevada","Nevada Test Site Area 19","Nevada Test Site Area 20","Nye County","PEST","Pahute Mesa","USGS:bb08a70e-38dd-4b9c-aed4-352879cd5ecb","environment","geoscientificInformation","inlandWaters","usgsgroundwatermodel"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-117.21, 36.62, -115.78, 37.74","theme":["geospatial"],"title":"MODFLOW-2005 and PEST models used to simulate multiple-well aquifer tests and characterize hydraulic properties of volcanic rocks in Pahute Mesa, Nevada"},"description":"A three-dimensional, groundwater-flow model (MODFLOW-2005) was developed to estimate the hydraulic \nproperties (e.g., transmissivity, hydraulic conductivity, specific yield, and specific storage) of volcanic rocks \nin Pahute Mesa, Nye County, Nevada. 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Himmelstoss","hasEmail":"mailto:ehimmelstoss@usgs.gov"},"description":"The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color aerial orthoimagery and 2007 topographic lidar datasets obtained from the National Oceanic and Atmospheric Administration's Ocean Service, Coastal Services Center. This 2018 data release includes rates that incorporate two new mean high water (MHW) shorelines for the Massachusetts coast extracted from lidar data collected between 2010 and 2014. The first new shoreline for the State includes data from 2010 along the North Shore and South Coast from lidar data collected by the U.S. Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise. Shorelines along the South Shore and Outer Cape are from 2011 lidar data collected by the U.S. Geological Survey's (USGS) National Geospatial Program Office. Shorelines along Nantucket and Martha\u2019s Vineyard are from a 2012 USACE Post Sandy Topographic lidar survey. The second new shoreline for the North Shore, Boston, South Shore, Cape Cod Bay, Outer Cape, South Cape, Nantucket, Martha\u2019s Vineyard, and the South Coast (around Buzzards Bay to the Rhode Island Border) is from 2013-14 lidar data collected by the (USGS) Coastal and Marine Geology Program. This 2018 update of the rate of shoreline change in Massachusetts includes two types of rates.  Some of rates include a proxy-datum bias correction, this is indicated in the filename with \u201cPDB\u201d. The rates that do not account for this correction have \u201cNB\u201d in their file names.  The proxy-datum bias is applied because in some areas a proxy shoreline (like a High Water Line shoreline) has a bias when compared to a datum shoreline (like a Mean High Water shoreline). In areas where it exists, this bias should be accounted for when calculating rates using a mix of proxy and datum shorelines. This issue is explained further in Ruggiero and List (2009) and in the process steps of the metadata associated with the rates.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9RRBEYK","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.5c644d31e4b0fe48cb37294b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5c644d31e4b0fe48cb37294b","keyword":["Atlantic Coast","Baseline","CMGP","Coastal and Marine Geology Program","DSAS","Digital Shoreline Analysis System","Massachusetts","New England","North America","Shoreline","Shoreline Change","U.S. Geological Survey","USGS","USGS:5c644d31e4b0fe48cb37294b","United States","WHCMSC","Woods Hole Coastal and Marine Science Center","coastal processes","environment","geoscientificInformation","geospatial datasets","oceans"],"modified":"2026-04-20T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-70.989790, 42.343469, -70.590365, 42.875152","theme":["geospatial"],"title":"Baseline for the coastal region north of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0"},"description":"The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. 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Shorelines along the South Shore and Outer Cape are from 2011 lidar data collected by the U.S. Geological Survey's (USGS) National Geospatial Program Office. Shorelines along Nantucket and Martha\u2019s Vineyard are from a 2012 USACE Post Sandy Topographic lidar survey. The second new shoreline for the North Shore, Boston, South Shore, Cape Cod Bay, Outer Cape, South Cape, Nantucket, Martha\u2019s Vineyard, and the South Coast (around Buzzards Bay to the Rhode Island Border) is from 2013-14 lidar data collected by the (USGS) Coastal and Marine Geology Program. This 2018 update of the rate of shoreline change in Massachusetts includes two types of rates.  Some of rates include a proxy-datum bias correction, this is indicated in the filename with \u201cPDB\u201d. The rates that do not account for this correction have \u201cNB\u201d in their file names.  The proxy-datum bias is applied because in some areas a proxy shoreline (like a High Water Line shoreline) has a bias when compared to a datum shoreline (like a Mean High Water shoreline). In areas where it exists, this bias should be accounted for when calculating rates using a mix of proxy and datum shorelines. This issue is explained further in Ruggiero and List (2009) and in the process steps of the metadata associated with the rates.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/a173343c-054a-4125-a438-cf588767d232","harvest_record_raw":"https://catalog.data.gov/harvest_record/a173343c-054a-4125-a438-cf588767d232/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5c644d31e4b0fe48cb37294b","keyword":["Atlantic Coast","Baseline","CMGP","Coastal and Marine Geology Program","DSAS","Digital Shoreline Analysis System","Massachusetts","New England","North America","Shoreline","Shoreline Change","U.S. Geological Survey","USGS","USGS:5c644d31e4b0fe48cb37294b","United States","WHCMSC","Woods Hole Coastal and Marine Science Center","coastal processes","environment","geoscientificInformation","geospatial datasets","oceans"],"last_harvested_date":"2026-09-10T22:50:53.623289","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"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"baseline-for-the-coastal-region-north-of-boston-massachusetts-generated-to-calculate-shore-12297","spatial_centroid":{"lat":42.5561422,"lon":-70.83002},"spatial_shape":{"coordinates":[[[-70.98979,42.343469],[-70.98979,42.875152],[-70.590365,42.875152],[-70.590365,42.343469],[-70.98979,42.343469]]],"type":"Polygon"},"theme":["geospatial"],"title":"Baseline for the coastal region north of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0","type":"dataset"},{"_score":6.4864173,"_sort":[1789080653411,6.4864173,1,"8d639ed3-0a14-44ad-8145-7e469982e562"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Marie K. 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Previously published historical shorelines for North Carolina (Kratzmann and others, 2017) were combined with the new lidar shoreline to calculate long-term (up to 169 years) and short-term (up to 20 years) rates of change. Files associated with the long-term and short-term rates are appended with \"LT\" and \"ST\", respectively. A proxy-datum bias reference line that accounts for the positional difference in a proxy shoreline (e.g. High Water Line (HWL) shoreline) and a datum shoreline (e.g. MHW shoreline) is also included in this release.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9HYNUNV","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.64de700cd34e5f6cd55350cd.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64de700cd34e5f6cd55350cd","keyword":["Atlantic Coast","DSAS","Digital Shoreline Analysis System","North America","North Carolina","Shoreline Change","Southeast Atlantic","U.S. Geological Survey","USGS","USGS:64de700cd34e5f6cd55350cd","United States","WHCMSC","Woods Hole Coastal and Marine Science Center","accretion","coastal processes","environment","erosion","geoscientificInformation","geospatial datasets","long-term change","oceans","shoreline accretion"],"modified":"2026-03-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-78.544289, 33.839336, -78.013996, 33.922937","theme":["geospatial"],"title":"North Carolina: west coast intersect points used in long-term (LT) and short-term (ST) shoreline change analyses from Cape Fear to the South Carolina border"},"description":"The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. 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Three flight transects were conducted from small aircraft over the National Park Service's Arctic Network (ARCN; Bering Land Bridge National Preserve, Cape Krusenstern National Monument, Gates of the Arctic National Park and Preserve, Kobuk Valley National Park, and Noatak National Preserve) and the U.S. Fish and Wildlife Service's Selawik National Wildlife Refuge.\n      The aerial photo surveys were flown for the WildCast Project (WILDlife Potential Habitat ForeCASTing), a collaboration of the U.S. Geological Survey, National Park Service, U.S. Fish and Wildlife Service, and U.S.D.A. Forest Service. WildCast was devised to provide models for projecting future land cover and wildlife habitat conditions in northwest Alaska under potential scenarios of climate change, and to provide an image database for future change-comparison research. More information is available at: https://www.usgs.gov/centers/alaska-science-center/science/wildlife-potential-habitat-forecasting-framework-wildcast#overview\n      Child Item 1: \"Flight Path GPS Logs and Browse Maps of Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 2: \"Nadir Photographs Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 3: \"Oblique Photographs Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 4: \"Nadir Videos Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 5: \"Oblique Videos Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9KFIRWQ","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.611595d4d34e3267c61166ce.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_611595d4d34e3267c61166ce","keyword":["Aerial Photography","Alaska","Arctic Network","Bering Land Bridge National Preserve","Biota","Cape Krusenstern National Monument","Climate Change","Coastal Ecosystems","Ecotypes","Environment","Frozen Ground","Gates of the Arctic National Park","Gates of the Arctic National Preserve","Geography","Geomorphic Landforms/Processes","GeoscientificInformation","Image Analysis","Image Collections","ImageryBaseMapsEarthCover","InlandWaters","Kobuk Valley National Park","Land Cover","Land Surface","Land Use and Land Cover","Land Use/Land Cover","Landscape","Low Altitude Air Photo","Low Altitude Video","Noatak National Preserve","Northwest Alaska","Northwest Arctic Borough","Photogrammetry","Selawik National Wildlife Refuge","Tundra Ecosystems","USGS:611595d4d34e3267c61166ce","Vegetation","Videography"],"modified":"2024-10-08T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-169.22, 64.78, -149.05, 68.87","theme":["geospatial"],"title":"Low-Altitude Photographic Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013"},"description":"This data release includes 5 child items with photos and videos taken during low altitude photo survey transects in northwest Alaska, July 2013. 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Both QWDATA and GWSI are subsystems of\nNWIS (National Water Inventory System)of the USGS (United States\nGeologic Survey).\n\t\t\nThis map is for Juab County, Utah.\n\t\t\nThe scope and purpose of NWIS is defined on the web site:\n\t\t\nhttp://water.usgs.gov/public/pubs/FS/FS-027-98/","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9UZP3GM","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.993fc00d-d143-4789-a035-fa62fdd6a144.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_993fc00d-d143-4789-a035-fa62fdd6a144","keyword":["Alkalinity","Ammonia","Ammonia unionize","Dissolved solids","Flow Rate","Hardness","Hardness total","Juab","Juab County","Nitrogen","Nitrogen nitrate","Quality","Specific conductance","State of Utah","USGS:993fc00d-d143-4789-a035-fa62fdd6a144","Utah","Water","Water Level","Water Quality","Water Quality Site","Water temperature","environment","geoscientificInformation","inlandWaters","pH"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-113.88916779, 39.37472153, -111.80361176, 39.80250168","theme":["geospatial"],"title":"Specific Water Quality Sites for Juab County, Utah"},"description":"This map shows specific water-quality items and hydrologic data site\ninformation which come from QWDATA (Water Quality) and GWSI (Ground\nWater Information System). 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Habitat suitability estimated for Colorado Parks and Wildlife critical habitat extent (southwestern Colorado).\nWe developed habitat selection models for Gunnison sage-grouse (Centrocercus minimus), a threatened species under the U.S. Endangered Species Act. We followed a management-centric modeling approach that sought to balance the need to evaluate the consistency of key habitat conditions and improvement actions across multiple, distinct populations, while allowing context-specific environmental variables and spatial scales to nuance selection responses. Models were developed for six isolated satellite populations (San Miguel, Crawford, Pi\u00f1on Mesa, Dove Creek, Cerro Summit-Cimarron-Sims, and Poncha Pass) from use locations collected between 1991 and 2016 (see larger citation for map of population boundaries). For each population, models were developed at two life stages (breeding and summer) and at two hierarchical scales (landscape and patch). We used multi-scale and seasonal resource selection analyses to quantify relationships between environmental conditions and sites used by animals. These resource selection function models relied on spatial data describing habitat conditions at different spatial scales, where environmental conditions differ, and habitat selection occur at different spatial scales for different available resources.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P93WFW13","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.61ae4803d34eb622f69a7180.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_61ae4803d34eb622f69a7180","keyword":["CO","Colorado","Conservation","Gunnison sage-grouse","Habitat selection","Landscape scale","Logistic regression","Pi\u00f1on Mesa satellite population","Resource selection function","Southwestern Colorado","Summer season","USGS:61ae4803d34eb622f69a7180","United States","biota","environment"],"modified":"2021-12-09T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-109.0772, 38.7617, -108.6223, 39.0684","theme":["geospatial"],"title":"Gunnison sage-grouse habitat suitability surface for Pinyon Mesa satellite population (summer, landscape): Colorado Parks and Wildlife critical habitat extent (southwestern Colorado)"},"description":"The Gunnison sage-grouse (Centrocercus minimus) habitat suitability surface for Pinyon Mesa satellite population represented here reflects summer season at a landscape scale context (window extents [radius] of 1 km, 3 km, and 6.4 km). 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The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/bc1b6a3b-6a1e-4b34-95f1-8f821e4698d3","harvest_record_raw":"https://catalog.data.gov/harvest_record/bc1b6a3b-6a1e-4b34-95f1-8f821e4698d3/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b413cab66b018f981b82e2","keyword":["US","USGS:69b413cab66b018f981b82e2","United States","WI","Wisconsin","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"last_harvested_date":"2026-09-10T22:50:39.491827","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"},"parent_identifier":null,"popularity":3,"publisher":"U.S. Geological Survey","slug":"imagery-station-215-lake-winnebago-at-jefferson-park","spatial_centroid":{"lat":44.203842,"lon":-88.425255},"spatial_shape":{"coordinates":[-88.425255,44.203842],"type":"Point"},"theme":["geospatial"],"title":"Imagery Station 215 [Lake Winnebago at Jefferson Park]","type":"dataset"},{"_score":6.2860165,"_sort":[1789080639073,6.2860165,3,"fa12b4c3-e83b-44b9-bb88-f36672f3ed76"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kara M. Watson","hasEmail":"mailto:kmwatson@usgs.gov"},"description":"The U.S. Geological Survey (USGS), in cooperation with the New Jersey Department of Environmental Protection (NJDEP), calculated several basin characteristics as part of the updated New Jersey StreamStats 2022 application (U.S. Geological Survey, 2022). These datasets are raster representations of various environmental, geological, and land use attributes within the New Jersey StreamStats 2022 study area; they are applied in the New Jersey 2022 application to describe delineated watersheds. This update features improvements in base elevation resolution from 10 meters to 10 feet and stream centerline hydrography from 1:24,000 to 1:2,400, as well as resampling of previously existing datasets to match the 10-foot resolution. The sixteen 8-digit Watershed Boundary Dataset  Hydrologic Unit Codes (HUCs) represented by these datasets are  02020007, 02030103, 02030104, 02030105, 02040101, 02040102, 02040103, 02040104, 02040105, 02040106, 02040201, 02040202, 02040203, 02040206, 02040301, and 02040302 (U.S. Geological Survey, 2016). The StreamStats application provides access to spatial analytical tools that are useful for water-resources planning and management, as well as engineering and design purposes. The map-based user interface can be used to delineate drainage areas, determine basin characteristics and estimate flow statistics, including instantaneous flood discharge, monthly flow-duration, and monthly low-flow frequency statistics for ungaged streams. \nReferences cited:  \nU.S. Geological Survey, 2016, National Hydrography: U.S. Geological Survey, accessed February 7, 2022, at https://www.usgs.gov/national-hydrography. \nU.S. Geological Survey, 2022, StreamStats v4.6.2: U.S. Geological Survey, accessed February 7, 2022, at https://streamstats.usgs.gov/ss/.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9QQ0JDF","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.5fe354bbd34ea5387deb47a9.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5fe354bbd34ea5387deb47a9","keyword":["USGS:5fe354bbd34ea5387deb47a9","datasets","digital elevation models","earth sciences","elevation","environment","field inventory and monitoring","forest ecosystems","geomorphology","geoscientificInformation","geospatial datasets","human impacts","hydrology","imageryBaseMapsEarthCover","land surface characteristics","land use and land cover","land use change","permeability","physiological processes","population (human)","precipitation (atmospheric)","soil sciences","vegetation","water resource management","water resources"],"modified":"2022-08-10T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-76.8713, 38.788, -73.5535, 42.5689","theme":["geospatial"],"title":"Basin characteristics rasters for New Jersey StreamStats 2022"},"description":"The U.S. Geological Survey (USGS), in cooperation with the New Jersey Department of Environmental Protection (NJDEP), calculated several basin characteristics as part of the updated New Jersey StreamStats 2022 application (U.S. Geological Survey, 2022). 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The map-based user interface can be used to delineate drainage areas, determine basin characteristics and estimate flow statistics, including instantaneous flood discharge, monthly flow-duration, and monthly low-flow frequency statistics for ungaged streams. \nReferences cited:  \nU.S. Geological Survey, 2016, National Hydrography: U.S. Geological Survey, accessed February 7, 2022, at https://www.usgs.gov/national-hydrography. \nU.S. Geological Survey, 2022, StreamStats v4.6.2: U.S. Geological Survey, accessed February 7, 2022, at https://streamstats.usgs.gov/ss/.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/36de4d27-58fc-4400-8e60-ec7ba325a863","harvest_record_raw":"https://catalog.data.gov/harvest_record/36de4d27-58fc-4400-8e60-ec7ba325a863/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5fe354bbd34ea5387deb47a9","keyword":["USGS:5fe354bbd34ea5387deb47a9","datasets","digital elevation models","earth sciences","elevation","environment","field inventory and monitoring","forest ecosystems","geomorphology","geoscientificInformation","geospatial datasets","human impacts","hydrology","imageryBaseMapsEarthCover","land surface characteristics","land use and land cover","land use change","permeability","physiological processes","population (human)","precipitation (atmospheric)","soil sciences","vegetation","water resource management","water resources"],"last_harvested_date":"2026-09-10T22:50:39.073198","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"},"parent_identifier":null,"popularity":3,"publisher":"U.S. Geological Survey","slug":"basin-characteristics-rasters-for-new-jersey-streamstats-2022","spatial_centroid":{"lat":40.30036,"lon":-75.54418000000001},"spatial_shape":{"coordinates":[[[-76.8713,38.788],[-76.8713,42.5689],[-73.5535,42.5689],[-73.5535,38.788],[-76.8713,38.788]]],"type":"Polygon"},"theme":["geospatial"],"title":"Basin characteristics rasters for New Jersey StreamStats 2022","type":"dataset"},{"_score":6.699102,"_sort":[1789080638651,6.699102,1,"2f990be1-ed62-4a54-81ea-846dc4140983"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"U.S. Geological Survey - CERSC Data Management Services Project","hasEmail":"mailto:datamgt@usgs.gov"},"description":"This ArcView shapefile contains a polygon representation\nof numerous themes for the Decker coalfield. These\nthemes are listed in the process steps.  Its sole purpose is\nto allow the users to perform multiple theme queries. This\ntheme was created specifically for the National Coal\nResources Assessment in the Northern Rocky Mountains\nand Great Plains Region.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9X9GH8G","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.60a808b5d34ea221ce4e5663.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_60a808b5d34ea221ce4e5663","keyword":["Anderson-Canyon coal zone","Big Horn County","Decker Coalfield","Federal coal ownership","Minimum overburden","Montana","Northern Rocky Mountains and Great Plains","Powder River Basin","Powder River County","Rosebud County","Total, net coal resources","USGS:60a808b5d34ea221ce4e5663","coal","economy","environment","geoscientificInformation","nca2000","nrockiescoal","powderbasincoal"],"modified":"2021-05-21T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-107.0195, 45.0003, -105.7425, 45.5365","theme":["geospatial"],"title":"Unioned layer for the the Decker coalfield, Montana (dkfing.shp)"},"description":"This ArcView shapefile contains a polygon representation\nof numerous themes for the Decker coalfield. 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Beginning in the Rogue River-Siskiyou National Forest, \nthe South Fork Coquille River gains the Middle Fork Coquille River (drainage area 798 square kilometers) and shortly thereafter the \nNorth Fork Coquille River (749 square kilometers). In cooperation with the U.S. Army Corps of Engineers, the U.S. Geological Survey \ncompleted a reconnaissance-level assessment of channel condition and bed-material transport relevant to the permitting of in-stream \ngravel extraction along the the South Fork Coquille River from river kilometer (RKM) 115.4 near its confluence with Upper Land Creek \nto RKM 58.5 at its confluence with the North Fork Coquille River, the mainstem Coquille River from RKM 58.5 at the confluence of the \nSouth and North Forks of the Coquille River to its mouth, the Middle Fork Coquille River from RKM 15.4 to its confluence with the \nSouth Fork Coquille River, and the North Fork Coquille River from RKM 14.6 to its confluence with the South Fork Coquille River. To \nsupport these analyses, digital channel maps were produced to depict channel and floodplain conditions in the Coquille River basin \nfrom different time periods. GIS layers defining the wetted channel and bar features and channel centerline of Hunter Creek were \ndeveloped for four time periods: 1939, 1967, 2005, and 2009. For this project, the active channel was defined as area typically \ninundated during annual high flows, and includes the low-flow channel as well as side channels, islands, and channel-flanking gravel \nbars. The wetted channel and bar feature datasets were developed by digitizing from aerial photographs. Aerial photographs from 1939 \nand 1967 were scanned, rectified, and mosaicked for this project (See metadata for each photograph set for more information on the \nrectification process and resolution of each dataset). 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Beginning in the Rogue River-Siskiyou National Forest, \nthe South Fork Coquille River gains the Middle Fork Coquille River (drainage area 798 square kilometers) and shortly thereafter the \nNorth Fork Coquille River (749 square kilometers). In cooperation with the U.S. Army Corps of Engineers, the U.S. Geological Survey \ncompleted a reconnaissance-level assessment of channel condition and bed-material transport relevant to the permitting of in-stream \ngravel extraction along the the South Fork Coquille River from river kilometer (RKM) 115.4 near its confluence with Upper Land Creek \nto RKM 58.5 at its confluence with the North Fork Coquille River, the mainstem Coquille River from RKM 58.5 at the confluence of the \nSouth and North Forks of the Coquille River to its mouth, the Middle Fork Coquille River from RKM 15.4 to its confluence with the \nSouth Fork Coquille River, and the North Fork Coquille River from RKM 14.6 to its confluence with the South Fork Coquille River. To \nsupport these analyses, digital channel maps were produced to depict channel and floodplain conditions in the Coquille River basin \nfrom different time periods. GIS layers defining the wetted channel and bar features and channel centerline of Hunter Creek were \ndeveloped for four time periods: 1939, 1967, 2005, and 2009. For this project, the active channel was defined as area typically \ninundated during annual high flows, and includes the low-flow channel as well as side channels, islands, and channel-flanking gravel \nbars. The wetted channel and bar feature datasets were developed by digitizing from aerial photographs. Aerial photographs from 1939 \nand 1967 were scanned, rectified, and mosaicked for this project (See metadata for each photograph set for more information on the \nrectification process and resolution of each dataset). 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Climate data in the form of temperature, relative humidity, barometric pressure, and precipitation 3-days leading up to sampling date are also included. There various forms to calculate fluxes using cone and frustrum shapes that were compared to LiDAR scans fromi the field, therefore surface area and volume for each geometric shape is also included in the dataset.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P164M78X","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.6647ad62d34e1955f5a4418e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6647ad62d34e1955f5a4418e","keyword":["Arkansas","Illinois","Louisiana","USGS:6647ad62d34e1955f5a4418e","carbon","cypress knees","cypress swamp","environment","freshwater wetlands","methane"],"modified":"2024-06-18T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-92.6367, 29.7644, -87.9785, 37.9269","theme":["geospatial"],"title":"Methane emissions associated with bald cypress knees across the Mississippi River Alluvial Valley"},"description":"Data shows CH4 fluxes from the upper portion of cypress knees across various climate and flooding gradients of the North American Baldcypress Swamp Network in the Mississippi River Alluvial Valley. 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Our results indicate coherent and logical class boundaries, and suggest that the dataset has promise for expanded use in ASC.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/ab7f4c13-3c3a-4c67-9e6f-34836187522f","harvest_record_raw":"https://catalog.data.gov/harvest_record/ab7f4c13-3c3a-4c67-9e6f-34836187522f/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_647d6c4dd34eac007b55d050","keyword":["Lake Huron","Lake Michigan","USGS:647d6c4dd34eac007b55d050","bathymetry","biota","cartography","elevation","environment","geoscientificInformation","inlandWaters","lakebed acoustic reflectivity","lakebed characteristics","single-beam echo sounder"],"last_harvested_date":"2026-09-10T22:50:24.285147","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"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"lakebed-features-extracted-from-single-beam-sonar-in-two-laurentian-great-lakes","spatial_centroid":{"lat":43.65754,"lon":-84.73678},"spatial_shape":{"coordinates":[[[-87.8617,42.0159],[-87.8617,46.12],[-80.0494,46.12],[-80.0494,42.0159],[-87.8617,42.0159]]],"type":"Polygon"},"theme":["geospatial"],"title":"Lakebed features extracted from single-beam sonar in two Laurentian Great Lakes.","type":"dataset"},{"_score":7.5820556,"_sort":[1789080623393,7.5820556,3,"99e46224-39d2-4926-9145-205f4393b14a"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Matthew A Struckhoff","hasEmail":"mailto:mstruckhoff@usgs.gov"},"description":"The orthoimagery, digital surface model and point cloud presented here document conditions at Lincoln Park in Milwaukee, Wisconsin. Photos were collected in August 2021, prior to planned ecological restoration within the Milwaukee Estuary Area of Concern. Aerial images were collected using a DJI Phantom with FC6310 sensor at an elevation of approximately 100 meters above the study site. Data were processed using structure-from-motion techniques to generate a point cloud (.las), digital surface model (DSM; .tif) and an orthomosaic (.tif) for the site. Products provide baseline information about terrestrial and emergent wetland communities against which to compare changes following restoration. Ground control points used to georeference the imagery and elevation products are provided as a .csv file, along with a processing report generated by the structure-from-motion processing software.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13ASWPH","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.6a2053dcb66b01180072ea27.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a2053dcb66b01180072ea27","keyword":["Lincoln Park","Milwaukee","Milwaukee County","Milwaukee River","USGS:6a2053dcb66b01180072ea27","United States","Wisconsin","biota","digital elevation models","ecosystem monitoring","elevation","environment","geospatial datasets","imageryBaseMapsEarthCover","remediation","structure from motion","vegetation"],"modified":"2026-06-29T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-87.935098, 43.115455, -87.905877, 43.095197","theme":["geospatial"],"title":"Pre-restoration orthomosaic and elevation products for Lincoln Park, Milwaukee, Wisconsin, August 2021"},"description":"The orthoimagery, digital surface model and point cloud presented here document conditions at Lincoln Park in Milwaukee, Wisconsin. 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The presence and spread of these genes in non-clinical and non-agricultural environments has created the need for background investigations to enhance our understanding of the magnitude and risks associated with this emerging field (Allen and others, 2010). The current global economic costs of antibiotic resistant microorganisms is about 5.8 trillion USD, which is approximately equivalent to the combined GDP of Germany and the United Kingdom (Taylor and others, 2014). In this study we screened soil and sediment samples for the presence of 15 antibiotic resistance gene targets and 5 species of Vibrio (a marker of marine inundation) to determine natural background concentrations. These data provide a foundation to address background prevalence of these genetic targets in the northeastern United States (U.S.) to address regional influences (sources of pollutants) and to contrast future influences due to sea-level rise and large scale storms.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7XS5SH2","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.356648ef-627a-40c0-8e13-d71276879f5b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_356648ef-627a-40c0-8e13-d71276879f5b","keyword":["Connecticut","Delaware","District of Columbia","Estuarine","Intertidal","Maine","Maryland","Massachusetts","New Hampshire","New Jersey","New York","Pennsylvania","Rhode Island","Sediment","Soil","South Carolina","Surface","USGS:356648ef-627a-40c0-8e13-d71276879f5b","Virginia","biota","contamination and pollution","environment","environmental health (human)","field monitoring stations","field sampling","genetic diversity","health","microbiology","soil resources","unconsolidated deposits"],"modified":"2020-10-13T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-79.303957, 33.352132, -66.983372, 44.972041","theme":["geospatial"],"title":"Digital Polymerase Chain Reaction (dPCR) Data from the Sediment-Bound Contaminant Resiliency and Response Strategy Pilot Study, Northeastern United States, 2015"},"description":"Due to the recognized proliferation and spread of antibiotic resistance genes by anthropogenic use of antibiotics for human, agriculture and aquaculture purposes, antibiotic resistance genes have been defined as an emerging contaminant (Laxminarayan and others, 2013; Rodriguez-Rojas and others, 2013; Niu and others, 2016). 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Geospatial Information </a>- Stream reach and catchment shapefiles </li> <li><a href=\"https://www.sciencebase.gov/catalog/item/6682f50bd34e57e93663d65a\"> 2. Model Inputs </a> - Meteorological data, river network matrices, and stream temperature observations </li> <li><a href=\"https://www.sciencebase.gov/catalog/item/6682f522d34e57e93663d65e\"> 3. Model Code </a>- Python files and README for reproducing model training and evaluation </li> <li><a href=\"https://www.sciencebase.gov/catalog/item/6682f545d34e57e93663d665\"> 4. Coarse Model </a>- Trained coarse stream temperature model to be downscaled </li> <li><a href=\"https://www.sciencebase.gov/catalog/item/6682f556d34e57e93663d668\"> 5. Model Outputs </a>- Model simulation outputs and evaluation metrics </li> </p>\n<p>The publication associated with this model archive is: Fan, Yingda, Runlong Yu, Janet R. Barclay, Alison P. Appling, Yiming Sun, Yiqun Xie, and Xiaowei Jia. 2025. \"Multi-Scale Graph Learning for Anti-Sparse Downscaling.\"  In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. Washington, DC, USA: AAAI Press.</p> <p>This data compilation was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Environmental System Science Data Management Program, as part of the ExaSheds project, under Award Number 89243021SSC000068. Work was also supported by the U.S. Geological Survey, Water Availability and Use Science Program.</p>","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1UP5DXN","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.66787f3ed34efbe36238c80a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_66787f3ed34efbe36238c80a","keyword":["DE","Delaware","MD","Maryland","NJ","NY","New Jersey","New York","PA","Pennsylvania","US","USGS:66787f3ed34efbe36238c80a","United States","deep learning","downscaling","environment","inlandWaters","machine learning","mathematical simulation","modeling","streams","water resources","water temperature"],"modified":"2025-03-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-76.3879905924101, 38.7894548062558, -74.380785128688, 42.4544544671721","theme":["geospatial"],"title":"Downscaling and multi-scale modeling of stream temperature in five watersheds of the Delaware River Basin, 1979-2021"},"description":"<p>This model archive (<a href=\"https://www.sciencebase.gov/catalog/item/66787f3ed34efbe36238c80a\">Fan et al. 2025a</a>) provides all data, code, and model outputs used in the corresponding manuscript (Fan et al. 2025b) to test machine learning (ML) methods for downscaling and multi-scale modeling of stream temperature to combine an ML model and/or input data at coarse spatial resolution with an ML model and/or input data at fine spatial resolution to predict stream temperatures at fine spatial resolution in a watershed.</p>\n<p>The data are organized into these child items: <li><a href=\"https://www.sciencebase.gov/catalog/item/6682f4f8d34e57e93663d655\"> 1. 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Appling, Yiming Sun, Yiqun Xie, and Xiaowei Jia. 2025. \"Multi-Scale Graph Learning for Anti-Sparse Downscaling.\"  In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. Washington, DC, USA: AAAI Press.</p> <p>This data compilation was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Environmental System Science Data Management Program, as part of the ExaSheds project, under Award Number 89243021SSC000068. 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Backscatter data are provided as separate grids depending on mapping system and processing method. These metadata describe acoustic-backscatter data collected by California State University, Monterey Bay and processed by the U.S. Geological Survey. The raster data files are included in \"BackscatterD_CSUMB_SWATH_MontereyCanyon.zip,\" which is accessible from https://doi.org/10.5066/F7XD0ZQ4. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series\u2014Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016\u20131072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072.\nThe acoustic-backscatter map of Monterey Canyon and Vicinity, California, were generated from acoustic-backscatter data collected by the U.S. Geological Survey (USGS), by Monterey Bay Aquarium Research Institute (MBARI), and by California State University, Monterey Bay (CSUMB). Mapping for the entire map area was completed between 1998 and 2014 using a combination of 30-kHz Simrad EM-300 and 200-kHz/400-kHz Reson 7125 multibeam echosounders, as well as 234-kHz and 468-kHz SEA SWATHplus bathymetric sidescan-sonar systems. The CSUMB mapping missions were completed in 2008 and 2009. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://cmgds.marine.usgs.gov/data/csmp/MontereyCanyon/data_catalog_MontereyCanyon.html","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.557613d6e4b032353cae50e0.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_557613d6e4b032353cae50e0","keyword":["Acoustic Reflectivity","CMGP","Coastal and Marine Geology Program","Continental/Island Shelf","Marine Nearshore Subtidal","Marine Offshore Subtidal","Monterey County","Moss Landing","Northern California Ecoregion","PCMSC","Pacific Coastal and Marine Science Center","Pacific Ocean","Rock Substrate","State of California","Transform Continental Margin","U.S. Geological Survey","USGS","USGS:557613d6e4b032353cae50e0","Unconsolidated Mineral Substrate","distributions","environment","geoscientificInformation","geospatial datasets","image mosaics","imageryBaseMapsEarthCover","interferometric sonar","maps and atlases","marine geophysics","multibeam echo sounder","oceans","physical habitats and geomorphology","sea-floor acoustic reflectivity","sea-floor characteristics","seabed","sonar methods"],"modified":"2022-04-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-122.06, 36.68, -121.74, 36.84","theme":["geospatial"],"title":"BackscatterD [CSUMB Swath]--Monterey Canyon and Vicinity, California"},"description":"This part of DS 781 presents data for the acoustic-backscatter map of Monterey Canyon and Vicinity map area, California. Backscatter data are provided as separate grids depending on mapping system and processing method. These metadata describe acoustic-backscatter data collected by California State University, Monterey Bay and processed by the U.S. Geological Survey. The raster data files are included in \"BackscatterD_CSUMB_SWATH_MontereyCanyon.zip,\" which is accessible from https://doi.org/10.5066/F7XD0ZQ4. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series\u2014Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016\u20131072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072.\nThe acoustic-backscatter map of Monterey Canyon and Vicinity, California, were generated from acoustic-backscatter data collected by the U.S. Geological Survey (USGS), by Monterey Bay Aquarium Research Institute (MBARI), and by California State University, Monterey Bay (CSUMB). Mapping for the entire map area was completed between 1998 and 2014 using a combination of 30-kHz Simrad EM-300 and 200-kHz/400-kHz Reson 7125 multibeam echosounders, as well as 234-kHz and 468-kHz SEA SWATHplus bathymetric sidescan-sonar systems. The CSUMB mapping missions were completed in 2008 and 2009. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c414942c-26e4-4f39-b7e3-a1ab5c15289f","harvest_record_raw":"https://catalog.data.gov/harvest_record/c414942c-26e4-4f39-b7e3-a1ab5c15289f/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_557613d6e4b032353cae50e0","keyword":["Acoustic Reflectivity","CMGP","Coastal and Marine Geology Program","Continental/Island Shelf","Marine Nearshore Subtidal","Marine Offshore Subtidal","Monterey County","Moss Landing","Northern California Ecoregion","PCMSC","Pacific Coastal and Marine Science Center","Pacific Ocean","Rock Substrate","State of California","Transform Continental Margin","U.S. Geological Survey","USGS","USGS:557613d6e4b032353cae50e0","Unconsolidated Mineral Substrate","distributions","environment","geoscientificInformation","geospatial datasets","image mosaics","imageryBaseMapsEarthCover","interferometric sonar","maps and atlases","marine geophysics","multibeam echo sounder","oceans","physical habitats and geomorphology","sea-floor acoustic reflectivity","sea-floor characteristics","seabed","sonar methods"],"last_harvested_date":"2026-09-10T22:50:03.828834","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"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"backscatterd-csumb-swath-monterey-canyon-and-vicinity-california","spatial_centroid":{"lat":36.744,"lon":-121.93199999999999},"spatial_shape":{"coordinates":[[[-122.06,36.68],[-122.06,36.84],[-121.74,36.84],[-121.74,36.68],[-122.06,36.68]]],"type":"Polygon"},"theme":["geospatial"],"title":"BackscatterD [CSUMB Swath]--Monterey Canyon and Vicinity, California","type":"dataset"},{"_score":6.39347,"_sort":[1789080602517,6.39347,4,"48162b2b-c850-4d1e-842c-b391a0ebdf40"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Legna M. 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As new morphology observations and storm predictions become available, this analysis will be updated to describe how coastal vulnerability to storms will vary in the future. The data presented here include the dune and cliff morphology observations, as derived from light detection and ranging (lidar) surveys.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9N01XLQ","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.d84db9e3-daed-4a49-86c4-58b85cd94b8f.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_d84db9e3-daed-4a49-86c4-58b85cd94b8f","keyword":["CMGP","Caribbean Sea","Coastal and Marine Geology Program","OFR 2012-1084","Open-File Report 2012-1084","Puerto Rico","SPCMSC","St. Petersburg Coastal and Marine Science Center","U.S. Geological Survey","USGS","USGS:d84db9e3-daed-4a49-86c4-58b85cd94b8f","United States","coastal","coastal processes","elevation","environment","erosion","geographic information systems","geoscientificInformation","hurricanes","oceans"],"modified":"2025-06-12T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-67.2710, 17.9269, -65.5896, 18.5160","theme":["geospatial"],"title":"National Assessment of Hurricane-Induced Coastal Erosion Hazards: Puerto Rico"},"description":"This dataset contains information on the probabilities of hurricane-induced erosion (collision, inundation and overwash) for each 100-meter (m) section of the Puerto Rico coast for category 1-5 hurricanes. The analysis is based on a storm-impact scaling model that uses observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast will respond to the direct landfall of category 1-5 hurricanes. Hurricane-induced water levels, due to both surge and waves, are compared to beach and dune elevations to determine the probabilities of three types of coastal change: collision (dune erosion), overwash, and inundation. Data on dune and cliff morphology (dune crest and toe elevation, cliff top and toe elevation) and hydrodynamics (storm surge, wave setup and runup) are also included in this data set. As new morphology observations and storm predictions become available, this analysis will be updated to describe how coastal vulnerability to storms will vary in the future. The data presented here include the dune and cliff morphology observations, as derived from light detection and ranging (lidar) surveys.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/783bbaa3-2087-4bed-b08d-bd958aa319cb","harvest_record_raw":"https://catalog.data.gov/harvest_record/783bbaa3-2087-4bed-b08d-bd958aa319cb/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_d84db9e3-daed-4a49-86c4-58b85cd94b8f","keyword":["CMGP","Caribbean Sea","Coastal and Marine Geology Program","OFR 2012-1084","Open-File Report 2012-1084","Puerto Rico","SPCMSC","St. Petersburg Coastal and Marine Science Center","U.S. Geological Survey","USGS","USGS:d84db9e3-daed-4a49-86c4-58b85cd94b8f","United States","coastal","coastal processes","elevation","environment","erosion","geographic information systems","geoscientificInformation","hurricanes","oceans"],"last_harvested_date":"2026-09-10T22:50:02.517520","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"},"parent_identifier":null,"popularity":4,"publisher":"U.S. Geological Survey","slug":"national-assessment-of-hurricane-induced-coastal-erosion-hazards-puerto-rico","spatial_centroid":{"lat":18.16254,"lon":-66.59844000000001},"spatial_shape":{"coordinates":[[[-67.271,17.9269],[-67.271,18.516],[-65.5896,18.516],[-65.5896,17.9269],[-67.271,17.9269]]],"type":"Polygon"},"theme":["geospatial"],"title":"National Assessment of Hurricane-Induced Coastal Erosion Hazards: Puerto Rico","type":"dataset"},{"_score":4.0876245,"_sort":[1789080602292,4.0876245,3,"3bbdcc8a-3289-4d77-9562-da8ee7774b01"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"David M. 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Created from a collection of digital orthorectified images from aerial photographs of the study area acquired during July 1998 using Stewart and Kantrud classification system.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://dx.doi.org/10.5066/F7J101DQ","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.58c82170e4b0849ce97960dd.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_58c82170e4b0849ce97960dd","keyword":["CHASE LAKE WETLAND MANAGEMENT DISTRICT>STUTSMAN COUNTY WATERFOWL PRODUCTION AREA>Eddy","CHASE LAKE WETLAND MANAGEMENT DISTRICT>STUTSMAN COUNTY WATERFOWL PRODUCTION AREA>Shuler","Cottonwood Lake Study Area","EARTH SCIENCE>BIOSPHERE>AQUATIC ECOSYSTEMS>WETLANDS>LACUSTRINE WETLANDS","EARTH SCIENCE>BIOSPHERE>AQUATIC ECOSYSTEMS>WETLANDS>PALUSTRINE WETLANDS","EARTH SCIENCE>BIOSPHERE>TERRESTRIAL ECOSYSTEMS>WETLANDS>LACUSTRINE WETLANDS","EARTH SCIENCE>BIOSPHERE>TERRESTRIAL ECOSYSTEMS>WETLANDS>PALUSTRINE WETLANDS","EARTH SCIENCE>BIOSPHERE>VEGETATION>VEGETATION COVER","North Dakota","Northern Great Plains","Prairie Pothole Region","Stutsman County","USGS:58c82170e4b0849ce97960dd","United States of America","Vegetation changes","Wetlands","aquatic vegetation","biota","environment","inlandWaters","surface water (non-marine)","wetland ecosystems"],"modified":"2022-04-21T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-99.152055, 47.094686, -99.089965, 47.111714","theme":["geospatial"],"title":"Cottonwood Lake Study Area-Wetland Vegetation Zones-1998"},"description":"Spatial polygons of vegetation zones in 1998 for wetlands; P1, P2, P3, P4, P6, P7, P8, P11, T1, T2, T3, T4, T5, T6, T7, T8, and T9 within the Cottonwood Lake Study Area, Stutsman County, North Dakota. Created from a collection of digital orthorectified images from aerial photographs of the study area acquired during July 1998 using Stewart and Kantrud classification system.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/9abe3aeb-27aa-4a97-9f00-2f38acc61cfe","harvest_record_raw":"https://catalog.data.gov/harvest_record/9abe3aeb-27aa-4a97-9f00-2f38acc61cfe/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_58c82170e4b0849ce97960dd","keyword":["CHASE LAKE WETLAND MANAGEMENT DISTRICT>STUTSMAN COUNTY WATERFOWL PRODUCTION AREA>Eddy","CHASE LAKE WETLAND MANAGEMENT DISTRICT>STUTSMAN COUNTY WATERFOWL PRODUCTION AREA>Shuler","Cottonwood Lake Study Area","EARTH SCIENCE>BIOSPHERE>AQUATIC ECOSYSTEMS>WETLANDS>LACUSTRINE WETLANDS","EARTH SCIENCE>BIOSPHERE>AQUATIC ECOSYSTEMS>WETLANDS>PALUSTRINE WETLANDS","EARTH SCIENCE>BIOSPHERE>TERRESTRIAL ECOSYSTEMS>WETLANDS>LACUSTRINE WETLANDS","EARTH SCIENCE>BIOSPHERE>TERRESTRIAL ECOSYSTEMS>WETLANDS>PALUSTRINE WETLANDS","EARTH SCIENCE>BIOSPHERE>VEGETATION>VEGETATION COVER","North Dakota","Northern Great Plains","Prairie Pothole Region","Stutsman County","USGS:58c82170e4b0849ce97960dd","United States of America","Vegetation changes","Wetlands","aquatic vegetation","biota","environment","inlandWaters","surface water (non-marine)","wetland ecosystems"],"last_harvested_date":"2026-09-10T22:50:02.292586","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"},"parent_identifier":null,"popularity":3,"publisher":"U.S. Geological Survey","slug":"cottonwood-lake-study-area-wetland-vegetation-zones-1998","spatial_centroid":{"lat":47.1014972,"lon":-99.127219},"spatial_shape":{"coordinates":[[[-99.152055,47.094686],[-99.152055,47.111714],[-99.089965,47.111714],[-99.089965,47.094686],[-99.152055,47.094686]]],"type":"Polygon"},"theme":["geospatial"],"title":"Cottonwood Lake Study Area-Wetland Vegetation Zones-1998","type":"dataset"},{"_score":5.231908,"_sort":[1789080601966,5.231908,2,"4876dba8-45a2-4b8f-be48-fb2cd93a7521"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Todd M. Hoefen","hasEmail":"mailto:thoefen@usgs.gov"},"description":"Corescan\u00a9 Hyperspectral Core Imager Mark III (HCI-III) system data were acquired for hand samples, and subsequent billets made from the hand samples, collected during the U.S. Geological Survey (USGS) 2014, 2015, and 2016 field seasons in the Nabesna area of the eastern Alaska Range. The HCI-III system consists of three different components. The first is an imaging spectrometer which collects reflectance data with a spatial resolution of approximately 500 nanometers (nm) for 514 spectral channels covering the 450-2,500 nm wavelength range of the electromagnetic spectrum (Martini and others, 2017). The second is a spectrally calibrated RGB camera that collects high resolution imagery of the samples with a 50 micrometer (\u03bcm) pixel size. The third component is a three-dimensional (3D) laser profiler that measures sample texture, surface features and shape with a vertical resolution of 20 \u03bcm (Martini and others, 2017).\nCorescan reflectance data were provided for a total of 63 hand samples and four billets analyzed using the HCI-III system in three scans.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7057DWM","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.5a048975e4b0dc0b45b50689.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5a048975e4b0dc0b45b50689","keyword":["Alaska","Alaska Range","Bond Creek","Canada","ENVI","Environment for Visualizing Images","GGGSC","Geology, Geophysics, and Geochemistry Science Center","MRP","Mineral Resources Program","Nabesna","Nabesna A-2 quadrangle","Nabesna A-4 quadrangle","Nabesna B-4 quadrangle","Nikonda Creek","Nutzotin Mountains","Orange Hill","SWIR","USGS:5a048975e4b0dc0b45b50689","VNIR","Valdez-Cordova County","Wrangell Mountains","Wrangell\u2013Saint Elias National Preserve","Yukon Territory","economy","geoscientificInformation","hyperspectral imaging","imaging spectroscopy","infrared imaging","mineral resources","mineralogy","remote sensing","shortwave infrared","spectroscopy","visible-near infrared"],"modified":"2020-09-30T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-142.85154, 62.08211, -141.86813, 62.25573","theme":["geospatial"],"title":"Corescan\u00a9 hyperspectral reflectance data"},"description":"Corescan\u00a9 Hyperspectral Core Imager Mark III (HCI-III) system data were acquired for hand samples, and subsequent billets made from the hand samples, collected during the U.S. Geological Survey (USGS) 2014, 2015, and 2016 field seasons in the Nabesna area of the eastern Alaska Range. The HCI-III system consists of three different components. The first is an imaging spectrometer which collects reflectance data with a spatial resolution of approximately 500 nanometers (nm) for 514 spectral channels covering the 450-2,500 nm wavelength range of the electromagnetic spectrum (Martini and others, 2017). The second is a spectrally calibrated RGB camera that collects high resolution imagery of the samples with a 50 micrometer (\u03bcm) pixel size. The third component is a three-dimensional (3D) laser profiler that measures sample texture, surface features and shape with a vertical resolution of 20 \u03bcm (Martini and others, 2017).\nCorescan reflectance data were provided for a total of 63 hand samples and four billets analyzed using the HCI-III system in three scans.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/1496fe93-2128-4079-9c70-77c22724201c","harvest_record_raw":"https://catalog.data.gov/harvest_record/1496fe93-2128-4079-9c70-77c22724201c/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5a048975e4b0dc0b45b50689","keyword":["Alaska","Alaska Range","Bond Creek","Canada","ENVI","Environment for Visualizing Images","GGGSC","Geology, Geophysics, and Geochemistry Science Center","MRP","Mineral Resources Program","Nabesna","Nabesna A-2 quadrangle","Nabesna A-4 quadrangle","Nabesna B-4 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The USGS, in cooperation with the BLM, conducted a study to assess the hydrologic and soil resources and potential effects of infrastructure and grazing within the monument area. Publicly available data as well as data provided by the BLM were used to assess these resources and effects and to identify data gaps in the monument area. The input and output files for the Rangeland Hydrologic Erosion Model are also included in this data release. This model was used to assess potential impacts from different climate and rangeland scenarios on the hydrologic and soil resources in the monument.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9JVHA4Z","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.5af5f24be4b0da30c1b5f9f4.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5af5f24be4b0da30c1b5f9f4","keyword":["Do\u00f1a Ana","New Mexico","Organ Mountains-Desert Peaks National Monument","Surface water","USGS:5af5f24be4b0da30c1b5f9f4","agriculture","biota","boundaries","environment","geologic maps","groundwater","infrastructure","land use and land cover","landforms","location","modeling","natural resources","soil survey","water resource management","water resources"],"modified":"2020-08-25T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-107.44079589774, 31.774505662462, -106.24877929623, 32.712987220591","theme":["geospatial"],"title":"Database Associated with the Assessment of Soil and Water Resources in The Organ Mountains-Desert Peaks National Monument, New Mexico"},"description":"This U.S. Geological Survey (USGS) data release presents the geospatial data used to assess the hydrologic and soil resources of the Organ Mountains-Desert Peaks National Monument managed by the U.S. Bureau of Land Management (BLM) in Do\u00f1a Ana County, New Mexico. The USGS, in cooperation with the BLM, conducted a study to assess the hydrologic and soil resources and potential effects of infrastructure and grazing within the monument area. Publicly available data as well as data provided by the BLM were used to assess these resources and effects and to identify data gaps in the monument area. The input and output files for the Rangeland Hydrologic Erosion Model are also included in this data release. 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More information on the Landsat program can be found online at \nhttp://landsat.usgs.gov/.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P910SBW4","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.43a3dee6-28f1-4958-ad6f-a70146ae6709.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_43a3dee6-28f1-4958-ad6f-a70146ae6709","keyword":["Klamath Basin Restoration Agreement","Landsat","Oregon","Sprague River Basin","USGS:43a3dee6-28f1-4958-ad6f-a70146ae6709","Upper Klamath Basin","Williamson River Basin","Wood River Basin","environment","geoscientificInformation","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-123.382600, 41.991760, -120.601579, 43.492919","theme":["geospatial"],"title":"Upper Klamath Basin Landsat Image for July 28, 2004: Path 44 Row 31"},"description":"This subset of a Landsat-5 image shows part of the upper Klamath Basin. \nThe original images were obtained from the U.S. Geological Survey Earth \nResources Observation and Science Center (EROS). 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More information on the Landsat program can be found online at \nhttp://landsat.usgs.gov/.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/cad5870e-f5c0-4fe5-88a2-486eb049d88b","harvest_record_raw":"https://catalog.data.gov/harvest_record/cad5870e-f5c0-4fe5-88a2-486eb049d88b/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_43a3dee6-28f1-4958-ad6f-a70146ae6709","keyword":["Klamath Basin Restoration Agreement","Landsat","Oregon","Sprague River Basin","USGS:43a3dee6-28f1-4958-ad6f-a70146ae6709","Upper Klamath Basin","Williamson River Basin","Wood River Basin","environment","geoscientificInformation","inlandWaters"],"last_harvested_date":"2026-09-10T22:49:59.712587","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"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"upper-klamath-basin-landsat-image-for-july-28-2004-path-44-row-31","spatial_centroid":{"lat":42.5922236,"lon":-122.2701916},"spatial_shape":{"coordinates":[[[-123.3826,41.99176],[-123.3826,43.492919],[-120.601579,43.492919],[-120.601579,41.99176],[-123.3826,41.99176]]],"type":"Polygon"},"theme":["geospatial"],"title":"Upper Klamath Basin Landsat Image for July 28, 2004: Path 44 Row 31","type":"dataset"},{"_score":6.0550375,"_sort":[1789080596366,6.0550375,1,"6ae9de98-acf1-4665-8919-2eacf03771f1"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"PCMSC Science Data Coordinator","hasEmail":"mailto:pcmsc_data@usgs.gov"},"description":"Organic and inorganic carbon content was measured on sediments from the Gal\u00e1pagos Spreading Center, a hydrothermal spreading center in the eastern equatorial Pacific Ocean. Samples were collected within Parque Nacional Gal\u00e1pagos, Ecuador or international waters.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P14XRQHO","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.67feca2fd4be02350c44bbeb.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_67feca2fd4be02350c44bbeb","keyword":["CMHRP","Coastal and Marine Hazards and Resources Program","Galapagos Islands","Mineral resources","Nonliving resources","PCMSC","Pacific Coastal and Marine Science Center","Pacific Ocean","Physical/Chemical features","Substrate","U.S. Geological Survey","USGS","USGS:67feca2fd4be02350c44bbeb","carbon cycle","chemical analysis","environment","geochemistry","geoscientificInformation","marine geology","mineral resources","oceans","petroleum resources","push coring","sediment geochemistry","seep and vent ecosystems","subsampling"],"modified":"2026-08-14T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-94.383414, 0.747778, -85.840941, 2.549431","theme":["geospatial"],"title":"Carbon data from Gal\u00e1pagos Spreading Center sediments, collected Aug-Nov 2023"},"description":"Organic and inorganic carbon content was measured on sediments from the Gal\u00e1pagos Spreading Center, a hydrothermal spreading center in the eastern equatorial Pacific Ocean. Samples were collected within Parque Nacional Gal\u00e1pagos, Ecuador or international waters.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/bbf884c2-7bea-45a8-be60-806eb7a05fa6","harvest_record_raw":"https://catalog.data.gov/harvest_record/bbf884c2-7bea-45a8-be60-806eb7a05fa6/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_67feca2fd4be02350c44bbeb","keyword":["CMHRP","Coastal and Marine Hazards and Resources Program","Galapagos Islands","Mineral resources","Nonliving resources","PCMSC","Pacific Coastal and Marine Science Center","Pacific Ocean","Physical/Chemical features","Substrate","U.S. Geological Survey","USGS","USGS:67feca2fd4be02350c44bbeb","carbon cycle","chemical analysis","environment","geochemistry","geoscientificInformation","marine geology","mineral resources","oceans","petroleum resources","push coring","sediment geochemistry","seep and vent ecosystems","subsampling"],"last_harvested_date":"2026-09-10T22:49:56.366733","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"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"carbon-data-from-galapagos-spreading-center-sediments-collected-aug-nov-2023","spatial_centroid":{"lat":1.4684392,"lon":-90.9664248},"spatial_shape":{"coordinates":[[[-94.383414,0.747778],[-94.383414,2.549431],[-85.840941,2.549431],[-85.840941,0.747778],[-94.383414,0.747778]]],"type":"Polygon"},"theme":["geospatial"],"title":"Carbon data from Gal\u00e1pagos Spreading Center sediments, collected Aug-Nov 2023","type":"dataset"},{"_score":8.487324,"_sort":[1789080595042,8.487324,2,"5c5bee6d-3696-4712-9838-7e1f822c6775"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"James W. Cain","hasEmail":"mailto:jwcain@usgs.gov"},"description":"The elk (Cervus canadensis) of the Jemez herd reside primarily in and around the Valles Caldera National Preserve, west of Los Alamos, NM and along the mesa tops to the north and west of the Valles Caldera. The area has experienced two wildfires, the stand replacing Las Conchas Fire and the mixed severity Thompson Ridge fire, within the last decade, burning a total of 180,555 acres. The data used in this report was collected to examine the responses of elk to these wildfires and forest restoration treatments. The Jemez herd is only partially migratory, with residents that consistently remain on the Valles Caldera and individuals that travel to the surrounding lower elevation slopes depending on the year and snowpack levels. The most consistent migration during  winter was south towards Bandelier National Monument, while a few individuals migrated east to the Santa Clara Indian Reservation or west to Lake Fork Mesa. Moreover, while most individuals returned to the Caldera in the early spring, some migrated north to the higher elevations of the La Grulla Plateau. These migrations were relatively short, averaging only 14 miles with the longest only reaching 28 miles. The Caldera contains a mix of ponderosa pine forests, mixed-conifer forests, and open grasslands, while the lower elevation slopes are predominantly pinyon-juniper woodlands. The primary challenge for individuals migrating would be crossing NM State Route 4 when traveling to Bandelier National Monument.\nThese data provide the location of migration stopovers for elk from the Jemez Herd in New Mexico. They were developed from Brownian Bridge movement models using 61 migration sequences collected from a sample size of 24 adult elk comprising GPS locations collected every 5 or 6 hours.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9TKA3L8","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.620e4aa6d34e6c7e83baa35a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_620e4aa6d34e6c7e83baa35a","keyword":["Jemez Mountains","New Mexico","USGS:620e4aa6d34e6c7e83baa35a","United States","animal behavior","economy","environment","migration","migration (organisms)","migratory species"],"modified":"2022-04-07T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.5548, 35.7683, -106.3245, 36.0771","theme":["geospatial"],"title":"Migration Stopovers of Elk in the Jemez Herd in New Mexico"},"description":"The elk (Cervus canadensis) of the Jemez herd reside primarily in and around the Valles Caldera National Preserve, west of Los Alamos, NM and along the mesa tops to the north and west of the Valles Caldera. The area has experienced two wildfires, the stand replacing Las Conchas Fire and the mixed severity Thompson Ridge fire, within the last decade, burning a total of 180,555 acres. The data used in this report was collected to examine the responses of elk to these wildfires and forest restoration treatments. The Jemez herd is only partially migratory, with residents that consistently remain on the Valles Caldera and individuals that travel to the surrounding lower elevation slopes depending on the year and snowpack levels. The most consistent migration during  winter was south towards Bandelier National Monument, while a few individuals migrated east to the Santa Clara Indian Reservation or west to Lake Fork Mesa. Moreover, while most individuals returned to the Caldera in the early spring, some migrated north to the higher elevations of the La Grulla Plateau. These migrations were relatively short, averaging only 14 miles with the longest only reaching 28 miles. The Caldera contains a mix of ponderosa pine forests, mixed-conifer forests, and open grasslands, while the lower elevation slopes are predominantly pinyon-juniper woodlands. The primary challenge for individuals migrating would be crossing NM State Route 4 when traveling to Bandelier National Monument.\nThese data provide the location of migration stopovers for elk from the Jemez Herd in New Mexico. They were developed from Brownian Bridge movement models using 61 migration sequences collected from a sample size of 24 adult elk comprising GPS locations collected every 5 or 6 hours.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/aafb3736-ab78-468c-a117-a9dcafb43a2d","harvest_record_raw":"https://catalog.data.gov/harvest_record/aafb3736-ab78-468c-a117-a9dcafb43a2d/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_620e4aa6d34e6c7e83baa35a","keyword":["Jemez Mountains","New Mexico","USGS:620e4aa6d34e6c7e83baa35a","United States","animal behavior","economy","environment","migration","migration (organisms)","migratory species"],"last_harvested_date":"2026-09-10T22:49:55.042142","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"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"migration-stopovers-of-elk-in-the-jemez-herd-in-new-mexico","spatial_centroid":{"lat":35.89182,"lon":-106.46268},"spatial_shape":{"coordinates":[[[-106.5548,35.7683],[-106.5548,36.0771],[-106.3245,36.0771],[-106.3245,35.7683],[-106.5548,35.7683]]],"type":"Polygon"},"theme":["geospatial"],"title":"Migration Stopovers of Elk in the Jemez Herd in New Mexico","type":"dataset"},{"_score":7.4102983,"_sort":[1789080592799,7.4102983,8,"ec85f1e0-3c96-4031-a0c5-b9ffe87c515a"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"U.S. Geological Survey, Fort Collins Science Center (FORT)","hasEmail":"mailto:fortdatamanagement@usgs.gov"},"description":"The values in this raster are unit-less scores ranging from 0 to 1 that represent normalized dollars per acre damage claims from elk on Wyoming lands.\nThis raster is one of 9 inputs used to calculate the \"Normalized Importance Index.\"","distribution":[{"@type":"dcat:Distribution","accessURL":"http://dx.doi.org/10.5066/F7ZC80X7","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.5785005ee4b0e02680bf1dba.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5785005ee4b0e02680bf1dba","keyword":["Aquatic Habitat","Energy Development","IA","Landscape-Scale Conservation","SW Wyoming","Southwest Wyoming","Terrestrial Habitat","USA","USGS:5785005ee4b0e02680bf1dba","United States","WLCI","WLCI Integrated Assessment","Wyoming","Wyoming Landscape Conservation Initiative","environment","geoscientificinformation"],"modified":"2022-07-14T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-111.048706059, 40.891696265, -105.911978033, 43.470064046","theme":["geospatial"],"title":"WLCI - 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Important Agricultural Lands Assessment (Input Raster: Normalized Elk Damage Claims)","type":"dataset"},{"_score":4.475273,"_sort":[1789080592583,4.475273,1,"12abd4f8-c210-4993-bb53-9e39ed52a6b8"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"P. 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Potential alongshore flux of SRBs was also estimated to identify regions more or less likely to have SRBs deposited under each scenario.  This methodology was developed to explain SRB movement and redistribution in the alongshore, interpret observed re-oiling events, and thus inform re-oiling mitigation efforts.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://pubs.usgs.gov/of/2012/1234/datafiles.html","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.273e39b5-e67d-46aa-8124-5b7ad82f5c6e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_273e39b5-e67d-46aa-8124-5b7ad82f5c6e","keyword":["Alabama","Atlantic Ocean","CMGP","Choctawhatchee Bay","Coastal and Marine Geology Program","Delft3D","Florida","Fort Pickens","Gulf Shores","Gulf of Mexico","Little Lagoon","Mobile Bay","North America","Panama City","Pensacola Bay","SPCMSC","SRBs","Santa Rosa","St. Petersburg Coastal and Marine Science Center","U.S. Geological Survey","USGS","USGS:273e39b5-e67d-46aa-8124-5b7ad82f5c6e","United States","WHCMSC","Woods Hole Coastal and Marine Science Center","alongshore currents","coastal processes","contaminant transport","current","environment","geoscientificInformation","industrial pollution","mathematical modeling","numerical modeling","ocean processes","oceans","oceans and coastal","oceans and estuaries","petroleum","petroleum spills","physical/chemical features","pollution","predictions","sediment mobility","surf zone","surface residual balls","tarballs","wave","wave-driven currents"],"modified":"2020-10-13T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-88.720441, 29.395074, -85.410772, 30.696090","theme":["geospatial"],"title":"Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance:  peak wave period"},"description":"The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs).  During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach.  A method was developed for estimating SRB mobilization and alongshore movement.  A representative suite of wave conditions was identified from buoy data for April, 2010, until August, 2012, and used to drive a numerical model of the spatially-variant alongshore currents.  Potential mobilization of SRBs was estimated by comparing combined wave- and current-induced shear stress from the model to critical stress values for several sized SRBs. Potential alongshore flux of SRBs was also estimated to identify regions more or less likely to have SRBs deposited under each scenario.  This methodology was developed to explain SRB movement and redistribution in the alongshore, interpret observed re-oiling events, and thus inform re-oiling mitigation efforts.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/4af5bdf3-2c03-47a9-958b-e0e2f22968c6","harvest_record_raw":"https://catalog.data.gov/harvest_record/4af5bdf3-2c03-47a9-958b-e0e2f22968c6/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_273e39b5-e67d-46aa-8124-5b7ad82f5c6e","keyword":["Alabama","Atlantic Ocean","CMGP","Choctawhatchee Bay","Coastal and Marine Geology Program","Delft3D","Florida","Fort Pickens","Gulf Shores","Gulf of Mexico","Little Lagoon","Mobile Bay","North America","Panama City","Pensacola Bay","SPCMSC","SRBs","Santa Rosa","St. Petersburg Coastal and Marine Science Center","U.S. Geological Survey","USGS","USGS:273e39b5-e67d-46aa-8124-5b7ad82f5c6e","United States","WHCMSC","Woods Hole Coastal and Marine Science Center","alongshore currents","coastal processes","contaminant transport","current","environment","geoscientificInformation","industrial pollution","mathematical modeling","numerical modeling","ocean processes","oceans","oceans and coastal","oceans and estuaries","petroleum","petroleum spills","physical/chemical features","pollution","predictions","sediment mobility","surf zone","surface residual balls","tarballs","wave","wave-driven currents"],"last_harvested_date":"2026-09-10T22:49:52.583233","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"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"hydrodynamic-and-sediment-transport-model-application-for-osat3-guidance-peak-wave-period","spatial_centroid":{"lat":29.9154804,"lon":-87.3965734},"spatial_shape":{"coordinates":[[[-88.720441,29.395074],[-88.720441,30.69609],[-85.410772,30.69609],[-85.410772,29.395074],[-88.720441,29.395074]]],"type":"Polygon"},"theme":["geospatial"],"title":"Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance:  peak wave period","type":"dataset"},{"_score":5.231018,"_sort":[1789080592151,5.231018,3,"6fb3691a-55bc-462e-82ed-7db02769e05c"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael E. Wieczorek","hasEmail":"mailto:mewieczo@usgs.gov"},"description":"This data set represents the area of Hydrologic Landscape Regions (HLR) compiled for every catchment \nof NHDPlus for the conterminous United States. The source data set is a 100-meter version of Hydrologic \nLandscape Regions of the United States (Wolock, 2003). HLR groups watersheds on the basis of similarities \nin land-surface form, geologic texture, and climate characteristics.\n\t\t\nThe NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates \nmany of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset \n(NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, \nnaming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments \n(drainage areas) produced using a drainage enforcement technique first widely used in New England, \nand thus referred to as \"the New England Method.\" This technique involves \"burning in\" the 1:100,000-scale \nNHD and when available building \"walls\" using the National Watershed Boundary Dataset (WBD). The \nresulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree \nwith the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey \n(USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method \nproduces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus \ndataset is organized by 18 Production Units that cover the conterminous United States.\n\t\t\nThe NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's  Major River Basins (MRBs, \nCrawford and others, 2006).  MRB1, covering the New England and Mid-Atlantic River basins, contains \nNHDPlus Production Units 1 and 2.  MRB2, covering the South Atlantic-Gulf and Tennessee River basins, \ncontains NHDPlus Production Units 3 and 6.  MRB3, covering the Great Lakes, Ohio, Upper Mississippi, \nand Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9.  MRB4, covering \nthe Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper.  MRB5, covering \nthe Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production \nUnits 8, 11 and 12.  MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains \nNHDPlus Production Units 13, 14, 15 and 16.  MRB7, covering the Pacific Northwest River basins, \ncontains NHDPlus Production Unit 17.  MRB8, covering California River basins, contains NHDPlus \nProduction Unit 18.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9142BM0","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.93b2f75c-33bc-4cae-b48e-41b6f85d2e39.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_93b2f75c-33bc-4cae-b48e-41b6f85d2e39","keyword":["CALI","COGB","California","Catchment","Conterminous United States","GLMR","Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy","Hydrologic landscape regions","Inlandwaters","LMTG","Lower Mississippi, Arkansas-White-Red, and Texas-Gulf","MORI","MRB","MRB1","MRB2","MRB3","MRB4","MRB5","MRB6","MRB7","MRB8","Major River Basin","Missouri","NAWQA","NEMA","NHDPlus","New England and Mid-Atlantic","PANW","Pacific Northwest","Rio Grande, Colorado, and Great Basin","SAGT","SPARROW","South Atlantic-Gulf and Tennessee","USGS:93b2f75c-33bc-4cae-b48e-41b6f85d2e39","environment","geoscientificInformation","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-127.910792, 23.243486, -65.327751, 51.657387","theme":["geospatial"],"title":"Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Hydrologic Landscape Regions"},"description":"This data set represents the area of Hydrologic Landscape Regions (HLR) compiled for every catchment \nof NHDPlus for the conterminous United States. The source data set is a 100-meter version of Hydrologic \nLandscape Regions of the United States (Wolock, 2003). HLR groups watersheds on the basis of similarities \nin land-surface form, geologic texture, and climate characteristics.\n\t\t\nThe NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates \nmany of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset \n(NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, \nnaming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments \n(drainage areas) produced using a drainage enforcement technique first widely used in New England, \nand thus referred to as \"the New England Method.\" This technique involves \"burning in\" the 1:100,000-scale \nNHD and when available building \"walls\" using the National Watershed Boundary Dataset (WBD). The \nresulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree \nwith the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey \n(USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method \nproduces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus \ndataset is organized by 18 Production Units that cover the conterminous United States.\n\t\t\nThe NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's  Major River Basins (MRBs, \nCrawford and others, 2006).  MRB1, covering the New England and Mid-Atlantic River basins, contains \nNHDPlus Production Units 1 and 2.  MRB2, covering the South Atlantic-Gulf and Tennessee River basins, \ncontains NHDPlus Production Units 3 and 6.  MRB3, covering the Great Lakes, Ohio, Upper Mississippi, \nand Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9.  MRB4, covering \nthe Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper.  MRB5, covering \nthe Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production \nUnits 8, 11 and 12.  MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains \nNHDPlus Production Units 13, 14, 15 and 16.  MRB7, covering the Pacific Northwest River basins, \ncontains NHDPlus Production Unit 17.  MRB8, covering California River basins, contains NHDPlus \nProduction Unit 18.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/02f8fbc2-a880-46ed-be94-44c4d195a819","harvest_record_raw":"https://catalog.data.gov/harvest_record/02f8fbc2-a880-46ed-be94-44c4d195a819/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_93b2f75c-33bc-4cae-b48e-41b6f85d2e39","keyword":["CALI","COGB","California","Catchment","Conterminous United States","GLMR","Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy","Hydrologic landscape regions","Inlandwaters","LMTG","Lower Mississippi, Arkansas-White-Red, and Texas-Gulf","MORI","MRB","MRB1","MRB2","MRB3","MRB4","MRB5","MRB6","MRB7","MRB8","Major River Basin","Missouri","NAWQA","NEMA","NHDPlus","New England and Mid-Atlantic","PANW","Pacific Northwest","Rio Grande, Colorado, and Great Basin","SAGT","SPARROW","South Atlantic-Gulf and Tennessee","USGS:93b2f75c-33bc-4cae-b48e-41b6f85d2e39","environment","geoscientificInformation","inlandWaters"],"last_harvested_date":"2026-09-10T22:49:52.151220","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"},"parent_identifier":null,"popularity":3,"publisher":"U.S. Geological Survey","slug":"attributes-for-nhdplus-catchments-version-1-1-for-the-conterminous-united-states-hydrologi","spatial_centroid":{"lat":34.6090464,"lon":-102.8775756},"spatial_shape":{"coordinates":[[[-127.910792,23.243486],[-127.910792,51.657387],[-65.327751,51.657387],[-65.327751,23.243486],[-127.910792,23.243486]]],"type":"Polygon"},"theme":["geospatial"],"title":"Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Hydrologic Landscape Regions","type":"dataset"},{"_score":9.802837,"_sort":[1789080591719,9.802837,2,"0c430a62-baaa-4085-9802-1fdadefee8d6"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jeff Starn","hasEmail":"mailto:jjstarn@usgs.gov"},"description":"Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large-scale numerical models can be resource intensive. With the data and computer codes in this data release users can (1) reconstruct and run 115 General Simulation Models (GSMs) of groundwater flow, (2) calculate groundwater age metrics at selected GSM cells, (3) train a boosted regression tree model using the provided data, (4) predict three-dimensional continuous groundwater age metrics across the Glacial Principal Aquifer, and (5) predict tritium concentrations at wells for comparison with measured tritium concentrations. The computer codes in this data release are in the form of Python scripts and Jupyter Notebooks. Users will need to have these Python resources installed on their computers to run the codes. \nInstructions for creating the Python environment can be found in the file Creating the Python environment.txt.\nUsers who would rather not run the scripts but who wish to obtain the final data sets can do so by downloading the file Output--Predictions.7z. \nUsers who wish to reproduce the data sets in this release can do so by downloading, unzipping, and running the data workflow in Starn_GW_Residence_Time_Data_and_Scripts.7z. The codes in this file use relative pathnames, so the directory structure within this file should not be changed. The \".7z\" file extension indicates 7-Zip files, http://www.7-zip.org\nExecutables--MODFLOW and MODPATH executable files provided for convenience. These are Windows 64-bit versions.\nStep 1--Create General Simulation Models--Codes to create 115 GSMs\nStep 2--Data preparation--Calculate residence time distributions at selected GSM cells\nStep 3--Metamodel training--Train a boosted regression tree metamodel (XGBoost)\nStep 4--Metamodel prediction--Predict age metrics throughout the Glacial Aquifer\nStep 5--Tritium simulation --Calculate tritium concentration at selected wells","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9BNWWCU","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.5ef5eb0e82ced62aaae8ccc2.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5ef5eb0e82ced62aaae8ccc2","keyword":["Boosted regression tree","Connecticut","Cycle 3","Glacial Principal Aquifer","Groundwater age","Groundwater model","Illinois","Indiana","Iowa","Kansas","Maine","Massachusetts","Metamodel","Michigan","Minnesota","Missouri","Montana","NAWQA","Nebraska","New Hampshire","New Jersey","New York","North Dakota","Ohio","Pennsylvania","Residence time distribution","Rhode Island","South Dakota","USGS:5ef5eb0e82ced62aaae8ccc2","Vermont","Washington","Wisconsin"],"modified":"2020-12-04T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.1591, 35.7294, -66.6053, 51.5004","theme":["geospatial"],"title":"Data for three-dimensional distribution of groundwater residence time metrics in the glaciated United States using metamodels trained on general numerical simulation models"},"description":"Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. 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Users will need to have these Python resources installed on their computers to run the codes. \nInstructions for creating the Python environment can be found in the file Creating the Python environment.txt.\nUsers who would rather not run the scripts but who wish to obtain the final data sets can do so by downloading the file Output--Predictions.7z. \nUsers who wish to reproduce the data sets in this release can do so by downloading, unzipping, and running the data workflow in Starn_GW_Residence_Time_Data_and_Scripts.7z. The codes in this file use relative pathnames, so the directory structure within this file should not be changed. The \".7z\" file extension indicates 7-Zip files, http://www.7-zip.org\nExecutables--MODFLOW and MODPATH executable files provided for convenience. 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This camera is part of the USGS CoastCam network, supported by the Total Water Level/Coastal Change Project under the Coastal and Marine Hazards and Resources Program (CMHRP). To learn more about this specific camera visit https://www.usgs.gov/centers/spcmsc/science/using-video-imagery-study-coastal-change-sand-key-florida.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P146YVZF","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.4ab47ffb-3aab-414c-b1ca-20030ed632b6.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_4ab47ffb-3aab-414c-b1ca-20030ed632b6","keyword":["CMHRP","CoastCam","Coastal and Marine Hazards and Resources Program","SPCMSC","Saint Petersburg","Sand Key","St. Petersburg Coastal and Marine Science Center","State of Florida","Total Water Level","U.S. Geological Survey","USGS","USGS:4ab47ffb-3aab-414c-b1ca-20030ed632b6","coastal processes","ecosystem management","environment","field methods","geomorphology","geoscientificInformation","image analysis","image collections","ocean sciences","optical methods","photography","remote sensing","video monitoring"],"modified":"2024-05-10T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-82.85683390, 27.89971348, -82.82813005, 27.93844384","theme":["geospatial"],"title":"USGS CoastCam at Sand Key, Florida: Intrinsic and Extrinsic Calibration Data (Camera 1)"},"description":"Two digital video cameras were installed at Sand Key, Florida (FL), facing south (camera 1) and north (camera 2) along the beach. 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To learn more about this specific camera visit https://www.usgs.gov/centers/spcmsc/science/using-video-imagery-study-coastal-change-sand-key-florida.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/bd0421e2-1485-493f-92bc-d7cefc30e557","harvest_record_raw":"https://catalog.data.gov/harvest_record/bd0421e2-1485-493f-92bc-d7cefc30e557/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_4ab47ffb-3aab-414c-b1ca-20030ed632b6","keyword":["CMHRP","CoastCam","Coastal and Marine Hazards and Resources Program","SPCMSC","Saint Petersburg","Sand Key","St. Petersburg Coastal and Marine Science Center","State of Florida","Total Water Level","U.S. Geological Survey","USGS","USGS:4ab47ffb-3aab-414c-b1ca-20030ed632b6","coastal processes","ecosystem management","environment","field methods","geomorphology","geoscientificInformation","image analysis","image collections","ocean sciences","optical methods","photography","remote sensing","video monitoring"],"last_harvested_date":"2026-09-10T22:49:50.321594","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"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"usgs-coastcam-at-sand-key-florida-intrinsic-and-extrinsic-calibration-data-camera-1","spatial_centroid":{"lat":27.915205624,"lon":-82.84535235999999},"spatial_shape":{"coordinates":[[[-82.8568339,27.89971348],[-82.8568339,27.93844384],[-82.82813005,27.93844384],[-82.82813005,27.89971348],[-82.8568339,27.89971348]]],"type":"Polygon"},"theme":["geospatial"],"title":"USGS CoastCam at Sand Key, Florida: Intrinsic and Extrinsic Calibration Data (Camera 1)","type":"dataset"},{"_score":6.971644,"_sort":[1789080589854,6.971644,2,"baf7dc9c-04b1-4a55-ab3a-c01befcadbf3"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"U.S. Geological Survey - CERSC Data Management Services Project","hasEmail":"mailto:datamgt@usgs.gov"},"description":"This is a shapefile and ARC/INFO coverage depicting\nstructure contours on top of the Trout Creek Sandstone\nMbr., Danforth Hills coal field.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9AF5AWL","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.60a8047ed34ea221ce4e54c9.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_60a8047ed34ea221ce4e54c9","keyword":["Colorado","Colorado Plateau","Colorado Plateau Coal Assessment","Danforth Hills","Mesaverde Formation","National Coal Resource Assessment (NCRA)","Piceance Basin","Trout Creek Sandstone Mbr.","USGS:60a8047ed34ea221ce4e54c9","Williams Fork Formation","coal","coal fields","coal rank","cplateaucoal","dhillscoal","economy","environment","geoscientificInformation","nca2000"],"modified":"2021-05-21T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-108.2131, 40.0036, -107.6930, 40.3982","theme":["geospatial"],"title":"Structure contours on top of the Trout Creek Sandstone, Danforth Hills, Colorado (danstrcg)"},"description":"This is a shapefile and ARC/INFO coverage depicting\nstructure contours on top of the Trout Creek Sandstone\nMbr., Danforth Hills coal field.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/305737e3-449e-42e1-b480-cd493475ce9c","harvest_record_raw":"https://catalog.data.gov/harvest_record/305737e3-449e-42e1-b480-cd493475ce9c/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_60a8047ed34ea221ce4e54c9","keyword":["Colorado","Colorado Plateau","Colorado Plateau Coal Assessment","Danforth Hills","Mesaverde Formation","National Coal Resource Assessment (NCRA)","Piceance Basin","Trout Creek Sandstone Mbr.","USGS:60a8047ed34ea221ce4e54c9","Williams Fork Formation","coal","coal fields","coal rank","cplateaucoal","dhillscoal","economy","environment","geoscientificInformation","nca2000"],"last_harvested_date":"2026-09-10T22:49:49.854749","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"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"structure-contours-on-top-of-the-trout-creek-sandstone-danforth-hills-colorado-danstrcg","spatial_centroid":{"lat":40.16144,"lon":-108.00506},"spatial_shape":{"coordinates":[[[-108.2131,40.0036],[-108.2131,40.3982],[-107.693,40.3982],[-107.693,40.0036],[-108.2131,40.0036]]],"type":"Polygon"},"theme":["geospatial"],"title":"Structure contours on top of the Trout Creek Sandstone, Danforth Hills, Colorado (danstrcg)","type":"dataset"},{"_score":4.66033,"_sort":[1789080589399,4.66033,1,"29333b7f-30a2-4ab8-9d92-714f2221576d"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Theresa Smith","hasEmail":"mailto:theresasmith@usgs.gov"},"description":"Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast.\nThe Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) in cooperation with the Massachusetts Office of Coastal Zone Management, has compiled reliable historical shoreline data along open-facing sections of the Massachusetts coast under the Massachusetts Shoreline Change Mapping and Analysis Project 2013 Update. Two oceanfront shorelines for Massachusetts (approximately 1,800 km) were (1) delineated using 2008/09 color aerial orthoimagery, and (2) extracted from topographic LIDAR datasets (2007) obtained from NOAA's Ocean Service, Coastal Services Center. The new shorelines were integrated with existing Massachusetts Office of Coastal Zone Management (MA CZM) and USGS historical shoreline data in order to compute long- and short-term rates using the latest version of the Digital Shoreline Analysis System (DSAS).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://pubs.usgs.gov/of/2012/1183/","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.38affa55-54ca-406a-b787-e09e8c142280.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_38affa55-54ca-406a-b787-e09e8c142280","keyword":["Atlantic Coast","CMGP","CSC","Chatham","Coastal Services Center","Coastal and Marine Geology Program","DSAS","Digital Shoreline Analysis System","Endpoint Rate","Linear Regression Rate","MA CZM","Massachusetts","Massachusetts Coastal Zone Management","Massachusetts Shoreline Change Project","NOAA","National Assessment of Shoreline Change Project","National Oceanic and Atmospheric Administration","New England","Nobska Point","Shoreline Change Rate","South Cape Cod","Stage Harbor Light","U.S. Army Corps of Engineers","U.S. Geological Survey","USACE","USGS","USGS:38affa55-54ca-406a-b787-e09e8c142280","United States","WHCMSC","Woods Hole","Woods Hole Coastal and Marine Science Center","coastal processes","environment","geoscientificInformation","geospatial datasets","oceans"],"modified":"2021-11-16T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-70.654471, 41.517485, -69.969121, 41.672666","theme":["geospatial"],"title":"Shorelines used to calculate shoreline change statistics from the South Cape Cod coastal region of Massachusetts from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_shorelines.shp)"},"description":"Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. 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This data release provides flight line data for the survey and a report describing the dataset and procedures","distribution":[{"@type":"dcat:Distribution","accessURL":"http://dx.doi.org/10.5066/F7DN435W","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.5953463de4b062508e3c7a29.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5953463de4b062508e3c7a29","keyword":["Alaska","Alaska Range","Bond Creek","Canada","Chisana River","ENVI","Environment for Visualizing Images","HyMap","MRP","Mineral Resources Program","Nabesna","Nabesna Glacier","Nabesna River","Nutzotin Mountains","Orange Hill","SWIR","USGS:5953463de4b062508e3c7a29","VNIR","Wrangell Mountains","Wrangell-Saint Elias National Park","Wrangell-Saint Elias National Preserve","Yukon Territory","hyperspectral imaging","imaging spectroscopy","infrared imaging","remote sensing","shortwave infrared","spectroscopy","visible light imaging","visible-near infrared"],"modified":"2020-09-29T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-143.37637891, 62.005457724, -141.921433884, 62.349099479","theme":["geospatial"],"title":"Airborne imaging spectrometer data collected for characterizing mineral resources near Nabesna, Alaska, 2014: CAL03"},"description":"Imaging spectrometer (hyperspectral) data were collected using the HyMap\u2122 sensor over the Nabesna Area of Interest (AOI) in the eastern Alaska Range, July 14 and July 21, 2014. The primary study area was a remote part of the eastern Alaska Range where porphyry deposits are exposed. The HyMap imaging spectrometer measured reflected sunlight in 126 narrow channels spanning the 0.4 to 2.5 micron wavelength region of the electromagnetic spectrum. The data were collected at a nominal 6-m ground-instantaneous field of view (GIFOV). A total 1,900 square kilometers were collected. This data release provides flight line data for the survey and a report describing the dataset and procedures","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/43b13408-bb19-480e-9258-6dee45972f66","harvest_record_raw":"https://catalog.data.gov/harvest_record/43b13408-bb19-480e-9258-6dee45972f66/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5953463de4b062508e3c7a29","keyword":["Alaska","Alaska Range","Bond Creek","Canada","Chisana River","ENVI","Environment for Visualizing Images","HyMap","MRP","Mineral Resources Program","Nabesna","Nabesna Glacier","Nabesna River","Nutzotin Mountains","Orange Hill","SWIR","USGS:5953463de4b062508e3c7a29","VNIR","Wrangell Mountains","Wrangell-Saint Elias National Park","Wrangell-Saint Elias National Preserve","Yukon Territory","hyperspectral imaging","imaging spectroscopy","infrared imaging","remote sensing","shortwave infrared","spectroscopy","visible light imaging","visible-near infrared"],"last_harvested_date":"2026-09-10T22:49:48.942765","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"},"parent_identifier":null,"popularity":3,"publisher":"U.S. Geological Survey","slug":"airborne-imaging-spectrometer-data-collected-for-characterizing-mineral-resources-near-nab-f4e71","spatial_centroid":{"lat":62.142914426000004,"lon":-142.79440089960002},"spatial_shape":{"coordinates":[[[-143.37637891,62.005457724],[-143.37637891,62.349099479],[-141.921433884,62.349099479],[-141.921433884,62.005457724],[-143.37637891,62.005457724]]],"type":"Polygon"},"theme":["geospatial"],"title":"Airborne imaging spectrometer data collected for characterizing mineral resources near Nabesna, Alaska, 2014: CAL03","type":"dataset"},{"_score":8.487324,"_sort":[1789080588714,8.487324,1,"e6499b2d-7586-4310-b3c6-66e469c0667b"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Nicholas M Enwright","hasEmail":"mailto:enwrightn@usgs.gov"},"description":"These data represent low-lying lands and intertidal lands on Dauphin Island, Alabama, USA for January 2015. These data were delineated using airborne lidar elevation data, in situ elevation observations, lidar metadata, and tide gauge information. We applied Monte Carlo simulations to incorporate uncertainty into a digital elevation model and produce probabilistic outputs with regards to elevation relative to tide and water levels. Specifically, these include three error treatments, including leaving error untreated, and treating error via simulating the propagation of error using Monte Carlo simulations with error and bias estimates from the lidar metadata and site-specific Real-time Kinematic Global Position System data, respectively. This data release also includes a sensitivity analysis to help visualize how a suite of alternative error and bias values affect the Monte Carlo simulation results for the identification of low-lying lands. This data release includes the following three components, which are included in the attached ZIP file: 1) low_lying_lands directory which contains presence/absence rasters for low-lying lands (Raster data) and validation points for low-lying lands (Vector data); 2) intertidal_lands which includes presence/absence rasters for intertidal lands (Raster data); and 3) senstivity_analysis which includes presence/absence rasters for low-lying lands for various alternative bias and error parameters (Raster data).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7125RVT","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.5a2575aee4b03852baf673f5.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5a2575aee4b03852baf673f5","keyword":["Dauphin Island","Gulf of Mexico","Mobile","Monte Carlo","USGS:5a2575aee4b03852baf673f5","barrier island","elevation","environment","intertidal wetland","lidar","uncertainty"],"modified":"2020-08-30T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-88.3464924997, 30.2161143471, -88.0662482165, 30.2863635509","theme":["geospatial"],"title":"The impact of lidar elevation uncertainty on mapping intertidal habitats on barrier islands"},"description":"These data represent low-lying lands and intertidal lands on Dauphin Island, Alabama, USA for January 2015. These data were delineated using airborne lidar elevation data, in situ elevation observations, lidar metadata, and tide gauge information. We applied Monte Carlo simulations to incorporate uncertainty into a digital elevation model and produce probabilistic outputs with regards to elevation relative to tide and water levels. Specifically, these include three error treatments, including leaving error untreated, and treating error via simulating the propagation of error using Monte Carlo simulations with error and bias estimates from the lidar metadata and site-specific Real-time Kinematic Global Position System data, respectively. This data release also includes a sensitivity analysis to help visualize how a suite of alternative error and bias values affect the Monte Carlo simulation results for the identification of low-lying lands. This data release includes the following three components, which are included in the attached ZIP file: 1) low_lying_lands directory which contains presence/absence rasters for low-lying lands (Raster data) and validation points for low-lying lands (Vector data); 2) intertidal_lands which includes presence/absence rasters for intertidal lands (Raster data); and 3) senstivity_analysis which includes presence/absence rasters for low-lying lands for various alternative bias and error parameters (Raster data).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/3f044ea3-6fb4-4303-8623-cf5e2f0e134e","harvest_record_raw":"https://catalog.data.gov/harvest_record/3f044ea3-6fb4-4303-8623-cf5e2f0e134e/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5a2575aee4b03852baf673f5","keyword":["Dauphin Island","Gulf of Mexico","Mobile","Monte Carlo","USGS:5a2575aee4b03852baf673f5","barrier island","elevation","environment","intertidal wetland","lidar","uncertainty"],"last_harvested_date":"2026-09-10T22:49:48.714909","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"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"the-impact-of-lidar-elevation-uncertainty-on-mapping-intertidal-habitats-on-barrier-island","spatial_centroid":{"lat":30.24421402862,"lon":-88.23439478642},"spatial_shape":{"coordinates":[[[-88.3464924997,30.2161143471],[-88.3464924997,30.2863635509],[-88.0662482165,30.2863635509],[-88.0662482165,30.2161143471],[-88.3464924997,30.2161143471]]],"type":"Polygon"},"theme":["geospatial"],"title":"The impact of lidar elevation uncertainty on mapping intertidal habitats on barrier islands","type":"dataset"},{"_score":8.9005165,"_sort":[1789080588500,8.9005165,1,"1921c88d-cd2a-4aba-a46f-58b22c0e49b0"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Courtney Creamer","hasEmail":"mailto:ccreamer@usgs.gov"},"description":"These datasets are from an incubation experiment with a combination of two minerals (feldspar or amorphous aluminum hydroxide), one living species of bacteria (Escherichia coli), and one added form of C (Arthrobacter crystallopoietes necromass).  We characterized the sorptive properties of the minerals with batch sorption experiments using four low molecular weight C substrates (glucose, oxalic acid, glutamic acid, p-hydroxybenzoic acid): this data is provided in the SterileSorptionData file.  We then conducted a 3-wk long incubation in serum vials or imaging chambers.  In both incubations, feldspar (200 mg) or amorphous aluminum hydroxide (100 mg) was given 1 of 4 different treatments: (1) a water control with autoclaved 18 M\u03a9 water, (2) a microbial necromass control with autoclaved and 99% <sup>13</sup>C enriched A. crystallopoietes necromass, (3) a E. coli control with living E. coli, or (4) a microbial necromass and E. coli incubation with both the autoclaved and 99% 13C enriched A. crystallopoietes necromass and living E. coli.  We measured the quantity and isotopic composition of respired CO2 through the incubation: this data is provided in the CO2data file.  Throughout the incubation we collected Raman spectra from incubations in imaging in order to quantity the 13C content of microbial biomass and changes in mineral-associated OC chemistry.  Microbial biomass 13C content predictions and processed Raman spectra across the phenylalanine regions used to create the predictions are provided in the AmAlOHBiomass and FeldBiomass files; the related processing steps are described under Data Quality: Process Step 4.  Processed Raman spectra used to examine changes in mineral-associated OC chemistry are provided in the RamanData file with the related processing steps described under Data Quality: Process Step 5. At the end of the incubation we extracted the minerals with water to remove DOC, this data is provided in the DOCData file.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9IHA9YN","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.5bef5105e4b045bfcadf7414.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5bef5105e4b045bfcadf7414","keyword":["Raman spectroscopy","USGS:5bef5105e4b045bfcadf7414","biota","carbon cycling","carbon isotope analysis","environment","mineralogy","sorption"],"modified":"2020-08-20T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-180.0000, -90.0000, 180.0000, 90.0000","theme":["geospatial"],"title":"Batch sorption data, respired CO2, extractable DOC, and Raman spectra collected from an incubation with microbial necromass on feldspar or amorphous aluminum hydroxide"},"description":"These datasets are from an incubation experiment with a combination of two minerals (feldspar or amorphous aluminum hydroxide), one living species of bacteria (Escherichia coli), and one added form of C (Arthrobacter crystallopoietes necromass).  We characterized the sorptive properties of the minerals with batch sorption experiments using four low molecular weight C substrates (glucose, oxalic acid, glutamic acid, p-hydroxybenzoic acid): this data is provided in the SterileSorptionData file.  We then conducted a 3-wk long incubation in serum vials or imaging chambers.  In both incubations, feldspar (200 mg) or amorphous aluminum hydroxide (100 mg) was given 1 of 4 different treatments: (1) a water control with autoclaved 18 M\u03a9 water, (2) a microbial necromass control with autoclaved and 99% <sup>13</sup>C enriched A. crystallopoietes necromass, (3) a E. coli control with living E. coli, or (4) a microbial necromass and E. coli incubation with both the autoclaved and 99% 13C enriched A. crystallopoietes necromass and living E. coli.  We measured the quantity and isotopic composition of respired CO2 through the incubation: this data is provided in the CO2data file.  Throughout the incubation we collected Raman spectra from incubations in imaging in order to quantity the 13C content of microbial biomass and changes in mineral-associated OC chemistry.  Microbial biomass 13C content predictions and processed Raman spectra across the phenylalanine regions used to create the predictions are provided in the AmAlOHBiomass and FeldBiomass files; the related processing steps are described under Data Quality: Process Step 4.  Processed Raman spectra used to examine changes in mineral-associated OC chemistry are provided in the RamanData file with the related processing steps described under Data Quality: Process Step 5. 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The raster data file for the shaded-relief map is included in \"BathymetryHS_OffshoreCarpinteria.zip.\" Both are accessible from https://pubs.usgs.gov/ds/781/OffshoreCarpinteria/data_catalog_OffshoreCarpinteria.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Endris, C.A., Seitz, G.G., Sliter, R.W., Erdey, M.D., Wong, F.L., Gutierrez, C.I., Krigsman, L.M., Draut, A.E., and Hart, P.E. (S.Y. Johnson and S.A. Cochran, eds.), 2013, California State Waters Map Series\u2014Offshore of Carpinteria, California: U.S. Geological Survey Scientific Investigations Map 3261, 42 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/sim3261.\nThe hillshaded bathymetry map of the Offshore of Carpinteria map area, California, was generated from bathymetry data collected by California State University, Monterey Bay, Seafloor Mapping Lab (CSUMB), by the U.S. Geological Survey (USGS), and by Fugro Pelagos for the U.S. Army Corps of Engineers (USACE) Joint Lidar Bathymetry Technical Center of Expertise. The southeastern nearshore and shelf areas, as well as the western midshelf area, were mapped by CSUMB in the summer of 2007, using a 244-kHz Reson 8101 multibeam echosounder. The western nearshore area, as well as the western outer shelf area, were mapped by the USGS in 2005 and 2006, using 117-kHz and 234.5-kHz SEA (AP) Ltd. SWATHplus-M phase-differencing sidescan sonars. The nearshore bathymetry and coastal topography were mapped for USACE by Fugro Pelagos in 2009, using the SHOALS-1000T bathymetric-lidar and Leica ALS60 topographic-lidar systems. All these mapping missions combined to collect bathymetry from the 0-m isobath to beyond the 3-nautical-mile limit of California's State Waters.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://pubs.usgs.gov/ds/781/OffshoreCarpinteria/data_catalog_OffshoreCarpinteria.html","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.c3046b62-3da2-4780-a2da-4d62b5ca62a5.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_c3046b62-3da2-4780-a2da-4d62b5ca62a5","keyword":["Bathymetry","Bight","Borderland","CMHRP","California","Carpinteria","Coastal and Marine Hazards and Resources Program","Continental/Island Shelf","Marine Nearshore Subtidal","Marine Offshore Subtidal","PCMSC","Pacific Coastal and Marine Science Center","Rock Substrate","Seafloor Topography","Southern California Bight Ecoregion","Summerland","Transform Continental Margin","U.S. Geological Survey","USGS","USGS:c3046b62-3da2-4780-a2da-4d62b5ca62a5","Unconsolidated Mineral Substrate","bathymetry","bathymetry and elevation","bathymetry measurement","distributions","environment","geoscientificInformation","geospatial datasets","imageryBaseMapsEarthCover","maps and atlases","marine geophysics","multibeam echo sounder","oceans","seabed","sonar methods"],"modified":"2023-03-20T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-119.63, 34.29, -119.43, 34.45","theme":["geospatial"],"title":"Bathymetry Hillshade--Offshore of Carpinteria, California"},"description":"This part of DS 781 presents data for the shaded-relief bathymetry map of the Offshore of Carpinteria map area, California. The raster data file for the shaded-relief map is included in \"BathymetryHS_OffshoreCarpinteria.zip.\" Both are accessible from https://pubs.usgs.gov/ds/781/OffshoreCarpinteria/data_catalog_OffshoreCarpinteria.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Endris, C.A., Seitz, G.G., Sliter, R.W., Erdey, M.D., Wong, F.L., Gutierrez, C.I., Krigsman, L.M., Draut, A.E., and Hart, P.E. (S.Y. Johnson and S.A. Cochran, eds.), 2013, California State Waters Map Series\u2014Offshore of Carpinteria, California: U.S. Geological Survey Scientific Investigations Map 3261, 42 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/sim3261.\nThe hillshaded bathymetry map of the Offshore of Carpinteria map area, California, was generated from bathymetry data collected by California State University, Monterey Bay, Seafloor Mapping Lab (CSUMB), by the U.S. Geological Survey (USGS), and by Fugro Pelagos for the U.S. Army Corps of Engineers (USACE) Joint Lidar Bathymetry Technical Center of Expertise. The southeastern nearshore and shelf areas, as well as the western midshelf area, were mapped by CSUMB in the summer of 2007, using a 244-kHz Reson 8101 multibeam echosounder. The western nearshore area, as well as the western outer shelf area, were mapped by the USGS in 2005 and 2006, using 117-kHz and 234.5-kHz SEA (AP) Ltd. SWATHplus-M phase-differencing sidescan sonars. 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All these mapping missions combined to collect bathymetry from the 0-m isobath to beyond the 3-nautical-mile limit of California's State Waters.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c9fe2f78-3bab-40c7-88d1-172cc41441e9","harvest_record_raw":"https://catalog.data.gov/harvest_record/c9fe2f78-3bab-40c7-88d1-172cc41441e9/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_c3046b62-3da2-4780-a2da-4d62b5ca62a5","keyword":["Bathymetry","Bight","Borderland","CMHRP","California","Carpinteria","Coastal and Marine Hazards and Resources Program","Continental/Island Shelf","Marine Nearshore Subtidal","Marine Offshore Subtidal","PCMSC","Pacific Coastal and Marine Science Center","Rock Substrate","Seafloor Topography","Southern California Bight Ecoregion","Summerland","Transform Continental Margin","U.S. Geological Survey","USGS","USGS:c3046b62-3da2-4780-a2da-4d62b5ca62a5","Unconsolidated Mineral Substrate","bathymetry","bathymetry and elevation","bathymetry measurement","distributions","environment","geoscientificInformation","geospatial datasets","imageryBaseMapsEarthCover","maps and atlases","marine geophysics","multibeam echo sounder","oceans","seabed","sonar methods"],"last_harvested_date":"2026-09-10T22:49:23.998683","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"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"bathymetry-hillshade-offshore-of-carpinteria-california","spatial_centroid":{"lat":34.354,"lon":-119.55},"spatial_shape":{"coordinates":[[[-119.63,34.29],[-119.63,34.45],[-119.43,34.45],[-119.43,34.29],[-119.63,34.29]]],"type":"Polygon"},"theme":["geospatial"],"title":"Bathymetry Hillshade--Offshore of Carpinteria, California","type":"dataset"},{"_score":6.8797073,"_sort":[1789080562547,6.8797073,2,"165fafa5-1ad3-4d8b-89bd-eafd384b929e"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Paul Heisig","hasEmail":"mailto:pmheisig@usgs.gov"},"description":"The hydrogeology of the valley-fill aquifer system along a 32-mile reach of the Susquehanna River \nvalley and adjacent areas was evaluated in eastern Broome and southeastern Chenango Counties, \nNew York. The surficial geology, inferred ice-marginal positions, and distribution of stratified-drift \naquifers were mapped from existing data. Ice-marginal positions, which represent pauses in the \nretreat of glacial ice from the region, favored the accumulation of coarse-grained deposits whereas \nmore steady or rapid ice retreat between these positions favored deposition of fine-grained lacustrine \ndeposits with limited coarse-grained deposits at depth. Unconfined aquifers with thick saturated \ncoarse-grained deposits are the most favorable settings for water-resource development, and three \nseveral-mile-long sections of valley were identified (mostly in Broome County) as potentially favorable: \n(1) the southernmost valley section, which extends from the New YorkPennsylvania border to about \n1 mile north of South Windsor, (2) the valley section that rounds the west side of the umlaufberg \n(an isolated bedrock hill within a valley) north of Windsor, and (3) the eastwest valley section at the \nBroome County Chenango County border from Nineveh to East of Bettsburg (including the lower reach \nof the Cornell Brook valley). Fine-grained lacustrine deposits form extensive confining units between \nthe unconfined areas, and the water-resource potential of confined aquifers is largely untested.Recharge,\nor replenishment, of these aquifers is dependent not only on infiltration of precipitation directly on \nunconfined aquifers, but perhaps more so from precipitation that falls in adjacent upland areas. \nSurface runoff and shallow groundwater from the valley walls flow downslope and recharge valley \naquifers. Tributary streams that drain upland areas lose flow as they enter main valleys on permeable \nalluvial fans. This infiltrating water also recharges valley aquifers.Current (2012) use of water resources \nin the area is primarily through domestic wells, most of which are completedin fractured bedrock in \nupland areas. A few villages in the Susquehanna River valley have supply wells that draw water from \nbeneath alluvial fans and near the Susquehanna River, which is a large potential source of water from \ninduced infiltration.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9MDTWPJ","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.3eb24ebe-0867-41ec-bd77-60140f131a9b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_3eb24ebe-0867-41ec-bd77-60140f131a9b","keyword":["Broome County, New York","Chenango County, New York","Inlandwaters","Susquehanna River valley","USGS SIR2012-5282_surficial_geology","USGS:3eb24ebe-0867-41ec-bd77-60140f131a9b","aquifer system","environment","geoscientificInformation","hydrogeology","inferred ice-marginal positions","stratified glacial deposits","stratified-drift aquifers","surficial geology"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-75.844489, 41.950064, -75.324973, 42.411385","theme":["geospatial"],"title":"SIR2012-5282 Surficial Geology: Hydrogeology of the Susquehanna River valley-fill aquifer system and adjacent areas in eastern Broome and southeastern Chenango Counties, New York"},"description":"The hydrogeology of the valley-fill aquifer system along a 32-mile reach of the Susquehanna River \nvalley and adjacent areas was evaluated in eastern Broome and southeastern Chenango Counties, \nNew York. The surficial geology, inferred ice-marginal positions, and distribution of stratified-drift \naquifers were mapped from existing data. Ice-marginal positions, which represent pauses in the \nretreat of glacial ice from the region, favored the accumulation of coarse-grained deposits whereas \nmore steady or rapid ice retreat between these positions favored deposition of fine-grained lacustrine \ndeposits with limited coarse-grained deposits at depth. Unconfined aquifers with thick saturated \ncoarse-grained deposits are the most favorable settings for water-resource development, and three \nseveral-mile-long sections of valley were identified (mostly in Broome County) as potentially favorable: \n(1) the southernmost valley section, which extends from the New YorkPennsylvania border to about \n1 mile north of South Windsor, (2) the valley section that rounds the west side of the umlaufberg \n(an isolated bedrock hill within a valley) north of Windsor, and (3) the eastwest valley section at the \nBroome County Chenango County border from Nineveh to East of Bettsburg (including the lower reach \nof the Cornell Brook valley). Fine-grained lacustrine deposits form extensive confining units between \nthe unconfined areas, and the water-resource potential of confined aquifers is largely untested.Recharge,\nor replenishment, of these aquifers is dependent not only on infiltration of precipitation directly on \nunconfined aquifers, but perhaps more so from precipitation that falls in adjacent upland areas. \nSurface runoff and shallow groundwater from the valley walls flow downslope and recharge valley \naquifers. Tributary streams that drain upland areas lose flow as they enter main valleys on permeable \nalluvial fans. This infiltrating water also recharges valley aquifers.Current (2012) use of water resources \nin the area is primarily through domestic wells, most of which are completedin fractured bedrock in \nupland areas. A few villages in the Susquehanna River valley have supply wells that draw water from \nbeneath alluvial fans and near the Susquehanna River, which is a large potential source of water from \ninduced infiltration.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c132b129-d377-40cb-93f6-8220f41d75ce","harvest_record_raw":"https://catalog.data.gov/harvest_record/c132b129-d377-40cb-93f6-8220f41d75ce/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_3eb24ebe-0867-41ec-bd77-60140f131a9b","keyword":["Broome County, New York","Chenango County, New York","Inlandwaters","Susquehanna River valley","USGS SIR2012-5282_surficial_geology","USGS:3eb24ebe-0867-41ec-bd77-60140f131a9b","aquifer system","environment","geoscientificInformation","hydrogeology","inferred ice-marginal positions","stratified glacial deposits","stratified-drift aquifers","surficial geology"],"last_harvested_date":"2026-09-10T22:49:22.547107","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"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"sir2012-5282-surficial-geology-hydrogeology-of-the-susquehanna-river-valley-fill-aquifer-s","spatial_centroid":{"lat":42.134592399999995,"lon":-75.6366826},"spatial_shape":{"coordinates":[[[-75.844489,41.950064],[-75.844489,42.411385],[-75.324973,42.411385],[-75.324973,41.950064],[-75.844489,41.950064]]],"type":"Polygon"},"theme":["geospatial"],"title":"SIR2012-5282 Surficial Geology: Hydrogeology of the Susquehanna River valley-fill aquifer system and adjacent areas in eastern Broome and southeastern Chenango Counties, New York","type":"dataset"},{"_score":7.9785004,"_sort":[1789080562083,7.9785004,5,"1b796757-4190-431d-b9c8-7504242e758e"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Nicholas M Enwright","hasEmail":"mailto:enwrightn@usgs.gov"},"description":"This product depicts the spatial coverage of seagrass beds from 0.5-m color-infrared orthoimagery for Ship Island, Mississippi from early fall of 2021. 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A minimum mapping unit of 4 square meters was used for this mapping effort.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P15IMOR8","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.679bf6dfd34ea8c183773976.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_679bf6dfd34ea8c183773976","keyword":["Gulf of America","Habitat alteration and disturbance","Mississippi","USGS:679bf6dfd34ea8c183773976","United States of America","aquatic vascular vegetation","aquatic vegetation","environment","geoscientificInformation","habitat map","mapping","seagrass bed"],"modified":"2025-09-19T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-88.988571, 30.211100, -88.937759, 30.219470","theme":["geospatial"],"title":"Seagrass map, Ship Island, Mississippi, 2021 (ver. 1.1, September 2025)"},"description":"This product depicts the spatial coverage of seagrass beds from 0.5-m color-infrared orthoimagery for Ship Island, Mississippi from early fall of 2021. 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