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The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory.\n\nFor more information regarding the PAD-US dataset please visit, https://www.usgs.gov/programs/gap-analysis-project/science/protected-areas/.  For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-manual .\n\nA version history of PAD-US updates is summarized below (See https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-history for more information): \n- Current Version - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B\n- Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT \n- Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE\n- Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ\n- Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD\n- Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov)\n- Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov)\n- First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov)\n \nComparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9Q9LQ4B","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.8cef209b-b188-4b1a-8b53-eead67d02da5.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_8cef209b-b188-4b1a-8b53-eead67d02da5","keyword":["2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021","Agricultural Research Service","Alabama (AL)","Alaska (AK)","American Samoa (AS)","Arizona (AZ)","Arkansas (AR)","Army Corps of Engineers","Biodiversity","Bureau of Land Management","Bureau of Reclamation","Cadastre Theme","California (CA)","Colorado (CO)","Connecticut (CT)","Conservation","Delaware (DE)","Department of Defense","Department of Energy","Federal Lands","Florida (FL)","Forest Service","GAP Status Code","Gap Analysis","Geography","Georgia (GA)","Governmental Units","Guam (GU)","Hawaii (HI)","IUCN Category","Idaho (ID)","Illinois (IL)","Indiana (IN)","Iowa (IA)","Kansas (KS)","Kentucky (KY)","Land Manager","Land Ownership","Land Stewardship","Land Use Change","Land Use and Land Cover","Local Government Lands","Louisiana (LA)","Maine (ME)","Mariana Islands (MP)","Maryland (MD)","Massachusetts (MA)","Michigan (MI)","Minnesota (MN)","Mississippi (MS)","Missouri (MO)","Montana (MT)","NGDA","NGDAID27","National Geospatial Data Asset","National Oceanic and Atmospheric Administration","National Park Service","Natural Resources Conservation Service","Nebraska (NE)","Nevada (NV)","New Hampshire (NH)","New Jersey (NJ)","New Mexico (NM)","New York (NY)","North Carolina (NC)","North Dakota (ND)","Ohio (OH)","Oklahoma (OK)","Oregon (OR)","Outdoor Recreation","Parks","Pennsylvania (PA)","Private Lands","Protected Area","Protection Status","Public Health","Public Lands","Public Open Space","Puerto Rico (PR)","Rhode Island (RI)","South Carolina (SC)","South Dakota (SD)","State Lands","Tennessee (TN)","Tennessee Valley Authority","Texas (TX)","U.S. Fish and Wildlife Service","U.S. Minor Outlying Islands (UM)","USGS:8cef209b-b188-4b1a-8b53-eead67d02da5","United States","United States Virgin Islands (VI)","Utah (UT)","Vermont (VT)","Virginia (VA)","Washington (WA)","West Virginia (WV)","Wisconsin (WI)","Wyoming (WY)"],"modified":"2022-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-180.0, -15.3861, 180.0, 71.312","theme":["geospatial"],"title":"Protected Areas Database of the United States (PAD-US)"},"description":"This is series-level metadata for the USGS Protected Areas Database of the United States (PAD-US) data released by the United States Geological Survey (USGS).  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For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-manual .\n\nA version history of PAD-US updates is summarized below (See https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-history for more information): \n- Current Version - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B\n- Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT \n- Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE\n- Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ\n- Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD\n- Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov)\n- Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov)\n- First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov)\n \nComparing protected area 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Parasites, bacteria, fungi, and viruses were not evident as a primary cause of death.  Pathology was suggestive of an environmental cause such as food limitation or toxin, possibilities that could be more aggressively pursued in future investigations.  These findings highlight the need for caution and additional tools to better assess health when translocating marine invertebrates to ensure maximal biosecurity.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P92DHFO5","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.6384daa5d34ed907bf77958f.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6384daa5d34ed907bf77958f","keyword":["Hawaii","Maui","Oahu","USGS:6384daa5d34ed907bf77958f","biota","echinoderms","epizootic","pathology","translocation"],"modified":"2023-01-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-160.3500, 18.8000, -154.6500, 22.6000","theme":["geospatial"],"title":"Mass mortality of collector urchins (Tripneustes gratilla) in Hawai`i"},"description":"As grazers, sea urchins are keystone species in tropical marine ecosystems, and their loss can have important ecological ramifications.  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Arsenic and uranium attribute data associated with the equal-area grid cells include the number of wells in each grid cell, the number of wells with constituent concentrations above three selected thresholds, the fraction of wells with constituent concentrations above three selected thresholds, and the percentage of wells with constituent concentrations above three selected thresholds. The three selected thresholds for arsenic include 3, 5, and 10 micrograms per liter (ug/L), with 10 ug/L representing the maximum contaminant level (MCL) established by the U.S. Environmental Protection Agency (EPA) for human health for arsenic. The three selected thresholds for uranium include 1, 10, and 30 ug/L, with 30 ug/L representing the EPA MCL for human health for uranium. The bedrock geology data table is table 4 from Gross and others (2020) formatted so that it can easily be joined with Connecticut's bedrock geology dataset (Connecticut Department of Environmental Protection, 2000) using the geologic unit abbreviation (UNIT attribute) in order to recreate figure 3 from Gross and others (2020). 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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). 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_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5b78ff80-87ca-45a3-b82b-2e94510c08e0","harvest_record_raw":"https://catalog.data.gov/harvest_record/5b78ff80-87ca-45a3-b82b-2e94510c08e0/raw","has_download":true,"has_spatial":true,"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"],"last_harvested_date":"2026-09-10T22:50:05.221491","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":"digital-polymerase-chain-reaction-dpcr-data-from-the-sediment-bound-contaminant-resil-2015","spatial_centroid":{"lat":38.000095599999995,"lon":-74.375723},"spatial_shape":{"coordinates":[[[-79.303957,33.352132],[-79.303957,44.972041],[-66.983372,44.972041],[-66.983372,33.352132],[-79.303957,33.352132]]],"type":"Polygon"},"theme":["geospatial"],"title":"Digital Polymerase Chain Reaction (dPCR) Data from the Sediment-Bound Contaminant Resiliency and Response Strategy Pilot Study, Northeastern United States, 2015","type":"dataset"},{"_score":7.2855015,"_sort":[1789080593907,7.2855015,1,"596d5ca2-9f51-4d61-a682-c4c9cc54c12c"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Leslie A DeSimone","hasEmail":"mailto:ldesimon@usgs.gov"},"description":"From October 2022 through September 2024, the U.S. Geological Survey (USGS) National Water Quality Network (NWQN) monitored 110 surface-water river and stream sites and about 700 groundwater wells for a large number of water-quality analytes, for which associated quality-control data and corresponding statistical summaries are included in this data release. The quality-control data\u2014for samples that were collected in the field (at 109 surface-water sites and 104 groundwater wells), prepared in the laboratory, or prepared by a third party\u2014can be used to assess the quality of environmental data collected by the NWQN through the estimation of bias and variability in reported results. The general analyte groups that were monitored at NWQN surface-water and (or) groundwater sites and have associated quality-control data in this data release include major ions, nutrients, trace elements, pesticides, volatile organic compounds, microbial indicators, sediment, and one environmental tracer (tritium). For each analyte group, the data tables contain results for one or more of the following types of quality-control samples, where relevant: blanks, matrix spikes, and replicates collected at field sites; laboratory blanks, reagent spikes, and matrix spikes prepared by the USGS National Water Quality Laboratory (NWQL) (quality-control samples prepared by other analyzing laboratories are not included in the current data release); and third-party blanks and reference samples prepared by the USGS Quality Systems Branch (QSB). For each relevant analyte, tables of summary statistics characterize the frequency and concentrations of blank detections, the typical magnitude of and variability in spike and reference-sample recoveries, and the typical variability between replicate concentrations.\nTables included in this data release:\nTable1_SiteList_WY23_24.txt: Information about National Water Quality Network sites that have associated quality-control data.\nTable2_AnalyteList_WY23_24.txt: Information about National Water Quality Network analytes that have associated quality-control data, including available aquatic-life and (or) human-health benchmarks and selected information regarding analytical methods.\nTable3_BlankData_WY23_24.txt: For all relevant analytes, results for blanks collected at field sites, prepared in the laboratory, or prepared by a third party.\nTable4_SpikeData_WY23_24.txt: For all relevant analytes, results for matrix spikes prepared in the field and(or) matrix or reagent spikes prepared in the laboratory. For matrix spikes, results of paired environmental samples are included. \nTable5_ReplicateData_WY23_24.txt: For all relevant analytes, results for field replicates and paired environmental samples.\nTable6_ReferenceData_WY23_24.txt: For all relevant analytes, results for third-party reference samples.\nTable7_BlankStats_WY23_24.txt: For all relevant analytes, summary statistics for each type of available blank sample.\nTable8_SpikeStats_WY23_24.txt: For all relevant analytes, summary statistics for each type of available spike sample.\nTable9_ReplicateStats_WY23_24.txt: For all relevant analytes, summary statistics for field replicates.\nTable10_ReferenceStats_WY23_24.txt: For all relevant analytes, summary statistics for reference samples.\nNWQN_QC_DataDictionary_WY23_24.txt: Descriptions of data fields and field values in the data tables.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066//P1ESIBMY","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.67bc6fd5d34e1a2e835b9776.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_67bc6fd5d34e1a2e835b9776","keyword":["National Water Quality Network","USGS:67bc6fd5d34e1a2e835b9776","geoscientificInformation","groundwater","groundwater quality","quality control","river systems","surface water (non marine)","surface water quality","transportation","water quality"],"modified":"2026-08-13T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-125.8594, 24.5271, -66.7969, 49.1530","theme":["geospatial"],"title":"Field, laboratory, and third-party quality-control data associated with sites and analytes monitored by the USGS National Water Quality Network, October 2022 through September 2024"},"description":"From October 2022 through September 2024, the U.S. Geological Survey (USGS) National Water Quality Network (NWQN) monitored 110 surface-water river and stream sites and about 700 groundwater wells for a large number of water-quality analytes, for which associated quality-control data and corresponding statistical summaries are included in this data release. The quality-control data\u2014for samples that were collected in the field (at 109 surface-water sites and 104 groundwater wells), prepared in the laboratory, or prepared by a third party\u2014can be used to assess the quality of environmental data collected by the NWQN through the estimation of bias and variability in reported results. The general analyte groups that were monitored at NWQN surface-water and (or) groundwater sites and have associated quality-control data in this data release include major ions, nutrients, trace elements, pesticides, volatile organic compounds, microbial indicators, sediment, and one environmental tracer (tritium). For each analyte group, the data tables contain results for one or more of the following types of quality-control samples, where relevant: blanks, matrix spikes, and replicates collected at field sites; laboratory blanks, reagent spikes, and matrix spikes prepared by the USGS National Water Quality Laboratory (NWQL) (quality-control samples prepared by other analyzing laboratories are not included in the current data release); and third-party blanks and reference samples prepared by the USGS Quality Systems Branch (QSB). For each relevant analyte, tables of summary statistics characterize the frequency and concentrations of blank detections, the typical magnitude of and variability in spike and reference-sample recoveries, and the typical variability between replicate concentrations.\nTables included in this data release:\nTable1_SiteList_WY23_24.txt: Information about National Water Quality Network sites that have associated quality-control data.\nTable2_AnalyteList_WY23_24.txt: Information about National Water Quality Network analytes that have associated quality-control data, including available aquatic-life and (or) human-health benchmarks and selected information regarding analytical methods.\nTable3_BlankData_WY23_24.txt: For all relevant analytes, results for blanks collected at field sites, prepared in the laboratory, or prepared by a third party.\nTable4_SpikeData_WY23_24.txt: For all relevant analytes, results for matrix spikes prepared in the field and(or) matrix or reagent spikes prepared in the laboratory. For matrix spikes, results of paired environmental samples are included. \nTable5_ReplicateData_WY23_24.txt: For all relevant analytes, results for field replicates and paired environmental samples.\nTable6_ReferenceData_WY23_24.txt: For all relevant analytes, results for third-party reference samples.\nTable7_BlankStats_WY23_24.txt: For all relevant analytes, summary statistics for each type of available blank sample.\nTable8_SpikeStats_WY23_24.txt: For all relevant analytes, summary statistics for each type of available spike sample.\nTable9_ReplicateStats_WY23_24.txt: For all relevant analytes, summary statistics for field replicates.\nTable10_ReferenceStats_WY23_24.txt: For all relevant analytes, summary statistics for reference samples.\nNWQN_QC_DataDictionary_WY23_24.txt: Descriptions of data fields and field values in the data tables.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/ac20adba-2122-43aa-aae3-ce432119c096","harvest_record_raw":"https://catalog.data.gov/harvest_record/ac20adba-2122-43aa-aae3-ce432119c096/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_67bc6fd5d34e1a2e835b9776","keyword":["National Water Quality Network","USGS:67bc6fd5d34e1a2e835b9776","geoscientificInformation","groundwater","groundwater quality","quality control","river systems","surface water (non marine)","surface water quality","transportation","water quality"],"last_harvested_date":"2026-09-10T22:49:53.907290","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":"field-laboratory-and-third-party-quality-control-data-associated-with-sites-and-analy-2024","spatial_centroid":{"lat":34.37746,"lon":-102.2344},"spatial_shape":{"coordinates":[[[-125.8594,24.5271],[-125.8594,49.153],[-66.7969,49.153],[-66.7969,24.5271],[-125.8594,24.5271]]],"type":"Polygon"},"theme":["geospatial"],"title":"Field, laboratory, and third-party quality-control data associated with sites and analytes monitored by the USGS National Water Quality Network, October 2022 through September 2024","type":"dataset"},{"_score":26.48188,"_sort":[1789080579907,26.48188,3,"c7c4d047-ecac-47c5-9888-7cb89fe374db"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Western Ecological Research Center","hasEmail":"mailto:gs-b-werc_data_management@usgs.gov"},"description":"This data release is comprised of sea otter capture data from the capture of wild sea otters in Big Sur and Monterey, CA between the years of 2008-2011.  These sea otters were captured for tagging and tracking during a comparison study designed to examine the biology, health, and survival of sea otters in a relatively pristine habitat (Big Sur) and a highly-impacted habitat (Monterey).  At the time of capture a variety of data are collected on each individual sea otter.  The otter may receive multiple identifiers including an otter number, color coded flipper tags, a PIT tag, VHF radio transmitter, and archival time-depth recorder.  Capture information (date, time, GPS location and general area, capture team, capture method, if the otter was caught with other otters, if the otter was previously captured) is reported in this data set as well.  A variety of physiological, morphometric, and health parameters are assessed and reported (reproductive status, sex, age class, age estimate, grizzle, nose wound status, weight, length, girth, tail length, right paw width, baculum length [if male], general tooth wear, comments on tooth condition or oral lesions, canine width, overall appearance).  If the otter was caught with a dependent pup, the pup was given an otter number and was sexed, weighed, and measured whenever possible.  Each otter is assigned a blood kit number corresponding to a blood sample.  Release coordinates, methodology, and team are also noted.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P98B08RO","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.5d4b3de5e4b01d82ce8df3f3.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d4b3de5e4b01d82ce8df3f3","keyword":["Big Sur","Monterey Bay","USGS:5d4b3de5e4b01d82ce8df3f3","biota","ecology","endangered species","environmental health","keystone species"],"modified":"2020-08-30T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-121.97865, 35.910017, -121.97865, 35.910017","theme":["geospatial"],"title":"Sea Otter Capture Data from the Big Sur-Monterey Study (2008-2011)"},"description":"This data release is comprised of sea otter capture data from the capture of wild sea otters in Big Sur and Monterey, CA between the years of 2008-2011.  These sea otters were captured for tagging and tracking during a comparison study designed to examine the biology, health, and survival of sea otters in a relatively pristine habitat (Big Sur) and a highly-impacted habitat (Monterey).  At the time of capture a variety of data are collected on each individual sea otter.  The otter may receive multiple identifiers including an otter number, color coded flipper tags, a PIT tag, VHF radio transmitter, and archival time-depth recorder.  Capture information (date, time, GPS location and general area, capture team, capture method, if the otter was caught with other otters, if the otter was previously captured) is reported in this data set as well.  A variety of physiological, morphometric, and health parameters are assessed and reported (reproductive status, sex, age class, age estimate, grizzle, nose wound status, weight, length, girth, tail length, right paw width, baculum length [if male], general tooth wear, comments on tooth condition or oral lesions, canine width, overall appearance).  If the otter was caught with a dependent pup, the pup was given an otter number and was sexed, weighed, and measured whenever possible.  Each otter is assigned a blood kit number corresponding to a blood sample.  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(The data release containing lifespan estimates for the Chesapeake Bay-facing portion of the Eastern Shore of Virginia is found here: https://doi.org/10.5066/P9FSPWSF.)","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P14GBAVB","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.664c9cbdd34e1955f5a4f45f.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_664c9cbdd34e1955f5a4f45f","keyword":["Atlantic Ocean","Cedar Island","Chincoteague National Wildlife Refuge","Eastern Shore National Wildlife Refuge","Fisherman Island National Wildlife Refuge","Hog Island","Metompkin Island","Mockhorn Island","Parramore Island","Smith Island","USGS:664c9cbdd34e1955f5a4f45f","United States","Virginia","Virginia Coast Reserve","Wallops Island","coastal ecosystems","coastal processes","elevation","environment","estuarine processes","estuary","geospatial datasets","inlandWaters","lifespan","marsh health","oceans","salt marsh","sea-level change","sediment transport","vegetation","wetland ecosystems","wetland functions"],"modified":"2026-04-16T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-75.9777, 37.0854, -75.4740, 37.8818","theme":["geospatial"],"title":"Lifespan of marsh units in Eastern Shore of Virginia salt marshes"},"description":"The lifespans of salt marshes in Atlantic-facing Eastern Shore of Virginia are calculated based on estimated sediment supply and sea-level rise (SLR) predictions, following the methodology of Ganju and others (2020). 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(The data release containing lifespan estimates for the Chesapeake Bay-facing portion of the Eastern Shore of Virginia is found here: https://doi.org/10.5066/P9FSPWSF.)","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f0011a01-df15-4beb-b172-fc95f6834ec0","harvest_record_raw":"https://catalog.data.gov/harvest_record/f0011a01-df15-4beb-b172-fc95f6834ec0/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_664c9cbdd34e1955f5a4f45f","keyword":["Atlantic Ocean","Cedar Island","Chincoteague National Wildlife Refuge","Eastern Shore National Wildlife Refuge","Fisherman Island National Wildlife Refuge","Hog Island","Metompkin Island","Mockhorn Island","Parramore Island","Smith Island","USGS:664c9cbdd34e1955f5a4f45f","United States","Virginia","Virginia Coast Reserve","Wallops Island","coastal ecosystems","coastal processes","elevation","environment","estuarine processes","estuary","geospatial datasets","inlandWaters","lifespan","marsh health","oceans","salt marsh","sea-level change","sediment transport","vegetation","wetland ecosystems","wetland functions"],"last_harvested_date":"2026-09-10T22:46:36.498051","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":"lifespan-of-marsh-units-in-eastern-shore-of-virginia-salt-marshes","spatial_centroid":{"lat":37.40396,"lon":-75.77622},"spatial_shape":{"coordinates":[[[-75.9777,37.0854],[-75.9777,37.8818],[-75.474,37.8818],[-75.474,37.0854],[-75.9777,37.0854]]],"type":"Polygon"},"theme":["geospatial"],"title":"Lifespan of marsh units in Eastern Shore of Virginia salt marshes","type":"dataset"},{"_score":4.9998035,"_sort":[1789080392361,4.9998035,5,"c4ce16d4-8bc5-4b25-95de-6bf31c549aeb"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Elizabeth Hittle","hasEmail":"mailto:ehittle@usgs.gov"},"description":"This data release supports the following publication:  \nHittle, Elizabeth, 2017, Longshore Water-Current Velocity and the Potential for Transport of Contaminants: A Pilot Study in Lake Erie from Walnut Creek to Presque Isle State Park beaches, Erie, Pennsylvania, June and August 2015: U.S. Geological Survey Open-File Report 2016\u20131206 126 p., https://doi.org/10.3133/ofr20161206 \nData were collected in Lake Erie between Walnut Creek and Presque Isle State Park (PSIP) Beach 1 in June and August 2015 to support a pilot study looking at water-current velocity and the potential for contaminant transport within that area.  Water-current velocity transects were collected on June 24, 25, August 18 and 19 with a Teledyne Rio Grande 1200 kHz acoustic Doppler current profiler (ADCP).  The data were processed within the Velocity Mapping Toolbox (Parsons and others., 2013) and visualized within ArcMap.     \nWater quality was measured on select transects by sampling water temperature, specific conductance, and turbidity from collection points at approximately 10 verticals along the transect on June 24 and June 25.  Measurements were collected with a YSI EXO water quality meter.  \nNear-shore water quality was measured by collecting grab samples from shore on June 24, August 11, and August 19.  Temperature was measured on site, and from the grab samples, turbidity and Escherichia coli (E. coli) bacteria concentration was measured.  Water-quality grab samples were collected about a meter from shore and coincide with the 25 longshore water-current velocity transects as closely as conditions would allow.  Samples were collected by Erie County Department of Health (ECDH) employees and Regional Science Consortium (RSC) interns.  The nearshore water-quality samples were collected using grab-sample techniques described in Myers and others (2007). To maintain sterile conditions, grab samples were collected in at least 1 meter of water at approximately 0.3 meters below the water surface, being careful not to stir up bottom sediments. Water samples for bacteria analysis were collected in pre-sterilized 500-mL polypropylene bottles, allowing about 2 inches of head space for proper mixing, and were kept on ice prior to processing. Bacteria samples were analyzed for Escherichia coli (E. coli) using modified mTEC membrane-filtration techniques (U.S. Environmental Protection Agency, 2002) and were processed by RSC staff in the RSC laboratory within 6 hours of sample collection. \nOn June 24 and August 11 an additional sample was collected near-shore for suspended sediment analysis.  Samples were collected in pre-tared 1000-mL polypropylene bottles by tilting the bottle at about a 45 degree angle away from the sampler and quickly moving it from just under the surface (where the bottle was uncapped) to just above the streambed and back in a smooth vertical motion to get as close to a depth-integrated, single-vertical grab sample as possible (Edwards and Glysson, 1999). There is no need to chill bottles for sediment analysis. Sediment samples were prepared for shipping and sent to the USGS sediment laboratory at the USGS Kentucky Water Science Center where they were analyzed for total suspended sediment concentration, sand/fine break (percent of sediment less than 4 mm), and fine components including percent fines less than 2 mm, 1mm, 0.5 mm, 0.25 mm, 0.125 mm, and <0.0625mm. \nEdwards, T.K., and Glysson, G.D., 1999, Field methods for measurement of fluvial sediment: Techniques of Water-Resources Investigations of the U.S. Geological Survey, book 3, chap. C2, 89 p  \nMyers, D.N., Stoeckel, D.M., Bushon, R.N., Francy, D.S., and Brady, A.M.G., 2007, Fecal indicator bacteria: U.S. Geological Survey Techniques of Water-Resources Investigations, book 9, chap. A7, section 7.1 (version 2.0), available from http://pubs.water.usgs.gov/twri9A/. \nParsons, D.R., Jackson, P.R., Czuba, J.A., Oberg, K.A., Mueller, D.S., Rhoads, B., Best, J.L., Johnson, K.K., Engel, F., and Riley, J. (2013) Velocity Mapping Toolbox (VMT): a processing and visualization suite for moving-vessel ADCP measurements, Earth Surface Processes and Landforms. doi: 10.1002/esp.3367. \nUS Environmental Protection Agency (USEPA). 2009,  Method 1603: Escherichia coli (E. coli) in Water by Membrane Filtration Using Modified membrane-Thermotolerant Escherichia coli Agar (Modified mTEC), EPA-821-R-09-007,  December 2009","distribution":[{"@type":"dcat:Distribution","accessURL":"http://dx.doi.org/10.5066/F7KP808D","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.5764424fe4b07657d19ba8a0.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5764424fe4b07657d19ba8a0","keyword":["Erie County","Lake Erie","Pennsylvania","Presque Isle State Park","USGS:5764424fe4b07657d19ba8a0","Water Circulation","Water Depth","Water Quality","Water Velocity"],"modified":"2020-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-80.241448, 42.078307, -80.15136, 42.122808","theme":["geospatial"],"title":"Data Collected in Support of the Longshore Water-Current Velocity and the Potential for Transport of Contaminants pilot study in Lake Erie"},"description":"This data release supports the following publication:  \nHittle, Elizabeth, 2017, Longshore Water-Current Velocity and the Potential for Transport of Contaminants: A Pilot Study in Lake Erie from Walnut Creek to Presque Isle State Park beaches, Erie, Pennsylvania, June and August 2015: U.S. Geological Survey Open-File Report 2016\u20131206 126 p., https://doi.org/10.3133/ofr20161206 \nData were collected in Lake Erie between Walnut Creek and Presque Isle State Park (PSIP) Beach 1 in June and August 2015 to support a pilot study looking at water-current velocity and the potential for contaminant transport within that area.  Water-current velocity transects were collected on June 24, 25, August 18 and 19 with a Teledyne Rio Grande 1200 kHz acoustic Doppler current profiler (ADCP).  The data were processed within the Velocity Mapping Toolbox (Parsons and others., 2013) and visualized within ArcMap.     \nWater quality was measured on select transects by sampling water temperature, specific conductance, and turbidity from collection points at approximately 10 verticals along the transect on June 24 and June 25.  Measurements were collected with a YSI EXO water quality meter.  \nNear-shore water quality was measured by collecting grab samples from shore on June 24, August 11, and August 19.  Temperature was measured on site, and from the grab samples, turbidity and Escherichia coli (E. coli) bacteria concentration was measured.  Water-quality grab samples were collected about a meter from shore and coincide with the 25 longshore water-current velocity transects as closely as conditions would allow.  Samples were collected by Erie County Department of Health (ECDH) employees and Regional Science Consortium (RSC) interns.  The nearshore water-quality samples were collected using grab-sample techniques described in Myers and others (2007). To maintain sterile conditions, grab samples were collected in at least 1 meter of water at approximately 0.3 meters below the water surface, being careful not to stir up bottom sediments. Water samples for bacteria analysis were collected in pre-sterilized 500-mL polypropylene bottles, allowing about 2 inches of head space for proper mixing, and were kept on ice prior to processing. Bacteria samples were analyzed for Escherichia coli (E. coli) using modified mTEC membrane-filtration techniques (U.S. Environmental Protection Agency, 2002) and were processed by RSC staff in the RSC laboratory within 6 hours of sample collection. \nOn June 24 and August 11 an additional sample was collected near-shore for suspended sediment analysis.  Samples were collected in pre-tared 1000-mL polypropylene bottles by tilting the bottle at about a 45 degree angle away from the sampler and quickly moving it from just under the surface (where the bottle was uncapped) to just above the streambed and back in a smooth vertical motion to get as close to a depth-integrated, single-vertical grab sample as possible (Edwards and Glysson, 1999). There is no need to chill bottles for sediment analysis. Sediment samples were prepared for shipping and sent to the USGS sediment laboratory at the USGS Kentucky Water Science Center where they were analyzed for total suspended sediment concentration, sand/fine break (percent of sediment less than 4 mm), and fine components including percent fines less than 2 mm, 1mm, 0.5 mm, 0.25 mm, 0.125 mm, and <0.0625mm. \nEdwards, T.K., and Glysson, G.D., 1999, Field methods for measurement of fluvial sediment: Techniques of Water-Resources Investigations of the U.S. Geological Survey, book 3, chap. C2, 89 p  \nMyers, D.N., Stoeckel, D.M., Bushon, R.N., Francy, D.S., and Brady, A.M.G., 2007, Fecal indicator bacteria: U.S. Geological Survey Techniques of Water-Resources Investigations, book 9, chap. A7, section 7.1 (version 2.0), available from http://pubs.water.usgs.gov/twri9A/. \nParsons, D.R., Jackson, P.R., Czuba, J.A., Oberg, K.A., Mueller, D.S., Rhoads, B., Best, J.L., Johnson, K.K., Engel, F., and Riley, J. (2013) Velocity Mapping Toolbox (VMT): a processing and visualization suite for moving-vessel ADCP measurements, Earth Surface Processes and Landforms. doi: 10.1002/esp.3367. \nUS Environmental Protection Agency (USEPA). 2009,  Method 1603: Escherichia coli (E. coli) in Water by Membrane Filtration Using Modified membrane-Thermotolerant Escherichia coli Agar (Modified mTEC), EPA-821-R-09-007,  December 2009","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6b9076aa-24bf-4e4d-b271-d230c0514ce4","harvest_record_raw":"https://catalog.data.gov/harvest_record/6b9076aa-24bf-4e4d-b271-d230c0514ce4/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5764424fe4b07657d19ba8a0","keyword":["Erie County","Lake Erie","Pennsylvania","Presque Isle State Park","USGS:5764424fe4b07657d19ba8a0","Water Circulation","Water Depth","Water Quality","Water Velocity"],"last_harvested_date":"2026-09-10T22:46:32.361990","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":5,"publisher":"U.S. Geological Survey","slug":"data-collected-in-support-of-the-longshore-water-current-velocity-and-the-potential-for-tr","spatial_centroid":{"lat":42.0961074,"lon":-80.2054128},"spatial_shape":{"coordinates":[[[-80.241448,42.078307],[-80.241448,42.122808],[-80.15136,42.122808],[-80.15136,42.078307],[-80.241448,42.078307]]],"type":"Polygon"},"theme":["geospatial"],"title":"Data Collected in Support of the Longshore Water-Current Velocity and the Potential for Transport of Contaminants pilot study in Lake Erie","type":"dataset"},{"_score":23.934189,"_sort":[1789080390528,23.934189,1,"a816d597-c732-4246-ae56-042ea3c0f001"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kate V. Ackerman","hasEmail":"mailto:kackerman@usgs.gov"},"description":"This data release contains coastal wetland synthesis products for Chesapeake Bay. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing federal, state, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P997EJYB","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.630f9b40d34e36012efa091d.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_630f9b40d34e36012efa091d","keyword":["Chesapeake Bay","Maryland","North Carolina","USGS:630f9b40d34e36012efa091d","United States","Virginia","coastal ecosystems","coastal processes","environment","estuary","geospatial datasets","inlandWaters","marsh health","oceans","salt marsh","vegetation","wetland ecosystems","wetland functions"],"modified":"2026-04-13T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-77.374707, 36.374384, -75.593417, 39.589813","theme":["geospatial"],"title":"Conceptual marsh units of Chesapeake Bay salt marshes"},"description":"This data release contains coastal wetland synthesis products for Chesapeake Bay. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing federal, state, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/39c54e19-e57d-4448-b813-3c2d76767d59","harvest_record_raw":"https://catalog.data.gov/harvest_record/39c54e19-e57d-4448-b813-3c2d76767d59/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_630f9b40d34e36012efa091d","keyword":["Chesapeake Bay","Maryland","North Carolina","USGS:630f9b40d34e36012efa091d","United States","Virginia","coastal ecosystems","coastal processes","environment","estuary","geospatial datasets","inlandWaters","marsh health","oceans","salt marsh","vegetation","wetland ecosystems","wetland functions"],"last_harvested_date":"2026-09-10T22:46:30.528303","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":"conceptual-marsh-units-of-chesapeake-bay-salt-marshes","spatial_centroid":{"lat":37.660555599999995,"lon":-76.662191},"spatial_shape":{"coordinates":[[[-77.374707,36.374384],[-77.374707,39.589813],[-75.593417,39.589813],[-75.593417,36.374384],[-77.374707,36.374384]]],"type":"Polygon"},"theme":["geospatial"],"title":"Conceptual marsh units of Chesapeake Bay salt marshes","type":"dataset"},{"_score":7.7967606,"_sort":[1789080381482,7.7967606,1,"a6b606e3-f7af-4046-9567-abdff6b91943"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Bethany K. Kunz","hasEmail":"mailto:bkunz@usgs.gov"},"description":"The health of soils along roadways is critical for maximizing habitat quality and minimizing negative ecological effects of roads. Adjacent to unpaved roads, soil chemistry may be altered by the deposition of dust, as well as by road treatment with dust suppressants or soil stabilizer products. If present in roadside soils, these product residues may be available to plants, terrestrial invertebrates, or small mammals. Unfortunately, very few studies have attempted to track the transport of dust suppressants after application. As part of a larger ongoing study on the environmental effects of dust suppressant products on roadside plants and animals, we sampled roadside soils at Squaw Creek National Wildlife Refuge (NWR). Replicated road sections at Squaw Creek NWR had been previously treated with two road products\u2014calcium chloride-based durablend-C\u2122 and synthetic iso-alkane EnviroKleen\u00ae. In order to quantify the effect of dust suppressant treatment on roadside soils, we took replicated composite soil samples one year after treatment at 1m and 4m from the road\u2019s edge, and analyzed samples for a suite of soil chemistry variables (pH, conductivity, NO3-N, P, K, Ca, Mg, Na and S). We also assessed dust suppressant product residues in the soil. For durablend-C\u2122, we used soil conductivity as an indicator. For EnviroKleen\u00ae, we developed a method for extraction and isolation, followed by analysis with gas chromatography/mass spectrometry to look for a specific EnviroKleen\u00ae signature. Surprisingly, soil conductivity was not elevated adjacent to road sections treated with durablend-C\u2122, relative to other sections. EnviroKleen\u00ae was detectable at both 1m and 4m from treated sections at concentrations from 1 to 1500 mg/kg, and was non-detectable in soils adjacent to the untreated section. The most notable characteristic of soils across all treated and untreated sections at 1m was elevated calcium (up to 30,000 mg/kg), likely as a result of dust deposition from the limestone surface aggregate. These results indicate that, at least in some cases, soil chemistry may be more influenced by proximity to the road itself than by treatment with a dust suppressant product. Importantly, this study is the first to detect and quantify a synthetic fluid product in soils adjacent to a treated roadway. In addition to informing ecological risk assessments for dust suppressants, these results can help practitioners make informed choices about environmentally responsible unpaved road management.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7K64G7D","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.5801036ee4b0824b2d18bbc1.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5801036ee4b0824b2d18bbc1","keyword":["Road ecology","USGS:5801036ee4b0824b2d18bbc1","Unpaved road","dust","dust suppressant"],"modified":"2020-08-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"theme":["geospatial"],"title":"Soil chemistry adjacent to roads treated with dust control products at Squaw Creek National Wildlife Refuge"},"description":"The health of soils along roadways is critical for maximizing habitat quality and minimizing negative ecological effects of roads. Adjacent to unpaved roads, soil chemistry may be altered by the deposition of dust, as well as by road treatment with dust suppressants or soil stabilizer products. If present in roadside soils, these product residues may be available to plants, terrestrial invertebrates, or small mammals. Unfortunately, very few studies have attempted to track the transport of dust suppressants after application. As part of a larger ongoing study on the environmental effects of dust suppressant products on roadside plants and animals, we sampled roadside soils at Squaw Creek National Wildlife Refuge (NWR). Replicated road sections at Squaw Creek NWR had been previously treated with two road products\u2014calcium chloride-based durablend-C\u2122 and synthetic iso-alkane EnviroKleen\u00ae. In order to quantify the effect of dust suppressant treatment on roadside soils, we took replicated composite soil samples one year after treatment at 1m and 4m from the road\u2019s edge, and analyzed samples for a suite of soil chemistry variables (pH, conductivity, NO3-N, P, K, Ca, Mg, Na and S). We also assessed dust suppressant product residues in the soil. For durablend-C\u2122, we used soil conductivity as an indicator. For EnviroKleen\u00ae, we developed a method for extraction and isolation, followed by analysis with gas chromatography/mass spectrometry to look for a specific EnviroKleen\u00ae signature. Surprisingly, soil conductivity was not elevated adjacent to road sections treated with durablend-C\u2122, relative to other sections. EnviroKleen\u00ae was detectable at both 1m and 4m from treated sections at concentrations from 1 to 1500 mg/kg, and was non-detectable in soils adjacent to the untreated section. The most notable characteristic of soils across all treated and untreated sections at 1m was elevated calcium (up to 30,000 mg/kg), likely as a result of dust deposition from the limestone surface aggregate. These results indicate that, at least in some cases, soil chemistry may be more influenced by proximity to the road itself than by treatment with a dust suppressant product. Importantly, this study is the first to detect and quantify a synthetic fluid product in soils adjacent to a treated roadway. 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Services include storm protection, water quality improvement, and biological carbon sequestration. Forest structural attributes including basal area, tree height, and stem density by species are used to calculate above ground biomass and above ground productivity. Percent cover is used to asses the forest canopy health. The data collected for the soils are: bulk density, percent total Nitrogen, percent total Carbon, and selected samples percent total Phosporus. The forest structure plots were placed in three zones; healthy, transition, and dead, along with a reference zone to compare how these plots change over time with the hydrologic restoration. 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Remote sensing provides a cost-effective and reliable method for monitoring change through time and attributing changes to drivers. We report an automated method of mapping rangeland fractional component cover over a large portion of the Northern Great Basin, USA, from 1986 to 2016 using a dense Landsat imagery time series. 2012 was excluded from the time-series due to a lack of quality imagery. Our method improved upon the traditional change vector method by considering the legacy of change at each pixel. We evaluate cover trends stratified by climate bin and assess spatial and temporal relationships with climate variables. Finally, we statistically evaluate the minimum time density needed to accurately characterize temporal patterns and relationships with climate drivers. Over the 30-yr period, shrub cover declined and bare ground increased. While few pixels had >10% cover change, a large majority had at least some change. 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All fractional components had significant spatial relationships with water year precipitation (WYPRCP), maximum temperature (WYTMAX), and minimum temperature (WYTMIN) in all years. Shrub and sagebrush cover in particular respond positively to warming WYTMIN, resulting from the largest increases in WYTMIN being in the coolest and wettest areas, and respond negatively to warming WYTMAX because the largest increases in WYTMAX are in the warmest and driest areas. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. Component products can be downloaded from www.mrlc.gov.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/964a559e-fc80-44d8-a518-b0272d012efc","harvest_record_raw":"https://catalog.data.gov/harvest_record/964a559e-fc80-44d8-a518-b0272d012efc/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5ed816ab82ce7e579c67003d","keyword":["AZ","Arizona","Arizona Plateau","Black Hills","Blue Mountains","CA","CO","California","Chihuahuan Desert","Colorado","Colorado Plateau","Columbia Plateau","Grand Canyon","Great Basin","Gunnison","ID","Idaho","MT","Middle Rockies","Montana","ND","NE","NM","NV","Nebraska","Nevada","New Mexico","North Dakota","Northern Mountainous","OR","Oregon","Rocky Mountains","SD","Sonoran Desert","South Dakota","Southwest Tablelands","TX","Texas","Three Forks","USGS:5ed816ab82ce7e579c67003d","UT","United States","Utah","WA","WY","Wasatch","Washington","Western US","Wyoming","Yellowstone","annual herbaceous","back-in-time","bare ground","big sagebrush","biota","climate change","environment","geoscientificInformation","grass","grassland change","herbaceous","imageryBaseMapsEarthCover","litter","rangeland","rangeland management","sagebrush","shrub","shrubland","shrubland change","shrubland ecosystems","shrublands","terrestrial ecosystems","time series","trends","vegetation","vegetation change"],"last_harvested_date":"2026-09-10T22:45:33.889512","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":"remote-sensing-shrub-grass-national-land-cover-database-nlcd-back-in-time-bit-ba-1985-2018","spatial_centroid":{"lat":36.83854,"lon":-118.01032000000001},"spatial_shape":{"coordinates":[[[-130.238,26.2039],[-130.238,52.7905],[-99.6688,52.7905],[-99.6688,26.2039],[-130.238,26.2039]]],"type":"Polygon"},"theme":["geospatial"],"title":"Remote Sensing Shrub/Grass National Land Cover Database (NLCD) Back-in-Time (BIT) Bare Ground Products for the Western U.S., 1985 - 2018","type":"dataset"},{"_score":12.345748,"_sort":[1789080329656,12.345748,2,"2df9527d-b16a-45bc-ab9e-6a0a6b75b5b2"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Ann Allert","hasEmail":"mailto:aallert@usgs.gov"},"description":"Deposits of lead (Pb) and other metals in southeastern Missouri, USA have been exploited since the 1700s.  Metal contamination of fish and other aquatic biota, alteration of fish and invertebrate communities, and public health advisories against human consumption of Pb-contaminated fish have resulted.  The Little Saint Francis River (LFSR) and its tributaries, which drain the mining-affected areas of Madison County, is inhabited by the St. Francis River crayfish (Faxonius quadruncus; formerly Orconectes quadruncus), an endemic species that has been petitioned for Federal listing as an endangered species.  Crayfish population density surveys and in-situ toxicity tests with laboratory-reared F. quadruncus were conducted at sites upstream and downstream of historical mining areas.  These data consist of attributes of habitat quality and water quality from study sites located in the LSFR watershed, Madison County, Missouri and attributes of growth and survival of crayfish (F. quadruncus) from 56 day in-situ exposures at the study sites.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9IKIEJH","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.5c92665de4b09388245734b4.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5c92665de4b09388245734b4","keyword":["Campostoma anomalum pullum","Campostoma oligolepis","Central stoneroller","Chapel Creek","Devil crayfish","Faxonius (Orconectes) luteus","Faxonius (Orconectes) puntimanus","Faxonius (Orconectes) quadruncus","Fredericktown","Golden crayfish","Lacunicambarus diogenes","Largescale stoneroller","Little Saint Francis River","Madison County","Mine waste","Rock Creek","Saint Francis River","Saint Francis River crayfish","Saint Francois County","Saline Creek","Spothanded crayfish","USGS:5c92665de4b09388245734b4","benthic ecosystems","biota","ecotoxicology","environment","invertebrates","mining and quarrying"],"modified":"2024-09-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-90.512530036999, 37.524860001, -90.273561117, 37.696499901","theme":["geospatial"],"title":"Effects of metals from historical mining on crayfish in Madison County Missouri USA, 2015"},"description":"Deposits of lead (Pb) and other metals in southeastern Missouri, USA have been exploited since the 1700s.  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Schmidt","hasEmail":"mailto:tschmidt@usgs.gov"},"description":"Biological, chemical, physical habitat, riparian, and land-use data collected from the Midwest streams by the National Water Quality Project Regional Stream Quality Team. Data were used to develop structural equation models for the purpose of understanding how networks of potential stressors influence stream ecological health. For more information about the Midwest Regional Stream Quality Assessment please go to https://pubs.er.usgs.gov/publication/fs20123124","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9RNKT7P","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.5b463148e4b060350a15a836.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5b463148e4b060350a15a836","keyword":["Illinois","Indiana","Iowa","KAnsas","Kentucky","Michigan","Midwest United States","Minnesota","Missouri","Nebraska","Ohio","South Dakota","USGS:5b463148e4b060350a15a836","Wisconsin","agriculture","algae","biota","ecological condition","fish","habitat","invertebrate","land use","multiple stressors","pesticides","urban"],"modified":"2020-08-26T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-98.1738, 37.2303, -82.6172, 45.2130","theme":["geospatial"],"title":"Linking the Agricultural Landscape of the Midwest to Stream Health with Structural Equation Modeling: Model Input Data"},"description":"Biological, chemical, physical habitat, riparian, and land-use data collected from the Midwest streams by the National Water Quality Project Regional Stream Quality Team. 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These models were developed to predict concentrations of cyanotoxins (anatoxin-a, microcystin, and saxitoxin) that occur within the blooms. Virtual Beach software (version 3.0.6) was used to develop four models: two cyanotoxin mixture (MIX) models and two microcystin (MC) models. Models include those using readily available environmental variables (for example, wind speed and specific conductance) and those using additional comprehensive variables (based on laboratory analyses). Many of the independent variables were averages over a certain time period prior to a sample date, whereas other independent variables were lagged between 4 and 8 days. Funding for this work was provided by the U.S Geological Survey \u2013 National Park Service Partnership and the U.S. Geological Survey Environmental Health Program (Toxic Substance Hydrology and Contaminant Biology). The resulting model equations and final datasets are included in this data release while an associated child item model archive includes all the files needed to run and develop these VB models.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9X7EO1K","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.5fc79816d34e4b9faad89521.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5fc79816d34e4b9faad89521","keyword":["Kabetogama Lake","Koochiching County Minnesota","Minnesota","USGS:5fc79816d34e4b9faad89521","Voyageurs National Park","cyanobacteria [\"blue-green algae\"]","cyanotoxin mixtures","harmful algal blooms","inlandWaters","recreational water quality"],"modified":"2021-04-07T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-93.1078, 48.4135, -92.7370, 48.5348","theme":["geospatial"],"title":"Data and model archive for multiple linear regression models for prediction of weighted cyanotoxin mixture concentrations and microcystin concentrations at three recurring bloom sites in Kabetogama Lake in Minnesota"},"description":"Multiple linear regression models were developed using data collected in 2016 and 2017 from three recurring bloom sites in Kabetogama Lake in northern Minnesota. These models were developed to predict concentrations of cyanotoxins (anatoxin-a, microcystin, and saxitoxin) that occur within the blooms. Virtual Beach software (version 3.0.6) was used to develop four models: two cyanotoxin mixture (MIX) models and two microcystin (MC) models. Models include those using readily available environmental variables (for example, wind speed and specific conductance) and those using additional comprehensive variables (based on laboratory analyses). Many of the independent variables were averages over a certain time period prior to a sample date, whereas other independent variables were lagged between 4 and 8 days. Funding for this work was provided by the U.S Geological Survey \u2013 National Park Service Partnership and the U.S. Geological Survey Environmental Health Program (Toxic Substance Hydrology and Contaminant Biology). 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The eight stands were mostly in or adjacent to the National Park of American Samoa (NPSA), but one stand was sampled near the western tip of Tutuila, outside NPSA. An additional 74 host trees were assessed for phenological status in the eight stands but were not surveyed for Papilio. 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The eight stands were mostly in or adjacent to the National Park of American Samoa (NPSA), but one stand was sampled near the western tip of Tutuila, outside NPSA. An additional 74 host trees were assessed for phenological status in the eight stands but were not surveyed for Papilio. 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Note that the text file contains special characters and a mix of date-time formats that reflect the original data provided by the authors. The text may not be displayed correctly if it is opened by proprietary software such as Microsoft Excel but will appear correctly when opened in a text editor software.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13STASQ","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.65cbade9d34ef4b119cb376c.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65cbade9d34ef4b119cb376c","keyword":["California","China","Debris Flow","Greece","Italy","USGS:65cbade9d34ef4b119cb376c","United States","Wildfire","environment","health","oceans"],"modified":"2024-05-29T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.4638, -37.7051, 149.0397, 64.2828","theme":["geospatial"],"title":"Postfire Debris-Flow Database (Literature Derived)"},"description":"The data presented in this data release represent observations of postfire debris flows that have been collected from publicly available datasets. Data originate from 13 different countries: the United States, Australia, China, Italy, Greece, Portugal, Spain, the United Kingdom, Austria, Switzerland, Canada, South Korea, and Japan. The data are located in the file called \u201cPFDF_database_sortedbyReference.txt\u201d and a description of each column header can be found in both the file \u201ccolumn_headers.txt\u201d and the metadata file (\u201cPost-fire Debris-Flow Database (Literature Derived).xml\u201d). The observations are derived from areas that have been burned by wildfire and are global in nature. However, this dataset is synthesized from information collected by many different researchers for different purposes, and therefore not all fields are available for each of the observations. Missing information is indicated by the value \u201c-9999\u201d in the \u201dPFDF_database_sortedbyReference.txt\u201d file.  Note that the text file contains special characters and a mix of date-time formats that reflect the original data provided by the authors. The text may not be displayed correctly if it is opened by proprietary software such as Microsoft Excel but will appear correctly when opened in a text editor software.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/750e3ad6-08cf-4912-ad5b-e75082145f5d","harvest_record_raw":"https://catalog.data.gov/harvest_record/750e3ad6-08cf-4912-ad5b-e75082145f5d/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65cbade9d34ef4b119cb376c","keyword":["California","China","Debris Flow","Greece","Italy","USGS:65cbade9d34ef4b119cb376c","United States","Wildfire","environment","health","oceans"],"last_harvested_date":"2026-09-10T22:44:53.030396","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":7,"publisher":"U.S. Geological Survey","slug":"postfire-debris-flow-database-literature-derived","spatial_centroid":{"lat":3.0900599999999967,"lon":-15.0624},"spatial_shape":{"coordinates":[[[-124.4638,-37.7051],[-124.4638,64.2828],[149.0397,64.2828],[149.0397,-37.7051],[-124.4638,-37.7051]]],"type":"Polygon"},"theme":["geospatial"],"title":"Postfire Debris-Flow Database (Literature Derived)","type":"dataset"},{"_score":14.834075,"_sort":[1789080292192,14.834075,1,"738e4712-2aad-4323-bd7e-b7c8e87daf53"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Adrian P Monroe","hasEmail":"mailto:amonroe@usgs.gov"},"description":"In 'Broad-scale analysis of greater sage-grouse population trends in response to grazing records in Wyoming, USA (2004-2014)', we provide data and R code necessary for analyzing state-space models for male greater sage-grouse (Centrocercus urophasianus) populations in response to grazing level, timing, and NDVI in Wyoming, USA, and then to compare models with 10-fold cross validation scores (Monroe et al. 2017). In 'Analysis of Land Health Standard failure among allotments in Wyoming, USA (2001-2009)', we provide data and R code necessary for logistic regression analyzing effects of grazing level and timing on the probability of an allotment failing one or more Land Health Standard (LHS) the previous year (Monroe et al. 2017). Relative predictive ability of models are then compared with a 10-fold cross-validation score. In 'Data to evaluate sensitivity of model results to scale and allotment overlap threshold', we provide data used to evaluate the sensitivity of our results to our choice of scale (6.44 km around lek sites) and the overlap threshold for allotments with grazing data (>75%).\nLiterature Cited: Monroe, A. P., C. L. Aldridge, T. J. Assal, K. E. Veblen, D. A. Pyke, and M. L. Casazza. 2017. Patterns in Greater Sage-grouse Population Dynamics Correspond with Public Grazing Records at Broad Scales. Ecological Applications. doi: 10.1002/eap.1512.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://dx.doi.org/10.5066/F70K26RK","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.58753705e4b0a829a324446a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_58753705e4b0a829a324446a","keyword":["USGS:58753705e4b0a829a324446a","United States","Wyoming","agriculture","ecology","environment","land use and land cover","natural resource management","wildlife population management"],"modified":"2020-08-20T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"theme":["geospatial"],"title":"Evaluating population responses of Greater sage-grouse to variation in public grazing records at broad scales"},"description":"In 'Broad-scale analysis of greater sage-grouse population trends in response to grazing records in Wyoming, USA (2004-2014)', we provide data and R code necessary for analyzing state-space models for male greater sage-grouse (Centrocercus urophasianus) populations in response to grazing level, timing, and NDVI in Wyoming, USA, and then to compare models with 10-fold cross validation scores (Monroe et al. 2017). In 'Analysis of Land Health Standard failure among allotments in Wyoming, USA (2001-2009)', we provide data and R code necessary for logistic regression analyzing effects of grazing level and timing on the probability of an allotment failing one or more Land Health Standard (LHS) the previous year (Monroe et al. 2017). Relative predictive ability of models are then compared with a 10-fold cross-validation score. In 'Data to evaluate sensitivity of model results to scale and allotment overlap threshold', we provide data used to evaluate the sensitivity of our results to our choice of scale (6.44 km around lek sites) and the overlap threshold for allotments with grazing data (>75%).\nLiterature Cited: Monroe, A. P., C. L. Aldridge, T. J. Assal, K. E. Veblen, D. A. Pyke, and M. L. Casazza. 2017. Patterns in Greater Sage-grouse Population Dynamics Correspond with Public Grazing Records at Broad Scales. Ecological Applications. doi: 10.1002/eap.1512.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/e861faf7-c4c8-4ebb-b76e-d51df3ab7fe3","harvest_record_raw":"https://catalog.data.gov/harvest_record/e861faf7-c4c8-4ebb-b76e-d51df3ab7fe3/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_58753705e4b0a829a324446a","keyword":["USGS:58753705e4b0a829a324446a","United States","Wyoming","agriculture","ecology","environment","land use and land cover","natural resource management","wildlife population management"],"last_harvested_date":"2026-09-10T22:44:52.192420","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":"evaluating-population-responses-of-greater-sage-grouse-to-variation-in-public-grazing-reco","spatial_centroid":null,"spatial_shape":null,"theme":["geospatial"],"title":"Evaluating population responses of Greater sage-grouse to variation in public grazing records at broad scales","type":"dataset"},{"_score":23.233835,"_sort":[1789080286378,23.233835,2,"ca3eae25-6639-4019-8151-6b2e834bcd4f"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kate Ackerman","hasEmail":"mailto:kackerman@usgs.gov"},"description":"This data release contains coastal wetland synthesis products for the state of Maine. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, and lifespan, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing federal, state, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9FRGLB0","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.657b3f47d34e952b2274bb44.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_657b3f47d34e952b2274bb44","keyword":["Acadia National Park","Maine","USGS:657b3f47d34e952b2274bb44","United States","coastal ecosystems","coastal processes","elevation","environment","estuarine processes","estuary","geospatial datasets","inlandWaters","lifespan","marsh health","oceans","salt marsh","sea-level change","sediment transport","vegetation","wetland ecosystems","wetland functions"],"modified":"2026-04-07T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-70.828578, 43.069179, -66.984779, 45.096044","theme":["geospatial"],"title":"Lifespan of marsh units in Maine salt marshes"},"description":"This data release contains coastal wetland synthesis products for the state of Maine. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, and lifespan, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing federal, state, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/3e88268c-4be9-4af3-a6df-3d5f8e95e41f","harvest_record_raw":"https://catalog.data.gov/harvest_record/3e88268c-4be9-4af3-a6df-3d5f8e95e41f/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_657b3f47d34e952b2274bb44","keyword":["Acadia National Park","Maine","USGS:657b3f47d34e952b2274bb44","United States","coastal ecosystems","coastal processes","elevation","environment","estuarine processes","estuary","geospatial datasets","inlandWaters","lifespan","marsh health","oceans","salt marsh","sea-level change","sediment transport","vegetation","wetland ecosystems","wetland functions"],"last_harvested_date":"2026-09-10T22:44:46.378459","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":"lifespan-of-marsh-units-in-maine-salt-marshes","spatial_centroid":{"lat":43.879925,"lon":-69.2910584},"spatial_shape":{"coordinates":[[[-70.828578,43.069179],[-70.828578,45.096044],[-66.984779,45.096044],[-66.984779,43.069179],[-70.828578,43.069179]]],"type":"Polygon"},"theme":["geospatial"],"title":"Lifespan of marsh units in Maine salt marshes","type":"dataset"},{"_score":5.569254,"_sort":[1789080285898,5.569254,17,"0ec90dcd-c52f-4f5f-a3d4-6e7a2c6658d9"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael O'Donnell","hasEmail":"mailto:odonnellm@usgs.gov"},"description":"We developed numerous datasets describing mining activity and landscape conditions for all known active, inactive, abandoned, and legacy surface mines in the Eastern United States Appalachian region to support monitoring and regulatory needs. These data include 1) a study area boundary, 2) a compiled set of spatial footprints for known mines from federal, state, academic research, and non-government organizations (extent of mining activity over time), 3) spatial subdivisions (subunits) of each mine footprint (based on the year of most probable vegetation loss since 1985), attributed with metrics to denote restoration status and recovery, 4) tabular attributes of subunits describing pre-European vegetation communities, annual surface conditions from remotely sensed vegetation indices (1985-2022), changes in land cover and land use types, and elevation changes, 5) tabular attributes of subunits describing the annual aggregated recovery metric and percent forest recovery, 6) areas mined within United States communities (Census tracts) and population demographics, and 7) eleven raster datasets (30-meter spatial resolution) describing annual and cumulative metrics for barren, grassland, grass/shrub, and planted forest year within mining footprints since 1985.\nSummary of data products: Refer to the supplemental section of this metadata file for a list, description, and potential use of the data (must download because not rendered on website). Descriptions also exist in other metadata files associated with the project.\nBackground:\nUntil the Surface Mining Control and Reclamation Act of 1977 (SMCRA) (refer to O\u2019Donnell and others, 2024 for a summary of state/federal/tribal regulations), there were no federal regulations on the reclamation of coal mines. The U.S. Department of the Interior Office of Surface Mining Reclamation and Enforcement (OSMRE) was also established in 1977 to \"protect citizens and the environment during mining and assure that the land is restored to beneficial use following mining.\" Mining regulations are encouraged through a monetary bond, a financial incentive between two parties (for example, mine company and state) to ensure agreed-upon obligations are met. Such obligations might include how to reclaim a mine after a company has completed mineral extraction. The Appalachian Regional Reforestation Initiative (ARRI; established in 2004) cooperates with OSMRE, state agencies (Alabama, Kentucky, Maryland, Ohio, Pennsylvania, Tennessee, Virginia, and West Virginia), the coal industry, environmental organizations, academic institutions, and landowners to assist with restoring forests and returning mine lands to pre-mining conditions or environmental services on coal mines in the Eastern United States. These efforts have more recently leaned on the forestry reclamation approach (FRA; Adams, 2017), a document that establishes best practices for reforesting mines, and OSMRE advisory reports. Until the establishment of ARRI, most reclamation focused on soil stabilization and establishment of grasses, shrubs, and nonnative plants (from 1977 to 2004). These sites primarily remain of little to no economic value because reclamation/restoration methods have resulted in compacted soils, an abundance of invasive species, and an inability to support forest growth and ecological succession.\nImportant caveats/limitations: \nPlease review the metadata accuracy reporting sections of each metadata file (data product) and all process steps describing data inputs and methods used in our analysis. \nData on mining locations within the United States are incomplete, and no single dataset provides sufficient information on where and when mining occurred. Because we are using data provided by federal and state government agencies, as well as published data based on mapping mine footprints using remotely sensed data, there is a significant variety of information and accuracy in source data. We, therefore, rely on the redundancy of data sources to improve mine location and the information documented for each mine. The aspatial information collected from the source data used to attribute footprints was intended to provide evidence that the footprint captures documented mining activity. When footprints indicated no mining activity from available source data, we discarded these data from the mine footprints.\nDue to the lack of publicly available data on mining and reclamation activities in the United States, our understanding of restoration success is limited. For example, we do not have complete records of when mining began and ended, or of the methods used for reclamation. A lack of this information might affect the success of soil remediation (for example, topsoil replacement and soil preparation before restoration) and restoration of vegetation (for example, species types used, planting methods, and mitigation of invasive species). The accompanying mine footprints provide evidence of mining activity that relies on aspatial information from independent data, which we include in our data and documentation. All subsequent analyses of data within mine footprints are based on multiple data sources and are intended to assess landscape changes as reflected in the temporal portrayal of those data.\nGiven that we have only investigated multispectral Landsat data without accompanying field data, as opposed to hyperspectral remotely sensed data (such as the Airborne Visible Infrared Imaging Spectrometer [AVIRIS]), we are limited to summarizing vegetation conditions at broad community levels (for example, trees, shrubs, grass). If data were available on mining activity and reclamation methods, using hyperspectral remotely sensed data would make more sense. We could then improve our understanding of species composition and whether invasive species are present (for example, kudzu and Autumn olive; other: mimosa, multiflora rose, bush honeysuckle, Japanese grass, Japanese spirea, and garlic mustard). We could also investigate the abundance and diversity of species within and beyond mine footprints to determine restoration success. Hyperspectral data, accompanied with field data, could also help detect if there are toxins absorbed by plants that cause threats to flora, wildlife, and people. Understanding site conditions post-mining is essential for understanding restoration success. Terrain characteristics (for example, slope and slope position, aspect, and elevation) affect moisture conditions, types of vegetation suitable for planting at a site, and time for vegetation recovery. Methods of soil preparation are important and usually require single to triple-shank rippers mounted on heavy equipment to uncompact soils (generally, at least four feet in depth). Due to a lack of data on on-site preparation and planting, we are limited in understanding why some sites recover more successfully or at faster rates, which would otherwise be helpful to mine operators and land stewards.    \nLandsat is a useful data product for regional assessments because it is free to the public and has a long history (1970s-present). Remotely sensed hyperspectral data is only available when acquired from aircraft and is therefore limited spatially and temporally. Hyperspectral data is also not freely available to the public and is more commonly used for local applications, with less frequent repeat collections. Our products are, therefore, useful for regional assessments of vegetation recovery but are limited to more general questions about vegetation cover and productivity. These products do not include information about site toxicity, alterations to soil pH (acidity versus alkaline/basic conditions that can be affected by mining), effects of heavy metals on vegetation, site preparation methods required for restoration, or similar characteristics that may affect restoration success. Such information was not publicly available but would be valuable for improving methods to measure future restoration success.\nTypes of mining activity:\nAbandoned mine lands: Mine lands where mining or processing activity is determined to have ceased. The Abandoned Mine Land (AML) Reclamation Program was established in 1981 to address the physical safety and environmental hazards posed by abandoned mines (both before and after SMCRA).\nLegacy mines: These are mines reclaimed under the SMCRA, where mine operators no longer have legal responsibilities (bonds released).\nInactive mines: These include mine lands where operators are not currently extracting resources, the lands have not been reclaimed, and bonds have not been released.\nActive mines: These include mine lands where operators are currently extracting resources and bonds have not been released.\nKeywords: vegetation, vegetation recovery, forest recovery, ecosystem condition, disturbance, land cover change, hydrology, watershed processes, mining, mine footprint, abandoned mine lands, legacy mines, inactive mines, active mines, surface mines, abandoned mines and quarries, reclamation, restoration, reforestation, revegetation, topography, elevation change, geomorphic processes, mountain top removal, valley infill, remote sensing, spectral indices, normalized difference vegetation index, normalized burn ratio, normalized difference moisture index, aggregated recovery metric, time series analysis, geography, land surface characteristics, land use change, land use and land cover, spatial analysis","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13ZNPX8","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.69cfd5ceb66b01c06b645af3.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69cfd5ceb66b01c06b645af3","keyword":["Alabama","Appalachian Mountains","Georgia","Illinois","Indiana","Kentucky","Maryland","New Jersey","New York","North Carolina","Ohio","Pennsylvania","South Carolina","Tennessee","USGS:69cfd5ceb66b01c06b645af3","United States","Virginia","West Virginia","abandoned mine lands","abandoned mines and quarries","active mines","aggregated recovery metric","biota","boundaries","climatologyMeteorologyAtmosphere","disturbance","ecosystem condition","elevation","elevation change","environment","forest recovery","geography","geomorphic processes","health","hydrology","inactive mines","land cover change","land surface characteristics","land use and land cover","land use change","legacy mines","mine footprint","mining","mountain top removal","normalized burn ratio","normalized difference moisture index","normalized difference vegetation index","reclamation","reforestation","remote sensing","restoration","revegetation","spatial analysis","spectral indices","surface mines","time series analysis","topography","valley infill","vegetation","vegetation recovery","watershed processes"],"modified":"2026-05-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-89.6648, 31.2522, -73.4753, 44.3350","theme":["geospatial"],"title":"Assessing all known mining activity and landscape changes within the Appalachian region of the Eastern United States (active, inactive, abandoned, and legacy surface mines)"},"description":"We developed numerous datasets describing mining activity and landscape conditions for all known active, inactive, abandoned, and legacy surface mines in the Eastern United States Appalachian region to support monitoring and regulatory needs. These data include 1) a study area boundary, 2) a compiled set of spatial footprints for known mines from federal, state, academic research, and non-government organizations (extent of mining activity over time), 3) spatial subdivisions (subunits) of each mine footprint (based on the year of most probable vegetation loss since 1985), attributed with metrics to denote restoration status and recovery, 4) tabular attributes of subunits describing pre-European vegetation communities, annual surface conditions from remotely sensed vegetation indices (1985-2022), changes in land cover and land use types, and elevation changes, 5) tabular attributes of subunits describing the annual aggregated recovery metric and percent forest recovery, 6) areas mined within United States communities (Census tracts) and population demographics, and 7) eleven raster datasets (30-meter spatial resolution) describing annual and cumulative metrics for barren, grassland, grass/shrub, and planted forest year within mining footprints since 1985.\nSummary of data products: Refer to the supplemental section of this metadata file for a list, description, and potential use of the data (must download because not rendered on website). Descriptions also exist in other metadata files associated with the project.\nBackground:\nUntil the Surface Mining Control and Reclamation Act of 1977 (SMCRA) (refer to O\u2019Donnell and others, 2024 for a summary of state/federal/tribal regulations), there were no federal regulations on the reclamation of coal mines. The U.S. Department of the Interior Office of Surface Mining Reclamation and Enforcement (OSMRE) was also established in 1977 to \"protect citizens and the environment during mining and assure that the land is restored to beneficial use following mining.\" Mining regulations are encouraged through a monetary bond, a financial incentive between two parties (for example, mine company and state) to ensure agreed-upon obligations are met. Such obligations might include how to reclaim a mine after a company has completed mineral extraction. The Appalachian Regional Reforestation Initiative (ARRI; established in 2004) cooperates with OSMRE, state agencies (Alabama, Kentucky, Maryland, Ohio, Pennsylvania, Tennessee, Virginia, and West Virginia), the coal industry, environmental organizations, academic institutions, and landowners to assist with restoring forests and returning mine lands to pre-mining conditions or environmental services on coal mines in the Eastern United States. These efforts have more recently leaned on the forestry reclamation approach (FRA; Adams, 2017), a document that establishes best practices for reforesting mines, and OSMRE advisory reports. Until the establishment of ARRI, most reclamation focused on soil stabilization and establishment of grasses, shrubs, and nonnative plants (from 1977 to 2004). These sites primarily remain of little to no economic value because reclamation/restoration methods have resulted in compacted soils, an abundance of invasive species, and an inability to support forest growth and ecological succession.\nImportant caveats/limitations: \nPlease review the metadata accuracy reporting sections of each metadata file (data product) and all process steps describing data inputs and methods used in our analysis. \nData on mining locations within the United States are incomplete, and no single dataset provides sufficient information on where and when mining occurred. Because we are using data provided by federal and state government agencies, as well as published data based on mapping mine footprints using remotely sensed data, there is a significant variety of information and accuracy in source data. We, therefore, rely on the redundancy of data sources to improve mine location and the information documented for each mine. The aspatial information collected from the source data used to attribute footprints was intended to provide evidence that the footprint captures documented mining activity. When footprints indicated no mining activity from available source data, we discarded these data from the mine footprints.\nDue to the lack of publicly available data on mining and reclamation activities in the United States, our understanding of restoration success is limited. For example, we do not have complete records of when mining began and ended, or of the methods used for reclamation. A lack of this information might affect the success of soil remediation (for example, topsoil replacement and soil preparation before restoration) and restoration of vegetation (for example, species types used, planting methods, and mitigation of invasive species). The accompanying mine footprints provide evidence of mining activity that relies on aspatial information from independent data, which we include in our data and documentation. All subsequent analyses of data within mine footprints are based on multiple data sources and are intended to assess landscape changes as reflected in the temporal portrayal of those data.\nGiven that we have only investigated multispectral Landsat data without accompanying field data, as opposed to hyperspectral remotely sensed data (such as the Airborne Visible Infrared Imaging Spectrometer [AVIRIS]), we are limited to summarizing vegetation conditions at broad community levels (for example, trees, shrubs, grass). If data were available on mining activity and reclamation methods, using hyperspectral remotely sensed data would make more sense. We could then improve our understanding of species composition and whether invasive species are present (for example, kudzu and Autumn olive; other: mimosa, multiflora rose, bush honeysuckle, Japanese grass, Japanese spirea, and garlic mustard). We could also investigate the abundance and diversity of species within and beyond mine footprints to determine restoration success. Hyperspectral data, accompanied with field data, could also help detect if there are toxins absorbed by plants that cause threats to flora, wildlife, and people. Understanding site conditions post-mining is essential for understanding restoration success. Terrain characteristics (for example, slope and slope position, aspect, and elevation) affect moisture conditions, types of vegetation suitable for planting at a site, and time for vegetation recovery. Methods of soil preparation are important and usually require single to triple-shank rippers mounted on heavy equipment to uncompact soils (generally, at least four feet in depth). Due to a lack of data on on-site preparation and planting, we are limited in understanding why some sites recover more successfully or at faster rates, which would otherwise be helpful to mine operators and land stewards.    \nLandsat is a useful data product for regional assessments because it is free to the public and has a long history (1970s-present). Remotely sensed hyperspectral data is only available when acquired from aircraft and is therefore limited spatially and temporally. Hyperspectral data is also not freely available to the public and is more commonly used for local applications, with less frequent repeat collections. Our products are, therefore, useful for regional assessments of vegetation recovery but are limited to more general questions about vegetation cover and productivity. These products do not include information about site toxicity, alterations to soil pH (acidity versus alkaline/basic conditions that can be affected by mining), effects of heavy metals on vegetation, site preparation methods required for restoration, or similar characteristics that may affect restoration success. Such information was not publicly available but would be valuable for improving methods to measure future restoration success.\nTypes of mining activity:\nAbandoned mine lands: Mine lands where mining or processing activity is determined to have ceased. The Abandoned Mine Land (AML) Reclamation Program was established in 1981 to address the physical safety and environmental hazards posed by abandoned mines (both before and after SMCRA).\nLegacy mines: These are mines reclaimed under the SMCRA, where mine operators no longer have legal responsibilities (bonds released).\nInactive mines: These include mine lands where operators are not currently extracting resources, the lands have not been reclaimed, and bonds have not been released.\nActive mines: These include mine lands where operators are currently extracting resources and bonds have not been released.\nKeywords: vegetation, vegetation recovery, forest recovery, ecosystem condition, disturbance, land cover change, hydrology, watershed processes, mining, mine footprint, abandoned mine lands, legacy mines, inactive mines, active mines, surface mines, abandoned mines and quarries, reclamation, restoration, reforestation, revegetation, topography, elevation change, geomorphic processes, mountain top removal, valley infill, remote sensing, spectral indices, normalized difference vegetation index, normalized burn ratio, normalized difference moisture index, aggregated recovery metric, time series analysis, geography, land surface characteristics, land use change, land use and land cover, spatial analysis","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6cd0329a-9ffa-4a0c-8f23-76903570d352","harvest_record_raw":"https://catalog.data.gov/harvest_record/6cd0329a-9ffa-4a0c-8f23-76903570d352/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69cfd5ceb66b01c06b645af3","keyword":["Alabama","Appalachian Mountains","Georgia","Illinois","Indiana","Kentucky","Maryland","New Jersey","New York","North Carolina","Ohio","Pennsylvania","South Carolina","Tennessee","USGS:69cfd5ceb66b01c06b645af3","United States","Virginia","West Virginia","abandoned mine lands","abandoned mines and quarries","active mines","aggregated recovery metric","biota","boundaries","climatologyMeteorologyAtmosphere","disturbance","ecosystem condition","elevation","elevation change","environment","forest recovery","geography","geomorphic processes","health","hydrology","inactive mines","land cover change","land surface characteristics","land use and land cover","land use change","legacy mines","mine footprint","mining","mountain top removal","normalized burn ratio","normalized difference moisture index","normalized difference vegetation index","reclamation","reforestation","remote sensing","restoration","revegetation","spatial analysis","spectral indices","surface mines","time series analysis","topography","valley infill","vegetation","vegetation recovery","watershed processes"],"last_harvested_date":"2026-09-10T22:44:45.898715","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":17,"publisher":"U.S. Geological Survey","slug":"assessing-all-known-mining-activity-and-landscape-changes-within-the-appalachian-region-of","spatial_centroid":{"lat":36.48532,"lon":-83.189},"spatial_shape":{"coordinates":[[[-89.6648,31.2522],[-89.6648,44.335],[-73.4753,44.335],[-73.4753,31.2522],[-89.6648,31.2522]]],"type":"Polygon"},"theme":["geospatial"],"title":"Assessing all known mining activity and landscape changes within the Appalachian region of the Eastern United States (active, inactive, abandoned, and legacy surface mines)","type":"dataset"},{"_score":33.98793,"_sort":[1789080275789,33.98793,1,"f09ca803-d1dd-4450-98f0-55c55c5f099a"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Joseph A Tomoleoni","hasEmail":"mailto:jtomoleoni@usgs.gov"},"description":"One CSV file with data from basic field surveys of southern sea otters at five sites in California, USA, including (from north to south) Elkhorn Slough, Monterey, Big Sur, Piedras Blancas, and San Luis Obispo, are provided. These are the data used to fit models in Law et al. 2024 (full citation in the larger work) publication in Science.  The data consist of otter age, sex, and size morphometrics, measured from sea otter captures; associated forage information collected by visual surveys; and hardness of forage prey species.  Complete description of the study objectives, methods, field sites, and uses of these data for analyses and interpretations can be found in Law et al. 2024.\n      \n      Although it is well documented that tool use can enable the utilization of novel resources, the fitness benefits associated with this innovative behavior are difficult to test. Using longitudinal data from 196 radio-tagged southern sea otters, we found that individuals, particularly females, with frequent tool use gained access to harder, larger prey items. In turn, the mechanical advantages of tool use during food processing translated to reduced tooth damage in tool users. We also found that tool use diminishes trade-offs between access to different prey types, tooth health, and caloric intake that are highly dependent on the relative availability of prey in the environment. 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We also found that tool use diminishes trade-offs between access to different prey types, tooth health, and caloric intake that are highly dependent on the relative availability of prey in the environment. 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NWHC Case 29381.\nMSF Sequence file used to construct a phylogenetic tree. Sequences were generated from samples at NWHC (Px) and using BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi) they were compared for base pair similarities with publicly available sequences from GenBank. Sequences selected for the Multi Sequence Fast File (MSF) was based on the number of base pair similarities AND stage of parasite from which the sequence was generated.  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The WHISPers database tracks reported morbidity and mortality (m/m) events, defined as one or more animals found dead linked in time and space, around the United States, reported by NWHC partners. 9,488 diagnostic cases submitted between January 1st, 2000 and December 31st, 2021 are included in this dataset.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13QPXBN","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.662c26efd34ea70bd5f123a6.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_662c26efd34ea70bd5f123a6","keyword":["USGS:662c26efd34ea70bd5f123a6","biota","outbreak","surveillance","wildlife disease"],"modified":"2024-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-175.9570, 13.2399, -53.0859, 71.9654","theme":["geospatial"],"title":"USGS NWHC Diagnostic Case Data to retrospectively evaluate NWHC\u2019s diagnosis likelihoods for different kinds of cases collected in the United States submitted to the NWHC from January 1st, 2000 to December 31st, 2021."},"description":"Diagnostic case-level data recorded by the USGS National Wildlife Health Center (NWHC) in their Laboratory Information Management system (LIMs) and wildlife health information sharing partnership event reporting system (WHISPers). 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For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P997EJYB","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.630f9ba4d34e36012efa0924.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_630f9ba4d34e36012efa0924","keyword":["Chesapeake Bay","Maryland","North Carolina","USGS:630f9ba4d34e36012efa0924","UVVR","United States","Virginia","coastal ecosystems","coastal processes","environment","estuary","geospatial datasets","inlandWaters","marsh health","oceans","salt marsh","vegetation","wetland ecosystems","wetland functions"],"modified":"2026-04-13T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-77.374707, 36.374384, -75.593417, 39.589813","theme":["geospatial"],"title":"Unvegetated to vegetated ratio of marsh units in Chesapeake Bay salt marshes"},"description":"This data release contains coastal wetland synthesis products for Chesapeake Bay. 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For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6c2d1a0d-dc3d-4986-99b3-e736e953abd9","harvest_record_raw":"https://catalog.data.gov/harvest_record/6c2d1a0d-dc3d-4986-99b3-e736e953abd9/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_630f9ba4d34e36012efa0924","keyword":["Chesapeake Bay","Maryland","North Carolina","USGS:630f9ba4d34e36012efa0924","UVVR","United States","Virginia","coastal ecosystems","coastal processes","environment","estuary","geospatial datasets","inlandWaters","marsh health","oceans","salt marsh","vegetation","wetland ecosystems","wetland functions"],"last_harvested_date":"2026-09-10T22:42:18.280954","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":"unvegetated-to-vegetated-ratio-of-marsh-units-in-chesapeake-bay-salt-marshes","spatial_centroid":{"lat":37.660555599999995,"lon":-76.662191},"spatial_shape":{"coordinates":[[[-77.374707,36.374384],[-77.374707,39.589813],[-75.593417,39.589813],[-75.593417,36.374384],[-77.374707,36.374384]]],"type":"Polygon"},"theme":["geospatial"],"title":"Unvegetated to vegetated ratio of marsh units in Chesapeake Bay salt marshes","type":"dataset"},{"_score":12.781002,"_sort":[1789080103599,12.781002,1,"9b830f71-a693-41b3-ae6d-4c960bf9f100"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"U.S. Geological Survey, Western Ecological Research Center","hasEmail":"mailto:gs-b-werc_data_management@usgs.gov"},"description":"Heavy fog was hypothesized to interfere with flight of waterfowl by obscuring visibility and cause waterfowl to fly more in search for habitat below, which in turn, is likely to increase bioenergetic (caloric) cost related to increased time flying. The data sets include variables used to analyze the relationship between fog characteristics (occurrence, density, and longevity) and flight activity (likelihood flying vs. not flying) of waterfowl, specifically of northern pintail (Anas acuta) during winter in the Central Valley of California, U.S. From analysis of pintail telemetry and fog observations for years 1991-93 and 2015-2023 using these data sets, the researchers inferred effects of fog reduction on the bioenergetics of waterfowl and related conservation implications since administration of the federal Clean Air Act (1972) (see related publication \"Clean Air Standards, a Win-win for Human Health and Bird Conservation\" for more details).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P14UBHN2","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.68925723d4be0275eef5d010.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_68925723d4be0275eef5d010","keyword":["USGS:68925723d4be0275eef5d010","animal tracking","biota","birds","environmental proxies","telemetry"],"modified":"2025-12-08T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-122.5489, 34.7909, -118.5327, 40.2425","theme":["geospatial"],"title":"Waterfowl tracking VHF and GPS data relative to Sacramento Delta Fog and Clean Air Act"},"description":"Heavy fog was hypothesized to interfere with flight of waterfowl by obscuring visibility and cause waterfowl to fly more in search for habitat below, which in turn, is likely to increase bioenergetic (caloric) cost related to increased time flying. 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The space available for landward migration is based on the NOAA marsh migration predictions under 2 feet of local sea-level rise (SLR). The migration space is further divided by National Hydrography Dataset (NHD) Plus catchments before assigning related catchment polygons to each marsh unit. The migration rates are then calculated using present day estimates at the prescribed rate of SLR, which correspond to the 0.5 meter increase in Global Mean Sea Level (GMSL) scenarios by 2100 from Sweet and others (2022). Through scientific efforts, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands, including the Chesapeake Bay salt marshes, with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. Marsh migration is one of the natural responses to SLR. \nReferences:\nAckerman, K.V., Defne, Z., and Ganju, N.K., 2022, Geospatial characterization of salt marshes in Chesapeake Bay: U.S. Geological Survey data release, https://doi.org/10.5066/P997EJYB.\nSweet, W.V., Hamlington, B.D., Kopp, R.E., Weaver, C.P., Barnard, P.L., Bekaert, D., Brooks, W., Craghan, M., Dusek, G., Frederikse, T., Garner, G., Genz, A.S., Krasting, J.P., Larour, E., Marcy, D., Marra, J.J., Obeysekera, J., Osler, M., Pendleton, M., Roman, D., Schmied, L., Veatch, W., White, K.D., and Zuzak, C., 2022, Global and regional sea level rise scenarios for the United States\u2014Updated mean projections and extreme water level probabilities along U.S. coastlines: National Oceanic and Atmospheric Administration, NOAA Technical Report NOS 01, 111 pp., https://oceanservice.noaa.gov/hazards/sealevelrise/noaa-nos-techrpt01-global-regional-SLR-scenarios-US.pdf","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P18BWN2U","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.677d87ded34e009b43365947.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_677d87ded34e009b43365947","keyword":["Atlantic","Chesapeake","LTER","Long-Term Ecological Research","Maryland","USGS:677d87ded34e009b43365947","United States","Virginia","coastal ecosystems","coastal processes","elevation","environment","estuarine processes","estuary","geospatial datasets","inlandWaters","lifespan","marsh health","oceans","salt marsh","sea-level change","sediment transport","vegetation","wetland ecosystems","wetland functions"],"modified":"2026-05-26T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-77.3747, 36.3744, -75.5934, 39.5898","theme":["geospatial"],"title":"Polygons for marsh migration under 2 feet of sea-level rise in Chesapeake Bay (ver. 2.0, May 2026)"},"description":"Marsh migration potential in the Chesapeake Bay (CB) salt marshes is calculated in terms of available migration area for each marsh unit defined by Ackerman and others (2022). 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For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. 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Environmental samples were analyzed for a suite of 29 antibiotics, plated on selective media for 15 types of bacteria growth, and DNA was extracted from culture growth and used in downstream polymerase chain reaction (PCR) assays for the detection of 24 antibiotic resistance genes.  Environmental field data were collected in-situ for water temperature (\u00b0C), specific conductivity (\u00b5s/cm), dissolved oxygen (mg/L), pH (standard units), and turbidity (FNU) using a multiparameter sonde and recorded in the National Water Information System (NWIS) (Table S2).  Stream discharge (ft3/s) and gage height (ft) values were estimated using available rating curves from adjacent USGS stream gages when available and relative flow was categorized by comparing discharge on the day of sample collection to discharge over the previous three to five years.  Samples were analyzed at U.S. Geological Survey laboratories: bacteria enumeration and growth and antibiotic resistance genes at the Michigan Bacteriological Research Laboratory and antibiotic concentrations at the Organic Geochemistry Research Laboratory.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9BUNGFS","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.64779a3ad34e3ac335becbd3.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64779a3ad34e3ac335becbd3","keyword":["Iowa","USGS:64779a3ad34e3ac335becbd3","United States","antibiotic resistance","antibiotics","bacteria","environment","environmental health","human health","sediment","water","water quality","wildlife health"],"modified":"2023-09-08T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-96.6400, 40.3700, -90.1400, 43.5000","theme":["geospatial"],"title":"Antibiotic and Antibiotic Resistance Signatures in Iowa Streams, 2019"},"description":"Chemical and microbiological results, quality assurance and quality control, site location, and method information for surface water, bed sediment, and wastewater effluent collected from 34 stream locations across Iowa (United States).  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The weighted sum of the layers was then calculated to achieve the aquifer \nvulnerability score","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9F9CGXB","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.2a38302b-243d-41cc-8823-31807da1d4e7.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_2a38302b-243d-41cc-8823-31807da1d4e7","keyword":["Aquifer media","Colorado","Colorado aquifer","DRASTIC","Depth to water","Hydraulic conductivity","Impact of the vadose zone","New Mexico","New Mexico aquifer","Recharge","Soil media","Topography","USGS:2a38302b-243d-41cc-8823-31807da1d4e7","elevation and derived products","geological and geophysical","human health and diease","imagery","imagery and base maps","inland waters","locations and geodetic networks","oceans and estuaries"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-110.803085, 31.239218, -101.360087, 41.522980","theme":["geospatial"],"title":"Aquifer vulnerability for Colorado and New Mexico"},"description":"The U.S. Geological Survey Data Series provides raster data representing an estimate of aquifer \nvulnerability calculated for each 30-meter raster cell. 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Evaluation of the architecture and cellular population of immune organs will shed light on potential functional immunological effects of exposure that may lead to increased susceptibility to infectious disease or affect normal growth and development of the chick.  \n(Luna LG. 1968.  Manual of histologic staining methods of the armed forces institute of pathology, 3rd edn. 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The lifespan represents the timescale by which the current sediment mass within a marsh parcel can no longer compensate for sediment export and deficits induced by sea-level rise. The lifespan calculation is based on vegetated cover, marsh elevation, sediment supply, and sea-level rise (SLR) predictions after Ganju and others (2020). Sea level rise scenarios are present day estimates corresponding to the 0.3, 0.5, and 1.0 meter increase in Global Mean Sea Level (GMSL) by 2100 from Sweet and others (2017). Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands, including the Assateague Island National Seashore and Chincoteague Bay salt marshes, with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.\nReferences:\nDefne, Z., and Ganju, N.K., 2018, Conceptual marsh units for Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia: U.S. Geological Survey data release, https://doi.org/10.5066/P92ZW4D9.\nGanju, N.K., Defne, Z., Fagherazzi, S., 2020, Are elevation and open-water conversion of salt marshes connected?, Geophysical Research Letters, https://doi.org/10.1029/2019GL086703.\nSweet, W.V., Kopp, R.E., Weaver, C.P., Obeysekera, J., Horton, R.M., Thieler, E.R., and Zervas, C., 2017, Global and regional sea level rise scenarios for the United States (Tech. Rep. NOS CO-OPS 083). 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For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.\nReferences:\nDefne, Z., and Ganju, N.K., 2018, Conceptual marsh units for Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia: U.S. Geological Survey data release, https://doi.org/10.5066/P92ZW4D9.\nGanju, N.K., Defne, Z., Fagherazzi, S., 2020, Are elevation and open-water conversion of salt marshes connected?, Geophysical Research Letters, https://doi.org/10.1029/2019GL086703.\nSweet, W.V., Kopp, R.E., Weaver, C.P., Obeysekera, J., Horton, R.M., Thieler, E.R., and Zervas, C., 2017, Global and regional sea level rise scenarios for the United States (Tech. Rep. NOS CO-OPS 083). 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The framework was developed first through a facilitated workshop with all units, followed by unit-specific workshops where priorities were adjusted to reflect the natural and cultural resources within the unit. The final product is a series of maps depicting performance metrics over a consistent geospatial framework. Integration of these metrics into decision support tools will help guide marsh restoration investments through identification of marsh units and complexes that maximize agency priorities and objectives. 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The final product is a series of maps depicting performance metrics over a consistent geospatial framework. Integration of these metrics into decision support tools will help guide marsh restoration investments through identification of marsh units and complexes that maximize agency priorities and objectives. 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The intent is to provide federal, state, and local managers with tools to estimate their vulnerability and ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. EBFNWR was selected as a pilot study area.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7CF9N7X","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.581ff0b3e4b06efbab38d9e2.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_581ff0b3e4b06efbab38d9e2","keyword":["Atlantic Ocean","Barnegat Bay","Edwin B. 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This dataset displays the spatial variation mean tidal range (i.e. Mean Range of Tides, MN) in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New Jersey, USA. MN was based on the calculated difference in height between mean high water (MHW) and mean low water (MLW) using the VDatum (v3.5) software (http://vdatum.noaa.gov/). The input elevation was set to zero in VDatum to calculate the relative difference between the two datums. \nAs part of the Hurricane Sandy Science Plan, the U.S. Geological Survey has started a Wetland Synthesis Project to expand National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate their vulnerability and ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. EBFNWR was selected as a pilot study area.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f29660a8-60db-4669-9c55-1be411006522","harvest_record_raw":"https://catalog.data.gov/harvest_record/f29660a8-60db-4669-9c55-1be411006522/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_581ff0b3e4b06efbab38d9e2","keyword":["Atlantic Ocean","Barnegat Bay","Edwin B. Forsythe National Wildlife Refuge","GeoTIFF","Great Bay","Little Egg Island","New Jersey","USGS:581ff0b3e4b06efbab38d9e2","United States","coastal ecosystems","coastal processes","environment","estuary","inlandWaters","marsh health","oceans","salt marsh","tidal range","tides (oceanic)","vegetation","wetland ecosystems","wetland functions"],"last_harvested_date":"2026-09-10T22:39:13.387657","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":"raster-image-of-mean-tidal-range-in-the-edwin-b-forsythe-national-wildlife-refuge-new-jers","spatial_centroid":{"lat":39.6819871456,"lon":-74.31182038540001},"spatial_shape":{"coordinates":[[[-74.487530855,39.432668022],[-74.487530855,40.055965831],[-74.048254681,40.055965831],[-74.048254681,39.432668022],[-74.487530855,39.432668022]]],"type":"Polygon"},"theme":["geospatial"],"title":"Raster image of mean tidal range in the Edwin B. Forsythe National Wildlife Refuge, New Jersey (32-bit GeoTIFF)","type":"dataset"},{"_score":7.5965652,"_sort":[1789079945749,7.5965652,2,"29e48926-c2f8-478e-a294-92d467264bf7"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Leslie A Desimone","hasEmail":"mailto:ldesimon@usgs.gov"},"description":"Data and preliminary machine-learning models used to predict manganese and 1,4-dioxane in groundwater on Long Island are documented in this data release. Concentration data used to develop the models were from 910 wells for manganese and 553 wells for 1,4-dioxane, primarily public supply wells, from U.S. Geological Survey, U.S. Environmental Protection Agency (USEPA), and Suffolk County Water Authority sources. Thirty-two explanatory variables describe depth, groundwater flow, land use, soil properties, and other features of the aquifer system. The models use XGBoost, an ensemble tree machine learning method. Four models are documented for manganese, predicting the probability of concentrations relative to four thresholds: 10 micrograms per liter (detection), 50 micrograms per liter (the USEPA Secondary Maximum Contaminant Level), 150 micrograms per liter, and 300 micrograms per liter (the USEPA lifetime health advisory). One model is documented for 1,4-dioxane, predicting the probability of concentrations relative to 0.07 micrograms per liter (detection). The models were used to predict concentrations in two layers of the upper glacial aquifer and three layers of the Magothy aquifer. Predictions were made at a 500-square-foot resolution across the entire island for manganese and across Suffolk County, which occupies the eastern two-thirds of Long Island, for 1,4-dioxane.   \nThe data are provided in data tables, raster files, and model files. One data table describes the 32 explanatory variables (LI_mn_14dx_exp_vars.txt). One data table describes the well data and includes the manganese and 1,4-dioxane concentrations, explanatory variables, and predictions for the wells (LI_mn_14dx_well_data.txt). There is a compressed group (zip file) of five files providing the explanatory variable data used to make predictions for the five aquifer layers (LI_mn_14dx_predinput_griddata.zip) and a zip file of 25 files providing model predictions for each model and aquifer layer (LI_mn_14dx_predoutput_rasters.zip). The data release also contains a tif-format raster file of the prediction grid (LI_mn_14dx_prediction_grid.tif). The models are documented in a zip file (LI_mn_14dx_models.zip) that contains the model object files (R data format) and scripts that can be used to run the models to produce the predictions provided in this data release. Filenames for prediction input and for model output are distinguished by names and numbers as follows: 1_upper_glacial, top layer of the upper glacial aquifer; 3_upper_glacial, bottom layer of the upper glacial aquifer; 5_Magothy, top layer of the Magothy aquifer; 14_Magothy, middle layer of the Magothy aquifer; and 23_Magothy, bottom layer of the Magothy aquifer.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P90AT9YG","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.614d0486d34e0df5fb986a43.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_614d0486d34e0df5fb986a43","keyword":["1,4-dioxane","Kings County","Long Island","NAWQA","Nassau County","New York","Queens County","Suffolk County","USGS:614d0486d34e0df5fb986a43","XGboost","groundwater quality","machine learning","manganese","regional groundwater quality"],"modified":"2023-03-22T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-74.083, 40.505, -71.808, 41.2","theme":["geospatial"],"title":"Data and Model Archive for Preliminary Machine Learning Models of Manganese and 1,4-Dioxane in Groundwater on Long Island, New York"},"description":"Data and preliminary machine-learning models used to predict manganese and 1,4-dioxane in groundwater on Long Island are documented in this data release. Concentration data used to develop the models were from 910 wells for manganese and 553 wells for 1,4-dioxane, primarily public supply wells, from U.S. Geological Survey, U.S. Environmental Protection Agency (USEPA), and Suffolk County Water Authority sources. Thirty-two explanatory variables describe depth, groundwater flow, land use, soil properties, and other features of the aquifer system. The models use XGBoost, an ensemble tree machine learning method. Four models are documented for manganese, predicting the probability of concentrations relative to four thresholds: 10 micrograms per liter (detection), 50 micrograms per liter (the USEPA Secondary Maximum Contaminant Level), 150 micrograms per liter, and 300 micrograms per liter (the USEPA lifetime health advisory). One model is documented for 1,4-dioxane, predicting the probability of concentrations relative to 0.07 micrograms per liter (detection). The models were used to predict concentrations in two layers of the upper glacial aquifer and three layers of the Magothy aquifer. Predictions were made at a 500-square-foot resolution across the entire island for manganese and across Suffolk County, which occupies the eastern two-thirds of Long Island, for 1,4-dioxane.   \nThe data are provided in data tables, raster files, and model files. One data table describes the 32 explanatory variables (LI_mn_14dx_exp_vars.txt). One data table describes the well data and includes the manganese and 1,4-dioxane concentrations, explanatory variables, and predictions for the wells (LI_mn_14dx_well_data.txt). There is a compressed group (zip file) of five files providing the explanatory variable data used to make predictions for the five aquifer layers (LI_mn_14dx_predinput_griddata.zip) and a zip file of 25 files providing model predictions for each model and aquifer layer (LI_mn_14dx_predoutput_rasters.zip). The data release also contains a tif-format raster file of the prediction grid (LI_mn_14dx_prediction_grid.tif). The models are documented in a zip file (LI_mn_14dx_models.zip) that contains the model object files (R data format) and scripts that can be used to run the models to produce the predictions provided in this data release. Filenames for prediction input and for model output are distinguished by names and numbers as follows: 1_upper_glacial, top layer of the upper glacial aquifer; 3_upper_glacial, bottom layer of the upper glacial aquifer; 5_Magothy, top layer of the Magothy aquifer; 14_Magothy, middle layer of the Magothy aquifer; and 23_Magothy, bottom layer of the Magothy aquifer.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f1cd777f-653d-40db-b065-c59a5994afa1","harvest_record_raw":"https://catalog.data.gov/harvest_record/f1cd777f-653d-40db-b065-c59a5994afa1/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_614d0486d34e0df5fb986a43","keyword":["1,4-dioxane","Kings County","Long Island","NAWQA","Nassau County","New York","Queens County","Suffolk County","USGS:614d0486d34e0df5fb986a43","XGboost","groundwater quality","machine learning","manganese","regional groundwater quality"],"last_harvested_date":"2026-09-10T22:39:05.749887","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":"data-and-model-archive-for-preliminary-machine-learning-models-of-manganese-and-14-dioxane","spatial_centroid":{"lat":40.783,"lon":-73.173},"spatial_shape":{"coordinates":[[[-74.083,40.505],[-74.083,41.2],[-71.808,41.2],[-71.808,40.505],[-74.083,40.505]]],"type":"Polygon"},"theme":["geospatial"],"title":"Data and Model Archive for Preliminary Machine Learning Models of Manganese and 1,4-Dioxane in Groundwater on Long Island, New York","type":"dataset"},{"_score":7.2986546,"_sort":[1789079885916,7.2986546,2,"164a2631-6680-4db1-a53b-b89f370e7202"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Laura M. Bexfield","hasEmail":"mailto:bexfield@usgs.gov"},"description":"From October 2017 through September 2022, the National Water Quality Network (NWQN) monitored 110 surface-water river and stream sites and more than 1,800 groundwater wells for a large number of water-quality analytes, for which associated quality-control data and corresponding statistical summaries are included in this data release. The quality-control data\u2014for samples that were collected in the field (at all 110 surface-water sites, 350 groundwater wells, and 16 quality-control-only sites), prepared in the laboratory, or prepared by a third party\u2014can be used to assess the quality of environmental data collected by the NWQN through the estimation of bias and variability in reported results. The general analyte groups that were monitored at NWQN surface-water and (or) groundwater sites and have associated quality-control data in this data release include major ions, nutrients, trace elements, pesticides, volatile organic compounds, hormones, pharmaceuticals, radionuclides, microbial indicators, sediment, and environmental tracers. For each analyte group, the data tables contain results for one or more of the following types of quality-control samples, where relevant: blanks, matrix spikes, and replicates collected at field sites; laboratory blanks, reagent spikes, and matrix spikes prepared by the USGS National Water Quality Laboratory (NWQL) (quality-control samples prepared by other analyzing laboratories are not included in the current data release); and third-party blanks, spikes, and reference samples prepared by the USGS Quality Systems Branch (QSB). For each relevant analyte, tables of summary statistics characterize the frequency and concentrations of blank detections, the typical magnitude of and variability in spike and reference-sample recoveries, and the typical variability between replicate concentrations.\nTables included in this data release:\nTable1_SiteList.txt: Information about National Water Quality Network sites that have associated quality-control data.\nTable2_AnalyteList.txt: Information about National Water Quality Network analytes that have associated quality-control data, including available aquatic-life and (or) human-health benchmarks and selected information regarding analytical methods.\nTable3_BlankData.txt: For all relevant analytes, results for blanks collected at field sites, prepared in the laboratory, or prepared by a third party.\nTable4_SpikeData.txt: For all relevant analytes, results for matrix spikes prepared in the field, matrix spikes prepared in the laboratory, reagent spikes prepared in the laboratory, or reagent spikes prepared by a third party. For matrix spikes, results of paired environmental samples are included. \nTable5_ReplicateData.txt: For all relevant analytes, results for field replicates and paired environmental samples.\nTable 6_ReferenceData.txt: For all relevant analytes, results for third-party reference samples.\nTable7_BlankStats.txt: For all relevant analytes, summary statistics for each type of available blank sample.\nTable8_SpikeStats.txt: For all relevant analytes, summary statistics for each type of available spike sample.\nTable9_ReplicateStats.txt: For all relevant analytes, summary statistics for field replicates.\nTable10_ReferenceStats.txt: For all relevant analytes, summary statistics for reference samples.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9NBXVSE","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.64b176cfd34e70357a2a013c.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64b176cfd34e70357a2a013c","keyword":["NAWQA","NWQN","USGS:64b176cfd34e70357a2a013c","groundwater","groundwater quality","quality control","river systems","surface water (non-marine)","surface water quality","water quality"],"modified":"2023-11-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-171.2109, 17.6440, -66.0938, 71.5249","theme":["geospatial"],"title":"Field, laboratory, and third-party quality-control data associated with sites and analytes monitored by the USGS National Water Quality Network, October 2017 through September 2022"},"description":"From October 2017 through September 2022, the National Water Quality Network (NWQN) monitored 110 surface-water river and stream sites and more than 1,800 groundwater wells for a large number of water-quality analytes, for which associated quality-control data and corresponding statistical summaries are included in this data release. The quality-control data\u2014for samples that were collected in the field (at all 110 surface-water sites, 350 groundwater wells, and 16 quality-control-only sites), prepared in the laboratory, or prepared by a third party\u2014can be used to assess the quality of environmental data collected by the NWQN through the estimation of bias and variability in reported results. The general analyte groups that were monitored at NWQN surface-water and (or) groundwater sites and have associated quality-control data in this data release include major ions, nutrients, trace elements, pesticides, volatile organic compounds, hormones, pharmaceuticals, radionuclides, microbial indicators, sediment, and environmental tracers. For each analyte group, the data tables contain results for one or more of the following types of quality-control samples, where relevant: blanks, matrix spikes, and replicates collected at field sites; laboratory blanks, reagent spikes, and matrix spikes prepared by the USGS National Water Quality Laboratory (NWQL) (quality-control samples prepared by other analyzing laboratories are not included in the current data release); and third-party blanks, spikes, and reference samples prepared by the USGS Quality Systems Branch (QSB). For each relevant analyte, tables of summary statistics characterize the frequency and concentrations of blank detections, the typical magnitude of and variability in spike and reference-sample recoveries, and the typical variability between replicate concentrations.\nTables included in this data release:\nTable1_SiteList.txt: Information about National Water Quality Network sites that have associated quality-control data.\nTable2_AnalyteList.txt: Information about National Water Quality Network analytes that have associated quality-control data, including available aquatic-life and (or) human-health benchmarks and selected information regarding analytical methods.\nTable3_BlankData.txt: For all relevant analytes, results for blanks collected at field sites, prepared in the laboratory, or prepared by a third party.\nTable4_SpikeData.txt: For all relevant analytes, results for matrix spikes prepared in the field, matrix spikes prepared in the laboratory, reagent spikes prepared in the laboratory, or reagent spikes prepared by a third party. For matrix spikes, results of paired environmental samples are included. \nTable5_ReplicateData.txt: For all relevant analytes, results for field replicates and paired environmental samples.\nTable 6_ReferenceData.txt: For all relevant analytes, results for third-party reference samples.\nTable7_BlankStats.txt: For all relevant analytes, summary statistics for each type of available blank sample.\nTable8_SpikeStats.txt: For all relevant analytes, summary statistics for each type of available spike sample.\nTable9_ReplicateStats.txt: For all relevant analytes, summary statistics for field replicates.\nTable10_ReferenceStats.txt: For all relevant analytes, summary statistics for reference samples.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f55e635f-e1af-4dee-b889-cfeb368e1e55","harvest_record_raw":"https://catalog.data.gov/harvest_record/f55e635f-e1af-4dee-b889-cfeb368e1e55/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64b176cfd34e70357a2a013c","keyword":["NAWQA","NWQN","USGS:64b176cfd34e70357a2a013c","groundwater","groundwater quality","quality control","river systems","surface water (non-marine)","surface water quality","water quality"],"last_harvested_date":"2026-09-10T22:38:05.916016","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":"field-laboratory-and-third-party-quality-control-data-associated-with-sites-and-analy-2022","spatial_centroid":{"lat":39.19636,"lon":-129.16406},"spatial_shape":{"coordinates":[[[-171.2109,17.644],[-171.2109,71.5249],[-66.0938,71.5249],[-66.0938,17.644],[-171.2109,17.644]]],"type":"Polygon"},"theme":["geospatial"],"title":"Field, laboratory, and third-party quality-control data associated with sites and analytes monitored by the USGS National Water Quality Network, October 2017 through September 2022","type":"dataset"},{"_score":9.788202,"_sort":[1789079881037,9.788202,1,"a9082bff-dc1c-4404-8f56-47ddb36a2af6"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"U.S. Geological Survey","hasEmail":"mailto:whsc_data_contact@usgs.gov"},"description":"The need to monitor change in sagebrush steppe is urgent due to the increasing impacts of climate change, shifting fire regimes, and management practices on ecosystem health. Remote sensing provides a cost-effective and reliable method for monitoring change through time and attributing changes to drivers. We report an automated method of mapping rangeland fractional component cover over a large portion of the Northern Great Basin, USA, from 1986 to 2016 using a dense Landsat imagery time series. 2012 was excluded from the time-series due to a lack of quality imagery. Our method improved upon the traditional change vector method by considering the legacy of change at each pixel. We evaluate cover trends stratified by climate bin and assess spatial and temporal relationships with climate variables. Finally, we statistically evaluate the minimum time density needed to accurately characterize temporal patterns and relationships with climate drivers. Over the 30-yr period, shrub cover declined and bare ground increased. While few pixels had >10% cover change, a large majority had at least some change. All fractional components had significant spatial relationships with water year precipitation (WYPRCP), maximum temperature (WYTMAX), and minimum temperature (WYTMIN) in all years. Shrub and sagebrush cover in particular respond positively to warming WYTMIN, resulting from the largest increases in WYTMIN being in the coolest and wettest areas, and respond negatively to warming WYTMAX because the largest increases in WYTMAX are in the warmest and driest areas. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. Component products can be downloaded from www.mrlc.gov.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9C9O66W","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.5ed816cf82ce7e579c670046.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5ed816cf82ce7e579c670046","keyword":["AZ","Arizona","Arizona Plateau","Black Hills","Blue Mountains","CA","CO","California","Chihuahuan Desert","Colorado","Colorado Plateau","Columbia Plateau","Grand Canyon","Great Basin","Gunnison","ID","Idaho","MT","Middle Rockies","Montana","ND","NE","NM","NV","Nebraska","Nevada","New Mexico","North Dakota","Northern Mountainous","OR","Oregon","Rocky Mountains","SD","Sonoran Desert","South Dakota","Southwest Tablelands","TX","Texas","Three Forks","USGS:5ed816cf82ce7e579c670046","UT","United States","Utah","WA","WY","Wasatch","Washington","Western US","Wyoming","Yellowstone","annual herbaceous","back-in-time","bare ground","big sagebrush","biota","climate change","environment","geoscientificInformation","grass","grassland change","herbaceous","imageryBaseMapsEarthCover","litter","rangeland","rangeland management","sagebrush","shrub","shrubland","shrubland change","shrubland ecosystems","shrublands","terrestrial ecosystems","time series","trends","vegetation","vegetation change"],"modified":"2020-08-18T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-130.2380, 26.2039, -99.6688, 52.7905","theme":["geospatial"],"title":"Remote Sensing Shrub/Grass National Land Cover Database (NLCD) Back-in-Time (BIT) Litter Products for the Western U.S., 1985 - 2018"},"description":"The need to monitor change in sagebrush steppe is urgent due to the increasing impacts of climate change, shifting fire regimes, and management practices on ecosystem health. Remote sensing provides a cost-effective and reliable method for monitoring change through time and attributing changes to drivers. We report an automated method of mapping rangeland fractional component cover over a large portion of the Northern Great Basin, USA, from 1986 to 2016 using a dense Landsat imagery time series. 2012 was excluded from the time-series due to a lack of quality imagery. Our method improved upon the traditional change vector method by considering the legacy of change at each pixel. We evaluate cover trends stratified by climate bin and assess spatial and temporal relationships with climate variables. Finally, we statistically evaluate the minimum time density needed to accurately characterize temporal patterns and relationships with climate drivers. Over the 30-yr period, shrub cover declined and bare ground increased. While few pixels had >10% cover change, a large majority had at least some change. All fractional components had significant spatial relationships with water year precipitation (WYPRCP), maximum temperature (WYTMAX), and minimum temperature (WYTMIN) in all years. Shrub and sagebrush cover in particular respond positively to warming WYTMIN, resulting from the largest increases in WYTMIN being in the coolest and wettest areas, and respond negatively to warming WYTMAX because the largest increases in WYTMAX are in the warmest and driest areas. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. 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This data release presents results from statistical summaries of the PAD-US 3.0 protection status (by GAP Status Code) and public access status for various land unit boundaries (Protected Areas Database of the United States 3.0 Vector Analysis and Summary Statistics). Summary statistics are also available to explore and download (Comma-separated Table [CSV], Microsoft Excel Workbook (.xlsx), Portable Document Format [.pdf] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). The vector GIS analysis file, source data used to summarize statistics for areas of interest to stakeholders (National, State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative), and complete Summary Statistics Tabular Data (CSV) are included in this data release. Raster GIS analysis files are also available for combination with other raster data (Protected Areas Database of the United States (PAD-US) 3.0 Raster Analysis). The PAD-US 3.0 Combined Fee, Designation, Easement feature class in the full inventory, with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class (Protected Areas Database of the United States (PAD-US) 3.0, https://doi.org/10.5066/P9Q9LQ4B), was modified to prioritize and remove overlapping management designations, limiting overestimation in protection status or public access statistics and to support user needs for vector and raster analysis data. Analysis files in this data release were clipped to the Census State boundary file to define the extent and fill in areas (largely private land) outside the PAD-US, providing a common denominator for statistical summaries.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9KLBB5D","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.61955a0bd34eb622f6908f88.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_61955a0bd34eb622f6908f88","keyword":["2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021","Agricultural Research Service","Alabama (AL)","Alaska (AK)","American Samoa (AS)","Arizona (AZ)","Arkansas (AR)","Army Corps of Engineers","Biodiversity","Bureau of Land Management","Bureau of Reclamation","California (CA)","Colorado (CO)","Connecticut (CT)","Conservation","Delaware (DE)","Department of Defense","Department of Energy","Federal Lands","Florida (FL)","Forest Service","GAP Status Code","Gap Analysis","Georgia (GA)","Governmental Units","Guam (GU)","Hawaii (HI)","IUCN Category","Idaho (ID)","Illinois (IL)","Indiana (IN)","Iowa (IA)","Kansas (KS)","Kentucky (KY)","Land Manager","Land Ownership","Land Stewardship","Local Government Lands","Louisiana (LA)","Maine (ME)","Mariana Islands (MP)","Maryland (MD)","Massachusetts (MA)","Michigan (MI)","Minnesota (MN)","Mississippi (MS)","Missouri (MO)","Montana (MT)","National Oceanic and Atmospheric Administration","National Park Service","Natural Resources Conservation Service","Nebraska (NE)","Nevada (NV)","New Hampshire (NH)","New Jersey (NJ)","New Mexico (NM)","New York (NY)","North Carolina (NC)","North Dakota (ND)","Ohio (OH)","Oklahoma (OK)","Oregon (OR)","Outdoor Recreation","Parks","Pennsylvania (PA)","Private Lands","Protected Area","Protection Status","Public Health","Public Lands","Public Open Space","Puerto Rico (PR)","Rhode Island (RI)","South Carolina (SC)","South Dakota (SD)","State Lands","Tennessee (TN)","Tennessee Valley Authority","Texas (TX)","U.S. Fish and Wildlife Service","U.S. Minor Outlying Islands (UM)","USGS:61955a0bd34eb622f6908f88","United States","United States Virgin Islands (VI)","Utah (UT)","Vermont (VT)","Virginia (VA)","Washington (WA)","West Virginia (WV)","Wisconsin (WI)","Wyoming (WY)","data integration","land use and land cover","natural resource management"],"modified":"2022-07-06T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-180.00, -14.60, 180.00, 71.36","theme":["geospatial"],"title":"Protected Areas Database of the United States (PAD-US) 3.0 Spatial Analysis and Statistics"},"description":"Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and outdoor recreation access across the nation. 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Raster GIS analysis files are also available for combination with other raster data (Protected Areas Database of the United States (PAD-US) 3.0 Raster Analysis). The PAD-US 3.0 Combined Fee, Designation, Easement feature class in the full inventory, with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class (Protected Areas Database of the United States (PAD-US) 3.0, https://doi.org/10.5066/P9Q9LQ4B), was modified to prioritize and remove overlapping management designations, limiting overestimation in protection status or public access statistics and to support user needs for vector and raster analysis data. Analysis files in this data release were clipped to the Census State boundary file to define the extent and fill in areas (largely private land) outside the PAD-US, providing a common denominator for statistical summaries.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/41216a5a-400b-493b-b78b-9e846be60f86","harvest_record_raw":"https://catalog.data.gov/harvest_record/41216a5a-400b-493b-b78b-9e846be60f86/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_61955a0bd34eb622f6908f88","keyword":["2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021","Agricultural Research Service","Alabama (AL)","Alaska (AK)","American Samoa (AS)","Arizona (AZ)","Arkansas (AR)","Army Corps of Engineers","Biodiversity","Bureau of Land Management","Bureau of Reclamation","California (CA)","Colorado (CO)","Connecticut (CT)","Conservation","Delaware (DE)","Department of Defense","Department of Energy","Federal Lands","Florida (FL)","Forest Service","GAP Status Code","Gap Analysis","Georgia (GA)","Governmental Units","Guam (GU)","Hawaii (HI)","IUCN Category","Idaho (ID)","Illinois (IL)","Indiana (IN)","Iowa (IA)","Kansas (KS)","Kentucky (KY)","Land Manager","Land Ownership","Land Stewardship","Local Government Lands","Louisiana (LA)","Maine (ME)","Mariana Islands (MP)","Maryland (MD)","Massachusetts (MA)","Michigan (MI)","Minnesota (MN)","Mississippi (MS)","Missouri (MO)","Montana (MT)","National Oceanic and Atmospheric Administration","National Park Service","Natural Resources Conservation Service","Nebraska (NE)","Nevada (NV)","New Hampshire (NH)","New Jersey (NJ)","New Mexico (NM)","New York (NY)","North Carolina (NC)","North Dakota (ND)","Ohio (OH)","Oklahoma (OK)","Oregon (OR)","Outdoor Recreation","Parks","Pennsylvania (PA)","Private Lands","Protected Area","Protection Status","Public Health","Public Lands","Public Open Space","Puerto Rico (PR)","Rhode Island (RI)","South Carolina (SC)","South Dakota (SD)","State Lands","Tennessee (TN)","Tennessee Valley Authority","Texas (TX)","U.S. Fish and Wildlife Service","U.S. Minor Outlying Islands (UM)","USGS:61955a0bd34eb622f6908f88","United States","United States Virgin Islands (VI)","Utah (UT)","Vermont (VT)","Virginia (VA)","Washington (WA)","West Virginia (WV)","Wisconsin (WI)","Wyoming (WY)","data integration","land use and land cover","natural resource management"],"last_harvested_date":"2026-09-10T22:37:57.879430","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":11,"publisher":"U.S. Geological Survey","slug":"protected-areas-database-of-the-united-states-pad-us-3-0-spatial-analysis-and-statistics","spatial_centroid":{"lat":19.784,"lon":-36.0},"spatial_shape":{"coordinates":[[[-180.0,-14.6],[-180.0,71.36],[180.0,71.36],[180.0,-14.6],[-180.0,-14.6]]],"type":"Polygon"},"theme":["geospatial"],"title":"Protected Areas Database of the United States (PAD-US) 3.0 Spatial Analysis and Statistics","type":"dataset"},{"_score":9.370237,"_sort":[1789079861691,9.370237,1,"7c2369ed-2d4d-4f55-ae72-d7a6a22a9ebb"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Martin Risch","hasEmail":"mailto:mrrisch@usgs.gov"},"description":"Mercury (Hg) is a persistent environmental contaminant and can accumulate and concentrate in food webs as methylmercury (MeHg), presenting a health risk to humans and wildlife. Multiyear monitoring and modeling studies have shown that atmospheric Hg in litterfall is an important form of Hg deposition to forests. Annual litterfall consists primarily of leaves with some amounts of needles, twigs, bark, flowers, seeds, fruits, and nuts. Atmospheric Hg accumulates in leaves and reaches an annual maximum concentration at autumn leaf drop. This data set is derived from autumn litterfall collected at 30 selected National Atmospheric Deposition Program (NADP) Mercury Deposition Network (MDN) sites in deciduous and mixed deciduous-coniferous forests from 16 states in the eastern United States during 2007-2009 and 2012-2015. The NADP administered litterfall collection at the MDN sites. The U.S. Geological Survey (USGS) distributed sets of passive litterfall sample collectors to MDN site operators for systematic retrieval of samples during the 8 to 16 weeks of autumn leaf drop each year at each site. Samples were processed and analyzed at the USGS Mercury Research Laboratory where concentrations of Hg and MeHg and litterfall dry mass and sample moisture were determined. All sites did not have data for all years. Most sites had four Hg concentrations per year and a few sites had less than or more than four Hg concentrations in specific years. MeHg concentrations were determined in one composite sample per site in 2007 and 2012-2015. Litterfall mass was determined from 4 to 8 samples per site per year. Seven annual groups of data were compiled into this dataset. More information is available from the NADP at http://nadp.sws.uiuc.edu/","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7KH0KHT","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.587007aae4b01a71ba0c5fa0.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_587007aae4b01a71ba0c5fa0","keyword":["Georgia","Indiana","Kentucky","Maryland","Michigan","Minnesota","Missouri","New York","Ohio","Pennsylvania","South Carolina","Tennessee","USGS:587007aae4b01a71ba0c5fa0","Vermont","Virginia","West Virginia","Wisconsin","atmospheric deposition","mercury"],"modified":"2020-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-93.469, 30.7404, -72.868, 47.5311","theme":["geospatial"],"title":"Mercury and Methylmercury Concentrations and Litterfall Mass in Autumn Litterfall Samples Collected at Selected National Atmospheric Deposition Program Sites in 2007-2009 and 2012-2015"},"description":"Mercury (Hg) is a persistent environmental contaminant and can accumulate and concentrate in food webs as methylmercury (MeHg), presenting a health risk to humans and wildlife. 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Samples were processed and analyzed at the USGS Mercury Research Laboratory where concentrations of Hg and MeHg and litterfall dry mass and sample moisture were determined. All sites did not have data for all years. Most sites had four Hg concentrations per year and a few sites had less than or more than four Hg concentrations in specific years. MeHg concentrations were determined in one composite sample per site in 2007 and 2012-2015. Litterfall mass was determined from 4 to 8 samples per site per year. Seven annual groups of data were compiled into this dataset. 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This particular dune field (DS-8) represents crescentic and parabolic dune fields and portions of Alkali Flats playa of the White Sands National Monument, New Mexico. The accompanying zip file contains linear deconvolution-derived mineral fractional abundance maps for a three-component mixture model of Quartz, Gypsum and Anhydrite, as well as RMS and residual errors. Each geotiff layer has an associated metadata file with further details.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://dx.doi.org/10.5066/F7CC0XTR","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.5a67adcde4b06e28e9c5717c.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5a67adcde4b06e28e9c5717c","keyword":["USGS:5a67adcde4b06e28e9c5717c","climatologyMeteorologyAtmosphere","elevation","environment","geoscientificInformation","health"],"modified":"2020-08-21T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.493762359, 32.379377611, -105.586826268, 33.060663007","theme":["geospatial"],"title":"Linear Deconvolution Results For Site DS-8 (3-component-model)"},"description":"These geotiffs represent the raster GIS outputs of Linear Deconvolution (Linear Spectral Unmixing) analysis of ASTER image pixels covering various sand dune and sand sheet fields throughout the Western United States and Alaska.  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Each geotiff layer has an associated metadata file with further details.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/efebb36a-f306-490d-81e1-c3764f8fde12","harvest_record_raw":"https://catalog.data.gov/harvest_record/efebb36a-f306-490d-81e1-c3764f8fde12/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5a67adcde4b06e28e9c5717c","keyword":["USGS:5a67adcde4b06e28e9c5717c","climatologyMeteorologyAtmosphere","elevation","environment","geoscientificInformation","health"],"last_harvested_date":"2026-09-10T22:37:41.226535","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":"linear-deconvolution-results-for-site-ds-8-3-component-model","spatial_centroid":{"lat":32.6518917694,"lon":-106.1309879226},"spatial_shape":{"coordinates":[[[-106.493762359,32.379377611],[-106.493762359,33.060663007],[-105.586826268,33.060663007],[-105.586826268,32.379377611],[-106.493762359,32.379377611]]],"type":"Polygon"},"theme":["geospatial"],"title":"Linear Deconvolution Results For Site DS-8 (3-component-model)","type":"dataset"},{"_score":10.108324,"_sort":[1789079859542,10.108324,2,"5121ee45-72d1-45e6-bdd0-09dbc8d8284d"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Zeno F. Levy","hasEmail":"mailto:zlevy@usgs.gov"},"description":"This data release documents time-series analyses of aqueous-chemistry data from public-supply wells (PSWs) and associated potential explanatory factors to characterize responses of groundwater quality to drought and recovery periods in California\u2019s San Joaquin Valley (SJV) during 2000-2022. Annual median nitrate values were computed for PSWs throughout the SJV during the period of study. Median annual nitrate values were calculated at all PSWs with available data in the SJV, resulting in a total of 698 PSWs with complete annual records after single-year linear gap imputation. A total of 237 of these records were classified as \u201clow variance\u201d because they contained proportions of identical values exceeding 80 percent or had standard deviations less than the study reporting level of 0.452 milligram of nitrate as nitrogen per liter. Annual nitrate anomalies were computed for the 461 remaining nitrate records not classified as \u201clow variance\u201d by detrending using the Hodrick-Prescott filter and z-score standardizing resultant values by subtracting the mean and dividing by the standard deviation. Resultant time series of nitrate anomalies were clustered into two groups sharing similar time-series characteristics quantified by the nonparametric correlation coefficient, Spearman\u2019s rho. Potential explanatory factors based on time-series characteristics or extrinsic factors used in prior studies to characterize groundwater nitrate concentrations in the SJV were compiled to help explain differences among cluster groups. Additionally, concentration changes for nitrate, total dissolved solids (TDS), arsenic, and fluoride in the regulatory monitoring dataset were computed for two selected hydrologic stress periods representative of an historic drought (Dsp; 2012-2016) and subsequent recovery period (Rsp; 2016-2019). Change values were computed by differencing the annual median concentration of the last year with available data in a given stress period with that of the first, including the year before the onset of the stress period to compare with antecedent conditions when possible.\nThis data release contains eight tables formatted as tab-delimited text files. The Nitrate_Medians.txt table contains complete, annual nitrate time series data for 698 PSWs in the SJV during 2000-2022. The Nitrate_Anomalies.txt table contains annual nitrate anomalies computed for the 461 annual nitrate median records not classified as \u201clow variance.\u201d The Nitrate_Clusters.txt table contains cluster group assignments and corresponding silhouette widths (a common cluster validation index) for the 461 records that were submitted for time-series clustering analysis. The Potential_Explanatory_Factors.txt table contains potential explanatory factors for the 698 PSWs that had complete annual nitrate records. The Nitrate_Stress_Period_Change_Analysis.txt, TDS_Stress_Period_Change_Analysis.txt, Arsenic_Stress_Period_Change_Analysis.txt, and Fluoride_Stress_Period_Change_Analysis.txt tables contain differenced median annual concentration values for nitrate, TDS, arsenic, and fluoride during Dsp and Rsp. 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Annual median nitrate values were computed for PSWs throughout the SJV during the period of study. Median annual nitrate values were calculated at all PSWs with available data in the SJV, resulting in a total of 698 PSWs with complete annual records after single-year linear gap imputation. A total of 237 of these records were classified as \u201clow variance\u201d because they contained proportions of identical values exceeding 80 percent or had standard deviations less than the study reporting level of 0.452 milligram of nitrate as nitrogen per liter. Annual nitrate anomalies were computed for the 461 remaining nitrate records not classified as \u201clow variance\u201d by detrending using the Hodrick-Prescott filter and z-score standardizing resultant values by subtracting the mean and dividing by the standard deviation. Resultant time series of nitrate anomalies were clustered into two groups sharing similar time-series characteristics quantified by the nonparametric correlation coefficient, Spearman\u2019s rho. Potential explanatory factors based on time-series characteristics or extrinsic factors used in prior studies to characterize groundwater nitrate concentrations in the SJV were compiled to help explain differences among cluster groups. Additionally, concentration changes for nitrate, total dissolved solids (TDS), arsenic, and fluoride in the regulatory monitoring dataset were computed for two selected hydrologic stress periods representative of an historic drought (Dsp; 2012-2016) and subsequent recovery period (Rsp; 2016-2019). 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These selected areas were examined with scanning electron microscopy and energy dispersive spectroscopy to determine the size and composition of particulate matter, if present, in these areas.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9YH0WAQ","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.5b3bda4ce4b060350a09d5f2.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5b3bda4ce4b060350a09d5f2","keyword":["USGS:5b3bda4ce4b060350a09d5f2","chemical analysis","geoscientificInformation","health","lung bioassay","scanning electron microscopy"],"modified":"2020-09-29T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-114.3182, 35.6350, -110.7806, 37.3527","theme":["geospatial"],"title":"Lung Bioassay of Ground Dwelling Mammals from the Grand Canyon Uranium Breccia Pipe Region Using Scanning Electron Microscopy"},"description":"Small mammal lung tissue sections were examined to help assess the effects of weathering on bioavailability and toxicity from sites in different phases of the mine cycle within the Grand Canyon area.  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This dataset identifies the STM produced topics and qualitative review of those topics to assess larger themes discussed in the news.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P144GSTD","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.69f4bd4db66b0122e4360fcc.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69f4bd4db66b0122e4360fcc","keyword":["Colorado River Headwaters","USGS:69f4bd4db66b0122e4360fcc","United States","Upper Colorado River Basin","elevation","environment","hazards","health","human environmental safety","natural resources","social sciences","society","state and transition modeling","water quality","water resources","water use"],"modified":"2026-07-10T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-112.3242, 35.2456, -105.8203, 41.2448","theme":["geospatial"],"title":"Quality Model Topics Metadata"},"description":"The curated Water Scarcity articles and curated metadata were fed into a structural topic model (STM). 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Surface-water quality and phytoplankton indicators of eutrophication were examined at Lake Bruin, Lake St. John, Lake St. Joseph, and False River Lake along an eutrophication gradient. These oxbow lakes are cut-off meanders of the Mississippi River that do not receive overbank flow from the river due to the levee system built in the early twentieth century. Oxbows have formed at various times in the last few hundred years as the Mississippi River carves a more efficient hydrologic route to the Gulf of Mexico and exhibit a succession of stages in lake evolution, from deep and oligotrophic to shallow and eutrophic.\nWater-quality samples were collected three times per year: once in late spring, once in late summer-early fall, and once in winter. Water samples were analyzed for major ions, nutrients, suspended sediments, pesticides, dissolved organic carbon, and chlorophyll. At each site, physiochemical properties (water temperature, specific conductance, dissolved oxygen, and pH) were recorded at multiple depths within a single vertical profile. Phytoplankton community samples and cyanotoxin samples were collected from the photic zone at the time of water-quality sample collection at one site per lake. This data release provides water quality profile and phytoplankton data for these lakes.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7DZ07J2","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.5a6a02cce4b06e28e9c8a583.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5a6a02cce4b06e28e9c8a583","keyword":["USGS:5a6a02cce4b06e28e9c8a583","algae","phytoplankton","surface water quality"],"modified":"2020-08-21T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-91.3615, 31.9923, -91.1204, 32.1226","theme":["geospatial"],"title":"Lake St. Joseph"},"description":"Nutrient and phytoplankton data indicate poor environmental health in four oxbow lakes in central Louisiana suggesting that long-term agriculture practices and increases in shoreline development have accelerated eutrophication. 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For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1NN2QYM","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.671933b3d34e23541cc171c8.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_671933b3d34e23541cc171c8","keyword":["Atlantic Ocean","Cape May National Wildlife Refuge","Edwin B. 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The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing federal, state, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/9d36bd77-1edf-4daa-baf0-41ba437317be","harvest_record_raw":"https://catalog.data.gov/harvest_record/9d36bd77-1edf-4daa-baf0-41ba437317be/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_671933b3d34e23541cc171c8","keyword":["Atlantic Ocean","Cape May National Wildlife Refuge","Edwin B. 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Culex quinquefasciatus is the vector of the avian malaria Plasmodium relictum a key limiting factor of forest birds. The main components of the study included: 1) the evaluation of trap designs and lures for adult mosquitoes in forested habitat and the prevalence of malaria in those mosquitoes (Hawaii Island 2 data files), 2)  Kawaikoi Stream surveys for larval mosquitoes and suitable larval habitat (Alakai Plateau, Kauai), 3) Larval mosquito control efficacy trials with the biopesticide VectoMax FG (Alakai Plateau, Kauai), 4) adult mosquito monitoring in the Kawaikoi Stream drainage study site and malaria prevalence determination (Alakai Plateau Kauai 2 data files). An additional data file contains geographical coordinates for key localities in the study.  The study was conducted by The US Geological Survey, Pacific Island Ecosystems Research Center personnel in collaboration wth biologists with the Kauai Forest Bird Recovery Project.  Studies were conducted in native forests on Kauai  and Hawaii islands during 2016 and 2017.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P96JOCVK","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.5f89e22682ce32418789385c.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f89e22682ce32418789385c","keyword":["Hawaii County","Kauai","USGS-EMA-LOW Fish and Wildlife Disease","USGS:5f89e22682ce32418789385c","avian malaria","biopesticide","biota","health","mosquito monitoring","wildlife disease"],"modified":"2020-12-11T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-159.9390, 18.7087, -154.6216, 22.4720","theme":["geospatial"],"title":"Alakai Plateau, Kauai, and Volcano Village,Hawaii biopesticides and traps for the control of Culex quinquefasciatus,  2017-2018"},"description":"This USGS data release consists of seven data sets and accompanying metadata for studies on the efficacy of adult mosquito traps and lures for monitoring populations of the invasive mosquito Culex quinquefasciatus and the efficacy of the biopesticide VectoMax FG for control of larval Culex quinquefasciatus in Hawaiian forest bird habitat.  Culex quinquefasciatus is the vector of the avian malaria Plasmodium relictum a key limiting factor of forest birds. The main components of the study included: 1) the evaluation of trap designs and lures for adult mosquitoes in forested habitat and the prevalence of malaria in those mosquitoes (Hawaii Island 2 data files), 2)  Kawaikoi Stream surveys for larval mosquitoes and suitable larval habitat (Alakai Plateau, Kauai), 3) Larval mosquito control efficacy trials with the biopesticide VectoMax FG (Alakai Plateau, Kauai), 4) adult mosquito monitoring in the Kawaikoi Stream drainage study site and malaria prevalence determination (Alakai Plateau Kauai 2 data files). An additional data file contains geographical coordinates for key localities in the study.  The study was conducted by The US Geological Survey, Pacific Island Ecosystems Research Center personnel in collaboration wth biologists with the Kauai Forest Bird Recovery Project.  Studies were conducted in native forests on Kauai  and Hawaii islands during 2016 and 2017.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/851e846d-620a-43a1-aa4b-fb5e75001c7b","harvest_record_raw":"https://catalog.data.gov/harvest_record/851e846d-620a-43a1-aa4b-fb5e75001c7b/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f89e22682ce32418789385c","keyword":["Hawaii County","Kauai","USGS-EMA-LOW Fish and Wildlife Disease","USGS:5f89e22682ce32418789385c","avian malaria","biopesticide","biota","health","mosquito monitoring","wildlife disease"],"last_harvested_date":"2026-09-10T22:35:19.663103","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":"alakai-plateau-kauai-and-volcano-villagehawaii-biopesticides-and-traps-for-the-c-2017-2018","spatial_centroid":{"lat":20.214019999999998,"lon":-157.81204},"spatial_shape":{"coordinates":[[[-159.939,18.7087],[-159.939,22.472],[-154.6216,22.472],[-154.6216,18.7087],[-159.939,18.7087]]],"type":"Polygon"},"theme":["geospatial"],"title":"Alakai Plateau, Kauai, and Volcano Village,Hawaii biopesticides and traps for the control of Culex quinquefasciatus,  2017-2018","type":"dataset"},{"_score":14.156185,"_sort":[1789079705497,14.156185,1,"3a5a1114-8730-4d33-8d26-fb087e7c81f0"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Travis S. 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The purpose of these analyses was to inform state and federal management agencies on transboundary coal mining impacts to downstream fish health.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9PDTNNS","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.640f47f5d34e254fd352e15e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_640f47f5d34e254fd352e15e","keyword":["Bioaccumulation","Biota","Columbia River Basin","Koocanusa Reservoir","Kootenai","Kootenai River Basin","Mercury","Montana","Selenium","USGS:640f47f5d34e254fd352e15e","United States","biota"],"modified":"2023-09-11T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-115.3510, 48.4055, -115.1038, 48.9928","theme":["geospatial"],"title":"Fish Tissue Analysis Results, Koocanusa Reservoir, Montana, 2021"},"description":"Montana Fish, Wildlife, and Parks (MT FWP), in collaboration with the United States Geological Survey (USGS) Wyoming-Montana Water Science Center (WY-MT WSC), Montana Department of Environmental Quality (MDEQ), and the United States Environmental Protection Agency (USEPA) collected fish from the Koocanusa Reservoir in 2021 for tissue analysis. Fish tissue collected included muscle, eggs, liver, and whole body. Analysis of tissues included characterization of the concentration of selenium, mercury and methyl mercury, nitrogen and carbon stable isotopes, and vitamins A and E. The purpose of these analyses was to inform state and federal management agencies on transboundary coal mining impacts to downstream fish health.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/a78c12f3-58de-422e-9e18-97b515a0f975","harvest_record_raw":"https://catalog.data.gov/harvest_record/a78c12f3-58de-422e-9e18-97b515a0f975/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_640f47f5d34e254fd352e15e","keyword":["Bioaccumulation","Biota","Columbia River Basin","Koocanusa Reservoir","Kootenai","Kootenai River Basin","Mercury","Montana","Selenium","USGS:640f47f5d34e254fd352e15e","United States","biota"],"last_harvested_date":"2026-09-10T22:35:05.497552","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":"fish-tissue-analysis-results-koocanusa-reservoir-montana-2021","spatial_centroid":{"lat":48.640420000000006,"lon":-115.25212000000002},"spatial_shape":{"coordinates":[[[-115.351,48.4055],[-115.351,48.9928],[-115.1038,48.9928],[-115.1038,48.4055],[-115.351,48.4055]]],"type":"Polygon"},"theme":["geospatial"],"title":"Fish Tissue Analysis Results, Koocanusa Reservoir, Montana, 2021","type":"dataset"},{"_score":7.3587427,"_sort":[1789079686205,7.3587427,1,"088eb11a-52b4-46c6-9520-57d331892e98"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"PCMSC Science Data Coordinator","hasEmail":"mailto:pcmsc_data@usgs.gov"},"description":"Conductivity-Temperature-Depth (CTD) profile data were collected along transects across study areas of west and east Hawaii Island between 2010 and 2014. 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All beach-cast sea otters (most of which are dead, but also live moribund animals that would have died without intervention) are examined and the date and geographic location of each recovered animal is recorded, as well as the sex, age class, general condition, and circumstantial cause(s) of death if evidence is apparent, e.g., boat-strike trauma, net entanglement, shark bite wounds.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F71J98P4","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.598e1cf6e4b09fa1cb14e184.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_598e1cf6e4b09fa1cb14e184","keyword":["Marine Nearshore","Stranded southern sea otters","USGS:598e1cf6e4b09fa1cb14e184","ecology","environment","health","health and disease","marine biology","marine ecosystems","sea otters"],"modified":"2021-06-30T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.4256591797, 32.539867193, -117.1197509766, 41.9982840178","theme":["geospatial"],"title":"Summary of stranded southern sea otters, 1985-2018"},"description":"The southern sea otter (Enhydra lutris nereis), also known as California sea otter, was listed as threatened in 1977 under the Endangered Species Act. 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Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, wave power, and exposure potential to environmental health stressors are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing federal, state, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. This project has been funded in part by the United States Environmental Protection Agency under assistance agreement DW-014-92531201-1 to N. 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Flow accumulation based on the relative elevation of each location is used to determine the ridge lines that separate each marsh unit while the surface slope is used to automatically assign each unit a drainage point, where water is expected to drain through. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands, including the  Assateague Island National Seashore and Chincoteague Bay salt marshes, with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. ","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P92ZW4D9","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.5b9909d8e4b0702d0e854873.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5b9909d8e4b0702d0e854873","keyword":["Accomack County","Assateague Island National Seashore","Assateague State Park","Atlantic Ocean","Chincoteague Bay","Chincoteague Island","E.A. 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","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d717eb43-9e22-4882-8bc9-cafb4abbc5df","harvest_record_raw":"https://catalog.data.gov/harvest_record/d717eb43-9e22-4882-8bc9-cafb4abbc5df/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5b9909d8e4b0702d0e854873","keyword":["Accomack County","Assateague Island National Seashore","Assateague State Park","Atlantic Ocean","Chincoteague Bay","Chincoteague Island","E.A. 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For information on the digital elevation model (DEM) source used to develop these datasets refer to the corresponding spatial metadata file (Danielson and others, 2023). This data release includes results from analyses of two local sea-level rise scenarios for two-time steps \u2014 the Intermediate-Low and Intermediate-High for 2040 and 2080 from Sweet and others (2022). Additionally, this data release includes maps of inundation probability under the minor, moderate, and major hight tide flooding thresholds. We estimated the probability of an area being inundated under a given scenario using Monte Carlo simulations with 1,000 iterations. For an individual iteration, each pixel of the DEM was randomly propagated based on the lidar data uncertainty, while the sea-level rise and high tide flooding water level estimates were also propagated based on uncertainty in the estimate (Sweet and others, 2022) and tidal datum transformation. Moreover, the probability of a pixel being inundated was calculated by summing the binary simulation outputs and dividing by 1,000. Following, probability was binned into the following classes: 1) Unlikely, probability \u22640.33; 2) Likely as not, probability >0.33 and \u22640.66; and 3) Likely, probability >0.66. Finally, depth statistics were only recorded when depth was equal to or greater than 0. We calculated the median depth, 25th percentile, 75th percentile, and interquartile range using all the pixels that met this criterion. When utilizing the depth statistics, it is important to also consider the probability of this pixel being flooded. In other words, the depth layers may show some depth returns, but the pixel may have rarely been inundated for the 1,000 iterations.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9FM6V0T","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.64fb3180d34ed30c2055b036.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64fb3180d34ed30c2055b036","keyword":["Biscayne National Park","Florida","National Park Service South Atlantic-Gulf Region","USGS:64fb3180d34ed30c2055b036","United States","biota","environment","health","wetland ecosystems"],"modified":"2024-09-03T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-80.4232, 25.2502, -80.0783, 25.7877","theme":["geospatial"],"title":"Sea-level rise and high tide flooding inundation probability and depth statistics at Biscayne National Park, Florida"},"description":"This dataset includes elevation-based probability and depth statistics for estimating inundation under various sea-level rise and high tide flooding scenarios in and around the National Park Service\u2019s Biscayne National Park. For information on the digital elevation model (DEM) source used to develop these datasets refer to the corresponding spatial metadata file (Danielson and others, 2023). This data release includes results from analyses of two local sea-level rise scenarios for two-time steps \u2014 the Intermediate-Low and Intermediate-High for 2040 and 2080 from Sweet and others (2022). Additionally, this data release includes maps of inundation probability under the minor, moderate, and major hight tide flooding thresholds. We estimated the probability of an area being inundated under a given scenario using Monte Carlo simulations with 1,000 iterations. For an individual iteration, each pixel of the DEM was randomly propagated based on the lidar data uncertainty, while the sea-level rise and high tide flooding water level estimates were also propagated based on uncertainty in the estimate (Sweet and others, 2022) and tidal datum transformation. Moreover, the probability of a pixel being inundated was calculated by summing the binary simulation outputs and dividing by 1,000. Following, probability was binned into the following classes: 1) Unlikely, probability \u22640.33; 2) Likely as not, probability >0.33 and \u22640.66; and 3) Likely, probability >0.66. Finally, depth statistics were only recorded when depth was equal to or greater than 0. We calculated the median depth, 25th percentile, 75th percentile, and interquartile range using all the pixels that met this criterion. When utilizing the depth statistics, it is important to also consider the probability of this pixel being flooded. In other words, the depth layers may show some depth returns, but the pixel may have rarely been inundated for the 1,000 iterations.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/01238c57-901a-4842-beb9-bb1a0c83e833","harvest_record_raw":"https://catalog.data.gov/harvest_record/01238c57-901a-4842-beb9-bb1a0c83e833/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64fb3180d34ed30c2055b036","keyword":["Biscayne National Park","Florida","National Park Service South Atlantic-Gulf Region","USGS:64fb3180d34ed30c2055b036","United States","biota","environment","health","wetland ecosystems"],"last_harvested_date":"2026-09-10T22:34:24.218423","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":"sea-level-rise-and-high-tide-flooding-inundation-probability-and-depth-statistics-at-bisca","spatial_centroid":{"lat":25.4652,"lon":-80.28524},"spatial_shape":{"coordinates":[[[-80.4232,25.2502],[-80.4232,25.7877],[-80.0783,25.7877],[-80.0783,25.2502],[-80.4232,25.2502]]],"type":"Polygon"},"theme":["geospatial"],"title":"Sea-level rise and high tide flooding inundation probability and depth statistics at Biscayne National Park, Florida","type":"dataset"},{"_score":7.609686,"_sort":[1789079662561,7.609686,3,"5103b54a-0867-4925-8cf8-d6d4f4ca2c11"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael O'Donnell","hasEmail":"mailto:odonnellm@usgs.gov"},"description":"We developed numerous datasets describing mining activity and landscape conditions for all known active, inactive, abandoned, and legacy surface mines in the Eastern United States Appalachian region to support monitoring and regulatory needs. These data include 1) a study area boundary, 2) a compiled set of spatial footprints for known mines from federal, state, academic research, and non-government organizations (extent of mining activity over time), 3) spatial subdivisions (subunits) of each mine footprint (based on the year of most probable vegetation loss since 1985), attributed with metrics to denote restoration status and recovery, 4) tabular attributes of subunits describing pre-European vegetation communities, annual surface conditions from remotely sensed vegetation indices (1985-2022), changes in land cover and land use types, and elevation changes, 5) tabular attributes of subunits describing the annual aggregated recovery metric and percent forest recovery, 6) areas mined within United States communities (Census tracts) and population demographics, and 7) eleven raster datasets (30-meter spatial resolution) describing annual and cumulative metrics for barren, grassland, grass/shrub, and planted forest year within mining footprints since 1985.\nData stored in the open-source geopackage include the following products:\napp_mtns_study_area (vector): Study area\napp_mtns_mines (vector): Mine footprints\napp_mtns_mines_su (vector): Subunits/polygons of mine units based on most probable year vegetation was lost\napp_mtns_mines_su_tab_cond (tabular): annual summaries (1985-2022) of landscape conditions for each subunit, including vegetation status, trends in vegetation indices, and elevation changes.\napp_mtns_mines_su_tab_arm (tabular): annual summaries of remote sensing vegetation indices and recovery metrics\napp_mtns_censustract_mines (vector): areal summaries of mine footprints within 2020 U.S. Census tracts and population demographic data.\nSummary of data products: Refer to the supplemental section of this metadata file for a list, description, and potential use of the data (must download because not rendered on website). Descriptions also exist in other metadata files associated with the project.\nBackground:\nUntil the Surface Mining Control and Reclamation Act of 1977 (SMCRA) (refer to O\u2019Donnell and others, 2024 for a summary of state/federal/tribal regulations), there were no federal regulations on the reclamation of coal mines. The U.S. Department of the Interior Office of Surface Mining Reclamation and Enforcement (OSMRE) was also established in 1977 to \"protect citizens and the environment during mining and assure that the land is restored to beneficial use following mining.\" Mining regulations are encouraged through a monetary bond, a financial incentive between two parties (for example, mine company and state) to ensure agreed-upon obligations are met. Such obligations might include how to reclaim a mine after a company has completed mineral extraction. The Appalachian Regional Reforestation Initiative (ARRI; established in 2004) cooperates with OSMRE, state agencies (Alabama, Kentucky, Maryland, Ohio, Pennsylvania, Tennessee, Virginia, and West Virginia), the coal industry, environmental organizations, academic institutions, and landowners to assist with restoring forests and returning mine lands to pre-mining conditions or environmental services on coal mines in the Eastern United States. These efforts have more recently leaned on the forestry reclamation approach (FRA; Adams, 2017), a document that establishes best practices for reforesting mines, and OSMRE advisory reports. Until the establishment of ARRI, most reclamation focused on soil stabilization and establishment of grasses, shrubs, and nonnative plants (from 1977 to 2004). These sites primarily remain of little to no economic value because reclamation/restoration methods have resulted in compacted soils, an abundance of invasive species, and an inability to support forest growth and ecological succession.\nImportant caveats/limitations: \nPlease review the metadata accuracy reporting sections of each metadata file (data product) and all process steps describing data inputs and methods used in our analysis. \nData on mining locations within the United States are incomplete, and no single dataset provides sufficient information on where and when mining occurred. Because we are using data provided by federal and state government agencies, as well as published data based on mapping mine footprints using remotely sensed data, there is a significant variety of information and accuracy in source data. We, therefore, rely on the redundancy of data sources to improve mine location and the information documented for each mine. The aspatial information collected from the source data used to attribute footprints was intended to provide evidence that the footprint captures documented mining activity. When footprints indicated no mining activity from available source data, we discarded these data from the mine footprints.\nDue to the lack of publicly available data on mining and reclamation activities in the United States, our understanding of restoration success is limited. For example, we do not have complete records of when mining began and ended, or of the methods used for reclamation. A lack of this information might affect the success of soil remediation (for example, topsoil replacement and soil preparation before restoration) and restoration of vegetation (for example, species types used, planting methods, and mitigation of invasive species). The accompanying mine footprints provide evidence of mining activity that relies on aspatial information from independent data, which we include in our data and documentation. All subsequent analyses of data within mine footprints are based on multiple data sources and are intended to assess landscape changes as reflected in the temporal portrayal of those data.\nGiven that we have only investigated multispectral Landsat data without accompanying field data, as opposed to hyperspectral remotely sensed data (such as the Airborne Visible Infrared Imaging Spectrometer [AVIRIS]), we are limited to summarizing vegetation conditions at broad community levels (for example, trees, shrubs, grass). If data were available on mining activity and reclamation methods, using hyperspectral remotely sensed data would make more sense. We could then improve our understanding of species composition and whether invasive species are present (for example, kudzu and Autumn olive; other: mimosa, multiflora rose, bush honeysuckle, Japanese grass, Japanese spirea, and garlic mustard). We could also investigate the abundance and diversity of species within and beyond mine footprints to determine restoration success. Hyperspectral data, accompanied with field data, could also help detect if there are toxins absorbed by plants that cause threats to flora, wildlife, and people. Understanding site conditions post-mining is essential for understanding restoration success. Terrain characteristics (for example, slope and slope position, aspect, and elevation) affect moisture conditions, types of vegetation suitable for planting at a site, and time for vegetation recovery. Methods of soil preparation are important and usually require single to triple-shank rippers mounted on heavy equipment to uncompact soils (generally, at least four feet in depth). Due to a lack of data on on-site preparation and planting, we are limited in understanding why some sites recover more successfully or at faster rates, which would otherwise be helpful to mine operators and land stewards.    \nLandsat is a useful data product for regional assessments because it is free to the public and has a long history (1970s-present). Remotely sensed hyperspectral data is only available when acquired from aircraft and is therefore limited spatially and temporally. Hyperspectral data is also not freely available to the public and is more commonly used for local applications, with less frequent repeat collections. Our products are, therefore, useful for regional assessments of vegetation recovery but are limited to more general questions about vegetation cover and productivity. These products do not include information about site toxicity, alterations to soil pH (acidity versus alkaline/basic conditions that can be affected by mining), effects of heavy metals on vegetation, site preparation methods required for restoration, or similar characteristics that may affect restoration success. Such information was not publicly available but would be valuable for improving methods to measure future restoration success.\nTypes of mining activity:\nActive mines: These include mine lands where operators are currently extracting resources and bonds have not been released.\nInactive mines: These include mine lands where operators are not currently extracting resources, the lands have not been reclaimed, and bonds have not been released.\nAbandoned mine lands: Mine lands where mining or processing activity is determined to have ceased. The Abandoned Mine Land (AML) Reclamation Program was established in 1981 to address the physical safety and environmental hazards posed by abandoned mines (both before and after SMCRA).\nLegacy mines: These are mines reclaimed under the SMCRA, where mine operators no longer have legal responsibilities (bonds released).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13ZNPX8","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.69f8fdc0b66b014d9c576d12.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69f8fdc0b66b014d9c576d12","keyword":["Alabama","Appalachian Mountains","Georgia","Illinois","Indiana","Kentucky","Maryland","New Jersey","New York","North Carolina","Ohio","Pennsylvania","South Carolina","Tennessee","USGS:69f8fdc0b66b014d9c576d12","United States","Virginia","West Virginia","abandoned mine lands","abandoned mines and quarries","active mines","biota","boundaries","climatologyMeteorologyAtmosphere","elevation","environment","geography","health","inactive mines","land surface characteristics","land use and land cover","land use change","legacy mines","remote sensing","spatial analysis","surface mines","topography","vegetation"],"modified":"2026-05-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-89.6648, 31.2522, -73.4753, 44.3350","theme":["geospatial"],"title":"GeoPackage Data Collection: Assessing all known mining activity and landscape changes within the Appalachian region of the Eastern United States (active, inactive, abandoned, and legacy surface mines)"},"description":"We developed numerous datasets describing mining activity and landscape conditions for all known active, inactive, abandoned, and legacy surface mines in the Eastern United States Appalachian region to support monitoring and regulatory needs. These data include 1) a study area boundary, 2) a compiled set of spatial footprints for known mines from federal, state, academic research, and non-government organizations (extent of mining activity over time), 3) spatial subdivisions (subunits) of each mine footprint (based on the year of most probable vegetation loss since 1985), attributed with metrics to denote restoration status and recovery, 4) tabular attributes of subunits describing pre-European vegetation communities, annual surface conditions from remotely sensed vegetation indices (1985-2022), changes in land cover and land use types, and elevation changes, 5) tabular attributes of subunits describing the annual aggregated recovery metric and percent forest recovery, 6) areas mined within United States communities (Census tracts) and population demographics, and 7) eleven raster datasets (30-meter spatial resolution) describing annual and cumulative metrics for barren, grassland, grass/shrub, and planted forest year within mining footprints since 1985.\nData stored in the open-source geopackage include the following products:\napp_mtns_study_area (vector): Study area\napp_mtns_mines (vector): Mine footprints\napp_mtns_mines_su (vector): Subunits/polygons of mine units based on most probable year vegetation was lost\napp_mtns_mines_su_tab_cond (tabular): annual summaries (1985-2022) of landscape conditions for each subunit, including vegetation status, trends in vegetation indices, and elevation changes.\napp_mtns_mines_su_tab_arm (tabular): annual summaries of remote sensing vegetation indices and recovery metrics\napp_mtns_censustract_mines (vector): areal summaries of mine footprints within 2020 U.S. Census tracts and population demographic data.\nSummary of data products: Refer to the supplemental section of this metadata file for a list, description, and potential use of the data (must download because not rendered on website). Descriptions also exist in other metadata files associated with the project.\nBackground:\nUntil the Surface Mining Control and Reclamation Act of 1977 (SMCRA) (refer to O\u2019Donnell and others, 2024 for a summary of state/federal/tribal regulations), there were no federal regulations on the reclamation of coal mines. The U.S. Department of the Interior Office of Surface Mining Reclamation and Enforcement (OSMRE) was also established in 1977 to \"protect citizens and the environment during mining and assure that the land is restored to beneficial use following mining.\" Mining regulations are encouraged through a monetary bond, a financial incentive between two parties (for example, mine company and state) to ensure agreed-upon obligations are met. Such obligations might include how to reclaim a mine after a company has completed mineral extraction. The Appalachian Regional Reforestation Initiative (ARRI; established in 2004) cooperates with OSMRE, state agencies (Alabama, Kentucky, Maryland, Ohio, Pennsylvania, Tennessee, Virginia, and West Virginia), the coal industry, environmental organizations, academic institutions, and landowners to assist with restoring forests and returning mine lands to pre-mining conditions or environmental services on coal mines in the Eastern United States. These efforts have more recently leaned on the forestry reclamation approach (FRA; Adams, 2017), a document that establishes best practices for reforesting mines, and OSMRE advisory reports. Until the establishment of ARRI, most reclamation focused on soil stabilization and establishment of grasses, shrubs, and nonnative plants (from 1977 to 2004). These sites primarily remain of little to no economic value because reclamation/restoration methods have resulted in compacted soils, an abundance of invasive species, and an inability to support forest growth and ecological succession.\nImportant caveats/limitations: \nPlease review the metadata accuracy reporting sections of each metadata file (data product) and all process steps describing data inputs and methods used in our analysis. \nData on mining locations within the United States are incomplete, and no single dataset provides sufficient information on where and when mining occurred. Because we are using data provided by federal and state government agencies, as well as published data based on mapping mine footprints using remotely sensed data, there is a significant variety of information and accuracy in source data. We, therefore, rely on the redundancy of data sources to improve mine location and the information documented for each mine. The aspatial information collected from the source data used to attribute footprints was intended to provide evidence that the footprint captures documented mining activity. When footprints indicated no mining activity from available source data, we discarded these data from the mine footprints.\nDue to the lack of publicly available data on mining and reclamation activities in the United States, our understanding of restoration success is limited. For example, we do not have complete records of when mining began and ended, or of the methods used for reclamation. A lack of this information might affect the success of soil remediation (for example, topsoil replacement and soil preparation before restoration) and restoration of vegetation (for example, species types used, planting methods, and mitigation of invasive species). The accompanying mine footprints provide evidence of mining activity that relies on aspatial information from independent data, which we include in our data and documentation. All subsequent analyses of data within mine footprints are based on multiple data sources and are intended to assess landscape changes as reflected in the temporal portrayal of those data.\nGiven that we have only investigated multispectral Landsat data without accompanying field data, as opposed to hyperspectral remotely sensed data (such as the Airborne Visible Infrared Imaging Spectrometer [AVIRIS]), we are limited to summarizing vegetation conditions at broad community levels (for example, trees, shrubs, grass). If data were available on mining activity and reclamation methods, using hyperspectral remotely sensed data would make more sense. We could then improve our understanding of species composition and whether invasive species are present (for example, kudzu and Autumn olive; other: mimosa, multiflora rose, bush honeysuckle, Japanese grass, Japanese spirea, and garlic mustard). We could also investigate the abundance and diversity of species within and beyond mine footprints to determine restoration success. Hyperspectral data, accompanied with field data, could also help detect if there are toxins absorbed by plants that cause threats to flora, wildlife, and people. Understanding site conditions post-mining is essential for understanding restoration success. Terrain characteristics (for example, slope and slope position, aspect, and elevation) affect moisture conditions, types of vegetation suitable for planting at a site, and time for vegetation recovery. Methods of soil preparation are important and usually require single to triple-shank rippers mounted on heavy equipment to uncompact soils (generally, at least four feet in depth). Due to a lack of data on on-site preparation and planting, we are limited in understanding why some sites recover more successfully or at faster rates, which would otherwise be helpful to mine operators and land stewards.    \nLandsat is a useful data product for regional assessments because it is free to the public and has a long history (1970s-present). Remotely sensed hyperspectral data is only available when acquired from aircraft and is therefore limited spatially and temporally. Hyperspectral data is also not freely available to the public and is more commonly used for local applications, with less frequent repeat collections. Our products are, therefore, useful for regional assessments of vegetation recovery but are limited to more general questions about vegetation cover and productivity. These products do not include information about site toxicity, alterations to soil pH (acidity versus alkaline/basic conditions that can be affected by mining), effects of heavy metals on vegetation, site preparation methods required for restoration, or similar characteristics that may affect restoration success. Such information was not publicly available but would be valuable for improving methods to measure future restoration success.\nTypes of mining activity:\nActive mines: These include mine lands where operators are currently extracting resources and bonds have not been released.\nInactive mines: These include mine lands where operators are not currently extracting resources, the lands have not been reclaimed, and bonds have not been released.\nAbandoned mine lands: Mine lands where mining or processing activity is determined to have ceased. The Abandoned Mine Land (AML) Reclamation Program was established in 1981 to address the physical safety and environmental hazards posed by abandoned mines (both before and after SMCRA).\nLegacy mines: These are mines reclaimed under the SMCRA, where mine operators no longer have legal responsibilities (bonds released).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/53cf5633-4ef1-4291-b067-b70f407ac647","harvest_record_raw":"https://catalog.data.gov/harvest_record/53cf5633-4ef1-4291-b067-b70f407ac647/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69f8fdc0b66b014d9c576d12","keyword":["Alabama","Appalachian Mountains","Georgia","Illinois","Indiana","Kentucky","Maryland","New Jersey","New York","North Carolina","Ohio","Pennsylvania","South Carolina","Tennessee","USGS:69f8fdc0b66b014d9c576d12","United States","Virginia","West Virginia","abandoned mine lands","abandoned mines and quarries","active mines","biota","boundaries","climatologyMeteorologyAtmosphere","elevation","environment","geography","health","inactive mines","land surface characteristics","land use and land cover","land use change","legacy mines","remote sensing","spatial analysis","surface mines","topography","vegetation"],"last_harvested_date":"2026-09-10T22:34:22.561521","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":"geopackage-data-collection-assessing-all-known-mining-activity-and-landscape-changes-withi","spatial_centroid":{"lat":36.48532,"lon":-83.189},"spatial_shape":{"coordinates":[[[-89.6648,31.2522],[-89.6648,44.335],[-73.4753,44.335],[-73.4753,31.2522],[-89.6648,31.2522]]],"type":"Polygon"},"theme":["geospatial"],"title":"GeoPackage Data Collection: Assessing all known mining activity and landscape changes within the Appalachian region of the Eastern United States (active, inactive, abandoned, and legacy surface mines)","type":"dataset"},{"_score":7.84814,"_sort":[1789079658780,7.84814,1,"487ddf5d-71d6-4ac5-8b1f-7af2782d01df"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Zafer Defne","hasEmail":"mailto:zdefne@usgs.gov"},"description":"As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate the vulnerability of coastal wetlands to various factors and to evaluate their ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. Edwin B. Forsythe National Wildlife Refuge (EBFNWR), New Jersey, was selected as a pilot study area.\nAs part of this data synthesis effort, hydrodynamic and sediment transport modeling of Barnegat Bay Little Egg Harbor (BBLEH) has been used to create the following wetland data layers in Edwin B. Forsythe National Wildlife Refuge (EBFNWR), New Jersey: 1) Hydrodynamic residence time , 2) salinity change and 3) salinity exposure change in wetlands, and 4) sediment supply to wetlands. The residence time layer was based on the hydrodynamic and particle tracking modeling of the period 3/1/2012 to 5/1/2012 by Defne and Ganju (2015). For this data layer, the residence time map of estuarine water has been projected over the EBFNWR salt marshes. The rest of the layers were derived from the BBLEH hydrodynamic modeling for the Hurricane Sandy period that spans from 10/27/2012 to 11/04/2012 (Defne and Ganju, 2016a). The model estimated changes in salinity and sediment concentrations over the salt marshes caused by storm-induced coastal flooding. The results are summarized over the previously determined conceptual salt marsh unit polygons (Defne and Ganju, 2016b).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7K64GZT","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.59655120e4b0d1f9f05b367a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_59655120e4b0d1f9f05b367a","keyword":["Atlantic Ocean","Barnegat Bay","Edwin B. Forsythe National Wildlife Refuge","Great Bay","Little Egg Island","New Jersey","USGS:59655120e4b0d1f9f05b367a","United States","coastal ecosystems","coastal processes","contaminant transport","contamination","ecological processes","environment","environmental assessment","estuary","flushing","inlandWaters","marsh health","oceans","polygon shapefile","residence time","resilience","salt marsh","vegetation","vulnerability","wetland ecosystems","wetland functions"],"modified":"2026-04-21T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-74.484623118, 39.435117592, -74.051158122, 40.053798801","theme":["geospatial"],"title":"Inferred hydrodynamic residence time in salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey"},"description":"As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate the vulnerability of coastal wetlands to various factors and to evaluate their ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. Edwin B. Forsythe National Wildlife Refuge (EBFNWR), New Jersey, was selected as a pilot study area.\nAs part of this data synthesis effort, hydrodynamic and sediment transport modeling of Barnegat Bay Little Egg Harbor (BBLEH) has been used to create the following wetland data layers in Edwin B. Forsythe National Wildlife Refuge (EBFNWR), New Jersey: 1) Hydrodynamic residence time , 2) salinity change and 3) salinity exposure change in wetlands, and 4) sediment supply to wetlands. The residence time layer was based on the hydrodynamic and particle tracking modeling of the period 3/1/2012 to 5/1/2012 by Defne and Ganju (2015). For this data layer, the residence time map of estuarine water has been projected over the EBFNWR salt marshes. The rest of the layers were derived from the BBLEH hydrodynamic modeling for the Hurricane Sandy period that spans from 10/27/2012 to 11/04/2012 (Defne and Ganju, 2016a). The model estimated changes in salinity and sediment concentrations over the salt marshes caused by storm-induced coastal flooding. The results are summarized over the previously determined conceptual salt marsh unit polygons (Defne and Ganju, 2016b).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5ea617ca-ad48-45b2-b846-95b6cdaba764","harvest_record_raw":"https://catalog.data.gov/harvest_record/5ea617ca-ad48-45b2-b846-95b6cdaba764/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_59655120e4b0d1f9f05b367a","keyword":["Atlantic Ocean","Barnegat Bay","Edwin B. Forsythe National Wildlife Refuge","Great Bay","Little Egg Island","New Jersey","USGS:59655120e4b0d1f9f05b367a","United States","coastal ecosystems","coastal processes","contaminant transport","contamination","ecological processes","environment","environmental assessment","estuary","flushing","inlandWaters","marsh health","oceans","polygon shapefile","residence time","resilience","salt marsh","vegetation","vulnerability","wetland ecosystems","wetland functions"],"last_harvested_date":"2026-09-10T22:34:18.780606","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":"inferred-hydrodynamic-residence-time-in-salt-marsh-units-in-edwin-b-forsythe-national-wild","spatial_centroid":{"lat":39.682590075600004,"lon":-74.31123711960001},"spatial_shape":{"coordinates":[[[-74.484623118,39.435117592],[-74.484623118,40.053798801],[-74.051158122,40.053798801],[-74.051158122,39.435117592],[-74.484623118,39.435117592]]],"type":"Polygon"},"theme":["geospatial"],"title":"Inferred hydrodynamic residence time in salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey","type":"dataset"},{"_score":10.726431,"_sort":[1789079658551,10.726431,3,"8bc36944-b98a-4d72-b75a-ca9ad6b6e995"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Samuel H Austin","hasEmail":"mailto:saustin@usgs.gov"},"description":"The Chesapeake Bay is greatly affected by freshwater flows from streams and rivers draining its watershed. Variations in the amount and timing of streamflow can change water temperature, salinity,  suspended sediment and contaminants levels, and affect the amount of nutrients in freshwater streams. Additionally freshwater flows affect the amount of in-stream habitat available to freshwater flora and fauna. Metrics characterizing streamflow extremes such as magnitude, frequency and duration of high and low flows are helpful indicators of long-term patterns, shifts in streamflow regimes, and the interpretation of their effects on stream biota. Generalized additive models were used to analyze long term changes in annual metrics of streamflow extrema from water year 1985 through water year 2022.  Detailed data preparation information, analytical methods and results are presented and discussed in the associated Scientific Investigative Report (https://doi.org/10.3133/sir20255072).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13FB2KQ","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.661feb43d34e7eb9eb7ed116.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_661feb43d34e7eb9eb7ed116","keyword":["Chesapeake Bay Watershed","Delaware","District of Columbia","Hydrology","Maryland","New York","Pennsylvania","Streamflow","USGS:661feb43d34e7eb9eb7ed116","Virginia","Water resources","West Virginia","environment","health","inlandWaters"],"modified":"2025-08-15T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-80.4381, 36.6971, -74.5926, 43.0066","theme":["geospatial"],"title":"Status and trends in streamflow across the Chesapeake Bay watershed"},"description":"The Chesapeake Bay is greatly affected by freshwater flows from streams and rivers draining its watershed. Variations in the amount and timing of streamflow can change water temperature, salinity,  suspended sediment and contaminants levels, and affect the amount of nutrients in freshwater streams. Additionally freshwater flows affect the amount of in-stream habitat available to freshwater flora and fauna. Metrics characterizing streamflow extremes such as magnitude, frequency and duration of high and low flows are helpful indicators of long-term patterns, shifts in streamflow regimes, and the interpretation of their effects on stream biota. Generalized additive models were used to analyze long term changes in annual metrics of streamflow extrema from water year 1985 through water year 2022.  Detailed data preparation information, analytical methods and results are presented and discussed in the associated Scientific Investigative Report (https://doi.org/10.3133/sir20255072).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/3c19e517-183a-4e16-bcb6-6f3a3adaf410","harvest_record_raw":"https://catalog.data.gov/harvest_record/3c19e517-183a-4e16-bcb6-6f3a3adaf410/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_661feb43d34e7eb9eb7ed116","keyword":["Chesapeake Bay Watershed","Delaware","District of Columbia","Hydrology","Maryland","New York","Pennsylvania","Streamflow","USGS:661feb43d34e7eb9eb7ed116","Virginia","Water resources","West Virginia","environment","health","inlandWaters"],"last_harvested_date":"2026-09-10T22:34:18.551363","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":"status-and-trends-in-streamflow-across-the-chesapeake-bay-watershed","spatial_centroid":{"lat":39.2209,"lon":-78.0999},"spatial_shape":{"coordinates":[[[-80.4381,36.6971],[-80.4381,43.0066],[-74.5926,43.0066],[-74.5926,36.6971],[-80.4381,36.6971]]],"type":"Polygon"},"theme":["geospatial"],"title":"Status and trends in streamflow across the Chesapeake Bay watershed","type":"dataset"},{"_score":11.701845,"_sort":[1789079657839,11.701845,4,"8cd97d14-5229-40f2-bc28-4e28b21aab61"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Francis Parchaso","hasEmail":"mailto:parchaso@usgs.gov"},"description":"Determining spatial distributions and temporal trends in trace metals in sediments and benthic organisms is common practice for monitoring environmental contamination. These data can be the basis for assessing metal exposure, the potential for adverse biological effects, and the response to regulatory or management actions (Suter, 2001). Another common method of environmental monitoring is to examine the community structure of sediment-dwelling benthic organisms (Simon, 2002). Spatial and temporal changes in community structure reflect the integrated response of resident species to environmental conditions, although the underlying cause(s) for the response may be difficult to identify and quantify. Together, measurements of metal exposure and biological response can provide a more complete view of anthropogenic disturbances and the associated effects on ecosystem health.\nDespite the complexities inherent in monitoring natural systems, the adopted approach has been effective in relating changes in near-field contamination to changes in reproductive activity of a clam (Hornberger and others, 2000) and in benthic community structure (Kennish, 1998). This study, with its basis in historical data, provides a rare multi-decadal context within which future environmental changes can be assessed.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9IBQ23S","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.606b596dd34edc0435c36bce.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_606b596dd34edc0435c36bce","keyword":["USGS:606b596dd34edc0435c36bce","arthropods","benthic ecosystems","benthos","biota","bryozoans and brachiopods","crustaceans","invertebrates","shellfish"],"modified":"2025-07-11T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-122.1015000, 37.4603000, -122.1007000, 37.4627000","theme":["geospatial"],"title":"Benthic community near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay (ver 3.0, July 2025)"},"description":"Determining spatial distributions and temporal trends in trace metals in sediments and benthic organisms is common practice for monitoring environmental contamination. 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Enhanced dispersion and concentration of these environmental health stressors in coastal regions can result from sea level rise and storm-derived disturbances. The combination of existing environmental health stressors and those mobilized by natural or anthropogenic disasters could adversely impact the health and resilience of coastal communities and ecosystems. This dataset displays the exposure potential to environmental health stressors in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New Jersey, USA. Exposure potential is calculated with the Sediment-bound Contaminant Resiliency and Response (SCoRR) ranking system (Reilly and others, 2015) designed to define baseline and post-event sediment-bound environmental health stressors. Facilities obtained from the Environmental Protection Agency\u2019s (EPA) Toxic Release Inventory (TRI) and Facility Registry Service (FRS) databases were ranked based on their potential contaminant hazard. Ranks were based in part on previous work by Olsen and others (2013), literature reviews, and an expert review panel. A 2000 meter search radius was used to identify nearby ranked facility locations. \nAs part of the Hurricane Sandy Science Plan, the U.S. Geological Survey has started a Wetland Synthesis Project to expand National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate their vulnerability and ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. EBFNWR was selected as a pilot study area.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7765CH1","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.585b1298e4b01224f329b88b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_585b1298e4b01224f329b88b","keyword":["Atlantic Ocean","Barnegat Bay","Edwin B. 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Exposure potential is calculated with the Sediment-bound Contaminant Resiliency and Response (SCoRR) ranking system (Reilly and others, 2015) designed to define baseline and post-event sediment-bound environmental health stressors. Facilities obtained from the Environmental Protection Agency\u2019s (EPA) Toxic Release Inventory (TRI) and Facility Registry Service (FRS) databases were ranked based on their potential contaminant hazard. Ranks were based in part on previous work by Olsen and others (2013), literature reviews, and an expert review panel. A 2000 meter search radius was used to identify nearby ranked facility locations. \nAs part of the Hurricane Sandy Science Plan, the U.S. Geological Survey has started a Wetland Synthesis Project to expand National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate their vulnerability and ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. EBFNWR was selected as a pilot study area.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/9e050e73-8b42-49d2-a2d2-4bd0de72e734","harvest_record_raw":"https://catalog.data.gov/harvest_record/9e050e73-8b42-49d2-a2d2-4bd0de72e734/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_585b1298e4b01224f329b88b","keyword":["Atlantic Ocean","Barnegat Bay","Edwin B. 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Forsythe National Wildlife Refuge to environmental health stressors (polygon shapefile)","type":"dataset"},{"_score":10.212536,"_sort":[1789079638011,10.212536,2,"38a0db98-3288-47d5-8613-5d5f133b9153"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jeffrey Duda","hasEmail":"mailto:jduda@usgs.gov"},"description":"This database is the result of an extensive literature search aimed at identifying documents relevant to the emerging field of dam removal science. In total the database contains 296 citations that contain empirical monitoring information associated with 207 different dam removals across the United States and abroad. Data includes publications through 2020 and supplemented with the U.S. Army Corps of Engineers National Inventory of Dams database, U.S. Geological Survey National Water Information System and aerial photos to estimate locations when coordinates were not provided. 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In total the database contains 296 citations that contain empirical monitoring information associated with 207 different dam removals across the United States and abroad. Data includes publications through 2020 and supplemented with the U.S. Army Corps of Engineers National Inventory of Dams database, U.S. Geological Survey National Water Information System and aerial photos to estimate locations when coordinates were not provided. Publications were located using the Web of Science, Google Scholar, and Clearinghouse for Dam Removal Information.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/a557f5ca-65f0-4b7d-bf4e-18993709fb86","harvest_record_raw":"https://catalog.data.gov/harvest_record/a557f5ca-65f0-4b7d-bf4e-18993709fb86/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5ace95e3e4b0e2c2dd1a688f","keyword":["Australia","Austria","Canada","China","Dam Removal","Denmark","Ecosystems","Environmental Health","Japan","Korea","Meta-Analysis","Norway","Spain","Sweden","Taiwan","USGS:5ace95e3e4b0e2c2dd1a688f","United States","Wales","Water","environment","inlandWaters"],"last_harvested_date":"2026-09-10T22:33:58.011967","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":"usgs-dam-removal-science-database-v4-0","spatial_centroid":{"lat":13.293362585344392,"lon":-41.19140500000002},"spatial_shape":{"coordinates":[[[-168.04687500000003,-25.2271],[-168.04687500000003,71.07405646336098],[149.0918,71.07405646336098],[149.0918,-25.2271],[-168.04687500000003,-25.2271]]],"type":"Polygon"},"theme":["geospatial"],"title":"USGS Dam Removal Science Database (ver. 4.0, June 2021)","type":"dataset"},{"_score":8.00325,"_sort":[1789079608947,8.00325,2,"7ce9bd6d-cd64-4a2e-bb1c-4f9a2edc7872"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Lizabeth Bowen","hasEmail":"mailto:lbowen@usgs.gov"},"description":"Marine mussels are a ubiquitous and crucial component of the nearshore environment, and new genomic technologies exist to quantify molecular responses of individual mussels to stimuli, including exposure to polycyclic aromatic hydrocarbons (PAHs). We used gene-based assays of exposure and physiological function to assess lingering oil damage from the 1989 Exxon Valdez oil spill using the Pacific blue mussel, Mytilus trossulus. We developed a diagnostic gene transcription panel to investigate exposure to PAHs and other contaminants and their effects on mussel physiology and health. Mussels were collected annually from 2012 through 2015 at five field sites (mussel beds) in western Prince William Sound: Herring Bay, Hogan Bay, Iktua Bay, Johnson Bay, and Whale Bay. These five sites were randomly selected and are sampled annually as part of the ongoing Gulf Watch Alaska long-term monitoring program (Dean et al. 2014, Bodkin et al. this volume). Reference samples of mussels were collected from three harbors that support high levels of commercial and recreational boating activity: Cordova Harbor in 2014, and Seward Harbor and Whittier Harbor in 2015. The five random sites represented approximately 3000 km2 in western Prince William Sound, and all were within the area of potential 1989 spill effects. Mussels were collected on the morning rising tide and dissected as soon as possible following collection, generally within one hour.  \nThese data support the following publication: Lizabeth Bowen, A. Keith Miles, Brenda Ballachey, Shannon Waters, James Bodkin, Mandy Lindeberg, Daniel Esler, Gene transcription patterns in response to low level petroleum contaminants in  from field sites and harbors in southcentral Alaska, Deep Sea Research Part II: Topical Studies in Oceanography, 2017, ISSN 0967-0645, https://doi.org/10.1016/j.dsr2.2017.08.007. \nReferences: \nDean, T. A., Bodkin, J.L., Coletti, H.A., 2014. Protocol Narrative for Nearshore Marine Ecosystem Monitoring in the Gulf of Alaska, version 1.1. Natural Resource Report NPS/SWAN/NRR -2014/756. Fort Collins, Colorado.  \nBodkin, J.L., Coletti, H.A., Ballachey, B.E., Monson, D.H., Esler, D., Dean, T.A., this volume. Variation in abundance of Pacific blue mussel in the northern Gulf of Alaska, 2006-2015. Deep Sea Res. 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We used gene-based assays of exposure and physiological function to assess lingering oil damage from the 1989 Exxon Valdez oil spill using the Pacific blue mussel, Mytilus trossulus. We developed a diagnostic gene transcription panel to investigate exposure to PAHs and other contaminants and their effects on mussel physiology and health. Mussels were collected annually from 2012 through 2015 at five field sites (mussel beds) in western Prince William Sound: Herring Bay, Hogan Bay, Iktua Bay, Johnson Bay, and Whale Bay. These five sites were randomly selected and are sampled annually as part of the ongoing Gulf Watch Alaska long-term monitoring program (Dean et al. 2014, Bodkin et al. this volume). Reference samples of mussels were collected from three harbors that support high levels of commercial and recreational boating activity: Cordova Harbor in 2014, and Seward Harbor and Whittier Harbor in 2015. The five random sites represented approximately 3000 km2 in western Prince William Sound, and all were within the area of potential 1989 spill effects. Mussels were collected on the morning rising tide and dissected as soon as possible following collection, generally within one hour.  \nThese data support the following publication: Lizabeth Bowen, A. Keith Miles, Brenda Ballachey, Shannon Waters, James Bodkin, Mandy Lindeberg, Daniel Esler, Gene transcription patterns in response to low level petroleum contaminants in  from field sites and harbors in southcentral Alaska, Deep Sea Research Part II: Topical Studies in Oceanography, 2017, ISSN 0967-0645, https://doi.org/10.1016/j.dsr2.2017.08.007. \nReferences: \nDean, T. A., Bodkin, J.L., Coletti, H.A., 2014. Protocol Narrative for Nearshore Marine Ecosystem Monitoring in the Gulf of Alaska, version 1.1. Natural Resource Report NPS/SWAN/NRR -2014/756. Fort Collins, Colorado.  \nBodkin, J.L., Coletti, H.A., Ballachey, B.E., Monson, D.H., Esler, D., Dean, T.A., this volume. Variation in abundance of Pacific blue mussel in the northern Gulf of Alaska, 2006-2015. Deep Sea Res. II.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/ab4f92bf-4f21-4201-83da-57a60263ca87","harvest_record_raw":"https://catalog.data.gov/harvest_record/ab4f92bf-4f21-4201-83da-57a60263ca87/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5909184fe4b0fc4e44900045","keyword":["Exxon Valdez oil spill","Gulf Watch Alaska","Mussels","Mytilus trossulus","PAH","Prince William Sound","USGS:5909184fe4b0fc4e44900045","gene expression"],"last_harvested_date":"2026-09-10T22:33:28.947471","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":"data-for-gene-transcription-patterns-in-response-to-low-level-petroleum-contaminants-in-my","spatial_centroid":{"lat":60.3732,"lon":-147.9662},"spatial_shape":{"coordinates":[[[-149.433,60.104],[-149.433,60.777],[-145.766,60.777],[-145.766,60.104],[-149.433,60.104]]],"type":"Polygon"},"theme":["geospatial"],"title":"Data for gene transcription patterns in response to low level petroleum contaminants in Mytilus trossulus from field sites and harbors in southcentral Alaska","type":"dataset"},{"_score":21.774996,"_sort":[1789079591833,21.774996,0,"1ad050ce-e2d3-4cb6-a9c2-a7c3ddcc8d35"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Robert J Welk","hasEmail":"mailto:rwelk@usgs.gov"},"description":"This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P91H426U","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.5dd56511e4b0695797628ca6.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5dd56511e4b0695797628ca6","keyword":["Atlantic Ocean","LTER","Long Island Sound","Long-Term Ecological Research","Moriches Bay","New York","Peconic Bay","Shinnecock Bay","Suffolk County","USGS:5dd56511e4b0695797628ca6","United States","coastal ecosystems","coastal processes","environment","estuary","geospatial datasets","inlandWaters","marsh health","oceans","salt marsh","tides (oceanic)","vegetation","wetland ecosystems","wetland functions"],"modified":"2026-04-13T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-72.748198, 40.766433, 71.885316, 41.291362","theme":["geospatial"],"title":"Coastal wetlands of eastern Long Island, New York (ver. 2.0, March 2024)"},"description":"This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f93894f0-3772-4687-b8a0-3bb54dab0220","harvest_record_raw":"https://catalog.data.gov/harvest_record/f93894f0-3772-4687-b8a0-3bb54dab0220/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5dd56511e4b0695797628ca6","keyword":["Atlantic Ocean","LTER","Long Island Sound","Long-Term Ecological Research","Moriches Bay","New York","Peconic Bay","Shinnecock Bay","Suffolk County","USGS:5dd56511e4b0695797628ca6","United States","coastal ecosystems","coastal processes","environment","estuary","geospatial datasets","inlandWaters","marsh health","oceans","salt marsh","tides (oceanic)","vegetation","wetland ecosystems","wetland functions"],"last_harvested_date":"2026-09-10T22:33:11.833266","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":"coastal-wetlands-of-eastern-long-island-new-york-ver-2-0-march-2024","spatial_centroid":{"lat":40.9764046,"lon":-14.8947924},"spatial_shape":{"coordinates":[[[-72.748198,40.766433],[-72.748198,41.291362],[71.885316,41.291362],[71.885316,40.766433],[-72.748198,40.766433]]],"type":"Polygon"},"theme":["geospatial"],"title":"Coastal wetlands of eastern Long Island, New York (ver. 2.0, March 2024)","type":"dataset"},{"_score":36.672714,"_sort":[1789079538534,36.672714,2,"ecf465a7-d55a-44fa-aeb1-35dc986c97f0"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Tristan G Mohs","hasEmail":"mailto:tmohs@usgs.gov"},"description":"U. S. Geological Survey (USGS) scientists completed a data collection campaign from the 3rd of June to the 22nd of July in 2024, using various methods to record geomorphic and habitat indicators throughout 30 streams in the Maryland-Washington, DC-Virginia Developed Piedmont region. Field methods included GNSS surveys, gravelometer-based pebble count readings, visual assessments, and riparian analyses. This metadata record contains all recorded observations from the campaign as well as numerous summary metrics to be used in model development. Summary metrics can be found in Developed_Summary_Metrics.csv in the parent item, while the two child items contain 1) recorded in-channel and Riparian Rapid Assessment (RRA) scores and 2) recorded survey data.\nAttached to this release is a data dictionary, Summary_Metrics_Dictionary.csv, containing general information about Developed_Summary_Metrics.csv.\nThe various data collected in this data release fall into the following categories describing both reach average and heterogeneity:\n- Study Information\n- Internal site ID\n- Riparian Statistics\n- RRA Qualitative Index Site Averages compiled from:\n- Natural Buffer Later Width\n- Natural Buffer Longitudinal Continuity\n- Lateral Land Surface Slope\n- Land Surface Topographic complexity\n- Lateral Rills and Channels\n- Soil Wetness\n- Soil Disturbance\n- Living Tree Basal Area\n- Natural Vegetation Disturbance\n- Coarse Woody Debris\n- Riparian Livestock Access\n- Channel Livestock Access\n- Forestry Activity\n- Active Cropping\n- Beaver Activity\n- Surface Hydrologic Connectivity\n- Sediment Deposition\n- Experiential Metrics\n- Riparian canopy cover\n- Active floodplain width\n- Bank Statistics\n- Bank erosion rates\n- Bank vegetation\n- Modified BEHI score\n- Total to bankfull ratio\n- Bank angle\n- Total bank height\n- Legacy sediment depth\n- In-Channel Statistics\n- Wetted width\n- Bankfull width\n- Mean\n- Standard deviation\n- Coefficient of Variance\n- Bankfull height\n- Pebble count\n- Average\n- Standard deviation\n- Heterogeneity metrics\n- Embeddedness\n- Average\n- Standard deviation\n- Heterogeneity metrics\n- Fine sediment depth\n- Average\n- Standard deviation\n- Heterogeneity metrics\n- Water depth\n- Average\n- Standard deviation\n- Heterogeneity metrics\n- Flow type\n- Average\n- Standard deviation\n- Heterogeneity metrics\n- In-channel canopy cover\n- Vegetation coverage\n- Average\n- Standard deviation\n- Heterogeneity metrics\n- Fish habitat coverage\n- Average\n- Standard deviation\n- Heterogeneity metrics\n- Woody debris\n- Survey Metrics\n- Thalweg sinuosity\n- Longitudinal roughness\n- Water surface slope\n- Cross section\n- Bankfull area\n- Total channel area\n- Total to bankfull area ratio\n- Cross sectional roughness\n- Wetted perimeter\nAdditionally, the following can be found attached to the child items within this data release:\n- Recorded Field Observations: All unsummarized data collected by field team members from the Habitat Assessment field teams.\n- Recorded Survey Data: All coordinate data from surveying campaign collected by Habitat Assessment field teams.\nAny use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13PCBSH","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.686ea01fd4be020e5c0a686e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_686ea01fd4be020e5c0a686e","keyword":["Chesapeake Bay","GPS measurement","Maryland","U.S. Geological Survey","USGS","USGS:686ea01fd4be020e5c0a686e","Virginia","agriculture","dendrogeomorphology","ecology","environmental health (human)","erosion","geography","geomorphology","geoscientificInformation","habitat","hydrology","inland waters","riparian","river reaches","stream survey","streams","transect sampling"],"modified":"2026-03-12T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-77.8052, 38.0048, -76.3330, 39.4744","theme":["geospatial"],"title":"Stream Health and Habitat Assessments for Streams in the Maryland-Washington, DC-Virginia Developed Piedmont Region (2024)"},"description":"U. S. Geological Survey (USGS) scientists completed a data collection campaign from the 3rd of June to the 22nd of July in 2024, using various methods to record geomorphic and habitat indicators throughout 30 streams in the Maryland-Washington, DC-Virginia Developed Piedmont region. Field methods included GNSS surveys, gravelometer-based pebble count readings, visual assessments, and riparian analyses. This metadata record contains all recorded observations from the campaign as well as numerous summary metrics to be used in model development. Summary metrics can be found in Developed_Summary_Metrics.csv in the parent item, while the two child items contain 1) recorded in-channel and Riparian Rapid Assessment (RRA) scores and 2) recorded survey data.\nAttached to this release is a data dictionary, Summary_Metrics_Dictionary.csv, containing general information about Developed_Summary_Metrics.csv.\nThe various data collected in this data release fall into the following categories describing both reach average and heterogeneity:\n- Study Information\n- Internal site ID\n- Riparian Statistics\n- RRA Qualitative Index Site Averages compiled from:\n- Natural Buffer Later Width\n- Natural Buffer Longitudinal Continuity\n- Lateral Land Surface Slope\n- Land Surface Topographic complexity\n- Lateral Rills and Channels\n- Soil Wetness\n- Soil Disturbance\n- Living Tree Basal Area\n- Natural Vegetation Disturbance\n- Coarse Woody Debris\n- Riparian Livestock Access\n- Channel Livestock Access\n- Forestry Activity\n- Active Cropping\n- Beaver Activity\n- Surface Hydrologic Connectivity\n- Sediment Deposition\n- Experiential Metrics\n- Riparian canopy cover\n- Active floodplain width\n- Bank Statistics\n- Bank erosion rates\n- Bank vegetation\n- Modified BEHI score\n- Total to bankfull ratio\n- Bank angle\n- Total bank height\n- Legacy sediment depth\n- In-Channel Statistics\n- Wetted width\n- Bankfull width\n- Mean\n- Standard deviation\n- Coefficient of Variance\n- Bankfull height\n- Pebble count\n- Average\n- Standard deviation\n- Heterogeneity metrics\n- Embeddedness\n- Average\n- Standard deviation\n- Heterogeneity metrics\n- Fine sediment depth\n- Average\n- Standard deviation\n- Heterogeneity metrics\n- Water depth\n- Average\n- Standard deviation\n- Heterogeneity metrics\n- Flow type\n- Average\n- Standard deviation\n- Heterogeneity metrics\n- In-channel canopy cover\n- Vegetation coverage\n- Average\n- Standard deviation\n- Heterogeneity metrics\n- Fish habitat coverage\n- Average\n- Standard deviation\n- Heterogeneity metrics\n- Woody debris\n- Survey Metrics\n- Thalweg sinuosity\n- Longitudinal roughness\n- Water surface slope\n- Cross section\n- Bankfull area\n- Total channel area\n- Total to bankfull area ratio\n- Cross sectional roughness\n- Wetted perimeter\nAdditionally, the following can be found attached to the child items within this data release:\n- Recorded Field Observations: All unsummarized data collected by field team members from the Habitat Assessment field teams.\n- Recorded Survey Data: All coordinate data from surveying campaign collected by Habitat Assessment field teams.\nAny use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/1496c46c-8c41-442f-8bad-d944babf724f","harvest_record_raw":"https://catalog.data.gov/harvest_record/1496c46c-8c41-442f-8bad-d944babf724f/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_686ea01fd4be020e5c0a686e","keyword":["Chesapeake Bay","GPS measurement","Maryland","U.S. Geological Survey","USGS","USGS:686ea01fd4be020e5c0a686e","Virginia","agriculture","dendrogeomorphology","ecology","environmental health (human)","erosion","geography","geomorphology","geoscientificInformation","habitat","hydrology","inland waters","riparian","river reaches","stream survey","streams","transect sampling"],"last_harvested_date":"2026-09-10T22:32:18.534648","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":"stream-health-and-habitat-assessments-for-streams-in-the-maryland-washington-dc-virgi-2024","spatial_centroid":{"lat":38.59264,"lon":-77.21632},"spatial_shape":{"coordinates":[[[-77.8052,38.0048],[-77.8052,39.4744],[-76.333,39.4744],[-76.333,38.0048],[-77.8052,38.0048]]],"type":"Polygon"},"theme":["geospatial"],"title":"Stream Health and Habitat Assessments for Streams in the Maryland-Washington, DC-Virginia Developed Piedmont Region (2024)","type":"dataset"},{"_score":19.351677,"_sort":[1789079537868,19.351677,0,"a70f89bd-ead2-4136-8689-a6780dd9b757"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Alison Meadow","hasEmail":"mailto:meadow@arizona.edu"},"description":"This data is from a survey of participants in the Arizona wine industry conducted in 2021. Participants in the Arizona wine industry generally include wine grape growers, wine makers, winery owners, vineyard or winery employees, and viticulture students. The data were collected in order to inform a broader economic analysis of the Arizona wine industry, which had been requested by a number of industry members during engagement with University of Arizona and Arizona Cooperative Extension researchers. The data were also collected in order to gauge the use of climate data presented to the Arizona wine industry in two Growing Season in Review workshops in 2019 and to identify possible future opportunities for climate and soil scientists at University of Arizona to engage with the Arizona wine industry.\nQuestions pertained to sales and production costs (for both vineyards and wineries), growing practices (vineyards), and marketing channels and revenue sources (wineries). The survey also asked questions about respondents\u2019 participation in grape growing workshops held by researchers at the University of Arizona, as well as respondents\u2019 interest in continued research on the wine industry\u2019s contribution to the state economy, the links between climate and viticulture, and the effects of soil health on wine grapes.\nInvitations to participate in the survey were sent to any person associated with the wine industry, as identified by contact lists from the Arizona Wine Growers Association, the Arizona Vignerons\u2019 Alliance, and researchers at the University of Arizona. The survey was sent out in mid-2021 and recipients included members of each association, individuals that have attended University of Arizona wine grape growing workshops and events, individuals that currently receive the University\u2019s Climate Viticulture Newsletter, or others that have otherwise been identified as associated with the industry. An email was sent to all individuals on these lists, resulting in 243 invitations to participate in the survey. Of these, 52 responded to a majority of the survey for an overall response rate of 21%. 82 people responded to at least part of the survey. Those responses are included in this dataset as well.\nThe data contains information about expenses and revenues from various components of the wine industry including sales and production, growing practices, and marketing - as well as revenue sources The survey also collected feedback from industry members about whether and how they would like to engage with University of Arizona and Arizona Cooperative Extension researchers on issues related to viticulture and climate in the state. This data could be used to pinpoint economic conditions for the Arizona wine industry in 2021. It could use used to compare Arizona's wine industry to other state's wine industries.\nThese data provide a snapshot of a subset of participants in the Arizona wine industry as of 2021. The respondents do not constitute a representative sample. While we presume that the data provided by respondents was accurate to the best of their ability, these findings are not necessarily replicable or generalizable beyond this group of respondents. These data have been deidentified to protect the privacy of survey respondents.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/h8xg-yc91","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.62ba0506d34e8f4977cc9f5c.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_62ba0506d34e8f4977cc9f5c","keyword":["USGS:62ba0506d34e8f4977cc9f5c","biota","climate","climate change","external research support","soil health","viticulture"],"modified":"2026-09-03T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-114.82, 31.33, -109.04, 37.0","theme":["geospatial"],"title":"Survey of Participants in the Arizona Wine Industry in Summer 2021 for the purposes of understanding their data needs"},"description":"This data is from a survey of participants in the Arizona wine industry conducted in 2021. Participants in the Arizona wine industry generally include wine grape growers, wine makers, winery owners, vineyard or winery employees, and viticulture students. The data were collected in order to inform a broader economic analysis of the Arizona wine industry, which had been requested by a number of industry members during engagement with University of Arizona and Arizona Cooperative Extension researchers. The data were also collected in order to gauge the use of climate data presented to the Arizona wine industry in two Growing Season in Review workshops in 2019 and to identify possible future opportunities for climate and soil scientists at University of Arizona to engage with the Arizona wine industry.\nQuestions pertained to sales and production costs (for both vineyards and wineries), growing practices (vineyards), and marketing channels and revenue sources (wineries). The survey also asked questions about respondents\u2019 participation in grape growing workshops held by researchers at the University of Arizona, as well as respondents\u2019 interest in continued research on the wine industry\u2019s contribution to the state economy, the links between climate and viticulture, and the effects of soil health on wine grapes.\nInvitations to participate in the survey were sent to any person associated with the wine industry, as identified by contact lists from the Arizona Wine Growers Association, the Arizona Vignerons\u2019 Alliance, and researchers at the University of Arizona. The survey was sent out in mid-2021 and recipients included members of each association, individuals that have attended University of Arizona wine grape growing workshops and events, individuals that currently receive the University\u2019s Climate Viticulture Newsletter, or others that have otherwise been identified as associated with the industry. An email was sent to all individuals on these lists, resulting in 243 invitations to participate in the survey. Of these, 52 responded to a majority of the survey for an overall response rate of 21%. 82 people responded to at least part of the survey. Those responses are included in this dataset as well.\nThe data contains information about expenses and revenues from various components of the wine industry including sales and production, growing practices, and marketing - as well as revenue sources The survey also collected feedback from industry members about whether and how they would like to engage with University of Arizona and Arizona Cooperative Extension researchers on issues related to viticulture and climate in the state. This data could be used to pinpoint economic conditions for the Arizona wine industry in 2021. It could use used to compare Arizona's wine industry to other state's wine industries.\nThese data provide a snapshot of a subset of participants in the Arizona wine industry as of 2021. The respondents do not constitute a representative sample. While we presume that the data provided by respondents was accurate to the best of their ability, these findings are not necessarily replicable or generalizable beyond this group of respondents. These data have been deidentified to protect the privacy of survey respondents.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/a2c4489c-0bd7-4e9a-ae3d-9d657d5ab241","harvest_record_raw":"https://catalog.data.gov/harvest_record/a2c4489c-0bd7-4e9a-ae3d-9d657d5ab241/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_62ba0506d34e8f4977cc9f5c","keyword":["USGS:62ba0506d34e8f4977cc9f5c","biota","climate","climate change","external research support","soil health","viticulture"],"last_harvested_date":"2026-09-10T22:32:17.868386","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":"survey-of-participants-in-the-arizona-wine-industry-in-summer-2021-for-the-purposes-of-und","spatial_centroid":{"lat":33.598,"lon":-112.508},"spatial_shape":{"coordinates":[[[-114.82,31.33],[-114.82,37.0],[-109.04,37.0],[-109.04,31.33],[-114.82,31.33]]],"type":"Polygon"},"theme":["geospatial"],"title":"Survey of Participants in the Arizona Wine Industry in Summer 2021 for the purposes of understanding their data needs","type":"dataset"},{"_score":8.990128,"_sort":[1789079534828,8.990128,1,"f062a0eb-f670-453f-b37f-594ff0305d70"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Natasha M Isaacs","hasEmail":"mailto:nisaacs@usgs.gov"},"description":"Avian botulism toxicity is a common cause of death to water and shore birds that live near or migrate through Lake Michigan.  The botulism neuro-toxin type E (bontE) gene is responsible for the production of botulinum neurotoxin type E.  Quantitative Polymerase Chain Reaction (qPCR) was performed using a Step One Plus Thermocycler (Applied Biosystems) and protocol described in Getchell and others, 2011, Journal of Aquatic Animal Health.  The assay was used to assess microbial community DNA obtained from environmental samples that were collected by Great Lakes Science Center and by National Park Service from 2011 to 2014 for the bontE gene.  Samples were obtained by ponar grab or by divers and matrices collected included sediment, Cladophora, mussels, mussel micro habitat, and invertebrates. This data set is comprised of qPCR data, reported in gene copies per gram wet weight of material for each environmental matrix assessed.   Ancillary information specific to sample collection is also included in the data-set, e.g., date, year, site, substrate type, depth, SER, \"other description\", MIBaRL ID, core depth in centimeter, matrix, collection agency, and copies per gram wet weight as an average reported value.  \nThe terms in the \"Remark Code I\" column are defined as follows: the term \"BDL\" is used when the average reported value is below the limit of detection.  The term \"DNQ\" is used when the value is higher than the BDL and lower than the Limit of Quantification (LoQ). The term \"--\" is used when the value is fully quantifiable (above the LoQ). \nThe terms in the \"Remark Code II\" column are defined as follows: the symbol < indicates that the result is less than the detection limit.  The letter E indicates that the value was estimated because it was higher than the upper range observed for the No Template Control (NTC) samples, but lower than the Limit of Quantification (LoQ). The letter Q indicates that the sample was fully quantifiable.  Where no information was given for the sample, the term \u201cNot Applicable\u201d, (N/A) was used.  \nQuantitative Polymerase Chain Reaction (qPCR) and Trophic Pathways: \nSediment samples collected at fixed and random sites during 2011-2014 were analyzed for the presence of the bontE gene (the C. botulinum gene that codes for botulinum type E toxin) using quantitative PCR (qPCR) using methods described in Getchell and others, 2011.  Journal of Aquatic Animal Health.  Quantitative PCR has been conducted on over 700 environmental samples collected during 2011-2014, including sediments, mussels and the mussel micro habitat, which is the immediate area surrounding mussel beds, attached and sloughed Cladophora, and several types of invertebrates from the waters near Sleeping Bear Dunes National Lakeshore.  Not all samples collected are represented in this data table as some samples were used for testing purposes or rendered not tested. This effort has involved challenging sample collection and coordinated environmental and laboratory approaches and is the first such comprehensive analysis using modern gene-based approaches on multiple environmental matrices for this area of the Great Lakes.  Preliminary results suggest that vegetative C. botulinum cells are widespread in sediments near SLBE and that seasonality may be a factor in C. botulinum proliferation.  In addition, qPCR bontE detections are being evaluated with respect to their association with depth, relation to temperature, and relation to conditions at depth, including the nature of the bottom materials, and whether mats of dead Cladophora are present.  When a unique identifier is seen more than one time, it indicates that the sample was run was run undiluted and diluted. \nThe bontE qPCR Assay: \nThe composited standard curve for this project has a slope of -3.546, an intercept of 42.5, an R2 value of .997, and an efficiency of 91%. \nThe Limit of Detection (LoD)and Limit of Quantification (LoQ) were calculated as follows: \nLoD: Choose Dilution (gc/rxn): 250; Ct0LoD: 34; Std. Dev. (Ct0LoD): 1; Standard Curve eqn. and R2 for Lod: 235.  LoQ: Ct 0LoQ: 32; Conc LoQ: Conc LoQ \nMobio PowerSoil Kits were used to extract sample DNA. \nTo test for inhibition, a subset of samples was spiked with standard DNA and Ct values were compared and ruled acceptable.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7P26XCW","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.5aaa99e1e4b09e9cadb7349f.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5aaa99e1e4b09e9cadb7349f","keyword":["Ecology","Lake Michigan","Leelanau","Microbiology","Polymerase Chain Reaction","Population and Community Ecology","Sleeping Bear Dunes National Lakeshore","USGS:5aaa99e1e4b09e9cadb7349f","Water Quality"],"modified":"2020-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-86.278381347804, 44.804913963961, -85.657653808766, 45.251380235812","theme":["geospatial"],"title":"Avian Botulism qPCR data, Leelanau Peninsula, Michigan.  Years 2011-2014."},"description":"Avian botulism toxicity is a common cause of death to water and shore birds that live near or migrate through Lake Michigan.  The botulism neuro-toxin type E (bontE) gene is responsible for the production of botulinum neurotoxin type E.  Quantitative Polymerase Chain Reaction (qPCR) was performed using a Step One Plus Thermocycler (Applied Biosystems) and protocol described in Getchell and others, 2011, Journal of Aquatic Animal Health.  The assay was used to assess microbial community DNA obtained from environmental samples that were collected by Great Lakes Science Center and by National Park Service from 2011 to 2014 for the bontE gene.  Samples were obtained by ponar grab or by divers and matrices collected included sediment, Cladophora, mussels, mussel micro habitat, and invertebrates. This data set is comprised of qPCR data, reported in gene copies per gram wet weight of material for each environmental matrix assessed.   Ancillary information specific to sample collection is also included in the data-set, e.g., date, year, site, substrate type, depth, SER, \"other description\", MIBaRL ID, core depth in centimeter, matrix, collection agency, and copies per gram wet weight as an average reported value.  \nThe terms in the \"Remark Code I\" column are defined as follows: the term \"BDL\" is used when the average reported value is below the limit of detection.  The term \"DNQ\" is used when the value is higher than the BDL and lower than the Limit of Quantification (LoQ). The term \"--\" is used when the value is fully quantifiable (above the LoQ). \nThe terms in the \"Remark Code II\" column are defined as follows: the symbol < indicates that the result is less than the detection limit.  The letter E indicates that the value was estimated because it was higher than the upper range observed for the No Template Control (NTC) samples, but lower than the Limit of Quantification (LoQ). The letter Q indicates that the sample was fully quantifiable.  Where no information was given for the sample, the term \u201cNot Applicable\u201d, (N/A) was used.  \nQuantitative Polymerase Chain Reaction (qPCR) and Trophic Pathways: \nSediment samples collected at fixed and random sites during 2011-2014 were analyzed for the presence of the bontE gene (the C. botulinum gene that codes for botulinum type E toxin) using quantitative PCR (qPCR) using methods described in Getchell and others, 2011.  Journal of Aquatic Animal Health.  Quantitative PCR has been conducted on over 700 environmental samples collected during 2011-2014, including sediments, mussels and the mussel micro habitat, which is the immediate area surrounding mussel beds, attached and sloughed Cladophora, and several types of invertebrates from the waters near Sleeping Bear Dunes National Lakeshore.  Not all samples collected are represented in this data table as some samples were used for testing purposes or rendered not tested. This effort has involved challenging sample collection and coordinated environmental and laboratory approaches and is the first such comprehensive analysis using modern gene-based approaches on multiple environmental matrices for this area of the Great Lakes.  Preliminary results suggest that vegetative C. botulinum cells are widespread in sediments near SLBE and that seasonality may be a factor in C. botulinum proliferation.  In addition, qPCR bontE detections are being evaluated with respect to their association with depth, relation to temperature, and relation to conditions at depth, including the nature of the bottom materials, and whether mats of dead Cladophora are present.  When a unique identifier is seen more than one time, it indicates that the sample was run was run undiluted and diluted. \nThe bontE qPCR Assay: \nThe composited standard curve for this project has a slope of -3.546, an intercept of 42.5, an R2 value of .997, and an efficiency of 91%. \nThe Limit of Detection (LoD)and Limit of Quantification (LoQ) were calculated as follows: \nLoD: Choose Dilution (gc/rxn): 250; Ct0LoD: 34; Std. Dev. (Ct0LoD): 1; Standard Curve eqn. and R2 for Lod: 235.  LoQ: Ct 0LoQ: 32; Conc LoQ: Conc LoQ \nMobio PowerSoil Kits were used to extract sample DNA. \nTo test for inhibition, a subset of samples was spiked with standard DNA and Ct values were compared and ruled acceptable.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/1b2e9f1a-e5d9-4d20-a622-fa54a9f7f2ed","harvest_record_raw":"https://catalog.data.gov/harvest_record/1b2e9f1a-e5d9-4d20-a622-fa54a9f7f2ed/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5aaa99e1e4b09e9cadb7349f","keyword":["Ecology","Lake Michigan","Leelanau","Microbiology","Polymerase Chain Reaction","Population and Community Ecology","Sleeping Bear Dunes National Lakeshore","USGS:5aaa99e1e4b09e9cadb7349f","Water Quality"],"last_harvested_date":"2026-09-10T22:32:14.828809","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":"avian-botulism-qpcr-data-leelanau-peninsula-michigan-years-2011-2014","spatial_centroid":{"lat":44.9835004727014,"lon":-86.03009033218879},"spatial_shape":{"coordinates":[[[-86.278381347804,44.804913963961],[-86.278381347804,45.251380235812],[-85.657653808766,45.251380235812],[-85.657653808766,44.804913963961],[-86.278381347804,44.804913963961]]],"type":"Polygon"},"theme":["geospatial"],"title":"Avian Botulism qPCR data, Leelanau Peninsula, Michigan.  Years 2011-2014.","type":"dataset"}],"sort":"last_harvested_date"}