Timeline / Data.gov — Environment Datasets
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A new raw object was archived. Both versions are preserved. 5626 line(s) added, 4733 line(s) removed.
Evidence
| Source | Data.gov — Environment Datasets |
|---|---|
| Agency | Data.gov |
| URL | https://api.gsa.gov/technology/datagov/v4/search?q=environment&sort=last_harvested_date&per_page=100&api_key=${DATAGOV_API_KEY} |
| Observed by | Civic Memory, directly, on 2026-10-04T06:22:57+00:00 |
| Content type | application/json |
| Current object |
6733e5344d45c596e61530ff72a8a2f55cb268f5c01bef530974845068de911e
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| Previous object |
cbe7ee1a3086a84eef33feec38fe58fe0e22d00b063d893347ceb28dad9490d3
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What changed derived
This diff is not evidence. It was produced by
civic-memory.diff_engine 1.1.0 at
2026-10-04T06:22:57+00:00 by normalizing the two archived objects above. The
objects are authoritative; this reading of them can be regenerated or deleted
without loss. 5626 line(s) added, 4733 line(s) removed.
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Notes on use of the code files are in 2_code_README_model_code.md.\nThis component is part of a model archive that provides all data, code, and model outputs used in the corresponding manuscript (Barclay and others, 2026) to test machine learning (ML) methods for downscaling and multi-scale modeling of stream temperature to combine an ML model and/or input data at coarse spatial resolution with an ML model and/or input data at fine spatial resolution to predict stream temperatures at fine spatial resolution in a watershed. The models were tested in eight watersheds across the conterminous United States for the period of 1979 through 2021.\nThe data are organized into these components: \n1. Model Inputs (https://www.sciencebase.gov/catalog/item/687127e6d4be026e1750edaf) - Meteorological data, river network matrices, and stream temperature observations \n2. 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Big data for small streams: Leveraging a coarse national model to enhance fine-resolution stream temperature predictions, http://doi.org/TBD/TBD.", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.5066/P13TRUEM", + "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.687128f8d4be026e1750ee0d.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_687128f8d4be026e1750ee0d", + "keyword": [ + "CA", + "CO", + "California", + "Colorado", + "GA", + "Georgia", + "MA", + "Massachusetts", + "NY", + "New York", + "OR", + "Oregon", + "TX", + "Texas", + "US", + "USGS:687128f8d4be026e1750ee0d", + "United States", + "WI", + "Wisconsin", + "deep learning", + "downscaling", + "environment", + "inlandWaters", + "machine learning", + "mathematical simulation", + "modeling", + "streams", + "water resources", + "water temperature" + ], + "modified": "2026-10-01T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-122.29435803014, 32.6258846826916, -72.4125519980412, 44.1608195676367", + "theme": [ + "geospatial" + ], + "title": "Model archive component 2, Model Code, in: Machine learning methods for downscaling stream temperature predictions, tested in eight watersheds of the conterminous United States" + }, + "description": "This model archive component contains model codes used in the methods experiments of Barclay and others (2026). 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The archived model input and output files are available \nin a USGS data release (https://doi.org/10.5066/F7S181FC). \n\t\t\nSeven additional MODFLOW-2000 scenarios (numbered 7-13), using this updated and recalibrated \nmodel, were developed to evaluate different withdrawal strategies which are included in this data \nrelease: (7) Mount Pleasant Waterworks bringing online a new well (located at the old well 5 location) \nat 3.51 million gallons per day (Mgal/d) in 2025; (8) Maximizing withdrawals from Mount Pleasant \nWaterworks wells 2 and 5 (3.51 Mgal/d each) in 2020 and 2025, respectively; (9) Same as Scenario \n7, but removing well 3 from production in 2025; (10) Same as Scenario 9, but removing well 4 from \nproduction in 2025 (11) Same as Scenario 7, but converting well 3 to an injection well in 2025 (12) \nSame as Scenario 11, but converting well 4 to an injection well in 2030; and (13) Same as scenario \n8, but with two injection wells added (one in 2025 and one in 2035) to Mount Pleasant Waterworks \nwell field. 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This USGS data \nrelease contains all the input and output files for the simulations described above and in the \nreadme.txt file of this data release (https://doi.org/10.5066/P9GZEE4E).", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.5066/P9GZEE4E", + "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.6ba49343-2436-45ea-b0d8-ec6f7d84577b.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6ba49343-2436-45ea-b0d8-ec6f7d84577b", + "keyword": [ + "Charleston County", + "Groundwater", + "Groundwater Model", + "InlandWaters", + "MODFLOW-2000", + "MODPATH", + "Middendorf aquifer", + "Mount Pleasant", + "South Carolina", + "USGS:6ba49343-2436-45ea-b0d8-ec6f7d84577b", + "environment", + "geoscientificInformation", + "inlandWaters", + "usgsgroundwatermodel" + ], + "modified": "2020-11-17T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-83.786434, 29.923548, -76.289479, 35.881171", + "theme": [ + "geospatial" + ], + "title": "MODFLOW-2000 and MODPATH model data sets used in scenarios of groundwater flow and pumping (1900-2500) near Mount Pleasant, South Carolina" + }, + "description": "An existing three-dimensional model (MODFLOW-2000) by Fine, Petkewich, and Campbell (2017) \n(https://doi.org/10.3133/sir20175128) was used to evaluate 7 water-management scenarios and \npredict the effects on the groundwater flow and groundwater-level conditions in the Mount Pleasant, \nSouth Carolina area. This model was originally developed in 2007, by Petkewich and Campbell \n(https://pubs.er.usgs.gov/publication/sir20075126), then updated and recalibrated to conditions \nfrom 1900 to 2015. Results of six previous scenario simulations (scenarios 1-6) for the Mount \nPleasant Water Works are published in a U.S. Geological Survey (USGS) Scientific Investigations \nReport (https://doi.org/10.3133/sir20175128). 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This USGS data \nrelease contains all the input and output files for the simulations described above and in the \nreadme.txt file of this data release (https://doi.org/10.5066/P9GZEE4E).", + "distribution_titles": [ + "Digital Data", + "Original Metadata" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/5f919b48-414c-4618-8dc7-cbdf364afd17", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/5f919b48-414c-4618-8dc7-cbdf364afd17/raw", + "has_download": true, + "has_spatial": true, + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6ba49343-2436-45ea-b0d8-ec6f7d84577b", + "keyword": [ + "Charleston County", + "Groundwater", + "Groundwater Model", + "InlandWaters", + "MODFLOW-2000", + "MODPATH", + "Middendorf aquifer", + "Mount Pleasant", + "South Carolina", + "USGS:6ba49343-2436-45ea-b0d8-ec6f7d84577b", + "environment", + "geoscientificInformation", + "inlandWaters", + "usgsgroundwatermodel" + ], + "last_harvested_date": "2026-10-04T03:37:22.340163", + "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": "modflow-2000-and-modpath-model-data-sets-used-in-scenarios-of-groundwater-flow-and-pumping", + "spatial_centroid": { + "lat": 32.3065972, + "lon": -80.78765200000001 + }, + "spatial_shape": { + "coordinates": [ + [ + [ + -83.786434, + 29.923548 + ], + [ + -83.786434, + 35.881171 + ], + [ + -76.289479, + 35.881171 + ], + [ + -76.289479, + 29.923548 + ], + [ + -83.786434, + 29.923548 + ] + ] + ], + "type": "Polygon" + }, + "theme": [ + "geospatial" + ], + "title": "MODFLOW-2000 and MODPATH model data sets used in scenarios of groundwater flow and pumping (1900-2500) near Mount Pleasant, South Carolina", + "type": "dataset" + }, + { + "_score": 8.975195, + "_sort": [ + 1791084889958, + 8.975195, + 3, + "896b089b-70fe-4cc9-922f-9a752c5182db" + ], + "access_level": "public", + "dcat": { + "accessLevel": "public", + "bureauCode": [ + "010:12" + ], + "contactPoint": { + "@type": "vcard:Contact", + "fn": "Sharon Qi", + "hasEmail": "mailto:slqi@usgs.gov" + }, + "description": "These files contain information acquired from a digital dataset\nof the Conterminous United States. 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Earth observing imagery sources used in this data release include (but are not limited to) optical imagery collections by: (1) the European Space Agency's Sentinel-2 mission and (2) the Plant Labs Planet Scope constellation, as well as synthetic aperture radar imagery collected by: (1) European Space Agency's Sentinel-1 mission; (2) the DLR (German Aerospace Agency) TerraSAR-X satellite (X-band), and (3) the Umbra Space satellite constellation (X-band).", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.5066/P97NFTDU", + "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.ASC557.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_ASC557", + "keyword": [ + "Alaska", + "Animals/vertebrates", + "Biota", + "Cape Serdtse-Kamen", + "Carnivores", + "Chukchi Sea", + "Chukotka", + "Coastal ecosystems", + "Environment", + "Field inventory and monitoring", + "Image collections", + "Mammals", + "Marine ecosystems", + "Marine mammals", + "Migratory species", + "Odobenus rosmarus divergens", + "Pacific Walrus", + "Pinniped", + "Point Lay", + "Remote sensing", + "Russia", + "Satellite imagery", + "Seals/sea lions/walruses", + "Seasonal distribution", + "USGS:ASC557", + "Walrus", + "Wildlife" + ], + "modified": "2023-09-14T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-171.7, 66.9, -163.0, 69.6", + "theme": [ + "geospatial" + ], + "title": "Pacific Walrus Coastal Haulout Occurrences Interpreted from Satellite Imagery, 2023" + }, + "description": "This data release contains maps, geospatial files, and a table of the satellite imagery types with the dates when they were collected and examined to interpret the presence of, and area occupied by, walruses at terrestrial haulouts. 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The models were tested in eight watersheds across the conterminous United States for the period of 1979 through 2021.\nThe data are organized into these components: \n1. Model Inputs (https://www.sciencebase.gov/catalog/item/687127e6d4be026e1750edaf) - Meteorological data, river network matrices, and stream temperature observations \n2. Model Code (https://www.sciencebase.gov/catalog/item/687128f8d4be026e1750ee0d) - Python files and README for reproducing model training and evaluation \n3. Coarse Model (https://www.sciencebase.gov/catalog/item/6871290dd4be026e1750ee0f) - Trained coarse stream temperature model to be downscaled \n4. Model Outputs (https://www.sciencebase.gov/catalog/item/6871292ad4be026e1750ee13) - Model simulation outputs and evaluation metrics\nThe publication associated with this model archive is: Barclay, J.R., Koenig, L.E., Fan, Y., Jia, X., Appling, A.P., (in review). Big data for small streams: Leveraging a coarse national model to enhance fine-resolution stream temperature predictions, http://doi.org/TBD/TBD.\nThis data compilation was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Environmental System Science Data Management Program, as part of the ExaSheds project, under Award Number 89243021SSC000068. Work was also supported by the U.S. Geological Survey, Water Availability and Use Science Program.", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.5066/P13TRUEM", + "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.67c5d1f2d34ea599a3b99394.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_67c5d1f2d34ea599a3b99394", + "keyword": [ + "CA", + "CO", + "California", + "Colorado", + "GA", + "Georgia", + "MA", + "Massachusetts", + "NY", + "New York", + "OR", + "Oregon", + "TX", + "Texas", + "US", + "USGS:67c5d1f2d34ea599a3b99394", + "United States", + "WI", + "Wisconsin", + "deep learning", + "downscaling", + "environment", + "inlandWaters", + "machine learning", + "mathematical simulation", + "modeling", + "streams", + "water resources", + "water temperature" + ], + "modified": "2026-10-01T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-122.29435803014, 32.6258846826916, -72.4125519980412, 44.1608195676367", + "theme": [ + "geospatial" + ], + "title": "Machine learning methods for downscaling stream temperature predictions, tested in eight watersheds of the conterminous United States" + }, + "description": "This model archive provides all data, code, and model outputs used in the corresponding manuscript (Barclay and others, 2026) to test machine learning (ML) methods for downscaling and multi-scale modeling of stream temperature to combine an ML model and/or input data at coarse spatial resolution with an ML model and/or input data at fine spatial resolution to predict stream temperatures at fine spatial resolution in a watershed. 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Big data for small streams: Leveraging a coarse national model to enhance fine-resolution stream temperature predictions, http://doi.org/TBD/TBD.\nThis data compilation was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Environmental System Science Data Management Program, as part of the ExaSheds project, under Award Number 89243021SSC000068. 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Step 4: Integrate historic shoreline models with geometric models to create spatial layers for maps and graphs.", "distribution": [ { "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/F7PR7T2W", + "accessURL": "https://doi.org/10.5066/P14GZ5WV", "description": "Landing page for access to the data", "format": "XML", "mediaType": "application/http", @@ -833,101 +6147,47 @@ { "@type": "dcat:Distribution", "description": "The metadata original format", - "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.ASC31.xml", + "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.58798bbee4b0847d353f4052.xml", "format": "XML", "mediaType": "text/xml", "title": "Original Metadata" } ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_ASC31", - "keyword": [ - "Alaska", - "Animals/Vertebrates", - "Arctic", - "Bearded seal", - "Bears", - "Biological informatics", - "Biota", - "Carnivores", - "Chukchi Sea", - "Coastal ecosystems", - "Diet composition", - "Diet estimation", - "Diets", - "Environment", - "Erignathus barbatus", - "Fatty Acids", - "Mammals", - "Marine ecosystems", - "Marine mammals", - "Pelagic habitat", - "Pinniped", - "Polar bear", - "Predator-prey", - "Pusa hispida", - "QFASA", - "Quantitative fatty acid signature analysis", - "Ringed seal", - "Seals/Sea lions/Walruses", - "USGS:ASC31", - "Ursus maritimus", - "Wildlife" - ], - "modified": "2024-11-30T00:00:00Z", + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_58798bbee4b0847d353f4052", + "keyword": [ + "USGS:58798bbee4b0847d353f4052", + "climate change", + "environment", + "geospatial datasets" + ], + "modified": "2026-09-30T00:00:00Z", "publisher": { "@type": "org:Organization", "name": "U.S. Geological Survey" }, - "spatial": "-173.0, 65.0, -155.0, 72.0", - "theme": [ - "geospatial" - ], - "title": "Assessing the Robustness of Quantitative Fatty Acid Signature Analysis to Assumption Violations (Supplementary Data)" - }, - "description": "This dataset contains fatty acid (FA) data expressed as mass percent of total FA for bearded seals, ringed seals and walrus. 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Because of an increase and intensification of agricultural production in the MRB since European settlement, plant and animal habitats have degraded. \nTo address water quality and wildlife issues in the MRB, a partnership between researchers at the US Geologic Service, Oregon State University, and Purdue University created a project to investigate the barriers and opportunities of adoption of conservation practices by agricultural producers in three sub-watersheds in the MRB. This investigation also gauged rates of adoption of different conservation practices which increase water quality or habitat that qualify for federal cost-share programs. Understanding what factors influence farmers’ management decisions can help researchers understand why practices are adopted or have a high likelihood of adoption now or in the future. 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Because of an increase and intensification of agricultural production in the MRB since European settlement, plant and animal habitats have degraded. \nTo address water quality and wildlife issues in the MRB, a partnership between researchers at the US Geologic Service, Oregon State University, and Purdue University created a project to investigate the barriers and opportunities of adoption of conservation practices by agricultural producers in three sub-watersheds in the MRB. This investigation also gauged rates of adoption of different conservation practices which increase water quality or habitat that qualify for federal cost-share programs. Understanding what factors influence farmers’ management decisions can help researchers understand why practices are adopted or have a high likelihood of adoption now or in the future. Understanding decision making as it relates to adoption can inform water quality and habitat models that predict what may happen to hypoxic areas in the Gulf of Mexico in the future if there are precipitation and temperature changes in the MRB. \nThe following data are the results of interviews with 36 agricultural producers in Big Creek watershed located in Posey County in southwestern Indiana and Lime Creek watershed located in Buchanan and Benton Counties in northeastern Iowa. Interviewees were asked to discuss their current use of conservation practices and their willingness to change their use of conservation practices in the future due to projected climate changes in the Upper Midwest.", "distribution_titles": [ "Digital Data", "Original Metadata" ], - "harvest_record": "https://catalog.data.gov/harvest_record/fbc2011b-63aa-4802-8de5-82a10ff2d5cb", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/fbc2011b-63aa-4802-8de5-82a10ff2d5cb/raw", + "harvest_record": "https://catalog.data.gov/harvest_record/e79356b8-80e5-4e05-975c-e1860ea336d5", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/e79356b8-80e5-4e05-975c-e1860ea336d5/raw", "has_download": true, "has_spatial": true, - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_58798bbee4b0847d353f4052", - "keyword": [ - "USGS:58798bbee4b0847d353f4052", + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5addee78e4b0e2c2dd2b7cfa", + "keyword": [ + "Indiana", + "Iowa", + "USGS:5addee78e4b0e2c2dd2b7cfa", + "agriculture", "climate change", - "environment", - "geospatial datasets" - ], - "last_harvested_date": "2026-10-03T03:09:07.551543", + "conservation", + "environment", + "external research support", + "society", + "water quality", + "watershed" + ], + "last_harvested_date": "2026-10-03T03:00:04.112973", "organization": { "aliases": [ "dept" @@ -1071,33 +6345,33 @@ "parent_identifier": null, "popularity": 0, "publisher": "U.S. Geological Survey", - "slug": "predicted-future-erosion-hazard-zones", + "slug": "agricultural-producer-perspectives-on-the-adoption-of-conservation-practices-water-quality", "spatial_centroid": { - "lat": 20.32, - "lon": -158.07 + "lat": 39.9894, + "lon": -91.40622 }, "spatial_shape": { "coordinates": [ [ [ - -160.35, - 18.8 - ], - [ - -160.35, - 22.6 - ], - [ - -154.65, - 22.6 - ], - [ - -154.65, - 18.8 - ], - [ - -160.35, - 18.8 + -96.3281, + 37.649 + ], + [ + -96.3281, + 43.5 + ], + [ + -84.0234, + 43.5 + ], + [ + -84.0234, + 37.649 + ], + [ + -96.3281, + 37.649 ] ] ], @@ -1106,16 +6380,16 @@ "theme": [ "geospatial" ], - "title": "Predicted future erosion hazard zones", + "title": "Agricultural Producer Perspectives on the Adoption of Conservation Practices, Water Quality, and Climate Change in Big Creek and Lime Creek Watersheds", "type": "dataset" }, { - "_score": 9.650475, + "_score": 10.458616, "_sort": [ - 1790996404112, - 9.650475, + 1790996224488, + 10.458616, 0, - "97e398c3-597a-4839-a863-748894a16840" + "e93f9c89-ef2b-4144-9ad2-02b32897427e" ], "access_level": "public", "dcat": { @@ -1125,14 +6399,14 @@ ], "contactPoint": { "@type": "vcard:Contact", - "fn": "Ajay Singh", - "hasEmail": "mailto:singh@csus.edu" - }, - "description": "The Mississippi River Basin (MRB) contains prime farmland that has produced high-value, nutrient intensive crops for food, fiber, and fuel. Prairie, forest and river ecosystems that support diverse plant and animal communities are also found within the MRB. Because of an increase and intensification of agricultural production in the MRB since European settlement, plant and animal habitats have degraded. \nTo address water quality and wildlife issues in the MRB, a partnership between researchers at the US Geologic Service, Oregon State University, and Purdue University created a project to investigate the barriers and opportunities of adoption of conservation practices by agricultural producers in three sub-watersheds in the MRB. This investigation also gauged rates of adoption of different conservation practices which increase water quality or habitat that qualify for federal cost-share programs. Understanding what factors influence farmers’ management decisions can help researchers understand why practices are adopted or have a high likelihood of adoption now or in the future. Understanding decision making as it relates to adoption can inform water quality and habitat models that predict what may happen to hypoxic areas in the Gulf of Mexico in the future if there are precipitation and temperature changes in the MRB. \nThe following data are the results of interviews with 36 agricultural producers in Big Creek watershed located in Posey County in southwestern Indiana and Lime Creek watershed located in Buchanan and Benton Counties in northeastern Iowa. Interviewees were asked to discuss their current use of conservation practices and their willingness to change their use of conservation practices in the future due to projected climate changes in the Upper Midwest.", + "fn": "Climate Adaptation Science Centers", + "hasEmail": "mailto:casc-data@usgs.gov" + }, + "description": "The purpose of this study was to look for evidence of montane meadow drying in the MODIS normalized difference vegetation index (NDVI) record over the period 2000 - 2012.", "distribution": [ { "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/P13V8TMH", + "accessURL": "https://doi.org/10.5066/P14JTAKW", "description": "Landing page for access to the data", "format": "XML", "mediaType": "application/http", @@ -1141,61 +6415,51 @@ { "@type": "dcat:Distribution", "description": "The metadata original format", - "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5addee78e4b0e2c2dd2b7cfa.xml", + "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.59b15c67e4b020cdf7d902d6.xml", "format": "XML", "mediaType": "text/xml", "title": "Original Metadata" } ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5addee78e4b0e2c2dd2b7cfa", - "keyword": [ - "Indiana", - "Iowa", - "USGS:5addee78e4b0e2c2dd2b7cfa", - "agriculture", - "climate change", - "conservation", - "environment", - "external research support", - "society", - "water quality", - "watershed" + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_59b15c67e4b020cdf7d902d6", + "keyword": [ + "USGS:59b15c67e4b020cdf7d902d6", + "biota", + "environment", + "geospatial analysis", + "meadow", + "remote sensing" ], "modified": "2026-09-30T00:00:00Z", "publisher": { "@type": "org:Organization", "name": "U.S. Geological Survey" }, - "spatial": "-96.3281, 37.6490, -84.0234, 43.5000", - "theme": [ - "geospatial" - ], - "title": "Agricultural Producer Perspectives on the Adoption of Conservation Practices, Water Quality, and Climate Change in Big Creek and Lime Creek Watersheds" - }, - "description": "The Mississippi River Basin (MRB) contains prime farmland that has produced high-value, nutrient intensive crops for food, fiber, and fuel. Prairie, forest and river ecosystems that support diverse plant and animal communities are also found within the MRB. Because of an increase and intensification of agricultural production in the MRB since European settlement, plant and animal habitats have degraded. \nTo address water quality and wildlife issues in the MRB, a partnership between researchers at the US Geologic Service, Oregon State University, and Purdue University created a project to investigate the barriers and opportunities of adoption of conservation practices by agricultural producers in three sub-watersheds in the MRB. This investigation also gauged rates of adoption of different conservation practices which increase water quality or habitat that qualify for federal cost-share programs. Understanding what factors influence farmers’ management decisions can help researchers understand why practices are adopted or have a high likelihood of adoption now or in the future. Understanding decision making as it relates to adoption can inform water quality and habitat models that predict what may happen to hypoxic areas in the Gulf of Mexico in the future if there are precipitation and temperature changes in the MRB. \nThe following data are the results of interviews with 36 agricultural producers in Big Creek watershed located in Posey County in southwestern Indiana and Lime Creek watershed located in Buchanan and Benton Counties in northeastern Iowa. Interviewees were asked to discuss their current use of conservation practices and their willingness to change their use of conservation practices in the future due to projected climate changes in the Upper Midwest.", + "spatial": "-111.3566, 44.9156, -111.0825, 45.2422", + "theme": [ + "geospatial" + ], + "title": "Hydrological Analysis of Greater Yellowstone Ecosystem Montane Meadow Condition using MODIS data" + }, + "description": "The purpose of this study was to look for evidence of montane meadow drying in the MODIS normalized difference vegetation index (NDVI) record over the period 2000 - 2012.", "distribution_titles": [ "Digital Data", "Original Metadata" ], - "harvest_record": "https://catalog.data.gov/harvest_record/e79356b8-80e5-4e05-975c-e1860ea336d5", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/e79356b8-80e5-4e05-975c-e1860ea336d5/raw", + "harvest_record": "https://catalog.data.gov/harvest_record/ca148696-f0cf-457f-bd16-964648a6de45", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/ca148696-f0cf-457f-bd16-964648a6de45/raw", "has_download": true, "has_spatial": true, - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5addee78e4b0e2c2dd2b7cfa", - "keyword": [ - "Indiana", - "Iowa", - "USGS:5addee78e4b0e2c2dd2b7cfa", - "agriculture", - "climate change", - "conservation", - "environment", - "external research support", - "society", - "water quality", - "watershed" - ], - "last_harvested_date": "2026-10-03T03:00:04.112973", + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_59b15c67e4b020cdf7d902d6", + "keyword": [ + "USGS:59b15c67e4b020cdf7d902d6", + "biota", + "environment", + "geospatial analysis", + "meadow", + "remote sensing" + ], + "last_harvested_date": "2026-10-03T02:57:04.488487", "organization": { "aliases": [ "dept" @@ -1212,33 +6476,33 @@ "parent_identifier": null, "popularity": 0, "publisher": "U.S. Geological Survey", - "slug": "agricultural-producer-perspectives-on-the-adoption-of-conservation-practices-water-quality", + "slug": "hydrological-analysis-of-greater-yellowstone-ecosystem-montane-meadow-condition-using-modi-a4568", "spatial_centroid": { - "lat": 39.9894, - "lon": -91.40622 + "lat": 45.04624, + "lon": -111.24695999999999 }, "spatial_shape": { "coordinates": [ [ [ - -96.3281, - 37.649 - ], - [ - -96.3281, - 43.5 - ], - [ - -84.0234, - 43.5 - ], - [ - -84.0234, - 37.649 - ], - [ - -96.3281, - 37.649 + -111.3566, + 44.9156 + ], + [ + -111.3566, + 45.2422 + ], + [ + -111.0825, + 45.2422 + ], + [ + -111.0825, + 44.9156 + ], + [ + -111.3566, + 44.9156 ] ] ], @@ -1247,16 +6511,16 @@ "theme": [ "geospatial" ], - "title": "Agricultural Producer Perspectives on the Adoption of Conservation Practices, Water Quality, and Climate Change in Big Creek and Lime Creek Watersheds", + "title": "Hydrological Analysis of Greater Yellowstone Ecosystem Montane Meadow Condition using MODIS data", "type": "dataset" }, { - "_score": 10.465647, + "_score": 9.619774, "_sort": [ - 1790996224488, - 10.465647, - 0, - "e93f9c89-ef2b-4144-9ad2-02b32897427e" + 1790996046443, + 9.619774, + 1, + "8780723e-ee78-48f9-a1e8-524b9e23942c" ], "access_level": "public", "dcat": { @@ -1266,14 +6530,14 @@ ], "contactPoint": { "@type": "vcard:Contact", - "fn": "Climate Adaptation Science Centers", - "hasEmail": "mailto:casc-data@usgs.gov" - }, - "description": "The purpose of this study was to look for evidence of montane meadow drying in the MODIS normalized difference vegetation index (NDVI) record over the period 2000 - 2012.", + "fn": "Nicholas M Enwright", + "hasEmail": "mailto:enwrightn@usgs.gov" + }, + "description": "This data release includes 2022 data for the Louisiana Outer Coast Restoration Project for Chenier Ronquille. Specifically, this data release includes a detailed habitat map, general habitat map, and georeferenced imagery. These habitat maps are developed using the methods and classification scheme from Louisiana Coastal Protection and Restoration Authority’s (CPRA) Barrier Island Comprehensive Monitoring (BICM) program. For more details on BICM habitat classes, see the Entity and Attribute Information section of the metadata. Please consult the accompanying readME.txt file for information and recommendations on the contents of this dataset (that is, dataset and recommended symbology). For more information about BICM habitat mapping, see Enwright and others (2020).\nEnwright, N.M., SooHoo, W.M., Dugas, J.L., Conzelmann, C.P., Laurenzano, C., Lee, D.M., Mouton, K., and Stelly, S.J., 2020, Louisiana Barrier Island Comprehensive Monitoring Program—Mapping habitats in beach, dune, and intertidal environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015–16: U.S. Geological Survey Open-File Report 2020–1030, 57 p.,\nhttps://doi.org/10.3133/ofr20201030.", "distribution": [ { "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/P14JTAKW", + "accessURL": "https://doi.org/10.5066/P13JWDKX", "description": "Landing page for access to the data", "format": "XML", "mediaType": "application/http", @@ -1282,51 +6546,59 @@ { "@type": "dcat:Distribution", "description": "The metadata original format", - "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.59b15c67e4b020cdf7d902d6.xml", + "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.65a7f3cdd34e02097d049a67.xml", "format": "XML", "mediaType": "text/xml", "title": "Original Metadata" } ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_59b15c67e4b020cdf7d902d6", - "keyword": [ - "USGS:59b15c67e4b020cdf7d902d6", - "biota", - "environment", - "geospatial analysis", - "meadow", - "remote sensing" + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_65a7f3cdd34e02097d049a67", + "keyword": [ + "Ecology", + "Gulf of America", + "Land Use Change", + "Louisiana", + "Plaquemines Parish", + "Remote Sensing", + "USGS:65a7f3cdd34e02097d049a67", + "environment", + "geoscientificInformation", + "imageryBaseMapsEarthCover" ], "modified": "2026-09-30T00:00:00Z", "publisher": { "@type": "org:Organization", "name": "U.S. Geological Survey" }, - "spatial": "-111.3566, 44.9156, -111.0825, 45.2422", - "theme": [ - "geospatial" - ], - "title": "Hydrological Analysis of Greater Yellowstone Ecosystem Montane Meadow Condition using MODIS data" - }, - "description": "The purpose of this study was to look for evidence of montane meadow drying in the MODIS normalized difference vegetation index (NDVI) record over the period 2000 - 2012.", + "spatial": "-89.86527, 29.28832, -89.66638, 29.34782", + "theme": [ + "geospatial" + ], + "title": "Louisiana Outer Coast Restoration Project – 2022 habitat map, Chenier Ronquille (ver. 2.0, September 2026)" + }, + "description": "This data release includes 2022 data for the Louisiana Outer Coast Restoration Project for Chenier Ronquille. Specifically, this data release includes a detailed habitat map, general habitat map, and georeferenced imagery. These habitat maps are developed using the methods and classification scheme from Louisiana Coastal Protection and Restoration Authority’s (CPRA) Barrier Island Comprehensive Monitoring (BICM) program. For more details on BICM habitat classes, see the Entity and Attribute Information section of the metadata. Please consult the accompanying readME.txt file for information and recommendations on the contents of this dataset (that is, dataset and recommended symbology). For more information about BICM habitat mapping, see Enwright and others (2020).\nEnwright, N.M., SooHoo, W.M., Dugas, J.L., Conzelmann, C.P., Laurenzano, C., Lee, D.M., Mouton, K., and Stelly, S.J., 2020, Louisiana Barrier Island Comprehensive Monitoring Program—Mapping habitats in beach, dune, and intertidal environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015–16: U.S. Geological Survey Open-File Report 2020–1030, 57 p.,\nhttps://doi.org/10.3133/ofr20201030.", "distribution_titles": [ "Digital Data", "Original Metadata" ], - "harvest_record": "https://catalog.data.gov/harvest_record/ca148696-f0cf-457f-bd16-964648a6de45", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/ca148696-f0cf-457f-bd16-964648a6de45/raw", + "harvest_record": "https://catalog.data.gov/harvest_record/61feaa84-60ad-41e3-a81e-6888fada1130", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/61feaa84-60ad-41e3-a81e-6888fada1130/raw", "has_download": true, "has_spatial": true, - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_59b15c67e4b020cdf7d902d6", - "keyword": [ - "USGS:59b15c67e4b020cdf7d902d6", - "biota", - "environment", - "geospatial analysis", - "meadow", - "remote sensing" - ], - "last_harvested_date": "2026-10-03T02:57:04.488487", + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_65a7f3cdd34e02097d049a67", + "keyword": [ + "Ecology", + "Gulf of America", + "Land Use Change", + "Louisiana", + "Plaquemines Parish", + "Remote Sensing", + "USGS:65a7f3cdd34e02097d049a67", + "environment", + "geoscientificInformation", + "imageryBaseMapsEarthCover" + ], + "last_harvested_date": "2026-10-03T02:54:06.443618", "organization": { "aliases": [ "dept" @@ -1341,35 +6613,35 @@ "slug": "doi" }, "parent_identifier": null, - "popularity": 0, + "popularity": 1, "publisher": "U.S. Geological Survey", - "slug": "hydrological-analysis-of-greater-yellowstone-ecosystem-montane-meadow-condition-using-modi-a4568", + "slug": "louisiana-outer-coast-restoration-project-2022-habitat-map-chenier-ronquille", "spatial_centroid": { - "lat": 45.04624, - "lon": -111.24695999999999 + "lat": 29.31212, + "lon": -89.785714 }, "spatial_shape": { "coordinates": [ [ [ - -111.3566, - 44.9156 - ], - [ - -111.3566, - 45.2422 - ], - [ - -111.0825, - 45.2422 - ], - [ - -111.0825, - 44.9156 - ], - [ - -111.3566, - 44.9156 + -89.86527, + 29.28832 + ], + [ + -89.86527, + 29.34782 + ], + [ + -89.66638, + 29.34782 + ], + [ + -89.66638, + 29.28832 + ], + [ + -89.86527, + 29.28832 ] ] ], @@ -1378,16 +6650,16 @@ "theme": [ "geospatial" ], - "title": "Hydrological Analysis of Greater Yellowstone Ecosystem Montane Meadow Condition using MODIS data", + "title": "Louisiana Outer Coast Restoration Project – 2022 habitat map, Chenier Ronquille (ver. 2.0, September 2026)", "type": "dataset" }, { - "_score": 9.621866, + "_score": 9.619774, "_sort": [ - 1790996046443, - 9.621866, - 1, - "8780723e-ee78-48f9-a1e8-524b9e23942c" + 1790995509499, + 9.619774, + 0, + "5344c088-bff6-4ed2-b875-0fba51f575a3" ], "access_level": "public", "dcat": { @@ -1397,14 +6669,14 @@ ], "contactPoint": { "@type": "vcard:Contact", - "fn": "Nicholas M Enwright", - "hasEmail": "mailto:enwrightn@usgs.gov" - }, - "description": "This data release includes 2022 data for the Louisiana Outer Coast Restoration Project for Chenier Ronquille. Specifically, this data release includes a detailed habitat map, general habitat map, and georeferenced imagery. These habitat maps are developed using the methods and classification scheme from Louisiana Coastal Protection and Restoration Authority’s (CPRA) Barrier Island Comprehensive Monitoring (BICM) program. For more details on BICM habitat classes, see the Entity and Attribute Information section of the metadata. Please consult the accompanying readME.txt file for information and recommendations on the contents of this dataset (that is, dataset and recommended symbology). For more information about BICM habitat mapping, see Enwright and others (2020).\nEnwright, N.M., SooHoo, W.M., Dugas, J.L., Conzelmann, C.P., Laurenzano, C., Lee, D.M., Mouton, K., and Stelly, S.J., 2020, Louisiana Barrier Island Comprehensive Monitoring Program—Mapping habitats in beach, dune, and intertidal environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015–16: U.S. Geological Survey Open-File Report 2020–1030, 57 p.,\nhttps://doi.org/10.3133/ofr20201030.", + "fn": "Jason H Chase", + "hasEmail": "mailto:jhchase@usgs.gov" + }, + "description": "This data release includes six comma delimited tables detailing elemental analysis results of sediment, surface water, and shallow groundwater samples collected in the vicinity of an unnamed tributary to Midway Branch in Patuxent Research Refuge, Odenton, Maryland. “MidwayBr_sediment.csv,” “MidwayBr_water_metals.csv,” and “MidwayBr_water_anions.csv” contain elemental analysis results for analyses of sediment samples, metal and other elemental analysis of water samples, and anion analysis of water samples, respectively. “MidwayBr_sediment_reporting.csv,” “MidwayBr_water_metals_reporting.csv,” and “MidwayBr_water_anions_reporting.csv” contain reporting limits for analyses of sediment samples, metal and other elemental analysis of water samples, and anion analysis of water samples, respectively. All elemental analysis results and reporting limits are provided by the Analytical Chemistry Laboratory at the Geology, Geophysics, and Geochemistry Science Center in Lakewood, Colorado. Any 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/P13JWDKX", + "accessURL": "https://doi.org/10.5066/P1GVX3SB", "description": "Landing page for access to the data", "format": "XML", "mediaType": "application/http", @@ -1413,59 +6685,55 @@ { "@type": "dcat:Distribution", "description": "The metadata original format", - "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.65a7f3cdd34e02097d049a67.xml", + "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.6aac2b381ba49b6a17baa4d2.xml", "format": "XML", "mediaType": "text/xml", "title": "Original Metadata" } ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_65a7f3cdd34e02097d049a67", - "keyword": [ - "Ecology", - "Gulf of America", - "Land Use Change", - "Louisiana", - "Plaquemines Parish", - "Remote Sensing", - "USGS:65a7f3cdd34e02097d049a67", - "environment", - "geoscientificInformation", - "imageryBaseMapsEarthCover" + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6aac2b381ba49b6a17baa4d2", + "keyword": [ + "Maryland", + "Patuxent Research Refuge", + "USGS:6aac2b381ba49b6a17baa4d2", + "environment", + "groundwater quality", + "metal elements", + "sediment transport", + "surface water quality" ], "modified": "2026-09-30T00:00:00Z", "publisher": { "@type": "org:Organization", "name": "U.S. Geological Survey" }, - "spatial": "-89.86527, 29.28832, -89.66638, 29.34782", - "theme": [ - "geospatial" - ], - "title": "Louisiana Outer Coast Restoration Project – 2022 habitat map, Chenier Ronquille (ver. 2.0, September 2026)" - }, - "description": "This data release includes 2022 data for the Louisiana Outer Coast Restoration Project for Chenier Ronquille. Specifically, this data release includes a detailed habitat map, general habitat map, and georeferenced imagery. These habitat maps are developed using the methods and classification scheme from Louisiana Coastal Protection and Restoration Authority’s (CPRA) Barrier Island Comprehensive Monitoring (BICM) program. For more details on BICM habitat classes, see the Entity and Attribute Information section of the metadata. Please consult the accompanying readME.txt file for information and recommendations on the contents of this dataset (that is, dataset and recommended symbology). For more information about BICM habitat mapping, see Enwright and others (2020).\nEnwright, N.M., SooHoo, W.M., Dugas, J.L., Conzelmann, C.P., Laurenzano, C., Lee, D.M., Mouton, K., and Stelly, S.J., 2020, Louisiana Barrier Island Comprehensive Monitoring Program—Mapping habitats in beach, dune, and intertidal environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015–16: U.S. Geological Survey Open-File Report 2020–1030, 57 p.,\nhttps://doi.org/10.3133/ofr20201030.", + "spatial": "-76.733307, 39.072412, -76.720200, 39.082400", + "theme": [ + "geospatial" + ], + "title": "Elemental analysis results of sediment, surface water, and shallow groundwater samples along unnamed tributary to Midway Branch in Patuxent Research Refuge, Odenton, MD" + }, + "description": "This data release includes six comma delimited tables detailing elemental analysis results of sediment, surface water, and shallow groundwater samples collected in the vicinity of an unnamed tributary to Midway Branch in Patuxent Research Refuge, Odenton, Maryland. “MidwayBr_sediment.csv,” “MidwayBr_water_metals.csv,” and “MidwayBr_water_anions.csv” contain elemental analysis results for analyses of sediment samples, metal and other elemental analysis of water samples, and anion analysis of water samples, respectively. “MidwayBr_sediment_reporting.csv,” “MidwayBr_water_metals_reporting.csv,” and “MidwayBr_water_anions_reporting.csv” contain reporting limits for analyses of sediment samples, metal and other elemental analysis of water samples, and anion analysis of water samples, respectively. All elemental analysis results and reporting limits are provided by the Analytical Chemistry Laboratory at the Geology, Geophysics, and Geochemistry Science Center in Lakewood, Colorado. Any 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/61feaa84-60ad-41e3-a81e-6888fada1130", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/61feaa84-60ad-41e3-a81e-6888fada1130/raw", + "harvest_record": "https://catalog.data.gov/harvest_record/af131813-bf89-40c6-a6b4-2644ea003d44", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/af131813-bf89-40c6-a6b4-2644ea003d44/raw", "has_download": true, "has_spatial": true, - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_65a7f3cdd34e02097d049a67", - "keyword": [ - "Ecology", - "Gulf of America", - "Land Use Change", - "Louisiana", - "Plaquemines Parish", - "Remote Sensing", - "USGS:65a7f3cdd34e02097d049a67", - "environment", - "geoscientificInformation", - "imageryBaseMapsEarthCover" - ], - "last_harvested_date": "2026-10-03T02:54:06.443618", + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6aac2b381ba49b6a17baa4d2", + "keyword": [ + "Maryland", + "Patuxent Research Refuge", + "USGS:6aac2b381ba49b6a17baa4d2", + "environment", + "groundwater quality", + "metal elements", + "sediment transport", + "surface water quality" + ], + "last_harvested_date": "2026-10-03T02:45:09.499527", "organization": { "aliases": [ "dept" @@ -1480,141 +6748,6 @@ "slug": "doi" }, "parent_identifier": null, - "popularity": 1, - "publisher": "U.S. Geological Survey", - "slug": "louisiana-outer-coast-restoration-project-2022-habitat-map-chenier-ronquille", - "spatial_centroid": { - "lat": 29.31212, - "lon": -89.785714 - }, - "spatial_shape": { - "coordinates": [ - [ - [ - -89.86527, - 29.28832 - ], - [ - -89.86527, - 29.34782 - ], - [ - -89.66638, - 29.34782 - ], - [ - -89.66638, - 29.28832 - ], - [ - -89.86527, - 29.28832 - ] - ] - ], - "type": "Polygon" - }, - "theme": [ - "geospatial" - ], - "title": "Louisiana Outer Coast Restoration Project – 2022 habitat map, Chenier Ronquille (ver. 2.0, September 2026)", - "type": "dataset" - }, - { - "_score": 9.621866, - "_sort": [ - 1790995509499, - 9.621866, - 0, - "5344c088-bff6-4ed2-b875-0fba51f575a3" - ], - "access_level": "public", - "dcat": { - "accessLevel": "public", - "bureauCode": [ - "010:12" - ], - "contactPoint": { - "@type": "vcard:Contact", - "fn": "Jason H Chase", - "hasEmail": "mailto:jhchase@usgs.gov" - }, - "description": "This data release includes six comma delimited tables detailing elemental analysis results of sediment, surface water, and shallow groundwater samples collected in the vicinity of an unnamed tributary to Midway Branch in Patuxent Research Refuge, Odenton, Maryland. “MidwayBr_sediment.csv,” “MidwayBr_water_metals.csv,” and “MidwayBr_water_anions.csv” contain elemental analysis results for analyses of sediment samples, metal and other elemental analysis of water samples, and anion analysis of water samples, respectively. “MidwayBr_sediment_reporting.csv,” “MidwayBr_water_metals_reporting.csv,” and “MidwayBr_water_anions_reporting.csv” contain reporting limits for analyses of sediment samples, metal and other elemental analysis of water samples, and anion analysis of water samples, respectively. All elemental analysis results and reporting limits are provided by the Analytical Chemistry Laboratory at the Geology, Geophysics, and Geochemistry Science Center in Lakewood, Colorado. Any 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/P1GVX3SB", - "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.6aac2b381ba49b6a17baa4d2.xml", - "format": "XML", - "mediaType": "text/xml", - "title": "Original Metadata" - } - ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6aac2b381ba49b6a17baa4d2", - "keyword": [ - "Maryland", - "Patuxent Research Refuge", - "USGS:6aac2b381ba49b6a17baa4d2", - "environment", - "groundwater quality", - "metal elements", - "sediment transport", - "surface water quality" - ], - "modified": "2026-09-30T00:00:00Z", - "publisher": { - "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-76.733307, 39.072412, -76.720200, 39.082400", - "theme": [ - "geospatial" - ], - "title": "Elemental analysis results of sediment, surface water, and shallow groundwater samples along unnamed tributary to Midway Branch in Patuxent Research Refuge, Odenton, MD" - }, - "description": "This data release includes six comma delimited tables detailing elemental analysis results of sediment, surface water, and shallow groundwater samples collected in the vicinity of an unnamed tributary to Midway Branch in Patuxent Research Refuge, Odenton, Maryland. “MidwayBr_sediment.csv,” “MidwayBr_water_metals.csv,” and “MidwayBr_water_anions.csv” contain elemental analysis results for analyses of sediment samples, metal and other elemental analysis of water samples, and anion analysis of water samples, respectively. “MidwayBr_sediment_reporting.csv,” “MidwayBr_water_metals_reporting.csv,” and “MidwayBr_water_anions_reporting.csv” contain reporting limits for analyses of sediment samples, metal and other elemental analysis of water samples, and anion analysis of water samples, respectively. All elemental analysis results and reporting limits are provided by the Analytical Chemistry Laboratory at the Geology, Geophysics, and Geochemistry Science Center in Lakewood, Colorado. 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Streamflow statistics were calculated in the R statistical software environment (R Core Team, 2025) and included three categories of metrics: basic flow statistics, flow duration statistics, and low-flow recurrence probability estimates. Users can reference the accompanying Scientific Investigations Report for specific details on the R packages used and the analytical methods applied. Basic streamflow statistics include maximum mean, minimum mean, and mean annual and monthly streamflow. Flow-duration statistics describe the percentage of time a specified flow was equaled or exceeded during a defined period (Searcy, 1959). These statistics include 1-, 10-, 25-, 50-, 75-, 90-, and 99-percent annual and monthly duration flows. Low-flow recurrence-probability calculations include the minimum 7-day mean flow observed for the period of record, the 7Q10 (the minimum consecutive 7-day average flow expected to occur on average once every ten years) and 1Q10 (the minimum consecutive 1-day mean flow expected to occur on average once every ten years) as described in Riggs (1972). Statistics were computed for 228 streamgages using daily mean flow (”daily value”) data from the USGS Water Data for the Nation (USGS, 2026). This data release consists of six separate comma-separated value (CSV) files containing non-peak flow statistics for the selected streamgages. A complete description of analytical methods is provided in the “Non Peak Statistics” section of the larger work.", - "distribution": [ - { - "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/P9GT0CCG", - "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.6894d59dd4be0274c8e010f1.xml", - "format": "XML", - "mediaType": "text/xml", - "title": "Original Metadata" - } - ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6894d59dd4be0274c8e010f1", - "keyword": [ - "Arizona", - "California", - "Nevada", - "USGS:6894d59dd4be0274c8e010f1", - "Utah", - "geospatial datasets", - "inlandWaters", - "river reaches", - "stream discharge", - "streamflow", - "surface water (non-marine)" - ], - "modified": "2026-09-30T00:00:00Z", - "publisher": { - "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-120.2380, 35.7905, -112.6044, 41.9740", - "theme": [ - "geospatial" - ], - "title": "Non-peak streamflow statistics for selected streamgages in or near Nevada through water year 2021" - }, - "description": "This dataset contains non-peak streamflow statistics for select USGS streamgages in and near the state of Nevada through water year 2021. 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Low-flow recurrence-probability calculations include the minimum 7-day mean flow observed for the period of record, the 7Q10 (the minimum consecutive 7-day average flow expected to occur on average once every ten years) and 1Q10 (the minimum consecutive 1-day mean flow expected to occur on average once every ten years) as described in Riggs (1972). Statistics were computed for 228 streamgages using daily mean flow (”daily value”) data from the USGS Water Data for the Nation (USGS, 2026). This data release consists of six separate comma-separated value (CSV) files containing non-peak flow statistics for the selected streamgages. 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For gaged streams on Maui, two sets of projected rainfall conditions for the late 21st century and associated estimates of changes in low flows and usable habitat for native stream fauna are also provided. (Tab 2) Drainage basin characteristics of ungaged streams on Maui used to apply the statistical models, two sets of projected rainfall conditions for the late 21st century, and associated estimates of changes in low flows and usable stream habitat. A list of the stream characteristics and study results are included in this metadata file as well as references for data sources. Refer to Bassiouni et al. (in press) for details of the methods and data sources used to determine these characteristics and results. Streams were classified based on the elasticity of low flows to rainfall and multiple linear regressions were developed using random effects panel regression methods as described in Bassiouni et al. 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