Timeline / Data.gov — Environment Datasets
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A new raw object was archived. Both versions are preserved. 4844 line(s) added, 5184 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 2026-10-05T06:23:19+00:00 |
| Content type | application/json |
| Current object |
491f364fbe008dc2cffcecd132ab5d3cd5e18ba6ffab941383bb0b395ff978e4
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| Previous object |
9c0cd4ea3a70bb1cc0f4ce4ecce71ec13a6e87fa8220877a58c235de5fb3c415
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What changed derived
This diff is not evidence. It was produced by
civic-memory.diff_engine 1.1.0 at
2026-10-05T06:23:19+00:00 by normalizing the two archived objects above. The
objects are authoritative; this reading of them can be regenerated or deleted
without loss. 4844 line(s) added, 5184 line(s) removed.
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Data tie- ins to local \nbenchmarks also are included", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.5066/P96EIQUW", + "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.ef9f62e5-57fc-4735-a1eb-52ee4c12dab1.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_ef9f62e5-57fc-4735-a1eb-52ee4c12dab1", + "keyword": [ + "Hydrographic Survey", + "USGS:ef9f62e5-57fc-4735-a1eb-52ee4c12dab1", + "environment", + "geoscientificInformation", + "inlandWaters" + ], + "modified": "2026-02-17T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-97.230886, 42.651279, -96.582338, 42.966037", + "theme": [ + "geospatial" + ], + "title": "GPS data collected for preconstruction hydrographic surveys of Missouri River downstream from Gavins Point Dam near river mile 761.4" + }, + "description": "This data set contains land surface elevations on dry and wadeable portions of transects for \npre-construction hydrographic surveys on the Missouri River below Gavins Point Dam for the \nEmergent Sandbar Habitat construction project near River Mile 761.4. 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This USGS data release contains all of the input and output files\n for the simulations described in the associated model documentation report \n(https://doi.org/10.3133/sir20205137).", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.5066/F7J102FK", + "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.226705a8-2a0e-4dda-8e36-84d2074d94c5.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_226705a8-2a0e-4dda-8e36-84d2074d94c5", + "keyword": [ + "Groundwater", + "Groundwater Model", + "InlandWaters", + "MOC3D", + "MODFLOW-2005", + "MODPATH", + "Milford", + "Milford-Souhegan River Valley", + "New Hampshire", + "Savage Municipal Water Supply Well Superfund site", + "Solute transport", + "USGS:226705a8-2a0e-4dda-8e36-84d2074d94c5", + "environment", + "geoscientificInformation", + "inlandWaters", + "remediation", + "usgsgroundwatermodel" + ], + "modified": "2025-08-05T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-71.761976, 42.800697, -71.642849, 42.889263", + "theme": [ + "geospatial" + ], + "title": "MODFLOW-2005, MODPATH, and MOC3D used for groundwater flow simulation, pathlines analysis, and solute transport in the crystalline-rock aquifer in the vicinity of the Savage Municipal Water-Supply Well Superfund Site, Milford, New Hampshire" + }, + "description": "The U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency and\nthe New Hampshire Department of Environmental Services, developed a model for used with \nMODFLOW-2005 and MODPATH5 to evaluate groundwater flow and advective transport under\npre- and post-remediation conditions in the crystalline-rock aquifer in the vicinity of the Savage\nMunicipal Water-Supply Well Superfund site Milford, New Hampshire. 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The current, area-wide model \nsimulates flow in the crystalline-rock aquifer and covers a much larger area than previous models\nwith the goal of improving the computation of groundwater flow from distal locations to the \nresidential wells and the area. This USGS data release contains all of the input and output files\n for the simulations described in the associated model documentation report \n(https://doi.org/10.3133/sir20205137).", + "distribution_titles": [ + "Digital Data", + "Original Metadata" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/9fcd5419-32f5-4bc0-8786-056c1d3288b0", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/9fcd5419-32f5-4bc0-8786-056c1d3288b0/raw", + "has_download": true, + "has_spatial": true, + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_226705a8-2a0e-4dda-8e36-84d2074d94c5", + "keyword": [ + "Groundwater", + "Groundwater Model", + "InlandWaters", + "MOC3D", + "MODFLOW-2005", + "MODPATH", + "Milford", + "Milford-Souhegan River Valley", + "New Hampshire", + "Savage Municipal Water Supply Well Superfund site", + "Solute transport", + "USGS:226705a8-2a0e-4dda-8e36-84d2074d94c5", + "environment", + "geoscientificInformation", + "inlandWaters", + "remediation", + "usgsgroundwatermodel" + ], + "last_harvested_date": "2026-10-05T04:06:30.351509", + "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-2005-modpath-and-moc3d-used-for-groundwater-flow-simulation-pathlines-analysis-and", + "spatial_centroid": { + "lat": 42.8361234, + "lon": -71.7143252 + }, + "spatial_shape": { + "coordinates": [ + [ + [ + -71.761976, + 42.800697 + ], + [ + -71.761976, + 42.889263 + ], + [ + -71.642849, + 42.889263 + ], + [ + -71.642849, + 42.800697 + ], + [ + -71.761976, + 42.800697 + ] + ] + ], + "type": "Polygon" + }, + "theme": [ + "geospatial" + ], + "title": "MODFLOW-2005, MODPATH, and MOC3D used for groundwater flow simulation, pathlines analysis, and solute transport in the crystalline-rock aquifer in the vicinity of the Savage Municipal Water-Supply Well Superfund Site, Milford, New Hampshire", + "type": "dataset" + }, + { + "_score": 9.108967, + "_sort": [ + 1791173187599, + 9.108967, + 0, + "c8a72df5-49da-4b1f-b112-c39c0d65d289" + ], + "access_level": "public", + "dcat": { + "accessLevel": "public", + "bureauCode": [ + "010:12" + ], + "contactPoint": { + "@type": "vcard:Contact", + "fn": "Steven (Paul) Berkowitz", + "hasEmail": "mailto:pberkowitz@usgs.gov" + }, + "description": "This is the primary output dataset from the project to access the potential impacts of climate change on vegetation management strategies within Hawaii Volcanoes National Park (HAVO). 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Filenames have a consistent naming convenion, as follows:\nSpecies name + island + file type + climate trajectory + year.TIF, where the following definitions apply:\nSpecies name = abbreviated code representing genus and species;\nIsland = 1 of the main 7 Hawaiian Islands (Hawaii, Maui, Kahoolwe, Lanai, Molokai, Oahu, and Kauai);\nFile type = one of 3 file types: \n(1) RANGE = present species range as of year 2000,\n(2) 80 PCT = binary raster of habitat suitability,\n(3) CHANGE TO 80 = raster showing the change in suitability between the year 2000 and the year indicated in the file name; \nClimate trajectory = lower (concave upward trajectory of change in rainfall and temperature over the century), middle (linear change in rainfall and temperature), upper (concave downward trajectory of change in rainfall and temperature), or future (where all three trajectories converge in 2090); \nYear = one of the following years: 2000, 2040, 2070, or 2090.\nFor example, consider this filename: Acakoa Hawaii 80 pct future2090.tif. This filename defines the following:\nSpecies name = Acakoa (Acacia koa), \nIsland = Hawaii island, \nFile type = 80 pct, indicating that it is a binary raster of habitat suitability where a value of 1 means 80% of model iterations forecast suitable habitat, and a value of 0 means less than 80% of model runs project suitability, \nClimate trajectory = future, which represents the point in the future (2090) where the lower, middle and upper trajectories converge, \nYear = 2090 (end of century since that's when our climate data set series ends).", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.5066/P1LJW4J4", + "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.57b27182e4b00148d3982d6b.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_57b27182e4b00148d3982d6b", + "keyword": [ + "Hawaii Volcanoes National Park", + "Hawaiian Islands", + "USGS:57b27182e4b00148d3982d6b", + "climate change", + "climatologyMeteorologyAtmosphere", + "envelope model", + "environment", + "geospatial datasets", + "modeling", + "species distribution", + "species range" + ], + "modified": "2026-10-02T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-159.80025, 18.88725, -154.80525, 22.25325", + "theme": [ + "geospatial" + ], + "title": "Plant species range models under different climate scenarios in Hawaii 2000-2090" + }, + "description": "This is the primary output dataset from the project to access the potential impacts of climate change on vegetation management strategies within Hawaii Volcanoes National Park (HAVO). The key objective of this project was to combine climate projections from the International Pacific Research Center (IPRC) and plant distribution models from Price et al. to produce a series of projected species range maps over the next century. Although the project focused on HAVO, the projected species range maps were created for seven of the main Hawaiian Islands. We stored the model output as rasters (.TIF files); additionally we created multi-panel maps of these rasters that are available separately.\nIn summary, this dataset consists of 4,095 rasters that delineate plant species range, both present and future, for various climate change scenarios and years. The series covers 39 species, 7 islands, and 15 different combinations of climate trajectory and year. The contents of each raster varies slightly, but the contents can be determined from the specific filename. Filenames have a consistent naming convenion, as follows:\nSpecies name + island + file type + climate trajectory + year.TIF, where the following definitions apply:\nSpecies name = abbreviated code representing genus and species;\nIsland = 1 of the main 7 Hawaiian Islands (Hawaii, Maui, Kahoolwe, Lanai, Molokai, Oahu, and Kauai);\nFile type = one of 3 file types: \n(1) RANGE = present species range as of year 2000,\n(2) 80 PCT = binary raster of habitat suitability,\n(3) CHANGE TO 80 = raster showing the change in suitability between the year 2000 and the year indicated in the file name; \nClimate trajectory = lower (concave upward trajectory of change in rainfall and temperature over the century), middle (linear change in rainfall and temperature), upper (concave downward trajectory of change in rainfall and temperature), or future (where all three trajectories converge in 2090); \nYear = one of the following years: 2000, 2040, 2070, or 2090.\nFor example, consider this filename: Acakoa Hawaii 80 pct future2090.tif. 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In addition to improving the scientific understanding of how the large-scale climate system works, GCM simulations of past and future climate conditions can be useful in applied research contexts. When seeking to apply information from global-scale climate projections to address local- and regional-scale climate questions, GCM-generated datasets often undergo statistical post-processing generally known as statistical downscaling (hereafter, SD). There are many different SD techniques, with all using information from observations to address GCM biases and to provide information at finer spatial scales than that of a GCM, thereby yielding data products often considered more suitable for use in climate impacts-related applications.\nThis collection of statistically downscaled future climate projections includes 81 sets of SD-processed projections of daily high temperature, daily low temperature, and daily total precipitation across the south-central United States. The 81 sets can be viewed as a 3x3x3x3 matrix, created based on a combination of three GCMs from the CMIP5 archive (CCSM4, MIROC5, and MPI-ESM-LR), each of which simulated 21st century climate responses for three different future atmospheric composition scenarios (known as representative concentration pathways or RCPs 2.6, 4.5, and 8.5). Three different SD techniques were employed, and each used three gridded observation-based data products to train (i.e. calibrate) the SD methods. The three downscaling techniques include a delta method (DeltaSD), an equi-distant quantile mapping method (EDQM), and a piecewise asynchronous regression method (PARM). The observational data products used for training were Daymet v. 2.1, Livneh v. 1.2, and PRISM AN81d v. D1. The resulting SD-processed projections are on a 10 km by 10 km grid covering the south-central United States (all of AR, KS, LA, NM, OK, TX, and portions of CO and MO). Both historical baseline files (1981-2005) and future projections (2006-2099) are provided, as appropriate.\nThough not exhaustive, these downscaled climate projections for the south central US region represent a range of potential future climate trajectories that can serve as a component of climate impacts research studies. That 81 sets of future projections, and not just one, are provided is indicative that some uncertainties exist regarding the trajectory of the 21st century climate change, though all show notable warming. Uncertainties in how human activity may change future atmospheric composition are represented by the different RCP scenarios. Differences in how sensitive the surface climate of this region will be to atmospheric composition changes are sampled by the use of different GCMs. Similarly, because each SD method has different performance characteristics and observational products differ, the use of different SD techniques and training data set combinations acknowledges that SD methodological choices influence the value-added statistically refined climate projection data products. Applied researchers may explore aspects of their applications’ sensitivities to some climate projection uncertainties by sampling from these 81 sets of SD data products. However, this collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the south central US region.", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/cprep", + "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.6778032fd34edab7af6e2387.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6778032fd34edab7af6e2387", + "keyword": [ + "USGS:6778032fd34edab7af6e2387", + "atmosphere", + "climate", + "climate change", + "climate projections", + "climatology", + "dataset", + "downscaling", + "environment", + "geospatial datasets", + "meteorology", + "service" + ], + "modified": "2026-10-02T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-108.6328, 26.0500, -90.0879, 39.9500", + "theme": [ + "geospatial" + ], + "title": "South Central Climate Projections Evaluation Project (C-PrEP)" + }, + "description": "Global climate models (GCMs) are numerically complex, computationally intensive, physics-based research tools used to simulate our planet’s inter-connected climate system. 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The 81 sets can be viewed as a 3x3x3x3 matrix, created based on a combination of three GCMs from the CMIP5 archive (CCSM4, MIROC5, and MPI-ESM-LR), each of which simulated 21st century climate responses for three different future atmospheric composition scenarios (known as representative concentration pathways or RCPs 2.6, 4.5, and 8.5). Three different SD techniques were employed, and each used three gridded observation-based data products to train (i.e. calibrate) the SD methods. The three downscaling techniques include a delta method (DeltaSD), an equi-distant quantile mapping method (EDQM), and a piecewise asynchronous regression method (PARM). The observational data products used for training were Daymet v. 2.1, Livneh v. 1.2, and PRISM AN81d v. D1. The resulting SD-processed projections are on a 10 km by 10 km grid covering the south-central United States (all of AR, KS, LA, NM, OK, TX, and portions of CO and MO). 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This \ndata provides land surface elevations of shallow-water, shore, and highbank for the Missouri River \nfollowing construction of Emergent Sandbar Habitat.", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.5066/P97LD4D7", + "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.e92cc781-ee80-4609-8733-54072c302ca2.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_e92cc781-ee80-4609-8733-54072c302ca2", + "keyword": [ + "Hydrographic Survey", + "USGS:e92cc781-ee80-4609-8733-54072c302ca2", + "environment", + "geoscientificInformation", + "inlandWaters" + ], + "modified": "2026-02-17T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-96.800230, 42.654601, -96.700315, 42.673551", + "theme": [ + "geospatial" + ], + "title": "GPS data collected for postconstruction hydrographic surveys of Missouri River downstream from Gavins Point Dam near river mile 761.4" + }, + "description": "This data set contains land surface elevations on dry and wadeable portions of transects for the \nhydrographic surveys on the Missouri River below Gavins Point Dam near River Mile 761.4. 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The dataset contains 2,445,875 records from nurseries in at least 2,795 unique locations, with the majority of catalogs published between 1890-1950. Nurseries were located in all conterminous states but were concentrated in the eastern U.S. and California. We identified 19,140 unique horticultural taxa, of which 8,642 matched taxa in the USDA Plants database. The USDA Plants database is limited to native and naturalized taxa in the US. Native or introduced status was listed in USDA Plants for 7,018 of included taxa, while 1,642 had an unknown status. The remaining 10,498 taxa are not naturalized according to USDA Plants or are of varieties of native and introduced taxa that did not match to USDA Plants taxonomy. The majority of taxa in the Historical Plant Sales (HPS) database with an identified status are native (65.5%; 4,596 of 7,018 taxa), of which 393 taxa are reported as invasive outside of the U.S. Of the 2381 introduced taxa, 1,103 (46.3%) are reported as invasive somewhere globally. Despite a richer pool of native taxa, most cataloged plant records with an identified status were of introduced taxa (54.1%; 1,045,684 of 1,933,925 records). Plants reported as invasive somewhere globally comprised a large portion of records with an identified status (38.7%; 747,953 of 1,933,925 records) underscoring the large role of ornamental introductions in facilitating plant invasions.", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.7275/0t5v-5r18", + "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.64872b51d34ef77fcafe1230.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_64872b51d34ef77fcafe1230", + "keyword": [ + "Biodiversity Heritage Library", + "Horticulture", + "Introduced plants", + "Invasive plants", + "Native plants", + "Nursery sales", + "Ornamental plants", + "USGS:64872b51d34ef77fcafe1230", + "datasets", + "environment" + ], + "modified": "2026-10-02T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-125.5078, 25.2447, -66.7969, 48.8647", + "theme": [ + "geospatial" + ], + "title": "Historical plant sales (HPS) database: Documenting the spatiotemporal history of plant sales in the conterminous U.S." + }, + "description": "We downloaded, cleaned, and combined records from Biodiversity Heritage Library’s (BHL) Seed and Nursery Catalog Collection with data from Restoring American Gardens: An encyclopedia of heirloom ornamental plants, 1640-1940 (RAG; Adams 2004) to create a single database of historical nursery sales in the U.S. Each record represents an individual taxon offered for sale at an individual time in a specific nursery’s catalog. We standardized records to the current World Flora Online (http://worldfloraonline.org) accepted taxonomy, and appended accepted USDA code, growth habit, and introduction status. We also appended whether taxa were reported as invasive in the Global Plant Invaders (GPI) dataset or the Global Invasive Species Database (GISD), or regulated in the conterminous U.S. Lastly, we geocoded all reported publication locations. The dataset contains 2,445,875 records from nurseries in at least 2,795 unique locations, with the majority of catalogs published between 1890-1950. Nurseries were located in all conterminous states but were concentrated in the eastern U.S. and California. We identified 19,140 unique horticultural taxa, of which 8,642 matched taxa in the USDA Plants database. The USDA Plants database is limited to native and naturalized taxa in the US. Native or introduced status was listed in USDA Plants for 7,018 of included taxa, while 1,642 had an unknown status. 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Future climate scenarios include statistically and dynamically downscaled RCP 8.5 in the year 2100 in addition to statistically downscaled RCP 8.5 in the year 2050. 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No data are provided here. The geospatial outlines and herd size estimates of Chukchi Sea autumn walrus haulouts, interpreted from Satellite imagery, have been combined into a single updated U.S. Geological Survey data release: https://doi.org/10.5066/P9CSM0KN\n 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. Estimates of the land area occupied by walruses are provided based on interpretation by experienced image reviewers. The images are from a variety of Earth observing satellite imagery sources collected over coastal areas of the Chukchi Sea (northwestern Alaska and northeastern Russia) in autumn when walruses come ashore in large numbers to rest in the absence of sea ice. 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": "2024-06-29T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-171.7, 66.9, -163.0, 69.6", + "theme": [ + "geospatial" + ], + "title": "SUPERSEDED: Pacific Walrus Coastal Haulout Occurrences Interpreted from Satellite Imagery, 2023" + }, + "description": "This data release has been SUPERSEDED. 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Water levels were recorded with pressure-transducing dataloggers (Solinst) for eight months (July 2016-March 2017). Elevation surveys (differential leveling) were used to convert water levels relative to the Earth Geoid Model of 2008.", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.5066/P96R8MZQ", + "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.608b4181d34e3d6ea566f894.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_608b4181d34e3d6ea566f894", + "keyword": [ + "Pacific Ocean", + "Pohnpei", + "Senyavin Islands", + "Tidal Mangrove Forest", + "USGS:608b4181d34e3d6ea566f894", + "environment", + "water level" + ], + "modified": "2026-10-02T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "158.1110, 6.7834, 158.3394, 6.9860", + "theme": [ + "geospatial" + ], + "title": "Water level across two mangrove sites in Pohnpei, Federated States of Micronesia, July 2016 - March 2017" + }, + "description": "Water level was monitored at two mangrove forest sites across Pohnpei, Federated States of Microneisa. 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This data was developed for use in a landscape simulation modeling study aimed at evaluating how well alternative management strategies maintain whitebark pine populations under historical climate and future climate conditions. For the study, we developed three spatial management alternatives for whitebark pine in the Greater Yellowstone Ecosystem representing no active management, current management, and climate-informed management. These management alternatives were implemented in the simulaton model FireBGCv2 under historical climate and three future climate change scenarios - the HadGEM-ES, CESM1-CAM5, and CNRM-CM5 Global Circulation Models under the RCP 8.5 emissions scenario. We worked with the Greater Yellowstone Coordinating Committee's (GYCC) Whitebark Pine Subcommittee to develop this spatial representation of their current management strategy. The treatments mapped represent a set of the treatments recommended in the GYCC Whitebark Pine 2011 Strategy document and include planting blister-rust resistant whitebark pine seedlings, competition removal thinning, wildland fire use and prescribed fire, and protection from mountain pine beetles using verbenone and carbaryl. We used historical and future projections of climate suitability based on species distribution models for whitebark pine (Chang et al. 2014) to map zones of core, deteriorating, and future whitebark pine habitat. Core zones were those areas that are currently suitable for whitebark and remain suitable in the future. Deteriorating zones were where the climatic conditions for whitebark pine are expected to decline. Future zones were areas that are projected to become newly suitable for whitebark pine. We then overlaid our climate zones for whitebark pine with similar projections of future climate suitability for all of whitebark pine’s competitors - Engelmann spruce, subalpine fir, lodgepole pine, and Douglas-fir (Piekielek et al. 2015. We discussed the different combinations of climate suitability zones (core, deteriorating, future) and potential future level of competition (low or high) from other species with the GYCC Whitebark Pine Subcommittee to determine which management activities should be prioritized within each management zone. The result is a map of management zones where different activities are prioritized to meet the goal of maintaining whitebark pine populations. This was used to determine which treatments would be implemented spatially during the simulation modeling, dependent upon additional criteria related to simulated stand-level conditions. In this dataset, we used the resulting map of spatially prioritized management activities to summarize the area prioritized for each management activity that fell within Core, Deteriorating, and Future climate suitability zones", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.5066/P1BLXPGK", + "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.5a85a50fe4b00f54eb3664d0.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5a85a50fe4b00f54eb3664d0", + "keyword": [ + "USGS:5a85a50fe4b00f54eb3664d0", + "biota", + "climate change", + "climatologyMeteorologyAmtmosphere", + "ecosystem management", + "environment", + "external research support", + "forest ecosystems", + "modeling", + "natural resource management" + ], + "modified": "2026-10-02T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-180.0, -90.0, 180.0, 90.0", + "theme": [ + "geospatial" + ], + "title": "Treatment area summarized by Whitebark Pine Climate Suitability Zone" + }, + "description": "This dataset represents the area in the Greater Yellowstone Ecosystem prioritized for different whitebark pine(Pinus albicaulis) management activities, summarized by climate suitability zones. 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Data include species observation details such as observers, dates, location, and number of individuals seen. 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EPA administers the Superfund program in cooperation with individual states and tribal governmentsFRS integrates facility data from EPA's national program systems, other federal agencies, and State and tribal master facility records and provides EPA with a centrally managed, single source of comprehensive and authoritative information on facilities. This data set contains the subset of FRS integrated facilities that link to CERCLIS non-NPL facilities once the CERCLIS data has been integrated into the FRS database. Additional information on FRS is available at the EPA website https://www.epa.gov/enviro/facility-registry-service-frs. These data are considered sensitive and are restricted to internal use only. The download file and map service are accessible only on the EPA intranet. 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