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Land Use, 21 Categories - Ipswich and Parker River Watersheds - 1999 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 21 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1999. This datalayer has complete information.
Land Use, 37 Categories - Ipswich and Parker River Watersheds - 1985 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for the Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 37 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1985. This datalayer has complete information.
Land Use, 37 Categories - Ipswich and Parker River Watersheds - 1991 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 37 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1991. This datalayer has complete information.
Land Use, 37 Categories - Ipswich and Parker River Watersheds - 1999 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 37 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1999. This datalayer has complete information. Provide land use information for general mapping and display, and landuse change analysis.
Boundaries of the designated study area - Ipswich and Parker River Watersheds - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for Marine Biological Laboratory (MBL) in Woods Hole. This layer shows a mask of the Plum Island Ecosystems (PIE) study area, for use with the corresponding land use maps. This datalayer has complete information. Display study area.
Individual Towns that are Fully or Partially in the Ipswich and Parker River Watersheds - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for Marine Biological Laboratory (MBL) in Woods Hole. This layer shows the boundaries for the towns in the Ipswich River Watershed and the Parker River Watershed. This data layer was created so that the town boundaries would correspond to the boundaries of the corresponding land use maps. This datalayer has complete information. Display town boundaries for the study area.
Annual Snow Timing Index Rasters for the Western US and Alaska, WY2001-2019
<p>Here, a collection of rasters describing annual snow onset (SO), snow cover duration (SCD), and day of snow disappearance (DSD) for WY2001-2019 over Alaska, Canada, and the Western United States (boundary box: -152W, -100W, 32N, 68N) are available. Units are in calendar Day of Year (DOY) for SO and DSD, and in days for SCD. Filenames follow the convention, "index_west_threshold_spatialsubdomain.tif". For example, "SCD_west_5-0000005376-0000005376.tif" indicates the file contains snow cover duration raster data obtained using a 5% threshold. Spatial subdomain naming convention is described below.</p> <p>These data accompany a manuscript entitled, "Investigating the Relationship Between Peak Snow-Water Equivalent and Snow Timing Indices in the Western U.S. and Alaska". A diagnostic model of peak SWE as a function of remotely sensed snow timing indices (the dataset provided here) was developed in the course of this study to address the following questions: 1) Are peak SWE and snow timing related?; 2) How does this relationship vary across the western United States?; and 3) What meteorological and topographical conditions affect this relationship?</p> <p>These rasters were created in the Google Earth Engine (GEE) from the MODIS MOD10A1 v006 product (MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 6). From the initial daily fSCA product, a moving median window (i.e., a low-pass filter) of filter length k=25 days was applied to obtain a binary snow cover series using fractional cover threshold of 1%, 5%, 10%, 20%, and 30% such that the timing indices may be extracted. The filter length smooths out small-scale short-lived snow deposition events that obscure snow timing. Pixels without seasonal snowpack (i.e., persistent year-round snow or little to no snow) were masked: within a given water year, pixels that 1) show no onset of snow between the 272nd day of year and end of calendar year (corresponding with the typical N. hemisphere timeframe for the start of snow accumulation), and/or 2) do not melt out between start of calendar year and the 272nd day of year (typical timeframe for the end of snowmelt) were masked. SCD was calculated as (DSD + 365 days - SO). Because of the large file size, rasters were split up spatially into multiple files by the GEE, and follow the naming convention for large file exports listed here: https://developers.google.com/earth-engine/guides/exporting. As stated, "the filename of each tile will be in the form baseFilename-yMin-xMin where xMin and yMin are the coordinates of each tile within the overall bounding box of the exported image." Each band represents a different water year and is labeled as such, e.g. "DSD_2001". </p>
GRIDCERF: Geospatial Raster Input Data for Capacity Expansion Regional Feasibility
<p><strong>Abstract:</strong></p> <p>Climate change, energy system transitions, and socioeconomic change are compounding influences affecting the growth of electricity demand. While energy efficiency initiatives and distributed resources can address a significant amount of this demand, the United States will likely still need new utility-scale generation resources. The energy sector uses capacity expansion planning models to determine the aggregate need for new generation, but these models are typically at the state or regional scale and are not equipped to address the wide range of location- and technology-specific issues that are increasingly a factor in power plant siting. To help address these challenges, we have developed the Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package, a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers 265 suitability layers for use with 56 power plant technology configurations in a harmonized format that can be easily ingested by geospatially-enabled modeling software. It also provides pre-compiled technology-specific suitability layers and allows for user customization to robustly address science objectives when evaluating varying future conditions.</p> <p><strong>Accompanying GitHub repository:</strong></p> <p>The following GitHub repository contains the code used to generate the data in this archive: https://github.com/IMMM-SFA/vernon-etal_2023_scidata</p> <p><strong>Contents:</strong></p> <p><strong>Note:</strong></p> <p>GRIDCERF does not provide the source data directly due to some license restrictions related for direct redistribution of the unaltered source data. However, the included file "gridcerf_source_data_description.csv" details the provenance associated with each source dataset and notes their individual licenses/disclaimers.</p> <p><strong>Common Rasters:</strong></p> <p><strong>Suitability Layer Type and Source</strong></p> <p><strong>GRIDCERF Raster Name</strong></p> <p><strong>Bureau of Land Management (BLM) Surface Management Agency Areas</strong><strong><sup>33</sup></strong></p> <p>gridcerf_blm_surface_management_agency_areas.tif</p> <p><strong>BLM National Landscape Conservation System (NLCS) - National Monuments</strong><strong><sup>34</sup></strong></p> <p>gridcerf_blm_nlcs_national_monument_conus.tif</p> <p><strong>BLM NLCS - Outstanding Natural Areas</strong><strong><sup>35</sup></strong></p> <p>gridcerf_blm_nlcs_outstanding_natural_areas_conus.tif</p> <p><strong>BLM NLCS - Wilderness</strong><strong><sup>36</sup></strong></p> <p>gridcerf_blm_nlcs_wilderness_conus.tif</p> <p><strong>BLM NLCS - Wilderness Study Areas</strong><strong><sup>37</sup></strong></p> <p>gridcerf_blm_nlcs_wilderness_study_areas_conus.tif</p> <p><strong>National Park Service (NPS) Class 1 airsheds</strong><strong><sup>38</sup></strong></p> <p>gridcerf_class1_airsheds_conus.tif</p> <p><strong>NPS Administrative Boundaries</strong><strong><sup>39</sup></strong></p> <p>gridcerf_nps_administrative_boundaries_conus.tif</p> <p><strong>NPS Historic Trails</strong><strong><sup>40</sup></strong></p> <p>gridcerf_nps_historic_trails_conus.tif</p> <p><strong>NPS Scenic Trails</strong><strong><sup>41</sup></strong></p> <p>gridcerf_nps_scenic_trails_conus.tif</p> <p><strong>U.S. Fish and Wildlife Service (USFWS) - Critical Habitat</strong><strong><sup>42</sup></strong></p> <p>gridcerf_usfws_critical_habitat_conus.tif</p> <p><strong>USFWS - Special Designation</strong><strong><sup>43</sup></strong></p> <p>gridcerf_usfws_special_designation_conus.tif</p> <p><strong>USFWS - Wild and Scenic River System</strong><strong><sup>44</sup></strong></p> <p>gridcerf_usfws_national_wild_scenic_river_system_conus.tif</p> <p><strong>USFWS - National Realty Tracts</strong><strong><sup>45</sup></strong></p> <p>gridcerf_usfws_national_realty_tracts_conus.tif</p> <p><strong>National Land Cover Dataset (NLCD) Wetlands</strong><strong><sup>46</sup></strong></p> <p>gridcerf_nlcd_wetlands_conus.tif</p> <p><strong>U.S. Forest Service (USFS) Administrative Boundaries</strong><strong><sup>47</sup></strong></p> <p>gridcerf_usfs_administrative_boundaries_conus.tif</p> <p><strong>USFS Wilderness Areas</strong><strong><sup>48</sup></strong></p> <p>gridcerf_usfs_wilderness_areas_conus.tif</p> <p><strong>U.S. Geological Survey (USGS) National Wilderness Lands</strong><strong><sup>49</sup></strong></p> <p>gridcerf_usgs_wilderness_areas_conus.tif</p> <p><strong>USGS Protected Areas of the U.S - Class 1&2</strong><strong><sup>50</sup></strong></p> <p>gridcerf_usgs_padus_class_1_to_2_conus.tif</p> <p><strong>U.S. State Protected Lands</strong><strong><sup>51</sup></strong></p> <p>gridcerf_wdpa_state_protected_lands_conus.tif</p> <p><strong>Nature Conservancy lands</strong><strong><sup>52</sup></strong></p> <p>gridcerf_wdpa_tnc_managed_lands_conus.tif</p> <p> </p> <p><strong>Technology-specific Rasters:</strong></p> <p><strong>Suitability Layer Type and Source</strong></p> <p><strong>GRIDCERF Raster Name</strong></p> <p><strong>Bureau of Indian Affairs (BIA) Land Area Representations Dataset</strong><strong><sup>53</sup></strong></p> <p>gridcerf_bia_land_area_representations_conus.tif</p> <p><strong>Slope 5% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_5pct_or_less.tif</p> <p><strong>Slope 10% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_10pct_or_less.tif</p> <p><strong>Slope 12% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_12pct_or_less.tif</p> <p><strong>Slope 20% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_20pct_or_less.tif</p> <p><strong>Airports (10-mile buffer)</strong><strong><sup>54</sup></strong></p> <p>gridcerf_airports_10mi_buffer_conus.tif</p> <p><strong>Airports (3-mile buffer)</strong><strong><sup>54</sup></strong></p> <p>gridcerf_airports_3mi_buffer_conus.tif</p> <p><strong>Proximity to Railroad and Navigable Waters (< 5 km)</strong> <strong><sup>55,56</sup></strong></p> <p>gridcerf_usdot_railnodes_navwaters_within5km.tif</p> <p><strong>Coal Supply</strong><strong><sup>55–57</sup></strong></p> <p>gridcerf_coalmines20km_railnodes5km_navwaters5km_conus.tif</p> <p><strong>United States Environmental Protection Agency (EPA) CO Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_co_conus.tif</p> <p><strong>EPA NOx Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_no2_conus.tif</p> <p><strong>EPA Ozone Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_ozone_conus.tif</p> <p><strong>EPA Lead Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_lead_conus.tif</p> <p><strong>EPA PM10 Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_pm10_conus.tif</p> <p><strong>EPA PM2.5 Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_pm2p5_conus.tif</p> <p><strong>EPA SOx Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_so2_conus.tif</p> <p><strong>Earthquake Potential</strong><strong><sup>59</sup></strong></p> <p>gridcerf_usgs_earthquake_pga_0.3_at_2pct_in_50yrs_conus.tif</p> <p><strong>Densely population areas</strong><strong><sup>11</sup></strong></p> <p>gridcerf_densely_populated_ssp[2,3,5]_[year].tif</p> <p><strong>Densely population areas buffered by 25 miles</strong><strong><sup>11</sup></strong></p> <p>gridcerf_densely_populated_ssp[2,3,5]_[year]_buff25mi.tif</p> <p><strong>Densely population areas – nuclear</strong><strong><sup>11</sup></strong></p> <p>gridcerf_densely_populated_ssp[2,3,5]_[year]_nuclear.tif</p> <p><strong>National Hydrography Dataset (version 2; NHDv2)</strong><strong><sup>32</sup></strong></p> <p>gridcerf_nhd2plus_surfaceflow_greaterthan[bin]mgd_buffer20km.tif</p> <p><strong>National Renewable Energy Laboratory (NREL) concentrating solar direct normal potential</strong><strong><sup>26</sup></strong></p> <p>gridcerf_nrel_solar_csp_centralized_potential.tif</p> <p><strong>NREL photovoltaic potential</strong><strong><sup>26</sup></strong></p> <p>gridcerf_nrel_solar_pv_centralized_potential.tif</p> <p><strong>NREL Wind Integration National Dataset (WIND) toolkit</strong><strong><sup>22</sup></strong></p> <p>gridcerf_nrel_wind_development_potential_hubheight[080,110,140]_cf35.tif</p> <p> </p> <p><strong>Compiled Technology Rasters:</strong></p> <p>The list of layers that make up each compiled technology raster can be found in the "reference/compiled_layer_configuration.txt" file in this data archive.</p> <p>The following technology raster file names are self-descriptive in the format "gridcerf_<technology>_<subtype>_<carbon_capture>_<cooling_type>.tif". Some technologies do not have a carbon capture or cooling type designation and will simply have technology specific considerations listed.</p> <pre>gridcerf_biomass_conventional_ccs_dry.tif gridcerf_biomass_conventional_ccs_oncethrough.tif gridcerf_biomass_conventional_ccs_recirculating.tif gridcerf_biomass_conventional_no-ccs_dry.tif gridcerf_biomass_conventional_no-ccs_oncethrough.tif gridcerf_biomass_conventional_no-ccs_pond.tif gridcerf_biomass_conventional_no-ccs_recirculating.tif gridcerf_biomass_igcc_no-ccs_dry.tif gridcerf_biomass_igcc_no-ccs_oncethrough.tif gridcerf_biomass_igcc_no-ccs_recirculating.tif gridcerf_biomass_igcc_with-ccs_dry.tif gridcerf_biomass_igcc_with-ccs_oncethrough.tif gridcerf_biomass_igcc_with-ccs_recirculating.tif gridcerf_coal_conventional_ccs_dry.tif gridcerf_coal_conventional_ccs_oncethrough.tif gridcerf_coal_conventional_ccs_recirculating.tif gridcerf_coal_conventional_no-ccs_dry.tif gridcerf_coal_conventional_no-ccs_oncethrough.tif gridcerf_coal_conventional_no-ccs_pond.tif gridcerf_coal_conventional_no-ccs_recirculating.tif gridcerf_coal_igcc_no-ccs_dry.tif gridcerf_coal_igcc_no-ccs_oncethrough.tif gridcerf_coal_igcc_no-ccs_recirculating.tif gridcerf_coal_igcc_with-ccs_dry.tif gridcerf_coal_igcc_with-ccs_oncethrough.tif gridcerf_coal_igcc_with-ccs_recirculating.tif gridcerf_gas_cc_ccs_dry.tif gridcerf_gas_cc_ccs_oncethrough.tif gridcerf_gas_cc_ccs_recirculating.tif gridcerf_gas_cc_no-ccs_dry.tif gridcerf_gas_cc_no-ccs_oncethrough.tif gridcerf_gas_cc_no-ccs_pond.tif gridcerf_gas_cc_no-ccs_recirculating.tif gridcerf_gas_turbine_dry.tif gridcerf_gas_turbine_oncethrough.tif gridcerf_gas_turbine_pond.tif gridcerf_gas_turbine_recirculating.tif gridcerf_nuclear_gen3_oncethrough.tif gridcerf_nuclear_gen3_pond.tif gridcerf_nuclear_gen3_recirculating.tif gridcerf_refinedliquids_cc_ccs_dry.tif gridcerf_refinedliquids_cc_ccs_oncethrough.tif gridcerf_refinedliquids_cc_ccs_recirculating.tif gridcerf_refinedliquids_cc_no-ccs_dry.tif gridcerf_refinedliquids_cc_no-ccs_oncethrough.tif gridcerf_refinedliquids_cc_no-ccs_recirculating.tif gridcerf_refinedliquids_ct_dry.tif gridcerf_refinedliquids_ct_oncethrough.tif gridcerf_refinedliquids_ct_pond.tif gridcerf_refinedliquids_ct_recirculating.tif gridcerf_solar_csp_centralized_dry-hybrid.tif gridcerf_solar_csp_centralized_recirculating.tif gridcerf_solar_pv_centralized.tif gridcerf_wind_onshore_hubheight080m.tif gridcerf_wind_onshore_hubheight110m.tif gridcerf_wind_onshore_hubheight140m.tif </pre> <p><strong>Reference Data: </strong>Contains land mask and other useful boundary data. Also contains additional literature review resource and the layers used to build the compiled suitability.</p> <p><strong>References:</strong></p> <p>1. Bureau of Land Management. BLM National Surface Management Agency Area Polygons - National Geospatial Data Asset (NGDA). <em>Landscape Approach Data Portal</em> https://gbp-blm-egis.hub.arcgis.com/datasets/blm-national-sma-surface-management-agency-area-polygons/about (2023).</p> <p>2. Bureau of Land Management. BLM National NLCS National Monuments, National Conservation Areas and Similar Designations Polygons. <em>U.S.Department of Interior Bureau of Land Management Geospatial Business Plaform</em> https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-natl-nlcs-national-monuments-national-conservation-areas-polygons/about (2023).</p> <p>3. Hartger, P. NLCS Outstanding Natural Areas. <em>ArcGIS Hub</em> https://hub.arcgis.com/datasets/Wilderness::nlcs-outstanding-natural-areas/about (2017).</p> <p>4. Bureau of Land Management. BLM National NLCS Wilderness Areas Polygons. <em>U.S.Department of Interior Bureau of Land Management Geospatial Business Plaform</em> https://arcg.is/a01uC (2023).</p> <p>5. Bureau of Land Management. BLM National NLCS Wilderness Study Areas Polygons. <em>U.S.Department of Interior</em> https://arcg.is/14XPiC (2023).</p> <p>6. United States Environmental Protection Agency. Mandatory Class 1 Federal Areas Web Service. <em>Mandatory Class 1 Federal Areas Web Service</em> https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7BF234E37B-E7CE-4A47-89D6-68399D540576%7D (2015).</p> <p>7. National Park Service. National Park Service Boundary. <em>National Park Service</em> https://public-nps.opendata.arcgis.com/datasets/nps::nps-boundary-4/about (2023).</p> <p>8. National Park Service. National Historic Trails. <em>Geospatial Energy Mapper</em> https://gem.anl.gov/tool (2019).</p> <p>9. National Park Service. National Scenic Trails. <em>Geospatial Energy Mapper</em> https://gem.anl.gov/tool (2019).</p> <p>10. U.S. Fish and Wildlife Service. USFWS Threatened & Endangered Species Active Critical Habitat Report. <em>ECOS Environmental Conservation Online System</em> https://ecos.fws.gov/ecp/report/table/critical-habitat.html (2023).</p> <p>11. U.S. Fish and Wildlife. U.S. Fish and Wildlife Special Designation. <em>U.S. Fish and Wildlife</em> https://gis-fws.opendata.arcgis.com/datasets/fws::fws-special-designation/about (2023).</p> <p>12. U.S. Fish and Wildlife Service. National Wild and Scenic Rivers System. <em>Geospatial Energy Mapper</em> https://data.fs.usda.gov/geodata/edw/edw_resources/shp/S_USA.WildScenicRiver_LN.zip (2014).</p> <p>13. U.S. Fish and WIldlife Service. USFWS National Realty Tracts. <em>ServCat Fish and Wildlife Service Catalog</em> https://ecos.fws.gov/ServCat/Reference/Profile/154057 (2023).</p> <p>14. Dewitz, J. & U.S. Geological Survey. National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021). <em>Multi-Resoltuion Land Characteristics Consortium</em> https://www.mrlc.gov/data/nlcd-2019-land-cover-conus (2021).</p> <p>15. USDA Forest Service. Administrative Forest Boundaries. <em>Download National Datasets</em> https://data.fs.usda.gov/geodata/edw/datasets.php?dsetCategory=boundaries (2015).</p> <p>16. U.S. Forest Service. National Wilderness Areas. <em>Download National Datasets</em> https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Wilderness+areas (2023).</p> <p>17. U.S. Geological Survey. Wilderness Areas in the United States. <em>ScienceBase-Catalog</em> https://www.sciencebase.gov/catalog/item/4fc8f0e4e4b0bffa8ab259e7 (2000).</p> <p>18. U.S. Geological Survey Gap Analysis Project. Protected Areas Database of the United States (PAD-US) 3.0 (ver. 2.0, March 2023). <em>ScienceBase-Catalog</em> https://doi.org/10.5066/P9Q9LQ4B (2023).</p> <p>19. Protected Planet. Protected Areas (WDPA). <em>Protected Areas (WDPA)</em> https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA (2023).</p> <p>20. The Nature Conservancy. The Nature Conservancy lands. <em>The Nature Conservancy (TNC) Lands</em> https://geospatial.tnc.org/datasets/TNC::tnc-lands-north-america/about?layer=0 (2022).</p> <p>21. Bureau of Indian Affairs. American Indian and Alaskan Native Land Area Representations (LAR). <em>U.S. Department of the Interior Indian Affairs</em> https://biamaps.doi.gov/bogs/datadownload.html (2019).</p> <p>22. Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-filled SRTM for the globe Version 4. <em>CGIAR Consortium for Spatial Information</em> https://research.utwente.nl/en/publications/hole-filled-srtm-for-the-globe-version-4-data-grid (2008).</p> <p>23. Bureau of Transportation Statistics. Aviation Facilities. <em>Transportation.gov U.S. Department of Transportation</em> https://geodata.bts.gov/maps/usdot::aviation-facilities (2023).</p> <p>24. U.S. Department of Transportation. North American Rail Network Nodes. <em>U.S. Department of Transportation</em> https://data-usdot.opendata.arcgis.com/datasets/usdot::north-american-rail-network-nodes/explore?location=34.085743%2C-108.310932%2C3.96 (2023).</p> <p>25. U.S. Department of Transportation. Navigable Waterway Network Lines. <em>Bureau of Transportation Statistics</em> https://geodata.bts.gov/datasets/usdot::navigable-waterway-network-lines/about (2023).</p> <p>26. Energy Information Association (EIA). Energy Information Association U.S. Coal Mining Locations. <em>ScienceBase-Catalog</em> https://www.sciencebase.gov/catalog/item/5748a4cbe4b07e28b664dd78 (2017).</p> <p>27. Environmental Protection Agency. Green Book GIS Download. https://www.epa.gov/green-book/green-book-gis-download (2023).</p> <p>28. Shumway, A. <em>Data Release for the 2014 National Seismic Hazard Model for the Conterminous U.S</em>. https://doi.org/10.5066/P9P77LGZ (2019).</p> <p>29. Zoraghein, H. & O’Neill, B. C. U.S. State-level Projections of the Spatial Distribution of Population Consistent with Shared Socioeconomic Pathways. <em>Sustainability</em> <strong>12</strong>, (2020).</p> <p>30. Moore, R. B. <em>et al.</em> <em>User’s guide for the national hydrography dataset plus (NHDPlus) high resolution: U.S. Geological Survey Open-File Report 2019–1096</em>. https://pubs.er.usgs.gov/publication/ofr20191096 (2019).</p> <p>31. Perez, R. <em>et al.</em> A new operational model for satellite-derived irradiances: description and validation. <em>Solar Energy</em> <strong>73</strong>, 307–317 (2002).</p> <p>32. Draxl, C., Clifton, A., Hodge, B.-M. & McCaa, J. The Wind Integration National Dataset (WIND) Toolkit. <em>Applied Energy</em> <strong>151</strong>, 355–366 (2015).</p> <p> </p> <p> </p>
La Laguna Catchment, Chile - Surface Elevation Change and Velocity Rasters
<p>Datasets from Robson et al 2022. The zip contains two sub-folders:</p> <p>1) Surface elevation changes 1956 to 2020 with time steps 1956 - 1978, 1978 - 2000, 2000 - 2012, 2012 - 2015, 2015 - 2020. Datasets cover Tapado Glacier, La Laguna Catchment, Chile. Additionally surface elevation changes 2012 - 2020 covering rock glaciers in the La Laguna catchment are included.</p> <p>2) Surface velocity raster (annual displacements between 2012 and 2020) for glaciers and rock glaciers in the La Laguna catchment.</p> <p>For details on the processing, please refer to the publication:</p> <p> Robson, B. A., MacDonell, S., Ayala, Á., Bolch, T., Nielsen, P. R., and Vivero, S (2022). Glacier and Rock Glacier changes since the 1950s in the La Laguna catchment, Chile, The Cryosphere</p> <p> </p>
Raster Image Correlation Spectroscopy and Brightness Measurements of AtLEA proteins from Arabidopsis thaliana
<p>Temporal sequences of various fluorescent leaves were captured using a confocal scanning microscope (Olympus FV1000 inverted microscope), equipped with a 1.3 NA oil immersion 60X objective and the photon counting detection mode. Utilizing a 488 nm laser at 0.1% power and GFP filters/cubes, each temporal sequence involved the acquisition of 100 frames of 64x64 pixels, with a dwell time of 10 μs (1.76 ms per line, 130.24 ms per frame) and a pixel size of 66 nm (50X digital zoom). The interval between frames was set at 131.6 ms.</p> <p>Five plants were analyzed, each expressing one of four distinct genetic constructs fused to complementary fragments of Yellow Fluorescent Protein: pYFN-4-/5pYFC-4-5 (representing the complete AtLEA4-5 protein), pYFN-4-51-77/pYFC-4-51-77 (associated with the N-terminal region of AtLEA4-5), pYFN-4-578-158/pYFC-4-578-158 (relating to the C-terminal region of AtLEA4-5), and pYFN-pYFC (serving as the control). The raw data (*.oib files) were collected during three imaging sessions within a one-week period:</p> <p>- 220618 raw oib dataset.zip</p> <p>- 220622 raw oib dataset.zip</p> <p>- 220623 raw oib dataset.zip</p> <p>Images were converted to *.tif format using FIJI/ImageJ for further analysis and were archived in "tif dataset RICS NB LEAs.zip," excluding files with excessive movement of biological specimens. These images were then subjected to "Raster Image Correlation Spectroscopy" and "Number and Brightness" techniques for analysis.</p> <p>Notation:</p> <p>- h1, h2, h3, h4, h5: Replicates (plants) expressing one of four specific genetic constructs fused to complementary fragments of Yellow Fluorescent Protein.</p> <p>- 45: Fused to the full-length AtLEA4-5 protein (pYFN-4-/5pYFC-4-5).</p> <p>- 4h: Fused to the N-terminal region of AtLEA4-5 (pYFN-4-51-77/pYFC-4-51-77).</p> <p>- rc: Fused to the C-terminal region of AtLEA4-5 (pYFN-4-578-158/pYFC-4-578-158).</p> <p>- ct: The control condition (pYFN-pYFC).</p>
Maldivian seagrass aerial extent raster layers 2021 - 2000
<p>Contemporary Seagrass Map (2021)<br>The contemporary product was derived from Sentinel-2 satellite imagery, operated by the European Space Agency (ESA). The imagery, with a spatial resolution of 10 meters was pre-processed in Google Earth Engine (GEE) following established methods for retrieval of benthic signals. A support vector machine (SVM) classifier was used for classification. Training data encompassed three classes: seagrass, non-seagrass (including coral reefs, mangroves, sand/rubble, and macroalgal beds), and optical-deep water (ODW), totaling 25,463 training pixels. Important: the classification output is a binary (seagrass/non-seagrass) class. Validation of the map was conducted independently using 1,019 in-situ field survey points collected from 2017-2023. Mapping accuracy was assessed through an error matrix. Overall accuracy = 82%</p> <p>Historical Seagrass Maps (2000-2021)<br>The historical mapping product is derived from Landsat data spanning 2000 to 2021. The Landsat missions, operated by the United States Geological Survey (USGS) in collaboration with NASA, provide satellite data with a spatial resolution of 30 meters. There are no suitable data for 2010-2011. Each composite, representing a two-year period, underwent radiometric normalisation relative to a reference image from 2020-2021. Training and validation data were designated using an identical methodology as the contemporary maps, with 823 validation points utilised for accuracy assessment from 2017-2023. A fixed pixel approach was adopted to assess accuracy across the entire time series, involving the manual delineation of seagrass and non-seagrass areas. Overall accuracy was >89% in all cases.</p> <p> </p> <p>These data represent GeoTIFF files of seagrass habitat extent (binary classification). Contemporary data come from habitat classification of Sentinel-2 imagery (10 m pixel size). Historical maps come from habitat classification of Landsat data (30 m pixel size). For further details of workflow and data specifications please see the original publication DOI: 10.1038/s41598-024-61088-1</p>
Potential Natural Vegetation of Eastern Africa (Burundi, Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia): raster and vector GIS files for each country
<p>The map of potential natural vegetation of eastern Africa (V4A) gives the distribution of potential natural vegetation in Ethiopia, Kenya, Tanzania, Uganda, Rwanda, Burundi, Malawi and Zambia.</p> <p>The map is based on national and local vegetation maps constructed from botanical field surveys - mainly carried out in the two decades after 1950 - in combination with input from national botanical experts. Potential natural vegetation (PNV) is defined as “vegetation that would persist under the current conditions without human interventions”. As such, it can be considered a baseline or null model to assess the vegetation that could be present in a landscape under the current climate and edaphic conditions and used as an input to model vegetation distribution under changing climate.</p> <p>Vegetation types are defined by their tree species composition, and the documentation of the maps thus includes the potential distribution for more than a thousand tree and shrub species, see the documentation (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fspecies.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=aeHdnF4n19CbTTznMdObr91vfZys%2FY1PrK1OxI%2BHif0%3D&reserved=0">https://vegetationmap4africa.org/species.html</a>)</p> <p>The map distinguishes 48 vegetation types, divided in four main vegetation groups: 16 forest types, 15 woodland and wooded grassland types, 5 bushland and thicket types and 12 other types. The map is available in various formats. The online version (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fvegetation_map.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=VKVkjZ8lTKMyoU9luZLAYFDwY5sbwDrGXceVEQAeGIQ%3D&reserved=0">https://vegetationmap4africa.org/vegetation_map.html</a>) and for PDF versions of the map, see the documentation (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fdocumentation.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=FIsoem3dYG4%2FIQFMPlM8B2Vf9Doqf2CS7p2fevpAwx0%3D&reserved=0">https://vegetationmap4africa.org/documentation.html</a>). Version 2.0 of the potential natural vegetation map and the woody species selection tool was published in 2015 (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fdocs%2Fversionhistory%2F&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=J1aJt1D0dUhDd2fF9uEo8k1uu%2F7josYZCnQG%2FXWj5Ks%3D&reserved=0">https://vegetationmap4africa.org/docs/versionhistory/</a>). The original data layers include country-specific vegetation types to maintain the maximum level of information available. This map might be most suitable when carrying out analysis at the national or sub-national level.</p> <p>When using V4A in your work, cite the publication: Lillesø, J-P.B., van Breugel, P., Kindt, R., Bingham, M., Demissew, S., Dudley, C., Friis, I., Gachathi, F., Kalema, J., Mbago, F., Minani, V., Moshi, H., Mulumba, J., Namaganda, M., Ndangalasi, H., Ruffo, C., Jamnadass, R. & Graudal, L. 2011, Potential Natural Vegetation of Eastern Africa (Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia). Volume 1: The Atlas. 61 ed. Forest & Landscape, University of Copenhagen. 155 p. (Forest & Landscape Working Papers; 61 - as well as this repository using the DOI <<span><a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.11125645&data=05%7C02%7Cjpbl%40ign.ku.dk%7C82eb48688be64612c08108dc70c1b2e9%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638509224465318531%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=BtOVb3lPqZXp45K%2BWKUaLQEK3CTn0uMg8ysuQh5aVpo%3D&reserved=0">https://doi.org/10.5281/zenodo.11125645</a></span>>.</p> <p>The development of V4A was mainly funded by the Rockefeller Foundation and supported by University of Copenhagen</p> <p>If you want to use the potential natural vegetation map of eastern Africa for your analysis, you can download the spatial data layers in raster format as well as in vector format from this repository <<span><a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.11125645&data=05%7C02%7Cjpbl%40ign.ku.dk%7C82eb48688be64612c08108dc70c1b2e9%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638509224465318531%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=BtOVb3lPqZXp45K%2BWKUaLQEK3CTn0uMg8ysuQh5aVpo%3D&reserved=0">https://doi.org/10.5281/zenodo.11125645</a></span>></p> <p>A simplified version of the map can be found on <u>Figshare <https://doi.org/10.6084/m9.figshare.1306936.v1>. </u>That version aggregates country specific vegetation types into regional types. This might be the better option when doing regional-level assessments.</p> <p> </p>
Forest segmentation of multi-source national forest inventory biomass rasters and canopy height model from 2021
<p>The dataset is produced at Natural Resources Institute Finland (Luke) and the study is funded by the European Union's Horizon 2020 research and innovation programme (Holisoils, grant agreement No 101000289).</p> <p>Source data (multi-source National Forest Inventory, MS-NFI and peatland fertility map of Finland) of varying resolution (10m -16m) was reprojected to 10mx10m resolution from which stand polygons were formulated based on automatic segmentation and regional minimum size limit for a stand.</p> <p>The dataset is a file geodatabase with 5 regional layers, all including the polygons of stands with stand attributes based on MS-NFI 2021 information on the site type, fertility class, dominant height, basal area, diameter, age, volume as total and per tree species, total and aboveground biomass as total and per tree species.</p> <p>Coordinate system: ETRS-TM35FIN (EPSG:3067)</p>
OEMC Hackathon 2023: Global FAPAR Modeling Dataset (including raster data)
<p>Dataset organized by the <a href="https://earthmonitor.org/">Open-Earth-Monitor (OEMC) project</a> within the context of <a href="http://www.kaggle.com/competitions/oemc-hackathon-eu-land-cover-classification/overview">Hackathon 2023</a>.</p> <p>The dataset contains monthly mean FAPAR values aggregated by each ground station. FAPAR represents the fraction of the incoming (photosynthetic active) radiation that is absorbed by vegetation, and is given in the range <code>0-1</code>. It is a measure of vegetation health and ecosystem functioning, and a key parameter in light use efficiency models that model primary productivity.</p> <p>For each monthly FAPAR value, a set of covariates / features were extracted from <strong>32</strong> raster spatial layers, including including satellite (spectral bands and indices) and temperature images (land surface temperature), climate images (precipitation) and digital terrain model (slope and elevation). The features are organized by columns, unique data points in time are identified by the <code>sample_id</code> column, and data points points belonging to the same location are identified by <code>station_number</code>.</p> <p><strong>Column names:</strong></p> <ul> <li><code>sample_id</code>: unique identifier of datapoint</li> <li><code>station</code>: ground station number</li> <li><code>fapar</code>: monthly mean FAPAR</li> <li><code>month</code>: month of measurement</li> <li><code>modis_{..}</code>: NDVI, EVI, reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared) based on <a href="https://lpdaac.usgs.gov/products/mod13q1v061/">MOD13Q1</a></li> <li><code>modis_lst_day_p{..}</code>: Land surface temperatures daytime of percentiles 5th, 50th and 95th based on <a href="https://lpdaac.usgs.gov/products/mod11a2v061/">MOD11A2</a></li> <li><code>modis_lst_night_p{..}</code>: Land surface temperatures nighttime of percentiles 5th, 50th and 95th based on <a href="https://lpdaac.usgs.gov/products/mod11a2v061/">MOD11A2</a></li> <li><code>wv_yearly_p{..}</code>: Water vapour aggregated yearly by percentiles 25th, 50th and 75th based on derived from <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>wv_monthly_lt_p{..}</code>: Water vapour aggregated long-term monthly by percentiles 25th, 50th and 75th based on <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>wv_monthly_lt_sd</code>: Water vapour aggregated long-term monthly standard deviation based on <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>wv_monthly_ts_raw</code>: Water vapour monthly time series based on <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>wv_monthly_ts_smooth</code>: Water vapour monthly time series smoothed using the Whittaker method based on <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>accum_pr_monthly</code>: Monthly accumulated precipitation based on <a href="https://doi.org/10.1038/sdata.2017.122">CHELSA timeseries</a></li> <li><code>dtm_{..}</code>: Several DTM derivatives (Elevation, Slope, aspect (sine, cosine), curvature (up- and downslope), openness (negative, positive), compound topographic index (cti), valley bottom flatness (vbf)) based on <a href="https://ui.adsabs.harvard.edu/abs/2017AGUFM.H12C..04Y">MERIT DEM</a></li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>train.csv</strong>: Training set with 3,461 rows and 36 columns, including sample id (<code>sample_id</code> - index column), ground station (<code>station</code>), reference month (<code>month</code>), measured FAPAR (<code>fapar</code>), and 32 features / covariates</li> <li><strong>test.csv</strong>: Test set with 4,939 rows and 34 columns, including sample id (<code>sample_id</code> - index column), ground station (<code>station</code>), reference month (<code>month</code>) and 32 features / covariates</li> <li><strong>sample_submission.csv</strong>: a sample submission file with 4,939 rows and 2 columns, including sample id (<code>sample_id</code> - index column) and measured FAPAR (<code>fapar</code>)</li> </ul>
Natural grasslands across mainland France: a dataset including a 10 m raster and ground reference points
<p>The data provided here include the first 10 m raster of natural grasslands across mainland France and related ground reference points. The latter consist of 1,770 field observations that describe natural and artificial grasslands from respectively a compilation of hundreds of field-based vegetation maps and the European Union Land Parcel Identification System (LPIS). The raster data of natural grasslands were derived from five annual 10 m land cover maps of France from 2016-2020.</p> <p>More details can be found on the following reference : Panhelleux, L., Rapinel, S., Hubert-Moy, L., 2023. Natural grasslands across mainland France: a dataset including a 10 m raster and ground reference points. Data in Brief 109348. https://doi.org/10.1016/j.dib.2023.109348</p>
Muddy water - WQeMS raster products
<p>This dataset contains samples of the Extreme Events / Muddy water service of the WQeMS H2020 project.</p>
Land Water Transition Zone Change Detection - Hydroperiod Maps - WQeMS raster products
<p>Within this dataset, Hydroperiod maps of the Polyphytos open surface water reservoir in Greece and Giaretta reservoir in Italy are available in GeoTIFF raster format. The maps provide information about the total number of days each pixel is inundated within a specific time period.They were generated by the Land Water Transition Zone Change Detection service of the WQeMS project. Each raster file in the dataset is named according to the water body and period during which the processing was performed. Copernicus Sentinel-2 data was utilized for the generation of the inundation maps which were provided as input for the production of the hydroperiod maps.</p>
Land Water Transition Zone Change Detection - Transition Maps between two dates - WQeMS raster products
<p>Within this dataset, land water transition zone maps, which indicate the land-water transition between two instances in time, of the Polyphytos open surface water reservoir in Greece and Giaretta reservoir in Italy, are available in GeoTIFF raster format. The maps depict the transition zones by indicating the change of the pixel status from non-inundated to inundated and vice versa between two provided dates. They were generated by the Land Water Transition Zone Change Detection service of the WQeMS project. Each raster file in the dataset is named according to the water body and the two dates during which the processing was performed. Copernicus Sentinel-2 data was utilized for the generation of the inundation maps on the two dates.</p>
Bloom Events Detection - WQeMS Raster Products
<p>This dataset contains samples of the Bloom Events Detection service of the WQeMS H2020 project.</p>
Output raster datasets from Apalachicola Regional Restoration Initiative Open Pine Ecological Condition Model (2023)
<p>Output raster datasets from the 2023 Ecological Condition Model (ECM) for open pine ecosystems in the Apalachicola Regional Restoration Initiative (ARRI) area of the eastern Florida Panhandle. Our goal was to develop an ECM that would span all lands in the Apalachicola Regional Restoration Initiative (ARRI) area. As such, we used only datasets that were available throughout this region and did not rely on any corporate data layers from specific landowners. Furthermore, we sought to assess ecological condition at a high enough resolution to inform management decisions down to the level of individual forest stands. By taking this approach, we hoped to create ecological condition scores that could be used to inform restoration activities across all lands, and which could be updated through time to measure progress and to gauge the effectiveness of management activities.</p> <p>Output raster datasets include ecological condition for canopy, midstory and groundcover/shrub layers as well as overall ecological condition. Each raster contains ranked scores of estimated ecological condition: 1- Excellent, 2- Good, 3-Fair, and 4-Poor. </p> <p>NOTE- These outputs were created using tools stored in this repository: <a href="https://doi.org/10.5281/zenodo.8236853">https://doi.org/10.5281/zenodo.8236853</a> as well as several raster input layers stored in this repository: https://doi.org/10.5281/zenodo.8234220. </p> <p> </p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.