Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
19
datasets available to search
ShareScore release 0.9.0
Dataset results
19 results for “GRASS GIS”
GRASS GIS database for CASAS-PBDM (www.casasglobal.org) geospatial mapping and analysis
<p>GRASS GIS database for geospatial mapping and analysis of physiologically based demographic modeling (PBDM) implemented by the Center for the Analysis of Sustainable Agricultural Systems (CASAS, <a href="https://www.casasglobal.org/" target="_blank" rel="noopener">www.casasglobal.org</a>).</p> <p>The <code>casas_gis_grass8data.zip</code> archive includes data updated for use with GRASS GIS version 8.</p>
GRASS GIS Location for South-East US urban growth case study
<p>GRASS GIS Location containing data prepared for a FUTURES urban growth modeling case study for the South East United States (NC, SC, TN, GA, AL, FL). Contains NLCD 2001-2019 (land cover for all years; 2001 and 2019 impervious descriptor), county boundaries, USGS DEM, PAD-US protected areas.</p>
South-East US historical and projected population per county for FUTURES urban growth modeling in GRASS GIS
<p>South East US historical (2001-2019) and projected (2020-2100) population per county for 6 states (NC, SC, TN, GA, AL, FL). Historical data come from The National Vital Statistics System (https://seer.cancer.gov/popdata/download.html) and future data are projected by Hauer 2019 (https://doi.org/10.1038/sdata.2019.5) for SSP2 scenario. Data are formatted for r.futures.demand module, which is a GRASS GIS addon for computing future land demand for FUTURES urban growth model. </p>
GRASS GIS Location for urban growth case study in Triangle area, NC, USA
<p>GRASS GIS Location containing data prepared for a FUTURES urban growth modeling case study for the broader Raleigh-Durham area in North Carolina, USA. Contains NLCD 2001-2019 (land cover for all years; 2001 and 2019 impervious descriptor), county boundaries, USGS DEM, PAD-US protected areas.</p>
Dataset for FOSS4G 2022 workshop: Unleash the power of GRASS GIS with Jupyter
<p>Dataset for FOSS4G 2022 workshop <em>Unleash the power of GRASS GIS with Jupyter.</em></p>
New Orleans Dataset for GRASS GIS
<p><strong>New Orleans Dataset for GRASS GIS</strong><br> This geospatial dataset contains raster and vector data for New Orleans, Louisiana, USA. The top level directory <em>new-orleans-dataset</em> is a GRASS GIS location for the North American Datum of 1983 (NAD 83) / Louisiana South State Plane Feet with <a href="https://epsg.io/3452">EPSG code 3452</a>. Inside the location there are the <em>PERMANENT</em> mapset with citywide data, a <em>vieux_carre</em> mapset with data for the French Quarter, Python scripts for data processing, data records, a color table, a license file, and readme file.</p> <p><strong>Instructions</strong><br> Install <a href="https://grass.osgeo.org/">GRASS GIS</a>, unzip this archive, and move the location into your <a href="https://grass.osgeo.org/grass77/manuals/grass_database.html">GRASS GIS database</a> directory. If you are new to GRASS GIS read the <a href="https://grass.osgeo.org/documentation/first-time-users/">first time users guide</a>.</p> <p><strong>Data Sources</strong></p> <ul> <li><a href="https://coast.noaa.gov/htdata/lidar2_z/geoid12b/data/6350/">U.S. Army Corps of Engineers 2012 Lidar Survey of New Orleans</a></li> <li><a href="https://datadriven.nola.gov/open-data/">New Orleans Open Data</a></li> </ul> <p><strong>License</strong><br> This dataset is licensed under the <a href="https://opendatacommons.org/licenses/pddl/index.html">ODC Public Domain Dedication and License 1.0 (PDDL)</a> by Brendan Harmon. The scripts are licensed under the<a href="https://www.gnu.org/licenses/gpl-3.0.en.html"> GNU General Public License 3.0</a> by Brendan Harmon. The graphics are licensed under the <a href="https://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)</a> by Brendan Harmon.<br> </p>
Louisiana Dataset for GRASS GIS
<p><strong>Louisiana Dataset for GRASS GIS</strong><br> This geospatial dataset contains statewide raster and vector data for Louisiana, USA. The top level directory <em>louisiana-dataset </em>is a GRASS GIS location for NAD 1983 / UTM zone 15N with <a href="https://epsg.io/26915">EPSG code 26915</a>. Inside the location there is the <em>PERMANENT</em> mapset, a license file, data record, and readme file.</p> <p><strong>Instructions</strong><br> Install <a href="https://grass.osgeo.org/">GRASS GIS</a>, unzip this archive, and move the location into your <a href="https://grass.osgeo.org/grass77/manuals/grass_database.html">GRASS GIS database</a> directory. If you are new to GRASS GIS read the <a href="https://grass.osgeo.org/documentation/first-time-users/">first time users guide</a>.</p> <p><strong>Data Sources</strong></p> <ul> <li>USGS National Elevation Dataset (NED)</li> <li>USGS National Landcover Dataset (NLCD)</li> <li>USGS National Hydrography Dataset (NHD)</li> <li>USGS National Transportation Dataset (NTD)</li> <li>USGS National Boundary Dataset (NBD)</li> </ul> <p><strong>License</strong><br> This dataset is licensed under the <a href="https://opendatacommons.org/licenses/pddl/index.html">ODC Public Domain Dedication and License 1.0 (PDDL)</a> by Brendan Harmon.<br> </p>
Sichuan Dataset for GRASS GIS
<p><strong>Sichuan Dataset for GRASS GIS</strong><br> This geospatial dataset contains raster and vector data for Sichuan Province, China. The top level directory <em>sichuan-dataset</em> is a GRASS GIS location for WGS 84 / UTM zone 48N with <a href="https://epsg.io/32648">EPSG code 32648</a>. Inside the location there is the <em>PERMANENT</em> mapset, color tables, category tables, a license file, and readme file.</p> <p><strong>Instructions</strong><br> Install <a href="https://grass.osgeo.org/">GRASS GIS</a>, unzip this archive, and move the location into your <a href="https://grass.osgeo.org/grass77/manuals/grass_database.html">GRASS GIS database</a><br> directory. If you are new to GRASS GIS read the <a href="https://grass.osgeo.org/documentation/first-time-users/">first time users guide</a>.</p> <p><strong>Data Sources</strong></p> <ul> <li><a href="https://www.eorc.jaxa.jp/ALOS/en/aw3d30/">ALOS Global Digital Surface Model</a></li> <li><a href="http://ecotope.org/anthromes/">Anthromes</a></li> <li><a href="https://earthexplorer.usgs.gov/">NASA Shuttle Radar Topography Mission (SRTM)</a></li> <li><a href="https://www.esa-landcover-cci.org/">ESA CCI Landcover</a></li> <li><a href="http://esdac.jrc.ec.europa.eu/">European Soil Data Centre (ESDAC)</a></li> <li><a href="https://gadm.org/">Database of Global Administrative Areas (GADM)</a></li> <li><a href="https://worldmap.harvard.edu/">Harvard World Map</a></li> <li><a href="https://www.iucnredlist.org/">IUCN Red List</a></li> <li><a href="https://www.protectedplanet.net/">Protected Planet</a></li> <li><a href="https://visibleearth.nasa.gov/">NASA Blue Marble</a></li> <li><a href="https://www.naturalearthdata.com/">Natural Earth</a></li> <li><a href="https://www.openstreetmap.org/">Open Street Map</a></li> <li><a href="https://www.worldwildlife.org/pages/hydrosheds">WWF HydroSHEDS</a></li> <li><a href="https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world">WWF Terrestrial Ecoregions of the World</a></li> </ul> <p><strong>License</strong><br> This dataset is licensed under the <a href="https://opendatacommons.org/licenses/pddl/index.html">ODC Public Domain Dedication and License 1.0 (PDDL)</a> by Brendan Harmon.<br> </p>
Natural Earth Dataset for GRASS GIS
<p><strong>Basic Global Dataset for GRASS GIS</strong><br>This geospatial dataset contains global raster and vector data from <a href="https://www.naturalearthdata.com/">Natural Earth</a>. The top level directory <em>global-dataset</em> is a GRASS GIS location for the World Geodetic System 1984 (WGS84) with <a href="https://epsg.io/4326">EPSG code 4326</a>. Inside the location there is the <em>PERMANENT</em> mapset, a license file, and readme file.</p> <p><strong>Instructions</strong><br>Install <a href="https://grass.osgeo.org/">GRASS GIS</a>, extract this archive, and move the location into your <a href="https://grass.osgeo.org/grass77/manuals/grass_database.html">GRASS GIS database</a> directory. If you are new to GRASS GIS read the <a href="https://grass.osgeo.org/documentation/first-time-users/">first time users guide.</a></p> <p><strong>Data Source</strong></p> <ul> <li><a href="https://www.naturalearthdata.com/">Natural Earth</a></li> </ul> <p><strong>License</strong><br>This dataset is licensed under the <a href="https://opendatacommons.org/licenses/pddl/index.html">ODC Public Domain Dedication and License 1.0 (PDDL)</a> by Brendan Harmon and Paulo van Breugel.<br> </p>
Global Dataset for GRASS GIS
<p><strong>Global Dataset for GRASS GIS</strong><br>This geospatial dataset contains global raster and vector data. The top level directory <em>global-dataset</em> is a GRASS GIS location for the World Geodetic System 1984 (WGS84) with <a href="https://epsg.io/4326">EPSG code 4326</a>. Inside the location there is the <em>PERMANENT</em> mapset, a license file, and readme file.</p> <p><strong>Instructions</strong><br>Install <a href="https://grass.osgeo.org/">GRASS GIS</a>, unzip this archive, and move the location into your <a href="https://grass.osgeo.org/grass77/manuals/grass_database.html">GRASS GIS database</a> directory. If you are new to GRASS GIS read the <a href="https://grass.osgeo.org/documentation/first-time-users/">first time users guide.</a></p> <p><strong>Data Sources</strong></p> <ul> <li><a href="https://www.naturalearthdata.com/">Natural Earth</a></li> <li><a href="https://www.protectedplanet.net/">Protected Planet</a></li> <li><a href="https://visibleearth.nasa.gov/">NASA Blue Marble</a></li> <li><a href="https://www.worldwildlife.org/pages/hydrosheds">WWF HydroSHEDS</a></li> <li><a href="https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world">WWF Terrestrial Ecoregions of the World</a></li> </ul> <p><strong>License</strong><br>This dataset is licensed under the <a href="https://opendatacommons.org/licenses/pddl/index.html">ODC Public Domain Dedication and License 1.0 (PDDL)</a> by Brendan Harmon and Paulo van Breugel.<br> </p>
The Hills of Governor's Island Dataset for GRASS GIS
<p><strong>The Hills of Governor's Island Dataset for GRASS GIS</strong><br> This geospatial dataset contains raster and vector data for the Hills region of Governor's Island, New York City, USA. The top level directory <em>governors_island_hills_for_grass</em> is a GRASS GIS location for <em>NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet </em>in US Surveyor's Feet with EPSG code 2263. Inside the location there is the <em>PERMANENT</em> mapset, a license file, data record, readme file, workspace, color table, category rules, and scripts for data processing. This dataset was created for the course <a href="http://baharmon.github.io/gis-for-designers">GIS for Designers</a>.</p> <p><strong>Instructions</strong><br> Install <a href="https://grass.osgeo.org/">GRASS GIS</a>, unzip this archive, and move the location into your <a href="https://grass.osgeo.org/grass78/manuals/grass_database.html">GRASS GIS database</a><br> directory. If you are new to GRASS GIS read the <a href="https://grass.osgeo.org/documentation/first-time-users/">first time users guide</a>.</p> <p><strong>Data Sources</strong></p> <ul> <li><a href="https://orthos.dhses.ny.gov/">https://orthos.dhses.ny.gov/</a></li> <li><a href="https://data.cityofnewyork.us/">https://data.cityofnewyork.us/</a></li> </ul> <p><strong>Maps</strong></p> <ul> <li>Orthophotographs from 2012, 2014, 2016, 2018, and 2020</li> <li>Digital elevation model from 2017</li> <li>Digital surface models from 2014 and 2017</li> <li>Landcover from 2014</li> </ul> <p><strong>License</strong><br> This dataset is licensed under the <a href="https://opendatacommons.org/licenses/pddl/index.htm">ODC Public Domain Dedication and License 1.0 (PDDL)</a> by Brendan Harmon.</p>
Governor's Island Dataset for GRASS GIS
<p><strong>Governor's Island Dataset for GRASS GIS</strong><br> This geospatial dataset contains raster and vector data for Governor's Island, New York City, USA. The top level directory <em>governors_island_for_grass</em> is a GRASS GIS location for <em>NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet </em>in US Surveyor's Feet with EPSG code 2263. Inside the location there is the <em>PERMANENT</em> mapset, a license file, data record, readme file, workspace, color table, category rules, and scripts for data processing. This dataset was created for the course <a href="http://baharmon.github.io/gis-for-designers">GIS for Designers</a>.</p> <p><strong>Instructions</strong><br> Install <a href="https://grass.osgeo.org/">GRASS GIS</a>, unzip this archive, and move the location into your <a href="https://grass.osgeo.org/grass78/manuals/grass_database.html">GRASS GIS database</a><br> directory. If you are new to GRASS GIS read the <a href="https://grass.osgeo.org/documentation/first-time-users/">first time users guide</a>.</p> <p><strong>Data Sources</strong></p> <ul> <li><a href="https://orthos.dhses.ny.gov/">https://orthos.dhses.ny.gov/</a></li> <li><a href="https://data.cityofnewyork.us/">https://data.cityofnewyork.us/</a></li> </ul> <p><strong>Maps</strong></p> <ul> <li>Orthophotographs from 2012, 2014, 2016, 2018, and 2020</li> <li>Digital elevation model from 2017</li> <li>Digital surface models from 2014 and 2017</li> <li>Landcover from 2014</li> </ul> <p><strong>License</strong><br> This dataset is licensed under the <a href="https://opendatacommons.org/licenses/pddl/index.htm">ODC Public Domain Dedication and License 1.0 (PDDL)</a> by Brendan Harmon.</p>
Mapping forests with different levels of naturalness using machine learning and landscape data mining - GRASS GIS DB
<p>The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled:</p> <p><strong>"Mapping forests with different levels of naturalness using machine learning and landscape data mining"</strong> (under review)</p> <p>Abstract:</p> <p><em>To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.</em></p> <p>This database was compiled from the following sources:</p> <p>1. <strong>HCVF</strong>. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency.</p> <p>source: <a href="https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip">https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip</a></p> <p>2. <strong>NMD</strong>. National Land Cover Data. Swedish Environmental Protection Agency.</p> <p>source: <a href="https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/">https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/</a></p> <p>3. <strong>DEM</strong>. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance.</p> <p>source: <a href="https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/">https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/</a></p> <p>4. <strong>GFC</strong>. Global Forest Change. Global Land Analysis and Discovery, University of Maryland.</p> <p>source: <a href="https://glad.earthengine.app">https://glad.earthengine.app</a></p> <p>5. <strong>LIGHTS</strong>. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN).</p> <p>source: <a href="https://doi.org/10.6084/m9.figshare.9828827.v2">https://doi.org/10.6084/m9.figshare.9828827.v2</a></p> <p>6. <strong>POPULATION</strong>. Total Population in Sweden. Statistics Sweden.</p> <p>source: <a href="https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/">https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/</a></p> <p> </p> <p>To learn more about the GRASS GIS database structure, see:</p> <p><a href="https://grass.osgeo.org/grass82/manuals/grass_database.html">https://grass.osgeo.org/grass82/manuals/grass_database.html</a></p>
GIS Shapefile - Riparian vegetation (forest and grass only) within a 100ft buffer of all 1:24K streams in Baltimore City.
Riparian vegetation (forest and grass only) within a 100ft buffer of all 1:24K streams in Baltimore City. Vegetation data used in this analysis came from the MD DNR Forest Service IKONOS-derived Strategic Urban Forest Assessment (SUFA) vegetation layer.
GIS Shapefile - Riparian vegetation (forest and grass only) within a 100ft buffer of all 1:24K streams in Baltimore City, block group
Block group summary of riparian vegetation (forest and grass only) within a 100ft buffer of all 1:24K streams in Baltimore City for only those block groups that intersect the stream buffers. The riparian area consists of all land within a 100 ft buffer of 1:24K streams. Vegetation data used in this analysis came from the MD DNR Forest Service IKONOS-derived Strategic Urban Forest Assessment (SUFA) vegetation layer.
GIS Shapefile - Urparian (non-parcel) vegetation (forest and grass) in Baltimore City
Urparian (non-parcel) vegetation (forest and grass) in Baltimore City. The urparian area consists of all roads and rights of way along roads. This area was delineated using Baltimore City parcel data by identifying all "non-parcel" areas. A cursory analysis of the "non-parcel" areas indicated that errors of omission were present due to insufficient parcel data. Vegetation data used in this analysis came from the MD DNR Forest Service IKONOS derived Strategic Urban Forest Assessment (SUFA) vegetation layer.
GIS Shapefile - Urparian (non-parcel) vegetation (forest and grass) in Baltimore City, block group
Block group summary of urparian (non-parcel) vegetation (forest and grass). The urparian area consists of all roads and rights of way along roads. This area was delineated using Baltimore City parcel data by identifying all "non-parcel" areas. A cursory analysis of the "non-parcel" areas indicated that errors of omission were present due to insufficient parcel data. Vegetation data used in this analysis came from the MD DNR Forest Service IKONOS derived Strategic Urban Forest Assessment (SUFA) vegetation layer.
Raster files catalog to forest growth simulation performed by r.recovery module (Grass Gis).
<p>This repository contains all publicly available raster files to calibrate/validate the parameters of the Diffusive-logistic growth (DLG) model and generate prognostics by means of the GRASS-GIS module r.recovery (Richit et al., 2019).</p> <p>Three .tiff files are needed for perform calibration/validation: two are EVI raster maps (two time-lapsed conditions of forest density) and a soil use file. In the repository the example files are:</p> <p>Calibration_EVI_2000;</p> <p>Calibration_EVI_2011 and</p> <p>Calibration_soil_use_2000, respectively.</p> <p>The others files in the repository are four EVI .tiff files and their respectively soil use .tiff files that were used to perform simulations by the means of calibrated parameters of the DGL model. The files are:</p> <p>AMNP_EVI_2016 and AMNP_soil_use_2016;</p> <p>FPSP_EVI_2016 and FPSP_soil_use_2016;</p> <p>MDRB_EVI_2016 and MDRB_soil_use_2016;</p> <p>MNPTS_EVI_2016 and MNPTS_soil_use_2016.</p> <p>For more details on r.recovery module please check </p> <p>Richit, L.A., Bonatto, C., da Silva, R.V., Grzybowski, J.M.V., 2019. Prognostics of forest recovery with r.recovery grass-gis module: an open source forest growth simulation model based on the diffusive-logistic equation. Environmental modelling & software 111, 108–120.</p>
Hilltop Arboretum Landform Dataset for GRASS GIS
<p><strong>Hilltop Arboretum Dataset for GRASS GIS</strong></p> <p>This geospatial dataset contains raster data for the landform at Hilltop Arboretum, Baton Rouge, Louisiana, USA. This data was collected in an aerial survey with a DJI Phantom 4 Pro drone over Hilltop Arboretum on 12/31/2019 by Brendan Harmon and Josef Horacek. The aerial photographs were processed in Agisoft Metashape using Structure from Motion (SfM) to generate a point cloud, orthophotograph, and digital surface model. The point cloud was processed in CloudCompare to generate a bare earth point cloud. The orthophoto, digital surface model, and bare earth point cloud were imported into GRASS GIS. The bare earth point cloud was interpolated as a digital elevation model using the Regularized Spline with Tension method. The top level directory <em>lousiana_s_spm_hilltop</em> is a GRASS GIS location for the North American Datum of 1983 (NAD 83) / Louisiana South State Plane Meters with <a href="https://epsg.io/26982">EPSG code 26982</a>. Inside the location there are the PERMANENT mapset, a license file, and readme file.</p> <p><strong>Survey</strong></p> <ul> <li>Location: LSU Hilltop Arboretum, Baton Rouge, Louisiana, USA.</li> <li>Drone: DJI Phantom 4 Pro</li> <li>Software: Agisoft Metashape, CloudCompare, GRASS GIS</li> <li>Team: Brendan Harmon and Josef Horacek</li> <li>Date: 12/31/2019</li> <li>GCPs: 10 AeroPoints</li> </ul> <p><strong>Instructions</strong><br> Install <a href="https://grass.osgeo.org/">GRASS GIS</a>, unzip this archive, and move the location into your <a href="https://grass.osgeo.org/grass77/manuals/grass_database.html">GRASS GIS database</a> directory. If you are new to GRASS GIS read the <a href="https://grass.osgeo.org/documentation/first-time-users/">first time users guide</a>.</p> <p><strong>License</strong><br> This dataset is licensed under the <a href="https://opendatacommons.org/licenses/pddl/index.html">ODC Public Domain Dedication and License 1.0 (PDDL)</a> by Brendan Harmon.</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.