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62 results for “geospatial data”
Quantification of spatio-temporal variation of aquaculture area in Satkhira, Bangladesh: Using Geospatial and social survey data
<p>This data shows the NDWI and MNDWI processed data of satkhira</p>
Figure 4 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Figure 4 Initial expertise (color of the bar) vs final confidence (y-axis) after the GRU workshop for participants responding to final survey. Example for how to interpret this graphic: the blue color bar at the top indicates that before the workshop roughly 50% of respondents said their knowledge of GEOLocate was "neither high nor low" but after the workshop these same respondents selected "much higher" for their knowledge of GEOLocate.
Figure 3 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Figure 3 An illustrative example of the two methods of uncertainty capture when georeferencing specimens. Method A, or polygon, creates a shape around the river (in blue). Method B, or point-radius, creates a circle of uncertainty around the origin. The illustration is based on output from GeoLocate software (Rios 2018) for both polygon and point-radius.
Figure 2 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Figure 2 This specimen record is an example from the University of California Collection Network Symbiota Portal. The large image is an edit of the record to include a medium size version of the image for easier viewing in this article. The portal software is open source and it is freely available for reuse through the Symbiota GitHub repository. The image is an example of a specimen record that includes an image of the specimen with label data. The image is contributed by the UCSB Invertebrate Zoology Collection at the Cheadle Center for Biodiversity and Ecological Restoration. The usage rights for the image is Creative Commons 0 (public domain).
Figure 1 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Figure 1 Map created using SimpleMappr (Shorthouse 2010) that illustrates geolocated specimens for Genus=Cicindela in California as found on iDigBio.
Merged HLS2 and GEDI data for estimating canopy height with IBM's granite-geospatial-canopyheight model
<p>This dataset contains merged Harmonized Landsat-Sentinel 2 (HLS2) (L30 only) and Global Ecosystem Dynamics Investigation (GEDI) L2A data following CRS:4326. It has been assembled for estimating canopy height with a fine-tuned granite geospatial foundation model developed by IBM Research. Please see https://huggingface.co/ibm-granite/granite-geospatial-canopyheight for more information on data preparation and model use.</p> <p><strong>HLS2—</strong>Masek, J., J. Ju, J. Roger, S. Skakun, E. Vermote, M. Claverie, J. Dungan, Z. Yin, B. Freitag, C. Justice. HLS Sentinel-2 MSI Surface Reflectance Daily Global 30m v2.0. 2021, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/HLS/HLSS30.002 </p> <p><strong>GEDI L2A—</strong>Lee, J., S. Favrichon, S. Mauceri, Y. Yang, J. Armston, and S. Saatchi. 2023. Addressing underestimation in global forest structure mapping. <a href="https://doi.org/10.22541/essoar.167276451.10705079/v1">https://doi.org/10.22541/essoar.167276451.10705079/v1</a></p>
Data from: The role of geospatial hotspots in the spatial spread of tuberculosis in rural Ethiopia: a mathematical modelling
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Data for: New Guinean orogenic dynamics and biota evolution revealed using a custom geospatial analysis pipeline
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MASTER: Flight Line Geospatial Polygons and Contextual Data
This dataset provides resources for identifying flight lines of interest for the MODIS/ASTER Airborne Simulator (MASTER) instrument based on spatial and temporal criteria. MASTER first flew in 1998 and has ongoing deployments as a Facility Instrument in the NASA Airborne Science Program (ASP). MASTER is a joint project involving the Airborne Sensor Facility (ASF) at the Ames Research Center, the Jet Propulsion Laboratory (JPL), and the Earth Resources Observation and Science Center (EROS). The primary goal of these airborne campaigns is to demonstrate important science and applications research that is uniquely enabled by the full suite of MASTER thermal infrared bands as well as the contiguous spectroscopic measurements of the AVIRIS (also flown in similar campaigns), or combinations of measurements from both instruments. This dataset includes a table of flight lines with dates, bounding coordinates, site names, investigators involved, flight attributes, and associated campaigns for the MASTER Facility Instrument Collection. A shapefile containing flights for all years, a GeoJSON version of the shapefile, and separate KMZ files for all years allow users to visualize flight line locations using GIS software.
AVIRIS Facility Instruments: Flight Line Geospatial and Contextual Data
This dataset provides attributed geospatial and tabular information for identifying and querying flight lines of interest for the Airborne Visible InfraRed Imaging Spectrometer-Classic (AVIRIS-C) and Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) Facility Instrument collections. It includes attributed shapefile and GeoJSON files containing polygon representation of individual flights lines for all years and separate KMZ files for each year. These files allow users to visualize and query flight line locations using Geographic Information System (GIS) software. Tables of AVIRIS-C and AVIRIS-NG flight lines with attributed information include dates, bounding coordinates, site names, investigators involved, flight attributes, associated campaigns, and corresponding file names for associated L1B (radiance) and L2 (reflectance) files in the AVIRIS-C and AVIRIS-NG Facility Instrument Collections. Tabular information is also provided in comma-separated values (CSV) format.
Merged HLS2 and GEDI data for estimating above ground biomass with IBM's granite-geospatial-biomass model
<p>This dataset contains merged Harmonized Landsat-Sentinel 2 (HLS2) (L30 only) and Global Ecosystem Dynamics Investigation (GEDI) L4A data following CRS:4326. It has been assembled for estimating above ground biomass with a fine-tuned granite geospatial foundation model developed by IBM Research. Please see https://huggingface.co/ibm-granite/granite-geospatial-biomass for more information on data preparation and model use.</p> <p><strong>HLS2—</strong>Masek, J., J. Ju, J. Roger, S. Skakun, E. Vermote, M. Claverie, J. Dungan, Z. Yin, B. Freitag, C. Justice. HLS Sentinel-2 MSI Surface Reflectance Daily Global 30m v2.0. 2021, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/HLS/HLSS30.002 </p> <p><strong>GEDI L4A—</strong>Dubayah, R.O., J. Armston, J.R. Kellner, L. Duncanson, S.P. Healey, P.L. Patterson, S. Hancock, H. Tang, M.A. Hofton, J.B. Blair, and S.B. Luthcke. 2021. GEDI L4A Footprint Level Aboveground Biomass Density, Version 1. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1907</p>
Global estimates of reach-level bankfull river width leveraging big-data geospatial analysis
<p><strong>1. Summary</strong></p> <p>Global estimates of reach-level bankfull river width generated in the article by Peirong Lin, Ming Pan, George H. Allen, Renato Frasson, Zhenzhong Zeng, Dai Yamazaki, Eric F. Wood entitled "Global reach-level bankfull river width leveraging big-data geospatial analysis", <em>Geophysical Research Letters (accepted)</em>.</p> <p> </p> <p><strong>2. File Description</strong></p> <p>Shapefile storing machine learning-derived bankfull river width, and environmental covariates used to predict the width (~1.4GB). The polylines were vectorized by Lin <em>et al.</em> (2019) based on the Multi-Error Removed Improved-Terrain (MERIT) DEM and MERIT Hydro (Yamazaki <em>et al.</em>, 2017, 2019), under a channelization threshold of 25 km<sup>2</sup>. Only rivers wider than 30 m are shown here; these locations were determined by jointly using the Global River Widths from Landsat (GRWL) database (Allen & Pavelsky, 2018) and the MERIT Hydro width estimates (Yamazaki <em>et al.</em>, 2019).</p> <p> </p> <p><strong>3. Attribute Description</strong></p> <ul> <li><strong>COMID</strong>: identification number of the river reach, same as that used in global river modeling by Lin <em>et al.</em>, (2019);</li> <li><strong>Order</strong>: Strahler-Horton stream order, with stream order 1 starting from those with an upstream drainage area of 25 km<sup>2</sup>;</li> <li><strong>Area</strong>: Upstream drainage basin area in km<sup>2</sup>;</li> <li><strong>Sin</strong>: Sinuosity of the river segment (unitless);</li> <li><strong>Slp</strong>: mean slope of the river segment (unitless);</li> <li><strong>Elev</strong>: mean elevation of the river segment;</li> <li><strong>K</strong>: mean bedrock permeability of the unit catchment surrounding the river segment, with data extracted from Huscroft <em>et al. </em>(2018);</li> <li><strong>P</strong>: mean bedrock porosity of the unit catchment surrounding the river segment, with data extracted from Huscroft <em>et al. </em>(2018);</li> <li><strong>AI</strong>: mean aridity index of the unit catchment; data extracted from Trabucco & Zomer (2019);</li> <li><strong>LAI</strong>: mean leaf area index of the unit catchment; data extracted from Zhu <em>et al. </em>(2013);</li> <li><strong>SND</strong>: mean sand content (mass percentage, %) of the unit catchment; data extracted from Hengl <em>et al.</em> (2017);</li> <li><strong>CLY</strong>: mean clay content (mass percentage, %) of the unit catchment; data extracted from Hengl <em>et al.</em> (2017);</li> <li><strong>SLT</strong>: mean silt content (mass percentage, %) of the unit catchment; data extracted from Hengl <em>et al.</em> (2017);</li> <li><strong>Urb</strong>: mean urban fraction of the unit catchment; data extracted from Liu <em>et al.</em> (2018);</li> <li><strong>WTD</strong>: mean water table depth (m below surface) of the unit catchment; data extracted from Fan <em>et al.</em> (2013);</li> <li><strong>HW</strong>: mean human water use (irrigational + industrial + domestic) of the unit catchment; data extracted from Wada <em>et al.</em> (2016)</li> <li><strong>DOR</strong>: degree of dam regulation for the river segment; the definition of DOR and data were sourced from Grill <em>et al.</em> (2019)</li> <li><strong>QMEAN</strong>: mean annual discharge (m<sup>3</sup>/s) for the river segment; the multi-year averaged were calculated from Lin <em>et al.</em> (2019);</li> <li><strong>Q2</strong>: 2-year return period flood discharge (m<sup>3</sup>/s) for the river segment; the 35-year data used to calculate the field was sourced from Lin <em>et al.</em> (2019);</li> <li><strong>Width_m</strong>: bankfull river width (m) estimated by using the optimized machine learning model of this study, applied to Q2 and other environmental covariates;</li> <li><strong>Width_DHG</strong>: bankfull river width (m) estimated by using the Moody & Troutman (2002) equation applied to Q2 estimated in this study</li> </ul> <p> </p> <p><strong>4. References</strong></p> <p>Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. <em>Science</em>, <em>361</em>(6402), 585–588. https://doi.org/10.1126/science.aat0636</p> <p>Fan, Y., Li, H., & Miguez-Macho, G. (2013). Global Patterns of Groundwater Table Depth. <em>Science</em>, <em>339</em>(6122), 940–943. https://doi.org/10.1126/science.1229881</p> <p>Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F., et al. (2019). Mapping the world’s free-flowing rivers. <em>Nature</em>, <em>569</em>(7755), 215. https://doi.org/10.1038/s41586-019-1111-9</p> <p>Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., et al. (2017). SoilGrids250m: Global gridded soil information based on machine learning. <em>PLOS ONE</em>, <em>12</em>(2), e0169748. https://doi.org/10.1371/journal.pone.0169748</p> <p>Huscroft, J., Gleeson, T., Hartmann, J., & Börker, J. (2018). Compiling and Mapping Global Permeability of the Unconsolidated and Consolidated Earth: GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0). <em>Geophysical Research Letters</em>, <em>45</em>(4), 1897–1904. https://doi.org/10.1002/2017GL075860</p> <p>Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., et al. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. <em>Water Resources Research</em>, <em>0</em>(0). https://doi.org/10.1029/2019WR025287</p> <p>Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., et al. (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. <em>Remote Sensing of Environment</em>, <em>209</em>, 227–239. https://doi.org/10.1016/j.rse.2018.02.055</p> <p>Trabucco, A., & Zomer, R. (2019, January 18). Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2. https://doi.org/10.6084/m9.figshare.7504448.v3</p> <p>Wada, Y., Graaf, I. E. M. de, & Beek, L. P. H. van. (2016). High-resolution modeling of human and climate impacts on global water resources. <em>Journal of Advances in Modeling Earth Systems</em>, <em>8</em>(2), 735–763. https://doi.org/10.1002/2015MS000618</p> <p>Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J. C., et al. (2017). A high-accuracy map of global terrain elevations. <em>Geophysical Research Letters</em>, <em>44</em>(11), 5844–5853. https://doi.org/10.1002/2017GL072874</p> <p>Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., & Pavelsky, T. M. (2019). MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset. <em>Water Resources Research</em>. https://doi.org/10.1029/2019WR024873</p> <p>Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., et al. (2013). Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011. <em>Remote Sensing</em>, <em>5</em>(2), 927–948. https://doi.org/10.3390/rs5020927</p> <p> </p>
India Village-Level Geospatial Socio-Economic Data Set: 1991, 2001
The India Village-Level Geospatial Socio-Economic Data Set: 1991, 2001 is a compilation of the finest level of administrative boundaries in India (village/town-level) and over 200 socio-economic variables collected during the Indian Census in 1991 and 2001. This data set was developed by digitizing village/town level boundaries from the official analog maps published by the Survey of India for 2001. This data set also utilized tabular data for 1991 and 2001 from the Primary Census Abstract (PCA) and Village Directory (VD) data series of the Indian census. The data are in UTM 44N projection and are distributed primarily as shapefiles. Separate files are provided for each of the 28 states (number of states during 1991 and 2001 census) and combined Union Territories for 1991 and 2001.
National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 3 (PLACE III)
The National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 3 (PLACE III) data set contains estimates of national-level aggregations in urban, rural, and total designations of territorial extent and population size by biome, climate zone, coastal proximity zone, elevation zone, and population density zone, for 232 statistical areas (countries and other UN recognized territories). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
GIBS Geospatial Data Abstraction Library (GDAL)
GDAL is an open source translator library for raster geospatial data formats that presents a single abstract data model to the calling application for all supported formats. By providing integration into the GDAL command line utilities, GIBS imagery can be easily included in imagery processing workflows, including bulk access.
National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 4 (PLACE IV)
The National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 4 (PLACE IV) provides measures of population (head counts) and land area (square kilometers) as totals and by urban and rural designation, within multiple biophysical themes for 248 statistical areas (countries and other territories recognized by the United Nations (UN)), UN geographic regions and subregions, and World Bank economic classifications. It improves upon previous versions by providing these estimates at both the national level, and where possible, at subnational administrative level 1 for the years 2000, 2005, 2010, 2015, and 2020, and by 5-year and broad age groups for the year 2010.
National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 2 (PLACE II)
The National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 2 (PLACE II) data set contains estimates of national-level aggregations of territorial extent and population size by biome, climate zone, coastal proximity zone, elevation zone, and population density zone, a compendium of nearly 300 variables for 228 countries. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates (PLACE)
The National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates (PLACE) data set contains estimates of national-level aggregations of territorial extent and population size by biome, climate zone, coastal proximity, elevation and slope, a compendium of nearly 300 variables for 222 countries. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
High-Resolution Mapping of Building Material Stocks in Major Urban Agglomerations in China Based on Multiple Geospatial Data
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Syntactic Geospatial data generated in RDF format
<p>This dataset represents synthetic generated data from CALLISTO data in RDF form. It contains the equivalent of 2 billion triples in TTL format.</p> <p>Each entity contains:</p> <ul> <li>Crop category: "Grasland" and "Bouwland"</li> <li>Geo information: as Multipolygon in Well Known Text (WKT) format</li> <li>Geometry area</li> <li>Geometry length</li> <li>Object id</li> <li>Parcel</li> <li>Rdf:type owl:NamedIndividual</li> </ul> <p>Send an email to: nagpal@infai.org to have access to the dataset if you need to test GeoSparql query engine on big data.</p>
ScienceDex guides
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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.