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138 results for “Geospatial”
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>
Figure1 from: Bachman S, Moat J, Hill A, de la Torre J, Scott B (2011) Supporting Red List threat assessments with GeoCAT: geospatial conservation assessment tool. ZooKeys 150: 117-126. https://doi.org/10.3897/zookeys.150.2109
Figure1 - GeoCAT workflow; Start a new project and add data to the map via the three options. Existing data may be derived from an output of an existing database or from an online source such as GBIF, Flickr or Scratchpads. Alternatively, click directly on the map to create markers to signify the occurrence of the taxon you wish to assess.The intuitive mapping interface allows interaction with the data to delete, move or hide points from analysis. The metadata window exposes the attributes of the occurrences e.g. date of collection, collector, location and provides a direct link to the raw data.<br> After editing the data the analysis can be enabled and the results are displayed as grpahics on the map and through a report window. The EOO/AOO values, preliminary IUCN categories and parameters are shown. AOO cell size can be adjusted.Statistics generated from the analysis and a basic map can be downloaded as a report. Occurrence data used in the analysis can be downloaded as a kml file for integration with Google Earth or as a CSV file. In addition, a single geocat. file encompassing all analysis results, parameters, map settings and occurrence data can be saved for later use, or to pass to collaborators for additional work.
Figure 2 from: Bachman S, Moat J, Hill A, de la Torre J, Scott B (2011) Supporting Red List threat assessments with GeoCAT: geospatial conservation assessment tool. ZooKeys 150: 117-126. https://doi.org/10.3897/zookeys.150.2109
Figure 2 - Illustration of a convex hull of a set of points. Imagine stretching a rubber band so that all points are inside it, then releasing it; when it becomes tight, the area enclosed is the convex hull.
Geospatial Interlinking Real Datasets
<p>This is a collection of 8 large-scale, real-world datasets for Geospatial Interlinking.</p>
Geospatial Dataset Estimating Corn and Soy Inputs for Biofuel Production
<p>This dataset provides estimates for the corn and soybean biomass used for biofuel production (corn ethanol and soybean biodiesel, respectively) based on the geographic locations of biofuel plants provided by US Energy Information Administration for the 2017 USDA crop census data. County-level biomass was allocated to each biofuel plant based on distance to the plant assuming all biomass is located at each county’s centroid. If two plants were competing for the same county’s biomass, priority was given to the biofuel plant with the highest production capacity. County-level biomass estimation was validated using reported state-level biomass to biofuel values. Reported values could only be found for 11 states, but the mean difference between reported and estimated biomass used for biofuel production was 1.4% for corn ethanol. </p>
MapMySmoke: Smoking Cessation App With Geospatial Capture
ClinicalTrials.gov study NCT02932917. IPD Sharing: NO. Countries: 0. Publications: 1.
Data from: The role of geospatial hotspots in the spatial spread of tuberculosis in rural Ethiopia: a mathematical modelling
Open the record for dataset details and reuse information.
Data for: New Guinean orogenic dynamics and biota evolution revealed using a custom geospatial analysis pipeline
Open the record for dataset details and reuse information.
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>
Figure 1 from: Della Chiesa S, Vianello A, Tritini S, Piva A (2021) AlpConv Atlas: The Geospatial Content Management System of the Alpine Convention. Research Ideas and Outcomes 7: e66106. https://doi.org/10.3897/rio.7.e66106
Figure 1 Homepage of the Alpine Convention Atlas.
Descriptive Statistics and Town level Geospatial Distribution of Archaeological Settlements of Turkey in Iron Age (1200 –330 BCE)
<p><strong>Context</strong></p> <p>This dataset is a byproduct of my phd thesis. It combines the Archaeological Settlements of Turkey (TAY) Project data with geo spatial data obtained from openstreetmaps.</p> <p><strong>Content</strong></p> <p>For each archaeological settlement, the data contains:</p> <ul> <li>active dates:</li> <li>geo spatial data which points to the town containing the settlement.</li> <li>information with respect to site type and its research status/methodology.<br> These are all contained in the file <code>taydata.json</code>.</li> </ul> <p>The associated <a href="https://www.kaggle.com/dkaane/data-extraction-protocol-for-iaasot">notebook</a> to this dataset gives how each file is produced.</p> <p>We give several important statistics with respect to regions, and cities of Turkey for the Iron Age.</p> <p>If you want to visualize the data on a map. You can use the <code>1200___330_bce_sites_of_turkey.umap</code> file.<br> Just download the file and visualize it on <a href="https://umap.openstreetmap.fr/en/">umap</a> or on <a href="https://www.kaggle.com/dkaane/framacarte.org/">framacarte</a></p> <p><strong>Acknowledgements</strong></p> <p>Without the immense effort of TAY Project and its researchers, this dataset would not be possible.</p>
Geospatial Analysis of Neighborhood Environmental Stress in Relation to Biological Markers of Cardiovascular Health and Health Behaviors in Women
ClinicalTrials.gov study NCT04014348. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Don't Go There: A Geospatial mHealth App for Gambling Disorder
ClinicalTrials.gov study NCT04158037. IPD Sharing: NO. Countries: 1. Publications: 0.
Delivering Geospatial Intelligence to Health Care Providers
ClinicalTrials.gov study NCT01567228. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.
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.