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26 results for “grid mapping”
Magnetic Anomaly Map of Paraná State - Final gridded data
<p>This gridded data is part of the article entitled: "THE MAGNETIC ANOMALY MAP OF PARANÁ STATE: AN<br>INTEGRATION OF AIRBORNE SURVEYS PERFORMED OVER THE YEARS", which was submitted in December 2023 to the Brazilian Journal of Geophysics. The article is still under review. </p> <p>These files include airborne magnetic data integrated at 1800m altitude, and the upwarded data to 2700m. Details of the integration and general interpretations are described in the related article.</p>
Stochastic Occupancy Grid Map Prediction in Dynamic Scenes: Dataset
<p>Three occupancy grid map (OGM) datasets for the paper titled "Stochastic Occupancy Grid Map Prediction in Dynamic Scenes" by Zhanteng Xie and Philip Dames</p> <p>1. OGM-Turtlebot2: collected by a simulated Turtlebot2 with a maximum speed of 0.8 m/s navigates around a lobby Gazebo environment with 34 moving pedestrians using random start points and goal points</p> <p>2. OGM-Jackal: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Jackal robot with a maximum speed of 2.0 m/s at the outdoor environment of the UT Austin</p> <p>3. OGM-Spot: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Spot robot with a maximum speed of 1.6 m/s at the Union Building of the UT Austin</p> <p>The relevant code is available at: <br> OGM prediction: https://github.com/TempleRAIL/SOGMP<br> OGM mapping with GPU: https://github.com/TempleRAIL/occupancy_grid_mapping_torch</p>
Dutch grid map layers for biodiversity surveys
<p>This dataset contains map layers in GeoPackage format with the following grids used in biodiversity surveys in the Netherlands:</p> <table> <tbody> <tr> <td><strong>Layer name</strong></td> <td><strong>Size (m)</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>IvonGrid Kwartierhok</td> <td>1250x1042 </td> <td>Used between 1902 and 1950 by IVON botanical surveys.</td> </tr> <tr> <td>IvonGrid Uurhok</td> <td>5000x4168 </td> <td>Used between 1902 and 1950 by IVON botanical surveys. Each square contains 16 'kwartierhok' squares with corresponding first four characters in column name.</td> </tr> <tr> <td>RdGrid Kilometerhok</td> <td>1000</td> <td>Used since 1950 in biodiversity surveys. </td> </tr> <tr> <td>RdGrid Uurhok</td> <td>5000</td> <td>Used since 1950 in biodiversity surveys. Each square contains 25 kilometer squares.</td> </tr> </tbody> </table> <p> </p> <p>The RdGrid maps are available in the WGS84 and RD New (EPSG:28992) spatial reference systems, as they are aligned to rounded RD coordinates. IvonGrid is only available in WGS84.</p> <p><strong>Grid square codes</strong></p> <p>Kilometerhok (1x1 km) and Uurhok (5x5) grids have corresponding grid square codes. A code consists of three concatenated numbers for map sheat (1-62), 5 km square (11-58) and 1 km square (11-55). E.g. a 5 km square code looks like 4017. In literature and on collection labels this often written as 40.17 or 40-17.</p> <p><strong>Maintanance</strong></p> <p>This dataset is being curated by FLORON Plant Conservation Netherlands since 1988 for use in botanical surveys (previously by Theo Peterbroers, Bart Vreeken and Ruud Beringen).</p> <p><strong>History</strong></p> <p>IVON stands for "Instituut voor Vegetatie-onderzoek Nederland", an NGO founded in 1930 was most active until the 1950's and then succeeded by FLORON in 1988. More information about the repartition of the IVON grid can be found in Atlas van de Nederlandse Flora part 1 (Mennema et al. 1989). The original map was hand drawn on topographical maps by J.W.C. Goethart en W. J. Jongmans (Van Ooststroom 1956) originally following the Bessel 1841 projection (EPSG:7004). The grid cells are equal in size, although not rounded to kilometers. The size was probably chosen to fit maps that were commercially available at that time.</p> <p> </p> <p><strong>References</strong></p> <p>Mennema, J., Quene-Boterenbrood, Plate, C. L., Van der Meijden, R., & Weeda, E. J. (1989). Atlas van de Nederlandse flora. Kosmos, Amsterdam.</p> <p>van Ooststroom, S.J. (1956). Het I.V.O.N. – Werk. Correspondentieblad ten dienste van de floristiek en het vegetatie-onderzoek van Nederland 1: 2–3.</p>
Estimated Annual Agricultural Pesticide Use for USA48 2000 to 2019 (gridded maps at 250-m resolution)
<p>Estimated low and high pescticide uses based on census data for USA48, provided for the cropland mask at 250-m spatial resolution. The original <a href="https://water.usgs.gov/nawqa/pnsp/usage/maps/">USGS maps</a> are only available as PNGs, so we have prepared here a code to rasterize the values and produce GeoTIFFs. For cropland mask we use the <a href="https://www.usgs.gov/centers/eros/science/national-land-cover-database">National Land Cover Database (NLCD)</a>. All processing is fully documented in: <a href="https://github.com/Envirometrix/pesticide-use-USA48">https://github.com/Envirometrix/pesticide-use-USA48</a>. All maps are projected in the <a href="https://epsg.io/5070-1252">EPSG:5070</a>. Description of the layers:</p> <ul> <li><code>croplands_20**_250m.tif</code> = cropland mask (0–100%) estimated based on NLCD.</li> <li><code>GLYPHOSATE_EPEST.HIGH.KG.KM2_20**_250m.tif</code> = estimated pesticide use per county (high estimate) expressed in kg/km-square.</li> <li><code>diff.GLYPHOSATE_EPEST.HIGH.KG.KM2_250m.tif</code> = annual difference in pesticide use from 2000 to 2019;</li> </ul> <p><strong>Disclaimer</strong>: this code is under construction and USGS makes is clearly available that there are some limitations to this data:</p> <ul> <li>These estimates are made by using projected county crop acres from the previous Census of Agriculture and are expected to be revised upon availability of updated crop acreages in the following Census of Agriculture.</li> <li>The files do not include pesticide use estimates for California. Data for California are obtained from the online Department of Pesticide Regulation-Pesticide Use Reporting (DPR-PUR) database and are typically not available at the time the preliminary pesticide use estimates are generated for the rest of the U.S.</li> </ul> <p>Pease have in mind these limitations when using these maps for further modeling.</p>
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).
Global gridded maps of yield potential of the Global Yield Gap Atlas (GYGA)
<p>A complete description of maps' methods, accuracy, strengths, and limitations is available in <a href="https://doi.org/10.1038/s43016-024-01029-3">Aramburu-Merlos et al. (Nat. Food, 2024)</a>. </p> <p>Briefly, we combined site-specific yield potential estimates of the <a href="https://www.yieldgap.org/">Global Yield Gap Atlas</a> with gridded environmental predictors in a machine-learning metamodel to generate global maps of yield potential at a 30-arc-second resolution for maize, wheat, and rice, separately for irrigated and rainfed conditions. Model predictions were restricted to their <a href="https://doi.org/10.1111/2041-210X.13650">area of applicability</a> and lands harvested with the given crop and water regime condition (harvested area > 0.5% according to <a href="https://doi.org/10.7910/DVN/PRFF8V">SPAM v2.0</a>).</p> <p> </p>
The complete set of neighborhood motions maps induced on a hexagonal grid
<p>The complete set of neighborhood motions maps induced on a hexagonal grid. This is a supplementary material for: Pluta K., Romon P., Kenmochi Y., Passat N.: Honeycomb geometry: Rigid Motions on the Hexagonal Grid, Accepted for DGCI17.</p>
Africa map showing population and grid infrastructure.
<p>Used for Wikipedia. Produced using <a href="https://github.com/pypsa-meets-earth/pypsa-earth">PyPSA-Earth</a> and the plotting scripts given in <a href="https://github.com/pypsa-meets-earth/documentation">https://github.com/pypsa-meets-earth/documentation</a>/ Credits to the PyPSA-Earth authors (previously PyPSA-Africa authors).</p> <p> </p>
Dakar population estimates at 100x100m spatial resolution - grid layer - Dasymetric mapping
<p>This dataset contains the a raster layer with the population estimates obtained using a dasymetric mapping procedure (top-down approach). For a detailed description of the methodology, please refer to the following paper:</p> <p>Grippa, Taïs, Catherine Linard, Moritz Lennert, Stefanos Georganos, Nicholus Mboga, Sabine Vanhuysse, Assane Gadiaga, and Eléonore Wolff. 2019. “Improving Urban Population Distribution Models with Very-High Resolution Satellite Information.” <em>Data</em> 4 (1): 13. <a href="https://doi.org/10.3390/data4010013">https://doi.org/10.3390/data4010013</a>.</p> <p>Funding and aknowledgement: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be/">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be/">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p> <p>The authors gratefully thanks the \href{http://assess-sn.org/}{ASSESS project}, funded by the \href{https://www.ares-ac.be}{ARES-CDD}, that provided the access to the census data.</p>
Open dataset for the research of "Assessing accuracy improvement of integrating digital footprints into gridded population mapping: spatiotemporal variations and data bias"
<p>Result datasets for "Assessing accuracy improvement of integrating digital footprints into gridded populationmapping:spatiotemporal variations and data bias":</p> <ol> <li> S1 is the results for gridded population mapping using different methods.</li> <li> S2 is the aggregate results of population mapping at county level.</li> <li> S3 is the results for intraday variation of population disaggregation accuracy,</li> <li> S4 is the data bias of different digital footprints.</li> </ol>
5 km square grid used in vascular plant mapping in Iceland
<p>This dataset contains 5 km square grid used in mapping the distribution of vascular plants in Iceland. The grid covers all land area of Iceland, including all smaller islands and archipelagoes close to mainland Iceland. The grid is available in two different formats: 1. ESRI shapefile ( UTF-8, Polygon [MultiPolygon], EPSG:3057 - ISN93 / Lambert 1993 - Projected) and 2. Keyhole Markup Language file (KML). </p>
FIGURE. Maps with overlapping quadrants showing the distribution of Drosera in Brazil. a, number of species per grid of 1 degree. b, number of species per grid of 0.1 degrees. in A synopsis of the genus Drosera (Droseraceae) in Brazil
FIGURE. Maps with overlapping quadrants showing the distribution of Drosera in Brazil. a, number of species per grid of 1 degree. b, number of species per grid of 0.1 degrees.
Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction
<p>This dataset contains the spatially-explicit gridded database based on social media data for over 150 different tourism-related classes that depicts tourism density (supply and demand) and perceived satisfaction in Europe, and the related exposure to selected climate extreme events. Information on tourism density (supply and demand) and perceived satisfaction are categorised for Attractions, Culinary, and Hospitality, while the exposure analysis of those clases are provided in separate, specific files. The provided dataset is made accessible to support large-scale and regional tourism research and extends its relevance to other fields that are part of tourism as a complex system, such as risk assessment and vulnerability studies. For citing this work, please refer to the research article "Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction", DOI 10.1088/1748-9326/ad3e91. Suggested citation: "Camatti, N., Hrast Essenfelder, A., & Giove, S. (2024). Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction. Environmental Research Letters."</p>
Gridded maps of global population scaled to match the 2023 Wittgenstein Center (WIC) Population projections
<p>The gridded population data used for calculating exposed populations is based on the population projections from the original SSPs (KC and Lutz, 2017) which were subsequently gridded (Jones and O’Neill, 2016). These gridded projections were aggregated to 0.5 ° spatial resolution and then scaled to match the latest available projections for population in line with the updated SSPs, v3.0 (KC <em>et al.</em>, 2024). The scaling is done on a country basis for all countries included in the latest SSP projections. Countries, which are not included in these projections, remain unchanged. The scaling process is done on a country-level basis using the following step:<br> </p> <ol> <li>The total population for the original gridded data is calculated using the ISIMIP fractional country raster (Perrette, 2023), excluding border cells containing contributions from more than one country.</li> <li>The population of the fractional border cells is subtracted from the total population of the SSP population projections and the required scalar to match the population from the gridded data to the SSP population projections is calculated.</li> <li>This scalar is applied to all non-fractional cells. </li> </ol> <p>While it is possible to calculate the scalar for each country including the proportion of the population in the fractional border cells, this would require the scalar to also be applied to that proportion of the population in the border cells to match the overall population number for each country. Applying different scalars to the population proportions for each country in the same cell would, however, change the ratios of the population in the fractional border cells and subsequently lead to skewed results when reapplying the fractional country raster to the scaled data for the aggregation to country level. For small countries, where more population lives in fractional border cells than in non-border cells, and for countries that only consist of border cells with contributions from more than one country, all cells were used in the scaling process. <br><br>It should be noted that some smaller countries cannot be scaled properly and that the latest SSP population projections do not contain values for all countries. Since there has been no release of updated gridded population projections yet, the gridded population data created using this approach still provide the closest match to the latest SSP population projections currently available.</p>
Validation of the Advisor HD Grid Mapping Catheter in Atrial Fibrillation Ablation
ClinicalTrials.gov study NCT04850508. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: Phenology of migrating game birds in Italy based on citizen science data: mean maps and grid maps for 23 species
Open the record for dataset details and reuse information.
Global Gridded Soil Phosphorus Distribution Maps at 0.5-degree Resolution
This data set provides estimates of different forms of naturally occurring soil phosphorus (P) including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P on a global scale at 0.5-degree resolution. The data were assembled from chronosequence information and global spatial databases to develop a map of total soil P and the distribution among mineral bound, labile, organic, occluded, and secondary P forms in soils. Uncertainty was calculated for the different forms. The data set has no explicit temporal component -- data were nominally for the pre-industrial period ca. 1850.The estimated global spatial variation and distribution of different soil P forms presented in this study will be useful for global biogeochemistry models that include P as a limiting element in biological production by providing initial estimates of the available soil P for plant uptake and microbial utilization (Yang et al., 2013).There is one netCDF data file (.nc) with this data set.
EnSite™ HD Grid Catheter AF/AT Mapping Study
ClinicalTrials.gov study NCT02656537. IPD Sharing: Not stated. Countries: 5. Publications: 0.
West Africa Coastal Vulnerability Mapping: Gridded Subset of Sub-national Poverty and Extreme Poverty Prevalence
The West Africa Coastal Vulnerability Mapping: Gridded Subset of Sub-national Poverty and Extreme Poverty Prevalence represents the HarvestChoice Subnational Poverty and Extreme Poverty Prevalence data set as a one kilometer raster, and includes values within 200 kilometers of the coast. Poverty levels affect the "defenselessness" of populations in the low elevation coastal zone. These data were developed by the Harvest Choice project funded by the Bill and Melinda Gates Foundation. Harvest Choice measured 2005 poverty levels using 2005 purchasing power parity data for two thresholds: $1.25/day and $2/day international poverty lines. The $2/day threshold was selected for this mapping exercise.
West Africa Coastal Vulnerability Mapping: Subset of Global Mammal and Amphibian Richness Grids, 2015 Release
The West Africa Coastal Vulnerability Mapping: Subset of Global Mammal and Amphibian Richness Grids, 2015 Release was extracted from the NASA Socioeconomic Data and Applications Center (SEDAC) Gridded Species Distribution collection created from vector data files acquired from the International Union for Conservation of Nature (IUCN) Red List collection. Threatened species include any species classified as vulnerable, endangered, or critically endangered. The data represent the density of threatened mammals and amphibians within 200 kilometers of the coast at a one kilometer resolution.
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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.