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13,117 results for “Maps”
Indoor maps
<p>Indoor maps of three testing buildings</p>
Routable map for Mexico (gmapsupp.img for Garmin devices)
<p>This is a routable map file for Garmin devices which was created by the scripts available at:</p> <p>https://bitbucket.org/lababi/mexico-garmin-map</p> <p>This script downloads the latest version of OpenStreetMap (OSM) data of Mexico and elevation lines (NASA SRTM) to create a gmapsupp.img file, which provides the map on Garmin devices. The resulting map is ~1.3 Gb (9 of June 2016) and works fine on an etrex Vista HCx. The cyclemap style included is from http://wiki.openstreetmap.org/wiki/OSM_Map_On_Garmin/Cycle_map.</p>
Modeling of the TFIIH complex using chemical cross-links and electron microscopy (EM) density maps
<p>TFIIH is essential for both RNA polymerase II transcription and DNA repair, and mutations in TFIIH can result in human disease. Here, we determine the molecular architecture of human and yeast TFIIH by an integrative approach using chemical crosslinking/mass spectrometry (CXMS) data, biochemical analyses, and previously published electron microscopy maps. We identified four new conserved "topological regions" that function as hubs for TFIIH assembly and more than 35 conserved topological features within TFIIH, illuminating a network of interactions involved in TFIIH assembly and regulation of its activities. We show that one of these conserved regions, the p62/Tfb1 Anchor region, directly interacts with the DNA helicase subunit XPD/Rad3 in native TFIIH and is required for the integrity and function of TFIIH. We also reveal the structural basis for defects in patients with xeroderma pigmentosum and trichothiodystrophy, with mutations found at the interface between the p62 Anchor region and the XPD subunit.</p> <p>For more information about how to reproduce this modeling, see https://salilab.org/tfiih or the README file.</p>
Ground deformation maps of the Visso and Norcia 2016 earthquakes captured from Sentinel-1 SAR
<p>The dataset contains the Line Of Sight (LOS) deformation maps obtained by applying Differential SAR Interferometry (DinSAR) to Sentinel-1 Interferometric Wide-swath (or TOPSAR) imagery.</p> <p>Two maps are provided: the first maps is the LOS deformation estimated from ascending data, the second map is the LOS deformation from descending images.</p> <p>Maps are expressed in metres and are provided in raster geotiff format.</p> <p>Data have been processed with GAMMA Interferometric processor.</p> <p>Ascending data details:</p> <p>SAR pairs are dated 2016/10/27 and 2016/11/02, acquired on relative orbit number 44, by Sentinel-1B and Sentinel-1A, respectively.</p> <p>The DEM used for removing the topographic phase is the SRTM 1 arc second. The interferogram has been generated by applying a 1x5 multi-look factor in azimuth and range, respectively.</p> <p>Final product has been geocoded in UTM WGS84 33 Nord projection, with a posting of 20 m.</p> <p>Descending data details:</p> <p>SAR pairs are dated 2016/10/26 and 2016/11/01, acquired on relative orbit number 22, by Sentinel-1B and Sentinel-1A, respectively.</p> <p>The DEM used for removing the topographic phase is the SRTM 1 arc second. The interferogram has been generated by applying a 2x10 multi-look factor in azimuth and range, respectively.</p> <p>Final product has been geocoded in UTM WGS84 33 Nord projection, with a posting of 40 m.</p>
Ground deformation maps of the Visso and Norcia (Italy) 2016 earthquakes from ALOS-2 SAR data
<p>The datasets consist of the Line of Sight (LoS) deformation maps obtained by applying Differential SAR Interferometry (DinSAR) to ALOS-2 (Strip Map acquisition mode) pairs.</p> <p>ALOS-2 is operated by the Japan Aerospace Exploration Agency (JAXA).</p> <p>Two maps were retrieved by processing data from both the ascending and descending track.</p> <p>Both the maps are in meters and provided in raster geotiff format.</p> <p>Data have been processed using the Sarscape© software.</p> <p>Ascending pair details:</p> <p>SAR images were acquired on 2016/08/24 and 2016/11/02, path 197, frame 850.</p> <p>The adopted DEM used for the topographic phase removal was the SRTM 1 arc second. The interferogram was generated by applying a 11x5 multi-look factor in azimuth and range, respectively.</p> <p>Final product were geocoded in UTM WGS84 33 Nord projection, and a final pixel size of 40 m.</p> <p>Descending pair details:</p> <p>SAR images were acquired on 2016/08/31 and 2016/11/09, path 92, frame 2750.</p> <p>The adopted DEM used for the topographic phase removal was the SRTM 1 arc second. The interferogram was generated by applying a 11x5 multi-look factor in azimuth and range, respectively.</p> <p>Final product were geocoded in UTM WGS84 33 Nord projection, and a final pixel size of 40 m.</p>
DNS resolved hostnames with organization and geographical mapping
<p>Hostnames mapped to their CNAMEs, IP addresses, ASes, Organization. IP addresses are obtained after resolving each hostname from 90,000+ open DNS servers across the Internet.</p> <p>The dataset is segregated as online ads and regular web content serving hostnames.</p> <p>The data was collected during January, 2013.</p>
3D neighborhood motion maps (6-neighborhood)
<p>The file is a sqlite3 database which contains 3D neighborhood motions maps of 6-neighborhood together with sample points. It was computed while using a variation of the algorithm described in: Pluta K., Moroz G., Kenmochi Y., Romon P. (2016) Quadric Arrangement in Classifying Rigid Motions of a 3D Digital Image. In: Gerdt V., Koepf W., Seiler W., Vorozhtsov E. (eds) Computer Algebra in Scientific Computing. CASC 2016. Lecture Notes in Computer Science, vol 9890. Springer, doi:10.1007/978-3-319-45641-6_27</p> <p>To refer to a version of the algorithm used to compute the file use DOI:10.5281/zenodo.573013 (https://zenodo.org/badge/latestdoi/53963129).</p>
3D neighborhood motion maps (26-neighborhood)
<p>The file is a sqlite3 database which contains 3D neighborhood motions maps of 26-neighborhood together with sample points. It was computed while using a variation of the algorithm described in: Pluta K., Moroz G., Kenmochi Y., Romon P. (2016) Quadric Arrangement in Classifying Rigid Motions of a 3D Digital Image. In: Gerdt V., Koepf W., Seiler W., Vorozhtsov E. (eds) Computer Algebra in Scientific Computing. CASC 2016. Lecture Notes in Computer Science, vol 9890. Springer, doi:10.1007/978-3-319-45641-6_27</p> <p>To refer to a version of the algorithm used to compute the file use DOI:10.5281/zenodo.573013 (https://zenodo.org/badge/latestdoi/53963129).</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>
Sets of second degree polynomials used in the problem of 3D neighborhood motion maps computations
<p>The files are Maple source files which contain sets of second degree polynomials used in the problem of computing 3D neighborhood motion maps. The files contain polynomials related to: 6-neighbrhood (see quadrics_N1.mpl); 18-neighbrhood (see quadrics_N2.mpl) and 26-neighbrhood (see quadrics_N3.mpl).</p> <p>The polynomials are related to the paper: Pluta K., Moroz G., Kenmochi Y., Romon P. (2016) Quadric Arrangement in Classifying Rigid Motions of a 3D Digital Image. In: Gerdt V., Koepf W., Seiler W., Vorozhtsov E. (eds) Computer Algebra in Scientific Computing. CASC 2016. Lecture Notes in Computer Science, vol 9890. Springer, doi:10.1007/978-3-319-45641-6_27</p> <p>To refer to a version of the algorithm used to compute the file use DOI:10.5281/zenodo.573013 (https://zenodo.org/badge/latestdoi/53963129).</p>
Large-scale Forest Stand Height mapping for the northeast of U.S. and China using L-band spaceborne repeat-pass InSAR and GEDI
<p><span>The forest height mosaic for the northeastern parts of China and U.S are generated based on a global-to-local inversion approach proposed in </span><span><span>(Yu et al., 2023)</span></span><span> by making use of Spaceborne repeat-pass InSAR and spaceborne GEDI data. The sparsely but extensively distributed LiDAR samples provided by NASA’s GEDI mission are used to parametrize the semi-empirical repeat-pass InSAR scattering model(Lei et al., 2017) and to obtain forest height estimates. Compared to our previous efforts </span><span><span>(Lei et al., 2018, Lei and Siqueira, 2022)</span></span><span>, this paper further removes the assumptions that were made given the limited availability of calibration samples at that time, and developed a new inversion approach based on a global-to-local two-stage inversion scheme. This approach allows a better use of local GEDI samples to achieve finer characterization of temporal decorrelation pattern and thus higher accuracy of forest height inversion.<span> </span>This approach is further fully automated to enable a large-scale forest mapping capability. Two forest height mosaic maps were generated for the entire northeastern regions of U.S. and China with total area of 18 million hectares and 112 million hectares, respectively. The validation of the forest height estimates demonstrates much improved accuracy achieved by the proposed approach compared to the previous efforts i.e., reducing from RMSE of 3-4 m on the order of 3-6-hectare aggregated pixel size to RMSE 3-4 m on the order of 0.81-hectare pixel size. The proposed fusion approach not only addresses the sparse spatial sampling problem inherent to the GEDI mission, but also improve the accuracy of forest height estimates compared to the GEDI-interpolated maps by a factor of 20% at 30-m resolution. The extensive evaluation of forest height inversion against LVIS LiDAR data indicates an accuracy 3-4 m on the order of 0.81 hectare over smooth areas and 4-5 m over hilly areas in U.S., whereas the forest height estimates over northeastern China are best compared with small footprint LiDAR validation data even at an accuracy of even below 3.5 m with R2 mostly above 0.6. Such a forest height inversion accuracy at sub-hectare pixel size provides promising values towards the existing and future spaceborne LiDAR (</span><span>JAXA’s MOLI, NASA’s GEDI, China’s TECIS</span><span>) and InSAR missions (</span><span>NASA-ISRO’s NISAR, JAXA’s ALOS-4 and China’s LuTan-1</span><span>). This fusion prototype can work as a cost-effective solution for public users to obtain a wall-to-wall forest height mapping at large scale when only spaceborne repeat-pass InSAR data are available and freely accessible.</span></p>
Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk
<p>sushie.molqtl.weights.tar.gz contains ancestry-specific eQTL and pQTL weights trained on mRNA and protein levels measured in American European, American African, and American Hispanic ancestries from TOPMed-MESA and GENOA studies. Column “a1” is the counting allele.</p> <p>mesa.*.fusion.tar.gz contains the weights in FUSION format.</p> <p>sushie_real_data_results.tar.gz contains all the real data analyzed in the sushie project.</p> <p>sushie_sim_data_results.tar.gz contains all the sim data analyzed in the sushie project.</p> <p>sushie_analysis_codes.tar.gz contains all the codes and scripts to generate and analyze these data.</p>
SYMBA project Industrial Symbiosis mapping
<p>The dataset includes the infomation collected during the elaboration of the Industrial Symbiosis (IS) mapping carried out in the framework of the SYMBA project (101135562). Concretely, it compiles the data included in the deliverable D3.1 Report on current Industrial Symbiosis solutions in Europe.</p> <p>The updated v3.0 of the dataset includes the prioritisation of IS solutions, resulting from the application of the Multicriteria Decision Analysis (MCDA) methodology developed under the SYMBA project. This prioritisation, detailed in Deliverable D3.3: Evaluation of IS Prioritised Solutions, applies weighted scoring factors across five readiness dimensions: Symbiosis, Environmental, Societal, Organisational, and Legal/Ethical. The weighting factors used for prioritisation are presented in the adjacent table. For more in-depth insight into the evaluation and prioritisation approach, consult Deliverable D3.3.</p>
A new map of South Manchurian mixed forests
<p>We publish the results of mapping the South Manchurian mixed forests (SMMFs) for 2021, with comparative analysis against 2001. SMMFs represent one of the most biodiverse temperate forest ecosystems globally, spanning northeastern China, southern Primorsky Krai (Russia), and the Korean Peninsula. These forests face significant degradation, with large areas replaced by secondary growth, agricultural land, and anthropogenic vegetation. Precise quantification of remaining SMMFs is critical due to their high conservation value.</p> <p>Our methodology integrated an original SMMFs distribution dataset (vegetation relevés, literature records, and regional maps) to train satellite data classifiers. We utilized Global Land Analysis and Discovery (GLAD) analysis-ready Landsat time-series at 30m resolution. Mapping employed decision tree ensembles calibrated with manually collected training data, with stratified sampling for accuracy assessment. Potential distribution modeling incorporated community-level presence/absence datasets using topographic (elevation, slope, aspect) and bioclimatic predictors.</p> <p><strong>Training Data.csv</strong> – Training dataset: Vegetation relevés and verified occurrence points for SMMFs</p> <p><strong>forest2001mask.tif</strong> and <strong>forest2021mask.tif</strong> – Binary forest cover masks (1: forest, 0: non-forest) for 2001 and 2021</p> <p><strong>smmf_2001_binary.tif</strong> and <strong>smmf_2021_binary.tif</strong> – SMMFs distribution maps (binary: 1 = SMMFs, 0 = other land cover)</p> <p><strong>Sampling.csv</strong> – Stratified sampling points for accuracy assessment</p> <p><strong>sampling_sum.tif</strong> – Stratified aggregation layer for error quantification</p> <p><strong>smmf_combined.tif</strong> – Composite SMMFs distribution raster (2021)</p> <p><strong>Map_SMMFs_area.shp</strong> – SMMF area distribution per 10×10 km grid cell</p> <p><strong>Map_SMMFs_patches.shp</strong> – Spatial boundaries of SMMFs forest patches with size attributes</p> <p><strong>proj_Current_SMMF.tb[...].tif</strong> – Potential SMMFs distribution models (target-background sampling ensemble)</p> <p><strong>proj_Current_SMMF.sample[...].tif</strong> – Potential distribution models (stratified sampling ensemble)</p> <p><strong>Results_by_algo_SMMF_tb.csv </strong>– Model evaluation metrics (target-background approach)</p> <p><strong>Results_by_algo_SMMF_sample.csv </strong>– Model evaluation metrics (stratified sampling approach)</p>
Global Sectoral GDP map at 30'' resolution (SectGDP30) v2.0
<p>- This dataset provides global sector-specific GDP distribution maps (in GeoTIFF format) with a 30-second spatial resolution. It allocates GDP at the 30-arcsecond grid level for three sectors (services, industry, and agriculture) by the distribution of country-level GDP data using high-resolution land cover map.<br>- The source GDP data for allocation is based on nominal GDP for the years 2010, 2015, and 2020, obtained from the World Bank. As the high-resolution land cover map, it uses the built-up area and non-residential area data by the Global Human Settlement Layer (Pesaresi and Politis, 2022) for the service and industrial sectors and the Global cropland map by Potapov et al. (2022) for the agriculture sector. Detailed descriptions of the data creation methodology can be found in Shoji et al. (In Review).<br>- Each pixel represents the monetary value of added value generated by economic activity hypothetically occurring within that pixel. The unit of each pixel value is in millions of USD (current prices for 2010, 2015, and 2020).</p> <p>(Updated to v2.0 on July 1, 2025)</p> <p>This update includes a major change to the spatial allocation method for service and agricultural GDP. For details on the new GDP mapping methodology, please refer to Shoji et al. (In Review). There are no changes to the industrial GDP map.</p> <p> </p> <p>Reference:<br>- Pesaresi M, Politis P.: GHS-BUILT-S R2022A: GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975–2030). European Commission, Joint Research Centre (JRC), 2022.<br>- Potapov P, Svetlana T, Matthew CH, Alexandra T, Viviana Z, Ahmad K, Xiao-Peng S, Amy P, Quan S, Jocelyn C.: Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nature Food 3: 19–28, 2022.<br>- Shoji T, Kajiyama K, Yamazaki D, Kita Y, Watanabe M.: Global spatially-distributed sectoral GDP map for disaster risk analysis. In Review.</p>
Australian Frog Atlas: Fine-scale species distribution maps informed by the FrogID dataset
<p>This is version three of the Australian Frog Atlas: shapefiles and KML files of the most up to date, accurate and detailed set of Australian frog species distribution maps available, and both a shapefile and detailed PDF map of Australian frog species richness. All of these are available as a free resource under the Creative Commons Attribution (CC-BY) 4.0 License. When using any material from the Australian Frog Atlas, please refer to and cite Cutajar et al. (2022), outlined in full below, which introduces the dataset and describes it in detail.</p> <p>Cutajar. T. P., Portway, C. D., Gillard, G. L., and Rowley, J. J. L. (2022) Australian Frog Atlas: Fine-scale species distribution maps informed by the FrogID dataset. <em>Technical Reports of the Australian Museum Online</em>. 36: 1-48. <a href="https://doi.org/10.3853/j.1835-4211.36.2022.1789">https://doi.org/10.3853/j.1835-4211.36.2022.1789</a></p> <p><strong>Caveat:</strong> The occurrence data used in the development of these maps are from a range of sources. Every effort has been made to ensure accuracy and completeness, including vetting of data using published literature. However, no guarantee is given, nor responsibility taken by the authors or their institutions for errors or omissions, nor in respect of any information or advice given in relation to, or as a consequence of, the maps and data provided herein. These maps are indicative only and aim to capture the known and presumed distributions of individual frog species and frog species richness within Australia. A taxonomic revision of Australo-Papuan tree frogs published in June 2025 proposes the reclassification of more than 120 Australian tree frog species (in the genera <em>Litoria</em> and <em>Cyclorana</em>) into 22 genera. This proposed change has not been adopted in this version of the Australian Frog Atlas. </p> <p> </p> <p> </p>
Dataset for "Mapping Water Flow Pathways in the Fengjiaping Landslide Using Self-Potential and Electrical Resistivity Tomography"
<p>This dataset includes soil temperature, moisture, and electrical conductivity measurements taken at a depth of approximately 50 cm, as well as the digital elevation model, electrical resistivity tomography, and self-potential data used in the manuscript "Mapping Water Flow Pathways in the Fengjiaping Landslide Using Self-Potential and Electrical Resistivity Tomography" submitted to <em>Comptes Rendus Geoscience</em>.</p>
Annual global grided livestock mapping from 1961 to 2021
<p>We release <strong>annual global livestock density maps (1961–2021) at 5-km resolution</strong> for eight groups: <strong>cattle, buffaloes, sheep, goats, horses, pigs, chickens, and ducks</strong>. Each raster represents <strong>heads per km²</strong> on a 5-km grid and is produced by a Random Forest workflow trained on multi-source predictors and constrained by theoretical suitability masks. We also provide a matched <strong>per-pixel, per-year uncertainty layer</strong> (0–1) that integrates (i) temporal extrapolation with local sample support, (ii) feature completeness by species/year, and (iii) MESS-like environmental similarity to the training domain. Uncertainty is<strong> </strong>higher in earlier decades (1960s–1990s) and data-sparse regions; users should consult the accompanying uncertainty layers when analyzing long-term trends or making regional inferences.</p>
Mapping Global Nitrogen Mineralization Rates: A Climate-Soil Perspective
<p>The file "Ecosystem_β.tif" represents the spatial distribution of nitrogen mineralization rates in global ecosystems (cropland, grassland, and forest) under different climate models (SSP1-2.6, SSP2-4.5, SSP5-8.5). "Cropland_β.tif" represents the spatial distribution of cropland ecosystems across various SSPβ scenarios. "Grassland_β.tif" represents the spatial distribution of grassland ecosystems under different SSPβ scenarios. "Forest_β.tif" represents the spatial distribution of forest ecosystems across different SSPβ scenarios.</p>
Indicative distribution map for Ecosystem Functional Group F1.3 Freeze-thaw rivers and streams
<p>This archive contains indicative distribution maps and profiles for <strong>F1.3 Freeze-thaw rivers and streams</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</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.