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13,117 results for “Maps”
Maps of the diversity and distribution of Raunkiær's life forms in European vegetation
<p>This repository contains raster files (TIF format) with a 50 km × 50 km resolution (over UTM grid EPSG:32633), showcasing the diversity and distribution of Raunkiær’s life forms in European vegetation. The maps are based on two key metrics: (i) the proportion (%) of species within each life form and (ii) the diversity of life forms, including richness and evenness.</p> <p>To generate these maps, we averaged plot-level metric values across a comprehensive dataset comprising 546,501 vegetation plots sourced from the European Vegetation Archive (EVA; Project 163; <a href="https://euroveg.org" target="_new">https://euroveg.org</a>). These plots cover diverse habitats, including 173,190 forests, 260,884 grasslands, 52,517 scrubs, and 59,910 wetlands.</p> <p>The maps encompass the entire dataset, offering a visualization of the geographical distribution patterns of life forms across Europe. Additionally, we created habitat-specific maps by subsetting the dataset to explore unique patterns within each habitat type (forest, grassland, scrub, and wetland).</p> <p>Furthermore, we generated additional maps based on standardised effect sizes (SES) of diversity metrics. Through 500 species identity shuffles without replacement, specific to each habitat type, we examined the deviations from random expectations. SES values outside the range of -1.96 to 1.96 indicate significantly lower or higher metric values than expected at random, respectively. </p> <p> </p> <table> <tbody> <tr> <td><strong>Folder name</strong></td> <td><strong>Description of TIF raster values</strong></td> </tr> <tr> <td>full.div</td> <td>Mean richness and evenness of life forms across all habitat types</td> </tr> <tr> <td>full.mean.rel.prop</td> <td>Mean proportion of each life form across all habitat types</td> </tr> <tr> <td>habitat.div</td> <td>Mean richness and evenness of life forms across separate habitat types (forest, grassland, scrub, and wetland)</td> </tr> <tr> <td>habitat.mean.rel.prop</td> <td>Mean proportion of each life form across separate habitat types (forest, grassland, scrub, and wetland)</td> </tr> <tr> <td>SES.full.div</td> <td>Mean richness and evenness of life forms across all habitat types measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.full.mean.rel.prop</td> <td>Mean proportion of each life form across all habitat types measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.habitat.div</td> <td>Mean richness and evenness of life forms across separate habitat types (forest, grassland, scrub, and wetland) measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.habitat.mean.rel.prop</td> <td>Mean proportion of each life form across separate habitat types (forest, grassland, scrub, and wetland) measured with standardized effect sizes (SES)</td> </tr> </tbody> </table> <p><br>Additional information is available in our publication:<br><br>Midolo, G., Axmanová, I., Divíšek, J., Dřevojan, P., Lososová, Z., Večeřa, M., Karger, D. N., Thuiller, W., Bruelheide, H., Aćić, S., Attorre, F., Biurrun, I., Boch, S., Bonari, G., Čarni, A., Chiarucci, A., Ćušterevska, R., Dengler, J., Dziuba, T., Garbolino, E., Jandt, U., Lenoir, J., Marcenò, C., Rūsiņa, S., Šibík, J., Škvorc, Ž., Stančić, Z., Stanišić-Vujačić, M., Svenning, J. C., Swacha, G., Vassilev, K., & Chytrý, M. (2024) Diversity and distribution of Raunkiær’s life forms in European vegetation.<em> Journal of Vegetation Science. </em>Accepted on the 10th of December 2023</p>
Globe230k: A Benchmark Dense-Pixel Annotation Dataset for Global Land Cover Mapping
<p>We (Intelligent Mining and Analysis of Remote Sensing big data, IMARS) create a large-scale annotated dataset (Globe230k) for land use/land cover (LULC) mapping, which is annotated on Google Earth image of 1 m spatial resolution. Globe230k is annotated by numerous experts and students major in survey and mapping after necessary training, through visual interpretation on very high-resolution images, as well as in-situ field survey, under the guidance of the organized annotation pipeline. Globe230k has three superiorities:</p> <p>1) Large scale: the Globe230k includes 232,819 annotated images with the size of 512x512 and spatial resolution of 1 m, with more than 3x1010 annotated pixels, and it includes 10 first-level categories. </p> <p>2) Rich diversity: the annotated images are sampled from worldwide regions, with coverage area of over 60,000 km2, indicating a high variability and diversity. Besides, in order to ensure the category balance, we intentionally give more chance to the rare categories to be sampled, such as wetland, ice/snow, etc.</p> <p>3) Multi-modal: Globe230k not only contains RGB bands, but also include other important features for Earth system research, such as Normalized differential vegetation index (NDVI), digital elevation model (DEM), vertical-vertical polarization (VV) bands, vertical-horizontal polarization (VH) bands, which can facilitate the multi-modal data fusion research. Due to the large size of the multi-modal dataset (DEM 1.91G, NDVI 164G, VVVH 372G), these dataset are stored on Baidu Yunpan, the download link is :https://pan.baidu.com/s/12AKbiqOXSf4fnm7mYkCE0g?pwd=230k, the extraction code is 230k.</p> <p>The image patches and their corresponding annotated patches are respectively stored in "image_patch.zip" and "label_patch.zip" file. The RGB image is in forms of ".jpg", with size of 512x512, the pixel value is ranged from 0-255. The annotated patches is in forms of ".png", also with size of 512x512, the pixel value is ranged from 1-10, which respectively represent 1#cropland, 2#forest, 3#grass, 4#shrubland, 5#wetland, 6#water, 7#tundra, 8#impervious, 9#bareland, 10#ice/snow. The corresponding DEM, NDVI and VVVH patches are all in form of ".tif", with size of 512x512 (due to the different resolution of DEM, NDVI and VVVH patches, they are all uniformly resized to the same scale as the image patch). </p> <p>The total 232,819 pairs are officially divided into training set, validation set, and test set, based on ratio of 7:1:2, which can be find in "train_num.txt","val_num.txt","test_num.txt" file. Based on this division, the official baseline accuracy of several state-of-the-art semantic segmentation can be found in the related arcticle (https://spj.science.org/doi/10.34133/remotesensing.0078).</p> <p>We hope it can be used as a benchmark to promote further development of global land cover mapping and semantic segmentation algorithm development.</p>
Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions
<p><span>Reliable maps of species distributions are fundamental for biodiversity research and conservation. Range maps created by the International Union for Conservation of Nature (IUCN) Red List are often considered authoritative but may not match species occurrence data. We tested concordance between occurrences from camera trap surveys and predicted occurrence from IUCN maps for 510 medium- to large-bodied mammalian species in 80 camera-trap sampling areas. Across all areas, cameras detected 39% of the species that were expected to occur based on IUCN ranges. The probability of mismatches between camera traps and IUCN range maps was significantly higher for smaller-bodied mammals and habitat specialists in the Neotropics and Indomalaya, and in areas with shorter canopy forests. Our results indicate that in many areas within their range map distributions species may be rare or absent. We suggest that combining range map data with accumulating data from ground-based biodiversity sensors, such as camera traps, acoustic recorders, and eDNA surveys, provides a richer knowledge base for conservation mapping and planning.</span></p>
Data from: Solanum pennellii (LA5240) backcross inbred lines (BILs) for high resolution mapping in tomato
<p>Wild species are an invaluable source of new traits for crop improvement. Over the years the tomato community bred cultivated lines that carry introgressions from different species of the tomato tribe to facilitate trait discovery and mapping. The next phase in such projects is to find the genes that drive the identified phenotypes. This can be achieved by genotyping a few thousand individuals resulting in fine-mapping that can potentially identify the causative gene. To couple trait discovery and fine mapping we are presenting large, recombination-rich, Backcross Inbred Line (BIL) populations involving an unexplored accession of the wild, green-fruited species Solanum pennellii (LA5240; the Lost Accession) with two modern tomato inbreds: LEA, determinate, and TOP indeterminate. The LEA and TOP BILs are in BC2F6-8 generation and include 1,400 and 500 lines respectively. The BILs were genotyped with ~5,000 SPET markers, showing that in the euchromatic regions there was one recombinant every 17-18 Kb while in the heterochromatin a recombinant every 600-700 Kb (TOP and LEA respectively). To gain perspective on the topography of recombination we compared five independent members of the self-pruning gene family with their respective neighboring genes; based on PCR markers, in all cases we found recombinants. Further mapping analysis of two known morphological mutations that segregated in the BILs (Self-pruning and Hair), showed that the maximal delimited intervals were 73 Kb and 210 Kb respectively and included the known causative genes. The LOST_BILs provide a solid framework to study traits derived from a tolerant wild tomato.</p>
High-density genetic linkage mapping in Sitka spruce advances the integration of genomic resources in conifers
<p><span>In species with large and complex genomes such as conifers, dense linkage maps are a useful for supporting genome assembly and laying the genomic groundwork at the structural, populational and functional levels. However, most of the 600+ extant conifer species still lack extensive genotyping resources, which hampers the development of high-density linkage maps. In this study, </span><span><span>we developed a linkage map relying on 21,570 SNP makers in </span></span><span>Sitka spruce (<em>Picea sitchensis</em> [Bong.] Carr.)</span><span><em><span>, </span></em></span><span><span>a long-lived conifer from western North America that is widely planted for productive forestry in the British Isles. </span></span><span>We used a single-step mapping approach to efficiently combine RAD-Seq and genotyping array SNP data for 528 individuals from two full-sib families. As expected for spruce taxa, the saturated map contained 12 linkages groups with a total length of 2,142 cM. The positioning of 5,414 unique gene coding sequences allowed us to compare our map with that of other Pinaceae species, which provided evidence for high levels of synteny and gene order conservation in this family. We then developed an integrated map for <em>P. sitchensis</em> and <em>P. glauca</em> based on 27,052 makers and 11,609 gene sequences. Altogether, these two linkage maps, the accompanying catalog of 286,159 SNPs and the genotyping chip developed herein opens new perspectives for a variety of fundamental and more applied research objectives, such as for the improvement of spruce genome assemblies, or for marker-assisted sustainable management of genetic resources in Sitka spruce and related species.</span></p>
Wasserstoffatlas (Hydrogen Map) Dataset
<p>This dataset contains the raw data from the Wasserstoffatlas / Hydrogen Map published on <a href="https://wasserstoffatlas.de/">wasserstoffatlas.de</a> as listed here:</p> <table> <tbody> <tr> <td>Description</td> <td>Dataset Name</td> <td>License of Raw Data</td> <td>Source/Additional Information</td> </tr> <tr> <td>Inventory, complete Dataset</td> <td>Bestand.csv</td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td><a href="https://wasserstoffatlas.de/">Methodology Paper Hydrogen Map</a></td> </tr> <tr> <td>Inventory, complete Dataset</td> <td>Bestand.geojson</td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td><a href="https://wasserstoffatlas.de/">Methodology Paper Hydrogen Map</a></td> </tr> <tr> <td>Potential, NUTS 0</td> <td>Potential_nuts0.csv</td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td><a href="https://wasserstoffatlas.de/">Methodology Paper Hydrogen Map</a></td> </tr> <tr> <td>Potential, NUTS 0</td> <td>Potential_nuts0.geojson</td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td><a href="https://wasserstoffatlas.de/">Methodology Paper Hydrogen Map</a></td> </tr> <tr> <td>Potential, NUTS 1</td> <td>Potential_nuts1.csv</td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td><a href="https://wasserstoffatlas.de/">Methodology Paper Hydrogen Map</a></td> </tr> <tr> <td>Potential, NUTS 1</td> <td>Potential_nuts1.geojson</td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td><a href="https://wasserstoffatlas.de/">Methodology Paper Hydrogen Map</a></td> </tr> <tr> <td>Potential, NUTS 2</td> <td>Potential_nuts2.csv</td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td><a href="https://wasserstoffatlas.de/">Methodology Paper Hydrogen Map</a></td> </tr> <tr> <td>Potential, NUTS 2</td> <td>Potential_nuts2.geojson</td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td><a href="https://wasserstoffatlas.de/">Methodology Paper Hydrogen Map</a></td> </tr> <tr> <td>Potential, NUTS 3</td> <td>Potential_nuts3.csv</td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td><a href="https://wasserstoffatlas.de/">Methodology Paper Hydrogen Map</a></td> </tr> <tr> <td>Potential, NUTS 3</td> <td>Potential_nuts3.geojson</td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td><a href="https://wasserstoffatlas.de/">Methodology Paper Hydrogen Map</a></td> </tr> </tbody> </table> <p> </p> <p>The Hydrogen Map relies on multiple data sources, the sources and licenses are as follows (other sources not listed here may be used as well):</p> <table> <tbody> <tr> <td>Description</td> <td>Raw Data</td> <td>License of Raw Data</td> <td>Source/Additional Information</td> </tr> <tr> <td>Solar potentials</td> <td><a href="https://opendata.ffe.de/dataset/vres-generation-potentials-europe-nuts-3/">vRES Generation Potentials (Europe NUTS-3)</a></td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td>München: Forschungsstelle für Energiewirtschaft e. V. (FfE), 2020.</td> </tr> <tr> <td>Electricity demand of Tertiary Sector</td> <td><a href="https://opendata.ffe.de/dataset/load-curves-of-the-tertiary-sector-extremos-solideu-scenario-europe-nuts-3/">Load Curves of the Tertiary Sector – eXtremOS solidEU Scenario (Europe NUTS-3)</a></td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td>München: Forschungsstelle für Energiewirtschaft e. V. (FfE), 2021.</td> </tr> <tr> <td>Electricity demand of Transport Sector</td> <td><a href="https://opendata.ffe.de/dataset/load-curves-of-the-transport-sector-extremos-solideu-scenario-europe-nuts-3/">Load Curves of the Transport Sector – eXtremOS solidEU Scenario (Europe NUTS-3)</a></td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td>München: Forschungsstelle für Energiewirtschaft e. V. (FfE), 2021.</td> </tr> <tr> <td>Electricity demand of Private Household Sector</td> <td><a href="https://opendata.ffe.de/dataset/load-curves-of-the-private-household-sector-extremos-solideu-scenario-europe-nuts-3/">Load Curves of the Private Household Sector – eXtremOS solidEU Scenario (Europe NUTS-3)</a></td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td>München: Forschungsstelle für Energiewirtschaft e. V. (FfE), 2021.</td> </tr> <tr> <td>Electricity demand of Industry Sector</td> <td><a href="https://opendata.ffe.de/dataset/load-curves-of-the-industry-sector-extremos-solideu-scenario-europe-nuts-3/">Load Curves of the Industry Sector – eXtremOS solidEU Scenario (Europe NUTS-3)</a></td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td>München: Forschungsstelle für Energiewirtschaft e. V. (FfE), 2021.</td> </tr> <tr> <td>Hydrogen refuelling stations</td> <td><a href="https://h2-map.eu/">European Hydrogen Refuelling Station Availability System (E-HRS-AS)</a></td> <td><a href="https://joinup.ec.europa.eu/collection/eupl/eupl-text-eupl-12">EUPL-1.2</a></td> <td>© Clean Hydrogen JU, 2020</td> </tr> <tr> <td>Atlite</td> <td><a href="https://pypi.org/project/atlite/">Atlite</a></td> <td>data: <a href="https://creativecommons.org/publicdomain/zero/1.0/legalcode">CC0-1.0</a> & docs:<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td>© 2016-2021 The Atlite Authors.</td> </tr> <tr> <td>PyPSA</td> <td><a href="https://pypi.org/project/pypsa/">PyPSA</a></td> <td>(<a href="https://github.com/PyPSA/PyPSA/blob/master/LICENSE.txt">MIT Licence</a></td> <td>© 2015-2022 PyPSA Developers</td> </tr> <tr> <td>Agora hydrogen demand</td> <td><a href="https://www.agora-energiewende.de/en/publications/no-regret-hydrogen">No regret hydrogen study</a></td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td>Agora Energiewende and AFRY Management Consulting (2021): No-regret hydrogen: Charting early steps for H₂ infrastructure in Europe.</td> </tr> <tr> <td>Bioethanolanlagen</td> <td><a href="https://www.bdbe.de/biokraftstoff-bioethanol/zellulose-ethanol">Bioethanolwerke</a></td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td>BDBe - Bundesverband der deutschen Bioethanolwirtschaft e.V.</td> </tr> <tr> <td>Biomethanaufbereitung</td> <td><a href="https://www.biogaspartner.de/einspeiseatlas/">Biomethanaufbereitungsanlagen</a></td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td>Deutsche Energie-Agentur </td></tr> <tr> <td>PRTR Germany</td> <td><a href="https://thru.de/daten/">Thru.de des Umweltbundesamtes</a></td> <td><a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a></td> <td>Thru.de des Umweltbundesamtes</td> </tr> <tr> <td>Raumordnungsplan BSH</td> <td><a href="https://www.geoseaportal.de/atomfeeds/Raumordnungsplan_AWZ_de.xml#download=1">GeoSeaPortal des BSH</a></td> <td><a href="http://www.govdata.de/dl-de/by-2-0">dl-de/by-2-0</a></td> <td>Bundesamt für Seeschifffahrt und Hydrographie (BSH) Download Raumordnungsplan AWZ (Veröffentlichung: 01.09.2021, letzte Änderung: 23.11.2021)</td> </tr> <tr> <td>Markstammdatenregister</td> <td><a href="https://www.marktstammdatenregister.de/MaStR/Datendownload">Marktstammdatenregister</a></td> <td><a href="http://www.govdata.de/dl-de/by-2-0">dl-de/by-2-0</a></td> <td>© 2022 Bundesnetzagentur für Elektrizität, Gas, Telekommunikation, Post und Eisenbahnen; Pressestelle</td> </tr> <tr> <td>Akteursatlas</td> <td> </td> <td> </td> <td> <p><a href="https://www.wasserstoff-kompass.de/">Wasserstoff-Kompass</a></p> </td> </tr> <tr> <td>OpenStreetMap</td> <td><a href="https://download.geofabrik.de/">Geofabrik</a></td> <td><a href="https://opendatacommons.org/licenses/odbl/1-0/">ODbL v1.0</a></td> <td>© 2023 OpenStreetMap</td> </tr> <tr> <td>Digitales Landbedeckungsmodell für Deutschland, Stand 2018</td> <td><a href="https://gdz.bkg.bund.de/index.php/default/digitale-geodaten/digitale-landschaftsmodelle/digitales-landbedeckungsmodell-fur-deutschland-stand-2018-lbm-de2018.html">lbm-de2018-003</a></td> <td><a href="https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/mnt/c/Users/bif38590/Documents/Git/htwo-map/www.govdata.de/dl-de/by-2-0">dl-de/by-2-0</a></td> <td>© GeoBasis-DE / BKG 2023</td> </tr> <tr> <td>Copernicus Slope</td> <td><a href="https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-and-derived-products">EU-DEM v1.1</a></td> <td><a href="https://opendatacommons.org/licenses/odbl/1-0/">ODbL v1.0</a></td> <td>© 2023 Copernicus</td> </tr> <tr> <td>FFH-Gebiete Deutschland</td> <td><a href="https://www.geoportal.de/Metadata/7930E9E6-2C9A-4A0B-8D48-99FA81E74086">Metadaten</a></td> <td><a href="https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf">GeoNutzV</a></td> <td>© 2023 Bundesamt für Naturschutz (BfN)</td> </tr> <tr> <td>Landschaftsschutzgebiete Deutschland</td> <td><a href="https://www.geoportal.de/Metadata/8dceff1a-7bc1-4a49-9c08-a3f6b37f1b2b">Metadaten</a></td> <td><a href="https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf">GeoNutzV</a></td> <td>© 2023 Bundesamt für Naturschutz (BfN)</td> </tr> <tr> <td>Ramsar-Gebiete Deutschland</td> <td><a href="https://www.geoportal.de/Metadata/9142A292-84C1-4859-A07F-39CD6185D076">Metadaten</a></td> <td><a href="https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf">GeoNutzV</a></td> <td>© 2023 Bundesamt für Naturschutz (BfN)</td> </tr> <tr> <td>Landschaftsschutzgebiete Nordrhein-Westfalen</td> <td><a href="https://www.geoportal.de/Metadata/ef9d0151-7746-44d6-9930-36482ea550cd">Metadaten</a></td> <td><a href="https://www.govdata.de/dl-de/zero-2-0">Datenlizenz Deutschland – Zero – Version 2.0</a></td> <td>© 2023 Landesamt für Natur, Umwelt und Verbraucherschutz Nordrhein-Westfalen</td> </tr> <tr> <td>Vogelschutzgebiete Deutschland</td> <td><a href="https://www.geoportal.de/Metadata/3F448817-6212-4AF0-B498-DB8981490571">Metadaten</a></td> <td><a href="https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf">GeoNutzV</a></td> <td>© 2023 Bundesamt für Naturschutz (BfN)</td> </tr> </tbody> </table> <p>Funded by BMBF Germany (FKZ.: 03EW0013A).</p>
Fast Radio Map Estimation and Automated Radio Network Design Using Deep Learning App and Dataset
<p>In this research, we present the Deep Learning architecture encoder/fully-connected for estimate optimal desings WLANs in indoor scenarios. This architecture was implemented for WLAN structures consisting of 1, 2, 3, 4, and 5 access points, with the capability to perform the <a href="https://link.springer.com/chapter/10.1007/978-3-662-44415-3_4">Balanced k-means algortihm</a>, but in a fast manner.</p> <p><a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset/blob/main/README.md#general-dataset-structure">General Dataset Structure</a></p> <p>A major initial difficulty for starting the research was the lack of data, in this case, indoor scenario floor plans, users posisitons and optimal designs for training the architecture. Therefore, it was necessary to create an appropriate database that would facilitate the respective trainings. The dataset was created in base the <a href="https://link.springer.com/chapter/10.1007/978-3-662-44415-3_4">Balanced k-means algortihm</a>. This implementation was carried out in the MATLAB software <a href="https://github.com/johanflorez98/WLANs-optimal-designs-methodology">WLANs-optimal-designs-methodology</a>.</p> <p>Thus, this research provides a dataset that can be used for training multiple Deep Learning architectures and can facilitate future investigations into similar problems.</p> <p>We show a dataset composed by optimal designs for the 5GHz band WiFi in indoor scenarios: it has 102 indoor constructions plans and around 20 users distributions per plan floor as image and APs positions per case as coordinates. These distributions are random and several WLAN's structures: 1 to 5 access points.</p> <p>The above explain that we got a total of 61000 RME and CME, this presents that is a model without interference between channels.</p> <p>The pictures have a depth of 8 bits and size of <em>256pixels X 256pixels</em> equivalents to indoor constructions of <em>20 X 20</em> m<sup>2</sup>. These ones make reference to offices's spaces at general or classroom.</p> <p><a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset/blob/main/README.md#obtained-models">Obtained Models</a></p> <p>To evaluate the obtained models, the dataset consisting of floor plans 911 to 102 can be used, with available user distributions for each case of APs configurations as a test set, or other new images can be used.</p> <p>To manipulate the codes better, click here in the <a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset">repository</a>.</p>
Dataset for ICDAR 21 paper "Vectorization of Historical Maps Using Deep Edge Filtering and Closed Shape Extraction"
<p>This is the dataset of the ICDAR 2021 conference paper "Vectorization of Historical Maps Using Deep Edge Filtering and Closed Shape Extraction".</p>
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France
<p>Maps of forest height, aboveground biomass (AGB)* and volume (VOL)* at 10 m spatial resolution for the year 2020 on France. </p> <p>* AGB and Volume maps are available on request.</p> <p>The methodology and validation of the maps are presented here: https://hal.science/hal-04249151</p> <p>Please cite :</p> <p>David Morin, Milena Planells, Stéphane Mermoz, Florian Mouret. Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France. 2023. hal-04249151</p>
A Parsec-Scale Galactic 3D Dust Map out to 1.25 kpc from the Sun -- Dataset for the 1.25 kpc 3D Dust Map and the 2 kpc 3D Dust Map
<p>Accompanying data products for Edenhofer et al. (2023).</p>
A Peatland Sub-Class Map for the Canadian Boreal Forest
<p><strong>Authors: </strong><br>Pontone, N., Millard, K., Thompson, D. K., Guindon, L., Beaudoin A. (2024)</p> <p><br><strong>Contact:</strong><br>NicholasPontone@cmail.carleton.ca</p> <p> </p> <p><strong>Description:</strong><br>A map of peatland sub-classes (bog, poor fen, rich fen and permafrost peat complex) for the Canadian Boreal Forest circa 2020 created using a three-stage hierarchical classification framework. Training and validation data consisted of peatland locations derived from various sources (field data, aerial photo interpretation, measurements documented in literature). A combination of multispectral data, L-band SAR and C-Band interferometric SAR coherence, forest structure, and ancillary variables were used as model predictors. Ancillary data were used to mask agricultural areas and urban regions, and account for regions that may exhibit permafrost</p> <p><br><strong>Pixel Values:</strong></p> <p>1: Bog<br>2: Rich Fen<br>3: Poor Fen<br>4: Peatland Permafrost Complex<br>5: Mineral Wetlands<br>6: Water<br>7: Upands<br>8: Agriculture<br>9: Urban</p> <p><br><strong>Recommended Colours</strong></p> <p>1: 4C0073<br>2: FFFF00<br>3: E64C00<br>4: 727272<br>5: F4C2C2<br>6: 0070FF<br>7: 4C7300<br>8: 623131<br>9: 000000</p> <p> </p> <p><strong>Please cite as:</strong></p> <p>Pontone, N., Millard, K., Thompson, D.K., Guindon, L. and Beaudoin, A. (2024), A hierarchical, multi-sensor framework for peatland sub-class and vegetation mapping throughout the Canadian boreal forest. Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.384</p> <p> </p> <p>This data was released in combination with PALSAR-2 L-band dual-polarized radar backscatter summer composites (circa 2020). </p> <p>Beaudoin, A., Villemaire, P., Gignac, C., Tolszczuk, S., Guindon, L., Pontone, N., Millard, C. (2024). Canada’s PALSAR-2 dual-polarized L-band radar summer backscatter composite, circa 2020. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. <a href="https://doi.org/10.23687/8ec4ee78-9240-4bd0-9c97-d3a27829e209" target="_blank" rel="nofollow noopener">https://doi.org/10.23687/8ec4ee78-9240-4bd0-9c97-d3a27829e209</a></p> <p>The peatland map is also available as a Google Earth Engine asset (projects/ee-peatlandthesis/assets/PeatlandMap8b_2023_07_17). </p> <p> </p>
Comparison of Cropland Maps Derived from Land Cover Maps in Sub-Saharan Africa
<p>This data repository provides the datasets associated with analysis conducted in Kerner et al. (2024), citation below.</p> <p>The CSV file `intercomparison-results.csv` gives a table metrics for each of 11 land cover maps (plus a majority vote ensemble of all maps) evaluated using the reference dataset for each of 8 countries (Kenya, Rwanda, Uganda, Tanzania, Mali, Malawi, Togo, and Zambia). The reference datasets are provided as zip files. To ensure these datasets can be used for independent evaluation and comparison between maps in the future, the reference datasets should ONLY be used for final, independent evaluation of data products/model outputs; they should NOT be used for training models, tuning hyperparameters, or any other decisions during model/map development. </p> <p>The provided tif files contain the consensus maps (sum of all 11 maps) used to compute consensus statistics in Kerner et al. (2024).</p> <p>Kerner, H., Nakalembe, C., Yang, A., Zvonkov, I., McWeeny, R., Tseng, G., and Becker-Reshef, I. (2024). How accurate are existing land cover maps for agriculture in Sub-Saharan Africa? <em>Under review</em>. </p>
Simulated and real NPP annual map
<p>The project is to reconstruct the NPP maps with tree ring data. We can get the simulated NPP maps 80 years ago using our models.</p>
Figure 1. Map showing the sampling site. A, B in A new species of Leipsuropus Stebbing, 1899 (Amphipoda: Podoceridae) from Japan
Figure 1. Map showing the sampling site. A, B, map showing the sampling site in Japan and the Kumano Sea. Maps were generated by using GMT6 (A, Wessel et al., 2019) or based on GSI Maps (B, https://maps.gsi.go.jp).
In vivo parameter maps for: Unconstrained quantitative magnetization transfer imaging: disentangling T1 of the free and semi-solid spin pools
<p>Quantitative magnetization transfer and relaxometry maps as described in the Paper <em>Unconstrained quantitative magnetization transfer imaging: disentangling T1 of the free and semi-solid spin pools</em>.</p> <p>.</p>
Sea-surface pCO2 maps for the Bay of Bengal based on machine learning algorithms
<p>The dataset contains two products, the first being sea-surface <em>p</em>CO<sub>2</sub> and the second being air-sea CO<sub>2</sub> flux for the Bay of Bengal region. The data is climatological data, with 12 months of the climatological year. Each of these data has a spatial resolution of 1/12<sup>o</sup>. The positive value of CO<sub>2</sub> flux indicates the outgassing of CO<sub>2</sub>, and the negative value shows the uptake of atmospheric CO<sub>2</sub>. This version contains an elaborate description in the attribute section of the NC files, which is missing from the previous versions.</p>
Mapping the AEC startup ecosystem - Survey (2023)
<p>This survey was part of a European research project that aims at leading construction sites into the Industry 4.0 revolution. The project's name is BIMProve: https://www.bimprove-h2020.eu/. We work with 3 demonstrators in Norway, Spain and Switzerland, where we test different technologies, among them drones, VR, AR, and ground-robots. We now want to map the global start-up ecosystem working with and for the building and construction industry, with a specific focus on technologies, business models, and collaboration with academia. SINTEF is the institution responsible for the project (https://www.sintef.no/).</p>
Fig. 8. Heat map resulting from the Species Distribution Model using MaxEnt, where 1 in A revision of the genus Armillipora Quate (Diptera: Psychodidae) with the descriptions of two new species
Fig. 8. Heat map resulting from the Species Distribution Model using MaxEnt, where 1 is equal to the highest probability of distribution, while 0 is the lowest probability.
Excel mapping tool for 2023 avian influenza data reporting
<p>Data collection is an important task of the European Food Safety Authority (EFSA) and a fundamental component of risk assessment (Articles 22 and 23 of Regulation (EC) No 178/2002). EFSA receives a large volume of data from Member States (MSs) that is used in support of its risk assessment mission.</p> <p>Council Directive 2005/94/EC and Commission Decision 2010/367/EU lay down the guidelines for monitoring and reporting avian influenza surveillance data on poultry and wild birds by European Union (EU) MSs. EFSA has been assigned the task of collating, validating, analysing, and summarising in an annual report the results from the avian influenza surveillance programmes in poultry and wild birds. For the reporting of data, EFSA provides a Data Collection Framework that allows data providers to submit data in eXtensible Markup Language (XML) format through a web interface or a web service. Here, a data model describing the format, and the content requested when submitting data through the DCF, is provided.</p> <p>The data model supports the reporting of laboratory testing results for avian influenza in both wild birds, and poultry. The use of this reporting standard ensures that reported results are comparable between reporting countries. Data reported in this format will be used to generate epidemiological updates on the avian influenza surveillance testing carry out in Europe, and to provide scientific advice to the European Commission.</p>
Organizing the fragmented landscape of multidisciplinary product development: A mapping of approaches, processes, methods and tools from the scientific literature - Searchable cartographies
<p>This document gathers cartographies for the development of mechatronic products, cyber-physical systems and smart products. The three cartographies presented are associated with an open-access article – see the citation box below – and differ from the ones provided in the article in that they are searchable, which makes it easier to pinpoint references, concepts and techniques. This document comprises a legend, the cartographies and a list of associated references. </p> <p>To contextualize the cartographies, the integration of digital and connectivity technologies in new products can invite companies to adapt their development. Organizing the fragmented landscape of multidisciplinary product development to help companies navigate the dense scientific literature corpus is a first step in supporting them in doing so. Multidisciplinary product development can be investigated by analyzing specific types of products that deal with both software and hardware development and can be referred to as cyber-physical systems, mechatronics, and smart products and systems in the literature. To support their development, 236 “concepts and techniques” (an expression that encompasses approaches, processes, methods and tools) were identified from 167 scientific papers through an extensive literature review and organized based on a four-level model paired with a decision tree. The mapping of the sorted concepts and techniques made it possible to generate graphical representations called “cartographies.” These cartographies represent a database of concepts and techniques for multidisciplinary product development and serve to support companies in their transformation from the product development perspective by providing them with a general overview of the related literature.</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.