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13,117 results for “Maps”
Supplemental Figures for: "The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves"
<p>Additional figures for the paper The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves. </p> <h2> </h2> <h2>Interactive Figure Data</h2> <p>Data files used to create the intreactive version of Figure 5 in the publication. There is a version of each file for each line species in the plot (i.e., Hα, Hβ, and MgII).</p> <p><strong>clouds_{line_name}.csv</strong>: A CSV file containing the cloud positions, line-of-sight velocities, and weights. The columns of the file are x [light-day], y [light-day], z [light-day], velocity [km/s], and weight.</p> <p><strong>transfer_function_velocity_{line_name}.csv</strong>: A CSV file containing x-axis of the transfer function panels, the rest-frame velocity.</p> <p><strong>transfer_function_tau_{line_name}.csv</strong>: A CSV file containing the y-axis of the transfer function panels, the rest-frame time delay τ in days.</p> <p><strong>transfer_function_{line_name}.csv</strong>: A CSV file containing the transfer function <span lang="el">Ψ.</span></p> <p> </p> <h2>Model-Related Figures</h2> <p><strong>fitplot_low.pdf</strong>: Same as Figure 4 in the publication, but for the low state.</p> <p><strong>fitplot_high.pdf</strong>: Same as Figure 4 in the publication, but for the high state.</p> <p><strong>geoplot_low.pdf</strong>: Same as Figure 5 in the publication, but for the low state.</p> <p><strong>geoplot_high.pdf</strong>: Same as Figure 5 in the publication, but for the high state.</p> <p><strong>lagplot_low.pdf</strong>: Same as Figure 6 in the publication, but for the low state.</p> <p><strong>lagplot_high.pdf</strong>: Same as Figure 6 in the publication, but for the high state. </p> <p> </p> <h2>Spectral Reduction Method Comparison</h2> <p><strong>spec_decomp_pyqsofit.pdf</strong>: A figure showing the spectral decomposition performed in PyQSOFit for the processed line profiles for Hβ, Hα, and MgII for an example epoch. The total spectrum is shown in black, and each of the decomposed elements are shown, color-coded using the legend above the three panels.</p> <p><strong>input_method_comp.pdf</strong>: A figure showing the processed multi-epoch line profiles for each spectral reduction method (PyQSOFit and PrepSpec). Each column corresponds to a given line (labeled above), and each row corresponds to a given spectral reduction method (labeled on the right). Note that the scales for each panel are different.</p> <p> </p> <h2>Published Value Comparison</h2> <p><strong>pubval_table.pdf</strong>: A table comparing the values obtained for certain physically relevant parameters obtained from our BRAINS modeling to those obtained in Shen et al. (2024). </p> <p> </p> <h2>Joint Posterior Analysis</h2> <p><strong>joint_line_posterior_table.pdf</strong>: A table containing the median values (and their uncertainties) extracted from the joint posteriors for a few key model parameters. These joint posteriors are produced for a given state, across all line species. </p> <p> </p> <h2>Virial Factor Analysis</h2> <p><strong>fcomp.pdf</strong>: A comparison of the virial factor values obtained by using the line dispersion (σ) and FWHM of each of the lines in each of the states.</p> <p><strong>fcorr_table.pdf</strong>: A table showing the correlations between the virial factor and model parameters (i.e., the slopes obtained using <a href="https://github.com/jmeyers314/linmix">LinMix</a> assuming a linear relationship, and the correlation coefficients). Values are given for virial factors obtained using both the line dispersion (σ) and FWHM.</p>
EUNIS Habitat Maps: Enhancing Thematic and Spatial Resolution for Europe through Machine Learning
<p>The EUNIS habitat classification is essential for categorising European habitats and supporting European policy on nature conservation and to implement the Nature Restoration Law. As such, to meet the growing demand for detailed and accurate habitat information, we provide spatial predictions for 260+ EUNIS habitat types at EUNIS level 3, together with validation and uncertainty analyses. </p> <p>More specifically, using ensemble machine learning models together with high-resolution satellite imagery and other climatic, terrain and soil variables, we produced an European habitat map at a 100-m resolution indicating the most likely EUNIS habitat at level 3 for every location across Europe. Predictions were validated for three independent countries, namely for France, the Netherlands and Austria. We also provide information on uncertainty and the most probable habitats at level 3 within each EUNIS level 1 formation. Products can be further refined with accurate and local land cover data. This product is thus likely to be particularly useful for restoration but also conservation purposes. </p> <p>Figure: <strong>Wall-to-wall map of EUNIS habitats at level 3 - (color coded at level 2 for visibility)</strong></p> <p></p>
Mapping of STEAM practices - anonymised dataset (updated)
<p>This dataset is the updated result of the dissemination of a survey submitted to STEAM practitioners, aiming at gaining insight on their on-going STEAM practices, which have informed the project's mapping of STEAM practices (Deliverable 4.2 and 4.4). </p>
Derived-ECVs for Case Studies (maps)
<p>Climate maps (raster layers .tif) of derived-ecvs with a spatial resolution of 5.5 km (1 km for Azores) obtained by statistically downscaling a set of CMIP6 simulations for different IPCC climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (reference, short time-horizon, medium time-horizon, long time-horizon). Data are representative of specific climate normals (yearly averaged values) and created by RethinkAction.</p> <p>We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.</p> <p>Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data:</p> <ul> <li>Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c.</li> <li>Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a</li> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47</li> <li>Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac</li> </ul> <p>Acknowledgement also to:</p> <ul> <li>DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (<a href="https://urldefense.com/v3/__https:/portal.azores.gov.pt/en/web/draac__;!!D9dNQwwGXtA!TAB9_FXZEEA4K_6AkmoIqX-krFMSiGcKRY--rOpV9psI98vjxa-sLAZQYR1s0G1fFmBrENoHHpZCdvP0s67vzss$" target="_blank" rel="noopener">https://portal.azores.gov.pt/en/web/draac</a>)</li> <li>SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), <a href="https://urldefense.com/v3/__https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search*/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1__;Iw!!D9dNQwwGXtA!SmILhnS0zICNt1ZcxuQ0VPP7VxtFeSEdLt4chAw8y5tsdlWqsgkyt9kESGhRu-00ZQqakzH36tvV-_DcQI7jGo-lOg$" target="_blank" rel="noopener">https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1</a></li> </ul> <p>This v2 includes metadata.</p>
Basic-ECVs for Case Studies (maps)
<p>Climate maps (raster layers .tif) of basic-ecvs with a spatial resolution of 5.5 km (1 km for Azores) obtained by statistically downscaling a set of CMIP6 simulations for different IPCC climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (reference, short time-horizon, medium time-horizon, long time-horizon). Data are representative of specific climate normals (yearly averaged values) and created by RethinkAction project.</p> <p>We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.</p> <p>Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data:</p> <ul> <li>Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c.</li> <li>Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a</li> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 </li> <li>Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac </li> </ul> <p>Acknowledgement also to: </p> <ul> <li>DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (<a href="https://urldefense.com/v3/__https:/portal.azores.gov.pt/en/web/draac__;!!D9dNQwwGXtA!TAB9_FXZEEA4K_6AkmoIqX-krFMSiGcKRY--rOpV9psI98vjxa-sLAZQYR1s0G1fFmBrENoHHpZCdvP0s67vzss$" target="_blank" rel="noopener">https://portal.azores.gov.pt/en/web/draac</a>)</li> <li>SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), <a href="https://urldefense.com/v3/__https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search*/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1__;Iw!!D9dNQwwGXtA!SmILhnS0zICNt1ZcxuQ0VPP7VxtFeSEdLt4chAw8y5tsdlWqsgkyt9kESGhRu-00ZQqakzH36tvV-_DcQI7jGo-lOg$" target="_blank" rel="noopener">https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1</a></li> </ul> <p> </p> <p>This v2 includes metadata.</p>
HR land use maps final version
<p>High resolution land use maps raster layers (.tif) with a spatial resolution of 10 m created by RethinkAcion project. The .tif files of the 12 maps are uploaded with visualization files. These datasets are in WGS84 in line with those that will be uploaded to the platform of the project and also this last version (v2) includes metadata</p>
Planting Date Maps (Low Resolution) and County Data for the US Corn Belt
<p>Aggregated dataset on planting dates for maize and soybean crops in the US Corn Belt from 2000-2020. This dataset was generated from Landsat satellite data and covers 12 states. The methodology is described in <a href="https://doi.org/10.1016/j.rse.2023.113551">Deines et al. 2023 </a>in Remote Sensing of Environment (open access).</p> <p>Note: Version 2 is identical to Version 1, but includes a file missing from v1 (soybeans_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv)</p> <p><strong>Preferred citation:</strong></p> <p>Deines, J.M., A. Swatantran, D. Ye, B. Myers, S. Archontoulis, & D.B. Lobell. 2023. Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020. <em><strong>Remote Sensing of Environment. </strong></em><a href="https://doi.org/10.1016/j.rse.2023.113551">https://doi.org/10.1016/j.rse.2023.113551</a></p> <p><strong>Contents</strong></p> <ol> <li>Gridded map datasets of planting dates aggregated to 10 km resolution. Map datasets are contained in zip files by crop type. Each zip file contains 21 annual geotiff rasters covering the full study region. <ol> <li>Maize_plantingDates_2000-2020_10000m_DeinesEtAl_vRSE.zip</li> <li>Soybeans_plantingDates_2000-2020_10000m_DeinesEtAl_vRSE.zip</li> </ol> </li> <li>Tabular county planting date statistics derived from the high-resolution (30 m) planting date maps. This data is presented in .csv format and includes the day-of-year when 10, 25, 50, 75, and 90 percent of crop area was planted for each year. There is one record for each county-year; data files are separated by crop type. <ol> <li>maize_countyStats_doyPercentPlanted_2000-2020_DeinesEtAl_vRSE.csv</li> <li>soybeans_countyStats_doyPercentPlanted_2000-2020_DeinesEtAl_vRSE.csv</li> </ol> </li> <li>Tabular planting progress by county. These .csv's present the area planted for each crop by county for each day. They can be considered the "raw" version of item #2 (county stats) and can therefore be used to derive other planting progress percentiles other than those provided in item #2. <ol> <li>maize_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv</li> <li>soybeans_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv</li> </ol> </li> </ol> <p><strong>Metadata</strong></p> <p>1. Planting date maps</p> <ul> <li>Value: day-of-year of planting</li> <li>No Data value = -999 (grid cells outside of the study area or lacking maize/soybeans that year)</li> <li>Map projection: EPSG:5070, CONUS Albers Equal Area</li> <li>Maps for 2008-2018 use the USDA's Cropland Data Layers to identify maize pixels; maps for 1999-2007 use the Corn-Soy Data Layer produced by <a href="https://www.nature.com/articles/s41597-020-00646-4">Wang et al. 2000</a> (data + manuscript are open access).</li> </ul> <p>2. Tabular datasets</p> <ul> <li>GEOID = 5 digit county FIPS, or the unique identifier assigned to each county by the US TIGER county dataset</li> <li>county_code = 3 digit county identifier. Represents places 3-5 in the 5 digit code.</li> <li>state_code = 2 digit state identifier. Represents places 1-2 in the 5 digit code.</li> <li>DOY = day-of-year</li> <li>area_daily_m2 = area of that crop planted that day in that county, in square meters</li> <li>doy_X = day-of-year when X% of area was planted that year</li> </ul> <p><br> </p>
Land use-based Adaptation and Mitigation Solution (LAMS) suitability maps final version
<div>These maps provide the suitable area (maximum available area) for the potential implementation of the specific proposed LAMS. The suitability map includes 4 classes: not suitable (0), least suitable (1), moderately suitable (2) and most suitable (3). Maps for 13 different LAMS (from the LAMS catalogue V1-1) have been developed for the six RethinkAction case studies, when possible. This v2 includes the metadata.</div> <div>The codes and the names of the LAMS are the following:</div> <div>LAMS03-EstGra: Establishment (conversion to) of permanent grassland</div> <div>LAMS14-SpaPla: Spatial planning for the sustainable deployment of energy on land</div> <div>LAMS15-PhoPla: Photovoltaic plants</div> <div>LAMS21-AgrPla: Agrovoltaic farms</div> <div>LAMS22-IncFor: Increased portion of forests included under protected areas</div> <div>LAMS23-RefAff: Reforestation/afforestation</div> <div>LAMS31-UrbSpr: Limiting urban sprawl</div> <div>LAMS32-GreUrb: Establishment and maintenance of green urban ecosystems</div> <div>LAMS44-IncCul: Increase in cultivated area</div> <div>LAMS49-FloSol: Floating solar photovoltaic panels in water bodies</div> <div>LAMS50-SolPan: Solar panels in rooftops/buildings</div> <div>LAMS55-WatHar: Water harvesting: collect and store rain water in reservoirs</div> <div>LAMS59-LanMan: Land management of solar photovoltaic systems land</div>
Package and Dependency Metadata for CZI Hackathon: Mapping the Impact of Research Software in Science
<p>A collection of useful datasets extracted from <a href="https://packages.ecosyste.ms">https://packages.ecosyste.ms</a> and <a href="https://repos.ecosyste.ms/">https://repos.ecosyste.ms</a> for use at the CZI Hackathon: Mapping the Impact of Research Software in Science.</p><p>All data is provided as NDJSON (new line delimited JSON), each line represents a valid JSON object, and they are separated by newline characters. There are <a href="https://pypi.org/project/ndjson/">python</a> and <a href="https://www.rdocumentation.org/packages/ndjson/versions/0.9.0/topics/stream_in">R</a> libraries for reading these files, or you can maually read each line and parse each line as a single JSON object.</p><p>Each ndjson file has been compressed with gzip (actual command: `tar -czvf`) to reduce download size, they expand to significantly bigger files after extraction.</p><h4>Package Data</h4><p>Package names from cran, bioconductor and pypi that have been parsed by the <a href="https://github.com/chanzuckerberg/software-mentions">software-mentions</a> project (data: <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.6wwpzgn2c">https://datadryad.org/stash/dataset/doi:10.5061/dryad.6wwpzgn2c</a>) are collected together with their latest release at time of publishing along with the names of their dependencies, those dependency names have then also been recursively fetched with latest release and dependencies until the full list of transitive dependencies is included. </p><p>Note: This approach uses a simplified method of dependency resolution, always picking the latest version of each package rather than taking into account each dependencies specific version range requirements, this is primarily due to time constraints and allows all software ecosystems to be processed in the same way. A future improvement would be to use each package ecosystem's specific dependency resolution algorithm to compute the full transitive dependency tree for each mentioned software package.</p><h4>GitHub Data</h4><p>Two different approaches were taken for collecting data for referenced GitHub mentions:</p><p>1. `github.ndjson` is metadata for each repository from GitHub, including "manifest" files which are known files that contain dependency information for a project such as requirements.txt, DESCRIPTION and package.json, parsed using <a href="https://github.com/ecosyste-ms/bibliothecary">https://github.com/ecosyste-ms/bibliothecary</a>, which may include transitive dependencies that have been discovered in a `lockfile` within the repository.</p><p>2. `github_packages.ndjson` is metadata for each package that was found on any package manager that references the GitHub url as it's repository url/source/homepage, these packages, like the cran and pypi data above, include the latest release and their direct dependencies. There may be more than one package for each GitHub URL as it is a one to many relationship. `github_packages_with_transitive.ndjson` follows the same format but also includes the extra resolved transitive dependencies of all packages using the same approach as with cran and pypi data above with the same caveats. </p><p>There are also many more ecosystems referenced in these files than just cran, bioconductor and pypi, https://packages.ecosyste.ms provides a standardized metadata format for all of them to enable comparison and simplification of automation.</p><h4>Contact</h4><p>If you would like any help, support or more data from Ecosyste.ms please do get in touch via email: hello@ecosyste.ms or open an issue on GitHub: https://github.com/ecosyste-ms/packages/issues</p>
NOAA NCCOS Assessment: Agency priorities for mapping coral reef ecosystems in American Samoa, 2023-06-06 to 2023-08-07
<p>Description:</p><p>NOAA's Coral Reef Conservation Program (CRCP) has identified a need for priority locations based on emerging management requirements in shallow coral reef areas (up to 40 meters depth) surrounding American Samoa. The priorities provided by participating agencies will inform research and monitoring activities, address current and future management needs, and maximize opportunities to leverage and complement existing regional efforts.</p><p>To meet this need, NOAA's National Centers for Coastal Ocean Science (NCCOS) developed a systematic, quantitative approach and online GIS application to gather seafloor mapping priorities from researchers and coral reef managers. Participants placed virtual coins into a grid overlaid on the project area to express the location of their mapping priorities. They also used pull-down menus to indicate specific mapping data needs and the rationale for their selections. Participants' inputs were compiled and analyzed to identify high priority areas along with their justifications and requirements. A total of nine participant groups entered their mapping priorities into the online tool. Identifying these high priority areas provide a critical spatial framework for prioritizing mapping efforts in shallow coral reef ecosystems in American Samoa.</p><p>Purpose:</p><p>The overall goal of the project was to systematically gather and quantify suggestions for mapping needs to support management of shallow coral reef ecosystems along the coast of the American Samoa. This dataset supports these goals by compiling input from a diversity of regional experts on their recommended priorities for mapping data collection.</p><p>Methods:</p><p>An advisory group was established which included individuals from NOAA CRCP and NOAA Fisheries. This advisory team customized the prioritization process specifically to meet the needs of CRCP and local coral reef manager priorities. In the online prioritization tool the study area was divided into 160 hexagonal grid cells 2.6 km2 in size. Existing relevant spatial datasets (e.g., bathymetry layers, Sanctuary Protection Areas, etc.) were provided as a digital atlas to help participants understand information and data gaps within the project area and to identify locations they wanted to prioritize for future data collections. Each participant was provided with 50 virtual coins to place into grid cells that they wished to prioritize. They were instructed to place more coins in grid cells that were higher priorities. A maximum of 5 coins could be placed into an individual grid cell. Respondents also reported why these locations were important by selecting a minimum of one, and a maximum of two, management uses from the following list: endangered species management (e.g.,), habitat restoration, monitoring, coastal vulnerability planning, watershed management, fisheries management, consultations and permitting, emergency response, and spatial protection and management. Respondents also reported requirements of data were needed in priority cells. A minimum of one, to a maximum of two choices were selected from the following list: delineations of large topographic features, delineations of hard vs. soft bottom, models of habitat suitability for key taxa or communities, delineations of substrate type (e.g. sand, mud, coral, rock), models of presence/absence or density of corals, identification of coral species and their local environments, documentation of individual specimen condition. Coin values were summarized and mapped to identify high priority areas, reasons for those priorities, and information needs. This ESRI shapefile contains the 2.6 km2 grid cells used in this prioritization and their associated coin values overall, as well as by management use, data product, and mapping methodology. Other summary values include the number of participants, number of participating groups, number of management uses, and number of data requirements. Additionally, coins for microscale (identification of coral species and their local environments and documentation of individual specimen condition), mesoscale (delineations of substrate type, models of presence/absence/density of corals), and regional (delineations of topographic features, delineations of hard vs. soft bottom, models of habitat suitability) requirements were summarized. Also included is a ranking of each grid cell based on the total number of coins, management uses, and participating groups allocating coins in the respective cell. For a complete description of the process and analysis see: Hile et al. 2023, in prep.</p>
An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')
<p>These data were produced as part of the study "An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')" (in press in Earth's Future, https://doi.org/10.1029/2023EF003895). We provide raster data at 30 arc seconds spatial resolution (folder 'raster') and vector and table data per administrative unit (folder 'admin') of five social vulnerability variables and the final Global Empirical Social Vulnerability Index (GlobE-SoVI) calculated from the five variables. Please see 'overview_table.pdf' for names and units.</p> <p>The code for data processing and analysis is available at https://github.com/lena-reimann/GlobE-SoVI (https://doi.org/10.5281/zenodo.10671539).</p>
Indicative distribution map for Ecosystem Functional Group MT1.2 Muddy Shorelines
<p>This archive contains indicative distribution maps and profiles for <strong>MT1.2 Muddy Shorelines</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.1). Please refer to Keith <em>et al.</em> (2020) and Keith <em>et al.</em> (2022) 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>
Indicative distribution map for Ecosystem Functional Group T4.5 Temperate subhumid grasslands
<p>This archive contains indicative distribution maps and profiles for <strong>T4.5 Temperate subhumid grasslands</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.1). Please refer to Keith <em>et al.</em> (2020) and Keith <em>et al.</em> (2022) 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>
A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, using Machine Learning: Supplemental data
<p>This dataset includes the crater map and equatorial crater depths and diameters presented in the paper: A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, using Machine Learning.</p>
Land cover map of the Greater Paramaribo region 2019
<p>This map shows the land cover of the Greater Paramaribo region for 2019 based on Sentinel 2 images. See details in the metadata document. </p><p>The map was made by Razi Taus for the Tropenbos Suriname and the University of Twente-Faculty Geo-information Science and Earth Observation (ITC) project "Naar een groen en leefbaarder Paramaribo" and must be accredited as follows.</p><p><i>Razia Taus, Lisa Best, Rudi van Kanten, Nina Schwarz, Louise Willemen, 2020, Land cover map of the Greater Paramaribo Region 2019, product of 'Naar een groen en leefbaarder Paramaribo' by Tropenbos Suriname and University of Twente-ITC. DOI: 10.5281/zenodo.7696951</i></p>
Map 1 in A new endogean species of Domene FAUVEL from a cave in Jaén, southern Spain (Coleoptera: Staphylinidae: Paederinae)
Map 1: Distribution of the Domene species known from southern Spain: D. fuelscheri (triangles), D. gevia (filled square), D. anichtchenkoi (open diamond), D. cavicola (filled diamond), D. perezi (open square), and D. lencinai (circle).
Fig. 1. Map showing the localities where Acropora corals were sampled during the first Snellius expedition, numbers correspond with Table 1 in The Acropora Humilis Group (Scleractinia) Of The Snellius Expedition (1929-30)
Fig. 1. Map showing the localities where Acropora corals were sampled during the first Snellius expedition, numbers correspond with Table 1.
Map 1 in A revision of Panscopaeus (Coleoptera: Staphylinidae: Paederinae)
Map 1: Distribution of Panscopaeus species in Taiwan: filled circles: P. yakushimanus; open circle: P. formosanus.
Fig. 14. Distribution map. 1 in New species and new records of terrestrial isopods (Crustacea, Isopoda, Oniscidea) of the families Philosciidae and Scleropactidae from Brazilian caves
Fig. 14. Distribution map. 1. Alboscia jotajota Campos-Filho, Bichuette & Taiti sp. nov. 2. Androdeloscia akuanduba Campos-Filho, Cardoso & Taiti sp nov. 3. Atlantoscia inflata Campos-Filho & Araujo, 2015. 4. Benthana iporangensis Lima & Serejo, 1993. 5. B. longicornis Verhoeff, 1941. 6. B. olfersii (Brandt, 1833). 7. B. picta (Brandt, 1833). 8. B. taeniata Araujo & Buckup, 1994. 9. Metaprosekia igatuensis Campos-Filho, Fernandes & Bichuette sp. nov. 10. Paratlantoscia rubromarginata (Araujo & Leistikow, 1999). 11. Amazoniscus spica Campos-Filho, Aguiar & Taiti sp. nov. 12. Circoniscus bezzii Arcangeli, 1931. Light gray areas denote Brazilian conservation units. AL = Alagoas; BA = Bahia; CE = Ceará; DF = Distrito Federal; ES = Espírito Santo; GO = Goiás; MA = Maranhão; MG = Minas Gerais; MT = Mato Grosso; PA = Pará; PB = Paraíba; PE = Pernambuco; PI = Piauí; PR = Paraná; RJ = Rio de Janeiro; RN = Rio Grande do Norte; SE = Sergipe; SP = São Paulo; TO = Tocantins.
Map 1 in Four new species and additional records of Staphylinidae from Spain (Insecta: Coleoptera)
Map 1: Distribution of Paraleptusa species in the Iberian Peninsula: P. ripicola (filled circles), P. spectans (open circle), P. anophthalma (square).
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.