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106 results for “map projections”
Data from: Incorporating interspecific competition into species-distribution mapping by upward scaling of small-scale model projections to the landscape
Open the record for dataset details and reuse information.
Integrated Terrain Unit Map (ITUM) for Niwot Saddle, Niwot Ridge LTER Project Area, Colorado
A 350 x 500 m integrated terrain unit map (ITUM) was produced at 1:500 scale inside the 350 x 500 m saddle grid, and the 1:500 digital elevation model (DEM). Vegetation was mapped using Komarkova's (1979) classification system (Braun-Blanquet) units. All map units were mapped to 1/8-inch minimum map-polygon-size resolution. The map is part of the Saddle grid geographic information system (GIS). Many GIS projects use an approach in which existing mapped information is digitized into the GIS database directly from the original sources. The maps may have different map scale, map-unit resolutions, dates of data collection, and classification systems. When these different sources are combined in a GIS, artifacts may arise due to boundary mismatches and scale incompatibility (Dangermond and Harnden 1990). Integrated geobotanical mapping can minimize many of these problems. This method simultaneously maps vegetation and other terrain features that are interpreted on a common air-photo base (Everett et al. 1978, Walker et al. 1980). We use the term geobotany in its traditional European sense to refer to the study of plant communities and their relationships to geology, landforms, and soils (Braun-Blanquet 1932). Terrain geomorphic boundaries are used to guide the delineation on aerial photographs of most major vegetation boundaries similiar to the landscape-guided vegetation mapping approach developed in Europe (Zonneveld 1988) and the integrated terrain unit mapping approach developed by the Environmental System Research Institute in Redlands, CA (Dangermond and Harnden 1990). Additional information concerning the Niwot Ridge LTER GIS can be found in Walker et al. (1993). [1]Braun-Blanquet, J. 1932. Plant sociology: The study of plant communities. New York: McGraw-Hill, 439 pp. [2]Everett, K.R., P.J. Webber, D.A. Walker, R.J. Parkinson, and J. Brown. 1978. A geoecological mapping scheme for Alaskan coastal tundra. Third International Conference on Permafrost, 10-13 July 19
The Sloan Digital Sky Survey Reverberation Mapping Project: the XMM-Newton X-ray source catalog and multi-band counterparts
<p>The XMM-RM project was designed to provide X-ray coverage of the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) field. 41 XMM-Newton exposures, placed surrounding the Chandra AEGIS field, were taken, covering an area of 6.13 deg2 and reaching a nominal exposure depth of ∼ 15 ks. We present an X-ray catalog of 3553 sources detected in these data, using a PSF-fitting algorithm and a sample selection threshold that produces a ∼ 5% fraction of spurious sources. The Bayesian method “NWAY” was employed to identify counterparts of the X-ray sources from the optical Legacy and the IR unWISE catalogs, using a 2-dimensional unWISE magnitude-color prior created from optical/IR counterparts of Chandra X-ray sources. 932 of the XMM-RM sources are covered by SDSS spectroscopic observations. 89% of them are classified as AGN, and 71% of these AGN are in the SDSS-RM quasar catalog. Among the SDSS-RM quasars, 80% are detectable at the depth of the XMM observations. Forced X-ray photometry is performed at the positions of the undetected SDSS-RM quasars.</p>
GMDN-EMDN Mapping Project - Data Release
<h2>Description</h2> <pre>This release contains the datasets described in the paper "A Data-driven Algorithm Streamlining Medical Device Nomenclature Interoperability Through Large Language Models". The repository provides the necessary input data to reproduce the results presented in the paper, as well as all the output files described. Access to the files can be obtained, following a reasonable request, by contacting the Corresponding Author at <code>enrico.caiani@polimi.it</code>.</pre> <h2>Included in this release</h2> <div> <ul> <li><code>EMDN.csv</code>: EMDN dataset</li> <li><code>foiclass.zip</code>: FDA Product Code Classification Database</li> <li><code>GUDID_full_release.zip</code>: FDA GUDID data of American medical devices</li> <li><code>italian_medical_device_full_list.csv</code>: Italian medical devices dataset</li> <li>All output files described in the paper</li> <li>Additional files for further analysis</li> </ul> </div> <div> <h2>Acknowledgements</h2> </div> <div> <div> <pre>We thank the <a href="https://www.salute.gov.it/portale/home.html" target="_blank" rel="noopener">Italian Ministry of Health</a>, the <a href="https://www.fda.gov/" target="_blank" rel="noopener">U.S. Food and Drug Administration</a>, and the <a href="https://commission.europa.eu/index_en" target="_blank" rel="noopener">European Commission</a> for providing the datasets used in this project with public access.<br><br>For more information, please visit either this <a href="https://doi.org/10.5281/zenodo.13891146" target="_blank" rel="noopener">Zenodo repository</a> or the <a href="https://github.com/riccardogibello/md_nomenclature_mapping_project.git" target="_blank" rel="noopener">official GitHub repository</a>.</pre> </div> </div>
Environmental map layers for Los Angeles environmental assessment project
Open the record for dataset details and reuse information.
Stewardship Mapping And Assessment Project (STEW-MAP)
9/19/2013
Ward USGS Quadrangle Geologic Map, Niwot Ridge LTER Project Area, Colorado
Geologic data digitized from 1:24,000 USGS Ward, CO quad. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.
Integrated Terrain Unit Map (ITUM) for Martinelli Slope, Niwot Ridge LTER Project Area, Colorado
A 300 x 600 m integrated terrain unit map (ITUM) was produced at 1:500 scale inside the 350 x 650 m Martinelli grid, and the 1:500 digital elevation model (DEM). Vegetation was mapped using Komarkova's (1979) classification system (Braun-Blanquet) units. All map units were mapped to 1/8-inch minimum map-polygon-size resolution. The map is part of the Martinelli grid geographic information system (GIS). Many GIS projects use an approach in which existing mapped information is digitized into the GIS database directly from the original sources. The maps may have different map scale, map-unit resolutions, dates of data collection, and classification systems. When these different sources are combined in a GIS, artifacts may arise due to boundary mismatches and scale incompatibility (Dangermond and Harnden 1990). Integrated geobotanical mapping can minimize many of these problems. This method simultaneously maps vegetation and other terrain features that are interpreted on a common air-photo base (Everett et al. 1978, Walker et al. 1980). We use the term geobotany in its traditional European sense to refer to the study of plant communities and their relationships to geology, landforms, and soils (Braun-Blanquet 1932). Terrain geomorphic boundaries are used to guide the delineation on aerial photographs of most major vegetation boundaries similiar to the landscape-guided vegetation mapping approach developed in Europe (Zonneveld 1988) and the integrated terrain unit mapping approach developed by the Environmental System Research Institute in Redlands, CA (Dangermond and Harnden 1990). Additional information concerning the Niwot Ridge LTER GIS can be found in Walker et al. (1993). [1]Braun-Blanquet, J. 1932. Plant sociology: The study of plant communities. New York: McGraw-Hill, 439 pp. [2]Everett, K.R., P.J. Webber, D.A. Walker, R.J. Parkinson, and J. Brown. 1978. A geoecological mapping scheme for Alaskan coastal tundra. Third International Conference on Permafrost, 10-13
Radarsat Antarctic Mapping Project Digital Elevation Model, Version 2
The high-resolution Radarsat Antarctic Mapping Project (RAMP) Digital Elevation Model (DEM) combines topographic data from a variety of sources to provide consistent coverage of all of Antarctica.
High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labelling
GEO Series GSE210174. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
University of Washington Human Reference Epigenome Mapping Project
GEO Series GSE18927. Homo sapiens. 758 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by array; Expression profiling by high throughput sequencing.
High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labelling [bulk RNA-seq]
GEO Series GSE210173. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
UCSF-UBC Human Reference Epigenome Mapping Project
GEO Series GSE16368. Homo sapiens. 529 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing; Expression profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
BI Human Reference Epigenome Mapping Project: ChIP-Seq in human subject
GEO Series GSE19465. Homo sapiens. 358 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
BI Human Reference Epigenome Mapping Project
GEO Series GSE17312. Homo sapiens. 633 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by array.
UCSD Human Reference Epigenome Mapping Project
GEO Series GSE16256. Homo sapiens. 878 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing; Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Broad Institute L1000 Connectivity Map and RNAseq gene expression profiles on tissue samples from the GTEx project
GEO Series GSE92743. Homo sapiens. 0 samples. Type: Expression profiling by array.
High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labelling [scRNA-seq]
GEO Series GSE210172. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
A mapping between Bot Commit, Projects, Files, and Blobs
<p>This dataset contains information about 150,633,947 file updates by 12,326,137 bot commits, in terms of blob updates.</p> <p>Each line in the file is of the following form:</p> <p>commit_sha; projects (projectOwner_projectName) associated with the commit separated by comma; filename; new blob associated with the file; old blob associated with the file (before the commit)</p> <p>Example:<br> 00000a0cee231959811e6c5d808730240b325417;guyellis_plant;package.json;fadda016e338f970adc62395e77aab80b09fa11b;2e7b221f2bfdeda4ef78d3a56e039e456b6901bb</p> <p><br> Contents of each blob can be accessed using the GitHub API:</p> <p>https://api.github.com/repos/<projectOwner>/<projectName>/git/blobs/<blob id></p> <p>Example:</p> <p>https://api.github.com/repos/guyellis/plant/git/blobs/fadda016e338f970adc62395e77aab80b09fa11b</p> <p>See <a href="https://developer.github.com/v3/git/blobs/">https://developer.github.com/v3/git/blobs/</a> for more deatils.</p> <p>If you're using our data, please do not forget it cite it !!!</p> <p> </p>
ECFAS Pan-EU Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211), www.ecfas.eu
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p> <p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. </p> <p><em><strong>Reference literature:</strong></em></p> <p><strong><em>Duo, E., Montes, J., Le Gal, M., Fernández-Montblanc, T., Ciavola, P., and Armaroli, C.: Validated probabilistic approach to estimate flood direct impacts on the population and assets on European coastlines, Nat. Hazards Earth Syst. Sci., 25, 13–39, <a href="https://doi.org/10.5194/nhess-25-13-2025">https://doi.org/10.5194/nhess-25-13-2025</a>, 2025.</em></strong></p> <p><em>Montes, J., Duo, E., Souto, P., Gastal, V., Grigoriadis, D., Le Gal, M., Fernández-Montblanc, T., Delbour, S., Ieronymidi, E., Armaroli, C., and Ciavola, P.: Evaluating coastal flood impacts at the EU-scale: the ECFAS approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11295, <a href="https://doi.org/10.5194/egusphere-egu22-11295">https://doi.org/10.5194/egusphere-egu22-11295</a>, 2022.</em></p> <p><strong>Description of the files contained in the Dataset</strong></p> <p>The ECFAS Pan-EU Impact Catalogue collects impact layers associated to the flood scenarios contained in the ECFAS Pan-EU Flood Catalogue. To produce the Flood Catalogue, the coast was divided into geographic regions embracing similar oceanographic conditions, and subsequently into coastal sectors. The coastal sectors can be identified by its region index RXXX and its own index CSYYY. Impacts associated to the flood maps were calculated following the approach described in the technical document of the ECFAS Deliverable 5.3 Algorithms for Impact Assessment (ECFAS Impact Tool; Duo et al., 2021). The ECFAS Impact Tool was adapted to assess the affected population, the damage to buildings, roads and railways and the exposure of a variety of other assets (e.g. agriculture, points of interest, etc.) for the flood scenarios included in the ECFAS Flood Catalogue.</p> <p>The shapefile of the polygons defining the coastal sectors as defined for the catalogue implementation is included in the database.</p> <p><strong>The Pan-EU Impact Catalogue is associated with the following additional ECFAS products: </strong></p> <p><strong>- The ECFAS Pan-EU Flood Catalogue</strong></p> <p><strong>Flood Catalogue in Zenodo</strong>: Le Gal, M., Fernández Montblanc, T., Montes Pérez, J., Duo, E., Souto Ceccon, P.E., Cabrita, P., & Ciavola, P. (2022). ECFAS Pan-EU Flood Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211), https://www.ecfas.eu/ (1.2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6778807">https://doi.org/10.5281/zenodo.6778807</a></p> <p><em><strong>Flood Catalogue Reference literature</strong>: Le Gal, M., Fernández-Montblanc, T., Duo, E., Montes Perez, J., Cabrita, P., Souto Ceccon, P., Gastal, V., Ciavola, P., and Armaroli, C.: A new European coastal flood database for low–medium intensity events, Nat. Hazards Earth Syst. Sci., 23, 3585–3602, <a href="https://doi.org/10.5194/nhess-23-3585-2023">https://doi.org/10.5194/nhess-23-3585-2023</a>, 2023.</em></p> <p><strong>- The ECFAS Impact Tool</strong></p> <p><strong>Impact Tool in Zenodo</strong>: Duo, E., Montes Pérez, J., and Souto-Ceccon, P.E. (2021). ECFAS Impact Tool, D5.3 – Algorithms for impact assessment - ECFAS project (GA 101004211), <a href="http://www.ecfas.eu/">www.ecfas.eu</a>, link: <a href="https://doi.org/10.5281/zenodo.5809296">https://doi.org/10.5281/zenodo.5809296</a></p> <p> </p> <p>The Impact Catalogue is accompanied by a technical document describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues:</p> <p>- Duo, E., Le Gal, M., Souto Ceccon, P.E., Montes Pérez, J., 2022. <a href="https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5ee287a5d&appId=PPGMS">Technical document</a> on the ECFAS Flood and Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). <a href="http://www.ecfas.eu/">www.ecfas.eu</a></p> <p> </p> <p>This ECFAS <strong>Impact Catalogue</strong> is made available under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</a>. Any rights in individual contents of the Impact Catalogue are licensed under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/dbcl/1.0/">http://opendatacommons.org/licenses/dbcl/1.0/</a>.</p> <p>The <strong>technical document</strong> describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues is made available under the <strong>Creative Commons Attribution 4.0 International License</strong>.</p> <p>*The size of the uncompressed dataset is 211 GB.</p> <p><em><strong>Disclaimer:</strong></em></p> <p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p> <p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p> <p> </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.