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13,117 results for “Maps”
Ecophysiology and specialized metabolite trait data for the sunflower association mapping population
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Data for: Oligonucleotide mapping via mass spectrometry to enable comprehensive primary structure characterization of an mRNA vaccine against SARS-CoV-2
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Integrating animal tracking datasets at a continental scale for mapping Eurasian lynx habitat
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Spatial probability maps of the superior parietal sulcus in the human brain
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Mapping of the QTLs governing grain micronutrients and thousand kernel weight in wheat (Triticum aestivum L.) using high density SNP markers
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Louse flies (Diptera: Hippoboscidae): United Kingdom, Republic of Ireland and Isle of Man: Citizen science data: Part 1, mapping
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ZipSeq : barcoding for real-time mapping of single cell transcriptomes
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Landuse classification map for the Central Arizona-Phoenix area for the year 2000
The simplified classification of Maricopa Association of Governments (MAG) land use map is created by combining the original land use categories into 5 major classes - urban, agriculture, recreation, desert, and other. This has been done for land use/land cover monitoring purposes and to compare the 2000 land use map with historic land use layers including the 1995 MAG land use map.
Martinelli slope 1:500 integrated terrain unit map (ITUM).
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 1g8-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).
Niwot Ridge saddle 1:500 integrated terrain unit map (ITUM).
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 1g8-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).
Source Index Map Layer for High-Resolution Orthorectified Imagery from Approximately 1990, Niwot Ridge LTER Project Area, Colorado
Citation Manley, W.F., Parrish, E.G., and Lestak, L.R., 2009, High-Resolution Orthorectified Imagery and Digital Elevation Models for Study of Environmental Change at Niwot Ridge and Green Lakes Valley, Colorado: Niwot Ridge LTER, INSTAAR, University of Colorado at Boulder, digital media. This vector shapefile is a source index map layer for the mosaic of orthorectified aerial photography from 1988 and 1990 for the Niwot Ridge Long Term Ecological Research (LTER) project. The index also covers the Green Lakes Valley portion of the Boulder Creek Critical Zone Observatory (CZO). The index polygons are attributed with source photo date and photo year. The mosaic is derived from approx. 1:40,000 scale, color infrared (CIR) photographs acquired by the United States Geological Survery (USGS) National Aerial Photography Program (NAPP). Other datasets available in this series includes orthorectified aerial photograph mosaics (for 1953, 1972, 1985, approximately 1990, 1999, 2000, 2002, 2004, 2006 and 2008), digital elevation models (DEM's), and accessory map layers. Together, the DEM's and imagery will be of interest to students, research scientists, and others for observation and analysis of natural features and ecosystems. 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.
Landscape Age Map for Hog Island, Northampton, Co., Virginia. 1852-1985
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Dataset 02: DS2.WP2_0.2 - Raman mapping results of LIFT printed graphene
<p>Raw Raman mapping results of LIFT printed graphene on SiO2/Si.</p>
Data package from: Mapping Atlantic rainforest degradation and regeneration history with indicator species using convolutional network
<p>This data package contains the very high resolution maps of natural forests as well as map of natural forests dominated by Cecropia hololeuca or Tibouchina pulchra presented in the Figure 8 of the paper "Mapping Atlantic rainforest degradation and regeneration history with indicator species using convolutional network". These maps have be produced with WorldView-2 and WorldView-3 very high resolution images and a deep learning method for image segmentation called U-net (methods and data are fully described in the article). When using this data, please cite the original article <a href="https://doi.org/10.1371/journal.pone.0229448">https://doi.org/10.1371/journal.pone.0229448</a>.</p> <p> </p>
Snow depth map and land cover map from satellite photogrammetry (Pleiades) in Tuolumne, California.
<p><strong>snow_depth_20170501_pleiades.tif</strong></p> <p>Snow depth from the difference of digital elevation models (DEMs) calculated from Pléiades images. The snow-on DEMs were acquired on 2017-05-01. The snow-off DEMs were acquired on 2017-08-08 and 2017-08-13.</p> <p> </p> <p><strong>land_cover_20170813-08_pleiades.tif land_cover_20170501_pleiades.tif</strong></p> <p>Land cover calculated from multi-spectral Pléiades images acquired on 2017-05-01 (snow-on) and 2017-08-08 and -13 (snow-off). Classes are snow (1), forest (2), bare rock/low vegetation (3), lake (4).</p>
Data from: Predictive mapping of the global power system using open data
<p>Three primary global data outputs from the research:</p> <ul> <li><strong>grid.gpkg:</strong> Vectorized predicted distribution and transmission line network, with existing OpenStreetMap lines tagged in the 'source' column</li> <li><strong>targets.tif:</strong> Binary raster showing locations predicted to be connected to distribution grid. </li> <li><strong>lv.tif:</strong> Raster of predicted low-voltage infrastructure in kilometres per cell.</li> </ul> <p>This data was created with code in the following three repositories:</p> <ul> <li>https://github.com/carderne/gridfinder</li> <li>https://github.com/carderne/predictive-mapping-global-power</li> <li>https://github.com/carderne/access-estimator</li> </ul> <p>Full steps to reproduce are contained in this file:</p> <ul> <li>https://github.com/carderne/predictive-mapping-global-power/blob/master/README.md</li> </ul> <p>The data can be visualized at the following location:</p> <ul> <li>https://gridfinder.org</li> </ul>
MeSDiCon subset for CodiEsp: MESH terms in MeSDiCon mapped to ICD10 CM and ICD10 PCS
<p>The MeSDiCon consists of a list or gazetteer of candidate names of diseases and symptoms mentioned in Spanish clinical texts. Thus MeSDiCon serves as a lexical resource or dictionary for automatic detection of disease/symptom mentions, as well as indexing or classification of medical texts with such concept types. Terms in MeSDiCon were mapped to MESH terminology.</p> <p>In this subset, we have mapped MESH codes to ICD10-CM and ICD10-PCS through UMLS Metathesaurus. Then, this resource contains diseases and symptoms terms from Spanish clinical texts mapped to MESH and ICD10.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020</p> <pre><code>@inproceedings{miranda2020overview, title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2020} }</code></pre> <p> </p> <p><strong>File structure</strong></p> <p>TSV. Data is separated by tabs (\t). Every row of the file has the following fields:</p> <pre><code>terminology identifier translatedTerm termCount documentCount ICD10CM-code ICD10PCS-code</code></pre> <p>In case one MESH term is mapped to more than one ICD10 code, they are separated by commas.</p>
SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections
<p>The submitted dataset, correspond to the RAW (*.czi format) and analysis files of the manuscript "SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections", which has been processed for revision (2020-01-31) by PLOS Biology.</p> <p>The files are in *.zip format and the naming follows thenumbering of the manuscript figures, which will be found in bioRxiv.org (<strong>ID#: BIORXIV/2020/938571</strong>). Each *.zip files contains a document with the description of all the provided files.</p>
MAP 2 in Studies in Australian Tettigoniidae: New Fully-winged Agraeciini From Northeastern Australia (Orthoptera: Tettigoniidae; Conocephalinae; Agraeciini)
MAP 2. Known distribution of two agraeciine species. - Greenagraecia cooloola gen. et sp. nov.yGeoffagraecia gwinganna gen. et sp. nov.
Maps of anomalies in heavy rainfall across Central Europe from the Little Ice Age to near present
<p>This dataset includes the data displayed in</p> <p>Förster, K., Thiele, L.-B. (2020): Variations in sub-daily precipitation at centennial scale. <em>npj Clim Atmos Sci</em> <strong>3, </strong>13. https://doi.org/10.1038/s41612-020-0117-1</p> <p>For each year, a csv file is provided containing anomalies as xyz point data (latitude, longitude, anomaly), referring to the center of each grid cell (~30 km).</p> <p> </p> <p> </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.