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13,117 results for “Maps”
Read-mapping for next-generation sequencing data (Drosophila melanogaster)
<p>Code, logs and quality-control data for whole-genome resequencing of Sussex-LH<sub>M</sub> and RG <em>Drosophila melanogaster</em>.</p> <p>Mapping code is in the archive lhm_mapping_scripts.zip</p> <p>Mapping logs are in the in the archive lhm_mapping_logs.zip</p> <p>Other zip archives contain the quality control data.</p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p>
Read-mapping for next-generation sequencing data (Wolbachia)
<p>Code, log files and QC data. NCBI SRA accession number SRP091004. Note that sequencing Wolbachia was not a central aim of the project, and was undertaken in order to maximise the amount of information that could be extracted from the raw genome sequence data targetted at the fruit-fly host.</p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p>
Fig. 1. Sampling map. A in Seven scuticociliates (Protozoa, Ciliophora) from Alabama, USA, with descriptions of two parasitic species isolated from a freshwater mussel Potamilus purpuratus
Fig. 1. Sampling map. A. Chewacla Creek, Auburn, Alabama (32º36′56″ N, 85º28ʹ58″ E). B. Orange Beach, Alabama (30º16′44″ N, 87º33′35″ E).
Metonymic mapping (CMT)
<p>Metonymic mappings in a conceptual metonymy as described by Conceptual Metaphor Theory (CMT) by George Lakoff and Mark Johnson. The actual image does not appear in their publications, but is based on them.</p>
Metaphorical mappings (CMT)
<p>Metaphorical mappings in a conceptual metaphor as described by Conceptual Metaphor Theory (CMT) by George Lakoff and Mark Johnson. The actual image does not appear in their publications, but is based on them.</p>
Friction Map for Brazil in 2014
<p><strong>Summary</strong></p> <p>This map shows friction values on a grid of 90x90 meters for Brazil. Friction values represent the average travel time by car, boat, train or foot (depending on the surface) to cross one grid-cell in horizontal or vertical space. Friction maps are fundamental for calculating accumulated costs maps that can serve as a substitute for infrastructure data in spatial modelling.</p> <p><strong>The base-data</strong> used for creating this friction map comprises:</p> <ol> <li>A layer of road-infrastructure data obtained from the Brazilian Ministry of transportation (Ministério dos Transportes e Departamento Nacional de Infraestrutura de Transportes). The data is available at http://pnlt.imagem-govfed.opendata.arcgis.com/ . Planned roads where filtered out of the dataset.</li> <li>A layer of rail-roads in Brazil from the same source as (1).</li> <li>A layer of oficial hidroways in Brazil from the same source as (1).</li> <li>A layer of land-use classes in 2014 obtained from Mapbiomas. The data is available for download here: http://mapbiomas.org/</li> <li>A layer of hydrological data that was composed from two different datasets from the Brazlian Water Agency (Agencia Nacional das Águas). The source data can be downloaded here: http://www.ana.gov.br/bibliotecavirtual/redeHidrografica.asp. The datasets from ANA are Drainage Network models based on SRTM data (2000) in the scale of 1:100.000 and in the scale of 1:250.000. We used the lower resolution data (1:250.000) to identify mayor rivers. However the spatial accuracy of this data is not sufficient for our purpose and affluent rivers to the main river are not included. We therefore developed a model to buffer the low resolution data-set with a 10km buffer on each side of the river. We than used this buffer areas to crop out the higher resolution data (1:100.000) thereby including affluent rivers of up to 10 km on each side of the main river. Furthermore the higher resolution data is spatially more accurate if compared to satellite imagery. </li> <li>A layer of sloped data based on global SRTM elevation data from 2000 available at: https://urs.earthdata.nasa.gov/</li> </ol> <p><strong>Description of the model</strong>:</p> <p>First all data was reprojected to WGS84, than the data was reclassified to hold travel times to cross one grid-cell for each class in seconds. The following assumptions on travel speed had been used for this purpose:</p> <ul> <li>Paved Roads: 60km/h</li> <li>Unpaved Roads: 40 km/h</li> <li>Waterbodies or navigable stretches in the flooding season: 10 km/h</li> <li>Hidroways: 20km/h</li> <li>Railways: 40km/h</li> <li>Forest: 3 km/h (comprises in the original data: forest, planted forest, coastal zone forest)</li> <li>Non-forest: 12 km/h (comprises: Agricultural areas, non-forest vegetation, pastures, others)</li> </ul> <p>Afterwards, all data-sources where rasterized at a resolution of 90x90meters and stacked in a rasterstack. Than the highest value of each grid-cell in the stack was extracted into a new summary raster. The summary layer is the raw friction map that needs to be corrected for the effect of slope. Slope was calculated from the SRTM1 data and rescaled to the same extent and resolution as the raw friction map. Slope was included as a constant factor with the following assumptions:</p> <ul> <li>slope between 0 and 5 degrees: slope constant 1 (travel speed is not impacted)</li> <li>slope between 5-10 degrees: slope constant 2 (travel speed is reduced by half)</li> <li>slope between 10-15 degrees: slope constant 3 ( travel speed is reduced to a third of the original speed)</li> <li>slope above 15 degrees: slope constant is 10 (travel speed is reduced to 10% of the original speed)</li> </ul> <p>Slope effects where not applied on water bodies. The corrected friction map was exported as a GeoTiff (WGS84) in a resolution of 90x90 meters.</p> <p><strong>Additional Information</strong>:</p> <p>The friction map was created using the R-software environment and GDAL. The script to replicated the results or modify the model with other base data can be obtained on request. The friction map can be used to calculate accumulated cost maps with GIS software (costdistance function in ArcGIS and r.cost function in QGIS).</p>
Flash Flood Risk Map Hallstatt / Gosau / Bad Goisern: Risk Map
Damage Risk Map of FFRM catchment Hallstatt / Gosau / Bad Goisern based on max. water depth in all timesteps, zoning plan, building density and specific damage function
IVMOOC 2017 - GloBI Data for Interactive Tableau Map of Spatial and Temporal Distribution of Interactions
<p>Global Biotic Interactions (GloBI, www.globalbioticinteractions.org) provides an infrastructure and data service that aggregates and archives known biotic interaction databases to provide easy access to species interaction data. This project explores the coverage of GloBI data against known taxonomic catalogues in order to <em>identify ‘gaps’ in knowledge of species interactions</em>. We examine the richness of GloBI’s datasets using itself as a frame of reference for comparison and explore interaction networks according to geographic regions over time. The resulting analysis and visualizations intend to provide insights that may help to enhance GloBI as a resource for research and education.</p> <p>Spatial and temporal biotic interactions data were used in the construction of an interactive Tableau map. The raw data (IVMOOC 2017 GloBI <em>Kingdom</em> Data Extracted 2017 04 17.csv) was extracted from the project-specific SQL database server. The raw data was clean and preprocessed (IVMOOC 2017 GloBI Cleaned Tableau Data.csv) for use in the Tableau map. Data cleaning and preprocessing steps are detailed in the companion paper.</p> <p>The <strong>interactive Tableau map</strong> can be found here: https://public.tableau.com/profile/publish/IVMOOC2017-GloBISpatialDistributionofInteractions/InteractionsMapTimeSeries#!/publish-confirm</p> <p>The<strong> companion paper</strong> can be found here: doi.org/10.5281/zenodo.814979</p> <p><strong>Complementary high resolution visualizations </strong>can be found here: doi.org/10.5281/zenodo.814922</p> <p><strong>Project-specific data </strong>can be found here: doi.org/10.5281/zenodo.804103 (SQL server database)</p>
Data set for Microscale and nanoscale strain mapping techniques applied to creep of rocks
<p>Data set (figures and data involved in their making) for Quintanilla-Terminel, A., M. E. Zimmerman, B. Evans, and D.L. Kohlstedt, Microscale and nanoscale strain mapping techniques applied to creep of rocks, Solid Earth Discuss., https://doi.org/10.5194/se-2017-27, in review, 2017.</p>
Ground Truth Dataset with mappings of companies to OpenCorporates legal entities
<p>Ground Truth Dataset with mappings of companies to OpenCorporates legal entities. Available data per company: company name, country of headquarters, state of headquarters (in case of US companies) and address of headquarters.</p>
Beikthano, Myanmar. Map of Beikthano.
<p>Beikthano, Myanmar. Map of Beikthano.</p>
EAST-WEST and VERTICAL deformation maps of Alto Tiberina Fault supersite: The Post-Proc service in Geohazard Exploitation Platform applied to Sentinel-1 dataset
<p>In the framework of the ESA funded project “MEMpHIS - Multi Scale and Multi Hazard Mapping Space based Solutions ”, the Istituto Nazionale di Geofisica e Vulcanologia (INGV) together with TRE-ALTAMIRA, generated the EAST-WEST and VERTICAL deformation maps of Alto Tiberina Fault supersite. The two maps of ground velocity were derived thanks to the adoption of a specific tool named "Post-Proc", implemented in MEMPHIS and with the support of Terradue, that is able to automatically re-project on the east-west and vertical directions the ascending and descending InSAR time series. In particular, the output refers to the deformation maps calculated by processing with SqueeSAR (TM) method, a large dataset acquired by the ESA Sentinel-1 mission.</p> <p>The Post-Proc tool also calculates the mean accelerations associated to each persistent scatterer in the scenes. Some additional features are also available from this tool:</p> <p>- Change the coherence threshold for selecting a subset of persistent scatterers</p> <p>- Activate a geometrical distortion filter to take into account the layover and foreshortening effects</p> <p>- Change the reference point (position and coherence)</p> <p>- Choose between two types of accelerations: 2<sup>nd</sup> order model or velocity derivative</p> <p>- Choose among three different projection: east-west, vertical, and downslope.</p> <p>The present dataset is composed of the EAST-WEST and VERTICAL acceleration maps. The data are in shapefile format: for each record (PS) the topography, velocity, acceleration, and InSAR coherence is reported.</p>
EOL Sample maps
<ol> <li><em>Gadus chalcogrammus</em> (species) </li> <li><em>Gadus macrocephalus</em> (species) </li> <li><em>Gadus morhua</em> (species) </li> <li><em>Gadus</em> (genus) </li> <li>Gadidae (family)</li> <li>Gadiformes (order) </li> </ol>
A cross-study transcriptional patient map of heart failure defines conserved multicellular coordination in cardiac remodeling
<p>Collection of auxiliary data to reproduce the results from "<strong>A cross-study transcriptional patient map of heart failure defines conserved multicellular coordination in cardiac remodeling</strong>". Source code is available at: https://github.com/saezlab/reheat2_pub<br><br>We provide processed data to facilitate access to the results, for the original count data, please see the associated manuscript for references to the original datasets. </p> <p> </p> <p> </p>
Map of the Roman mining hydraulic system - Las Médulas, León, Spain
<p>This resource is related with the Roman hydraulic system linked to Las Médulas gold mining complex in northwest Iberia. The dataset includes a detailed digital cartography of the hydraulic network, which extends over 1100km. It identifies 41 canals distributed between La Cabrera and El Bierzo regions, (33 and 8, respectively), with 14 canals supplying water to Las Médulas. Additionally, the dataset includes the locations of the Roman mining sites.</p> <p><a title="Map of the Roman mining hydraulic system - Las Médulas, León, Spain" href="https://earth.google.com/earth/d/1argDbqGaEMdlE7Uomb4z70LGYce9pCBb?usp=sharing">View on Google Earth (3D)</a></p> <p><a title="Map of the Roman mining hydraulic system - Las Médulas, León, Spain" href="https://www.google.com/maps/d/edit?mid=12yHtyAN3qJcmrt-P_3abv3rd6PaI_ZQ&usp=sharing">View on Google Maps (2D)</a></p> <p> </p>
Data from: Area of habitat maps and validated occurrences for neotropical birds of conservation concern
<p>Understanding species distributions is essential for advancing bird conservation, especially in the rapidly changing landscapes of the Neotropics, where habitat loss and degradation are accelerating. Area of Habitat (AOH) maps offer valuable spatial tools for illustrating species distributions by highlighting potentially suitable habitats within their geographic range. In this study, we generated AOH maps for 713 neotropical bird species of conservation concern, which includes species listed as globally or nationally threatened, endemic, or with restricted ranges. Using primary biodiversity data and a structured geospatial workflow, we refined approximately 2.5 million occurrence records through a flagging process and validated 50,743 records manually.<strong> </strong>This unparalleled effort led to the creation of high-quality AOH maps, along with altitude-corrected Extent of Occurrence (EOO-DEM) and Inverse Distance Weighted (IDW) range maps. Our AOH maps significantly improved species distribution predictions for 82% of species, over EOO-DEM maps. The validated occurrences and AOH maps produced in this study have wide-ranging applications, providing a valuable basis for the development of new species distribution models and for evaluating species’ natural history, extinction risk, and habitat threats. They also support the identification of priority areas for strategic conservation investments. Importantly, these maps played a key role in systematic conservation planning analyses for the Conserva Aves initiative, which is facilitating the creation of more than 80 new protected areas across Latin America, safeguarding 2 million hectares and improving the management of an additional 2 million hectares (<a href="https://conserva-aves.org/">https://conserva-aves.org/</a>).</p>
SeMRA Raw Semantic Mappings Database
<p>An automatically assembled dataset of raw semantic mappings produced by <code>python -m semra.database</code>. This incorporates mappings from the following places:</p> <ol> <li>Ontologies indexed in the Bioregistry (primary)</li> <li>Databases integrated in PyOBO (primary)</li> <li>Biomappings (secondary)</li> <li>Wikidata (primary/secondary)</li> <li>Custom resources integrated in SeMRA (primary)</li> </ol> <p>This is a database of raw mapping without further processing. For processed mapping datasets, we suggest smaller domain-specific processing rules (see <a href="https://github.com/biopragmatics/semra/tree/main/notebooks/landscape">https://github.com/biopragmatics/semra/tree/main/notebooks/landscape</a> for examples). It can be accessed directly via:</p> <ul> <li><code>mappings.sssom.tsv.gz</code> - loadable through any tools supporting SSSOM</li> <li><code>mappings.jsonl.gz</code> - loadable through SeMRA using <a href="https://semra.readthedocs.io/en/latest/api/semra.io.from_jsonl.html" target="_blank" rel="noopener"><code>semra.from_jsonl</code></a></li> </ul> <h2>How to Run the Web App</h2> <ol> <li>Download all artifacts from this Record</li> <li>Make sure that you have Docker running locally</li> <li>Run <code>sh run_on_docker.sh</code> from the command line</li> <li>Navigate to http://localhost:8773 to see the SeMRA dashboard or to http://localhost:7474 for direct access to the Neo4j graph database</li> </ol> <h2>Licensing</h2> <p>Mappings are licensed according to their primary resources. These are explicitly annotated in the SSSOM file on each row (when available) and on the mapping set level in the Neo4j graph database artifacts.</p>
Semantic Enrichment of the Laboratory Data Dictionary of the Study of Health in Pomerania (SHIP-START-4) with LOINC; Detailed Mapping Results
<p>Unlike West Germany, high morbidity and mortality have been observed in East Germany over the last century. The regional population-based Study of Health in Pomerania (SHIP) therefore investigates the long-term progression of sub-clinical findings, their determinants and prognostic values, to acquire knowledge that facilitates early diagnosis and thus helps prevent the progression of disease. The SHIP covers various areas of patient health. Each SHIP data set is accompanied by a data dictionary (DD) which provides descriptions of variables and definitions.</p> <p>This work shows the detailed mapping results of the semantic enrichment of the SHIP-START-4 medical laboratory data dictionary with LOINC codes. This work also provides detailed descriptions of the concepts applied in the semnatic enrichment. The results of this work serve as a critical step towards improving its interoperability and hence FAIRness for the SHIP laboratory-related measurements. </p>
World Map of Agricultural Robot Manufacturer & services
<p>This Data Base is a World Map of all the manufacturers of agricultural robot for field. this data base was created by 3 student of the University of UniLaSalle Beauvais. You can fin the LINK of the GOOGLE EARTH "only reading" on the PDF document.</p>
Global MVL loss map-induced by human expansions and natural disasters; Global mountain-PAs; Global AHRTMS
<p><span lang="EN-US">(1) Global MVL loss map-induced by human expansions and natural disasters</span></p> <p><span lang="EN-US">Global MVL loss map: a global mountain vegetated landscapes (MVL) loss map (during 2000-2020) at 30-m resolution was developed using global datasets on mountain boundaries, human land use, natural disasters together with Landsat imageries-derived NDVI. This map includes seven drivers that cause MVL loss (i.e. human expansions and natural disasters). The losses of MVL caused by human expansions include (i) human settlement growth, (ii) agriculture expansion, and (iii) mining. The losses of MVL caused by natural disasters (i.e. a net loss after deducting restored areas in disaster areas) include (vi) wildfires, (v) floods, (vi) landslides, and (xii) droughts. The data was stored in Global MVL loss map.gdb and can be opened through mxd file in ArcGIS software.</span></p> <p><span lang="EN-US">(2) Global mountain-PAs; </span></p> <p><span lang="EN-US">Global mountain PAs: the mountain-protected areas (PAs) was mapped using World Database of Protected Areas (WDPA) and GMBA mountain boundaries (<a name="OLE_LINK1"></a>v2.0 standard). The data was stored in</span><span lang="EN-US"> </span><span lang="EN-US">Global mountain-PAs.gdb and can be opened through ArcGIS software.</span></p> <p><span lang="EN-US">(3) Global AHRTMS</span></p> <p><span lang="EN-US">Global AHRTMS: the areas with high richness of threatened mountain-occurring species (AHRTMS) was produced with IUCN Red List threatened species (including mammals, amphibians, reptiles, birds and plants) and GMBA mountain boundaries (v2.0 standard). The data was stored in</span><span lang="EN-US"> </span><span lang="EN-US">Global AHRTMS.gdb and can be opened through ArcGIS software.</span></p> <p><strong><span lang="EN-US">A manuscript related to above data analysis has submitted to a journal.</span></strong></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.