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13,117 results for “Maps”
DH duplicate page mappings: Duplicate DH1.1 page mappings
<p>DH1.1 taxa that don__t have unique page mappings, i.e., more than one DH1.1 taxon is mapped to the same EOLid.</p>
Singing Insects of North America: SINA maps
Singing Insects of North America (SINA) is a unique resource providing extensive content covering the crickets and katydids (and, to a much lesser extent, cicadas) of North America north of Mexico. This content includes audio recordings, images, text, and identification keys. SINA was created and is still maintained by Dr. Thomas J. Walker of the University of Florida, a pioneer and leader in the study of North American crickets and katydids beginning in the late 1950s and continuing into the 21st century. <p></p>http://entnemdept.ifas.ufl.edu/walker/Buzz/
Guided wave tomography for thickness mapping
<p>This dataset is for mapping thickness variation using non-dispersive guided waves through geometrical full waveform inversion.</p>
Fig. 2 in Allopatric chromosomal variation in Nematocharax venustus Weitzman, Menezes & Britski, 1986 (Actinopterygii: Characiformes) based on mapping of repetitive sequences
Fig. 2. Representative karyotype of Nematocharax venustus. Bar = 5 µm.
Map 3 in Erratum ad Linzer biol. Beitr. 38/1: 165-187 (2006) Crocus biflorus (Liliiflorae, Iridaceae) in Anatolia (Part Three)
Map 3: Phytogeographical classification.
Radar mosaic maps for wavelike banded convection over southern China (2013-2021)
<p>This radar mosaic maps dataset concludes 112 wavelike banded convective events over southern China from 2013 to 2021. Among 112 cases, 42 cases were identified to evolve with the presence of a wave duct.</p>
Blowin' in the wind: Mapping the dispersion of Metal(loid)s from Atacama Mining
<p><strong>Raw data from elemental and mineralogical analyses of surface sediments from Alto El Loa, Antofagasta Region, Chile.</strong></p> <p><strong>Blowin' in the wind: Mapping the dispersion of Metal(loid)s from Atacama Mining</strong></p> <p><strong>Authors: Nicolás C. Zanetta-Colombo<sup>1,2</sup> *, Carlos A. Manzano<sup>3,4 </sup>, Dagmar Brombierstäudl<sup>1</sup>, Zoë L. Fleming<sup>5,6</sup>, Eugenia M. Gayo<sup>6,7</sup>, David A. Rubinos<sup>8</sup>, Óscar Jerez<sup>9</sup>, Jorge Valdés<sup>10</sup>, Manuel Prieto<sup>11,12</sup>, Marcus Nüsser<sup>1,2</sup></strong></p> <p><strong><sup>1</sup></strong><sup> </sup>Department of Geography, South Asia Institute, Heidelberg University, Heidelberg, Germany.</p> <p><strong><sup>2</sup></strong><sup> </sup>Heidelberg Center for the Environment (HCE), Heidelberg University, Heidelberg, Germany.</p> <p><strong><sup>3</sup></strong><sup> </sup>Departamento de Química, Facultad de Ciencias, Universidad de Chile, Santiago, Chile.</p> <p><strong><sup>4</sup></strong><sup> </sup>School of Public Health, San Diego State University, San Diego, CA, USA</p> <p><strong><sup>5</sup></strong><sup> </sup>Centro de Investigación en Tecnologías para la Sociedad, Universidad Del Desarrollo, Santiago, Chile.</p> <p><strong><sup>6</sup></strong><sup> </sup>Center for Climate and Resilience Research (CR)2, Chile.</p> <p><strong><sup>7</sup></strong><sup> </sup>Departamento de Geografía, Universidad de Chile, Santiago, Chile.</p> <p><strong><sup>8</sup></strong><sup> </sup>Sustainable Minerals Institute–International Centre of Excellence Chile (SMI-ICE-Chile), The University of Queensland, Australia, Las Condes, Santiago, Chile.</p> <p><strong><sup>9</sup></strong><sup> </sup>Instituto de Geología Económica Aplicada (GEA), University of Concepción, Chile. Barrio Universitario S/N, Concepción, Chile.</p> <p><strong><sup>10</sup></strong><sup> </sup>Laboratorio de Sedimentología y Paleoambientes (LASPAL), Instituto de Ciencias Naturales Alexander von Humboldt, Facultad de Ciencias del Mar y de Recursos Biológicos, Universidad de Antofagasta, Antofagasta, Chile.</p> <p><strong><sup>11 </sup></strong>Millenium Nucleus in Andean Peatlands (AndesPeat), Chile</p> <p><strong><sup>12 </sup></strong>Departamento de Ciencias Históricas y Geográficas, Universidad de Tarapacá, 18 de Septiembre 2222, Arica, Chile</p> <p> </p> <p><strong><span>Data Set 1 (ds1): </span></strong><span>Elemental concentrations (mg/kg) of metal(loid)s in surface sediment samples. The dataset also includes geographic coordinates (LONG, LAT), distance to the nearest mines (Dist_mines), distance to tailings (Dist_tailing), and buffer zone classifications for tailings (Buffer_T) and mines (Buffer_M).</span></p> <p><strong>Data set 2 (ds2)</strong>: Mineralogical composition of selected surface sediment samples. </p>
Bivariate GWA mapping reveals associations between aliphatic glucosinolates and plant responses to thrips and heat stress
<p>Supplemental data on bivariate GWA mapping of stress phenotypes and metabolomes of Arabidopsis.</p>
ForestPaths: European canopy cover map
<p>This repository contains a 10 m resolution canopy cover (at 5m height) map of the year 2020 over Europe derived from Sentinel-1 and Sentinel-2 data. The map is available as COGs over a 100 km grid in the spatial reference system EPSG 3035 (ETRS89 / LAEA Europe). </p> <p><strong>Known issues</strong></p> <p>- Due to the lack of GEDI data over the north of Europe, the accuracy of the model is expected to be lower above 52 degrees latitude. </p>
ForestPaths: European canopy height map
<p>This repository contains a 10 m resolution canopy height map of the year 2020 over Europe derived from Sentinel-1 and Sentinel-2 data. The canopy height model was trained using both GEDI and ICESat-2 data.</p> <p>The map is available as COGs over a 100 km grid in the spatial reference system EPSG 3035 (ETRS89 / LAEA Europe). </p> <p> </p>
ForestPaths: European FHD map
<p>This repository contains a 10 m resolution FHD map of the year 2020 over Europe derived from Sentinel-1 and Sentinel-2 data. The map is available as COGs over a 100 km grid in the spatial reference system EPSG 3035 (ETRS89 / LAEA Europe). </p> <p><strong>Known issues</strong></p> <p>- Due to the lack of GEDI data in the north of Europe, the accuracy of the model is expected to be lower above 52 degrees latitude.</p>
Inputs & results for "Identifying circular DNA using short-read mapping"
<div>* `inputs`: Contains genome assemblies, annotations, and sample sheets for the Nextflow pipeline</div> <div> * `parasite_species`: Contains the sample sheet and input data for the parasite/related species dataset</div> <div> * `parasitoid_wasps`: Contains the sample sheet and input data for the parasitoid wasp dataset</div> <div> </div> <div>* `results`: Contains filtered BAM and coverage files, figures, and `GenomeInfo`-filtered example files for the parasitoid wasp and parasite/related species datasets</div> <div> * `parasite_species`:</div> <div> * `bam_files`: Contains BAM files with mapped distances >= 1 kb</div> <div> * `coverage_files`: Contains coverage depth files filtered by the BAM files</div> <div> * `insert_filtered_results`: Contains examples of filtered file outputs from the `GenomeInfo` class. The complete set of outputs can be found on Zenodo.</div> <div> * `fig`: Figures generated for the pub.</div> <div> * `parasitoid_wasps`:</div> <div> * `bam_files`: Contains BAM files with mapped distances >= 1 kb</div> <div> * `coverage_files`: Contains coverage depth files filtered by the BAM files</div> <div> * `fig`: Figures generated for the pub.</div> <div> * `hyposoter_didymator_blastx_results`: Contains the manual BLASTx results from the _Hyposoter didymator_ search.</div>
PMF-LP: the first 10 m plastic-mulched farmland distribution map (2019-2021) in the Loess Plateau of China generated using training sample generation and classifier transfer method
<p>This dataset provides 10-m resolution plastic-mulched farmland distribution map in the Loess Plateau of China from 2019 to 2021</p> <p>*** The data file is in ".tif" format</p> <p>*** Temporal resolution: Annually</p> <p>*** Temporal coverage: 2019-2021</p> <p>*** Pixel size: 10 m</p> <p>*** Projection information: EPSG: 4326</p> <p>*** Values: 1 denotes plastic-mulched farmland (PMF) and 0 denotes non-plastic-mulched farmland (non-PMF)</p>
Drone-based mapping of Hiorthhamn in Svalbard
<p>This datasets contains more than 3,300 pictures that were during a drone-based mapping survey on 29 August 2020 in Hiorthhamn, Svalbard. The dataset was obtained with a DJI Mavic 2 Zoom drone during a one day campaign. The total coverage is about 8.5 km² and the ground resolution is about 5.5 cm/pixel. This dataset contains the raw images only. This dataset is used in the following publication: Nicu, I.C., Rubensdotter, L., Stalsberg, K., Nau, E. (2021). Coastal Erosion of Arctic Cultural Heritage in Danger: A Case Study from Svalbard, Norway. Water, 13(6), 784. doi.org/10.3390/w13060784.</p>
Aladdin: High-Resolution Maps of Left Atrial Displacements and Strains Estimated with 3D Cine MRI
<p>The uploaded files include high-resolution 3D images of the left atrium from 18 individuals—10 healthy volunteers and 8 patients with various cardiovascular diseases—along with their corresponding left atrium segmentation maps. Additionally, a deformation atlas based on the 10 healthy cases is also provided.</p> <p>For more information, visit: <a href="https://github.com/cgalaz01/aladdin_cmr_la" target="_new" rel="noopener">https://github.com/cgalaz01/aladdin_cmr_la</a></p>
Full summary statistics from eQTL mapping in heterogeneous differentiating cultures (HDCs)
<p>Full summary statistics, and significant gene-variant pairs, for the cis-eQTL mapping results presented in the manuscript: <em>Cell-type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures</em>. These include cell-type specific eQTL mapping in 29 cell types, dynamic eQTL mapping in 3 trajectories, and topic eQTL mapping with 10 topics. We additionally include a mash object, which includes posterior effect size estimates and local false sign rate for the analysis of eQTL effects across cell types. See README and manuscript for details.</p>
20m Africa rice distribution map in 2023
<p>This dataset provides a 20 m Resolution Annual Rice Distribution Map in 2023 of 34 African countries with rice area statistics over 5000 Ha(FAO, 2022). The accuracy of the dataset achieves an overall accuracy of 86.30%, with both user’s and producer’s accuracies exceeding 85%. The reliability of the dataset is verified by an R<sup>2</sup> values ranging between 0.93 and 0.98 with respect to different official statistics.</p> <p><strong>File format:</strong> tif</p> <p><strong>Spatial extent:</strong> Africa</p> <p><strong>Pixel size:</strong> 20 m</p> <p><strong>Projection information:</strong> EPSG: 4326 (WGS84)</p> <p><strong>Crop type: </strong>Paddy rice</p> <p><strong>Year:</strong> 2023</p>
Mapped Industrial Tree Plantations (ITP) in Caraga Region, Mindanao, Philippines for the Year 2019
<p>The Esri Shapefile, in UTM 51 WGS 1984 coordinate reference system, contains polygons of industrial tree plantation species (Falcata, Bagras, Yemane, and Mangium) in the Caraga Region, Mindanao, Philippines. The plantations were mapped through the classification of year 2019 Sentinel-2 satellite images, complemented by high-resolution satellite images available in Google Earth, as well as field surveys conducted between October 1, 2019 to September 30, 2021.</p> <p>Please refer to the Project 1 terminal report (<a href="../records/13735736" target="_blank" rel="noopener">https://zenodo.org/records/13735736</a>) for more details.</p>
Activation and connectivity maps - A chronometric relationship between circuits underlying learning and error monitoring in the basal ganglia and salience network
<p>Activation and connectivity maps of the study "A chronometric relationship between circuits underlying learning and error monitoring in the basal ganglia and salience network".</p> <ul> <li>Error-correct.nii corresponds to the statistical map of group-level differences in BOLD signal between correct and erroneous responses shown in figure 4;</li> <li>Late-initial.nii corresponds to the statistical map of group-level differences in BOLD signal between the initial and late learning periods shown in figure 5;</li> <li>Conn_error-correct_dACC.nii corresponds to the results from the seed-to-voxel gPPI analysis, using the dACC as seed region, showing areas of higher functional connectivity in erroneous compared to correct responses, shown in figure 8.</li> </ul>
Mapping the potential habitat suitability, and opportunities of bush encroacher species in Southern Africa: a case study of the SteamBioAfrica project.
<p>Senegalia mellifera (Benth) Seigler & Ebinger. , Dichrostachys cinerea (L.) Wight & Arn. and Terminalia sericea Burch. Ex DC., are three important bush encroacher species that contribute to the well-known ecological process named "thicketization" in Southern Africa. This issue has persisted for many years, impacting species distribution, plant communities, soil, and fauna dynamics. According to climate change projections, Southern Africa is expected to become drier and warmer in future scenarios, creating favourable conditions for proliferation of bush encroacher species.</p> <p>In the paper, we analyze the habitat suitability of these bush encroachers through MaxEnt 3.4.4 under three different future climate models and scenarios. Future projections were made using the shared socio-economic pathways (SSPs) for greenhouse gas concentrations, specifically SSP245-585 across two-time horizons: 2041-2060 and 2061-2180. We utilized <a href="https://www.worldclim.org/data/cmip6/cmip6climate.html">WorldClim 1-km resolution climate data</a> from three global Earth System Models (ESMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) that obtained better results than CMIP5 models (Bouramdane 2022; Mmame and Ngongondo 2024): the Max Planck Institute for Meteorology Earth System Model (MPI-ESM1.2-HR), the Hadley Climate Center Earth System Model (UK-ESM1.0-LL) and the Institute Model for Numerical Mathematics (INM-CM5-0). </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.