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13,117 results for “Maps”
Open-SEA-Rice-10: Open Access High-Resolution Maps of Rice Harvested Area and Cropping Intensity in Southeast Asia
<h2>Cite this article</h2> <p>Ginting, F.I., Rudiyanto, R., Fatchurrachman, F., Mohd Shah, R., Che Soh, N., Goh Eng Giap, S., Fiantis, D., Setiawan, B.I., Schiller, S., Davitt, A., Minasny, B. High-resolution maps of rice cropping intensity across Southeast Asia. Scientific Data [12, 1408] (2025). <a href="https://www.nature.com/articles/s41597-025-05722-1">https://doi.org/10.1038/s41597-025-05722-1</a></p> <p> </p> <h2>Data Description</h2> <p>The datasets are high-resolution mapping of rice cropping intensity across Southeast Asia using the integration of Sentinel-1 and Sentinel-2 data</p> <ul> <li>The data file is in “.tif" format</li> <li>Spatial extent: Southeast Asia</li> <li>Pixel size: 10 m</li> <li>Projection information: EPSG: 4326</li> <li>CropType: Paddy rice.</li> <li>Year: Values from 2021</li> <li>Raster class:<br>-1 is single rice cropping area; <br>-2 is double rice cropping area and<br>-3 is triple rice cropping area</li> <li>The data also can be viewed on the GEE App (<a href="https://ee-rudiyanto.projects.earthengine.app/view/open-sea-rice-10">https://ee-rudiyanto.projects.earthengine.app/view/open-sea-rice-10</a>) and the Climate TRACE platform (<a href="https://climatetrace.org/">https://climatetrace.org/</a>) </li> <li>Correspondence to: Rudiyanto (rudiyanto@umt.edu.my) and Budiman Minasny (budiman.minasny@sydney.edu.au)</li> </ul> <p> </p> <p> </p>
Ultra-high scale cytometry-based cellular interaction mapping - Data repository
<p>This is the repository for datasets used in Vonficht, Jopp-Saile, Yousefian, Flore <em>et al. </em>Ultra-high scale cytometry-based cellular interaction mapping, <em>Nature Methods </em>(2025) <a href="https://doi.org/10.1038/s41592-025-02744-w" rel="nofollow">https://doi.org/10.1038/s41592-025-02744-w</a>. Associated analysis code can be found at <a href="https://github.com/agSHaas/ultra-high-scale-cytometry-based-cellular-interaction-mapping">https://github.com/agSHaas/ultra-high-scale-cytometry-based-cellular-interaction-mapping</a>, and the repository for the accompanying R package is hosted at <a href="https://github.com/agSHaas/PICtR">https://github.com/agSHaas/PICtR</a>. </p>
Map of livestock density in Central Appenines: a standardised protocol
<p>Shapefile and final table of the distribution of the grazing livestock in a mid-mountain area of Central Apennines, relatives to the paper entitled "A standardised protocol for mapping grazing density: a case study from Central Italy"</p>
Glacial Lake Image Dataset for "Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset"
<p>Glacial lake dataset for the paper <strong>"Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset" (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jhydrol.2025.133072" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jhydrol.2025.133072</a>)</strong></p> <p>The GLID dataset contains a total of 18,367 samples, and the size of each sample is 512*512. Each sample consists of an image and a corresponding label. Four glacial lake types including supraglacial lake, proglacial lake, ice-marginal lake, and unconnected glacial lake are involved. The pixel value of the glacier lake in annotation map is labeled 255 and background is labeled 0.</p> <p>GLID.rar contains the training dataset (16,000 samples), and val.zip contains the validation dataset (2,367 samples).</p> <p>GLID_annotation.zip contains the annotated shapefile of GLID with a CRS of WGS 84.</p> <p>Optical_images_source.xlsx contains the optical images ID/names and acquisition time of each platform (e.g., WV2, LC08, S2B, and GF02) used in GLID.</p> <p>Transferability validation.zip contains the images, labels, and predictions for transferability validation, which is independent of GLID (not used for model training or validation). The file structure is shown below:</p> <p>Transferability validation.zip</p> <ul> <li>images <ul> <li>AS.tif</li> <li>GL.tif</li> <li>NA.tif</li> <li>SA.tif</li> </ul> </li> <li>labels <ul> <li>AS_gt.tif</li> <li>GL_gt.tif</li> <li>NA_gt.tif</li> <li>SA_gt.tif</li> </ul> </li> <li>predictions <ul> <li>AS_pred.tif</li> <li>GL_pred.tif</li> <li>NA_pred.tif</li> <li>SA_pred.tif</li> </ul> </li> </ul> <p>AS, GL, NA, and SA represent Asia, Greenland, North America, and South America, respectively. Four high-quality Landsat-8/9 images (each cloud cover less than 6%) were used for testing, and we manually annotated the glacial lakes in each image as labels. The pixel value of the glacier lake in annotation map is labeled 255 and background is labeled 0. Files in transferability validation.zip have a same CRS of WGS 84.</p> <p>If you find this dataset is helpful in your research, please consider <strong><em>cite </em></strong>this paper:</p> <blockquote> <p><em>Ma D, Li J, Jiang L. 2025. Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset. Journal of Hydrology 657: 133072.</em></p> </blockquote>
Developmental pyrethroid exposure disrupts molecular pathways for MAP kinase and circadian rhythms in mouse brain
<p><span>Neurodevelopmental disorders (NDDs) are a category of pervasive disorders of the developing nervous system with few or no recognized biomarkers. A significant portion of the risk for NDDs, including attention deficit hyperactivity disorder (ADHD), is contributed by the environment, and exposure to pyrethroid pesticides during pregnancy has been identified as a potential risk factor for NDD in the unborn child. We recently showed that low-dose developmental exposure to the pyrethroid pesticide deltamethrin in mice causes male-biased changes to ADHD- and NDD-relevant behaviors as well as the striatal dopamine system. Here, we used an integrated multiomics approach to determine the broadest possible set of biological changes in the mouse brain caused by developmental pyrethroid exposure (DPE). Using a litter-based, split-sample design, we exposed mouse dams during pregnancy and lactation to deltamethrin (3 mg/kg or vehicle every 3 days) at a concentration well below the EPA-determined benchmark dose used for regulatory guidance. We raised male offspring to adulthood, euthanized them, and pulverized and divided whole brain samples for split-sample transcriptomics, kinomics and multiomics integration. Transcriptome analysis revealed alterations to multiple canonical clock genes, and kinome analysis revealed changes in the activity of multiple kinases involved in synaptic plasticity, including the mitogen-activated protein (MAP) kinase ERK. Multiomics integration revealed a dysregulated protein-protein interaction network containing primary clusters for MAP kinase cascades, regulation of apoptosis, and synaptic function. These results demonstrate that DPE causes a multi-modal biophenotype in the brain relevant to ADHD and identifies new potential mechanisms of action.</span></p>
Perceptual maps of Heliconiini butterflies: images, 3D spaces, 2D maps, and mimicry ring listings
<h3>Summary</h3> <p>This repository contains<strong> images</strong>, <strong>3D animated spaces</strong>, <strong>2D perceptual maps with GMM</strong>, and <strong>mimicry ring lists</strong> for heliconiine butterflies complementing the analyses presented in this research paper:<em><strong> </strong>"Doré et al., 2025 - Perceptual maps reveal rampant convergence in butterfly wing patterns across the Neotropics. in prep."</em>.</p> <h3>Abstract</h3> <p> In 1879, Fritz Müller formulated the first mathematical evolutionary model to explain mutualistic mimicry between coexisting defended prey. Yet, the degree to which local mimicry drives the structure of prey aposematic signals at continental scale remains unclear, because the perception of pattern similarity has never been assessed at large spatial scale. Here, we implement a Citizen Science survey to quantify and analyze the structure of perceived variation in the wing patterns of heliconiine butterflies (Nymphalidae: Heliconiini) throughout the entire Neotropics. Despite a continuum of perceived wing patterns at the continental scale, we show that the convergence of sympatric species into discrete mimicry rings is ubiquitous among communities. These results expand Müller’s historical predictions by supporting the rampant convergence of prey signals across an entire continent. </p> <h3>Contents</h3> <p>This repository contains three folders:</p> <ul> <li><em>"3D_maps"</em> contains the <strong>animated 3D perceptual spaces</strong> of heliconiine wing patterns for the Citizen Science dataset (N = 432) and the Local reference for the five local communities highlighted in the article.</li> <li><em>"Clustering"</em> contains the <strong>2D perceptual maps</strong> and associated <strong>lists of mimicry rings</strong> built for each of the five local communities, for different level of clustering from GMM (K from 5 to 10).</li> <li><em>"Images"</em> contains the 432 <strong>images of dorsal wing patterns</strong> of heliconiine butterflies used in the online survey (<a href="https://memometic.cleverapps.io/">https://memometic.cleverapps.io/</a>) designed for this study.</li> </ul> <h3>How to cite</h3> <p>Please cite this research article as:</p> <blockquote> <p>Doré, M., Pérochon, E., Aubier, T.G., Le Poul, Y., Joron, M., Elias, M., 2025. Perceptual maps reveal rampant convergence in butterfly wing patterns across the Neotropics. in prep. <a href="https://doi.org/TBA">https://doi.org/TBA</a></p> </blockquote> <h3>Associated ressources</h3> <p>The source codes for the analyses carried out in the study are available on <a href="https://github.com/MaelDore/Perceptual_map_Heliconiini">GitHub</a>.<br> <br>The occurrences data and distribution maps used in this study are publicly available from Zenodo: Occurrences data at <a href="https://doi.org/10.5281/zenodo.10906853">https://doi.org/10.5281/zenodo.10906853</a>; Distribution maps at <a href="https://doi.org/10.5281/zenodo.10903661">https://doi.org/10.5281/zenodo.10903661</a>.</p> <p>The online Citizen Science survey on the perception of mimicry in wing color patterns of heliconiine butterflies is temporary available at <a href="https://memometic.cleverapps.io/">https://memometic.cleverapps.io/</a>.<br>Source code for the online Citizen Science survey are accessible on <a href="https://github.com/MaelDore/Memometic_website">GitHub</a>.</p>
WELCOME to the "Old Survey of India Maps" Collection
<p>Free downloads of about 26,900 classic maps issued by the Survey of India and its descendant organizations in Pakistan, Bangladesh and Burma. The collection includes maps of Pakistan, India, Nepal, Bhutan, Bangladesh and Burma dating from the 1880s through to the 2010s, as well as some even older historical maps.</p> <p>The "Map Selection and Download Spreadsheet" file below can be downloaded to provide an easy-to-use tool to view the file names of all the maps available on this website. <strong>Each of the filenames in the spreadsheet is a link to the map file, and a click on the file name will download the map to the viewers computer. </strong>This file can be stored by the viewer for future use, or, as the collection grows, an updated file can be obtained periodically from this website. The file is issued in an MS Excel format, but it can be opened by Google Sheets or other spreadsheet software.</p> <p>The map collection is broken down into 19 different categories based on topic, scale and geographic area. A tab at the bottom of the spreadsheet opens the page for each category.</p>
Mapping the interaction surface between CaVβ and actin and its role in calcium channel clearance
<p><strong>HADDOCK protein-protein docking data for the Ca<sub>V</sub>β/F-actin complex models reported in "Mapping the interaction surface between Ca<sub>V</sub>β and actin and its role in calcium channel clearance".</strong></p> <p> </p> <p>The dataset is divided in four different folders:</p> <ol> <li><strong>Cavbeta2:</strong> data for the docking between dimeric actin (PDB 5OOE) and Ca<sub>V</sub>β<sub>2</sub> (PDB 5V2P) using XL-MS-derived distance restraints </li> <li><strong>Cavbeta4:</strong> data for the docking between dimeric actin (PDB 5OOE) and Ca<sub>V</sub>β<sub>4</sub> (PDB 1VYV) using XL-MS-derived distance restraints </li> <li><strong>Monomer:</strong> data for the control docking between <em>monomeric</em> actin (PDB 5OOE) and Ca<sub>V</sub>β<sub>2</sub> (PDB 5V2P) using XL-MS-derived distance restraints </li> <li><strong>Ab_initio:</strong> data for the control dockings between dimeric actin (PDB 5OOE) and Ca<sub>V</sub>β<sub>2</sub> (PDB 5V2P) <em>without</em> XL-MS-derived distance restraints and using the <em>ab initio</em> options available in HADDOCK 2.4</li> </ol> <p> </p> <p>Each of the <strong>Cavbeta</strong> folders (1-2) and the <strong>Monomer</strong> folder (3) contain:</p> <p>- Inputs: </p> <ul> <li>HADDOCK run parameter file (job_params.json)</li> <li>XL/MS-derived unambiguous distance restraints (unambig.tbl)</li> <li>Bioinformatics-derived (CPORT and NACCESS) ambiguous distance restraints (ambig.tbl)</li> </ul> <p>- Outputs:</p> <ul> <li>Top 4 models of the selected HADDOCK cluster (cluster1_1.pdb, ..., cluster1_4.pdb)</li> <li>Source data for the docking analyses presented in the Supplementary Information (Supplementary Figures 4, 6, 9, 13 and 14 and Supplementary Table 4)</li> </ul> <p> </p> <p>The <strong>Ab_initio</strong> folder (4) contains:</p> <p>- Input: </p> <ul> <li>HADDOCK run parameter files for each of the six control simulations presented in Supplementary Table 6 (job_params.json)</li> </ul> <p>- Outputs:</p> <ul> <li>Source data for the docking analyses presented in Supplementary Table 6</li> <li>Model from control simulation 1 shown in Supplementary Figure 5</li> </ul> <p> </p>
Conceptual map.
<p>Part of the thesis of Sonia Verdugo Castro (USAL, Spain). <br> Title of the article: "The gender gap in STEM higher education studies: visualisation of literature".<br> Authors of the publication: Sonia Verdugo-Castro, Mª Cruz Sánchez-Gómez, Alicia García-Holgado, Francisco J. García-Peñalvo</p>
Landsat-derived annual maps of agricultural greenhouse in Shandong province, China from 1989 to 2018
<p>Using 8,450 Landsat images on the Google Earth Engine, we built the first Landsat-derived annual maps of agricultural greenhouse (AG) in Shandong province, China from 1989 to 2018. Two types of reference datasets, including AG and Non-AG, were labeled via visual inspection of high-resolution imagery available in Google Earth or Landsat imagery based on a 10 km grid sampling structure. The mapping window for each year was selected based on the vegetation growth and the phenological information. Classification for each year was carried out initially based on the random forest classifier after the feature optimization. A temporal consistency correction algorithm based on classification probability was then proposed to the classified AG maps for further improvement.</p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl 85)
<p>Ensembl 85 derived ID mapping database for use with <a href="https://bridgedb.github.io/">BridgeDb</a> and was created with <a href="https://github.com/bridgedb/create-bridgedb-genedb">custom code</a>.</p>
MAP 19 in Taxonomic Revision of the Spider Genus Actinopus Perty, 1833 (Araneae, Mygalomorphae, Actinopodidae)
MAP 19. Distribution map of Actinopus wallacei Chamberlin, 1917.
MAP 16 in Taxonomic Revision of the Spider Genus Actinopus Perty, 1833 (Araneae, Mygalomorphae, Actinopodidae)
MAP 16. Distribution map of Actinopus echinus Mello-Leit"o, 1949.
MAP 14 in Taxonomic Revision of the Spider Genus Actinopus Perty, 1833 (Araneae, Mygalomorphae, Actinopodidae)
MAP 14. Distribution map of Actinopus panguana sp. nov.
MAP 13 in Taxonomic Revision of the Spider Genus Actinopus Perty, 1833 (Araneae, Mygalomorphae, Actinopodidae)
MAP 13. Distribution map of Actinopus apiacas sp. nov.
MAP 9 in Taxonomic Revision of the Spider Genus Actinopus Perty, 1833 (Araneae, Mygalomorphae, Actinopodidae)
MAP 9. Distribution map of Actinopus parafundulus sp. nov. and Actinopus urucui sp. nov.
MAP 18 in Taxonomic Revision of the Spider Genus Actinopus Perty, 1833 (Araneae, Mygalomorphae, Actinopodidae)
MAP 18. Distribution map of Actinopus trinotatus Mello-Leit"o, 1938.
MAP 17 in Taxonomic Revision of the Spider Genus Actinopus Perty, 1833 (Araneae, Mygalomorphae, Actinopodidae)
MAP 17. Distribution map of Actinopus princeps Chamberlin, 1917.
MAP 2 in Taxonomic Revision of the Spider Genus Actinopus Perty, 1833 (Araneae, Mygalomorphae, Actinopodidae)
MAP 2. Distribution map of Actinopus mairinquensis sp. nov., and Actinopus apalai sp. nov.
MAP 7 in Taxonomic Revision of the Spider Genus Actinopus Perty, 1833 (Araneae, Mygalomorphae, Actinopodidae)
MAP 7. Distribution map of Actinopus dioi sp. nov. and Actinopus osbournei sp. nov.
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.