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13,117 results for “Maps”
MAP 1 in Seven new species of the enigmatic spider genus Paratropis Simon, 1889 (Mygalomorphae, Paratropididae) from Ecuador
MAP 1. Distribution of newly described Paratropis. Paratropis carcosita sp. nov. (yellow circle), Paratropis pukallucha sp. nov. (orange circle), Paratropis esmeraldas sp. nov. (blue circle), Paratropis kapak sp. nov. (purple circle), Paratropis pasochoa sp. nov. (yellow star), Paratropis arenosa sp. nov. (red inverted triangle) and, Paratropis cryptica sp. nov. (green inverted triangle). Provinces abbreviations: Azuay (AZ), Bolívar (BO), Carchi (CA), Cañar (CAN), Chimborazo (CH), Cotopaxi (CO), El Oro (EO), Esmeralsdas (ES), Guayas (GU), Imbabura (IM), Loja (LO), Los Ríos (LR), Manabí (MA), Morona Santiago (MS), Napo (NA), Orellana (OR), Pastaza (PA), Pichincha (PI), Santa Elena (SE), Santo Domingo de los Tsáchilas (SD), Sucumbíos (SU), Tugurahua (TU), Zamora Chinchipe (ZC).
Experimental data of genetic mapping, fine mapping or agronomic traits, related to Figures 1 and 3
<p>This dataset reports the experimental data of <span>g</span>enetic mapping<span>,</span> <span>f</span>ine mapping<span>, </span>agronomic, flowering time and yield-related traits for NIL<sup>DAN340</sup> and NIL<sup>K22</sup>, agronomic, flowering time and yield-related traits for knockout of <em>qKDR1</em>, agronomic, flowering time and yield-related traits for knockout of microRPG1<span>.</span></p>
DMGAT: Predicting ncRNA-Drug resistance associations based on diffusion map and heterogeneous graph attention network
<p>Dataset for the paper: DMGAT: Predicting ncRNA-Drug resistance associations based on diffusion map and heterogeneous graph attention network</p>
Samples and 4-class predictions of agroforest-forest frontiers mapping in Peru
<p>This is a database for the study of agroforest-forest frontiers mapping conducted by Wanting Yang, etc.</p> <p>The data contained in the repository includes two parts.</p> <p>1. the training samples(polygons and rectagle_roi) annotated with Google Earth as reference. We used Google Earth Pro to make the date of Google Earth imagery match with PlanetScope imagery.</p> <p>2. the prediction from the 4-class model.</p> <p>Users can visualize the data with QGIS or other GIS software.</p>
SD4EO: Open Street Maps Northern France Patch Dataset
<p>This dataset has been created as part of the deliverables for ESA’s <a href="https://eo4society.esa.int/projects/sd4eo/">SD4EO project</a>.</p> <p>It consists of orthogonal patches from Open Street Maps taken on 2024, which cover large areas in northern France. They corresponds to large Sentinel-2 Tile areas: T31TCJ, T31TCN, T31TDN, T31UDP, T31UEP, T30UWU, T31TCL, T31TDL, T31TDM, T31TEM, T31TEN, T31TFM, T31UDQ, T31UEQ, T30TXT, T30TYS, T30UXU, T30UYU, T31TFN, T31UFP, T31UFQ and T30TVK.</p> <p>Each patch covers an area of approximately 1700x1700 meters (though the exact size may vary slightly with latitude). Patches located at the edges of Sentinel-2 tiles were excluded to avoid potential cutting or distortion issues. Likewise, patches near large bodies of water, including coastlines, were also discarded.</p> <p>It's important to note that, unlike the synthetic image dataset [link], in this case, the patches are always disjoint.</p> <p>To facilitate the use of these images for training generative AI models (such as GANs and Diffusion models), the patch size has been standardized to 512x512 pixels, providing an effective resolution of 3.3 meters per pixel.</p> <p>To simplify their usage, the images are stored in lossless PNG format.</p> <p>Each patch is also accompanied by real RGB+NIR pixel-by-pixel correspondent satellite images from Sentinel-2 in the sibling dataset <a href="https://zenodo.org/records/13957879">doi:10.5281/zenodo.13957879</a> Both this dataset and the Sentinel-2 dataset have been used to train a conditional diffusion model, which generated a synthetic dataset of 46.5 GB that can be accessed at this [<a href="https://zenodo.org/records/13208361">link</a>].</p> <p>The file naming convention is straightforward:</p> <p><span><span> </span><span> </span><span> </span><span> </span>Patch_<Tile Name>_<Column Number>_<Row Number>_OSM.png</span></p> <p>Columns and rows are always referenced within the same tile.</p> <p> </p> <p>The data from OpenStreetMap (OSM) is licensed under the <strong>Open Database License (ODbL)</strong>. This license allows you to freely use, modify, and distribute the data, provided we follow two key conditions:</p> <ol> <li><strong>Attribution</strong>: We must give appropriate credit to OpenStreetMap and its contributors when using their data.</li> <li><strong>Share-Alike</strong>: If we modify or build upon the data, you must distribute your derivative works under the same ODbL license.</li> </ol> <p>As a derivative dataset, we share it under the same terms. This ensures that any improvements or enhancements made to the data remain open and available to the public.</p> <p> </p> <p>The SD4EO Project is funded by ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and is supervised by ESA Φ-lab.</p>
D1.3 - Map and report of baseline exposure and vulnerability
<p>This document primarily describes the methodology and summarises the results and conclusions of Task 1.3 – Mapping of coastal cities exposure and vulnerability to climate effects and sea level rise. Itis part of the work included in WP1, whose final objective is to produce a high-level baseline risk map of extreme climate impacts and sea-level rise based on a semi-quantitative assessment of exposure and vulnerability for the ten CCLLs.</p>
FIGURE 8. Distribution map for Helicosina. Blue circles indicate H in Helicosina, a new genus of Neotropical Limosininae (Diptera: Sphaeroceridae)
FIGURE 8. Distribution map for Helicosina. Blue circles indicate H. crena sp. nov., red circles indicate H. sica sp. nov., and green circles indicate H. valga sp. nov.
Supplemental Data: Rapid Automated Mapping of Clouds on Titan with Instance Segmentation
<p>Supplemental data provided in conjuction with paper submitted to AGU JGR: Machine Learning and Computation.</p>
Flood Hazard Maps using Google Earth Engine: Thrace and Thessaly River Basin Districts (Greece)
<p>This dataset contains three raster files with a spatial resolution of 10 m, derived by the Google Earth Engine:</p> <p> </p> <p>1) DynamicWorld_Floods_2015_2023.tif: Number of days flooded for the River Basin District of Thrace (Greece) starting from 2015 until 2023</p> <p>2) Thessaly_2015_August2023.tiff: Number of days flooded for the River Basin District of Thessaly (Greece) starting from 2015 until August 2023</p> <p>3) Thessaly_2015_now.tiff: Number of days flooded for the River Basin District of Thessaly (Greece) starting from 2015 until January 2024</p> <p> </p>
Swift/BAT-GUANO upper limits map for S241009l
Open the record for dataset details and reuse information.
Map of possible raw material areas in Emilia-Romagna
<p><span>Preliminary map of areas in the Emilia-Romagna region of Italy with potentially significant concentrations of CRMs.</span></p>
Interactive maps: Unveiling hidden risks in healthcare from flood-induced transportation disruption
<p>We provide interactive maps in HTML format, viewable in standard web browsers, to illustrate the impact of flood events (indicated by the index in the title) on specific regions, focusing on hospitals at risk. This collection includes 321 maps, each representing a hospital where the change in service population exceeds 0.3. Hovering over the hospital symbols (marked with a plus) reveals meta information, including service population details before and after a flood event. Additionally, hovering over the polygons provides further insights into the hospital in focus, offering a detailed view of each facility’s status and impact zone.</p>
50k Maps of Burma--additional 8--20241029
<p>97 50k maps of Burma </p>
50k Maps of Burma--additional 5--20241029
<p>97 50k maps of Burma</p>
50k Maps of Burma--additional 4--20241029
<p>96 50k maps of Burma </p>
50k Maps of Burma--additional 2--20241029
<p>97 50k maps of Burma </p>
Simulation Code for Plotting the Range-Doppler Map
<p>This file contains simulation code for plotting a Range-Doppler map. Key parameters have been left blank and should be populated with your specific data before running the code. Ensure all required fields are correctly set to generate accurate simulation results.</p>
50k Maps of Burma--additional 12--20241029
<p>43 50k Maps of Burma</p>
Theory Use In Software Engineering: A Systematic Mapping Study
<p><strong>Abstract </strong></p> <p><strong>Context</strong>: The use of theories in Software Engineering research is not as common or explicit as in other fields,<br>with most studies focusing on technical aspects. However, establishing a more robust theoretical foundation could<br>significantly contribute to the maturation of Software Engineering as a science.</p> <p><strong>Objective</strong>: Therefore, this study investigates the use of theory in software engineering by applying the snowballing<br>technique to systematic literature reviews indexed by the main online databases over the last 16 years. It also analyzes<br>the extent of theory use and what, how, and where these theories are used.</p> <p><strong>Method</strong>: We conducted a systematic mapping study to classify evidence on theory definitions, papers’ quality,<br>research topics, methods, types, theory types, theory roles, and publication venues.</p> <p><strong>Results</strong>: Our results showed that the term “theory” varied among the literature due to inconsistent terminology,<br>thus necessitating a comprehensive approach for accurate identification. Although many theories are cited, only a tiny<br>percentage see repeated application across studies, with limited testing for relevance to practical software engineering<br>contexts.</p> <p><strong>Conclusion</strong>: Despite the increase in proposed theories, software engineering requires further attention to mature, as<br>most papers primarily use theory to justify or motivate experimental research questions. Furthermore, although there<br>is a diversity of research topics and an adaptation of external theories, only 16% of studies explicitly operationalize<br>theory, highlighting the need for more intentional theoretical integration and developing SE-specific frameworks.</p>
Regionalpcs ROSMAP Fine-mapped QTLs
<p>This dataset contains DAP-G fine-mapped QTL results for the following study:</p> <p><strong><em>"regionalpcs improve discovery of DNA methylation associations with complex traits"</em></strong></p> <p>Tiffany Eulalio*<sup>1</sup>, Min Woo Sun<sup>1</sup>, Olivier Gevaert<sup>1</sup>, Michael D. Greicius<sup>2</sup>, Thomas J. Montine<sup>3</sup>, Daniel Nachun*‡<sup>3</sup>, Stephen B. Montgomery*‡<sup>1,3</sup></p> <p>‡ These authors contributed equally as senior authors</p> <p>* Corresponding authors: Tiffany Eulalio (<a href="mailto:eulalio@alumn.stanford.edu">eulalio@alumn.stanford.edu</a>), Daniel Nachun (<a href="mailto:dnachun@stanford.edu">dnachun@stanford.edu</a>), Stephen B. Montgomery (<a href="mailto:smontgom@stanford.edu">smontgom@stanford.edu</a>)</p> <p> Author affiliations:</p> <p>1. Department of Biomedical Data Science, Stanford University, Stanford, CA</p> <p>2. Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA</p> <p>3. Department of Pathology, Stanford University, Stanford, CA</p> <p> </p> <p><strong>Dataset description</strong>:</p> <p>The DAP-G results are organized by region type (full gene, gene body, preTSS, and promoters), cell type (astrocytes, endothelial cells, neurons, oligodendrocytes, and bulk), and summary type (averages and regionalpcs).</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.