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13,117 results for “Maps”
NeurOST-SSH Maps for Ocean Data Challenge 2023a_SSH_mapping_OSE
<p>Global maps of sea surface height (SSH) and surface geostrophic currents generated using NeurOST, a deep learning for mapping SSH from nadir satellite altimetry and sea surface temperature, generated for the observing system experiment outlined in the Ocean Data Challenge '2023a_SSH_mapping_OSE'.</p> <p>Ocean Data Challenge link: https://github.com/ocean-data-challenges/2023a_SSH_mapping_OSE/tree/main </p> <p>These maps were made using only L3 SSH (not including SST).</p> <p>NeurOST citations:</p> <ul> <li>Martin, S. A., Manucharyan, G. E., and Klein, P. (2024). Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics. Geophysical Research Letters, 51, e2024GL110059. https://doi.org/10.1029/2024GL110059</li> <li>Martin, S. A., Manucharyan, G. E., and Klein, P. (2023). Synthesizing Sea Surface Temperature and Satellite Altimetry Observations Using Deep Learning Improves the Accuracy and Resolution of Gridded Sea Surface Height Anomalies. Journal of Advances in Modeling Earth Systems, 15, e2022MS003589. https://doi.org/10.1029/2022MS003589</li> </ul>
A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology
<p>Source Data and Supplementary Data associated with the paper “A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology” (DOI: https://doi.org/10.1101/2023.03.06.531441).</p>
Mean boat speed over the new NW Mediterranean PSSA on the UNEP-MAP Knowledge Management Platform
<p>This video was created to represent on KMaP (<a href="https://kmap.info-rac.org/">https://kmap.info-rac.org/</a>) mean boat speed during 2023 summer, extrapolated from AIS data taken from NETCCOBAMS (<a href="https://accobams.org/">https://accobams.org/</a>) over 1km square cells grid covering the entire <a href="https://wwwcdn.imo.org/localresources/en/KnowledgeCentre/IndexofIMOResolutions/MEPCDocuments/MEPC.380%2880%29.pdf">new PSSA</a> (Particularly Sensitive Sea Area). The purpose of the video is to showcase the variation of the mean speed of all the boats travelling through each cell of the grid on weekly basis, from the 5th of June to the 2nd of September 2023 and represents a starting point to simulate how AIS data could be used to monitor cetacean-boats strike risk.</p>
Mapping the relative accuracy of cross-ancestry prediction
<p>GWAS for six traits for SNPs from the UK Biobank arrays set, six traits for SNPs from the HapMap set, and three traits for SNPs from the ARIC study set.</p>
Genetic fine-mapping results for 56 NMR metabolites measured in 246,683 UK Biobank participants
<p>Fine-mapping credible sets for 56 metabolites measured in 246,683 UK Biobank participants using the Nightingale Health platform. Fine-mapping was performed using the https://github.com/AlasooLab/reGSusie workflow.<br><br>The 56_metabolites_finemapping_credible_sets.tsv file contains the fine-mapped credible sets for all 56 metabolites. The *_coloc5_final.tsv.gz files contain the log Bayes factors for each metabolite in each fine-mapped region. </p>
Data for Urban Seismic Source Mapping
<p>In this study, we explore the innovative use of existing and ubiquitous urban infrastructure--telecommunication optical fibers--to map urban seismic sources. We integrate seismic interferometry and beamforming algorithms to overcome the proximity limitations of previous urban seismology approaches. <br>Our method models the propagation of seismic surface waves to estimate the spatio-temporal distribution of seismic source power (SSP; average seismic energy per unit of time), enabling the detection and localization of urban seismic sources occurring remotely from optical fibers.</p> <p><code>Put these pickle files into '/urban_das/data/das_data/' and run the scripts at https://github.com/jingxiaoliu/urban_das</code></p> <p>If you use this implementation, please cite our papers:</p> <blockquote> <p>[1] Liu, J., Li, H., Noh, H. Y., Santi, P., Biondi, B., & Ratti, C. (2025). Urban sensing using existing fiber-optic networks. <em>Nature Communications</em>, <em>16</em>(1), 3091.</p> <p>[2] Yuan, S., Liu, J., Noh, H. Y., Clapp, R., & Biondi, B. (2024). Using vehicle‐induced DAS signals for near‐surface characterization with high spatiotemporal resolution. <em>Journal of Geophysical Research: Solid Earth</em>, <em>129</em>(4), e2023JB028033.</p> <p>[3] Liu, J., Yuan, S., Dong, Y., Biondi, B., & Noh, H. Y. (2023). TelecomTM: A fine-grained and ubiquitous traffic monitoring system using pre-existing telecommunication fiber-optic cables as sensors. <em>Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies</em>, <em>7</em>(2), 1-24.</p> </blockquote>
Mapping Soil Organic Carbon in the World's Largest Arid Mangrove Forest (Indus Delta, Pakistan): A Multi-Sensor Remote Sensing and Machine Learning Approach
<p>Mangrove forests play a crucial role in carbon sequestration, especially in arid regions where their ability to store carbon in soil is vital for mitigating climate change. The Indus Delta in Pakistan, the world’s largest arid mangrove forest system, lacks spatially explicit data on Soil Organic Carbon (SOC) despite its importance for conservation and carbon budgeting. This study aims to establish a baseline SOC map 2020 at 10 m spatial resolution using Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (MultiSpectral Instrument) satellite imagery, integrated with in-situ soil sampling. SOC predictions were made using a Classification and Regression Tree (CART) machine learning model within the Google Earth Engine platform, leveraging 40 predictor variables, including spectral bands and derived indices. A total of 53 topsoil (0-10 cm) samples were collected in February 2020 across the Indus Delta, and SOC was analyzed using the Walkley-Black method. The results showed an average SOC value of 65.88 Mg C ha⁻¹ with substantial spatial variability, ranging from 15.06 Mg C ha⁻¹ to 138.03 Mg C ha⁻¹ with a total of 0.91 Pg C. The CART model demonstrated high accuracy, with an R² of 0.95 and an RMSE of 9.18 Mg C ha⁻¹. However, the region faces challenges such as seawater intrusion and salinity, which threaten its ability to sequester carbon. With the first high-resolution SOC map for the Indus Delta, this study provides valuable insights for ecosystem management, conservation planning, and carbon budgeting. These findings of this study have the potential to significantly influence initiatives like REDD+ and Blue Carbon projects, which aim to enhance carbon sequestration while addressing the ecological challenges facing Pakistan’s mangroves</p>
Augmented Spaces and Maps, research project 2015–2019
<div> <p>"Augmented Space” (2015-2019) is a research project initiated by Dr. Christine Schranz which brings together both theoretical and and practical parts as a basic introduction to the field of digital cartographies. Complementary to the book publication “Augmented Spaces and Maps. Das Design von kartenbasierten Interfaces”, the AR application offers a historical perspective on the Campus of the Arts (formerly the site of the Basel bonded warehouse) and combines this view with a contemporary critique of the practice of art-related bonded warehouses. The project was carried at the FHNW Academy of Arts and Design in Basel, with the support from the Swiss National Science Foundation (SNSF).</p> <p>Principal Investigator, Book Editor: Dr. Christine Schranz</p> <p>Video Production: Maria Smigielska</p> </div>
Probabilistic Data Generating Process-based Crop Type Map for the EU 2010-2020
<h3>General Description</h3> <p>This dataset consists of probabilistic crop type maps for the EU-28 for the years 2010-2020 that distinguish 28 crop types at 1km resolution (EPSG:3035). The maps were generated using the Data Generating Process-based procedure developed by Baumert, Heckelei and Storm (2024) [<em><span><a href="https://doi.org/10.1016/j.ecoinf.2024.102836">https://doi.org/10.1016/j.ecoinf.2024.102836</a></span></em>]. We refer to this paper for details on the generation and validation of the maps. The code used to create the maps including a detailed list of the input data can be found here: <a href="https://github.com/JoBaumert/Probabilistic_Crop_Mapping_EU">GitHub - JoBaumert/Probabilistic_Crop_Mapping_EU</a> . </p> <h3>Downloadable Data</h3> <p>The file “EU_expected_crop_shares.zip” consists of 11 raster files, one for each year from 2010 – 2020. The raster files indicate the expected shares for each of the 28 distinguished crop types in a grid cell for the entire EU-28 (see readme.txt contained in the zipped folder). Note that this raster file does not contain uncertainty information.</p> <p>The other 28 zip files contain the entire crop map ensemble (i.e., including uncertainty information), each for one of the EU countries and the United Kingdom. Each of those zip files contain 11 raster files, one for each year from 2010 – 2020. Each raster file has 2830 bands: the first two bands indicate the weight of the cell (proportional to the utilized agricultural area in a cell) and the estimated number of agricultural fields in a cell, respectively. The next 28 bands indicate the expected shares for each of the 28 crops in the respective cell. The remaining 2800 bands compose the crop type map ensemble, i.e., 100 simulated crop shares for each of the 28 crops. The zipped country folder also includes a csv file named “bands” that describes which band refers to which crop. Note that all crop shares were multiplied by 1000 when writing them to the raster files (saving them as integers requires less storage capacity), i.e., if a crop share is 0.325 or 32.5% it will appear as 325 in the raster files. </p> <p>The distinguished crops are (with abbreviation used in "bands.csv"):</p> <ul> <li>Apples and other fruits, nuts and berries (APPL+OFRU)</li> <li>Barley (BARL)</li> <li>Citrus fruits (CITR)</li> <li>Durum wheat (DWHE)</li> <li>Flowers and ornamental plants (FLOW)</li> <li>Grassland (GRAS)</li> <li>Maize (both green maize as well as grain maize, LMAIZ)</li> <li>Rape and turnip (LRAPE)</li> <li>Nurseries (NURS)</li> <li>Oats (OATS)</li> <li>Other cereals (OCER)</li> <li>Other permanent crops (OCRO)</li> <li>Other forage plants (OFAR)</li> <li>Other industrial plants (OIND)</li> <li>Olives (OLIVGR)</li> <li>Rice (PARI)</li> <li>Potatoes (POTA)</li> <li>Pulses (PULS)</li> <li>Fodder roots and brassicas (ROOF)</li> <li>Rye (RYEM)</li> <li>Soybeans (SOYA)</li> <li>Sugar beets (SUGB)</li> <li>Sunflowers (SUNF)</li> <li>Soft/common wheat (SWHE)</li> <li>Other oilseeds and fibre crops (TEXT)</li> <li>Tobacco (TOBA)</li> <li>Fresh vegetables, melons, strawberries (TOMA+OVEG)</li> <li>Vineyards (VINY)</li> </ul> <p> </p> <p> </p>
A Systematic Mapping Study on Student Reflection in Software Engineering Project-Based Learning (supplementary material)
<p>This repository contains supplementary material for the manuscript "A Systematic Mapping Study on Student Reflection in Software Engineering Project-Based Learning". The CSV file is structured as follows:</p> <ul> <li>Column A represents the paper citation key generated by Zotero that we used as a unique identifier for all papers in this study.</li> <li>Columns B-I include the results of our data extraction as per the data extraction form.</li> <li>Columns J-AB include metadata about each paper.</li> </ul>
Open dataset for the research of "Assessing accuracy improvement of integrating digital footprints into gridded population mapping: spatiotemporal variations and data bias"
<p>Result datasets for "Assessing accuracy improvement of integrating digital footprints into gridded populationmapping:spatiotemporal variations and data bias":</p> <ol> <li> S1 is the results for gridded population mapping using different methods.</li> <li> S2 is the aggregate results of population mapping at county level.</li> <li> S3 is the results for intraday variation of population disaggregation accuracy,</li> <li> S4 is the data bias of different digital footprints.</li> </ol>
Potential map generated by the RF spatial model to define ideal zones for the occurrence of high density of giant trees in the Amazon
<p>The provided image is a theoretical map of giant tree density in the Amazon, generated from a spatial model based on the **Random Forest** algorithm. The map displays the spatial distribution of tree density, representing the number of trees taller than 60 meters per square kilometer (trees/km²). The model was developed using climatic, topographic, and soil variables to predict areas with higher concentrations of these giant trees.</p> <p>The areas are color-coded according to different density ranges, where:<br>- Lighter shades indicate lower tree density (≤ 5 trees/km²),<br>- Darker shades indicate higher density (up to 141 trees/km²).</p> <p>Biogeographic provinces within the Amazon biome, such as the **Guiana Shield**, **Xingu-Tapajós**, and **Roraima**, are highlighted, showing distinct density patterns across the Amazon region. This map is a valuable tool for understanding the spatial distribution of giant trees in the Amazon and plays a crucial role in conservation efforts and ecological monitoring in the region.</p>
Meta-data and coding for Resilience of Coastal Communities Nexus Evidence Map
<p>Data extracted and coded for studies relevant to the Evidence Map associated with the manuscript "Mapping research at the nexus of resilience, wellbeing and environmental sustainability in UK marine systems"</p>
Data from the article titled Mapping of networks and professional capital in the La Algarabía occupational center.
<p>Project PID2020-117020GB-I00 , funded by : Ministerio de Ciencia e Innovación de España/<br>AEI/10.13039/501100011033 and by the predoctoral contracts grant for the training of PhD implemented by<br>the [grant number PRE2021-098075]</p>
Supplementary tables for the paper: "Comprehensive Mapping of the AOP-Wiki Database: Identifying Biological and Disease Gaps"
<p><strong>Supplementary tables for the paper: "Comprehensive Mapping of the AOP-Wiki Database: Identifying Biological and Disease Gaps"</strong></p> <p><em>Original Research Article</em><br><strong>Frontiers in Toxicology</strong>, March 8, 2024<br>Section: Regulatory Toxicology<br><strong>Volume 6 - 2024</strong> | <a href="https://doi.org/10.3389/ftox.2024.1285768" target="_new" rel="noopener">https://doi.org/10.3389/ftox.2024.1285768</a></p> <p><strong>Authors</strong>:<br>Thomas Jaylet, Thibaut Coustillet, Nicola M. Smith, Barbara Viviani, Birgitte Lindeman, Lucia Vergauwen, Oddvar Myhre, Nurettin Yarar, Johanna M. Gostner, Pablo Monfort-Lanzas, Florence Jornod, Henrik Holbech, Xavier Coumoul, Dimosthenis A. Sarigiannis, Philipp Antczak, Anna Bal-Price, Ellen Fritsche, Eliska Kuchovska, Antonios K. Stratidakis, Robert Barouki, Min Ji Kim, Olivier Taboureau, Marcin W. Wojewodzic, Dries Knapen, Karine Audouze</p>
Lotus2 on Mycorrhizal Fungi in the Galaxy Training Network - Sample files for creating Mapping TSV
<p>Sample files for the last step on creating a Mapping TSV in the Galaxy Training Network tutorial on "Identifying Mycorrhizal Fungi from ITS2 sequencing using LotuS2"</p>
Updated 30 m resolution global annual burned area map
<p>Updated 30 m resolution global annual burned area maps (GABAM) of 2014-2021 are released for free download.The annual burned area map is defined as <strong>spatial extent of fires that occurs within a whole year and not of fires that occurred in previous years.</strong>GABAM was generated via an automated pipeline based on Google Earth Engine (GEE), using all the available Landsat images on GEE platform. The product was projected in a Geographic (Lat/Long) projection at 0.00025° (approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10°×10° tiles spanning the range 180°W–180°E and 80°N–60°S.<br>contacts: zhangzhaoming@aircas.ac.cn/zhangzm@radi.ac.cn</p>
Asprogiali orthophoto map
<p>The product was developed in the context of the project "Protecting the Inner Ionian Archipelago and Formicula' implemented by iSea in 2024 and funded by Blue Marine Foundation. The orthophoto map was created using drone imagery for the collection of groundtrouthing points on habitat types to assist in the mapping of Posidonia oceanica in close association with aquaculture fascilities in the municipality of Xiromero, Greece. </p>
BIL20 and BIL24 Assembled, Annotated, and Mapped Genomes
<p>The dataset is featured in the article <em>Beyond Buro</em>, which investigates the probiotic potential of <em>Limosilactobacillus fermentum</em> BIL20 and BIL24 using genomic analysis and probiotic assays. These strains were isolated from <em>burong isda</em>, a traditional fermented fish product from Arayat, Pampanga. The study adds to the limited research from the Philippines that applies genomic analysis and probiotic testing on LAB isolates from fermented foods, revealing their promising probiotic attributes and potential health advantages. The dataset includes genome assemblies, RAST and Prokka annotations, and mapped assemblies locating the probiotic-related genes and their functional categories.</p>
Data related to Mapping user-related comfort conditions to identify urban planning issues for improved quality of places in cities: The case of Ljubljana, Slovenia
<p>The data consists of parameters including air temperature, noise levels, humidity, and air quality (PM 2.5), measured using a portable ICT device while cycling through the city of Ljubljana between August 1 and August 30, 2022. The data is available as raw CSV files, with each measurement session stored in a separate file. An additional QGIS Project file (qgz) is included, along with maps (in jpg format in ppt) that visually represent the results presented in the article.</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.