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3,197 results for “atlas”
A global atlas of extreme wind speeds for wind energy applications
<p>Here we present a global, homogenized and geospatially explicit digital atlas of the sustained fifty-year return period wind speed (<em>U<sub>50</sub></em>)<em><sub> </sub></em>and associated confidence intervals based on ERA5 reanalysis output at 100 m a.g.l.. Four different approaches are used to derive <em>U<sub>50</sub></em> estimates using 40 years of hourly disjunct 20-minute sustained wind speeds. All rely on use of the Gumbel distribution to fit extreme wind speeds but differ in how the distribution parameters are derived. Resulting values of <em>U<sub>50</sub></em> are compared to reference wind speeds <em>U<sub>ref</sub></em> derived using five times the mean wind speed as specified in the International Electrotechnical Commission (IEC) wind turbine design standards. An observationally derived dataset used in evaluation of the atlas is also included, along with a MATLAB script used in deriving the <em>U<sub>50</sub></em> estimates.</p> <p>Associated publication is: Pryor S.C. and Barthelmie R.J. (2021): A global assessment of extreme wind speeds for wind energy applications. <em>Nature Energy</em> DOI: 10.1038/s41560-020-00773-7</p>
Linguistic Atlas Projects - Atlanta Survey Project - Version 1
<p>Director:<br> William A. Kretzschmar, Jr.</p> <p>Status of work:<br> Field work complete in 2003. Direct CD recordings; some full transcriptions have been completed.</p> <p>Areas covered:<br> Fulton and DeKalb counties in the Atlanta, GA, metropolitan area.</p> <p>Subjects:<br> 18 speakers selected by random phone sampling, with later in-person interviews. African American and nonAfrican American subjects, stratified by gender and occupational status.</p> <p>Nature of recordings:<br> Guided conversational interview expected to last one hour, adapted from Western States interview. Fixed format elicitation in which speakers read words from cards. </p> <p>Archives:<br> Original materials are archived in the Special Collections repository of the Library, University of Georgia, Athens, Georgia 30602. For access to original materials, contact the Linguistic Atlas Project office.</p>
Tonsil Atlas: CellRanger outputs
<p>This repository contains all the expression and accessibility matrices for each of the five data modalities (scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics) associated with the article "An Atlas of Cells in The Human Tonsil," published in Immunity in 2024. In particular, each modality was produced with different platforms from 10X Genomics and processed with different versions of CellRanger. This repository contains the most important outputs from each CellRanger run of each library. In addition, it also includes de expression and accessiblity matrices for the two mantle cell lymphoma (MCL) patients analyzed in the paper.</p> <p>Version 4.0 of this repository adds all high-quality TIFF images of the Visium slides, as requested by a user.</p>
Seatizen Atlas
This deposit offers a comprehensive collection of geospatial and metadata files that constitute the Seatizen Atlas dataset, facilitating the management and analysis of spatial information. <br><br>To navigate through the data, you can use an interface available at <a href="http://seatizenmonitoring.ifremer.re" target="_blank">seatizenmonitoring.ifremer.re</a>, which provides a condensed CSV file tailored to your choice of metadata and the selected area.<br>To retrieve the associated images, you will need to use a script that extracts the relevant frames. A brief tutorial is available here: <a href="https://github.com/SeatizenDOI/zenodo-tools/blob/master/Tutorial.md" target="_blank">Tutorial</a>.<br>All the scripts for processing sessions, creating the geopackage, and generating files can be found here: <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI github repository</a>.<br>All our CSV files are also available in Parquet format.<br><br>The repository includes:<br><ul> <li> <strong> seatizen_atlas_db.gpkg: </strong> geopackage file that stores extensive geospatial data, allowing for efficient management and analysis of spatial information. </li><br> <li> <strong> session_doi.csv: </strong> a CSV file listing all sessions published on Zenodo. This file contains the following columns: </li> <br> <ul> <li> session_name: identifies the session. </li> <li> session_doi: indicates the URL of the session. </li> <li> place: indicates the location of the session. </li> <li> date: indicates the date of the session. </li> <li> raw_data: indicates whether the session contains raw data or not. </li> <li> processed_data: indicates whether the session contains processed data. </li> </ul> <br> <li><strong> metadata_images.csv:</strong> a CSV file describing all metadata for each image published in open access. This file contains the following columns: </li> <br> <ul> <li> OriginalFileName: indicates the original name of the photo. </li> <li> FileName: indicates the name of the photo adapted to the naming convention adopted by the Seatizen team (i.e., YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number_originalimagename). </li> <li> relative_file_path: indicates the path of the image in the deposit. </li> <li> frames_doi: indicates the DOI of the version where the image is located. </li> <li> GPSLatitude: indicates the latitude of the image (if available). </li> <li> GPSLongitude: indicates the longitude of the image (if available). </li> <li> GPSAltitude: indicates the depth of the frame (if available). </li> <li> GPSRoll: indicates the roll of the image (if available). </li> <li> GPSPitch: indicates the pitch of the image (if available). </li> <li> GPSTrack: indicates the track of the image (if available). </li> <li> GPSDatetime: indicates when frames was take (if available). </li> <li> GPSFix: indicates GNSS quality levels (if available). </li> </ul> <br> <li> <strong> metadata_multilabel_predictions.csv:</strong> a CSV file describing all predictions from last multilabel model with georeferenced data. </li><br> <ul> <li> FileName: indicates the name of the photo adapted to the naming convention adopted by the Seatizen team (i.e., YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number_originalimagename). </li> <li> frames_doi: indicates the DOI of the version where the image is located. </li> <li> GPSLatitude: indicates the latitude of the image (if available). </li> <li> GPSLongitude: indicates the longitude of the image (if available). </li> <li> GPSAltitude: indicates the depth of the frame (if available). </li> <li> GPSRoll: indicates the roll of the image (if available). </li> <li> GPSPitch: indicates the pitch of the image (if available). </li> <li> GPSTrack: indicates the track of the image (if available). </li> <li> GPSFix: indicates GNSS quality levels (if available). </li> <li> prediction_doi: refers to a specific AI model prediction on the current image (if available). </li> <li> A column for each class predicted by the AI model. </li> </ul> <br> <li> <strong>metadata_multilabel_annotation.csv: </strong> a CSV file listing the subset of all the images that are annotated, along with their annotations. This file contains the following columns: </li><br> <ul> <li> FileName: indicates the name of the photo. </li> <li> frame_doi: indicates the DOI of the version where the image is located. </li> <li> relative_file_path: indicates the path of the image in the deposit. </li> <li> annotation_date: indicates the date when the image was annotated. </li> <li> A column for each class with values: </li> <ul> <li> 1: if the class is present. </li> <li> 0: if the class is absent. </li> <li> -1: if the class was not annotated. </li> </ul> </ul> <br> <li> <strong>darwincore_multilabel_annotations.zip: </strong> a Darwin Core Archive (DwC-A) file listing the subset of all the images that are annotated, along with their annotations. </li><br></ul><h2> Scientific Publication </h2>If you use this dataset in your research, please consider citing the associated paper: <br><br><pre>@article{contini2025seatizen,<br> title={Seatizen Atlas: a collaborative dataset of underwater and aerial marine imagery},<br> author={Contini, Matteo and Illien, Victor and Julien, Mohan and Ravitchandirane, Mervyn and Russias, Victor <br> and Lazennec, Arthur and Chevrier, Thomas and Rintz, Cam Ly and Carpentier, L{\'e}anne and Gogendeau, Pierre and others}, <br> journal={Scientific Data}, <br> volume={12}, <br> number={1}, <br> pages={67}, <br> year={2025}, <br> publisher={Nature Publishing Group UK London} <br>} <br></pre> <br>For detailed information about the dataset and experimental results, please refer to the previous paper.
Ostwald_colour-atlas_reflectance-measurements
<p>This repository contains the results of visible reflectance spectroscopy that have been performed on three colour atlases in the 1920s made by Verlag Unesma under the supervision of Wilhelm Ostwald. These colour atlases are currently held at the Rijksmuseum Research Library in Amsterdam under the following inventory numbers (339D32 / 339D33 and GF386G3). </p><p>The atlases are physical representations of the colour space developed by Wilhelm Ostwald in the 1910s. They are composed of hundreds of small swatches of paint, where each one represents a specific colour and can be characterised by a hue number (ranging from F01 to F24) and a two-letter code that indicates the position of the swatch in the Ostwald colour space. </p><p>In addition to photographs stored in the zip file, each colour swatch were measured three times with a spectrophotometer from Konica Minolta (CM-2600d), where the UV radiation had been cut-off. Subsequently, the mean and standard deviation were calculated and stored inside the Ostwald_DB.csv file. The Lab values were calculated with the help of the Colour Science python package (https://www.colour-science.org/) according to a 10° observer and a D65 illuminant.</p><p>A Jupyter notebook along with a python script have been created to help users to manipulate the data contains in the Ostwald_DB.csv file.</p>
SEM atlas of selected carbon nanomaterials
<p>Atlas of SEM images of commercially available carbon nanomaterials. Magnifications: x2000 - x100000.</p>
Additional data: Longitudinal single-cell multiomic atlas of high-risk neuroblastoma reveals chemotherapy-induced tumor microenvironment rewiring
<p>This repository provides additional data for the manuscript titled "Longitudinal single-cell multiomic atlas of high-risk neuroblastoma reveals chemotherapy-induced tumor microenvironment rewiring", currently under revision at Nature Genetics. The primary data cohort has been deposited in the HTAN data portal. This repository includes processed 10x Xenium spatial transcriptomic data for six TH-MYCN mice (three chemotherapy-treated and three treatment-naive) as well as processed scRNA-seq data for CHLA15 and CHLA20 neuroblastoma (NBL) cells. The scRNA-seq data includes mono-cultured, co-cultured cells with THP-1 macrophages, and co-culture cells treated with Afatinib/CRM197. </p>
An extended and improved CCFv3 annotation and Nissl atlas of the entire mouse brain
<p>This archive contains the dataset produced by the Blue Brain Project (BBP) for improving the Common Coordinate Framework version 3 (CCFv3) mouse brain atlas from the Allen Institute for Brain Science (AIBS). The dataset nomenclature is aligned with AIBS standards, utilizing the Allen Reference Atlas (ARA) Nissl-stained volume sectioned in the coronal incidence (ARA NisslCOR) and the annotation file version 3 (ANNOTv3). Additional data were used, such as the AIBS Nissl-stained volume sectioned in the sagittal incidence (AIBS NisslSAG, Allen Mouse Brain Atlas ID 100042147) as well as a Waxholm (WAXH) Nissl-stained volume sectioned in the horizontal incidence (WAXH NisslHOR; https://www.nitrc.org/projects/incfwhsmouse).</p> <p>Here is a list of the files produced and shared below with their descriptions:</p> <p><strong>ara_bbp_nisslCOR_25 - 10</strong>: ARA Nissl-stained volume sectioned in the coronal incidence at 25 and 10 μm isotropic resolution accurately aligned in the CCFv3.</p> <p><strong>arav3a_bbp_nisslCOR_25 - 10</strong>: ARA Nissl-stained volume sectioned in the coronal incidence at 25 and 10 μm isotropic resolution accurately aligned in the CCFv3 and extended for covering the entire brain.</p> <p><strong>annotv3am_bbp_manual_25</strong>: Expert manual delineation of the extended tissue based on ARAv3aBBP NisslCOR at 25 isotropic resolution as well as of the granular and molecular layers in the cerebellum.</p> <p><strong>annotv3a_bbp_25 - 10</strong>: Extended CCFv3 annotation at 25 and 10 μm isotropic resolution covering the entire mouse brain plus including new granular and molecular layers in all cerebellar lobules, assessed using ANNOTv3am.</p> <p><strong>annotv3c_bbp</strong>: Extended CCFv3aBBP annotation covering the mouse central nervous system including spinal cord as well as barrel columns in the isocortex.</p> <p><strong>aibs_bbp_nisslSAG_25</strong>: AIBS sagittal Nissl-stained volume aligned in the CCFv3aBBP at 25 μm isotropic resolution.</p> <p><strong>waxh_bbp_nisslHOR_25</strong>: WAXH horizontal Nissl-stained volume aligned in the CCFv3aBBP at 25 μm isotropic resolution.</p> <p><strong>annotation_bbp_atlas_pipeline_25</strong>: Annotation file from the Blue Brain cell atlas pipeline including the extended version annotv3a_bbp_25, plus some additional sublayers such as layer 2 and layer 3, as well as the barrel columns in the isocortex.</p> <p><strong>hierarchy_bbp_atlas_pipeline</strong>: Hierarchy file attached to the annotation_bbp_atlas_pipeline_25 version.</p> <p><strong>average_nissl_init_25_v3a_CBcorrected</strong>: Average Nissl-stained template composed of the average of arav3a_bbp_nisslCOR_25, aibs_bbp_nisslSAG_25, and waxh_bbp_nisslHOR_25 and including some automated corrections of the artifacts in the cerebellum. This was used as a reference and initialization for building the average Nissl-stained template.</p> <p><strong>average_nissl_template</strong>: Symmetric (symmetric_full) and non symmetric (nissl_average_full) averaged Nissl-stained template in the CCFv3aBBP, as well as the number of occurences per voxel (frequency) in the averaging process.</p> <p><strong>QuickNII-CCFv3a-extended</strong>: Extended CCFv3a atlas file compatible with QuickNII software.</p> <p><strong>VisuAlign-v0.91</strong>: Extended CCFv3a atlas file compatible with VisuAlign software.</p> <p>An additional video (<strong>FullBrainAtlas_bbp</strong>) is provided in that archive, presenting the different mouse brain annotations from AIBS to BBP ones, as well as the BBP computed neuron distribution among the entire mouse brain colored by regions given AIBS standards. The code for creating the data in the video is accessible at https://github.com/favreau/BioExplorer/tree/master/bioexplorer%2Fpythonsdk%2Fnotebooks%2Fccfv3.</p> <p>For accessing to the code related to that work, please go to the corresponding GitHub repository: https://github.com/BlueBrain/ccfv3a-extended-atlas.</p> <p>--</p> <p>Citation:</p> <p>Piluso, S., Verasztó, C., Carey, H., Delattre, É., L’Yvonnet, T., Colnot, É., Romani, A., Bjaalie, J. G., & Keller, D. (2024). An extended and improved CCFv3 annotation and Nissl atlas of the entire mouse brain. Zenodo. <a href="https://doi.org/10.5281/zenodo.13640418" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.13640418</a></p> <p>--</p> <p>Reference paper:</p> <p>Sébastien Piluso, Csaba Verasztó, Harry Carey, Émilie Delattre, Thibaud L’Yvonnet, Éloïse Colnot, Armando Romani, Jan G. Bjaalie, Henry Markram, Daniel Keller; An extended and improved CCFv3 annotation and Nissl atlas of the entire mouse brain. <em>Imaging Neuroscience</em> 2025; doi: <a href="https://doi.org/10.1162/imag_a_00565" target="_blank" rel="noopener">https://doi.org/10.1162/imag_a_00565</a></p> <p>--</p> <p>Funding:</p> <p><em>This study was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology. This project/research has received funding from the European Union’s Research and Innovation Program Horizon Europe under Grant Agreement no. 101147319 (EBRAINS 2.0).</em></p>
Datasets and Pipeline V1.0 from An Atlas of Plant Transposable Elements
<p>In this repository, we deposited support data for the article "An Atlas of Plant Transposable Elements", available at <a href="http://apte.cp.utfpr.edu.br/">http://apte.cp.utfpr.edu.br/</a>.</p> <p>Here, we included:</p> <p><strong>1.) Supplementary material data:</strong><br> A) SuppMat_1.xlsx: The genome assembly reference access from Ensembl Plants species used.<br> B) SuppMat_2.docx: A brief transposable elements annotation steps are used in this work.</p> <p><strong>2.) Code and software: </strong>all script code create, third-party software, how we are used it, are detailed using Arabidopsis thaliana genome as an example in the GitHub: <a href="https://github.com/alerpaschoal/apte_pipeline">https://github.com/alerpaschoal/apte_pipeline</a> under the MIT license (please see details in licence.txt file). For the third part-software, consult their terms.</p> <p>To report bugs, to ask for help, and to give any feedback, please contact Alexandre R. Paschoal (paschoal@utfpr.edu.br) or Douglas S. Domingues (douglas.domingues@unesp.br).</p>
Data set for "Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas"
<p>Data set for: Liu Y, Foustoukos G, Crochet S and Petersen CCH (2022) Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas. Front Neuroanat 15: 791015. https://doi.org/10.3389/fnana.2021.791015</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2022_Liu_FrontNeuroanat.pdf</strong>" is the Open Access pdf of the online publication in Frontiers in Neuroanatomy.</p> <p>2. The file named "<strong>Liu_data_code.zip</strong>" (~1 GB) is a zipped version of a folder ‘<em>Liu_data_code</em>’, which contains the data analyzed in the study along with the Python codes used to generate the published figures. The original high resolution image stacks obtained through whole-brain two-photon serial tomography are unfortunately too large for Zenodo, and only highly-downsampled data are included in this upload, which were used for registration with the Allen CCFv3. Instructions on how to view and analyse the anatomical data are provided in the 'README.docx' file, which you will find upon unzipping the folder.</p> <p> </p>
Data Sources for the World Atlas of late Quaternary Foraminiferal Oxygen and Carbon Isotope Ratios 2021
<p>A tabulated text file containing all data sources used for the World Atlas of late Quaternary Foraminiferal Oxygen and Carbon Isotope Ratios 2021 (WA_Foraminiferal_Isotopes_2021), https://doi.org/10.1594/PANGAEA.936747 (Mulitza et al. 2021)</p>
A Single-Cell Tumor Immune Atlas for Precision Oncology
<p><strong>Publication version of the Single-Cell Tumor Immune Atlas</strong></p> <p>This upload contains:</p> <ul> <li><strong>TICAtlas.rds:</strong> an rds file containing a Seurat object with the whole Atlas</li> <li><strong>TICAtlas.h5ad:</strong> an h5ad file with the whole Atlas</li> <li><strong>TICAtlas_downsampled.rds:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li><strong>TICAtlas_downsampled.h5ad:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li><strong>TICAtlas_metadata.csv: </strong>a comma-separated text file with the metadata for each of the cells</li> </ul> <p>All the files contain the following patient/sample metadata variables:</p> <ul> <li>patient: assigned patient identifiers</li> <li>nCountRNA and nFeatureRNA: number of UMIs and genes per cell</li> <li>percent.mt: percentage of mitochondrial genes</li> <li>gender: the patient's gender (male/female/unknown)</li> <li>source: dataset of origin</li> <li>subtype: cancer type (abbreviations as indicated in the preprint)</li> <li>kmeans_cluster: patients clusters, NA if filtered out before clustering</li> <li>lv1 and lv2: annotated cell type for each of the cells, two level annotation (lv2 has more cell types)</li> </ul> <pre> </pre> <p>If you have any issues with the metadata (i.e. unexpected factors, NA values...) you can use the <strong>TICAtlas_metadata.csv </strong>file.</p> <p>For more information, <a href="https://genome.cshlp.org/content/early/2021/09/21/gr.273300.120.">read our paper</a>, <a href="https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/Tumor-Immune-Cell-Atlas">check our GitHub</a> and our <a href="https://singlecellgenomics-cnag-crg.shinyapps.io/TICA/">ShinyApp</a>.</p> <p>h5ad files can be read with Python using <a href="https://scanpy.readthedocs.io/en/stable/">Scanpy</a>, rds files can be read in R using <a href="https://satijalab.org/seurat/">Seurat</a>. For format conversion between AnnData and Seurat we recommend <a href="https://mojaveazure.github.io/seurat-disk/">SeuratDisk</a>. For other single-cell data formats you can use <a href="https://github.com/cellgeni/sceasy">sceasy</a>.</p>
EOOffshore: New European Wind Atlas (NEWA) Data for the Irish Continental Shelf Region
<p><a href="https://eooffshore.github.io/">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.</p> <p>The <a href="https://www.neweuropeanwindatlas.eu/">New European Wind Atlas (NEWA)</a> provides wind statistics covering onshore Europe, 100km offshore over European seas, and the complete North and Baltic Seas, based on <a href="https://map.neweuropeanwindatlas.eu/about">30 years of mesoscale simulations</a>. These catalog data sets contain 2009-2018 products for the ICS region, provided by the <a href="https://map.neweuropeanwindatlas.eu/">NEWA Map Layers and Datasets</a> website, featuring variables at multiple heights (metres above surface level). They were used in the EOOffshore project outputs presented (<a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html"><em>Scalable Offshore Wind Analysis With Pangeo</em></a>) at the <a href="https://meetingorganizer.copernicus.org/EGU22/session/42046"><em>Meeting Exascale Computing Challenges with Compression and Pangeo</em></a> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session.</p> <ul> <li><em>eooffshore_ics_newa_celticsea.zarr.tar.gz</em> <ul> <li>Data set for a North Celtic Sea area of interest.</li> </ul> </li> <li><em>eooffshore_ics_newa_irishsea.zarr.tar.gz</em> <ul> <li>Data set for an Irish Sea area of interest.</li> </ul> </li> <li><em>eooffshore_ics_newa_m3.zarr.tar.gz</em> <ul> <li>Data set for the area surrounding the <a href="http://www.marine.ie/Home/site-area/data-services/real-time-observations/irish-weather-buoy-network-imos">Irish Weather Buoy Network - M3 buoy</a> coordinates.</li> </ul> </li> <li><em>eooffshore_ics_newa_m4.zarr.tar.gz</em> <ul> <li>Data set for the area surrounding the <a href="http://www.marine.ie/Home/site-area/data-services/real-time-observations/irish-weather-buoy-network-imos">Irish Weather Buoy Network - M4 buoy</a> coordinates.</li> </ul> </li> </ul> <p>Description and example usage of the NEWA data sets in EOOffshore:</p> <ul> <li><a href="https://eooffshore.github.io/NEWA_ICS_Wind_Data.html">NEWA Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> <p>As requested by the <a href="https://map.neweuropeanwindatlas.eu/about">NEWA Terms of use</a>, the following attribution is declared:</p> <ul> <li>Data [2009 - 2018] obtained from the New European Wind Atlas (NEWA), a free, web-based application developed, owned and operated by the NEWA Consortium. For additional information see <a href="http://www.neweuropeanwindatlas.eu/">www.neweuropeanwindatlas.eu</a>.</li> </ul>
F-TRACT atlas
<p>F-TRACT atlas release - December 2021<br> ======================================</p> <p>The F-TRACT atlas is provided as .csv (comma-separated values) files that can be read in any table editor. In addition, we provide a Matlab routine allowing to read the features of the atlas as Matlab variables. The atlas is provided for free use for research use only, with limited accuracy, which hopefully will improve with subsequent releases. Please cite David et al. (2013) Probabilistic functional tractography of the human cortex, NeuroImage, and Trebaul et al. (2018) Probabilistic functional tractography of the human cortex revisited, NeuroImage, Lemarechal et al. (2022) A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials, Brain, when using the F-TRACT atlas.</p> <p>- f-tract_v2112 : Connectivity probability as well as features describing fibers biophysical properties, estimated from CCEP data recorded in 780 patients, in the AAL, AICHA, Brodmann, Freesurfer, Hammers, HCP-MMP1, Lausanne2008 (resolutions 33, 60, 125, 250, 500) and MarsAtlas parcellation schemes. The CCEP features are: peak and onset latency (LatStart), amplitude, duration, integral, velocity estimated from the onset latency and the fibers distance between the parcels and axonal conduction delays. Synaptic excitatory and inhibitory delays are also provided for each parcel. All features have been estimated separately for patients younger than 15 y.o. (group "0-15") and patients older than 15 y.o. (group "15-100").</p> <p>- Features maps : Images representing the connectivity probability and response features for all the regions in the Lausanne2008-60 parcellation.<br> </p>
Dataset and code from the ATLAS phone survey to replicate analysis of HIVST positivity rates and linkage to confirmatory testing
<p>This dataset and code allow to replicate the analysis presented in the paper entitled "HIV self-testing positivity rate and linkage to confirmatory testing and care: a telephone survey in Côte d'Ivoire, Mali and Senegal" by Arsène Kra Kouassi et al. Preprint available at <a href="https://doi.org/10.1101/2023.06.10.23291206">https://doi.org/10.1101/2023.06.10.23291206</a></p><p>The data was collected within the ATLAS project, funded by Unitaid and coordinated by Solthis and IRD.</p><p>ATLAS website: <a href="https://atlas.solthis.org/">https://atlas.solthis.org/</a></p><p>ATLAS presentation on Ceped website: <a href="https://www.ceped.org/atlas">https://www.ceped.org/atlas</a></p><p>ATLAS publications portal: <a href="https://hal.science/ATLAS_ADVIH/">https://hal.science/ATLAS_ADVIH/</a></p><p> </p><p> </p>
Larval dispersal histogram data used for ATLAS deliverable D1.6: Biologically realistic Lagrangian dispersal and connectivity
<p>Larval dispersal histogram data for ATLAS deliverable D1.6 "Biologically realistic Lagrangian connectivity" (https://www.eu-atlas.org/resources/atlas-partners-document-area/atlas-deliverables/455-d1-6-biologically-realistic-lagrangian-connectivity/file). Tar archive files are ordered by ATLAS case study source region and with folders by larval behaviour type. The numbered behaviour types are described in deliverable D1.6. Each netcdf histogram file, e.g. hists_age_21.nc, contains the histogram for larvae of a single age in 5-day steps, from 00 (0 days) to 37 (185 days).</p> <p>Within each file histogram file, particle counts in each Viking20 model grid-cell are contained in a 4-d array with dimensions (launch month, lauch year, model gridsquare y index, model gridsquare x index). The Viking20 grid in the North Atlantic is the ORCA tripolar grid. Details of the model mesh are in the included file viking20_mesh_mask.tgz</p> <p>Histograms are in netcdf files:</p> <p>============================</p> <p>$ ncdump -h hists_age_00.nc<br> netcdf hists_age_00 {<br> dimensions:<br> coordinate = 4 ;<br> coordinate_1 = 50 ;<br> coordinate_2 = 1719 ;<br> coordinate_3 = 1784 ;<br> variables:<br> int64 coordinate(coordinate) ;<br> coordinate:units = "month" ;<br> coordinate:long_name = "Launch month" ;<br> int64 coordinate_1(coordinate_1) ;<br> coordinate_1:units = "year" ;<br> coordinate_1:long_name = "Launch year" ;<br> int64 coordinate_2(coordinate_2) ;<br> coordinate_2:units = "index" ;<br> coordinate_2:long_name = "J index" ;<br> int64 coordinate_3(coordinate_3) ;<br> coordinate_3:units = "index" ;<br> coordinate_3:long_name = "I index" ;<br> int64 data(coordinate, coordinate_1, coordinate_2, coordinate_3) ;<br> data :long_name = "particle count" ;</p> <p>// global attributes:<br> :Conventions = "CF-1.6" ;<br> }</p> <p>==========================================</p> <p> </p> <p> </p>
European Forest Disturbance Atlas
<p><strong>Description</strong></p> <p>This repository holds maps of annual forest disturbances across 38 European countries derived from Landsat satellite data. The European Forest Disturbance Atlas currently covers the period 1985-2023 and consists of a set of maps:</p> <ul> <li>The <em>year of disturbance</em> layers contain the year of the most recent disturbance event in the time-series, the greatest disturbance in terms of spectral change and stack of annual disturbances indicating undisturbed (0) and disturbed (1).</li> <li>The <em>number of disturbances</em> layer shows the number of disturbance events detected within the time-series.</li> <li>The <em>disturbance severity</em> layer indicates the spectral change in NBR relative to pre-disturbance.</li> <li>The <em>disturbance agent</em> layer summarises the attribution of agents over the full time series. The causal agents assigned are wind/bark beetle complex (1), fire (2), harvest (3) and mixed agents (4, where more than one agent occurred). The stack of disturbance agents provides annual information on causal agent assigned.</li> </ul> <p>The maps are available per country as GeoTIFF. The spatial reference system is EPSG 3035 (ETRS89 / LAEA Europe). The most current version is 2.1.1. The maps will be updated regularly. <a href="https://albaviana.users.earthengine.app/view/european-forest-disturbance-map">The maps can also be explored online</a>.</p> <p><strong>Version history</strong></p> <ul> <li>2.0.0 - Initial version, covering 1985-2021.</li> <li>2.1.0 - Maps updated until 2023. Added forest land use layer and annual stacks of disturbances (including annual disturbance probabilities and disturbance agents).</li> <li>2.1.1 - Improvements to the disturbance maps introduced and disturbance severity layer added.</li> </ul> <p><strong>Known issues</strong></p> <ul> <li>Some SLC-off artefacts (both version 2.0.0 and 2.1.0).</li> <li>Known errors related to the forest mask of Belarus, Moldova and Ukraine (now corrected in 2.1.0)</li> <li>Known some false disturbances mapped north of 67°N in Norway, Sweeden and Finland.</li> </ul>
The World Atlas of Language Structures Online
<p>Cite the source of the dataset as:</p> <blockquote> <p>Dryer, Matthew S. & Haspelmath, Martin (eds.) 2013. The World Atlas of Language Structures Online. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at https://wals.info)</p> </blockquote>
Seatizen Atlas image dataset
<h1>Seatizen Atlas image dataset</h1> <p>This repository contains the resources and tools for accessing and utilizing the annotated images within the Seatizen Atlas dataset, as described in the paper <em><a href="https://www.nature.com/articles/s41597-024-04267-z">Seatizen Atlas: a collaborative dataset of underwater and aerial marine imagery</a></em>.</p> <h2>Download the Dataset</h2> <p>This annotated dataset is part of a bigger dataset composed of labeled and unlabeled images. To access information about the whole dataset, please visit the <a href="https://zenodo.org/record/11125847">Zenodo repository</a> and follow the download instructions provided.</p> <p>If you are interested in training AI models using this dataset, you can directly access the processed version on <a href="https://huggingface.co/datasets/lombardata/seatizen_atlas_image_dataset" target="_new" rel="noopener">Hugging Face</a>.<br>This version is already split into training, validation and test sets, and includes only the classes with more than 200 annotations for more robust model training.</p> <p>An example of a trained model based on this dataset is <a href="https://doi.org/10.57967/hf/2947">DinoVdeau.</a></p> <h2>Scientific Publication</h2> <p>If you use this dataset in your research, please consider citing the associated paper:</p> <pre>@article{contini2025seatizen,<br> title={Seatizen Atlas: a collaborative dataset of underwater and aerial marine imagery},<br> author={Contini, Matteo and Illien, Victor and Julien, Mohan and Ravitchandirane, Mervyn and Russias, Victor and Lazennec, Arthur and Chevrier, Thomas and Rintz, Cam Ly and Carpentier, L{\'e}anne and Gogendeau, Pierre and others},<br> journal={Scientific Data},<br> volume={12},<br> number={1},<br> pages={67},<br> year={2025},<br> publisher={Nature Publishing Group UK London}<br>}</pre> <p>For detailed information about the dataset and experimental results, please refer to the previous paper.</p> <h2>Overview</h2> <p>The Seatizen Atlas dataset includes 14,492 multilabel and 1,200 instance segmentation annotated images. These images are useful for training and evaluating AI models for marine biodiversity research. The annotations follow standards from the Global Coral Reef Monitoring Network (GCRMN).</p> <h3>Annotation Details</h3> <ul> <li>Annotation Types:</li> <li><strong>Multilabel Convention</strong>: Identifies all observed classes in an image.</li> <li><strong>Instance Segmentation</strong>: Highlights contours of each instance for each class.</li> </ul> <h2>List of Classes</h2> <h2>Algae</h2> <ol> <li>Algal Assemblage</li> <li>Algae Halimeda</li> <li>Algae Coralline</li> <li>Algae Turf</li> </ol> <h2>Coral</h2> <ol> <li>Acropora Branching</li> <li>Acropora Digitate</li> <li>Acropora Submassive</li> <li>Acropora Tabular</li> <li>Bleached Coral</li> <li>Dead Coral</li> <li>Gorgonian</li> <li>Living Coral</li> <li>Non-acropora Millepora</li> <li>Non-acropora Branching</li> <li>Non-acropora Encrusting</li> <li>Non-acropora Foliose</li> <li>Non-acropora Massive</li> <li>Non-acropora Coral Free</li> <li>Non-acropora Submassive</li> </ol> <h2>Seagrass</h2> <ol> <li>Syringodium Isoetifolium</li> <li>Thalassodendron Ciliatum</li> </ol> <h2>Habitat</h2> <ol> <li>Rock</li> <li>Rubble</li> <li>Sand</li> </ol> <h2>Other Organisms</h2> <ol> <li>Thorny Starfish</li> <li>Sea Anemone</li> <li>Ascidians</li> <li>Giant Clam</li> <li>Fish</li> <li>Other Starfish</li> <li>Sea Cucumber</li> <li>Sea Urchin</li> <li>Sponges</li> <li>Turtle</li> </ol> <h2>Custom Classes</h2> <ol> <li>Blurred</li> <li>Homo Sapiens</li> <li>Human Object</li> <li>Trample</li> <li>Useless</li> <li>Waste</li> </ol> <p>These classes reflect the biodiversity and variety of habitats captured in the Seatizen Atlas dataset, providing valuable resources for training AI models in marine biodiversity research.</p> <h2>Usage Notes</h2> <p>The annotated images are available for non-commercial use. Users are requested to cite the related publication in any resulting works. A GitHub repository has been set up to facilitate data reuse and sharing: <a href="https://github.com/SeatizenDOI">GitHub Repository</a>.</p> <h2>Code Availability</h2> <p>All related codes for data processing, downloading, and AI model training can be found in the following GitHub repositories:</p> <ul> <li><a href="https://github.com/SeatizenDOI/plancha-workflow">Plancha Workflow</a></li> <li><a href="https://github.com/SeatizenDOI/zenodo-tools">Zenodo Tools</a></li> <li><a href="https://github.com/SeatizenDOI/DinoVdeau">DinoVdeau Model</a></li> </ul> <h2>Acknowledgements</h2> <p>This dataset and associated research have been supported by several organizations, including the Seychelles Islands Foundation, Réserve Naturelle Marine de la Réunion, and Monaco Explorations, among others.</p> <p>For any questions or collaboration inquiries, please contact <a href="mailto:seatizen.ifremer@gmail.com">seatizen.ifremer@gmail.com</a>.</p>
Symphony pre-built single-cell reference atlases
<p>Pre-built Symphony reference objects that can be downloaded and used to map new query datasets.</p> <p>The Symphony algorithm is used to perform reference mapping to these atlases. </p> <ul> <li>Preprint: <a href="https://www.biorxiv.org/content/10.1101/2020.11.18.389189v2">https://www.biorxiv.org/content/10.1101/2020.11.18.389189v2</a></li> <li>Usage: <a href="https://github.com/immunogenomics/symphony">https://github.com/immunogenomics/symphony</a></li> </ul> <p><strong>References available for download:</strong></p> <ol> <li>10x PBMCs Atlas (pbmcs_10x_reference.rds)</li> <li>Pancreatic Islet Cells Atlas (pancreas_plate-based_reference.rds)</li> <li>Fetal Liver Hematopoiesis Atlas (fetal_liver_reference_3p.rds)</li> <li>Healthy Fetal Kidney Atlas (kidney_healthy_fetal_reference.rds)</li> <li>T cell CITE-seq atlas (tbru_ref.rds)</li> <li>Cross-tissue Fibroblast Atlas (see <a href="https://sandbox.zenodo.org/record/772596#.YOqMiBNKhTY">here</a>)</li> <li>Cross-tissue Inflammatory Immune Atlas (<a href="https://sandbox.zenodo.org/record/888445#.YV9In2ZKijB">here</a>)</li> <li>Tabula Muris Senis (FACS) Atlas (TMS_facs_reference.rds)</li> </ol> <p>To read in a reference into R, one may simply execute: reference = readRDS('path/to/reference_name.rds')</p> <p>Note: To be able to map query datasets into the reference UMAP coordinates, you must also download the corresponding 'uwot_model' file and set the reference$save_uwot_path.</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.