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428 results for “image map”
Counting animals in aerial images with a density map estimation model
<p>Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual post-processing has been used extensively, however, volumes of such data are increasing, necessitating some level of automation, either for complete counting or as a labour-saving tool. Any automated processing can be challenging when using such tools on species that nest in close formation such as <em>Pygoscelis</em> penguins. We present here a customized CNN-based density map estimation method for counting of penguins from low-resolution aerial photography. Our model, an indirect regression algorithm, performed significantly better in terms of counting accuracy than standard detection algorithm (Faster RCNN) when counting small objects from low-resolution images and gave an error rate of only 0.8 percent. Density map estimation methods as demonstrated here can vastly improve our ability to count animals in tight aggregations, and demonstrably improve monitoring efforts from aerial imagery. </p>
Data for: Quantitative Multi-Parameter Mapping in Magnetic Resonance Imaging
<p>Magnetic Resonance Imaging measurement data used in the PhD thesis "Quantitative Multi-Parameter Mapping in Magnetic Resonance Imaging". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).<br> </p> <p>Further information about the individual datasets:</p> <p>data_invivo_b0map<br> Type: B0 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence<br> TR|TE1|TE2 [ms]: 400|4.92|7.38<br> FA [deg]: 60<br> FOV [mm]: 240</p> <p>data_invivo_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 6830|2.19<br> FA [deg]: 8<br> FOV [mm]: 240</p> <p>data_invivo_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.8|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7</p> <p>data_invivo_irbssfp<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7</p> <p>data_invivo_irbssfp_shim<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7<br> missing shim break</p> <p>data_vfa_b0map<br> Type: B0 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence<br> TR|TE1|TE2 [ms]: 400|4.92|7.38<br> FA [deg]: 60<br> FOV [mm]: 240</p> <p>data_vfa_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 6830|2.19<br> FA [deg]: 8<br> FOV [mm]: 240</p> <p>data_vfa_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.8|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_20<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 20<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_40<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 40<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_45<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_50<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 50<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_60<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 60<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_70<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 70<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_77<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 77<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p>
Counting animals in aerial images with a density map estimation model
Open the record for dataset details and reuse information.
Data package from "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images"
<p>This data package contains the very high resolution maps of canopy palms from the paper "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images". These maps have been produced with two GeoEye-1 very high resolution images (0.5 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. The total size of the decompressed archive is 2.56 Go and is distributed in two shapefiles, one for each GeoEye-1 image. When using this dataset, please cite the original article https://doi.org/10.3390/rs12142225</p>
Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations - data
<p>Data from the paper:<em> Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.</em></p> <p>Preprint: https://www.biorxiv.org/content/10.1101/840256v1</p> <p>Marek A. Pedziwiatr<br> marek.pedziwi@gmail.com<br> September 2020</p> <p> </p>
Satellite Image Maps and GIS File of the Masara, Maco, Davao de Oro Landslide
<p>These maps and GIS file provide visualizations of the landslide that occurred in Brgy. Masara, Maco, Davao de Oro, Philippines on 06 February 2024.<br><br>The PlanetScope Super Dove satellite image was acquired four (4) days after the landslide event (10 February 2024). Using the satellite image, the extents of the landslide were manually delineated using GIS software. We estimated that the larger landslide has a surface area of approximately 85,960 sq. m., while the smaller one measures approximately 759 sq. m. Unfortunately, some portions of the image near the landslide area are contaminated by cloud shadows, making it challenging to visualize and accurately map the extent of the landslide.<br><br>Also included is a high-resolution satellite image depicting Brgy. Masara before the occurrence of the landslide. The image was acquired sometime in November 2019. The manually delineated extent of the landslide area (from the PlanetScope image) is overlaid to easily visualize the impact of the landslide on the nearby community.<br><br>Credits:<br><br>* GIS analysis and map preparation: Jojene R. Santillan<br>* Imagery © 2024 Planet Labs Inc., Maxar<br><br>Access to the PlanetScope image was made possible by the Planet Education and Research Program.<br><br>****<br>Disclaimers:</p> <p>The maps and GIS files ("Products") shall be used for non-commercial, reference purposes only. They shall not be used as replacements to authoritative maps and information provided/issued by mandated government agencies.</p> <p>Accuracy and Limitations: The accuracies of the Products are dependent on the source datasets, and limitations of the software and algorithms used and implemented procedures. The Products are provided “as is” without any warranty of any kind, expressed or implied. CCGeo does not warrant that the Products will be complete, and meet the needs or expectations of the user, or that the operations or use of the maps and GIS files will be error-free.</p> <p><br>Limitation of Liability: CCGeo and Caraga State University will not be held liable for any incidental, consequential, special, exemplary, or indirect damages (including lost profits and lost data) arising from or relating to the use of the maps and GIS files.</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh outward facing state (OFS) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh outward facing state (OFS) conformation at apo condition, imaged from the cytoplasmic side, in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state closed (IFSclosed) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state closed (IFSclosed) conformation at apo condition, imaged from the cytoplasmic side, in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open (IFSopen) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open (IFSopen) conformation at apo condition, imaged from the cytoplasmic side , in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
HiRISE and MOC-NA Images Used in "Mapping of Western Valles Marineris Light-toned Layered Deposits and Newly Classified Rim Deposits"
<p>These files contain the images used in the paper "Mapping of Western Valles Marineris Light-toned Layered Deposits and Newly Classified Rim Deposits"</p> <p>All images examined, and all images used in the creation of figures are listed within these documents. They are seperated by region, and type of image (HiRISE, MOC-NA)</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open, kinetically locked, (IFSopen-1) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open, kinetically locked, (IFSopen-1) conformation at apo condition, imaged from the cytoplasmic side , in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</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>
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>
Evolutionary Map of the Universe (EMU): discovering 18-cm OH maser sources in ASKAP continuum images of the SCORPIO field
<p>Data cubes and spectra from the ATCA C3414 project.</p> <p>Data cubes are in fits format.</p> <p>Spectra were extracted from the data cubes following what described in the text. The format is the standard output of the CASA profile tools. Each spectrum contains an header with the column description.</p>
Raw Data - Part 4 : Spatial multi-omic map of human myocardial infarction ---- Raw image
<p>We provide here the raw image for the visium data for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022</p>
Dataset for image segmentation of tree trunks from depth maps captured with a an Android app using Google ARCore
<p>This dataset consists of pairs of depth maps created with a custom-built Android app using Google's ARCore for depth estimates, and tree trunk segments obtained from depth maps captured by Huawei''s AREngine and processed with a tree diameter estimation algorithm. This dataset was used for a machine learning segmentation task that aimed to improve the inputs from ARCore tree trunk depths to the tree diameter estimation algorithm. For each pair of depth maps and segments an RGB image of the scene where samples were captured is also included.</p>
Manually mapped traces from UAV-acquired images of Loviisa shoreline outcrops
<p>This dataset contains manually mapped two-dimensional bedrock fracture traces from shoreline outcrop orthomosaics generated from UAV-acquired images (https://doi.org/10.5281/zenodo.7077518). The outcrops are located along the shoreline of Loviisa, South-East Finland. Mapping has been done in ArcGIS with snapping toggled on to document the topological abutment relationships between traces.</p> <p>Data is published as ESRI Shapefiles in the ETRS-TM35FIN (EPSG:3067) coordinate system. </p> <p>The work in manual mapping was done as part of a Geological Survey of<br> Finland project, KYT KARIKKO, with funding from Finnish National Nuclear Waste<br> Management Fund (KYT) during the years 2019 and 2020.</p> <p>This dataset has been previously used as part of a Master's Thesis, which can be located here: <a href="https://urn.fi/URN:NBN:fi-fe202003259211">https://urn.fi/URN:NBN:fi-fe202003259211</a>, and in a Journal of Structural Geology article (OG1 target area), located here: https://doi.org/10.1016/j.jsg.2021.104304.</p>
Automatically mapped traces from UAV-acquired images of Loviisa shoreline outcrops
<p>This dataset contains automatically mapped two-dimensional bedrock fracture traces from shoreline outcrop orthomosaics generated from UAV-acquired images (https://doi.org/10.5281/zenodo.7077518). The outcrops are located along the shoreline of Loviisa, South-East Finland. Code for automatic mapping is available on GitHub (https://github.com/nialov/ALSA).</p> <p>Data is published as ESRI Shapefiles in the ETRS-TM35FIN (EPSG:3067) coordinate system. </p> <p>The work in automatic mapping was done as part of a Geological Survey of<br> Finland project, Kallioperän Rikkonaisuus, during 2021-2022.</p>
PL maps, optical images and PLQY analyses
<p>Large data set of PL maps, optical images and PLQY analyses</p>
Barcelona light impact impact maps using ISS images
<p>Environmental impact maps of Barcelona based on the ISS image ISS035-e-023385 from https://eol.jsc.nasa.gov.</p> <p>Includes:</p> <p>Estimations of the corrections to all Lucas et al. 2013 pigments using the ratios BG and GR. Also, Aubé et al 2013 index.</p> <p>These files have been produced calibrating procedure Sánchez de Miguel 2015 and updated procedures and Sánchez de Miguel et al 2019a and early stages of Sánchez de Miguel et. al 2019b.</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.