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78 results for “Image Database”
Image Databases for Computer Vision Coded for Subject Traceability
<p>This document consists of the corpus of image databases examined for traceability of dataset subjects as published in:</p> <p>Morgan Klaus Scheuerman, Katy Weathington, Tarun Mugunthan, Emily Denton, and Casey Fiesler. 2023. From Human to Data to Dataset: Mapping the Traceability of Human Subjects in Computer Vision Datasets. Proc. ACM Hum.-Comput. Interact. 7, CSCW1, Article 55 (April 2023), 33 pages. https://doi.org/10.1145/3579488</p>
MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall
<p><strong>Dataset overview</strong></p> <p>This dataset provides data and images of snowflakes in free fall collected with a <a href="https://amt.copernicus.org/articles/5/2625/2012/">Multi-Angle Snowflake Camera (MASC)</a> The dataset includes, for each recorded snowflakes:</p> <ol> <li>A triplet of gray-scale images corresponding to the three cameras of the MASC</li> <li>A large quantity of geometrical, textural descriptors and the pre-compiled output of published retrieval algorithms as well as basic environmental information at the location and time of each measurement.</li> </ol> <p>The pre-computed descriptors and retrievals are available either individually for each camera view or, some of them, available as descriptors of the triplet as a whole. A non exhaustive list of precomputed quantities includes for example:</p> <ul> <li>Textural and geometrical descriptors as in <a href="https://amt.copernicus.org/articles/10/1335/2017/"><em>Praz et al 2017</em></a></li> <li>Hydrometeor classification, riming degree estimation, melting identification, as in <a href="https://amt.copernicus.org/articles/10/1335/2017/"><em>Praz et al 2017</em></a></li> <li>Blowing snow identification, as in <a href="https://tc.copernicus.org/articles/14/367/2020/"><em>Schaer et al 2020 </em></a></li> <li>Mass, volume, gyration estimation<em>, as in <a href="https://amt.copernicus.org/preprints/amt-2021-176/">Leinonen et al 2021</a></em></li> </ul> <p><strong>Data format and structure</strong></p> <p>The dataset is divided into four <em>.parquet</em> file (for scalar descriptors) and a <em>Zarr</em> database (for the images). A detailed description of the data content and of the data records is available <a href="https://pymascdb.readthedocs.io/en/latest/data.html#data">here</a>.</p> <p><strong>Supporting code</strong></p> <p>A python-based API is available to manipulate, display and organize the data of our dataset. It can be found on <a href="https://github.com/ltelab/pymascdb">GitHub</a>. See also the code documentation on <a href="https://pymascdb.readthedocs.io/en/latest/index.html">ReadTheDocs</a>.</p> <p><strong>Download notes</strong></p> <ul> <li>All files available here for download should be stored in the same folder, if the python-based API is used</li> <li><em>MASCdb.zarr.zip</em> must be unzipped after download</li> </ul> <p><strong>Field campaigns</strong></p> <p>A list of campaigns included in the dataset, with a minimal description is given in the following table</p> <table> <tbody> <tr> <td><strong>Campaign_name</strong></td> <td><strong>Information</strong></td> <td> <p><strong>Shielded / Not shielded</strong></p> <p><em>DFIR = Double Fence Intercomparison Reference</em></p> </td> </tr> <tr> <td> <p><em>APRES3-2016 & APRES3-2017</em></p> </td> <td>Installed in Antarctica in the context of the APRES3 project. See for example <a href="https://essd.copernicus.org/articles/10/1605/2018/essd-10-1605-2018.html">Genthon et al, 2018</a> or <a href="https://tc.copernicus.org/articles/11/1797/2017/">Grazioli et al 2017</a></td> <td>Not shielded</td> </tr> <tr> <td><em>Davos-2015</em></td> <td>Installed in the Swiss Alps within the context of <a href="https://public.wmo.int/en/resources/meteoworld/spice-%E2%80%93-improving-snowfall-measurements">SPICE</a> (Solid Precipitation InterComparison Experiment)</td> <td>Shielded (DFIR)</td> </tr> <tr> <td><em>Davos-2019</em></td> <td>Installed in the Swiss Alps within the context of <a href="https://www.envidat.ch/group/about/raclets-field-campaign">RACLETS</a> (<em>Role of Aerosols and CLouds Enhanced by Topography on Snow</em>)</td> <td>Not shielded</td> </tr> <tr> <td><em>ICEGENESIS-2021</em></td> <td>Installed in the Swiss Jura in a MeteoSwiss ground measurement site, within the context of ICE-GENESIS. See for example <a href="https://doi.org/10.1175/BAMS-D-21-0184.1">Billault-Roux et al, 2023</a></td> <td>Not shielded</td> </tr> <tr> <td><em>ICEPOP-2018</em></td> <td>Installed in Korea, in the context of ICEPOP. See for example <a href="https://doi.org/10.5194/essd-13-417-2021">Gehring et al 2021</a>.</td> <td>Shielded (DFIR)</td> </tr> <tr> <td><em>Jura-2019 & Jura-2023</em></td> <td>Installed in the Swiss Jura within a MeteoSwiss measurement site</td> <td>Not shielded</td> </tr> <tr> <td><em>Norway-2016</em></td> <td>Installed in Norway during the High-Latitude Measurement of Snowfall (HiLaMS). See for example <a href="https://doi.org/10.1175/BAMS-D-21-0007.1">Cooper et al, 2022</a>.</td> <td>Not shielded</td> </tr> <tr> <td><em>PLATO-2019</em></td> <td>Installed in the "Davis" Antarctic base during the <a href="https://www.osti.gov/biblio/1524773">PLATO</a> field campaign</td> <td>Not shielded</td> </tr> <tr> <td><em>POPE-2020</em></td> <td>Installed in the "Princess Elizabeth Antarctica" base during the POPE campaign. See for example <a href="https://essd.copernicus.org/articles/15/1115/2023/essd-15-1115-2023.html">Ferrone et al, 2023</a>.</td> <td>Not shielded</td> </tr> <tr> <td><em>Remoray-2022</em></td> <td>Installed in the French Jura.</td> <td>Not shielded</td> </tr> <tr> <td><em>Valais-2016</em></td> <td>Installed in the Swiss Alps in a ski resort.</td> <td>Not shielded</td> </tr> <tr> <td>ISLAS-2022</td> <td>Installed in Norway during the <a href="https://www.uib.no/en/rg/meten/150202/islas2022-field-campaign">ISLAS campaign</a></td> <td>Not shielded</td> </tr> <tr> <td>Norway-2023</td> <td>Installed in Norway during the MC2-ICEPACKS campaign</td> <td>Not shielded</td> </tr> </tbody> </table> <p> </p> <p><strong>Version</strong></p> <p>1.1 - Two new campaigns ("ISLAS-2022", "Norway-2023") added.</p> <p>1.0 - Two new campaigns ("Jura-2023", "Norway-2016") added. Added references and list of campaigns.</p> <p>0.3 - a new campaign is added to the dataset ("Remoray-2022")</p> <p>0.2 - rename of variables. Variable precision (digits) standardized</p> <p>0.1 - first upload</p>
Comprehensive Mini-Database of the Northern Hemisphere's Winter Sky: 100 Raw Images from Ensenada, Mexico
<p>We carried out several test sessions for data collection to adjust the settings of our optical system. From October 2022 to June 2023, we executed numerous sessions to assemble our primary catalog, capturing an extensive array of sky views. A total of 100 sky observations were recorded from various directions without restrictions. These sessions were held at the peak of a hill where CICESE, our research institute, is situated at coordinates 31°52′21.5′′ N 116°40′11.8′′ W in Ensenada, Baja California, Mexico. This location was chosen because it is relatively free from urban light pollution and noise, despite its proximity to the city outskirts. This position minimizes city light interference on one side, slightly reducing light pollution, although image quality was occasionally compromised by the light pollution and facility lighting.</p> <p>Using the ASI Studio software, we captured high-resolution images of 5496 × 3672 pixels without employing pixel binning to achieve the highest possible resolution. The camera's settings were adjusted to an exposure time of 0.5 seconds and standard gain, with the lens focused at infinity and an aperture set at f/4. This setup enabled us to detect significant background noise and numerous areas that could potentially contain stars.</p> <p>More information about the article is in the:</p> <p><a href="https://doi.org/10.3390/aerospace10090748">https://doi.org/10.3390/aerospace10090748</a></p>
Image database to supplement "Paulus, F.M. et al. Pain empathy but not surprise in response to unexpected action explains arousal related pupil dilation." (VIPER database)
<p>This folder contains the 282 images of the "VIPER" database (visually-induced pain empathy repository) along with ratings of 24 independent raters. Details are described in the following publication:</p> <p>Paulus, F.M., Müller-Pinzler, L., Walper, D., Marx, S., Hamschmidt, L., Rademacher, L., Krach, S., Einhäuser, W. Pain empathy but not surprise in response to unexpected action explains arousal related pupil dilation.</p> <p>The material can be used for scientific purposes, provided this reference is appropriately cited. Please check the download site to get the up-to-date reference at the time of your publication.</p> <p> </p> <p>Conditions are identified by the filename of the image, which consists of the number of the scenario (1-83) and the condition identifier:<br> pain<br> neut(ral)<br> mism(atch)<br> tool<br> Note that the tool and the mismatch condition do not exist for all scenarios.</p> <p>The file ratings_viper.csv contains the ratings. Each image corresponds to a line, the columns are as follows:<br> Column 1: Filename of the image<br> Column 2: Scenario number<br> Column 3: condition<br> Columns 4 through 27: ratings of the 24 individuals (between 0 and 4, NaN if there was no rating recorded)</p> <p>The file thumbnail_viper.jpg provides an overview over all images in the database.</p> <p>For ease of download, the images are available as tar-archive (allImages_viper.tar) and as inidivual files.</p> <p> </p>
Data from: The MRi-Share database: Brain imaging in a cross-sectional cohort of 1,870 university students
<p>We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1,870 young healthy adults, aged 18 to 35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1,722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early aging.</p>
Database that contains all images (plus 180 more) employed in the article: "Image features for quality analysis of thick blood smears employed in malaria diagnosis"
<p>We share with you a bank of images obtained from microscopic fields of thick blood smears employed in the malaria diagnosis, and also the .csv file that contains the labels for each image.</p> <p>The images are saved with a unique name that is found in the first column of the .csv file. The second column contains the labels from each image, according to their unique names.</p> <p>The labeling process was done with the online toolbox Labelbox. Labelbox, "Labelbox," Online, 2020. [Online]. Available: https://labelbox.com </p> <p>If you are interested in using our database, cite our article as a way to recognize our work. We will be grateful for that. </p> <p>CITATION: Fong Amaris, W.M., Martinez, C., Cortés-Cortés, L.J. et al. Image features for quality analysis of thick blood smears employed in malaria diagnosis. Malar J 21, 74 (2022). https://doi.org/10.1186/s12936-022-04064-2</p> <p>URL of our paper: https://malariajournal.biomedcentral.com/articles/10.1186/s12936-022-04064-2</p> <p><strong>--- This is the link where you can find our images Bank: https://drive.google.com/drive/folders/1Qrv0e4bSEtkeqtPABz-klQp-6D6OjU-X?usp=sharing </strong></p> <p>It is important you to know that along with this .txt file, we are sharing the .csv file that contains 600 names of images (in the first column) with their respective labels (second column aside)</p> <p>This file corresponds to the instructions of an extended label file related to 600 images (and 600 new labels) in contrast to our previous label file with 420 labels from 420 images (https://www.researchgate.net/publication/359439520_Database420LabelsInstructionstxt ; https://www.researchgate.net/publication/359438904_Database420Labelscsv).</p> <p>Best Regards</p> <p> </p>
Figure1. Three-layer image decomposition with content protection-Access Management in Medical Image Databases Based on New Format and Contents Protection with Inverse Pyramid Decomposition
<p>The image preparation for the image database with layered access is shown on Fig. 1. The<br> image is archived layer by layer and the watermarks are inserted together with the image<br> processing. The ROI (if there is one in the image) is processed in such a way, that to permit direct<br> access for authorized users (separate pyramid is developed for the ROI representation).</p>
A normative database of free-breathing pediatric thoracic 4D dynamic MRI images
<p>In pediatric patients with respiratory abnormalities, it is important to understand the alterations in regional dynamics of the lungs and other thoracoabdominal components, which in turn requires a quantitative understanding of what is considered as normal in healthy children. Currently, such a normative database of regional respiratory structure and function in healthy children does not exist. The shared open-source normative database is from our ongoing virtual growing child (VGC) project, which includes 4D dynamic magnetic resonance imaging (dMRI) images during one breathing cycle for each normal child and also 10 object segmentations at end expiration (EE) and end inspiration (EI) phases of the respiratory cycle in the 4D image. The lung volumes at EE and EI as well as the excursion volumes of chest wall and diaphragm from EE to EI, left and right separately, are also reported. The database has 2,820 3D segmentations from 141 healthy children, which to our knowledge is the largest dMRI dataset of healthy children to date. The database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for healthy children. The database can serve as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The database can be useful to advance future AI-based research on image-based object segmentation and analysis.</p>
Figure 2. – Otolith images from a in Automatic method to transform routine otolith images for a standardized otolith database using R
Figure 2. – Otolith images from a binocular dissecting microscope under different types of illumination: A. Reflected light and B. Transmitted light.
Figure 1 in Automatic method to transform routine otolith images for a standardized otolith database using R
Figure 1. – Different types of otolith images showing common issues. Scale is different between images depending on the sample, some broken otoliths, various exposures and colors, some particles may be present (hair, bubbles).
The CAPA Apple Quality Grading Multi-Spectral Image Database
<p>The CAPA Apple Quality Grading Multi-Spectral Image Database consists of multispectral (450nm, 500nm, 750nm, and 800nm) images of health and defected apples of bi-color, manual segmentations of defected regions, and expert evaluations of the apples into 4 quality categories. The defect types consist of bruise, rot, flesh damage, frost damage, russet, etc. The database can be used for academic or research purposes with the aim of computer vision based apple quality inspection.</p> <p>The CAPA Apple Quality Grading Multi-Spectral Image Database is a propriety of ULG (Gembloux Agro-Bio Tech) - Belgium, and cannot be used without the consent of the ULG (Gembloux Agro-Bio Tech), Belgium. <br> For consent, contact<br> Devrim Unay, İzmir University of Economics, Turkey: unaydevrim@gmail.com<br> OR<br> Marie-France Destain, Gembloux Agro-Bio Tech, Belgium: mfdestain@ulg.ac.be</p> <p><br> In disseminating results using this database, <br> 1. the author should indicate in the manuscript that it was acquired by ULG (Gembloux Agro-Bio Tech), Belgium.<br> 2. cite the following article Kleynen, O., Leemans, V., & Destain, M.-F. (2005). Development of a multi-spectral vision system for the detection of defects on apples. Journal of Food Engineering, 69(1), 41-49.</p> <p>Relevant publications:<br> Kleynen et al., 2003 O. Kleynen, V. Leemans and M.F. Destain, Selection of the most efficient wavelength bands for ‘Jonagold’ apple sorting. Postharv. Biol. Technol., 30 (2003), pp. 221–232.<br> Leemans and Destain, 2004 V. Leemans and M.F. Destain, A real-time grading method of apples based on features extracted from defects. J. Food Eng., 61 (2004), pp. 83–89.<br> Leemans et al., 2002 V. Leemans, H. Magein and M.F. Destain, On-line fruit grading according to their external quality using machine vision. Biosyst. Eng., 83 (2002), pp. 397–404.<br> Unay and Gosselin, 2006 D. Unay and B. Gosselin, Automatic defect detection of ‘Jonagold’ apples on multi-spectral images: A comparative study. Postharv. Biol. Technol., 42 (2006), pp. 271–279.<br> Unay and Gosselin, 2007 D. Unay and B. Gosselin, Stem and calyx recognition on ‘Jonagold’ apples by pattern recognition. J. Food Eng., 78 (2007), pp. 597–605.<br> Unay et al., 2011 Unay, D., Gosselin, B., Kleynen, O, Leemans, V., Destain, M.-F., Debeir, O, “Automatic Grading of Bi-Colored Apples by Multispectral Machine Vision”, Computers and Electronics in Agriculture, 75(1), 204-212, 2011.<br> </p>
Subglacial Topography Training Image Database
<p>This database contains elevation data for 166 topographic training images. These training images should be cited as Yin et al., (2022). The file contains a 40,000 x166 matrix, where each column contains one TI. Each column should be reshaped to 200x200. The resolution is 500 m, and each TI is 100x100 km<sup>2</sup>. The elevation data were obtained from ArcticDEM (Porter et al., 2018) and IBCSO (Arndt et al., 2013). </p> <p>Arndt, J. E., Schenke, H. W., Jakobsson, M., Nitsche, F. O., Buys, G., Goleby, B., ... & Wigley, R. (2013). The International Bathymetric Chart of the Southern Ocean (IBCSO) Version 1.0—A new bathymetric compilation covering circum‐Antarctic waters. <em>Geophysical Research Letters</em>, <em>40</em>(12), 3111-3117.</p> <p>Porter, C., Morin, P., Howat, I., Noh, M. J., Bates, B., Peterman, K., ... & Bojesen, M. (2018). ArcticDEM. <em>Harvard Dataverse</em>, <em>1</em>, 2018-30.</p> <p>Yin, Z., Zuo, C., MacKie, E. J., & Caers, J. (2022). Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1). <em>Geoscientific Model Development</em>, <em>15</em>(4), 1477-1497.</p>
Images of the data brushes generated for the ApPEARS deliverable D5.1 Database of vector based brush strokes and sample prints that demonstrate the range of printed materials
<p>These images are appendices of ApPEARS deliverable D5.1 Database of vector based brush strokes and sample prints that demonstrate the range of printed materials. They show the generated data brushes.</p>
Empa HDR Image Database
<p>Empa HDR Image Database evolved from projects on HDR research carried out at Empa in the years 2011-2013 with partial support of the COST action IC1005 on HDRI. There are 33 scenes, consisting of the images from the exposure bracketing, resulting exr or hdr files, and a tone mapped jpeg. The zip FIles of the exr and hdr files can be downloaded individually, if desired. Furthermore, there are 3 image sequences that can be turned into videos using the time lapse or stop motion technique.</p>
Computer-rendered HDR and LDR 4k images database
<p>Realistic image computation mimics the natural process of acquiring pictures by simulating the physical interactions of light between all the objects, lights and cameras lying within a modelled 3D scene. This process is known as global illumination and was formalised by Kajiya with the following rendering Equation:<br> <span class="math-tex">\(\begin{equation} \label{eq:rendering_equation} L_o(x, \omega_o) = {L_e(x, \omega_o)} + \int_{\Omega}^{} {L_i(x, \omega_i)} \cdot f_r(x, \omega_i \rightarrow \omega_o) \cdot \cos \theta_i d\omega_i \end{equation}\)</span></p> <p>where:</p> <ul> <li> <span class="math-tex">\(L_o(x, \omega_o)\)</span> is the luminance traveling from point <span class="math-tex">\(x\)</span> in direction <span class="math-tex">\(\omega_o\)</span>;</li> <li><span class="math-tex">\(L_e(x, \omega_o)\)</span> is point <span class="math-tex">\(x\)</span> emitted luminance (it is null if point x does not lie on a ligth source surface);</li> <li>the integral represents the set of luminances <span class="math-tex">\(L_i\)</span>incident in <span class="math-tex">\(x \)</span> from the hemisphere of the directions <span class="math-tex">\(\Omega\)</span> and reflected in the direction <span class="math-tex">\(\omega_o\)</span>. The reflected luminances are weighted by the materials reflecting properties (bidirectionnal reflectance function <span class="math-tex">\(f_r(x, \omega_i \rightarrow \omega_o)\)</span>) and the cosinus of the incident angle.</li> </ul> <p>This equation cannot be analytically solved and Monte Carlo approaches are generally used to estimate the value of the pixels of the final image.</p> <p>This proposed dataset is composed of 32 points of view of photo realistics images with different level of samples (following the Monte Carlo approach) for each. Each image is 3840 × 2160 pixels in size. The most noisy image is of 2⁰ samples and the reference one (the most converged image obtained) is of 2²⁰ samples. The <a href="https://www.pbrt.org/index.html">pbrt</a> rendering engine (version 4) was used to generate these images.</p>
Data from: The MRi-Share database: Brain imaging in a cross-sectional cohort of 1,870 university students
Open the record for dataset details and reuse information.
A normative database of free-breathing pediatric thoracic 4D dynamic MRI images
Open the record for dataset details and reuse information.
High Resolution Fundus Image Database for Monomodal Single-Channel Image Registration of Thin Features
<p>A high resolution image database of 42 image pairs (related by elastic deformations) created from original images from the High Resolution Fundus Image Database.<br> <br> Consists of thin linear structures that lack sufficient overlap to pose a challenge for classic similarity measures based on overlapping pixels commonly used in image registration.</p> <p>The dataset contains the intensity grayscale images, as well as binary masks of the retinal area, and labels of the vessels, segmented by expert annotators.<br> </p>
Zurich Natural Image Database
<p>Zip-File containing a set of 128 natural images that have been used in various eye-tracking studies. Thumbnails.jpg provides an overview. Images were captured with a 3.3 Mega pixel colour mosaic CCD camera (Nikon Coolpix 995, Tokyo, Japan) in RGB and have a resolution (WxH) of 2048 x 1536 pixels.</p><p>You are free to use these images for scientific purposes, provided at least one of the following papers is appropriately cited:</p><p>Einhäuser, W., & König, P. (2003). Does luminance‐contrast contribute to a saliency map for overt visual attention?. <i>European Journal of Neuroscience</i>, <i>17</i>(5), 1089-1097. <a href="https://doi.org/10.1046/j.1460-9568.2003.02508.x">https://doi.org/10.1046/j.1460-9568.2003.02508.x </a>[used the first 8 images in grayscale]</p><p>Einhäuser, W., Kruse, W., Hoffmann, K. P., & König, P. (2006). Differences of monkey and human overt attention under natural conditions. <i>Vision Research</i>, <i>46</i>(8-9), 1194-1209. <a href="https://doi.org/10.1016/j.visres.2005.08.032">https://doi.org/10.1016/j.visres.2005.08.032 </a>[used the first 108 images in grayscale]</p><p>Frey, HP., König, P. & Einhäuser, W. The role of first- and second-order stimulus features for human overt attention. <i>Perception & Psychophysics, 69</i>, 153–161 (2007). <a href="https://doi.org/10.3758/BF03193738">https://doi.org/10.3758/BF03193738 </a>[used the images in color]</p>
Bird predation on Roseau cane scale as revealed by a web image search and querying a citizen monitoring database
<p>NA</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.