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979 results for “image dataset”
Pictures of diseased soybean leaves by category captured in field and with controlled backgrounds: Auburn soybean disease image dataset (ASDID)
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Dataset: Segmentation of cortical bone, trabecular bone, and medullary pores from micro-CT images using 2D and 3D deep learning models
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Demo dataset for: SPACEc, a streamlined, interactive Python workflow for multiplexed image processing and analysis
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Dataset for: Image-based screen capturing misfolding status of Niemann-Pick type C1 identifies potential candidates for chaperone drugs
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Imaging dataset from: Longitudinal tracking of acute kidney injury reveals injury propagation along the nephron
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Datasets for neuronal imaging, extracellular recordings and behavioral rig code
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Girasol, a sky imaging and global solar irradiance dataset
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Transmission electron microscope images dataset for AutoDetect-mNP (triangular prisms)
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Transmission electron microscope images dataset for AutoDetect-mNP (nanorods)
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TOPCLASS Raw Image Dataset
<p>Topology Classification Dataset: raw images of simulated proton-proton LHC collisions at 13 TeV</p> <p>Full description at https://arxiv.org/abs/1807.00083</p>
TOPCLASS Abstract Image Dataset
<p>Topology Classification Dataset: abstract images of simulated proton-proton LHC collisions at 13 TeV</p> <p>Full description at https://arxiv.org/abs/1807.00083</p>
BACH Dataset : Grand Challenge on Breast Cancer Histology images
<p><strong>i3S Annotated Datasets on Digital Pathology</strong></p> <p> </p> <p><strong>WELCOME</strong></p> <p>In an effort to contribute and push forward the field of Digital Pathology, <a href="https://www.ipatimup.pt/">Ipatimup</a> and <a href="http://www.ineb.up.pt/">INEB</a>, two major research institutions in Portugal, have joined forces in the construction of histology datasets to support grand Challenges on automatic classification of tissue malignancy. The researchers/pathologists responsible for the datasets are:</p> <p><a href="mailto:apolonia@ipatimup.pt">António Polónia</a> (MD), Ipatimup/i3S</p> <p><a href="mailto:celoy@ipatimup.pt">Catarina Eloy</a> (MD, PhD), Ipatimup/i3S</p> <p><a href="mailto:pauloaguiar@ineb.up.pt">Paulo Aguiar</a> (PhD), INEB/i3S</p> <p> </p> <p>This specific page refers to the <a href="https://iciar2018-challenge.grand-challenge.org/home/">Grand Challenge on Breast Cancer Histology images</a>, or BACH Challenge</p> <p> </p> <p><strong>THE BACH CHALLENGE DATASET</strong></p> <p><a href="https://iciar2018-challenge.grand-challenge.org/home/">ICIAR 2018 - Grand Challenge on Breast Cancer Histology images</a> [Challenge organized by Teresa Araújo, Guilherme Aresta, António Polónia, Catarina Eloy and Paulo Aguiar]</p> <p>For detailed information visit: <a href="https://iciar2018-challenge.grand-challenge.org/home/">https://iciar2018-challenge.grand-challenge.org/home/</a></p> <p> </p> <p>THIS DATASET IS PUBLICALLY AVAILABLE UNDER A CREATIVE COMMONS CC BY-NC-ND LICENSE (<a href="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode">ATTRIBUTION-NONCOMMERCIAL-NODERIVS</a>)<br> ESSENCIALLY, YOU ARE GRANTED ACCESS TO THE DATASET FOR USE IN YOUR RESEARCH AS LONG AS YOU CREDIT OUR WORK/PUBLICATIONS(*), BUT YOU CANNOT CHANGE THEM IN ANY WAY OR USE THEM COMMERCIALLY</p> <ul> <li>(*) Aresta, Guilherme, et al. "BACH: Grand challenge on breast cancer histology images." Medical image analysis (2019).</li> <li>(*) Araújo, Teresa, et al. "Classification of breast cancer histology images using convolutional neural networks." PloS one 12.6 (2017): e0177544.</li> <li>(*) Fondón, Irene, et al. "Automatic classification of tissue malignancy for breast carcinoma diagnosis." Computers in biology and medicine 96 (2018): 41-51.</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Macroscopic and histological image dataset of the European plaice (Pleuronectes platessa) ovaries
<p><strong>Macroscopic and histological image dataset of the European plaice (</strong><em><strong>Pleuronectes platessa</strong></em><strong>) ovaries </strong></p> <p> </p> <p><strong>Authors:</strong></p> <p> </p> <p>Carine Sauger<sup>1</sup>, Jérôme Quinquis<sup>1</sup>, Kristell Kellner<sup>2</sup>, Clothilde Heude-Berthelin<sup>2</sup>, Mélanie Lepoittevin<sup>2</sup>, Nicolas Elie<sup>3</sup>, Laurent Dubroca<sup>1</sup></p> <p> </p> <p><strong>Affiliations:</strong></p> <p> </p> <p>1 : Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER). Laboratoire Ressources Halieutiques de Port-en-Bessin, Avenue du Général de Gaulle, 14520, Port-en-Bessin-Huppain, Calvados</p> <p>2 : Biologie des Organismes et Ecosystèmes Aquatiques (FRE 2030 BOREA). Université de Caen Normandie, Esplanade de la Paix, CS 14032, Caen, Calvados</p> <p>3 : Centre de Microscopie Appliquée à la Biologie (SF 4206 ICORE, CMABIO3). Université de Caen Normandie, Esplanade de la Paix, CS 14032, Caen, Calvados</p> <p> </p> <p><strong>Contents: </strong>This dataset was established during a 6 month long Master’s degree internship (February to July 2019), under the IFREMER (Institut Français de Recherche pour l'Exploitation de la Mer) project MATO (MATurité Objective des poissons par l’histologie quantitative), with the collaboration of two research facilities from the University of Caen-Normandie : BOREA (Biologie des Organismes et Ecosystèmes Aquatiques) and CMABIO3 (Centre de Microscopie Appliquée à la Biologie).</p> <p>This dataset contains the macroscopic and the histological images of the ovaries of 151 European plaices (female, <em>Pleuronectes platessa</em>) collected along the French Coast of the English Channel (ICES area 27.7.d) in 2017, 2018 and 2019.</p> <p><br> </p> <p><strong>Images:</strong></p> <ul> <li> <p><strong>Full_Ovaries_Data.zip: </strong>archive in zip format of 151 pictures (.JPG; 8Mo-9Mo; sRGB; 6016x4000 pixels) of both ovaries from 151 female plaice dissected during this study. Each photo was taken by the same person with a Nikon camera (D3200), in the same room with identical lightening methods (no flash). For each picture, both ovaries were set on a blue background, with a 0.50€ coin for size calibration. The upper most ovary is the dorsal gonad of the fish while the lower one is the ventral gonad. The name of the picture is the same as the fish’s ID number.</p> </li> </ul> <p><br> </p> <ul> <li> <p><strong>Stereology_Readings_Data.zip:</strong> archive in zip format of two directories containing the images</p> </li> </ul> <ul> <li> <ul> <li> <p><strong>Interagent_Calibration</strong>: the ovarian histological slides were digitized using an Aperio slide scanner (Scan Scope Console software, v.10.2.0.2352, Leica Biosystems), x20 lens. The pictures (.svs: Aperio single-file pyramidal tiled TIFF, with non-standard metadata and compression) are of the 20 histological slides used for the stereological count. 20 slides of 20 fish (with one slide per fish) were analyzed for the intercalibration analysis. The slides used were from the central position of the ventral ovary (V2).</p> </li> <li> <p><strong>Ovary_Slides</strong>: the ovarian histological slides were digitized using an Aperio slide scanner (Scan Scope Console software, v.10.2.0.2352, Leica Biosystems), x20 lens. The pictures (Aperio single-file pyramidal tiled TIFF, with non-standard metadata and compression) in this dataset are the 226 histological slides read during this study. With a total of 151 fish dissected, 151 ovarian histological slides of the median position of the ventral ovary were read. Among the remaining slides, 90 were read to analyze the homogeneous distribution of the different cell types. These 90 slides belong to 15 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the dorsal (D) and ventral (V) ovaries.</p> </li> </ul> </li> </ul> <p><strong>Data frames:</strong></p> <ul> <li> <p><strong>Intergaent_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Interagent.csv</strong> file, as well as their meaning.</p> </li> <li> <p><strong>Interagent.csv</strong>: a text data file (.csv) with the output of two stereological readings, done by three agents for 15 slides, and by two agents for 20 slides. Between the first and second reading, a reading protocol was set up to help in the determination of the different structures. This protocol allowed the three agents to calibrate themselves with a determination key. This key was necessary for the identification of specific complex structures. The information contained in this table is as follows:</p> <ul> <li> <p>agent: code id for the three agents that did the calibration exercise (A, B and C)</p> </li> <li> <p>num_fish: fish number for this study. Here we have 20 different fish</p> </li> <li> <p>fish_id: identification number of the fish. This id number is identical to the name given to the pictures of the full ovaries (<strong>Full_Ovaries_Data</strong>)</p> </li> <li> <p>scan_id: identification number of the digitized histological slide that was used for the stereological count (<strong>Stereology_Readings_Data </strong>/ <strong>Interagent_Calibration</strong>)</p> </li> <li> <p>total_points: total number of identified structures for the stereological sampling grid of a slide</p> </li> <li> <p>cell_type: abbreviation of the structure identified (reading protocol available here: https://archimer.ifremer.fr/doc/00501/61235/). In this study, we have 20 different structures</p> </li> <li> <p>hit_points: number of time a structure has been counted on a single slide</p> </li> <li> <p>Fract_estim: percentage (%) of times a structure was counted on a single slide =<em> (100 / total_point) * hit_points</em></p> </li> <li> <p>reading: reading number. In this study, we have two readings, the first (1) and the second (2)</p> </li> </ul> </li> </ul> <p><br> </p> <ul> <li> <p><strong>Macros_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Macros.csv</strong> file, as well as their meaning.</p> </li> <li> <p><strong>Macros.csv</strong>: a text data file (.csv) containing macroscopic parameters measurements for all 151 fish that have been used during this study. The information contained in this table is as follows:</p> <ul> <li> <p>num_fish: fish number for this study. Here we have 151 different female fish</p> </li> <li> <p>fish_id: identification number of the fish. This id number is identical to the name given to the pictures of the full ovaries (<strong>Full_Ovaries_Data</strong>)</p> </li> <li> <p>gon_pos: gonad position, with D being the dorsal gonad of the individual, and V being the ventral gonad.</p> </li> <li> <p>date: the date the fish was caught (dd/mm/yyyy)</p> </li> <li> <p>L_fish: total length of the fish (cm)</p> </li> <li> <p>W_fish: total weight of the fish (g)</p> </li> <li> <p>mat_estim: visually estimated maturity, after observation of the fish’s gonad with the naked eye, following the WKMATCH (ICES, 2012) scale</p> </li> <li> <p>age: estimated age (in years) of the fish, after analysis of the fish’s otolith. The IFREMER laboratory executed this analysis in Boulogne-sur-Mer (FRANCE)</p> </li> <li> <p>W_gon: gonad weight (g)</p> </li> <li> <p>Kurtosis*: kurtosis parameter</p> </li> <li> <p>Skewness*: skewness coefficient</p> </li> <li> <p>gon_area*: gonad area (mm²)</p> </li> <li> <p>L_gon*: gonad length (mm)</p> </li> <li> <p>width_gon*: maximum gonad width (mm)</p> </li> <li> <p>width_mid_L_gon*: width at mid-length of the gonad (mm)</p> </li> <li> <p>mean_col_index*: the mean color value of the different hues found on the ovary</p> </li> <li> <p>std_dev*: standard deviation of the mean_col_index</p> </li> <li> <p>modal*: modal value or the most frequently occurring color value within the selected ovary</p> </li> </ul> </li> </ul> <p>*: values determined after image analysis of the <strong>Full_Ovaries_Data</strong> with the ImageJ software (v. 1.50J)</p> <p><br> </p> <ul> <li> <p><strong>Stereology_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Stereology.csv</strong> file, as well as their meaning.</p> </li> <li> <p><strong>Stereology.csv</strong>: a text data file (.csv) of the stereology count results of 226 slides read during this study. Among these slides, 90 were read to test the homogeneity distribution of different cell types found throughout each ovary (15 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), 20 slides were read by two agents for calibration purposes, and 15 of these 20 slides were also read by a third agent for calibration purposes. Finally, 151 median histological slides of the ventral ovary were also read. The information contained in this table is as follows:</p> <ul> <li> <p>agent: code id for the 3 agents that did the calibration exercise (A, B and C)</p> </li> <li> <p>num_fish: fish number for this study. Here we have a total of 151 fish</p> </li> <li> <p>fish_id: identification number of the fish. This id number is identical to the name given to the pictures of the full ovaries (<strong>Full_Ovaries_Data</strong>)</p> </li> <li> <p>scan_id: identification number of the digitized histological slide that was used for the stereological count (<strong>Stereology_Readings_Data </strong>/ <strong>Ovary_Slides</strong>)</p> </li> <li> <p>reading: reading data to test the homogeneity of cell distributions throughout the ovaries (homogeneity), reading data of the 20 slides read for the inter-agent calibration, and reading data of all the slides (median)</p> </li> <li> <p>cell_type: abbreviation of the structure identified (reading protocol available here: https://archimer.ifremer.fr/doc/00501/61235/). In this study, we have 20 different structures</p> </li> <li> <p>point_id: identification number of the point inside the stereological sampling grid placed over the ovarian histology slide</p> </li> <li> <p>coord_x: x coordinate of the sampling point</p> </li> <li> <p>coord_y: y coordinate of the sampling point</p> </li> </ul> </li> </ul> <p><br> </p> <p><strong>Contact :</strong></p> <p>For questions, please contact: <a href="mailto:carine.sauger@gmail.com">carine.sauger@gmail.com</a> or <a href="mailto:laurent.dubroca@ifremer.fr">laurent.dubroca@ifremer.fr</a></p>
A Dataset of Fact-Checked Images Shared on WhatsApp during the Brazilian and Indian Elections
<p>(Dataset paper). In <em>Proceedings of the Int'l AAAI Conference on Weblogs and Social Media (ICWSM’20). </em>Atlanta, Georgia, U.S. June 2020.</p> <p>Abstract: Recently, messaging applications, such as WhatsApp, have been reportedly abused by misinformation campaigns, especially in Brazil and India. A notable form of abuse in WhatsApp relies on several manipulated images and memes containing all kinds of fake stories. In this work, we performed an extensive data collection from a large set of WhatsApp publicly accessible groups and fact-checking agency websites. This paper opens a novel dataset to the research community containing fact-checked fake images shared through WhatsApp for two distinct scenarios known for the spread of fake news on the platform: the 2018 Brazilian elections and the 2019 Indian elections. </p>
ImageCLEF 2012 Image annotation and retrieval dataset (MIRFLICKR)
<p>DESCRIPTION<br> For this task, we use a subset of the MIRFLICKR (http://mirflickr.liacs.nl) collection. The entire collection contains 1 million images from the social photo sharing website Flickr and was formed by downloading up to a thousand photos per day that were deemed to be the most interesting according to Flickr. All photos in this collection were released by their users under a Creative Commons license, allowing them to be freely used for research purposes. Of the entire collection, 25 thousand images were manually annotated with a limited number of concepts and many of these annotations have been further refined and expanded over the lifetime of the ImageCLEF photo annotation task. This year we used crowd sourcing to annotate all of these 25 thousand images with the concepts.</p> <p>On this page we provide you with more information about the textual features, visual features and concept features we supply with each image in the collection we use for this year's task.</p> <p><br> TEXTUAL FEATURES<br> All images are accompanied by the following textual features:</p> <p>- Flickr user tags<br> These are the tags that the users assigned to the photos their uploaded to Flickr. The 'raw' tags are the original tags, while the 'clean' tags are those collapsed to lowercase and condensed to removed spaces.</p> <p>- EXIF metadata<br> If available, the EXIF metadata contains information about the camera that took the photo and the parameters used. The 'raw' exif is the original camera data, while the 'clean' exif reduces the verbosity.</p> <p>- User information and Creative Commons license information<br> This contains information about the user that took the photo and the license associated with it.</p> <p><br> VISUAL FEATURES<br> Over the previous years of the photo annotation task we noticed that often the same types of visual features are used by the participants, in particular features based on interest points and bag-of-words are popular. To assist you we have extracted several features for you that you may want to use, so you can focus on the concept detection instead. We additionally give you some pointers to easy to use toolkits that will help you extract other features or the same features but with different default settings.</p> <p>- SIFT, C-SIFT, RGB-SIFT, OPPONENT-SIFT<br> We used the ISIS Color Descriptors (http://www.colordescriptors.com) toolkit to extract these descriptors. This package provides you with many different types of features based on interest points, mostly using SIFT. It furthermore assists you with building codebooks for bag-of-words. The toolkit is available for Windows, Linux and Mac OS X.</p> <p>- SURF<br> We used the OpenSURF (http://www.chrisevansdev.com/computer-vision-opensurf.html) toolkit to extract this descriptor. The open source code is available in C++, C#, Java and many more languages.</p> <p>- TOP-SURF<br> We used the TOP-SURF (http://press.liacs.nl/researchdownloads/topsurf) toolkit to extract this descriptor, which represents images with SURF-based bag-of-words. The website provides codebooks of several different sizes that were created using a combination of images from the MIR-FLICKR collection and from the internet. The toolkit also offers the ability to create custom codebooks from your own image collection. The code is open source, written in C++ and available for Windows, Linux and Mac OS X.</p> <p>- GIST<br> We used the LabelMe (http://labelme.csail.mit.edu) toolkit to extract this descriptor. The MATLAB-based library offers a comprehensive set of tools for annotating images.</p> <p>For the interest point-based features above we used a Fast Hessian-based technique to detect the interest points in each image. This detector is built into the OpenSURF library. In comparison with the Hessian-Laplace technique built into the ColorDescriptors toolkit it detects fewer points, resulting in a considerably reduced memory footprint. We therefore also provide you with the interest point locations in each image that the Fast Hessian-based technique detected, so when you would like to recalculate some features you can use them as a starting point for the extraction. The ColorDescriptors toolkit for instance accepts these locations as a separate parameter. Please go to http://www.imageclef.org/2012/photo-flickr/descriptors for more information on the file format of the visual features and how you can extract them yourself if you want to change the default settings.</p> <p><br> CONCEPT FEATURES<br> We have solicited the help of workers on the Amazon Mechanical Turk platform to perform the concept annotation for us. To ensure a high standard of annotation we used the CrowdFlower platform that acts as a quality control layer by removing the judgments of workers that fail to annotate properly. We reused several concepts of last year's task and for most of these we annotated the remaining photos of the MIRFLICKR-25K collection that had not yet been used before in the previous task; for some concepts we reannotated all 25,000 images to boost their quality. For the new concepts we naturally had to annotate all of the images.</p> <p>- Concepts<br> For each concept we indicate in which images it is present. The 'raw' concepts contain the judgments of all annotators for each image, where a '1' means an annotator indicated the concept was present whereas a '0' means the concept was not present, while the 'clean' concepts only contain the images for which the majority of annotators indicated the concept was present. Some images in the raw data for which we reused last year's annotations only have one judgment for a concept, whereas the other images have between three and five judgments; the single judgment does not mean only one annotator looked at it, as it is the result of a majority vote amongst last year's annotators.</p> <p>- Annotations<br> For each image we indicate which concepts are present, so this is the reverse version of the data above. The 'raw' annotations contain the average agreement of the annotators on the presence of each concept, while the 'clean' annotations only include those for which there was a majority agreement amongst the annotators.</p> <p>You will notice that the annotations are not perfect. Especially when the concepts are more subjective or abstract, the annotators tend to disagree more with each other. The raw versions of the concept annotations should help you get an understanding of the exact judgments given by the annotators.</p>
The V2S Dataset: A Set of Android Screen Recordings, Training Images, and Models
<p>This is the dataset and models used in the paper entitled "Translating Video Recordings of Mobile App Usages into Replayable Scenarios" published at the 42nd International Conference on Software Engineering (ICSE'20)</p> <p>Link to V2S Paper: https://arxiv.org/abs/2005.09057</p>
ICFHR 2020 Competition on Image Retrieval for Historical Handwritten Fragments (HisFrag20) Dataset
<p>This competition investigates the performance of large-scale retrieval of historical document fragments based on writer recognition. The analysis of historic fragments is a difficult challenge commonly solved by trained humanists.<br> We focus on the task of automatic image retrieval to simulate common scenarios of humanities research, such as fragment or writer retrieval. Therefore, we created a large dataset consisting of more than 120000 fragments.<br> The goal is then to find similar patches of the same page or manuscript. contains ~100 000 fragments using the Historical-IR19 as base dataset, they should all contain some text, however some fragments are quite small.</p> <p>Training-set: contains ~100 000 fragments using the Historical-IR19 as base dataset, they should all contain some text, however some fragments are quite small.</p> <p>Test-set: contains about 20 000 new fragments</p> <p>Naming-convention: WID_PID_FID.jpg , where WID=writer id, PID: page id, FID= fragment id</p> <p>For more information visit: <a href="https://lme.tf.fau.de/research/competitions/hisfragir20/">https://lme.tf.fau.de/research/competitions/hisfragir20/</a></p>
Natural-Color-Cloud-and-Contrail-Image-Dataset
<p>The natural color cloud and contrail image (NCCI) dataset is a dataset for satellite cloud image super resolution. The NCCI dataset is generated based on Himawari-8 satellite data. The NCCI dataset contains 1100 satellite images, including 1000 natural color images and 100 contrail images. It is worth noting that contrails have consideration in our dataset.</p>
A Dataset for Evaluating Blood Detection in Hyperspectral Images
<p>The sensitivity of hyperspectral imaging (imaging spectroscopy) to haemoglobin derivatives makes it a promising tool for detection and classification of blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms is challenging. To facilitate their development, we present a new hyperspectral blood detection dataset. This dataset consists of 14 hyperspectral images (ENVI format) of a mock-up scene containing blood and visually similar substances (e.g. artificial blood or tomato concentrate). Images were taken over a period of three weeks and differ in terms of background composition and lighting intensity. To facilitate the use of data, the dataset includes an annotation of classes: pixels where blood and similar substances are visible have been marked by the authors. The main intention behind the dataset is to serve as testing data for Machine Learning methods for hyperspectral target detection and classification.</p>
Continental Splitted Non-IID Image Classification Dataset
<p>A non-IID dataset with images from Flickr and labels from Open Image Dataset. The dataset is split into parts from three continents, North America, Europe, and Asia, between which the data turned out to be non-IID, with same objects looking different. The images in the dataset were cropped from original images with the help of bounding boxes in Open Image Dataset.</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.