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26 results for “MNIST”
Dataset for training the Surrogate Model of microlaser neurons on the reduced MNIST classification task
<p>This dataset was used to train a surrogate multilayer perceptron surrogate model of microlaser neurons.</p> <p>It is in csv format. It was generated using the Yamada Model as found in </p> <p><span>Selmi F, Braive R, Beaudoin G, Sagnes I, Kuszelewicz R and Barbay S 2014 Relative Refractory Period in an Excitable Semiconductor Laser <em>Phys. Rev. Lett.</em> <strong>112</strong> 183902</span>.</p>
MNIST-Federated-Learning
<p>Please find below the descriptions of the three configurations for partitioning <strong>the MNIST Train dataset into 10 clients and the MNIST Train data: </strong><br> </p> <ol> <li><strong>Balanced Distribution:</strong> In the first configuration, the MNIST dataset is partitioned among 10 clients in a balanced manner. This means that the data samples from each class are evenly distributed among the clients. Each client receives a roughly equal number of images from each digit class, ensuring that the distribution of samples across clients is proportional and representative of the overall dataset. <strong> [ Config 1]</strong></li> <li><strong>Heterogeneous Distribution (One Class per Client)</strong>: In the second configuration, the MNIST dataset is partitioned in a heterogeneous manner, where each client is assigned a single digit class exclusively. This means that one client will only receive images of the digit '0', another client will receive images of the digit '1', and so on. In this setup, each client becomes an expert in classifying a specific digit, allowing for specialized training and evaluation. <strong>[ Config 2]</strong></li> <li><strong>Mixed Distribution:</strong> In the third configuration, the MNIST dataset is partitioned using a mixed distribution approach. This means that the data samples from all digit classes are distributed among the 10 clients, but the distribution is not necessarily balanced. The number of samples assigned to each client may vary for different digit classes, resulting in an uneven distribution across the clients. This configuration aims to capture both the overall diversity of the dataset and the varying difficulty levels of classifying different digits. <strong>[ Config 3 ]</strong></li> </ol> <p> </p> <p>Mnist-dataset/<br> ├── config1/<br> │ ├── client-1/<br> │ │ └── data.csv<br> │ ├── client-2/<br> │ │ └── data.csv<br> │ ├── client-3/<br> │ │ └── data.csv<br> │ └── ...<br> ├── config2/<br> │ ├── client-1/<br> │ │ └── data.csv<br> │ ├── client-2/<br> │ │ └── data.csv<br> │ ├── client-3/<br> │ │ └── data.csv<br> │ └── ...<br> ├── config3/<br> │ ├── client-1/<br> │ │ └── data.csv<br> │ ├── client-2/<br> │ │ └── data.csv<br> │ ├── client-3/<br> │ │ └── data.csv<br> │ └── ...<br> └── mnist_test.csv<br> </p> <p>***</p> <p>License: Yann LeCun and Corinna Cortes hold the copyright of MNIST dataset, which is a derivative work from original NIST datasets. MNIST dataset is made available under the terms of the <a href="https://creativecommons.org/licenses/by-sa/3.0/">Creative Commons Attribution-Share Alike 3.0 license.</a></p> <p>***</p>
[MedMNIST+] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification with Multiple Size Options: 28 (MNIST-Like), 64, 128, and 224
<h2><strong>Code</strong> [<a href="https://github.com/MedMNIST/MedMNIST" target="_blank" rel="noopener">GitHub</a>] | <strong>Publication</strong> [<a href="https://doi.org/10.1038/s41597-022-01721-8" target="_blank" rel="noopener">Nature Scientific Data'23</a> / <a href="https://doi.org/10.1109/ISBI48211.2021.9434062" target="_blank" rel="noopener">ISBI'21</a>] | <strong>Preprint</strong> [<a href="https://arxiv.org/abs/2110.14795" target="_blank" rel="noopener">arXiv</a>]</h2> <p> </p> <p><strong>Abstract</strong></p> <p>We introduce MedMNIST, a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into 28x28 (2D) or 28x28x28 (3D) with the corresponding classification labels, so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST is designed to perform classification on lightweight 2D and 3D images with various data scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression and multi-label). The resulting dataset, consisting of approximately 708K 2D images and 10K 3D images in total, could support numerous research and educational purposes in biomedical image analysis, computer vision and machine learning. We benchmark several baseline methods on MedMNIST, including 2D / 3D neural networks and open-source / commercial AutoML tools. The data and code are publicly available at <a href="https://medmnist.com/">https://medmnist.com/</a>.</p> <p><em><strong>Disclaimer</strong></em>: The only official distribution link for the MedMNIST dataset is <a href="https://doi.org/10.5281/zenodo.10519652">Zenodo</a>. We kindly request users to refer to this original dataset link for accurate and up-to-date data.</p> <p><strong><em>Update</em>:</strong> We are thrilled to release <a href="https://github.com/MedMNIST/MedMNIST/blob/main/on_medmnist_plus.md">MedMNIST+</a> with larger sizes: 64x64, 128x128, and 224x224 for 2D, and 64x64x64 for 3D. As a complement to the previous 28-size MedMNIST, the large-size version could serve as a standardized benchmark for medical foundation models. Install the latest API to try it out!</p> <p> </p> <p><strong>Python Usage</strong></p> <p>We recommend our official <a href="https://github.com/MedMNIST/MedMNIST">code</a> to download, parse and use the MedMNIST dataset:</p> <blockquote> <pre>% pip install medmnist<br>% python</pre> <div> <div>To use the standard 28-size (MNIST-like) version utilizing the downloaded files:</div> <br> <div>>>> from medmnist import PathMNIST</div> <div>>>> train_dataset = PathMNIST(split="train")</div> <br> <div>To enable automatic downloading by setting `download=True`:</div> <br> <div>>>> from medmnist import NoduleMNIST3D</div> <div>>>> val_dataset = NoduleMNIST3D(split="val", download=True)</div> <br> <div>Alternatively, you can access MedMNIST+ with larger image sizes by specifying the `size` parameter:</div> <br> <div>>>> from medmnist import ChestMNIST</div> <div>>>> test_dataset = ChestMNIST(split="test", download=True, size=224)</div> </div> </blockquote> <p> </p> <p><strong>Citation</strong></p> <p>If you find this project useful, please cite both v1 and v2 paper as:</p> <blockquote> <p>Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni. Yang, Jiancheng, et al. "MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification." Scientific Data, 2023.</p> <p>Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis". IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021.</p> </blockquote> <p>or using bibtex:</p> <blockquote> <pre>@article{medmnistv2, title={MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification}, author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing}, journal={Scientific Data}, volume={10}, number={1}, pages={41}, year={2023}, publisher={Nature Publishing Group UK London} } @inproceedings{medmnistv1, title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis}, author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing}, booktitle={IEEE 18th International Symposium on Biomedical Imaging (ISBI)}, pages={191--195}, year={2021} }</pre> </blockquote> <p>Please also cite the corresponding paper(s) of source data if you use any subset of MedMNIST as per the description on the <a href="https://medmnist.github.io/">project website</a>.</p> <p> </p> <p><strong>License</strong></p> <p>The MedMNIST dataset is licensed under <em>Creative Commons Attribution 4.0 International</em> (<a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>), except DermaMNIST under <em>Creative Commons Attribution-NonCommercial 4.0 International</em> (<a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC 4.0</a>).</p> <p>The code is under <a href="https://github.com/MedMNIST/MedMNIST/blob/main/LICENSE">Apache-2.0 License</a>.</p> <p> </p> <p><strong>Changelog</strong></p> <p><a href="https://doi.org/10.5281/zenodo.10519652">v3.0</a> (this repository): Released MedMNIST+ featuring larger sizes: 64x64, 128x128, and 224x224 for 2D, and 64x64x64 for 3D.</p> <p><a href="https://doi.org/10.5281/zenodo.10519195">v2.2</a>: Removed a small number of mistakenly included blank samples in OrganAMNIST, OrganCMNIST, OrganSMNIST, OrganMNIST3D, and VesselMNIST3D. </p> <p><a href="https://doi.org/10.5281/zenodo.6496656">v2.1</a>: Addressed an issue in the NoduleMNIST3D file (i.e., nodulemnist3d.npz). Further details can be found in this <a href="https://github.com/MedMNIST/MedMNIST/issues/22#issuecomment-1103438191">issue</a>.</p> <p><a href="https://doi.org/10.5281/zenodo.5208230">v2.0</a>: Launched the initial repository of MedMNIST v2, adding 6 datasets for 3D and 2 for 2D.</p> <p><a href="https://doi.org/10.5281/zenodo.4269852">v1.0</a>: Established the initial repository (in a separate repository) of MedMNIST v1, featuring 10 datasets for 2D.</p> <p> </p> <p><strong>Note</strong>: This dataset is <strong>NOT</strong> intended for clinical use.</p>
Model Zoo: A Dataset of Diverse Populations of Neural Network Models - Fashion-MNIST
<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from Fashion-MNIST. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p>This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "fmnist_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models.</p> <p>For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.</p>
Model Zoo: A Dataset of Diverse Populations of Neural Network Models - MNIST
<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from MNIST. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p>This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "mnist_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models.</p> <p>For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.</p>
Execution Traces of an MNIST Workflow on a Serverless Edge Testbed
<p>For the evaluation of a Serverless Edge Computing platform, we built an Edge Cloud testbed consisting of several machines:</p> <ul> <li>A Cloud VM</li> <li>An Nvidia Jetson TX</li> <li>Four Raspberry Pi 3b+</li> <li>Two Intel NUCs with i5 processors</li> </ul> <p>We were interested in profiling these devices with a Machine Learning workflow deployed as a serverless application. To that end, we implemented three functions: Preprocessing, Training, and Serving as OpenFaaS functions. The workflow trains an MNIST model.</p> <p> </p>
Sparsified Model Zoo Twins: A Dataset of Sparsified Populations of Neural Network Models - MNIST
<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 27 model zoos with varying hyperparameter combinations are generated and includes 50’360 unique neural network models resulting in over 2’585’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the sparsified twins of models trained on MNIST. The original population is made available at https://doi.org/10.5281/zenodo.6632086. Sparsification is done using Variational Dropout, starting from the last epoch of the original population. The zip file contains the sparsification trajectory for 25 epochs for all 1000 models. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p> </p>
MNIST_train_test_set.hdf5
<div> <div> <div> <p>The MNIST_784 dataset is a widely used machine learning benchmark dataset. It consists of 60,000 training and 10,000 test grayscale images of handwritten digits ranging from 0 to 9, making it visually straightforward and easy to work with. Moreover, each image is 28x28 pixels in size, resulting in a total of 784 dimensions when flattened, allowing researchers to explore algorithms in the context of high-dimensional data.</p> </div> </div> </div>
Wildlife MNIST
<p>The Wildlife MNIST dataset contains MNIST digits with colored backgrounds and foregrounds with annotations, suitable for benchmarking disentangling or factor identification. Originally used for the project https://github.com/vitskvara/sgad. There are two versions - non-mixed and mixed. In the non-mixed version (data.npy and label.npy), the background and foreground textures are the same for all digits of a single MNIST class, therefore only a single label describes each sample. In the mixed version (data_test.npy and labels_test.npy), each sample image has a random digit, background and foreground (out of 10 classes for each factor of variation). Then, the label is a tuple of three numbers, describing the individual (digit,background,foreground) labels. Note that the data is scaled to the interval [-1,1], so rescaling them by computing "x*0.5 + 0.5" is necessary for some applications that require them to be in the interval [0,1]. Example images from both versions of the dataset are included. Note that the dataset was originally used in "Sauer, Axel, and Andreas Geiger. Counterfactual generative networks. 2021."</p>
scMARK an 'MNIST' like benchmark to evaluate and optimize models for unifying scRNA data
<p>Here we present a novel benchmark dataset (scMARK.v2), that consists of 11 published cancer scRNA-seq studies, for which we standardized cell-type author labels and gene identifiers. scMARK.v2 can be used to ask how well models integrate data from different scRNA studies. We also provide a 12th standardized study (Wu et al 2021) that we held-out for evaluation of alignment of data "never seen" before, and a 13th study of newly generated in-vitro scRNA-seq data from cancer and fibroblast cells.</p> <ul> <li>Data is provided as aData *h5ad files that can be read with Python's library <a href="https://scanpy.readthedocs.io/en/stable/">Scanpy</a>.</li> <li>Studies inclided in scMARK.v2 were downsampled to 10,000 cells per study.</li> <li>The difference between <a href="https://zenodo.org/record/5765804">scMARK.v1</a> and scMARK.v2, is that in v2, we provide at least two studies for each cancer type and each cell type; whereas in v1 a handfull of cell types were present only in one study.</li> </ul>
Stroke Based MNIST Data
<p>The following dataset contains the MNIST dataset in stroke/point form. The data in this repository was based on the data obtained from the following project: https://github.com/edwin-de-jong/mnist-digits-stroke-sequence-data</p>
Safran-MNIST-D
<p>This dataset contains images of serial numbers extracted from diverse avionic parts manufactured by <strong><a href="https://www.safran-group.com/">SAFRAN</a></strong>, the international high-technology group and world leader operating in the aviation (propulsion, equipment and interiors), defense and space markets. This dataset resembles the well-known MNIST dataset, but with a focus to industrial contexts, encompassing variations in lighting conditions, orientations, writing styles and surface textures. </p> <p>The dataset contains 10 classes depicting numbers, from 0 to 9</p> <p>August 14th, 2024 : Validation dataset containing 421 images without labels is released.</p> <p>December 5th, 2024 : Testing dataset released, ground-truth labels for validation and testing released.</p> <p>This dataset has been proposed in the context of <a title="https://dagecc-challenge.github.io/icpr2024/" href="https://dagecc-challenge.github.io/icpr2024/" target="_blank" rel="noreferrer noopener">ICPR24 DAGECC Competition</a></p>
Safran-MNIST-DLS
<p>This dataset contains images of serial numbers extracted from diverse avionic parts manufactured by <strong><a href="https://www.safran-group.com/">SAFRAN</a></strong>, the international high-technology group and world leader operating in the aviation (propulsion, equipment and interiors), defense and space markets. This dataset resembles the well-known MNIST dataset, but with a focus to industrial contexts, encompassing variations in lighting conditions, orientations, writing styles and surface textures. </p> <p>The dataset contains 32 classes depicting numbers, alphabetic characters, and symbols, namely: [0, 1, 2, 3, 4, 5, 5, 6, 7, 8, 9, A, B, C, D, E, F, G, H, J, K, L, M, N, P, R, S, T, U, W, Y, /, .]</p> <p>April 30th, 2024 : Training dataset containing 9314 images without labels is released.</p> <p>December 5th, 2024 : Testing and validation datasets released, ground-truth labels for training, validation and testing released.</p> <p>This dataset has been proposed in the context of <a title="https://dagecc-challenge.github.io/icpr2024/" href="https://dagecc-challenge.github.io/icpr2024/" target="_blank" rel="noreferrer noopener">ICPR24 DAGECC Competition</a></p>
Trained MNIST predictions for label shift experiments
<p>Trained MNIST predictions for label shift experiments:<br> https://github.com/kundajelab/label_shift_experiments/</p>
Mechanical MNIST crack path extended version
<p>The Mechanical MNIST Crack Path dataset contains Finite Element simulation results from phase-field models of quasi-static brittle fracture in heterogeneous material domains subjected to prescribed loading and boundary conditions. For all samples, the material domain is a square with a side length of 1. There is an initial crack of fixed length (0.25) on the left edge of each domain. The bottom edge of the domain is fixed in x (horizontal) and y (vertical), the right edge of the domain is fixed in x and free in y, and the left edge is free in both x and y. The top edge is free in x, and in y it is displaced such that, at each step, the displacement increases linearly from zero at the top right corner to the maximum displacement on the top left corner. Maximum displacement starts at 0.0 and increases to 0.02 by increments of 0.0001 (200 simulation steps in total). The heterogeneous material distribution is obtained by adding rigid circular inclusions to the domain using the Fashion MNIST bitmaps as the reference location for the center of the inclusions. Specifically, each center point location is generated randomly inside a square region defined by the corresponding Fashion MNIST pixel when the pixel has an intensity value higher than 10. In addition, a minimum center-to-center distance limit of 0.0525 is applied while generating these center points for each sample. The values of Young's Modulus (E), Fracture Toughness (G<sub>f</sub>), and Failure Strength (f<sub>t</sub>) near each inclusion are increased with respect to the background domain by a variable rigidity ratio r. The background value for E is 210000, the background value for G<sub>f</sub> is 2.7, and the background value for f<sub>t</sub> is 2445.42. The rigidity ratio throughout the domain depends on position with respect to all inclusion centers such that the closer a point is to the inclusion center the higher the rigidity ratio will be. We note that the full algorithm for constructing the heterogeneous material property distribution is included in the simulation scripts shared on GitHub. The following information is included in our dataset:</p> <p><span><span><span><span><span><span><span><span><span>(1) Locations of the center of the inclusions (the script to extract rigidity ratio matrices with the desired resolution is available on GitHub), (2) the displacement and damage fields every ten simulation step reported over a uniform 256×256 grid (3) the full resolution displacements and damage fields at both the final displacement step and the damage initiation state, and (4) the force-displacement curves for each simulation.</span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span>All simulations are conducted with the FEniCS computing platform (FEniCS Project). The code to reproduce these simulations is hosted on GitHub (https://github.com/saeedmhz/phase-field).</span></span></span></span></span></span></span></span></span></p>
Mechanical MNIST crack path extended version
Open the record for dataset details and reuse information.
MNIST dataset
Open the record for dataset details and reuse information.
MNIST_dataset
<p>Archivo de prueba para la signatura Ciclo de vida de los datos</p>
mnist-train
<p>.</p>
Model and sample data for MNIST classification
<p>Model and sample data for MNIST classification. The data is used in conjunction with <a href="https://github.com/freitaglab/LightToInformation">https://github.com/freitaglab/LightToInformation</a>.</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.