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In vivo treatment with insulin-like growth factor 1 reduces CCR5 expression on vaccine-induced activated CD4+ T-cells
<p>Dataset of the publication "In vivo treatment with insulin-like growth factor 1 reduces CCR5 expression on vaccine-induced activated CD4+ T-cells" by Bissa et al. on the journal Vaccines. </p><p>Each folder contains the original files reporting the data used to generate the manuscript.</p><p>For flowcytometry based assays the Flow panel is included in the folders. </p><p>For ELISA based assays the schemes of the plates are included in the folders. </p><p>The excel table "Bissa et al._Vaccines_2023_Animal IDs and viral acquisition" reports the IDs and grouping of the animals together with their viral acquisition</p><p>The excel table "Bissa et al._Vaccines_2023_Master table" reports each data used to generate the figures and supplemental materials included in the publication </p>
Taking Antibiotics for a virus is like trying to sweeten lake Victoria with a bag of sugar
<p>Video in English (see also the Swahili version) warns that attempting to treat a virus or common cold by self-medication with antibiotics can lead to the development of Antimicrobial Resistance (AMR). It uses humour and Tanzanian metaphors for foolishness to make its point. This was originally developed as an audio piece, but still images have been added in post-production to create a video. </p><p>Audio and images produced as part of a Participatory Action Research (PAR) Workshop with young professionals in Mwanza, Tanzania to create public health messages on Antimicrobial Resistance in a post-COVID East Africa in June 2022. The project built upon data gathered in two international, interdisciplinary research projects (HATUA – 'Holistic Approaches to Understanding Antimicrobial Resistance in East Africa' and CARE – 'COVID-19 and Antimicrobial Resistance in East Africa – Impact and Response'), seeking to understand the wider medical and societal drivers of AMR in East Africa and identify possible interventions to curb the spread of AMR. The workshop ran for 9 days over a 3 week period and consisted of focus group style discussions with participants to explore issues surrounding AMR, antibiotic use, and public health messaging awareness in local communities (days 1-2), participant-led design of poster, radio, and video messages with feedback from the research team and introduction to filming/recording equipment (days 3-4), filming, shooting and recording materials within local settings in Mwanza with participants serving as actors, directors, and crew with guidance from research team (days 4-8) and a final in-person review and hands-on feedback of preliminary mock-ups of posters and videos (day 9). Participants have continued to collaborate via email and WhatsApp as materials were finalised. Final video production and editing was undertaken by the researchers. </p><p>Correspondence: mgk@st-andrews.ac.uk; kjf4@st-andrews.ac.uk</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>
Multiple Evolution Modes of Megaripples in the Qaidam Basin and Implications for Ripple-Like Aeolian Landforms on Mars
<p>The dataset includes wind regime data for Golmud, Sebei, and the west bank of the Narin Gol River in the Qaidam Basin, as well as sediment grain size and morphological parameters of the megaripples. In addition, we provide R language source code for data processing and visualization.</p><p>The primary directory contains the data and the source code in the R language. The data includes sediment grain size, morphological parameters and wind regime analysis data of megaripples. Modifying the working path and installation package is necessary to call the R source code for data loading.</p>
Savanna-like Mammalian Community in the Mid-Miocene of Northern China--Supplementary Data
<p><strong><span>Supplementary Data 1:</span></strong><span> Stable Carbon and Oxygen isotopes data of Junggar Basin and Tongxin region, China.</span></p> <p><strong><span>Supplementary Data 2:</span></strong><span> Teeth hypsodonty of herbivorous mammals from China and Europe, including Sandelzhausen, Steinheim, Sansan, Madrid Basin, Qaidam Basin, Tunggur region, Baode, Lantian, Junggar Basin and Tongxin region.</span></p> <p><strong><span>Supplementary Data 3: </span></strong><span>Body mass of herbivorous mammals from different locations in China. </span></p>
Data for publication 'Detection of Artificial Seed-like Objects from UAV Imagery'
<p>This resource contains the datasets supporting the model development as published in the article 'Detection of Artificial Seed-like Objects from UAV Imagery' (https://doi.org/10.3390/rs15061637).</p> <p>In the last two decades, unmanned aerial vehicle (UAV) technology has been widely utilized as an aerial survey method. Recently, a unique system of self-deployable and biodegradable microrobots akin to winged achene seeds was introduced to monitor environmental parameters in the air above the soil interface, which requires geo-localization. This research focuses on detecting these artificial seed-like objects from UAV RGB images in real-time scenarios, employing the object detection algorithm YOLO (You Only Look Once). Three environmental parameters, namely, daylight condition, background type, and flying altitude, were investigated to encompass varying data acquisition situations and their influence on detection accuracy. Artificial seeds were detected using four variants of the YOLO version 5 (YOLOv5) algorithm, which were compared in terms of accuracy and speed. The most accurate model variant was used in combination with slice-aided hyper inference (SAHI) on full resolution images to evaluate the model’s performance. It was found that the YOLOv5n variant had the highest accuracy and fastest inference speed. After model training, the best conditions for detecting artificial seed-like objects were found at a flight altitude of 4 m, on an overcast day, and against a concrete background, obtaining accuracies of 0.91, 0.90, and 0.99, respectively. YOLOv5n outperformed the other models by achieving a mAP0.5 score of 84.6% on the validation set and 83.2% on the test set. This study can be used as a baseline for detecting seed-like objects under the tested conditions in future studies.</p>
FASTA file containing the MYB encoding gene An2-like and Ant1 coding sequences corresponding to wild and cultivated tomato accessions
<p>The coding sequence (CDS) of the MYB encoding genes <em>Ant1</em> and <em>An2-like</em>. Sequences were retrieved from regions corresponding to the<em> Aft</em> locus from <em>Solanum galapagense </em>accession LA1141, <em>S. lycopersicum</em> variety OH8245, and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences were compared to available CDS available from the Sol genomics network (SGN) and the National Center for Biotechnology Information. The CDS was retrieved from <em>S. lycopersicum</em> variety Indigo Rose [MN433087 (Yan et al., 2020)], <em>S. lycopersicum</em> accession LA1996 [MN242011.1, EF433417.1( Sapir et al., 2008; Colanero et al., 2020)], and <em>S. chilense </em>accession LA1930 [MN242012.1 (Colanero et al., 2020)], The orthologous CDS corresponding to the <em>Aft </em>MYB encoding genes from <em>Solanum tuberosum</em> L. Group Phureja clone DM1-3 genome (PGSC DM v4.03 Pseudomolecules) was retrieved from the Potato Genome Sequence Consortium (PGSC: Potato Genome Sequencing Consortium et al., 2011), and the Capsicum annum cv. CM334 genome was retrieved from <em>Capsicum annuum </em>cv CM334 genome chromosome release 1.55 (Hulse-Kemp et al. 2018). These CDS were obtained using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at https://solgenomics.net/tools/blast/). Comparison of syntenic chromosomal regions using known positions of tomato, potato, and pepper markers with comparative map viewer from SGN: (available at https://solgenomics.net/cview) on chromosome 10, was used as a quality check for S.<em> tuberosom</em> and <em>C. annuum.</em> Orthologous CDS corresponding to <em>Salvia miltiorrhiza, Arabidopsis thaliana</em>, [NM_105308.2, NM_105310.4 (Teng et al., 2005, Cominelli et al., 2008; Beradini et al., 2015)] were chosen based on tomato <em>Aft</em> sequence homology and gene annotations of positive R2R3 MYB regulation of anthocyanin. The CDS corresponding to the <em>Aft</em> genes were retrieved from the CDS reference genomes available from the Sol Genomics Network SGN: Tomato Genome CDS (ITAG release 4.0), Potato PGSC DM v3.4 CDS sequences, <em>Capsicum annuum </em>cv CM334 Genome CDS (release 1.55), or from the National Center for Biotechnology Information (NCBI: https://www.ncbi.nlm.nih.gov) reference sequences (RefSeq) section of the Genbank records. When accessed from Genank records, the CDS sequence was extracted from the “features” section and exported as a FASTA file.</p>
Subgrid-scale effects in cloud-like atmospheric flows: Colliding thermals - volume 2
<p>Datasets to perform surrogate modeling of subgrid-scale effects in cloud-like atmospheric flows. Please see README.md file for detailed description and metadata.</p>
Database of Webcam Photos Recorded in Videoconference-like Settings
<p><br> <strong>Webcam photos of persons (senders) locking at targets on their computer screens</strong></p> <p>n: number of the sender<br> hp: head position<br> tp: target position</p> <p>Positioning of hp:</p> <pre><code>[7] [5] [10] [3] [8] [1][0][2] [11] [4] [9] [6] [12]</code></pre> <p>Positioning of tp:</p> <pre><code>[ 0][ 1][ 2][ 3][ 4][ 5][ 6] [ 7][ 8][ 9][10][11][12][13] [14][15][16][17][18][19][20]</code></pre> <p><br> File structure:<br> P1-8: A directory for each sender.<br> T1: Collection day 1, T2: Collection day 2.<br> Each combination of hp and tp was recorded once per collection day.</p> <p>Naming of the images:<br> n_hp_tp.png</p>
Visual-inertial input datasets for SLAM applications containing extreme and human-like motion patterns
<p>Recorded datasets in compressed rosbag format, which contain visual and IMU sensor information that are bearing high resemblance to the movement of a human player with a handheld AR-capable device.</p> <p>For machine learning training and validation tasks, separate dataset are available containing motion patterns in a wide range from steady camera image to extremely challenging movements.</p>
Infrared spectroscopy of the benzylium-like (and tropylium-like) isomers formed in the --H dissociative ionization of methylated PAHs
<p>Dataset for article "Infrared spectroscopy of the benzylium-like (and tropylium-like) isomers formed in the --H dissociative ionization of methylated PAHs". DOI: 10.1016/j.jms.2022.111620</p> <p> - folder Experimental contains<br> - folder Fig2_IRPDspectra containing<br> - with IRPD spectra data of all three species (Fig. 2)<br> - folder Fig3_Depletion containing<br> - the saturation depletion measurements on six bands (Fig. 3)<br> - folder FigS1_MassSpectra containing<br> - the mass spectra of all three species in Trap ON/OFF modes (Fig. S1)</p> <p> - folder Theoretical contains<br> - .log files for each considered species<br> - e.g.: folder C11H9+ contains folders for the<br> - NapC7+<br> - NapC7+Ne<br> - NapCH2+<br> - NapCH2+Ne<br> These contain all .log files needed to reproduce Figs. 4, 5, 6 of the main<br> mansucript and Figs. S3, S4, S5, S6, S7, S8, S9 of the supplementary material<br> - folder Fig7_EnergyProfile containing<br> - all minima and transition states for the computed energy profile (doublet<br> spin state surface) for the H loss from NapCH3+ leading to NapCH2+ and NapC7+<br> (Fig. 7)</p> <p> </p>
Supplementary Data: Cosmological constraints on decaying axion-like particles: a global analysis
<p><strong>Supplementary Data</strong></p> <p><em>Cosmological constraints on decaying axion-like particles: a global analysis</em></p> <p>This record contains the supplemetary data for the GAMBIT article, "Cosmological constraints on decaying axion-like particles: a global analysis". </p>
joemacgregor/GBaTSv2: Greenland Ice Sheet Likely Basal Thermal State version 2 [dataset+code] FINAL
<p>Greenland Ice Sheet Likely Basal Thermal State version 2, FINAL dataset+code to be published in The Cryosphere (tc-2022-40)</p>
Perceptions on the utility of community question and answer websites like Stack Overflow to software developers (Replication package)
<p>Interview Questions on the perception of the utility of CQAs like Stack Overflow to software developers. In this study, we focused on the questions highlighted in yellow.</p>
Thimble-like object with shrine and tree
<p>Thimble-like object cast in a copper alloy; on one side a rectangular panel showing a sacred tree, shrine, altar and standard; pierced. Probably from central India. British Museum number 1995,1018.1</p>
Dataset - Gravisensors in plant cells behave like an active granular liquid
<p>Dataset corresponding to data shown in article : https://hal.archives-ouvertes.fr/hal-01425298v2 (avalanches of statoliths pile in wheat coleoptile cells, avalanches of silica micro-particles in biomimetic cells, vertical fluctuations of statoliths in wheat coleoptile cells or extracted from their cells).</p> <p>Examples of python scripts that open and plot the data are also included.</p>
Human Kelch-like ECH Associated Protein 1 (KEAP1); A Target Enabling Package
<p>KEAP1 is a highly redox-sensitive member of the BTB-Kelch family that assembles with the CUL3 protein to form a Cullin-RING E3 ligase complex for the degradation of NRF2. Oxidative stress disables KEAP1 allowing NRF2 protein levels to accumulate for the transactivation of critical stress response genes. Consequently, the KEAP1-NRF2 system is a highly attractive target for the development of protein-protein interaction inhibitors that will stabilise NRF2 for therapeutic effect in conditions of neurodegeneration and inflammation. As part of this TEP we have solved the first crystal structure of a KEAP1-CUL3 complex as well as a structure of the apo-Kelch domain suitable for small molecule soaking. We further established a selectivity assay panel of 17 human Kelch domain-containing proteins and have shown that non-covalent KEAP1 inhibitors from the literature are highly selective for KEAP1. This protein panel offers a resource for future work on KEAP1 as well as 16 other human Kelch proteins.</p>
Supplementary files for "Pressure-enhanced ferroelectric polarisation in polar perovskite-like [C2H5NH3]Na0.5Cr0.5(HCOO)3 metal-organic framework"
<p>DFT optimised structures for the paper: Pressure-enhanced ferroelectric polarisation in polar perovskite-like [C2H5NH3]Na0.5Cr0.5(HCOO)3 metal-organic framework. See the paper for additional information on the computational setup.</p> <p>Files are named HP or LP for high-pressure and low-pressure phases, followed by the pressure as calculated by DFT. The HP structure was optimized in two different space groups and are labelled accordingly. The DFT optimized structure without volume restrictions is found in opt-vol.POSCAR</p> <p> </p>
LIKE-A-PRO_D.1.2_qualitative_data
<p>This file includes qualitative data developed in Task 1.2, LIKE-A-PRO project. Respective data file presents the results of coding of findings reported in the original studies, included in the systematic review conducted in T.1.2. The file also reports the coding of the risk of bias of the original studies, included in the systematic review conducted in T.1.2.</p>
Dataset from "Venomous Peptides: Molecular Origin of the Toxicity of Snake Venom PLA2‑like Peptides"
<p>Dataset from "Venomous Peptides: Molecular Origin of the Toxicity of Snake Venom PLA2‑like Peptides", containing the most relevant all-atom output trajectories and input files ran with GROMACS 2021:</p> <p>1) <strong>pure_membrane_systems.7z</strong> - pure bilayer systems (AA1, AA2, AA5), including equilibration, calcium insertion, and umbrella sampling simulations;</p> <p>2) <strong>single_peptide_systems.7z</strong> - single peptide-containing systems (AA3, AA4, AA6), including equilibration, calcium insertion, and umbrella sampling simulations;</p> <p>3) <strong>multiple_peptide_systems.7z</strong> - multiple peptide-containing systems (AA7, AA8), including equilibration, calcium insertion, and umbrella sampling simulations.</p> <p>We have included the input files (.mdp), system topology (.top and .itp), initial and final structure files (.gro), the index file (.ndx), and the portable binary run input files (.tpr). We have also included the output trajectories of systems AA3, AA4, AA6-8 in .xtc format, and spaced every 500 ps.</p> <p>System composition is given in Table Z1. More details can be found in the related publication.</p> <p><strong>Table Z1. Simulated systems' details, including name, composition (in number of lipid and peptide molecules), number of atoms composing the systems, simulation (sim.) time, and total umbrella sampling (US) time.</strong> </p> <table> <tbody> <tr> <td><strong>System</strong></td> <td><strong>POPC/POPS/Peptide</strong></td> <td><strong>no. atoms (a)</strong></td> <td><strong>sim. time (µs)</strong></td> <td><strong>US time (µs)</strong></td> </tr> <tr> <td><strong>AA1</strong></td> <td>128/0/0</td> <td>40,226</td> <td>0.3</td> <td>10.8</td> </tr> <tr> <td><strong>AA2</strong></td> <td>0/128/0</td> <td>39,458</td> <td>0.3</td> <td>10.8</td> </tr> <tr> <td><strong>AA3</strong></td> <td>128/0/1</td> <td>40,504</td> <td>0.5</td> <td>32.3</td> </tr> <tr> <td><strong>AA4</strong></td> <td>0/128/1</td> <td>39,724</td> <td>0.5</td> <td>32.3</td> </tr> <tr> <td><strong>AA5</strong></td> <td>96/32/0</td> <td>40,034</td> <td>1.0</td> <td>-</td> </tr> <tr> <td><strong>AA6</strong></td> <td>96/32/1</td> <td>40,300</td> <td>1.0</td> <td>-</td> </tr> <tr> <td><strong>AA7</strong></td> <td>96/32/5</td> <td>41,364</td> <td>1.0</td> <td>10.8</td> </tr> <tr> <td><strong>AA8</strong></td> <td>96/32/13</td> <td>55,128</td> <td>2.0</td> <td>10.8</td> </tr> </tbody> </table> <p>(a) for the US simulations, the total number of atoms was reduced in 1 because two sodium ions were substituted by a single calcium ion.</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.