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285 results for “Machine learning dataset”
Dataset for "The State of the ML-universe: 10 Years of Artificial Intelligence & Machine Learning Software Development on GitHub"
<p>Supplementary data to "The State of the ML-universe: 10 Years of Artificial Intelligence & Machine Learning Software Development on GitHub" accepted for publication at MSR 2020.</p> <p>The data included in this package were used to conduct analyses to characterize the AI & ML software development community hosted on GitHub. Please read the paper for a full understanding of what data was collected and how it was used.</p> <p>Questions and comments can be directed to Danielle Gonzalez dng2551@rit.edu</p>
Fermilab LHC Physics Center Machine Learning Hands-On Advanced Tutorial Session Datasets
<p>The dataset jet_raw.tar.gz contains a sample of 2011 CMS Open Simulation in numpy arrays. The columns correspond to</p> <pre><code>['run', 'lumi', 'event', 'met', 'sumet', 'rho', 'pthat', 'mcweight', 'njet_ak7', 'jet_pt_ak7', 'jet_eta_ak7', 'jet_phi_ak7', 'jet_E_ak7', 'jet_msd_ak7', 'jet_area_ak7', 'jet_jes_ak7', 'jet_tau21_ak7', 'jet_isW_ak7', 'jet_ncand_ak7', 'ak7pfcand_ijet']</code></pre> <p>Each row is a separate anti-k<sub>T</sub> R=0.7 (AK7) jet. The code to produce the numpy arrays is located at https://doi.org/10.5281/zenodo.3901871</p> <p>The dataset jet_images.h5 contains preprocessed 2D jet images.</p> <p>The datasets ntuple_4mu_bkg.root, ntuple_4mu_gg.root, and ntuple_4mu_VV.root contain simulated LHC events with 4 muons for the background process, gluon fusion Higgs boson production, and vector boson fusion Higgs boson production.</p> <p> </p>
Dataset used for detecting DNS over HTTPS by Machine Learning.
<p><strong> </strong>The dataset consists of three different data sources:</p> <ol> <li> DoH enabled Firefox</li> <li>DoH enabled Google Chrome</li> <li>Cloudflared DoH proxy</li> </ol> <p>The capture of web browser data was made using the Selenium framework, which simulated classical user browsing. The browsers received command for visiting domains taken from Alexa's top 10K most visited websites. The capturing was performed on the host by listening to the network interface of the virtual machine. Overall the dataset contains almost 5,000 web-page visits by Mozilla and 1,000 pages visited by Chrome.</p> <p>The Cloudflared DoH proxy was installed in Raspberry PI, and the IP address of the Raspberry was set as the default DNS resolver in two separate offices in our university. It was continuously capturing the DNS/DoH traffic created up to 20 devices for around three months.</p> <p>The dataset contains 1,128,904 flows from which is around 33,000 labeled as DoH. We provide raw pcap data, CSV with flow data, and CSV file with extracted features.</p> <p>The CSV with extracted features has the following data fields:</p> <p>- Label (1 - Doh, 0 - regular HTTPS)<br> - Data source<br> - Duration<br> - Minimal Inter-Packet Delay<br> - Maximal Inter-Packet Delay<br> - Average Inter-Packet Delay<br> - A variance of Incoming Packet Sizes<br> - A variance of Outgoing Packet Sizes<br> - A ratio of the number of Incoming and outgoing bytes<br> - A ration of the number of Incoming and outgoing packets<br> - Average of Incoming Packet sizes<br> - Average of Outgoing Packet sizes<br> - The median value of Incoming Packet sizes<br> - The median value of outgoing Packet sizes<br> - The ratio of bursts and pauses<br> - Number of bursts<br> - Number of pauses<br> - Autocorrelation<br> - Transmission symmetry in the 1st third of connection<br> - Transmission symmetry in the 2nd third of connection<br> - Transmission symmetry in the last third of connection</p> <p>The observed network traffic does not contain privacy-sensitive information. </p> <p>The zip file structure is:</p> <pre><code>|-- data | |-- extracted-features...extracted features used in ML for DoH recognition | | |-- chrome | | |-- cloudflared | | `-- firefox | |-- flows...............................................exported flow data | | |-- chrome | | |-- cloudflared | | `-- firefox | `-- pcaps....................................................raw PCAP data | |-- chrome | |-- cloudflared | `-- firefox |-- LICENSE `-- README.md</code></pre> <p><br> When using this dataset, please cite the original work as follows:</p> <pre><code>@inproceedings{vekshin2020, author = {Vekshin, Dmitrii and Hynek, Karel and Cejka, Tomas}, title = {DoH Insight: Detecting DNS over HTTPS by Machine Learning}, year = {2020}, isbn = {9781450388337}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3407023.3409192}, doi = {10.1145/3407023.3409192}, booktitle = {Proceedings of the 15th International Conference on Availability, Reliability and Security}, articleno = {87}, numpages = {8}, keywords = {classification, DoH, DNS over HTTPS, machine learning, detection, datasets}, location = {Virtual Event, Ireland}, series = {ARES '20} } </code></pre> <p> </p>
Machine learning datasets for epigenomic landscapes in epidermal differentiation
<p>Datasets for training classification and regression models on sequence and epigenomic features. The data used here is generated from an integrative analysis of the Genomics of Gene Regulation dataset (https://www.encodeproject.org/awards/U01HG007919). For classification models, the peak files used to label genomic regions as positives or negatives can be found in `ggr.label_files.tar.gz`. For regression models, the bigwig files used for target signals can be found at the ENCODE portal. he processed dataset stored in hdf5 format files along with processing details are in the file `nn.ggr.hdf5_files.tar.gz`.</p>
RotconML: A theoretical dataset for machine learning of spectroscopic parameters
<p>This dataset comprises ~83,000 small organic molecules containing [H,C,O,N], with structures and harmonic frequency calculations performed at the ωB97X-D/6-31+G(d) level of theory with Gaussian '16.</p> <p>The purpose of this dataset is for training machine learning models—in particular, for use in rotational spectroscopy and identifying unknown molecules from spectroscopic parameters. Details of the model and the data can be found <a href="https://pubs.acs.org/doi/10.1021/acs.jpca.0c01376">in this paper</a>.</p> <p>This particular combination of electronic structure method and basis set was benchmarked in earlier work to provide relatively low uncertainties in the predicted rotational constants, and through a cancellation of errors, provides equilibrium constants that are extremely close to the vibrationally averaged (experimental) values. <a href="http://10.1021/acs.jpca.9b09982">More details can be found in this paper</a>.</p> <p> </p> <p>The dataset is included as a comma-separated value (CSV) file, which can be a little difficult to parse as plain text; I recommend using the `pandas` Python package to parse and manipulate as a Dataframe instead. The columns of this dataset include: rotational constants, moments of inertia and derived values (such as inertial defect and asymmetry parameter), harmonic frequencies and intensities, dipole moments, zero-point energy, the electronic energy, the cartesian coordinates, the SMILES identifier, the final energy difference after optimization, and the molecular mass.</p> <p> </p> <p>For more details, users are referred to our papers above and/or contact the author. If you are using this dataset for your research/work, please cite this Zenodo entry, and this reference:</p> <p>McCarthy, M.; Lee, K. L. K. Molecule Identification with Rotational Spectroscopy and Probabilistic Deep Learning. <em>J. Phys. Chem. A</em> <strong>2020</strong>, <em>124</em> (15), 3002–3017. <a href="https://doi.org/10.1021/acs.jpca.0c01376">https://doi.org/10.1021/acs.jpca.0c01376</a>.</p>
Machine Learning Dataset for Rabies Diagnosis and Outbreak Prediction
Open the record for dataset details and reuse information.
Graph Machine Learning Dataset LPWC
<p><strong>LPWC</strong> is a <strong>heterogenous graph machine learning dataset</strong> based on the RDF knowledge graph <a href="https://arxiv.org/pdf/2310.20475.pdf">Linked Papers With Code</a> (version from 2023-06-24).</p><p>LPWC contains four node types - papers (376,557 nodes), datasets (8,322 nodes), tasks (4,267 nodes) and methods (2,101), and six edge types.</p><p>Each node has rich semantic node features for node representation (content-based and topology-based node features are available).</p><p> </p><p>More information can be found in README.txt and on <a href="https://github.com/davidlamprecht/AutoRDF2GML">https://github.com/davidlamprecht/AutoRDF2GML</a></p>
Graph Machine Learning Dataset SOA-SW
<p><strong>SOA-SW</strong> is a heterogeneous graph machine learning dataset based on the RDF knowledge graph <a href="doi.org/10.5281/zenodo.10299132">SemOpenAlex-SemanticWeb</a>.</p><p>SOA-SW contains six node types - works (95,575 nodes), authors (19,970 nodes), concepts (38,050 nodes), sources (10,739 nodes), institutions (5,846 nodes), and publishers (786 nodes), and seven edge types. </p><p>Each node has rich semantic node features as node representation (content-based and topology-based node features are available).</p><p>More information can be found in the README.txt and on <a href="https://github.com/davidlamprecht/AutoRDF2GML">https://github.com/davidlamprecht/AutoRDF2GML.</a></p><p> </p><p><strong>soa-sw-homogeneous-author</strong> only models the co-author network of SOA-SW.</p><p>It is a homogeneous graph containing the author node type (19,970 nodes) and the edge type author- author.</p><p>The authors' content-based features (nodes-nld) are based on the titles and abstracts of the authors' works (128-dimensional SciBERT embeddings).</p>
dataset for machine learning example in WarpX / ImpactX
<p>This contains data for a machine learning workflow using codes from the BLAST suite supported at Lawrence Berkeley National Laboratory. There are 2 directories: </p> <p>lab_particle_diags: contains particle diagnostics in openPMD format for use in training neural networks<br>models: parameters of pre-trained neural networks for use in inference</p>
Dataset and code of groudwater nitrate for machine learning
<p>Data of groudwater nitrate and related data in North China Plain (NCP). The data including nitrate concentration of groudwater collected from more than 4,000 sites (wells) in NCP from 2005 to 2021. The groundwater samples were collected in 2005–2021, and the collection was conducted in May (before rainy season) and October (after rainy season) in each year for every site.During sampling, basic information about well location, groundwater depth, farmland planting pattern and soil types were collected. Sampling wells were divided into three types according to depth, shallow (0–30 m), medium (30–100 m) and deep (> 100 m). The planting pattern mainly involved intensive croplands, grain crops, vegetable crops and orchards. Soil types of each sampling site were obtained from the China soil database (<a href="http://vdb3.soil.csdb.cn/">http://vdb3.soil.csdb.cn/</a>).The socio-economic and agricultural information of the study areas (take the districts of municipalities and prefecture-level cities of provinces as basic units) were acquired via the <a>China Statistical Yearbook</a> (<a href="http://www.stats.gov.cn/sj/ndsj/">http://www.stats.gov.cn/sj/ndsj/</a>). The data includes agricultural planting area, grain crop area, vegetable planting area, orchard planting area, total facility agricultural area; fertilizer amount, nitrogen fertilizer amount, unit area nitrogen fertilizer amount; total output value of agricultural, forestry, animal and fishery husbandry, agricultural output value, forestry output value, animal husbandry output value, fishery output value; Gross Domestic Product (GDP), per capita GDP; total population, and rural population.</p>
Dataset: Uncovering Obscured Phonon Dynamics from Powder Inelastic Neutron Scattering using Machine Learning
<p>Dataset of simulated and experimental spectra for the manuscript: Uncovering Obscured Phonon Dynamics from Powder Inelastic Neutron Scattering using Machine Learning. </p> <p>The data.zip contains all the simulated spectra and labels.</p> <p>The dataset.zip contains the divided subsets for training, validation and testing purposes, as well as the experimental dataset.</p>
Graph Machine Learning Dataset AIFB (AIFB-GML)
<p>AIFB-GML is a comprehensive, heterogeneous graph machine learning dataset derived from the <a href="https://figshare.com/articles/dataset/AIFB_DataSet/745364" target="_blank" rel="noopener">AIFB RDF knowledge graph</a>. This dataset integrates a variety of node types, including individuals (:person), research groups (:group), and scholarly publications (:publication).</p> <p>Developed using the innovative <a href="https://github.com/davidlamprecht/AutoRDF2GML" target="_new">AutoRDF2GML framework</a>, AIFB-GML is specifically formatted to facilitate graph-based tasks such as node classification and link prediction.</p>
Dataset and machine learning models for seismic response predictions of small-to-medium continuous girder bridges
<p>This upload includes the dataset and machine learning models (based on Matlab platform) for longitudinal seismic response predictions of multi-span highway girder bridges, which have a typical span length of 30 m supported by reinforced concrete (RC) bridge bents and abutments through spherical steel bearings. The input variables (features) are five structural parameters of studied bridges and seven intensity measures of earthquakes. The output variables (labels) are peak column drifts and peak bearing deformations. The dataset is developed by conducting a total number of 720 nonlinear time-history analyses considering the uncertainty of bridges and earthquakes. Machine learning models are developed using two popular machine learning algorithms named artificial neural network (ANN) and support vector regression (SVR).</p>
DRAMMA: A multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data (dataset 2 of 2)
<p>Dataset for the support of a journal publication. </p> <p>Part 2/2 of a dataset used for running and training the ML model.</p> <p>Part 1 is available at <a href="https://doi.org/10.5281/zenodo.14513933">10.5281/zenodo.14513933</a></p> <p>The code associated with this publication is available at: https://github.com/burstein-lab/DRAMMA </p>
DRAMMA: A multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data (dataset 1 of 2)
<p>Dataset for the support of a journal publication. </p> <p>Part 1/2 of a dataset used for running and training the ML model.</p> <div> <div>Part 2 is available at <a href="https://doi.org/10.5281/zenodo.14524613">10.5281/zenodo.14524613</a></div> <div> </div> </div> <p>The code associated with this publication is available at: https://github.com/burstein-lab/DRAMMA </p>
Datasets for: Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy
<p>The datasets and supplementary materials for the manuscript "Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy". <strong>Citations should refer directly to the manuscript (refer to the DOI </strong><a href="https://doi.org/10.1021/acs.jcim.1c01103">10.1021/acs.jcim.1c01103</a><strong>)</strong>.</p> <p>The preprint version of of the manuscript is also available at: <a href="https://doi.org/10.33774/chemrxiv-2021-djd3d-v2">10.33774/chemrxiv-2021-djd3d-v2</a></p> <p> </p> <p>Regarding "<strong>Solvation_data-1.0.0.zip</strong>":</p> <p>The datasets include the curated data for: (1) Abraham solute parameters, (2) solvation free energy, (3) solvation enthalpy, (4) gas-water partition coefficient (logKw), (5) water-1-octanol partition coefficient (logPow). The fitted Abraham and Mintz solvent parameters are also included.</p> <p>Detailed information can be found in the "README.txt" file of the zip file.</p> <p> </p> <p>Regarding "<strong>ML_model_files.zip</strong>":</p> <p>This contains the machine learning model files for SoluteML and DirectML. For the instruction on how to use it, please refer to the <em>chemprop_solvation</em> git repository (<a href="https://github.com/fhvermei/chemprop_solvation">https://github.com/fhvermei/chemprop_solvation</a>)</p> <p> </p>
Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning Dataset
<p>Data files were used in support of the research paper titled “<em>Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning</em>" which has been submitted to the IET Communications journal.</p> <p>---------------------------------------------------------------------------------------------</p> <p>All data was collected using the SDR implementation shown here: https://github.com/mainland/dragonradio/tree/iet-paper. Particularly for antenna state selection, the files developed for this paper are located in 'dragonradio/scripts/:'</p> <ul> <li>'ModeSelect.py': class used to defined the antenna state selection algorithm</li> <li>'standalone-radio.py': SDR implementation for normal radio operation with reconfigurable antenna</li> <li>'standalone-radio-tuning.py': SDR implementation for hyperparameter tunning</li> <li>'standalone-radio-onmi.py': SDR implementation for omnidirectional mode only</li> </ul> <p>---------------------------------------------------------------------------------------------</p> <p>Authors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar<br> Contact: krd26@drexel.edu</p> <p>---------------------------------------------------------------------------------------------</p> <p>Top-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper.</p> <p>---------------------------------------------------------------------------------------------</p> <p>classifier_training: files used for training classifiers that are integrated into SDR platform</p> <ul> <li>'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)</li> <li>'classTrain.py' is the main parser for training the classifiers</li> <li>'trainedClassifiers' contains the output classifiers generated by 'classTrain.py'</li> </ul> <p>post_processing_classifier: contains logs of online classifier outputs and processing script</p> <ul> <li>'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario</li> <li>'classProcess.py' parses the log files and provides classification report and confusion matrix for each multi-class and binary classifiers for each observed scenario - found in 'results->classifier_performance'</li> </ul> <p>post_processing_mgen: contains MGEN receiver logs and parser</p> <ul> <li>'configs' contains JSON files to be used with parser for each experiment</li> <li>'mgenLogs' contains MGEN receiver logs for each OTA and RTE experiment described. Within each experiment logs are separated by 'mit' for mitigation used, 'nj' for no jammer, and 'noMit' for no mitigation technique used. File names take the form *_cj_* for constant jammer, *_pj_* for periodic jammer, *_rj_* for reactive jammer, and *_nj_* for no jammer. Performance figures are found in 'results->mitigation_performance'</li> </ul> <p>ray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies.</p> <ul> <li>Directory contains detailed 'readme.txt' for understanding.</li> <li>Please note: the processing files and data logs present in 'validation' folder were developed by Wolfe et al. and should be cited as such, unless explicitly stated differently. <ul> <li>S. Wolfe, S. Begashaw, Y. Liu and K. R. Dandekar, "Adaptive Link Optimization for 802.11 UAV Uplink Using a Reconfigurable Antenna," MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), 2018, pp. 1-6, doi: 10.1109/MILCOM.2018.8599696.</li> </ul> </li> </ul> <p>results: contains results obtained from study</p> <ul> <li>'classifier_performance' contains .txt files summarizing binary and multi-class performance of online SDR system. Files obtained using 'post_processing_classifier.'</li> <li>'mitigation_performance' contains figures generated by 'post_processing_mgen.'</li> <li>'validation' contains RTE and OTA performance comparison obtained by 'ray_tracing_emulation->validation->matlab->outdoor_hover_plots.m'</li> </ul> <p>tuning_parameter_study: contains the OTA log files for antenna state selection hyperparameter study</p> <ul> <li>'dataCollect' contains a folder for each jammer considered in the study, and inside each folder there is a CSV file corresponding to a different configuration of the learning parameters of the reconfigurable antenna. The configuration selected was the one that performed the best across all these experiments and is described in the paper.</li> <li>'data_summary.txt'this file contains the summaries from all the CSV files for convenience.</li> </ul>
SynPhoRest - Synthetic Photorealistic Forest Dataset with Depth Information for Machine Learning Model Training
<p><strong>SynPhoRest </strong>is a synthetic dataset collected on virtual forests. It features RGB images, semantic segmentation maps, depth maps and the projection of LIDAR point clouds on the RGB FOV for two different LIDAR scanning patterns. The dataset has a total of 3154 frames.<br> <br> A description of the available data follows:</p> <p><strong>RGB images</strong></p> <ul> <li>Resolution: 848 x 480 pixels.</li> <li>PNG files with 8 bits encoding per channel.</li> </ul> <p><strong>Segmentation Maps</strong></p> <ul> <li>Resolution: 848 x 480 pixels.</li> <li>PNG files with a single 8 bit channel.</li> <li>Classes are encoded as follows:</li> <li> <table> <thead> <tr> <th scope="col"><strong>Value</strong></th> <th scope="col"><strong>Class</strong></th> </tr> </thead> <tbody> <tr> <td>0</td> <td>Background</td> </tr> <tr> <td>1</td> <td>Soil</td> </tr> <tr> <td>2</td> <td>Traversable</td> </tr> <tr> <td>3</td> <td>Canopy</td> </tr> <tr> <td>4</td> <td>Fuel</td> </tr> <tr> <td>5</td> <td>Trunks</td> </tr> </tbody> </table> <p>Fuel represents flammable material such as shrubbery and grass.</p> </li> </ul> <p><strong>Depth Maps</strong></p> <ul> <li>Resolution: 848 x 480 pixels.</li> <li>PNG files with a single 16 bit channel</li> <li>The depth value is encoded in the unsigned integer format.</li> <li>Infinite depth is represented by the value 65535.</li> <li>To obtain the depth values in meters, the original values must by divided by 256.</li> <li>The FOV of the virtual depth camera was the same as the FOV of the RGB camera.</li> </ul> <p><strong>LIDAR Point Cloud Projections on the RGB Camera FOV</strong></p> <ul> <li>Resolution: 848 x 480 pixels.</li> <li>PNG files with a single 16 bit channel.</li> <li>The distance values are encoded in the unsigned integer format.</li> <li>To obtain the distance values in meters the original values must by divided by 256.</li> <li>On average, the projection images have a point density of 5.5%. In practice, this means that 5.5% of the pixels in the projection image have distance information.</li> <li>Two LIDAR Point Cloud Projections were made available. One for a LIDAR with a repeating line pattern and other resembling the commercially available Livox Horizon LIDAR scanner.</li> </ul>
Modified version of the Physionet database "MIT Normal Sinus Rhythm" as Machine Learning dataset
<p>ECGs from the MIT-NSR database with some modifications to make them more suitable as playground data set for machine learning.</p> <ul> <li>all 18 ECGs are trimmed to approx. 50000 heart beats from a region without recording errors</li> <li>scaled to a range -1 to 1 (non-linear/tanh)</li> <li>heart beats annotation as time series with value 1.0 at the point of the annotated beat and 0.0 for all other times</li> <li>additional heart beat column smoothed by applying a gaussian filter</li> <li>provided as csv with columns "time in sec", "channel 1", "channel 2", "beat" and "smooth"</li> <li>an example that uses the dataset to implement heart-beat detection can be found here: <a href="https://github.com/KnetML/NNHelferlein.jl/blob/main/examples/62-ECG-tagger.ipynb">Heart beat detection with Peephole LSTM</a>.</li> </ul> <p><strong>Original data set description:</strong></p> <p>MIT-BIH Normal Sinus Rhythm Database</p> <p>George Moody, Published: Aug. 3, 1999. Version: 1.0.0</p> <p>This database includes 18 long-term ECG recordings of subjects referred to the Arrhythmia Laboratory at Boston's Beth Israel Hospital (now the Beth Israel Deaconess Medical Center). Subjects included in this database were found to have had no significant arrhythmias; they include 5 men, aged 26 to 45, and 13 women, aged 20 to 50.</p> <p>DOI: <a href="https://doi.org/10.13026/C2NK5R">https://doi.org/10.13026/C2NK5R</a></p> <p>Link: <a href="https://www.physionet.org/content/nsrdb/1.0.0/">https://www.physionet.org/content/nsrdb/1.0.0/</a></p> <p>Ref: Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.</p> <p> </p>
Machine learning for Material Science 2022 - Neural Networks Assignment Dataset
<p>Data for the neural network assignment</p> <p> </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.