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1,943 results for “machine learning”
Data from: Olfactory testing in Parkinson's disease & REM behavior disorder: a machine learning approach
<p><span><span><span><span><span><span><span><span><span><span><span><b>Objective: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>We sought to identify an abbreviated test of impaired olfaction, amenable for use in busy clinical environments in prodromal (isolated REM sleep Behavior Disorder (iRBD)) and manifest Parkinson's.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Methods: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>890 PD and 313 control participants in the Discovery cohort study underwent Sniffin' stick odour identification assessment. Random forests were initially trained to distinguish individuals with poor (functional anosmia/hyposmia) and good (normosmia/super-smeller) smell ability using all 16 Sniffin' sticks. Models were retrained using the top 3 sticks ranked by order of predictor importance. One randomly selected 3-stick model was tested in a second independent Parkinson's dataset (n=452) and in two iRBD datasets (Discovery n=241; Marburg n=37) before being compared to previously described abbreviated Sniffin' stick combinations.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>In differentiating poor from good smell ability, the overall area under the curve (AUC) value associated with the top 3 sticks (Anise, Licorice and Banana) was 0.95 in the development dataset (sensitivity:90%, specificity:92%, PPV:92%, NPV:90%). Internal and external validation confirmed AUCs≥0.90. The combination of 3-stick model determined poor smell and an RBD screening questionnaire score of ≥5, separated iRBD from controls with a sensitivity, specificity, PPV and NPV of 65%, 100%, 100% and 30%. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>Our 3-Sniffin'-stick model holds potential utility as a brief screening test in the stratification of individuals with Parkinson's and iRBD according to olfactory dysfunction.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Classification of Evidence: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>This study provides Class III evidence that a 3-Sniffin'-stick model distinguishes individuals with poor and good smell ability and can be used to screen for individuals with iRBD.</span></span></span></span></span></span></span></span></span></span></span></p>
Code and data for "Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction" By Guan et al. Submitted to JGR Oceans.
<p>This repository contains the code and data for the machine learning analysis of "Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction”By Guan et al. Submitted to JGR Oceans.</p> <p>Specifically, this repository contains the following items:<br> (1) Codes for assessing the representation skill of the machine learning and linear regression (LR) methods. Three machine learning methods are considered: random forest (RF), back-propagation neural network (BP), and convolutional neural network (CNN).<br> (2) Codes for assessing the prediction skill of the machine learning and LR methods. <br> (3) Seasonal-mean and annual-mean input data to run these codes. <br> (4) The package needed to run the random forest code, i.e. the RF_MexStandalone-v0.02 program package from https://code.google.com/archive/p/randomforest-matlab/downloads .</p>
Linkage of hospital records and death certificates by a search engine and machine learning: training and test set data
<p>INTRODUCTION: Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly available dataset containing death-certificate data for over 25 million individuals. The objective of this study was to link French death certificates to the Bordeaux University Hospital records to complete the vital status information.</p> <p>MATERIALS AND METHODS: Our linkage strategy was composed of a search engine to reduce the number of comparisons and machine-learning algorithms. The overall pipeline was evaluated by assembling a file containing 3,565 in-hospital deaths and 15,000 alive persons.</p> <p>RESULTS: The recall and precision of our linkage strategy were 97.5% and 99.97% for the upper threshold and 99.4% and 98.9% for the lower threshold, respectively.</p> <p>CONCLUSION: In this article, we demonstrated the feasibility of accurately linking hospital records with death certificates using a search engine and machine learning.</p>
Data for "Explainable Machine Learning for Predicting Homicide Clearance in the United States"
<p>These pickle files can be used to replicate the analyses carried out in the paper "Explainable Machine Learning for Predicting Homicide Clearance in the United States", currently under review.</p>
Code for: Comparison and interpretability of machine learning models to predict severity of chest injury
<p><span><span><span><span><span><span><span><span><span><span><span><b>Objective:</b> Trauma quality improvement programs and registries improve care and outcomes for injured patients. Designated trauma centers calculate injury scores using dedicated trauma registrars; however, many injuries arrive at non-trauma centers, leaving a substantial amount of data uncaptured. We propose automated methods to identify severe chest injury using machine learning (ML) and natural language processing (NLP) methods from the electronic health record (EHR) for quality reporting.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Materials and Methods:</b> A level I trauma center was queried for patients presenting after injury between 2014 and 2018. Prediction modeling was performed to classify severe chest injury using a reference dataset labeled by certified registrars. Clinical documents from trauma encounters were processed into concept unique identifiers for inputs to ML models: logistic regression with elastic net regularization (EN), extreme gradient boosted machines (XGB), and convolutional neural networks (CNN). The optimal model was identified by examining predictive and face validity metrics using global explanations.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results:</b> Of 8,952 encounters, 542 (6.1%) had a severe chest injury. CNN and EN had the highest discrimination, with an area under the receiver operating characteristic curve of 0.93 and calibration slopes between 0.88 and 0.97. CNN had better performance across risk thresholds with fewer discordant cases. Examination of global explanations demonstrated the CNN model had better face validity, with top features including "contusion of lung" and "hemopneumothorax." </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Discussion: </b>The CNN model featured optimal discrimination, calibration, and clinically relevant features selected. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusion:</b> NLP and ML methods to populate trauma registries for quality analyses are feasible.</span></span></span></span></span></span></span></span></span></span></span></p>
Contrast MR Based Radiomics and Machine Learning Analysis to assess clinical outcomes following liver resec-tion in Colorectal Liver Metastases: a preliminary study
<p>We uploaded the images of the manuscript "Contrast MR Based Radiomics and Machine Learning Analysis to assess clinical outcomes following liver resection in Colorectal Liver Metastases: a preliminary study" accepted on Cancers.</p> <p>Vincenza Granata1*, Roberta Fusco2, Federica De Muzio3, Carmen Cutolo4, Sergio Venanzio Setola1, Federica dell’ Aversana5, Alessandro Ottaiano6, Antonio Avallone6, Guglielmo Nasti6, Francesca Grassi5, Vincenzo Pilone4, Vittorio Miele7-8, Luca Brunese3, Francesco Izzo9, Antonella Petrillo1</p> <p>1Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy</p> <p>2Medical Oncology Division, Igea SpA, Napoli, Italy</p> <p>3Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy</p> <p>4Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy</p> <p>5Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, Naples, Italy</p> <p>.6Division of Abdominal Oncology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy</p> <p>7Division of Radiology, “Azienda Ospedaliera Universitaria Careggi”, Florence, Italy</p> <p>8Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122 Milan, Italy</p> <p>9Division of Epatobiliary Surgical Oncology,“Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy</p> <p> </p>
Supplementary Data: Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China using Machine Learning Emulation.
<p>Temporary duplicate of:</p> <p>Conibear, L., Reddington, C. L., Silver, B. J., Chen, Y., Arnold, S. R., & Spracklen, D. V. (2022). <em>Supplementary Data: Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China using Machine Learning Emulation. University of Leeds. [Dataset]</em>. https://doi.org/doi.org/10.5518/1055</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>
Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis
<p>data files, including ML models of dynamic classifiers, trajectories of electronic structure and geometric features, optimized geometries, and final csv files.</p>
Data for "Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection"
<p>Once decompressed, the file contains a folder which contains:</p> <ul> <li>The files "s100_Nth.fasta" (where "N" is 5, 6, 7 or 8), which are the output of the SELEX experiment described in the paper with DOI: <a href="https://doi.org/10.1002/cbic.201900265">10.1002/cbic.201900265</a>. They are standard fasta files, and the descriptor of each sequence is of the form "seqX-Y", where "X" is an increasing label, and "Y" is the number of times "seqX" has been obtained (number of counts of "seqX").</li> <li>The file "Aptamer_Exp_Results.csv", which contains the sequences tested experimentally for the paper "Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection" (preprint available at https://doi.org/10.1101/2022.03.12.484094), with the following experimental results for each sequence: (i) whether the sequence was able to bind thrombin ('B' for binders, 'NB' for non-binders); (ii) the thrombin exosite used for binding ('I' for exosite I, 'II' for exosite II, 'n/a' for sequences not tested).</li> </ul> <p>Examples of usage of the data are available at https://github.com/adigioacchino/RBMsForAptamers.</p>
Data for "Machine Learning Parameterization of Subgrid-Scale Orographic Gravity Wave Drag in a Middle-Atmosphere General Circulation Model" by Lu et al., submitted to JAMES, 2022.
<p>The NetCDF data file involving the decision tree strucutre attributes of the random forest emulator.</p> <p>gcm_regressors/<br> The data file involving the decision tree strucutre attributes (in NetCDF format)</p>
Machine Learning Potentials for Metal-Organic Frameworks with Thermodynamic Transferability: training data
<p>This dataset contains potential energies, forces, and virial stress for a large set of reference configurations for UiO-66(Zr) and MIL-53(Al), computed at the PBE-D3 level using CP2K 7.1. The basis set contained both TZVP Gaussian basis functions as well as plane waves (cutoff energy 800 Ry for UiO-66(Zr) and 900 Ry for MIL-53(Al)). The sampling of the Brillouin zone was restricted to the gamma point.</p>
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>
Master thesis: Comparison of Machine Learning Methods for Pedestrian Trajectory Generation
<p>These videos are the result videos of the pedestrian model with trained neural networks of different Machine Learning methods in the master thesis: Comparison of Machine Learning Methods for Pedestrian Trajectory Generation.</p> <p>Turing_Learning_1: the first experiment of the Turing Learning method.</p> <p>Turing_Learning_1: the second experiment of the Turing Learning method.</p> <p>Inverse_Reinforcement_Learning_1: the first experiment of the Inverse Reinforcement Learning method.</p> <p>Inverse_Reinforcement_Learning_2: the second experiment of the Inverse Reinforcement Learning method.</p> <p>Deep_Q_Network_1: the first experiment of the Deep Q-Learning method.</p> <p>Deep_Q_Network_2: the second experiment of the Deep Q-Learning method.</p> <p>Deep_Q_Network_3: the third experiment of the Deep Q-Learning method.</p> <p>Deep_Q_Network_4: the fourth experiment of the Deep Q-Learning method.</p> <p>Policy_Distillation_1: the first experiment of the Policy Distillation method.</p> <p>Policy_Distillation_2: the second experiment of the Policy Distillation method.</p> <p>Policy_Distillation_3: the third experiment of the Policy Distillation method.</p>
To Seed or Not to Seed? An Empirical Analysis of Usage of Seeds for Testing in Machine Learning Projects
<p>This contains the experimental dataset for our paper: "To Seed or Not to Seed? An Empirical Analysis of Usage of Seeds for Testing<br> in Machine Learning Projects" published at ICST 2022. Go to <a href="https://github.com/uiuc-arc/xseed">Github</a> for more details.</p>
Research data for "A machine-learned interatomic potential for silica and its relation to empirical models"
<p>This dataset supports the paper "A machine-learned interatomic potential for silica and its relation to empirical models". The paper is online here:</p> <p>The following files are provided:</p> <ul> <li>xyz-file containing all structures in the training database including forces and energies</li> <li>GAP file containing the corresponding parameters, which can be used for example for Lammps MD simulations</li> <li>Amorphous structure files for silica created by different interatomic potentials.</li> </ul>
NYU -Machine Learning Data (coronal_pd_fs directory converted to BART file format)
<p>This is part of the training and test data that was used for the 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data (If you work with this data, please cite this paper):</p> <p>Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018)</p> <p>The data has been converted to the BART cfl format. The same data is available in the MATLAB format on GLOBUS (https://app.globus.org/file-manager?origin_id=15c7de28-a76b-11e9-821c-02b7a92d8e58&origin_path=%2F). k-Space data is partly available in the ismrmrd file format at mridata.org.</p>
Radiomics and Machine Learning Analysis Based on Magnetic Resonance Imaging in the Assessment of Colorectal Liver Metastases Growth Pattern
<p>I upload the images of the manuscript "Radiomics and Machine Learning Analysis Based on Magnetic Resonance Imaging in the Assessment of Colorectal Liver Metastases Growth Pattern".</p>
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>
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
<p>Supplementary data for the paper "GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations" by the GPUMD developers.</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.