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Version 2 of Dataset for paper "Drivers' Manoeuvre Prediction for Safe HRI"
<p>Second version of the dataset published for "Drivers’ Manoeuvre Prediction for Safe HRI".</p> <p>Details in the README file.</p>
Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution
<p>Data and model files supporting the manuscript: </p> <p>Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution.</p> <p>Repository: https://github.com/alcantarar/Recurrent_GRF_Prediction</p>
Embeddings from protein language models predict conservation and variant effects
<p>For this work, we used protein language model representations (embeddings) to predict sequence conservation without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthew Correlation Coefficient – MCC - for ProtT5 embeddings of 0.596±0.006 vs. 0.608±0.006 for ConSeq).</p> <p><strong><em>ConSurf10k</em>- Dataset for the development of ProtT5cons:</strong> The method (ProtT5cons) predicting residue conservation used <em>ConSurf-DB </em>(Ben Chorin et al. 2020). This resource provided sequences and conservation for 89,673 proteins. For all, experimental high-resolution three-dimensional (3D) structures were available in the Protein Data Bank (PDB) (Berman et al. 2000). As standard-of-truth for the conservation prediction, we used the values from ConSurf-DB generated using HMMER (Mistry et al. 2013), CD-HIT (Fu et al. 2012), and MAFFT-LINSi (Katoh and Standley 2013) to align proteins in the PDB (Burley et al. 2019). For proteins from families with over 50 proteins in the resulting MSA, an evolutionary rate at each residue position is computed and used along with the MSA to reconstruct a phylogenetic tree. The ConSurf-DB conservation scores ranged from 1 (most variable) to 9 (most conserved). The PISCES server (Wang and Dunbrack 2003) was used to redundancy reduce the data set such that no pair of proteins had more than 25% pairwise sequence identity. We removed proteins with resolutions >2.5Å, those shorter than 40 residues, and those longer than 10,000 residues. The resulting data set (ConSurf10k) with 10,507 proteins (or domains) was randomly partitioned into training (9,392 sequences), cross-training/validation (555) and test (519) sets.</p> <p>Uploaded data:</p> <ul> <li>ConSuf10k_PDBid_seq_cons.fasta: fasta file with PDBid, sequence and conservation annotation</li> <li>consurf10k_test_ids.txt: txt file with id's of test set</li> <li>consurf10k_train_ids.txt: txt file with id's of train set</li> <li>consurf10k_val_ids.txt: txt file with id's of cross-validation set</li> </ul>
A computational intelligence approach to predict energy demand using Random Forest in a Cloudera cluster
<p>Society’s energy consumption has shot up in recent years, making the prediction of its demand a current challenge to ensure an efficient and responsible use. Artificial intelligence techniques have proven to be potential tools in handling tedious tasks and making sense of large-scale data to make better business decisions in different areas of knowledge. In this article, the use of random forests algorithms in a Big Data environment is proposed for households energy demand forecasting. The predictions are based on the use of information from different sources, confirming a fundamental role of socioeconomic data in consumer’s behaviours. On the other hand, the use of Big Data architectures is proposed to perform horizontal and vertical scaling of the solution to be used in real environments. Finally, a tool for high-resolution predictions with great efficiency is introduced, which enables energy management in a very accurate way.</p> <p>Raw data is incuded in data.csv. This file contains half hourly home electricity consumption registers for 4404 households with fix tariffs (not subject to dynamic time of use) for a period between November 2011 and February 2014. Original information was acquired from the Low Carbon London project led by UK Power Networks (https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households)</p> <p>RFResults.zip contains the energy predictions for each ACORN group using the generated Random Forest algorithm. For this purpose, the first 613 days of a total of 818 observations of each group were considered for training and the last 205 days for testing.</p> <p>Meteorological data was adquired from the darksky app (https://darksky.net). These data are included in the weather_hourly_darksky.csv</p> <p>uk_bank_holidays. xlsx contains the dated of UK bank holidays for the studied period, used as additional variable related to occupancy</p>
Dataset for "A Bayesian neural network predicts the dissolution of compact planetary systems"
<p>The dataset used for training and evaluating the models in the paper "A Bayesian neural network predicts the dissolution of compact planetary systems": https://arxiv.org/abs/2101.04117. </p> <p>The code for working with this dataset, and other links, can be found at: https://github.com/MilesCranmer/bnn_chaos_model.</p>
Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Supplementary Tables
<p>This repository contains the Supplementary Tables for Suriyalaksh et al. Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes.</p> <p>The list of table files can be found in <a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/Supplementary%20table%20guide.pdf">Supplementary Tables guide.pdf</a></p> <p>Tables S1, S2 and S3 corresponding to physical gene-gene interaction data are in a separate repository doi:10.5281/zenodo.4382337</p> <p>Details about some of the Supplementary tables:</p> <p>TableS4_inferred_networks.csv - list of inferred GRNs for specified input combinations (set of input regulators, length of the time sequence, NI tool and prior used).</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS5_consensus_network_member.xlsx">TableS5_consensus_network_member.xlsx</a> - list of groups of topologically similar GRNs (from Table S4)</p> <p>Table S6: edge lists (source,target) for each one of the three consensus networks selected according to the GS validation metrics: middle PFE/AUFE, max AUFE, max PFE.<br> TableS6a_max_AUFE_GRN.txt - max AUFE; largest network - this is the one we used in the main analysis and discussion<br> TableS6b_max_PFE_GRN.xt - max PFE<br> TableS6c_middle_AUFE_PFE_GRN.txt - middle PFE/AUFE</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS7_qRTPCR_ddCt_network_accuracy.csv">TableS7_qRTPCR_ddCt_network_accuracy.csv</a> - gene expression count differences for RNAi knockdown GRN validation experiments. </p> <p>Table S8: Group membership for each one of the nodes in each one of the selected networks according to the SBM that best describes the observed network topology. Each column shows the group membership for each level in a SBM block hierarchy. Our analysis is in the second most coarse-grained level (level 1).</p> <p>TableS8a_max_AUFE_SBM.csv<br> TableS8b_max_PFE_SBM.csv<br> TableS8c_middle_AUFE_PFE_SBM.csv</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS9_glp_gs_datasets.pdf">TableS9_glp_gs_datasets.pdf</a> - list of datasets used for defining functional clusters.</p> <p>TableS14a_glp_l1_vs_fem_l1_lifespan_assay.xlsx - Day13 survival of fem-3(q20)ts vs day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L1</p> <p>TableS14b_glp_l1_vs_glp_l4_lifespan_assay.xlsx - Day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L4 vs day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L1</p> <p>TableS15a_glp1_in_vivo_fluorescence_data.xlsx - in vivo fluorescent reporter data of glp-1(e2144)ts;rrf-3(pk1426)</p> <p>TableS15b_fem3_in_vivo_fluorescence_data.xlsx - in vivo fluorescent reporter data of fem-3(q20)ts</p> <p>TableS17_input_regulators_annotated.csv - list input regulators used as input for Network Inference Tools annotated by source type (2nd column): GenAge, known transcription factors (TF) and gene with high variability in the gene expression time series (HV). The third column lists whether that regulator has an orthologue in human (y) according to WormBase (v 278).</p> <p>TableS20_epistasis_lifespan_data.xlsx - Epistasis lifespan data of glp-1(e2144)ts</p> <p>All the image (TIF) files represent representative images in the following genetic backgrounds (below) that have been treated </p> <p>with empty vector (EV) or RNAi against the gene highlighted in the title of the image. See methods section for details. </p> <p><strong>femliu1: </strong></p> <p><em>fem-3(q20)ts.; dhs-3p::dhs-3::gfp</em></p> <p><strong>femsod3:</strong></p> <p><em>fem-3(q20)ts.; sod-3p::gfp</em></p> <p><strong>glp1lgg1:</strong></p> <p><em>glp-1(e2144); lgg-1p:lgg-1:gfp</em></p>
Predicting COVID-19 Incidence Through Spatiotemporal Human Interactions
<p>This repository contains data (features) necessary to run STXGB model. STXGB is a spatiotemporal autoregressive model that predicts county-level new cases of COVID-19 in the coterminous US in 1- to 4-week prediction horizons using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features.</p>
Spatiotemporal Prediction of COVID-19 Cases using Inter- and Intra-County Proxies of Human Interactions (dataset)
<p>This repository contains data (features) necessary to run STXGB model and accompanies the paper titled "Spatiotemporal Prediction of COVID-19 Cases using Inter- and Intra-County Proxies of Human Interactions".</p> <p> </p> <p>STXGB is a spatiotemporal autoregressive model that predicts county-level new cases of COVID-19 in the coterminous US in 1- to 4-week prediction horizons using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features.</p>
Is this bug severe? A text-cum-graph based model for bug severity prediction
<p>A snapshot of the dataset has been updated. For the time being, we are publishing a snapshot of the dataset where the bugs were reported after 2017.</p> <p>Paper link: <a href="https://arxiv.org/abs/2207.00623">https://arxiv.org/abs/2207.00623</a> (ECML-PKDD 2022)</p> <p>Cite our paper:</p> <p>@InProceedings{10.1007/978-3-031-26422-1_15,<br> author="Hazra, Rima<br> and Dwivedi, Arpit<br> and Mukherjee, Animesh",<br> editor="Amini, Massih-Reza<br> and Canu, St{\'e}phane<br> and Fischer, Asja<br> and Guns, Tias<br> and Kralj Novak, Petra<br> and Tsoumakas, Grigorios",<br> title="Is This Bug Severe? A Text-Cum-Graph Based Model for Bug Severity Prediction",<br> booktitle="Machine Learning and Knowledge Discovery in Databases",<br> year="2023",<br> publisher="Springer Nature Switzerland",<br> address="Cham",<br> pages="236--252",<br> isbn="978-3-031-26422-1"<br> }</p> <p><strong>*** Please see the new version. (10.5281/zenodo.5554978)</strong></p> <p>There is a total of six files.</p> <ul> <li><strong>bug_descriptions.csv:</strong> This file contains the bug id and its description.</li> <li><strong>bug_comments.csv:</strong> This file contains three columns. The columns are the bug ids, comments and timestamp of the comment.</li> <li><strong>bug_REPORTED_ON_details.csv:</strong> This file contains the bug id and the package name on which the bug is reported</li> <li><strong>affect_dataset.csv: </strong>This file contains the bug id and the affected packages along with the affect timestamp.</li> <li><strong>bug_heat_2019.csv:</strong> This file contains the bug ids and its bug heats crawled in November 2019.</li> <li><strong>bug_heat_2020.csv:</strong> This file contains the bug ids and its bug heats crawled in November 2020.</li> </ul>
Data for: Machine learning for predicting environmental mobility based on retention behaviour
<p>This repository contains the data and supplementary information for the paper: "Machine learning for predicting environmental mobility based on retention behaviour".</p>
Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.
<p>## Abstract</p> <p>from [1]:</p> <p>Mechanochemistry is a fast-developing field of interdisciplinary research with a growing number of applications. Therefore, many theoretical methods have been developed to quickly predict the outcome of mechanically induced reactions. Constrained geometries simulate External Force (CoGEF) is one of the earlier methods in this field. It is easily implemented and can be conducted with most DFT codes. However, recently, we observed totally different predictions for model systems of epoxy resins in different conformations and with different density functionals. To better understand the conformational and functional dependence in typical CoGEF calculations we present a systematic evaluation of the CoGEF method for different model systems covering homolytic and heterolytic bond cleavage reactions, electrocyclic ring opening reactions and scission of non-covalent interactions in hydrogen-bond complexes. From our calculations we observe that many mechanochemical descriptors strongly depend on the functional used, however, a systematic trend exists for the relative maximum Force. In general, we observe that the CoGEF procedure is forcing the system to high energetic regions on the molecular potential energy profiles, which can lead to unexpected and uncorrelated predictions of mechanochemical reactions. This is questioning the true predictive character of the method.</p> <p> </p> <p>## Contact</p> <p>Christian R. Wick</p> <p>Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Faculty of Science, Department of Physics, PULS Group, Interdisciplinary Center for Nanostructured Films (IZNF), Cauerstrasse 3, 91058, Germany</p> <p> </p> <p>## License</p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p>## Context</p> <p>Dataset to paper [1]</p> <p> </p> <p>## Contents</p> <ul> <li>All COGEF trajectories in xyz format.</li> <li>All CoGEF distances and DFT Energies in csv format.</li> </ul> <p>The following DFT levels of theory were investigated:</p> <ul> <li>B3LYP/6-31G(d)</li> <li>B3LYP-D3BJ/def2-SVP</li> <li>BP86-D3/def2-SVP</li> <li>PBE1PBE/def2-SVP</li> <li>M06-D3/def2-SVP</li> </ul> <p> </p> <p>## Folder structure</p> <ul> <li>- compound_X : data set for compound number X (numbering corresponds to the numbering scheme in [1]) <ul> <li>the xyz trajectories follow the following naming convention: "DFT_method"_"unrestricted/restricted".xyz</li> <li>the csv files follow the naming convention: "DFT_method"_"unrestricted/restricted".xyz.csv</li> </ul> </li> </ul> <p>## Software</p> <p>### COGEFF calculations: COGEF.py v1.8.0</p> <p>Zenodo release:</p> <p>https://doi.org/10.5281/zenodo.7079733</p> <p>### DFT calculations:</p> <p>Gaussian 16 Rev B [2]</p> <p> </p> <p>## Funding</p> <p>This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/1-2019 FRASCAL.</p> <p><br> ## References</p> <p>[1] C. R. Wick, E. Topraksal, D. M. Smith, A.-S. Smith, "Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.", Forces in Mechanics, 9, 100143; doi:10.1016/j.finmec.2022.100143</p> <p>[2] Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; et al. Gaussian 16 Rev. B.01, 2016.</p>
DATASET: Predicting Protein Function and Orientation on a Gold Nanoparticle Surface Using a Residue-Based Affinity Scale
<p>This upload contains data for the manuscript "<strong>Predicting Protein Function and Orientation on a Gold Nanoparticle Surface Using a Residue-Based Affinity Scale</strong>." It contains kinetics data, UV-vis data, surface calculations, and activity assays for the systems described in the manuscript.</p>
Predictive modeling of moonlighting DNA-binding proteins
<p>This repository contains the codes used for the prediction of moonlighting proteins in the paper "Predictive modeling of moonlighting DNA binding proteins".</p> <p>The repository is organized as the following:</p> <p>1. The DNA binding protein identifiers and their features that were used to train the models for the prediction of DNA binding Moonlighting proteins.</p> <p>2. Five feature sets were used to create Catboost models that make predictions. The source code for generating predictions based on all the features and predictions based on particular features is supplied. In addition, the source code for generating maximum and average ensemble predictions has been made available. A detailed explanation is given in README file.</p>
Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer - pre-processed data
<p>A multicentre, prospective observational study was conducted of colorectal cancer (CRC) patients undergoing primary surgical resection in the United Kingdom and Czech Republic. Analysis was performed using metataxonomics (microbiome) and ultra-performance liquid chromatography mass spectrometry (UPLC-MS, metabolomics). Both datasets were pre-processed as described in the methods section of the main article. The data here were used as the input to the data analysis workflows available from <a href="https://github.com/jmp111/CRC">Github</a>.</p>
A common NFKB1 variant detected through antibody analysis in UK Biobank predicts risk of infection and allergy: Summary statistics - Health records
<p>Infectious agents contribute significantly to the global burden of diseases, through both acute infection and their chronic sequelae. We leveraged the UK Biobank to identify genetic loci that influence humoral immune response to multiple infections. From 45 genome-wide association studies in 9,611 participants from UK Biobank, we identified NFKB1 as a locus associated with quantitative antibody responses to multiple pathogens including those from the herpes, retro- and polyoma-virus families. An insertion-deletion variant thought to affect NFKB1 expression (rs28362491), was mapped as the likely causal variant. This variant has persisted throughout hominid evolution and could play a key role in regulation of the immune response. Using 121 infection and inflammation related traits in 487,297 UK Biobank participants, we show that the deletion allele was associated with an increased risk of infection from diverse pathogens but had a protective effect against allergic disease. We propose that altered expression of NFKB1, as a result of the deletion, modulates haematopoietic pathways, and likely impacts cell survival, antibody production, and inflammation. Taken together, we show that disruptions to the tightly regulated immune processes may tip the balance between exacerbated immune responses and allergy, or increased risk of infection and impaired resolution of inflammation. </p> <p>-------------------------------------------------------------------------------------</p> <p>This dataset contains GWAS summary statistics for infection, inflammation, and allergy related traits in 487,297 individuals</p>
A common NFKB1 variant detected through antibody analysis in UK Biobank predicts risk of infection and allergy: Summary statistics - Serology
<p>Infectious agents contribute significantly to the global burden of diseases, through both acute infection and their chronic sequelae. We leveraged the UK Biobank to identify genetic loci that influence humoral immune response to multiple infections. From 45 genome-wide association studies in 9,611 participants from UK Biobank, we identified NFKB1 as a locus associated with quantitative antibody responses to multiple pathogens including those from the herpes, retro- and polyoma-virus families. An insertion-deletion variant thought to affect NFKB1 expression (rs28362491), was mapped as the likely causal variant. This variant has persisted throughout hominid evolution and could play a key role in regulation of the immune response. Using 121 infection and inflammation related traits in 487,297 UK Biobank participants, we show that the deletion allele was associated with an increased risk of infection from diverse pathogens but had a protective effect against allergic disease. We propose that altered expression of NFKB1, as a result of the deletion, modulates haematopoietic pathways, and likely impacts cell survival, antibody production, and inflammation. Taken together, we show that disruptions to the tightly regulated immune processes may tip the balance between exacerbated immune responses and allergy, or increased risk of infection and impaired resolution of inflammation. </p> <p>-------------------------------------------------------------------------------------</p> <p>This dataset contains GWAS summary statistics for quantitative antibody responses in 9611 individuals and results for a meta-analysis of UK Biobank and CoLaus/PsyCoLaus antibody responses.</p> <p> </p>
predicted_summary_stats_ecoli
<p>The published dataset shows the summary statistics of predicted <em>E.coli </em>concentrations at the Seine river in Paris at Pont d'Iena. The data is provided on a log<sub>10 </sub>scale and shows the geometric mean and the predicted upper percentiles, which can be compared to the thresholds provided by the European Bathing Water Directive.</p> <p>Model predictions have a root mean squared error of 0.25 and a coverage rate of 97%, 95%, and 98% for the ratio of test data points falling below the 95th, the 90th percentile, and inside the 95% prediction interval. However, model predictions are very uncertain and are not able to accurately predict variations of measured <em>E.coli</em> concentrations. Therefore, the model is not able to discriminate between periods of good and impaired water quality and always predicts "poor" water quality. The used features were not suitable to accurately predict the observed concentrations. Further model improvements are considered necessary.</p> <p> </p>
Data for paper "An adaptive nonlinear iterative method for predicting seafloor topography from altimetry-derived gravity data"
<p>LM is the linear inversion seafloor topography model</p> <p>NLM is the nonlinear inversion seafloor topography model</p> <p>PM is the prior seafloor topography model</p>
Research data supporting "Tin phosphide anodes for potassium-ion batteries: insights from crystal structure prediction"
<p>This dataset contains the output files of crystal structure prediction calculations (density-functional theory relaxations, bandstructures, phonon calculations, GIPAW-NMR calculations) on the ternary K-Sn-P phase diagram. All calculations were performed with the CASTEP DFT package (https://www.castep.org/) and the "matador" Python library (https://github.com/ml-evs/matador).</p> <p><strong>Contents:</strong></p> <ul> <li>"convergence_tests.zip": contains the results of convergence tests on the K-P system at two levels of accuracy "polish" and "searches" on the corresponding edge of the K-Sn-P ternary system</li> <li>"phonons.zip": contains CASTEP output files for phonon calculations on the predicted low-lying phases on the corresponding edge of the K-Sn-P phase diagram</li> <li>"polish.zip": contains CASTEP output files of relaxations on the corresponding edge of the K-Sn-P system at the "polish" level of accuracy using various different xc-functionals or external pressures.</li> <li>"searches.zip": contains ".res" files that provide the relaxed structure from each different crystal structure prediction method on the corresponding edge of the K-Sn-P system.</li> <li>"bulk_modulus.zip" contains CASTEP output files for calculation of E(V) curves for low-lying KP phases with different xc-functionals.</li> <li>"nmr.zip" contains CASTEP output files for GIPAW-NMR calculations of chemical shifts for low-lying K-Sn-P phases.</li> <li>"spectral.zip" contains CASTEP and OptaDOS output files for projected bandstructure and DOS calculations of low-lying K-Sn-P phases.</li> <li>"digests.zip" contains JSON representations of all the structures from polish and searches, broken down into K-P and K-Sn-P specific digests.</li> </ul>
Predicting Survival of Tongue Cancer Patients by Machine Learning Models
<p>This repository contains the dataset used in the paper "Predicting Survival of Tongue Cancer Patients by Machine Learning Models." The dataset contains information on 1712 tongue cancer curative surgery recipients. Each row represents one patient. The meaning of each variable is summarized here:</p> <ul> <li>id: patient identifier</li> <li>gender: patient sex</li> <li>survival: patient survival status at follow-up; 0: survival, 1: death</li> <li>follow_time: length of follow-up period in days</li> <li>part: site of operation</li> <li>stage: tumor stage; 0: very small, no spreading, 1: small, no spreading, 2: some growth, spreading, 3: large, spreading to surrounding tissue or lymph nodes, 4A/4B/4C: larger, metastasis to at least one other organ</li> <li>op: operation status; 1: complete</li> <li>rt: radiation therapy status; 0: not received, 1: received</li> <li>ct: chemotherapy status; 0: not received, 1: received</li> <li>t_stage: tumor size; 1 (small) to 4 (large)</li> <li>n_stage: metastasis to lymph nodes; 0 (no metastasis) to 3 (metastasis to multiple lymph nodes)</li> <li>grade: tumor grade; 1 (no proliferation) to 3 (aggressive proliferation)</li> </ul>
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.