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2,562 results for “SARS CoV 2”
Supplementary data of the paper 'Adaptive trends of sequence compositional complexity over pandemic time in the SARS CoV 2 coronavirus'
<p>Supplement of the paper<br> "Adaptive trends of sequence compositional complexity over pandemic time in the SARS-CoV-2 coronavirus”<br> During the spread of the COVID-19 pandemic, the SARS-CoV-2 coronavirus underwent mutation and recombination events that altered its genome compositional structure, thus providing an unprecedented opportunity to check an evolutionary process in real time. The mutation rate is known to be lower than expected for neutral evolution, suggesting natural selection and convergent evolution. We begin by summarizing the compositional heterogeneity of each viral genome by computing its Sequence Compositional Complexity (SCC). To analyze the full range of SCC diversity, we select random samples of high quality coronavirus genomes covering the full span of the pandemic. We then search for evolutionary trends that could inform us on the adaptive process of the virus to its human host by computing the phylogenetic ridge regression of SCC against time (i.e., the collection date of each viral isolate). In early samples, we find no statistical support for any trend in SCC values, although the viral genome appears to evolve faster than Brownian Motion (BM) expectation. However, in samples taken after the emergence of high fitness variants, and despite the brief time span elapsed, a driven decreasing trend for SCC and an increasing one for its absolute evolutionary rate are detected, pointing to a role for selection in the evolution of SCC in the coronavirus. We conclude that the higher fitness of variant genomes may have leads to adaptive trends of SCC over pandemic time in the coronavirus.</p> <p>Supplementary files</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>SupplementaryTables S1-S19.zip</p> </td> <td> <p>Excel supplementary tables: The strain name, the collection date, and the SCC values for each analyzed genome.</p> </td> </tr> <tr> <td>nextstrain_ncov_open_global_timetree.nwk</td> <td>ML phylodynamic tree for the Nextstrain sample</td> </tr> <tr> <td> <p>SupplementaryTable S20.pdf</p> </td> <td> <p>A complete list acknowledging the authors, originating and submitting laboratories of the genetic sequences we used for the analysis of the Nextstrain sample.</p> </td> </tr> <tr> <td>Nextstrain_sample_fasta_3059.zip</td> <td>Nextstrain sample (sequences in Fasta format)</td> </tr> <tr> <td> <p>PhylogeneticTimetrees_NewickFormat.zip</p> </td> <td> <p>Phylogenetic timetrees (Newick format).</p> </td> </tr> </tbody> </table> <p> </p>
The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France
<p><strong>Objective</strong></p> <p>The aim of this study was to develop an accurate regional forecast algorithm to predict the number of hospitalized patients and to assess the benefit of the Electronic Health Records (EHR) information to perform those predictions. Materials and Methods Aggregated data from SARS-CoV-2 and weather public database and data warehouse of the Bordeaux hospital were extracted from May 16, 2020, to January 17, 2022. The outcomes were the number of hospitalized patients in the Bordeaux Hospital at 7 and 14 days. We compared the performance of different data sources, feature engineering, and machine learning models.</p> <p><strong>Results </strong></p> <p>During the period of 88 weeks, 2561 hospitalizations due to COVID-19 were recorded at the Bordeaux Hospital. The model achieving the best performance was an elastic-net penalized linear regression using all available data with a median relative error at 7 and 14 days of 0.136 [0.063; 0.223] and 0.198 [0.105; 0.302] hospitalizations, respectively. Electronic health records (EHRs) from the hospital data warehouse improved median relative error at 7 and 14 days by 10.9% and 19.8%, respectively. Graphical evaluation showed remaining forecast error was mainly due to delay in slope shift detection.</p> <p><strong>Discussion </strong></p> <p>Forecast models showed overall good performance both at 7 and 14 days which was improved by the addition of the data from Bordeaux Hospital data warehouse.</p> <p><strong>Conclusions </strong></p> <p>The development of hospital data warehouses might help to get more specific and faster information than traditional surveillance systems, which in turn will help to improve epidemic forecasting at a larger and finer scale.</p>
FASTA consensus sequences obtained using amplicon-based genome sequencing of SARS-CoV-2
<p>Set of 22 FASTA consensus sequences that were produced during routine SARS-CoV-2 sequencing obtained using amplicon-based sequencing (ARTIC protocol). Those sequences were compared to those generated in NASCarD applications.</p>
SARS-CoV-2 Omicron Boosting Induces De Novo B Cell Response in Humans
<p>These are the<strong> processed</strong> BCR repertoire and transcriptomics data described in <a href="https://doi.org/10.1038/s41586-023-06025-4">Alsoussi & Malladi et al., <em>Nature</em>, 2023</a>. The <strong>raw</strong> sequencing data new to this study are available on SRA under BioProject <a href="https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA800176">PRJNA800176</a>. This study also used BCR repertoire data from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">PRJNA731610</a>), <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., <em>Immunity</em>, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA741267">PRJNA741267</a>), and <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., <em>Nature</em>, 2022</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA777934/">PRJNA777934</a>).</p> <p> </p> <p><strong>Code</strong></p> <p>Code along with Docker containers for reproducing the NGS data-based figures and analyses in the published paper can be <a href="https://github.com/julianqz/wustl_published/tree/main/nature_2023">found on GitHub</a>.</p> <p> </p> <p><strong>Metadata</strong></p> <p>File: WU382_alsoussi_et_al_nature_2023_meta.tsv.gz</p> <p>Notes:</p> <ul> <li>181 samples in total, including: <ul> <li>78 new</li> <li>90 from <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., <em>Nature</em>, 2022</a></li> <li>8 from <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., <em>Immunity</em>, 2021</a></li> <li>5 from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a></li> </ul> </li> <li>Participant IDs: 6 participants who were in previous studies and who continued in the new study were referenced by new participant IDs. Correspondence with previous participant IDs is as follows: <ul> <li>382-01 = 368-22</li> <li>382-02 = 368-20</li> <li>382-07 = 368-02a</li> <li>382-08 = 368-04</li> <li>382-13 = 368-01a</li> <li>382-15 = 368-10</li> </ul> </li> <li>Sample collection time was originally recorded in days in the `timepoint` column. Values in parentheses indicate variations in which the BCR data was coded. Timepoints were mainly referenced in weeks in the manuscript, as shown in the `timepoint_ms` column. </li> <li>Pre-3rd dose ("pre-boost") samples were coded `b0` in the `booster_num` column; post-3rd dose ("post-boost") samples were coded `b1`.</li> <li>The `booster_type` column records the 3rd dose ("booster") variant. <ul> <li>`regular` = mRNA-1273 (WA1/2020)</li> <li>`beta_delta` = mRNA-1273.213 (Beta & Delta)</li> <li>`v1.1.529` = mRNA-1273.529 (Omicron)</li> </ul> </li> <li>382-02/07/08 received mRNA-1273; 382-01/13/15 received mRNA-1273.213; 382-53/54/55 received mRNA-1273.529.</li> <li>The `seq_type` column indicates the platform from which sequences originated. <ul> <li>`bulk` = bulk BCR sequencing</li> <li>`tgx` = 10x Genomics single-cell VDJ + 5' gene expression</li> <li>`mab`, `mab_1`, `mab_2`: single-cell sorted mAb synthesis. The suffixes were purely for the convenience of distinguishing originating studies.</li> </ul> </li> </ul> <p>Abbreviations:</p> <ul> <li>LN = lymph node</li> <li>BM = bone marrow</li> <li>PB = plasmablast</li> <li>GC = germinal centre</li> <li>LLPC = long-lived plasma cell</li> <li>NS = no sorting</li> <li>mAb = monoclonal antibody</li> </ul> <p> </p> <p><strong>[Beta & Delta booster] Processed BCR data - heavy chains</strong></p> <p>File: WU382_alsoussi_et_al_nature_2023_betaDelta_bcr_heavy.tsv.gz</p> <p><em>Analysis was based on heavy chain-based clonal inference.</em></p> <p>Notes on columns:</p> <p>The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s were used, as opposed to IMGT-defined "junctions". Nonetheless, junction-related columns are included here as some repositories such as <a href="https://gateway.ireceptor.org/login"><em>iReceptor</em></a> use these. Non-standard columns are noted below.</p> <ul> <li>`cell_id`: Only sequences from single-cell samples and synthesized mAbs have cell IDs. 10x sequences follow the format `[donor]_[sample]@[id]`. `NA` for bulk sequences.</li> <li>`sequence_id`: Sequence IDs follow the format `[donor]_[sample]@[id]`.</li> <li>`v_call_genotyped`: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a>.</li> <li>`germline_[vdj]_call`: Clonal consensus germline calls after corresponding clonal consensus sequences were reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` from Change-O</a>.</li> <li>`collapse_count`: Number of duplicate IMGT-aligned V(D)J sequences that were collapsed by <a href="https://alakazam.readthedocs.io/en/stable/topics/collapseDuplicates/">`alakazam::collapseDuplicates`</a>.</li> <li>`timepoint`: Timepoints follow the format `b[01]_d*`, where `b0` and `b1` correspond to pre-3rd dose ("pre-boost") and post-3rd dose ("post-boost") respectively, and `d*` indicates the timepoint in days. There's one exception: `b0_m6or9` for pre-3rd dose d201 or d280 (m6or9 = 6 or 9 months).</li> <li>`gex_anno`: Cell type identity annotation based on transcriptomic profiles. Mapped from `anno_leiden_0.35` from WU382_alsoussi_et_al_nature_2023_betaDelta_gex_b_cells.h5ad.</li> <li>`compartment`: B cell compartment</li> <li>`clone_id`: B cell clonal lineage IDs follow the format `[donor]@[id]`.</li> <li>`s_pos_clone`: `TRUE` if a sequence belonged to a B cell clone that was designated as S-binding by virtue of containing one of the recombinant mAbs that tested positive via ELISA.</li> <li>`expressed_id`: mAb IDs of mAbs from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a> and the current study; and of recombinant mAbs generated based on 10x BCRs from <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., <em>Nature</em>, 2022</a>. `NA` for everything else.</li> <li>`elisa`: ELISA results for binding of recombinant mAbs to SARS-CoV-2 S. `TRUE` if positive (WA1+); `FALSE` if negaive; `NA` if not tested or test failed.</li> <li>`nuc_RS_19_312`: number of replacement and silent mutations between IMGT-numbered nucleotide positions 19-312 along IGHV sequences, calculated by <a href="https://shazam.readthedocs.io/en/stable/topics/calcObservedMutations/">`shazam::calcObservedMutations`</a>.</li> <li>`nuc_denom_19_312`: number of informative nucleotide positions for counting mutations, excluding non-A/T/G/C positions (such as "N", "-", ".").</li> <li>`nuc_RS_freq_19_312`: nucleotide-level mutation frequency (= nuc_RS_19_312 / nuc_denom_19_312).</li> </ul> <p> </p> <p><strong>[Beta & Delta booster] Processed BCR data - light chains</strong></p> <p>File: WU382_alsoussi_et_al_nature_2023_betaDelta_bcr_light.tsv.gz</p> <p><em>Light chains were not used for heavy chain-based clonal inference or analysis.</em></p> <p> </p> <p><strong>[Beta & Delta booster] Processed transcriptomics data</strong></p> <p>Files: </p> <ul> <li>WU382_alsoussi_et_al_nature_2023_betaDelta_gex_all_cells.h5ad</li> <li>WU382_alsoussi_et_al_nature_2023_betaDelta_gex_b_cells.h5ad</li> <li>WU382_alsoussi_et_al_nature_2023_betaDelta_gex_b_cell_umap.tsv.gz</li> </ul> <p>Notes on the `h5ad` files:</p> <ul> <li>These files can be imported into <a href="https://scanpy.readthedocs.io/en/stable/index.html">Scanpy</a> as an <a href="https://scanpy.readthedocs.io/en/stable/usage-principles.html#anndata">AnnData object</a>.</li> <li>Each `AnnData` object has 3 `.layers`, each representing a version of the count matrix. <ul> <li>`raw_counts`: Imported from `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` output by `scanpy.read_10x_mtx`.</li> <li>`log_norm`: Log-noramlized expression values outputted by `scanpy.pp.normalize_total` followed by `scanpy.pp.log1p`.</li> <li>`scaled`: The `log_norm` layer scaled to unit variance and zero mean by `scanpy.pp.scale`. </li> </ul> </li> <li>The `gene_name` and `biotype` columns in `.var` were extracted from GENCODE v32 GTF.</li> <li>Columns in `.obs` (each row corresponds to a cell) <ul> <li>`n_feature`: The `n_genes_by_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The number of genes expressed. This is before subsetting the genes.</li> <li>`n_umi`: The `total_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The total UMI counts in a cell.</li> <li>`pct_mt`: The `pct_counts_mt` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The percentage of counts in mitochondrial genes.</li> <li>`n_hkg`: The number of housekeeping genes for which expression was detected.</li> <li>`n_gene_expressed`: The total number of genes for which expression was detected. This is after subsetting the genes.</li> <li>`pre_qc_bcr`: `TRUE` if a cell also had paired BCR data available. Produced by cross-referencing the cellular barcodes in `cell_barcodes.json` outputted by `cellranger vdj`. At this point the BCR data had not gone through the QC process in the BCR processing pipeline (hence `pre_qc`). </li> <li>`leiden_[resolution]`: Cluster assignment by `scanpy.tl.leiden`.</li> <li>`anno_leiden_[resolution]`: Cell type identity annotations based on transcriptomic profiles. This was mapped onto the `gex_anno` column in the processed heavy chain BCR data.</li> </ul> </li> <li>UMAP coordinates can be found in `.obsm["X_umap"]`.</li> <li>`.X` has been set to `None` in order to reduce file size.</li> </ul> <p>Note on the `tsv.gz` file: This file was derived from WU382_alsoussi_et_al_nature_2023_betaDelta_gex_b_cells.h5ad. It contains UMAP coordinates and select attributes of the cells, including their log-normalized expression values of XBP1 (`ln_XBP1`). For analysis and visualization in conjunction with BCR data.</p> <p>In addition, the preprocessed count matrix outputted by `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` is available from <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE227562">GEO under BioProject PRJNA800176</a>.</p> <p> </p> <p><strong>[Omicron booster] Processed BCR data - heavy chains</strong></p> <p>File: WU382_alsoussi_et_al_nature_2023_omicron_bcr_heavy.tsv.gz</p> <p>Notes on columns:</p> <ul> <li>`elisa`: ELISA results for mAbs, with values being one of `WA1+`, `BA1+WA1-`, or `negative`. `NA` for bulk sequences.</li> <li>`clone_type`: If a sequence was in an S-binding B cell clone (`TRUE` for `s_pos_clone`), its `clone_type` was based on the `elisa` value of the S-binding mAb in that clone -- either `WA1+` or `BA1+WA1-`; otherwise `NA`.</li> </ul>
Dataset of the Article "Reconstruction of the unbinding pathways of new inhibitors of the SARS-CoV-2 Papain-like protease using molecular dynamics simulation"
<p>This dataset contains concatenated trajectory files of the SuMD simulation of the unbinding pathways of the new inhibitors for SARS-CoV-2 papain-like protease. This data will be published in an article titled: "<strong>Reconstruction of the unbinding pathways of new inhibitors of the SARS-CoV-2 Papain-like protease using molecular dynamics simulation".</strong></p>
COVFlow: performing virus phylodynamics analyses from selected SARS-CoV-2 genome sequences
<p>This upload contains pipeline configuration files, output data, scripts and data identifiers (GISAID EPI_ISL_ID) required to reproduce the results of the article entitled "COVFlow: performing virus phylodynamics analyses from selected SARS-CoV-2 genome sequences".</p>
CORONASTEP: Monitoring of SARS-CoV-2 in Luxembourg wastewater
<p>This dataset presents the results of national-wide wastewater monitoring efforts in Luxembourg through the sampling of 13 different wastewater treatment plants across the country from March 2020 to 2023.</p>
Simulated reads for benchmarking SARS-CoV-2 lineage abundance estimation
<p>To evaluate the accuracy of lineage abundance estimates from amplicon-based and whole genome-based sequencing, we simulated paired-end reads from amplicons determined by AmpliDiff, and reads spanning full genomes. Abundances of lineages are based on the relative abundance of a lineage within the dataset. The data consists of the following 8 independent datasets:</p> <ul> <li>200 bp reads from the Netherlands based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage,</li> <li>400 bp reads from the Netherlands based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage,</li> <li>200 bp reads from the Netherlands based on whole genome sequencing at 100x coverage,</li> <li>400 bp reads from the Netherlands based on whole genome sequencing at 100x coverage,</li> <li>200 bp reads from Texas based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage,</li> <li>400 bp reads from Texas based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage,</li> <li>200 bp reads from Texas based on whole genome sequencing at 100x coverage,</li> <li>400 bp reads from Texas based on whole genome sequencing at 100x coverage.</li> </ul> <p>Every independent dataset contains 20 sets of reads (generated with different random seeds). The genomes used for the Netherlands-based simulations can be obtained via GISAID through accession id <a href="https://doi.org/10.55876/gis8.230825fe">EPI_SET_230825fe</a>, and the genomes used for the Texas-based simulations can be obtained via GISAID through accession id <a href="https://doi.org/10.55876/gis8.230825pe">EPI_SET_230825pe</a>.</p>
Dataset SeBluCo study: SARS-CoV-2-antibodies among German blood donors 2020 – 2022, a repetitive cross-sectional study
<p>The dataset is the result of a repetitive cross-sectional study in 28 regions in Germany on SARS-CoV-2 antibodies in residual samples of blood donors from April 2020 to April 2021, September 2021 and April/May 2022. These data were used to aide in monitoring the pandemic in Germany. Data were completely anonymised at the site of sample collection. Serological test results are accompanied by demographic data including sex, age and area of residence (assigned a level two Nomenclature des Unités Territoriales Statistiques (NUTS2)). </p><p>The file contains data (sheet "data") as well as the description of variable content and coding (sheet "variables").</p>
Exploring the Immune Response to SARS-CoV-2 modRNA Vaccines in Patients With Secondary Progressive Multiple Sclerosis (AMA-VACC)
ClinicalTrials.gov study NCT04792567. IPD Sharing: YES. Countries: 1. Publications: 1.
SARS-CoV-2 Immune Responses After COVID-19 Therapy and Subsequent Vaccine
ClinicalTrials.gov study NCT04952402. IPD Sharing: YES. Countries: 1. Publications: 0.
COVID-19 Study Assessing the Virologic Efficacy of REGN10933+REGN10987 Across Different Dose Regimens in Adult Outpatients With SARS-CoV-2 Infection
ClinicalTrials.gov study NCT04666441. IPD Sharing: YES. Countries: 1. Publications: 3.
Data from: Live imaging of SARS-CoV-2 infected airway epithelium cultures
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The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France
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Dataset for: Ultrarapid detection of SARS-CoV-2 RNA using a reverse transcription-free exponential amplification reaction, RTF-EXPAR
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Alternative Covid-19 mitigation measures in school classrooms: Analysis using an agent-based model of SARS-CoV-2 transmission
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PI3Kg inhibition circumvents inflammation and mortality in SARS-CoV-2 and other infections
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Online phylogenetics with matOptimize for SARS-CoV-2
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Raw diffraction data for structure of SARS-CoV-2 main protease with PCM-0102974 (ID: mpro-x1458 / PDB: 5RFY)
Raw diffraction data for mpro-x1458 / PDB ID 5RFY (see: https://www.ebi.ac.uk/pdbe/entry/pdb/5RFY) - SARS-CoV-2 main protease in complex with PCM-0102974 (SMILES:CC(C)N(C)C(=O)C1CCN(CC1)C(=O)CCl) collected as part of an XChem crystallographic fragment screening campaign on beamline i04-1 at Diamond Light Source. The deposited structure was automatically processed with standard Diamond tools and PanDDA, however the raw data are being made available to allow reanalysis by any interested party. For more details see: https://www.diamond.ac.uk/covid-19/for-scientists/Main-protease-structure-and-XChem.html
Raw diffraction data for structure of SARS-CoV-2 main protease with PCM-0102254 (ID: mpro-x1425 / PDB: 5RFX)
Raw diffraction data for mpro-x1425 / PDB ID 5RFX (see: https://www.ebi.ac.uk/pdbe/entry/pdb/5RFX) - SARS-CoV-2 main protease in complex with PCM-0102254 (SMILES:COc1ccc(cc1)N2CCN(CC2)C(=O)CCl) collected as part of an XChem crystallographic fragment screening campaign on beamline i04-1 at Diamond Light Source. The deposited structure was automatically processed with standard Diamond tools and PanDDA, however the raw data are being made available to allow reanalysis by any interested party. For more details see: https://www.diamond.ac.uk/covid-19/for-scientists/Main-protease-structure-and-XChem.html
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.