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2,800 results for “CD8+ T cells”
PD1+CD8+ cells are an independent prognostic marker in patients with head and neck cancer
<p><strong>Data open:</strong> file with parametres used for the multivariate evaluation. </p>
CD8+ T cell differentiation status correlates with the feasibility of sustained unresponsiveness following oral immunotherapy
<p>While food allergy oral immunotherapy (OIT) can provide safe and effective desensitization (DS), the immune mechanisms underlying development of sustained unresponsiveness (SU) following a period of avoidance are largely unknown. We compared high dimensional immunophenotypes of innate and adaptive immune cell subsets of participants in a phase 2 randomized, controlled, peanut OIT trial who achieved SU vs. DS (no vs. with allergic reactions upon food challenge after a withdrawal period; n=21 vs. 30 respectively among total 120 intent-to-treat participants). Lower frequencies of naïve CD8<sup>+</sup> T cells and terminally differentiated CD57+ CD8<sup>+</sup> T cell subsets at baseline (pre-OIT) were highly associated with SU. Frequency of naïve CD8<sup>+</sup> T cells showed a significant positive correlation with peanut- and Ara h 2-specific IgE at baseline. Higher frequencies of IL-4<sup>+</sup> and IFNg<sup>+</sup> CD8<sup>+</sup> T cells post-OIT were negatively correlated with SU. Our findings provide compelling evidence that an immune signature consisting of certain CD8<sup>+</sup> T cell subset frequencies is predictive of SU following OIT.</p>
Impact of low pH on glutamine incorporation in CD8+ T cells
<p>OT-I CTLs were cultured for 4 hours with <sup><span>13</span></sup><span>C</span>-glutamine in the presence, or absence, of IL-2 at pH7.4 or pH6.6. Metabolites that incorporated isotopic glutamine were identified by HILIC-HRMS.</p>
Spatial dynamics of CD39⁺CD8⁺ exhausted T cells reveal tertiary lymphoid structures-mediated response to PD-1 blockade in esophageal cancer
<p><strong>Data related to the paper</strong>: <em>"Spatial dynamics of CD39+CD8+ exhausted T cells reveal tertiary lymphoid structures-mediated response to PD-1 blockade in esophageal cancer,”</em> <em>Nature Communications</em> (2024)</p> <p>The repository data consists of two main folders: <strong>IMC_dataset</strong> and <strong>MC_normalized_dataset</strong>.</p> <p><strong>IMC_dataset</strong> includes:</p> <ol> <li> <p><strong>IMC_denoised_dataset</strong>: This folder contains cell mask images and noise-reduced images for each sample.</p> </li> <li> <p><strong>IMC_raw_dataset</strong>: This folder contains raw, unprocessed data.</p> </li> <li> <p><strong>IMC_processed_data</strong>: This folder contains standardized single-cell information and spillover-corrected FCS files, along with the compensation matrix.</p> </li> </ol> <p>The <strong>MC_normalized dataset</strong> includes FCS files that have been sorted by barcode.</p> <p><strong>Please note</strong> that in the IMC dataset, the following mass channels are blank:</p> <ul> <li><strong>Tumor-ROI</strong>: 80Ar, 127I, 131Xe, 145Nd, 146Nd, 149Sm, 160Gd, 171Yb, 174Yb, 176Yb, 190Os</li> <li><strong>SLO-ROI</strong>: 80Ar, 127I, 131Xe, 145Nd, 146Nd, 149Sm, 160Gd, 176Yb, 190Os</li> </ul> <p>The names attached to the file names are IDs.</p>
Supplemental Figures S1-S8: Direct CD137 costimulation of CD8 T cells promotes retention and innate-like function within nascent atherogenic foci
<p>Supplemental Figures and Table S1-S8 for "Direct CD137 costimulation of CD8 T cells promotes retention and innate-like function within nascent atherogenic foci" H-00088-2019, to the American Journal of Physiology-Heart and Circulatory Physiology.</p>
NGS data produced in 'Rapid selection and identification of functional CD8+ T-cell epitopes from large peptide-coding libraries'; Nature Communications (2019)
<p>Sharma, G et al. Rapid selection and identification of functional CD8+ T-cell epitopes from large peptide-coding libraries. <em>Nature Communications</em>. Accepted (August 2019)</p> <p><strong>Abstract:</strong></p> <p>Cytotoxic CD8+ T-cells recognize and eliminate infected or malignant cells that present, at their cell surfaces, short peptide epitopes derived from intracellularly processed antigens. However, broadly searching for specific major histocompatibility complex (MHC)-bound peptide epitopes that are naturally processed and capable of eliciting a functional T-cell response has been challenging. Here, we report a method for deep and unbiased T-cell epitope profiling, which is done by using <em>in vitro</em> co-culture of CD8+ T-cells and target cells transduced with high-complexity epitope-encoding minigene libraries. Target cells that are subject to cytotoxic attack from T-cells in co-culture are isolated, before they are lost to apoptosis, by fluorescence-activated cell sorting and characterized by sequencing the minigenes encoded within. In the present study, we validate this highly parallelized method using known murine T-cell receptor/peptide-MHC pairs and diverse minigene-encoded epitope libraries to identify naturally processed and MHC-presented peptide epitopes unambiguously and with high sensitivity.</p>
Single-cell RNA-Seq and TCR-Seq analysis of PD-1+ CD8+ T-cells responding to anti-PD-1 and anti-PD-1/CTLA-4 immunotherapy in melanoma
<p><strong>This dataset details the scRNASeq and TCR-Seq analysis of sorted PD-1+ CD8+ T cells from patients with melanoma treated with checkpoint therapy (anti-PD-1 monotherapy and anti-PD-1 & anti-CTLA-4 combination therapy) at baseline and after the first cycle of therapy. A major publication using this dataset is accessible here: (reference) </strong></p> <p> </p> <p><strong>*experimental design</strong></p> <p> Single-cell RNA sequencing was performed using 10x Genomics with feature barcoding technology to multiplex cell samples from different patients undergoing mono or dual therapy so that they can be loaded on one well to reduce costs and minimize technical variability. Hashtag oligomers (oligos) were obtained as purified and already oligo-conjugated in TotalSeq-C format from BioLegend. Cells were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.</p> <p> </p> <p><strong>*extract protocol</strong></p> <p> PBMCs were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions.</p> <p> </p> <p><strong>*library construction protocol</strong></p> <p> Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.</p> <p> </p> <p><strong>*library strategy</strong></p> <p> scRNA-seq and scTCR-seq</p> <p> </p> <p><strong>*data processing step</strong></p> <p> Pre-processing of sequencing results to generate count matrices (gene expression and HTO barcode counts) was performed using the 10x genomics Cell Ranger pipeline.</p> <p> Further processing was done with Seurat (cell and gene filtering, hashtag identification, clustering, differential gene expression analysis based on gene expression).</p> <p> </p> <p> <strong>*genome build/assembly</strong></p> <p> Alignment was performed using prebuilt Cell Ranger human reference GRCh38.</p> <p> </p> <p><strong>*processed data files format and content</strong></p> <p> RNA counts and HTO counts are in sparse matrix format and TCR clonotypes are in csv format.</p> <p>Datasets were merged and analyzed by Seurat and the analyzed objects are in rds format.</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>file name</strong></p> </td> <td> <p><strong>file checksum</strong></p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>da2e006d2b39485fd8cf8701742c6d77</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>e125fc5031899bba71e1171888d78205</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_contig_annotations.csv</p> </td> <td> <p>927241805d507204fbe9ef7045d0ccf4</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_contig_annotations.csv</p> </td> <td> <p>8ca544d27f06e66592b567d3ab86551e</p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p><strong>*processed data file </strong></p> </td> <td> <p><strong>antibodies/tags</strong></p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M1_base_monotherapy<br>TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M1_post_monotherapy<br>TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - C1_base_combined_therapy<br>TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - C1_post_combined_therapy<br>TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C2_base_combined_therapy<br>TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C2_post_combined_therapy</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_contig_annotations.csv</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M2_base_monotherapy<br>TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M2_post_monotherapy<br>TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - M3_base_monotherapy<br>TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - M3_post_monotherapy<br>TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C3_base_combined_therapy<br>TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C3_post_combined_therapy</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_contig_annotations.csv</p> </td> <td> <p>none</p> </td> </tr> </tbody> </table> <p> </p>
Data from: Postnatal administration of S-adenosylmethionine restores developmental AHR activation-induced deficits in CD8+ T cell function during influenza A virus infection
<p>Abstract Developmental exposures can influence life-long health; yet, counteracting negative consequences is challenging due to poor understanding of cellular mechanisms. The aryl hydrocarbon receptor (AHR) binds many small molecules, including numerous pollutants. Developmental exposure to the signature environmental AHR ligand 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) significantly dampens adaptive immune responses to influenza A virus (IAV) in adult offspring. CD8+ cytotoxic T lymphocytes (CTL) are crucial for successful infection resolution, which depends on the number generated and the complexity of their functionality. Prior studies showed developmental AHR activation significantly reduced the number of virus-specific CD8+ T cells, but impact on their functions is less clear. Other studies showed developmental exposure was associated with differences in DNA methylation in CD8+ T cells. Yet, empirical evidence that differences in DNA methylation are causally related to altered CD8+ T cell function is lacking. The two objectives were to ascertain whether developmental AHR activation affects CTL function, and whether differences in methylation contribute to reduced CD8+ T cell responses to infection. Developmental AHR triggering significantly reduced CTL polyfunctionality, and modified the transcriptional program of CD8+ T cells. S-adenosylmethionine (SAM), which increases DNA methylation, but not Zebularine, which diminishes DNA methylation, restored polyfunctionality and boosted the number of virus-specific CD8+ T cells. These findings suggest that diminished methylation, initiated by developmental exposure to an AHR-binding chemical, contributes to durable changes in antiviral CD8+ CTL functions later in life. Thus, deleterious consequence of development exposure to environmental chemicals are not permanently fixed, opening the door for interventional strategies to improve health.</p>
Prospective Randomized Comparative Study of Cell Transfer Therapy Using CD8+-Enriched Short-Term Cultured Anti-Tumor Autologous Lymphocytes Following a Non-Myeloablative Lymphocyte Depleting Chemother
ClinicalTrials.gov study NCT01118091. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Cell Therapy for Metastatic Melanoma Using CD8 Enriched Tumor Infiltrating Lymphocytes
ClinicalTrials.gov study NCT01236573. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Data from: Postnatal administration of S-adenosylmethionine restores developmental AHR activation-induced deficits in CD8+ T cell function during influenza A virus infection
Open the record for dataset details and reuse information.
Interleukin-17A signaling promotes CD8+ T cell cytotoxicity against West Nile virus infection through enhancing PI3K-mTOR-mediated metabolism
Open the record for dataset details and reuse information.
Data from: Coordinated ARP2/3 and glycolytic activities regulate the morphological and functional fitness of human CD8+ T cells
Open the record for dataset details and reuse information.
CD8+ T cell differentiation status correlates with the feasibility of sustained unresponsiveness following oral immunotherapy
Open the record for dataset details and reuse information.
IL-32 producing CD8+ memory T cells and Tregs define the IDO1/PD-L1 niche in human cutaneous leishmaniasis skin lesions.
<p>https://github.com/NidhiSDey/leish-ME/</p> <p>Human cutaneous leishmaniasis (CL) is characterised by chronic skin pathology. Experimental and clinical data suggest that immune checkpoints (ICs) play a crucial role in disease outcome but the cellular and molecular niches that facilitate IC expression during leishmaniasis are ill-defined. We previously showed that in Sri Lankan patients with CL two ICs, indoleamine 2,3-dioxygenase 1 (IDO1) and programmed death-ligand 1 (PD-L1) are enriched in lesional skin and that reduced PD-L1 expression early after treatment onset predicts cure rate following antimonial therapy. Here, we use spatial cell interaction mapping to identify IL-32-expressing CD8+ memory cells and regulatory T cells as key components of the IDO1 / PD-L1 niche in a cohort of Sri Lankan CL patients. This finding was confirmed in patients with distinct forms of dermal leishmaniasis in Brazil and India. Furthermore, in our Sri Lankan cohort the abundance of IL-32+ cells and IL-32+CD8+ T cells at treatment onset was prognostic for rate of cure. This study provides a unique spatial perspective on the expression of key ICs in these important skin diseases and a novel route to identify biomarkers of treatment response.</p>
An mRNA-encoded, long-lasting Interleukin-2 restores CD8+ T cell neoantigen immunity in MHC class I-deficient cancers
<ul> <li><strong><span>Cd45Summit-IntgrAllgroups_finalUMAP_simplified_colorordered.RData </span></strong><span>contains all final result of the workflow (data integration and annotation) without subsetting the cell types into the different treatment groups.</span></li> <li><strong><span>Cd45Summit-IntgrAllgroups_finalUMAP_Only_CD8Tcells_subsetted.RData </span></strong><span>contains combined dataset of all present CD8+ T cell types (final UMAP, all annotated), which are subsetted into the four treatment groups.</span></li> <li><strong><span>Cd45Summit-IntgrAllgroups_finalUMAP_Only_ProlifMacros_subsetted.RData </span></strong><span>contains only the proliferating CD8+ T cells, which are subsetted into the four different treatment groups.</span></li> <li><strong><span>Cd45Summit-IntgrAllgroups_forJan_Macors_Gr1to4.RData </span></strong><span>contains only the combined macrophages of the dataset, subsetted into the four treatment groups.</span></li> </ul>
Blood memory CD8 T cell phenotypes in lung cancer patients predict immune checkpoint treatment responses
<p>Rscript for figure generation and data analysis:</p> <p>GenerateFigures.R</p> <p> </p> <p>Seurat objects containing processed data after quality control:</p> <p><a href="../api/records/10867209/draft/files/NCCS_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">NCCS_For_Zenodo.RDS</a> - NCCS discovery cohort.</p> <p><a href="../api/records/10867209/draft/files/Pavia_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">Pavia_For_Zenodo.RDS</a> - Pavia validation cohort.</p> <p> </p> <p>RDS files containing DEGs or differentially abundant surface markers:</p> <p>TestResults2Groups.rds - Cell type specific LTR vs Non Responder DEG </p> <p>TestResults2GroupsADT.rds - Cell type specific LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnly.rds - Cell type specific LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyADT.rds - Cell type specific LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3Groups.rds - Cell type specific LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p>TestResults2GroupsGeneralRNA.rds - Across cell type LTR vs Non Responder DEG </p> <p>TestResults2GroupsGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnlyGeneralRNA.rds - Across cell type LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3GroupsGeneralRNA.rds - Across cell type LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p> </p> <p>Logistic regression models trained on the NCCS discovery cohort:</p> <p>PerCellPredictions <CellType> * - Celltype specific models predicting either LTR, R or control group trained on all NCCS samples</p> <p>PerCellPredictions_2Groups_LungOnly <CellType> * - Celltype specific models predicting either LTR or NonResponder group, trained on lung samples only.</p> <p>PerCellPredictions_2Groups_<CellType> * - Celltype specific models predicting either LTR or NonResponder trained on all NCCS samples</p>
Signature of long-lived memory CD8+ T cells in acute SARS-CoV-2 infection
<p>The datasets uploaded in this Zenodo entry were generated in the single cell RNA sequencing part of the project.</p> <p>For the scRNAseq analysis, cells from ten patients and the same time point were pooled together, generating four individual sample sets in total: (1) patients CoV2_T001- CoV2_T010, acute; (2) patients CoV2_T001- CoV2_T010, six months post-infection; (3) patients CoV2_T011- CoV2_T020, acute; (4) patients CoV2_T011- CoV2_T011-20, six months post-infection.</p> <p>We additionally generated two more sample sets: using 5000 unsorted PBMCs from each patient’s sample: (5) patients CoV2_T001- CoV2_T010, six months post-infection unsorted; (6) patients CoV2_T011- CoV2_T020, six months post-infection unsorted. The cells in these two sample sets were hashed.</p> <p>Finally, using PBMCs from four healthy donors, we generated sample set (7) by sorting and pooling 2000 CD8+ T cells from each healthy donor sample.</p> <p>We are here providing the pre-processed sets for each sample set (1-7), i.e. :<br> - "filtered feature bc matrix" files, as output from the ‘cellranger multi’ pipeline (Cell Ranger version 5.0.0), containing cell-RNA count matrices and cell-ADT matrices. ADTs comprise counts for TotalSeq antibodies and dCODE Dextramers.<br> - "filtered_contig_annotations.csv" files, as output from the ‘cellranger multi’ pipeline (Cell Ranger version 5.0.0), containing High-level annotations of each high-confidence, cellular contig for TCR clonal analysis. This file is not present for sets 5 and 6, because we did not perform TCR profiling for these samples.<br> - "clusters.tsv" files, as output from the souporcell SNP analysis (version 2). To cluster cells based on their patient specific genetic variants, we merged sample sets 1, 2 and 5 (comprising sorted cells from both time points of patients CoV2_T001- CoV2_T010 and unsorted cells of the same patients) and sets 3, 4 and 6 (comprising cells from both time points of patients CoV2_T011- CoV2_T020 and unsorted cells of the same patients). Then, we executed the souporcell pipeline with option <em>k=10 </em>(number of clusters to be determined) for each of the two merged sample sets.</p> <p>Together, these files allow to reproduce the analysis as reported in the paper.</p> <p>Additionally, we provide the Seurat Objects "Integrated.h5seurat" and "Integrated_NA_filtered.h5seurat" which can be used to skip the pre-processing steps of the data analysis. See the code provided on https://github.com/Moors-Code/SARS-CoV-2-Tcell-Boyman-collaboration for details.</p> <p> </p>
TCRb sequencing of CD4+ and CD8+ T cells from hematological patients (part 2)
<p>This dataset contains TCRb sequencing of 177 samples from patients with aplastic anemia, myelodysplastic syndrome, immune thrombocytopenia, immunodeficiency or graft-versus-host disease and healthy controls. Samples are separated CD4+ or CD8+ cells from peripheral blood or bone marrow. The data has been produced with immunoSEQ platform (Adaptive Biotechnologies). The details regarding sample processing, sequencing and metadata can be found from the publication <em>Somatic mutations associate with clonal expansion of CD8+ T cells</em> (Lundgren et al, Science Advances, in press).</p> <p>The dataset is divided in 2 parts containing 91 (part 1) and 86 (part 2) files. Data is in immunoSEQ format (v2).</p>
scTCR-seq analysis of CD8+ T cells
<p><span>To investigate whether a higher ratio of KC-derived LMAMs could change T cell population, we conducted single-cell TCR sequencing combined with scRNA-seq on CD8<sup>+</sup> T cells infiltrating liver metastases from <em>Ccr2<sup>GFP/WT</sup></em> background or <em>Ccr2<sup>GFP/GFP</sup></em> background. </span></p>
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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.