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11,105 results for “single cells”
Datasets for CASSL: A cell-type annotation method for single cell transcriptomics data using semi-supervised learning
<p>This repository contains datasets used in the project CASSL: A cell-type annotation method for single cell transcriptomics data using semi-supervised learning. This project aims at learning cell annotations for missing cell labels via NMF and recursive k-Means clustering.</p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p> </p> <p><strong>Data:</strong></p> <p>1. negative_cDC1_relative_signatures.csv : Negative signatures for performing Connectivity Map (cMAP) Analysis</p> <p>2. positive_cDC1_relative_signatures.csv : Positive signatures for performing Connectivity Map (cMAP) Analysis</p>
# Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease
<p>Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment) with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals dysregulated cell type gene dysregulatory mechanisms in AD.</p>
Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data
<p>These data files contain single-cell RNA-sequencing expression data (expression_data.zip) and pseudotime files (pseudotime.zip) used to conduct comparisons of network inference methods on six published single-cell RNA-sequencing datasets. The resulting networks generated from the network inference methods are also uploaded here (normalized_inferred_networks.zip and imputed_inferred_networks.zip). Finally, the gold standard networks we used as ground truth to measure accuracy of the inferred networks are uploaded here (gold_standard_datasets.zip).</p>
Integrated Data of Single cell RNA sequencing for Human Pancreatic Adenocarcinoma
<p>These data are collected and integrated from five available deposit data and one original data of single cell RNA sequencing from human pancreatic adenocarcinoma. Further analyses data for bulk transcriptomics (such as TCGA )using scRNAseq data and re-clustering for ductal epithelial cells and fibroblasts are also stored in step by step. Moreover, all R code is uploaded.</p>
SEACells: Inference of transcriptional and epigenomic cellular states from single-cell genomics data
<p>Processed data for the manuscript "" available on bioRxiv at ""</p> <p>Data is available for the following samples</p> <ol> <li>CD34+ Multiome data : 2 replicates </li> <li>T-cell depleted bone marrow Multiome data: 2 replicates </li> </ol> <p> </p> <p>The following counts and fragments files are available for each replicate </p> <ol> <li><sample>_filtered_feature_bc_matrix.h5: Feature counts from CellRanger ARC</li> <li><sample>_atac_fragments.tsv.gz: ATAC fragments file from Cellranger ARC</li> <li><sample>_atac_fragments.tsv.gz.tbi: Index files for ATAC fragments file from Cellranger ARC</li> </ol> <p> </p> <p>The following scanpy anndata objects are also available</p> <ol> <li>cd34_multiome_rna.h5ad: Anndata object with normalized data, cell type annotations and clusters for the RNA modality of CD34+ hematopoietic stem and progenitor cells </li> <li>cd34_multiome_atac.h5ad: Anndata object with peak counts, cell type annotations and clusters for the ATAC modality of of CD34+ hematopoietic stem and progenitor cells.</li> <li>cd34_multiome_rna.h5ad: Anndata object with normalized data, cell type annotations and clusters for the RNA modality of T-cell depleted bone marrow dataset.</li> <li>bm_multiome_atac.h5ad: Anndata object with peak counts, cell type annotations and clusters for the ATAC modality of T-cell depleted bone marrow dataset.</li> </ol>
Computational Analysis of Two-dimensional High-throughput Data from Large-scale RNAi Screens and Single-cell Transcriptomics
<p>This publication provides a singularity definition file to reproduce the computational environment along with the scripts to reproduce every figure or table in the revised manuscript using ZetaSuite Perl module and R package.</p> <p>First, generate a new folder and then download all the files into the folder.</p> <p>Then, uncompressed the files DataSets_part1.tar.gz,DataSets_part2.tar.gz,DataSets_part3.tar.gz,DataSets_part4.tar.gz, and scripts.tar.gz. within the folder.</p> <p>Next, move all the files in DataSets_part1 folder, DataSets_part2 folder,DataSets_part3 folder and DataSets_part4 folder to a new folder called DataSets.</p> <p>Finally, run the following scripts to generate the figures and tables in our manuscript.</p> <p>Regeneration of Figure2 and S2: singularity exec ZetaSuite.sif sh Figure2andS2.sh </p> <p>Regeneration of Figure3 and S3: singularity exec ZetaSuite.sif sh Figure3andS3.sh </p> <p>Regeneration of Figure4 and S4: singularity exec ZetaSuite.sif sh Figure4andS4.sh </p> <p>Regeneration of Figure5 and S5: singularity exec ZetaSuite.sif sh Figure5andS5.sh </p> <p>Regeneration of Figure6 and S6: singularity exec ZetaSuite.sif sh Figure6andS6.sh </p> <p>Regeneration of Figure7 and S7: singularity exec ZetaSuite.sif sh Figure7andS7.sh </p> <p> </p>
IGM Population of HFF structures using Hi-C, laminB1 DamID, 3D HIPMAp FISH and single cell SPRITE data
<p>This repository accompanies the manuscript "<strong>Integrative Genome Modeling Platform reveals essentiality of rare contact events in 3D genome organizations</strong>", to appear in Nat. Methods (2022), see also https://www.biorxiv.org/content/10.1101/2021.08.22.457288v1.</p> <p>It contains the preprocessed input data files (Hi-C, laminB1 DamID, 3D HIPMAp FISH and single cell SPRITE) for the HFF fibroblast cell line to be used in the Integrative Genome Modeling platform (IGM) developed in the Alber lab at UCLA (https://github.com/alberlab/igm).</p> <p>Also, we provide the configuration file to run IGM with those datasets, as we did in generating the HDSF population discussed in the accompanying manuscript. Such population is also provided as an "hss" file. Documentation and a simple demo/tutorial on how IGM can be run is given on the Alber lab Github @ https://github.com/alberlab/igm.</p> <p>All files can be read in using the <em>h5py</em> and <em>alabtools</em> (available @https://github.com/alberlab/alabtools) Python packages. More detailed information is provided in the manuscript and associated Supplementary Information file. </p> <p>For any inquiry/suggestions/doubts please reach out to Lorenzo Boninsegna (bonimba@g.ucla.edu) or Dr. Frank Alber (falber@g.ucla.edu).</p> <p> </p>
Human single-cell TCR data from irradiated sporozoite trial
<p>This is the MiAIRR-compliant single-cell AIRR-seq data of the study "Clonal evolution and TCR specificity of the human<br> TFH cell response to Plasmodium falciparum CSP" by Wahl <em>et al.,</em> <em>Sci Immunol</em> 7:eabm9644 (2022). The study defines the clonal evolution and epitope specificity of the human cTFH cell response to Plasmodium falciparum CSP.</p>
Single-Cell Autism data stored as sce object
<p>The raw autism dataset is from UCSC Cell Browser Dataset, Autism section (<a href="https://cells.ucsc.edu/">https://cells.ucsc.edu</a>). It is stored as SingleCellExperiment object for further usage. </p>
Data of: Imputation-free reconstructions of three-dimensional chromosome architectures in human diploid single-cells using allele-specified contacts
<p>These files are results obtained in<br><span><span><span><span>Imputation-free reconstructions of three-dimensional chromosome architectures in human diploid single-cells using allele-specified contacts</span></span></span></span><br>by Yoshito Hirata, Arisa H. Oda, Chie Motono, Masanori Shiro & Kunihiro Ohta.</p> <p>There are 33 files for the corresponding each reconstruction of three-dimensional chromosomone structures<br>for each cell.<br>There are 3D structures for 15 GM cells and 18 PBMC cells, which are obtained from the single cell Hi-C data of Tan et al. Science (2018).</p> <p>For each file, there are 6 columns:<br>The first column corresponds to the allele (0: maternal, 1: paternal)<br>The second column corresponds to the chromosome (1-22: chromosome's number, 23: X, 24: Y)<br>The third column corrsponds to the base point.<br>The fourth column, the fifth column and the sixth column correspond to x-, y-, and z-axes of our reconstruction.</p>
Data files: Single-cell RNA sequencing of Plasmodium vivax sporozoites reveals stage- and species-specific transcriptomic signatures
<p>Scripts, preprocessed count matrices, single-cell data objects, and generated data (tables and .rds files) from the scRNA-seq analyses performed in <strong>“Single-cell RNA sequencing of Plasmodium vivax sporozoites reveals stage- and species-specific transcriptomic signatures".</strong></p> <p> </p>
Image data for bioRxiv article named: mtFociCounter - Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci
<p>Raw imaging data to reproduce and test the findings of the bioRxiv article: <strong>mtFociCounter </strong>- Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci. It contains data from three imaging days and 2 or three technical replicates on each day.</p> <p> </p>
[Demo Input Data] for SCAFE: a software suite for analysis of transcribed cis-regulatory elements in single cells
<p>This archive (input.tar.gz) contains the demo data for SCAFE v1.0.0 (on <a href="https://doi.org/10.5281/zenodo.7023163">Zenodo</a> or <a href="https://github.com/chung-lab/SCAFE/releases/tag/v1.0.0">Github</a>)</p> <p><em>SCAFE</em> (Single Cell Analysis of Five-prime Ends) provides an end-to-end solution for processing of single cell 5’end RNA-seq data. It takes a read alignment file (*.bam) from single-cell RNA-5’end-sequencing (e.g. 10xGenomics Chromimum®), precisely maps the cDNA 5'ends (i.e. transcription start sites, TSS), filters for the artefacts and identifies genuine TSS clusters using logistic regression. Based on the TSS clusters, it defines transcribed cis-regulatory elements (tCRE) and annotated them to gene models. It then counts the UMI in tCRE in single cells and returns a tCRE UMI/cellbarcode matrix ready for downstream analyses, e.g. cell-type clustering, linking promoters to enhancers by co-activity <em>etc</em>.</p> <p>For details on installation, usage and test run on demo data, visit <a href="https://github.com/chung-lab/SCAFE">https://github.com/chung-lab/SCAFE</a></p>
Long-Term, Single-Molecule Imaging of Proteins in Live Cells with Photoregulated Fluxional Fluorophores
<p>Supporting data for paper "Long-Term, Single-Molecule Imaging of Proteins in Live Cells with Photoregulated Fluxional Fluorophores"</p>
Text-fig. 7. Scanning electron micrographs (a, b, d, e, g–k), X-ray microtomographic orthoslices (c) and synchrotron radiation X-ray tomographic microscopy orthoslices (f) of fruits and endocarps of uncertain affinity from Zliv-Řídká Blana locality. a–c: Trebecenia sarcocalis, a – tricarpellate fruit, no. NM-F 3637, b – fruits supported by pentamerous and persistent calyx, no. NMF 3637, c – fruit almost circular in transverse section, no. NM-F 3637; d: Taxon 17, small fruit with slightly sunken stylar region, no. NM-F 3201; e: Taxon 19, spherical fruit, the fruit wall composed of large isodiametric, thick walled cells, no. NM-F 3181; f: Taxon 19, single-seeded fruit, no. NM-F 3621; g: Taxon 20, syncarpous, multicarpellate fruit of ten carpels, no. NM-F 3200; h: Taxon 22, syncarpous, multicarpellate fruit of seven carpels, no. NM-F 3159; i: cf. Sabia menispermoides, endocarp of drupaceous fruits, no. NM-F 4624; j: Taxon 25, endocarp triangular in cross-section, no. NM-F 3218; k: Taxon 24, endocarp spherical in cross-section with a distinctly ribbed and foveolate surface, no. NM-F 3217. in Plant Mesofossils From The Late Cretaceous Klikov Formation, The Czech Republic
Text-fig. 7. Scanning electron micrographs (a, b, d, e, g–k), X-ray microtomographic orthoslices (c) and synchrotron radiation X-ray tomographic microscopy orthoslices (f) of fruits and endocarps of uncertain affinity from Zliv-Řídká Blana locality. a–c: Trebecenia sarcocalis, a – tricarpellate fruit, no. NM-F 3637, b – fruits supported by pentamerous and persistent calyx, no. NMF 3637, c – fruit almost circular in transverse section, no. NM-F 3637; d: Taxon 17, small fruit with slightly sunken stylar region, no. NM-F 3201; e: Taxon 19, spherical fruit, the fruit wall composed of large isodiametric, thick walled cells, no. NM-F 3181; f: Taxon 19, single-seeded fruit, no. NM-F 3621; g: Taxon 20, syncarpous, multicarpellate fruit of ten carpels, no. NM-F 3200; h: Taxon 22, syncarpous, multicarpellate fruit of seven carpels, no. NM-F 3159; i: cf. Sabia menispermoides, endocarp of drupaceous fruits, no. NM-F 4624; j: Taxon 25, endocarp triangular in cross-section, no. NM-F 3218; k: Taxon 24, endocarp spherical in cross-section with a distinctly ribbed and foveolate surface, no. NM-F 3217.
Text-fig. 2. Scanning electron micrographs of non-angiosperm remains (a–d) and insect remains (e–f) from Zliv-Řídká Blana locality. a: Eopolytrichium sp. small leafy shoot, no. NM-F 3631; b: Taxon 1, single tip of a young fern frond with circinate vernation, no. NM-F 4130; c: Taxon 2, fern with simple leaves and circinate vernation, no. NM-F 3459; d: Pagiophyllum sp., small needle-like leaf, no. NM-F 4132; e: Microcarpolithes hexagonalis, a faecal pellet/coprolite with subcylindrical shape, no. NMF 4520; f: Palaeoaldrovanda splendens, pieces of compact walls formed by rectangular cells, no. NM-F 3237. in Plant Mesofossils From The Late Cretaceous Klikov Formation, The Czech Republic
Text-fig. 2. Scanning electron micrographs of non-angiosperm remains (a–d) and insect remains (e–f) from Zliv-Řídká Blana locality. a: Eopolytrichium sp. small leafy shoot, no. NM-F 3631; b: Taxon 1, single tip of a young fern frond with circinate vernation, no. NM-F 4130; c: Taxon 2, fern with simple leaves and circinate vernation, no. NM-F 3459; d: Pagiophyllum sp., small needle-like leaf, no. NM-F 4132; e: Microcarpolithes hexagonalis, a faecal pellet/coprolite with subcylindrical shape, no. NMF 4520; f: Palaeoaldrovanda splendens, pieces of compact walls formed by rectangular cells, no. NM-F 3237.
Text-fig. 5. Lithocarpoxylon ashwillii sp. nov., UF 279-24544. a, b: Semi-ring porous wood, exclusively solitary vessels in a radial/ diagonal arrangement, diffuse axial parenchyma, TS. c: Homocellular rays composed of procumbent cells; vasicentric tracheids; vessel elements with simple perforation plates (PP), TLS. d: Vessel-ray parenchyma pits (VRP) with reduced borders, vertical, RLS. e: Crystalliferous axial parenchyma strand (C) with a single crystal per chamber; ray with procumbent cells, RLS. f: Aggregate ray (right) composed of loosely associated 1–2-seriate rays, TLS. g: Uniseriate rays, thin-walled tyloses in vessels, vasicentric tracheids (VT). Lithocarpoxylon sp., UF 279-84864. h, i: Semi-ring porous wood, exclusively solitary vessels in radial arrangement. j: Aggregate ray and uniseriate rays. k: Scalariform perforation plate with fewer than 10 bars. l: Vessel-ray parenchyma pits with reduced borders to simple, vertical. Scale bars: 200 µm in a, b, f, h, j; 100 µm in c, i; 50 µm in d. e; 20 µm in k, l. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 5. Lithocarpoxylon ashwillii sp. nov., UF 279-24544. a, b: Semi-ring porous wood, exclusively solitary vessels in a radial/ diagonal arrangement, diffuse axial parenchyma, TS. c: Homocellular rays composed of procumbent cells; vasicentric tracheids; vessel elements with simple perforation plates (PP), TLS. d: Vessel-ray parenchyma pits (VRP) with reduced borders, vertical, RLS. e: Crystalliferous axial parenchyma strand (C) with a single crystal per chamber; ray with procumbent cells, RLS. f: Aggregate ray (right) composed of loosely associated 1–2-seriate rays, TLS. g: Uniseriate rays, thin-walled tyloses in vessels, vasicentric tracheids (VT). Lithocarpoxylon sp., UF 279-84864. h, i: Semi-ring porous wood, exclusively solitary vessels in radial arrangement. j: Aggregate ray and uniseriate rays. k: Scalariform perforation plate with fewer than 10 bars. l: Vessel-ray parenchyma pits with reduced borders to simple, vertical. Scale bars: 200 µm in a, b, f, h, j; 100 µm in c, i; 50 µm in d. e; 20 µm in k, l.
Text-fig. 8. Carpolithes (a–t). a–e: Carpolithes sp. 1. USNM PAL 772366. Scale bar = 1 cm. a: Lateral view of endocarp, note two longitudinal ridges. b: Lateral view of endocarp rotated 90° from (a), note single lateral ridge in center, a, b reflected light, palladium coated. c: Lateral view, Micro-CT scan surface rendering. d: View of rounded end of the endocarp, reflected light, palladium coated. e: View of the opposite (pointed) end of the endocarp, note split; reflected light, palladium coated. f–j: Carpolithes sp. 2. USNM PAL 772367. Scale bar = 5 mm. f: Lateral view, base down; note raphe-like structure (arrow), reflected light, palladium coated. g: Lateral view, the raphe-like structure extending vertically from the base. h: Lateral view, rotated 90° from (g). i: Lateral view, the opposite face to that in (h). j: Basal view, raphe-like structure running from the center to the right of the image. g–j: CT scan surface renderings. k–o: Carpolithes sp. 3 USNM PAL 772368. Scale bar = 5 mm. k: Ventral view of the specimen, note flared apical extension, reflected light, uncoated. l: Dorsal view illustrating the flared apical extension, rotated 180o from (k). m: Lateral view rotated 90° from that in (l). n: Apical view, the apical extension with central pore (arrow) and a clear lineation running down the side to the top of the image. o: Basal view. l–o: Micro-CT scan surface renderings. p–t: Carpolithes sp. 4. USNM PAL 772369. Scale bar = 3 mm. p: Basal view illustrating the concentric rings of radiating possible cells surrounding a central depression. q: Lateral view, base down, note possible cellular pattern. r: Lateral view, rotated 180° from (q), base down; p–r: reflected light, palladium coated. s, t: Basal and lateral views, micro-CT scan surface renderings. in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 8. Carpolithes (a–t). a–e: Carpolithes sp. 1. USNM PAL 772366. Scale bar = 1 cm. a: Lateral view of endocarp, note two longitudinal ridges. b: Lateral view of endocarp rotated 90° from (a), note single lateral ridge in center, a, b reflected light, palladium coated. c: Lateral view, Micro-CT scan surface rendering. d: View of rounded end of the endocarp, reflected light, palladium coated. e: View of the opposite (pointed) end of the endocarp, note split; reflected light, palladium coated. f–j: Carpolithes sp. 2. USNM PAL 772367. Scale bar = 5 mm. f: Lateral view, base down; note raphe-like structure (arrow), reflected light, palladium coated. g: Lateral view, the raphe-like structure extending vertically from the base. h: Lateral view, rotated 90° from (g). i: Lateral view, the opposite face to that in (h). j: Basal view, raphe-like structure running from the center to the right of the image. g–j: CT scan surface renderings. k–o: Carpolithes sp. 3 USNM PAL 772368. Scale bar = 5 mm. k: Ventral view of the specimen, note flared apical extension, reflected light, uncoated. l: Dorsal view illustrating the flared apical extension, rotated 180o from (k). m: Lateral view rotated 90° from that in (l). n: Apical view, the apical extension with central pore (arrow) and a clear lineation running down the side to the top of the image. o: Basal view. l–o: Micro-CT scan surface renderings. p–t: Carpolithes sp. 4. USNM PAL 772369. Scale bar = 3 mm. p: Basal view illustrating the concentric rings of radiating possible cells surrounding a central depression. q: Lateral view, base down, note possible cellular pattern. r: Lateral view, rotated 180° from (q), base down; p–r: reflected light, palladium coated. s, t: Basal and lateral views, micro-CT scan surface renderings.
Dataset for: Phenotyping single-cell motility in microfluidic confinement
<p>Associated dataset and simulation codes for the publication "Phenotyping single-cell motility in microfluidic confinement" (2022), by Samuel A. Bentley, Hannah Laeverenz-Schlogelhofer, Vasileios Anagnostidis, Jan Cammann, Marco G. Mazza, Fabrice Gielen, Kirsty Y. Wan. </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.