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241 results for “glia”
Dataset related to article "Lipoprotein receptor loss in forebrain radial glia results in neurological deficits and severe seizures"
<p>This dataset is related to the article entitled: Lipoprotein receptor loss in forebrain radial glia results in neurological deficits and severe seizures. This article is published in the Journal GLIA.</p> <p>Bres EE et al.<br> Lipoprotein receptor loss in forebrain radial glia results in<br> neurological deficits and severe seizures. Glia. 2020;1–33.</p>
Data for Altered Glia-Neuron Communication in Alzheimer's Disease Affects WNT, p53, and NFkB Signaling Determined by snRNA-seq
<p><strong>data.tar.gz contains all files from the data directory associated with the 230313_TS_CCCinHumanAD GitHub project and includes the following:</strong></p><ul><li><strong>CellRangerCounts/</strong><ul><li><strong>GSE157827/</strong><ul><li><strong>post_soupX/ : </strong>contains 21 directories for 21 samples, which each contain 3 files obtained from ambient RNA removal with soupX. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN16100290_S01_AD/</strong><ul><li><strong>barcodes.tsv</strong></li><li><strong>genes.tsv</strong></li><li><strong>matrix.mtx</strong></li></ul></li></ul></li><li><strong>pre_soupX/ : </strong>contains 21 directories for 21 samples, which each contain 2 files obtained from Cell Ranger after aligning fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN16100290_S01_AD/</strong><ul><li><strong>filtered_feature_ bc_matrix.h5</strong></li><li><strong>Raw_feature_bc_matrix.h5</strong></li></ul></li></ul></li></ul></li><li><strong>GSE174367/ : </strong>contains 19 directories for 19 samples, which contain 3 files each from Cell Ranger alignment of fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN19128610_S1_CTRL/</strong><ul><li><strong>barcodes.tsv</strong></li><li><strong>genes.tsv</strong></li><li><strong>Matrix.mtx</strong></li></ul></li></ul></li></ul></li><li><strong>ccc/</strong><ul><li><strong>nichenet_grn/</strong><ul><li><strong>gr_network_human_21122021.rds : </strong>accessed in October 2023, gene regulation network – gene regulatory information from MultiNicheNet</li><li><strong>ligand_tf_matrix_nsga2r_final.rds: </strong>accessed in October 2023, ligand tf matrix for signaling path determination from MultiNicheNet</li><li><strong>signaling_network_human_21122021.rds : </strong>accessed in October 2023, signaling network – protein-protein interaction information from MultiNicheNet</li><li><strong>weighted_networks_nsga2r_final.rds : </strong>accessed in October 2023, networks weighted by literature evidence from MultiNicheNet</li></ul></li><li><strong>nichenet_prior/</strong><ul><li><strong>ligand_target_matrix.rds : </strong>accessed in April 2023, ligand to target matrix from NicheNet</li><li><strong>lr_network.rds : </strong>accessed in April 2023, ligand-receptor matrix from NicheNet</li></ul></li><li><strong>nichenet_v2_prior/</strong><ul><li><strong>ligand_target_matrix_nsga2r_final.rds : </strong>accessed in June 2023, ligand to target matrix from MultiNicheNet used to predict target genes.</li><li><strong>lr_network_human_21122021.rds : </strong>accessed in June 2023, ligand-receptor matrix from MultiNicheNet used to predict ligand-receptor pairs.</li></ul></li><li><strong>geo_multinichenet_output.rds </strong>: MultiNicheNet output for Morabito et al., 2021 data</li><li><strong>geo_signaling_igraph_objects.rds </strong>: list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Morabito et al., 2021 dataset. </li><li><strong>gse_multinichenet_output.rds</strong> : MultiNicheNet output for Lau et al., 2020 data</li><li><strong>gse_signaling_igraph_objects.rds</strong> : list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Lau et al., 2020 dataset </li></ul></li><li><strong>seurat_preprocessing/</strong><ul><li><strong>geo_filtered_seurat.rds : </strong>merged and filtered seurat object of Morabito et al., 2021 data</li><li><strong>geo_integrated_seurat.rds :</strong> seurat object integrated using harmony of Morabito et al., 2021 data</li><li><strong>geo_clustered_seurat.rds : </strong>clustered seurat object of Morabito et al., 2021 data</li><li><strong>geo_processed_seurat.rds : </strong>processed seurat object with final cell type assignments at specified resolution of Morabito et al., 2021 data</li><li><strong>gse_filtered_seurat.rds : </strong>merged and filtered seurat object of Lau et al., 2020 data</li><li><strong>gse_integrated_seurat.rds : </strong>seurat object integrated using harmony of Lau et al., 2020 data</li><li><strong>gse_clustered_seurat.rds :</strong> clustered seurat object of Lau et al., 2020 data</li><li><strong>gse_processed_seurat.rds : </strong>processed seurat object with final cell type assignments at specified resolution of Lau et al., 2020 data </li></ul></li></ul>
Dataset related to article "Glia-to-neuron transfer of miRNAs via extracellular vesicles: a new mechanism underlying inflammation-induced synaptic alterations"
<p>This record contains raw data related to article "Glia-to-neuron transfer of miRNAs via extracellular vesicles: a new mechanism underlying inflammation-induced synaptic alterations"</p> <p>Recent evidence indicates synaptic dysfunction as an early mechanism affected in neuroinflammatory diseases, such as multiple sclerosis, which are characterized by chronic microglia activation. However, the mode(s) of action of reactive microglia in causing synaptic defects are not fully understood. In this study, we show that inflammatory microglia produce extracellular vesicles (EVs) which are enriched in a set of miRNAs that regulate the expression of key synaptic proteins. Among them, miR-146a-5p, a microglia-specific miRNA not present in hippocampal neurons, controls the expression of presynaptic synaptotagmin1 (Syt1) and postsynaptic neuroligin1 (Nlg1), an adhesion protein which play a crucial role in dendritic spine formation and synaptic stability. Using a Renilla-based sensor, we provide formal proof that inflammatory EVs transfer their miR-146a-5p cargo to neuron. By western blot and immunofluorescence analysis we show that vesicular miR-146a-5p suppresses Syt1 and Nlg1 expression in receiving neurons. Microglia-to-neuron miR-146a-5p transfer and Syt1 and Nlg1 downregulation do not occur when EV-neuron contact is inhibited by cloaking vesicular phosphatidylserine residues and when neurons are exposed to EVs either depleted of miR-146a-5p, produced by pro-regenerative microglia, or storing inactive miR-146a-5p, produced by cells transfected with an anti-miR-146a-5p. Morphological analysis reveals that prolonged exposure to inflammatory EVs leads to significant decrease in dendritic spine density in hippocampal neurons in vivo and in primary culture, which is rescued in vitro by transfection of a miR-insensitive Nlg1 form. Dendritic spine loss is accompanied by a decrease in the density and strength of excitatory synapses, as indicated by reduced mEPSC frequency and amplitude. These findings link inflammatory microglia and enhanced EV production to loss of excitatory synapses, uncovering a previously unrecognized role for microglia-enriched miRNAs, released in association to EVs, in silencing of key synaptic genes.</p>
Spatiotemporal dysregulation of neuron-glia related genes and pro-/anti-inflammatory miRNAs in the 5xFAD mouse model of Alzheimer's disease - Supplementary data
<p><strong>Supplementary Table 1. </strong> Gene expression profile by RT-qPCR analysis revealed no significant differences when simultaneously considering the genotype (WT/5xFAD), age (6/9 months) and brain region (HPC, hippocampus/PFC, prefrontal cortex). </p> <p><strong>Supplementary Table 2.</strong> miRNA-target table for the Analyzed microRNAs and targets selected for this study. Obtained in the online platform https://www.mirnet.ca/</p> <p><strong>Supplementary Table 3. </strong> Node table for the analyzed microRNAs and targets selected for this study. We only considered miRNAs and/or targets with a node degree of at least 2. Obtained in the online platform https://www.mirnet.ca/</p> <p><strong>Supplementary Table 4.</strong> Bivariate Pearson’s correlation coefficients and respective p-values obtained between all miRNAs and genes.</p> <p><strong>Supplementary Table 5.</strong> List of microRNAs analyzed by RT-qPCR and their primer sequences.</p> <p><strong>Supplementary Table 6.</strong> List of genes and respective primer sequences used for mRNA analysis by RT-qPCR.</p> <p><strong>Supplementary Table 7.</strong> Raw data used for correlational analysis in hippocampus (HPC) and prefrontal cortex (PFC) using the cor function in RStudio software.</p>
Whole Brain Drosophila Larval Glia
<p>Example intact whole brains from female and male Drosophila larva expressing a glial marker associated with manuscript -</p> <p><strong>Intact Drosophila Whole Brain Cellular Quantitation reveals Sexual Dimorphism</strong>. Wei Jiao, Gard Spreemann, Evelyne Ruchti, Soumya Banerjee, Ying Shi, R. Steven Stowers, Kathryn Hess and Brian D. McCabe</p> <p>Brain Mind Institute, EPFL - Swiss Federal Institute of Technology Lausanne, Switzerland <a href="https://mccabelab.org">https://mccabelab.org</a></p> <p>See ReadMe file for detailed genotype and methods.</p>
Modulation of glia activation by TRPA1 antagonism in a pre-clinical model of migraine
<p>This dataset comprises the findings obtained in the study aimed at investigating the relationship between TRPA1 channels and glia activation in the modulation of trigeminal hyperalgesia in preclinical models of migraine.</p> <p><strong><em>In vivo</em></strong> assessments performed in male Sprague-Dawley rats four hours after treatment with nitroglycerin (10mg/kg, i.p.) or its vehicle in the acute model, and 24h after the last treatment with nitroglycerin (5mg/kg, i.p.) or its vehicle in the chronic model (injections every other day over a 9-day period for a total of five injections):</p> <p>- evaluation of trigeminal hyperalgesia in the orofacial formalin test: the face rubbing was measured counting the seconds the animal spent grooming the injected area (upper lip, lateral to the nose) with the ipsilateral forepaw or hindpaw 0–3 min (Phase I) or 12–45 min (Phase II) after formalin injection (50 µl, s.c.). The observation time was divided into 15 blocks of 3 min each.</p> <p><strong><em>Ex vivo</em></strong> assessments performed on samples collected at the end of the behavioral test:</p> <p>- Microglia and astroglia activation was evaluated the trigeminal nucleus caudalis by means of immunofluorescence staining. Specifically, the medullary segment containing the trigeminal nucleus caudalis (between +1 and -5 mm from the obex) was removed and cut transversely at 30 µm thickness on a freezing sliding microtome. The anti-cluster of differentiation molecule 11b (anti-CD11b, 1:300) primary antibody (to detect microglia) or rabbit anti-glial fibrillary acidic protein (anti-GFAP, 1:500) primary antibody (to label astrocytes) were used. Cell count for microglia and astroglia was assessed by counting CD11b- or GFAP-positive cells, respectively, from a stack of 16 pictures (1μm-thick, 20x magnification) taken from four representative sections alongside the trigeminal nucleus caudalis. The total number of CD11b- and GFAP-positive cells of the four sections were summed and expressed per mm<sup>2</sup>. Glial activation state was rated on a scale ranging from score 0 to 3. The final activation state was calculated as the average of the scores assessed for each of the four representative sections of the trigeminal nucleus caudalis.</p> <p>- Satellite glial cells activation (anti-GFAP, 1:600), calcitonin gene-related peptide (CGRP) immunoreactivity (anti-CGRP, 1:500) and macrophage infiltration (anti-Iba1, 1:500) were evaluated in trigeminal ganglia using immunofluorescence staining. Specifically, trigeminal ganglia were removed and sectioned longitudinally at 20 µm thickness using a cryostat. In the trigeminal ganglia, the number of Iba1+ cells/area and the % of GFAP-encircled neurons and of CGRP+ neurons were assessed in whole TG sections acquired at 20X magnification.</p> <p>- Gene expression analysis: mRNA expression levels of (TNF-alpha), interleukin (IL) -1beta, IL-6, IL-10, inducible nitric oxide synthase (iNOS), GFAP, CD11b and CGRP in medulla oblongata and trigeminal ganglia. mRNA levels were measured by rt-PCR. All samples were assayed in triplicate and gene expression levels were calculated according to 2−∆∆Ct = 2− (∆Ct gene − ∆Ct housekeeping gene) formula by using Ct (cycle threshold) values.</p> <p><strong>Results </strong>- In nitroglycerin-treated rats, ADM_12 showed an anti-hyperalgesic effect in both the acute and chronic models. ADM_12 was able to counteract the acute and chronic nitroglycerin-induced changes in cytokines’ gene expression in both the areas of interest. In the acute nitroglycerin model, we reported a significant increase in microglial and astroglial activation in the trigeminalis nucleus caudalis area, which was abolished by ADM_12 treatment. In the chronic model, we detected an activation of satellite glial cells in the trigeminal ganglia in nitroglycerin-treated rats; this was inhibited by ADM_12. This latter also prevented the increase in CGRP gene expression levels in the trigeminal ganglia of nitroglycerin-treated animals.</p> <p> </p> <p> </p>
Data from: Aging and memory are altered by genetically manipulating lactate dehydrogenase in the neurons or glia of flies
<p>The astrocyte-neuron lactate shuttle hypothesis posits that glial-generated lactate is transported to neurons to fuel metabolic processes required for long-term memory. Although studies in vertebrates have revealed that lactate shuttling is important for cognitive function, it is uncertain if this form of metabolic coupling is conserved in invertebrates or is influenced by age. Lactate dehydrogenase (Ldh) is a rate-limiting enzyme that interconverts lactate and pyruvate. Here we genetically manipulated expression of <em>Drosophila</em> <em>melanogaster</em> lactate dehydrogenase (dLdh) in neurons or glia to assess the impact of altered lactate metabolism on invertebrate aging and long-term courtship memory at different ages. We also assessed survival, negative geotaxis, brain-neutral lipids (the core component of lipid droplets), and whole head metabolites. Both upregulation and downregulation of dLdh in neurons resulted in decreased survival and memory impairment with age. Glial downregulation of dLdh expression caused age-related memory impairment without altering survival, while upregulated glial dLdh expression lowered survival without disrupting memory. Both neuronal and glial dLdh upregulation increased neutral lipid accumulation. We provide evidence that altered lactate metabolism with age affects the tricarboxylic acid (TCA) cycle, 2-hydroxyglutarate (2HG), and neutral lipid accumulation. Collectively, our findings indicate that the direct alteration of lactate metabolism in either glia or neurons affects memory and survival but only in an age-dependent manner. </p>
Study of the Safety, Pharmacodynamics, Efficacy, and PK of TIMP-GLIA in Subjects With Celiac Disease
ClinicalTrials.gov study NCT03738475. IPD Sharing: YES. Countries: 1. Publications: 1.
Study of the Safety, Tolerability and Pharmacokinetics of TIMP-GLIA in Subjects With Celiac Disease
ClinicalTrials.gov study NCT03486990. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Aging and memory are altered by genetically manipulating lactate dehydrogenase in the neurons or glia of flies
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Stochastic cell-cycle entry and cell-state-dependent fate outputs of injury-reactivated tectal radial glia in zebrafish
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Sphingosine-1-phosphate signaling regulates the ability of Müller glia to become neurogenic, proliferating progenitor-like cells
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Data from: Three-photon in vivo imaging of neurons and glia in the medial prefrontal cortex with sub-cellular resolution
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Excitatory neurotransmission activates compartmentalized calcium transients in Müller glia without affecting lateral process motility
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Data for Evaluation of altered cell-cell communication between glia and neurons in the hippocampus of 3xTg-AD mice at two time points
<p>processed_data.tar.gz contains all files from the data directory associated with the 230418_TS_AgingCCC GitHub project and includes the following:</p> <ul> <li> <p>CellRangerCounts/</p> </li> <ul> <li> <p>post_soupX/ : contains 12 directories for 12 samples, which each contain 3 files obtained from ambient RNA removal with soupX. Below is a representative example, but the post_soupX directory contains one directory for each of the 12 samples:</p> </li> <ul> <li> <p>S01_6m_AD/</p> </li> <ul> <li> <p>barcodes.tsv</p> </li> <li> <p>genes.tsv</p> </li> <li> <p>matrix.mtx</p> </li> </ul> </ul> <li> <p>pre_soupX/ : contains 12 directories for 12 samples, which each contain 2 files obtained from Cell Ranger after aligning fastq files to the reference genome. Below is a representative example, but this directory contains 1 directory for each individual sample:</p> </li> <ul> <li> <p>S01_6m_AD/outs/</p> </li> <ul> <li> <p>filtered_feature_ bc_matrix.h5</p> </li> <li> <p>raw_feature_bc_matrix.h5 </p> </li> </ul> </ul> </ul> <li> <p>PANDA_inputs/</p> </li> <ul> <li> <p>PANDA_exp_files_array.txt: Text files with files paths to expression inputs for PANDA gene regulatory networks.</p> </li> <li> <p>mm10_TFmotifs.txt: Mouse TF motif input for PANDA gene regulatory networks. Previously published in Whitlock et al. 2023 </p> </li> <li> <p>mm10_ppi.txt: Mouse protein-protein interaction information from SringDB input for PANDA gene regulatory networks. Previously published in Whitlock et al. 2023 </p> </li> </ul> <li> <p>ccc/</p> </li> <ul> <li> <p>nichenet_v2_prior/</p> </li> <ul> <li> <p>gr_network_mouse_21122021.rds : accessed in December 2023, gene regulation network – gene regulatory information from MultiNicheNet</p> </li> <li> <p>ligand_target_matrix_nsga2r_final_mouse.rds: accessed in December 2023, ligand target matrix for mouse from MultiNicheNet.</p> </li> <li> <p>ligand_tf_matrix_nsga2r_final_mouse.rds: accessed in December 2023, mouse ligand tf matrix for signaling path determination from MultiNicheNet</p> </li> <li> <p>lr_network_mouse_21122021.rds : accessed in December 2023, ligand-receptor matrix from MultiNicheNet</p> </li> <li> <p>signaling_network_mouse_21122021.rds : accessed in December 2023, signaling network – protein-protein interaction information from MultiNicheNet for mouse</p> </li> <li> <p>weighted_networks_nsga2r_final_mouse.rds : accessed in October 2023, networks weighted by literature evidence from MultiNicheNet for mouse</p> </li> </ul> <li> <p>multinichenet_output.rds : MultiNicheNet output for 3xTg-AD snRNA-seq data</p> </li> <li> <p>12m_signaling_igraph_objects.rds : list of igraph objects for 93 LRTs and their signaling mediators at 12 months</p> </li> <li> <p>6m_signaling_igraph_objects.rds :list of igraph objects for 2 LRTs and their signaling mediators at 6 months</p> </li> </ul> <li> <p>elisa/: CSV files of measured OD values for every ELISA.</p> </li> <ul> <li> <p>240319_ELISA_Ab40.csv: OD measurements for Ab40</p> </li> <li> <p>240319_ELISA_Ab42.csv: OD measurements for Ab42</p> </li> <li> <p>240319_ELISA_total_tau.csv: OD measurements for Total Tau</p> </li> </ul> <li> <p>panda/: PANDA gene regulatory networks for each time point and condition in excitatory and inhibitory neurons. Used for differential gene targeting.</p> </li> <ul> <li> <p>excitatory_neurons_AD12.Rdata</p> </li> <li> <p>excitatory_neurons_AD6.Rdata</p> </li> <li> <p>excitatory_neurons_WT12.Rdata</p> </li> <li> <p>excitatory_neurons_WT6.Rdata</p> </li> <li> <p>inhibitory_neurons_AD12.Rdata</p> </li> <li> <p>inhibitory_neurons_AD6.Rdata</p> </li> <li> <p>inhibitory_neurons_WT12.Rdata</p> </li> <li> <p>inhibitory_neurons_WT6.Rdata</p> </li> </ul> <li> <p>pseudobulk/: includes pseudo bulk matrices for every cell type which were used for downstream analyses. Each matrix also includes metadata information on condition and time point.</p> </li> <ul> <li> <p>all_counts_ls.rds: List of all the pseudo bulk matrices (below).</p> </li> <li> <p>astrocytes.rds: pseudobulk matrix for astrocytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>endothelial_cells.rds: pseudobulk matrix for endothelial cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>ependymal_cells.rds: pseudobulk matrix for ependymal cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>excitatory_neurons.rds: pseudobulk matrix for excitatory neurons. Include time point and condition information for downstream analyses.</p> </li> <li> <p>fibroblasts.rds: pseudobulk matrix for fibroblasts. Include time point and condition information for downstream analyses.</p> </li> <li> <p>inhibitory_neurons.rds: pseudobulk matrix for inhibitory neurons. Include time point and condition information for downstream analyses.</p> </li> <li> <p>meningeal_cells.rds: pseudobulk matrix for meningeal cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>microglia.rds: pseudobulk matrix for microglia. Include time point and condition information for downstream analyses.</p> </li> <li> <p>oligodendrocytes.rds: pseudobulk matrix for oligodendrocytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>opcs.rds: pseudobulk matrix for oligodendrocyte progenitor cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>percicytes.rds: pseudobulk matrix for pericytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>rgcs.rds: pseudobulk matrix for retinal ganglion cells. Include time point and condition information for downstream analyses.</p> </li> </ul> <li> <p>pseudobulk_split/: Includes pseudo bulk count matrices split by time point and condition. Used for input to PANDA for gene regulatory network construction.</p> </li> <ul> <li> <p>excitatory_neurons_AD12.Rdata</p> </li> <li> <p>excitatory_neurons_AD6.Rdata</p> </li> <li> <p>excitatory_neurons_WT12.Rdata</p> </li> <li> <p>excitatory_neurons_WT6.Rdata</p> </li> <li> <p>inhibitory_neurons_AD12.Rdata</p> </li> <li> <p>inhibitory_neurons_AD6.Rdata</p> </li> <li> <p>inhibitory_neurons_WT12.Rdata</p> </li> <li> <p>inhibitory_neurons_WT6.Rdata</p> </li> </ul> <li> <p>seurat_preprocessing/</p> </li> <ul> <li> <p>filtered_seurat.rds : merged and filtered seurat object</p> </li> <li> <p>integrated_seurat.rds : seurat object integrated using harmony</p> </li> <li> <p>clustered_seurat.rds : clustered seurat object</p> </li> <li> <p>processed_seurat.rds : processed seurat object with final cell type assignments at specified resolution</p> </li> </ul> </ul> <p> </p> <p>Raw data publicly available on GEO under series accession: GSE261596</p>
Myelin, Glia and Depression in Type 2 Diabetes
ClinicalTrials.gov study NCT01327404. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Modeling Macrophages Activation Pattern in X-linked Adrenoleukodystrophy, Metachromatic Leukodystrophy and Adult Onset Leukoencephalopathy With Axonal Spheroids and Pigmented Glia
ClinicalTrials.gov study NCT04925349. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Radial glia
Drawing uploaded to scidraw.io on: 21 May 2020
Radial glia
Drawing uploaded to scidraw.io on: 21 May 2020
Effect of Antibiotics on Enteric Neurons and Glia
ClinicalTrials.gov study NCT05834036. IPD Sharing: NO. Countries: 1. Publications: 0.
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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.