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4,540 results for “inflammation”
Increased inflammation and oxidative stress caused by accumulated metal particle exposure among metro station staff, Tianjin, China, 2023
Metro is a significant part of world transport, delivering over 58 billion passengers annually. The dilution effect of particulate matter (PM) from natural ventilation was limited in underground metro stations. What's worse, train operation processes generated PM rich in heavy metals. Though PM pollution in metro stations was reported widely, there is limited evidence of the adverse health effect of metro station PM. This dataset collected urinary samples from 74 metro station staff from three different metro stations in Tianjin, China, for inflammation and oxidative stress biomarker tests to better understand the potential health effects induced by metal particulates in metro stations. Also, an indoor air quality survey was conducted simultaneously in the metro stations.
Reprocessing of the dataset "Plasma Proteome Profiling Reveals the Effects of Weight Loss on the Apolipoprotein Family and Systemic Inflammation Status"
<p>Reprocessing of the MassIVE repository MSV000080596, originally generated to investigate the dynamic changes in the plasma proteomes of a cohort of individuals with obesity following weight loss and maintenance. The reprocessing included all samples from 52 individuals taken right after the weight-loss process and during the weight maintenance phase of the study (Weeks 0, 4, 13, 26, 39, and 52).</p> <p>We used the sequence database generated by ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) representing all populations from the 1000 Genomes Project (doi.org/10.5281/zenodo.10149277). For the search, SearchGUI version 4.3.1 and PeptideShaker version 3.0.0 were used with the X!Tandem and Tide search engines. The modification settings specified were carbamidomethylation of C as fixed and oxidation of M, deamidation of N and Q, Pyrrolidone of E and Q, and acetylation of protein N-terminus as variable modifications. The maximum peptide length was set to 40 amino acids and the precursor and fragment ion tolerances were set to 7 and 20 ppm, respectively. Resulting PSMs were processed as described in (doi.org/10.1021/acs.jproteome.3c00243) using Percolator version 3.5 provided with features based on peptide retention time (DeepLC version 1.1.2) and fragmentation predictors (MS2PIP version 3.9.0), and filtered at a 1% estimated FDR.</p> <p>The attached file contains all the peptide-spectrum matches identified at 1% FDR. The peptides have been annotated with transcripts, genes, and alleles using the ProHap Peptide Annotator v1.1 (<a href="https://github.com/ProGenNo/ProHap_PeptideAnnotator">https://github.com/ProGenNo/ProHap_PeptideAnnotator</a>).</p>
Highly multiplexed histology reveals phenotypic and spatial characteristics of human Innate Lymphoid Cells in chronic inflammation - MELC tonsil data-set
<p><strong>53 marker MELC Run in human tonsil</strong>. Each image depicts the same field of view, sequentially stained with the depicted fluorescence-labelled antibodies. Images contain 2048 x 2048 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have not been normalized and intensities have not been adjusted.</p> <p> </p>
Mural cells sustain a homeostatic vascular macrophage niche limiting chronic inflammation
<p>If you use any of these data, please cite the corresponding publication:</p><p><a href="https://doi.org/10.1016/j.immuni.2023.08.002">Mural cell-derived chemokines provide a protective niche to safeguard vascular macrophages and limit chronic inflammation</a><br><a href="https://doi.org/10.1016/j.immuni.2023.08.002">Pekayvaz et al., Immunity, 2023</a> . ( <a href="https://doi.org/10.1016/j.immuni.2023.08.002">https://doi.org/10.1016/j.immuni.2023.08.002</a> )</p><p><strong>scRNA-seq data</strong></p><p><i>processed:</i></p><p>processed_kidney_seurat.Rds<br>processed_lung_seurat.Rds</p><p><i>count matrices:</i></p><p>sample_M1_raw_feature_bc_matrix.h5<br>sample_M2_raw_feature_bc_matrix.h5</p><p><i>script:</i></p><p>process_sctransform.R</p><p>functions.R with helper functions (mainly for plotting) used in the process-script.</p><p><strong>RNA-seq</strong></p><p><i>macrophages:</i></p><p>macs_control_vs_knockout.hisat2.DirectDESeq2.tsv|xlsx (count data and DESeq2 results)</p><p><i>aorta:</i></p><p>aorta_control_vs_knockout.hisat2.DirectDESeq2.tsv|xlsx (count data and DESeq2 results)</p><p><i>Dead vs. Living:</i></p><p>peri_Dead_vs_Living.exon_all.DirectDESeq2.tsv|xlsx (count data and DESeq2 results)</p><p> </p>
Analysis of bacteria, inflammation, and exudation in epidermal suction blister wounds reveals dynamic changes during wound healing - Dataset
<p>Dataset for "<strong>Analysis of bacteria, inflammation, and exudation in epidermal suction blister wounds reveals dynamic changes during wound healing</strong>". Data were generated from samples collected to a biobank from suction blister wounds at various timepoints during a clinical trial (<a href="https://clinicaltrials.gov/study/NCT05378997?term=NCT05378997&rank=1"><strong>NCT05378997</strong></a>). Data include concentration of bacteria in swab and dressing fluid samples, neutrophil proteins (HNE, MPO, and HBP), cytokines (IFN-gamma, interleukin IL-1beta, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, TNF-alpha) and total protein measured in dressing fluid samples, and bacterial species identified by MALDI-TOF in swab and dressing fluid samples.</p>
PI3Kg inhibition circumvents inflammation and mortality in SARS-CoV-2 and other infections
<p>Virulent infectious agents such as SARS-CoV-2 and Methicillin Resistant <em>Staphylococcus Aureus</em> (MRSA) induce tissue damage that recruits neutrophils and monocyte/macrophages that promote T cell exhaustion, fibrosis, vascular leak, epithelial cell depletion, and fatal organ damage. Neutrophils and macrophages recruited to pathogen infected lungs, including SARS-CoV-2 infected lungs, express phosphatidylinositol 3-kinase gamma (PI3Kg), a signaling protein that coordinately controls granulocyte and monocyte trafficking to diseased tissues and immune suppressive, pro-fibrotic transcription in myeloid cells. PI3Kg deletion and inhibition with the clinical PI3Kg inhibitor eganelisib promoted survival in models of infectious diseases, including SARS-CoV-2 and MRSA, by suppressing inflammation, vascular leak, organ damage and cytokine storm. These results demonstrate essential roles for PI3Kg in inflammatory lung disease and support the potential use of PI3Kg inhibitors to suppress inflammation in severe infectious diseases.</p>
Datasets for: Integrative Mapping of Human CD8+ T Cell in Inflammation and Cancer
<p>Files for the integrated pan-disease CD8+ T cell atlas: </p> <ul> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.all_genes.h5ad.gz?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.all_genes.h5ad.gz</a> : 19957 genes raw matrix </li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.h5ad?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.h5ad</a> : 4000 highly variable genes raw matrix</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.X_gex.npy?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.X_gex.npy</a> : scatlasavae model embedding</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.clone_subtype.csv?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.clone_subtype.csv</a> : clone type definition</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.supervised.model?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.supervised.model</a>. scatlasavae model checkpoint</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.h5ad?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.h5ad</a> : Tex subset of 4000 highly variable genes raw matrix</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.Tex.supervised.model?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.Tex.supervised.model</a>. scatlasavae model checkpoint for the Tex subset</li> </ul> <p>Files for the TILs CD8+ T cell atlas</p> <ul> <li><a href="https://zenodo.org/records/13382785/files/adata_cd8_chu.h5ad?download=1" target="_blank" rel="noopener">adata_cd8_chu.h5ad</a>. the Chu <em>et al.</em>, 2023 Dataset</li> <li><a href="https://zenodo.org/records/13382785/files/adata_cd8_zheng.h5ad?download=1" target="_blank" rel="noopener">adata_cd8_zheng.h5ad.</a> The Zheng <em>et al.</em>, 2021 Dataset</li> </ul> <p>Files for the transfer (query) datasets:</p> <ul> <li><a href="https://zenodo.org/records/13382785/files/Zhang_LC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Zhang_LC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Luoma_HNSCC_TIL.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Luoma_HNSCC_TIL.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Luoma_HNSCC_PBMC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Luoma_HNSCC_PBMC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Watson_MELA.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Watson_MELA.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Bassez_BC.cohort1.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Bassez_BC.cohort1.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Bi_RCC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Bi_RCC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Caushi_NSCLC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Caushi_NSCLC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Liu_TNBC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Liu_TNBC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Borra%CC%80s_2024_Colorectal_cancer.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Borràs_2024_Colorectal_cancer.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Garner_2023_MAIT.h5ad" target="_blank" rel="noopener">Garner_2023.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Vorkas_2022_MAIT.h5ad?download=1" target="_blank" rel="noopener">Vorkas_2022_MAIT.h5ad</a></li> </ul>
Polyphenols' Inflammation-mediated Deglucuronidation and Promiscuous-binding Behavior for Antiviral Assaying and Trialing
<p>Dataset based on EMSKE Phytochem's pre-existing library of more than 250 Pubchem compounds that supports the research work Polyphenols’ Inflammation-mediated Deglucuronidation and Promiscuous-binding Behavior for Antiviral Assaying and Trialing. It refers to Jasial et al. 2016's dataset uploaded to Zenodo, <br> <br> Jasial, S.; Hu, Y.; Bajorath, J. PubChem Compounds Tested in Primary and Confirmatory Assays, 2016<br> <br> located at https://doi.org/10.5281/zenodo.44593 , which itself supports their work Jasial et al 2017: <br> <br> Jasial, S.; Hu, Y.; Bajorath, J. Determining the Degree of Promiscuity of Extensively Assayed Compounds. PLOS ONE 2016, 11 (4), e0153873. https://doi.org/10.1371/journal.pone.0153873.</p>
MyD88-TLR4-dependent choroid plexus activation precedes perilesional inflammation and secondary brain edema in a mouse model of intracerebral hemorrhage
<p>Supplementary data and code of the article "MyD88-TLR4-dependent choroid plexus activation precedes perilesional inflammation and secondary brain edema in a mouse model of intracerebral hemorrhage".</p>
A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities
<p>This file contains the data that was used in the paper titled "A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities" which will be published in Frontiers of Pharmacology.</p>
Figure 7 in Inflammation reduction potential of nanostructured lipid carriers encapsulated with rat's bone marrow cells' lysate
Figure 7. In-vivo Gene Expression Analysis: (A) represents apoptotic markers; BAX and Caspase-3, expression in treated nanostructured lipid carriers (T-NLC), treated bone marrow-derived mesenchymal stromal cells (BMSCs) lysate (C-I-BMSCs-L) and NLC loaded BMSCs lysate (C-I-NLC-BMSCs-L) groups as compared to normal (N) and carrageenan injected injury (C) groups (B) shows proinflammatory markers (IL-6 and IL-8) expression levels in treated C-I-BMSCs-L and treated C-I-NLC-BMSCs-L groups as compared to N and C groups (C) shows Proliferative markers (Ki-67, PCNA and TOP2A) expression in treated C-I-BMSCs-L and treated C-INLC-BMSCs-L group as compared to N and C groups. Whereas N-NS represents normal rats injected with normal saline, C-NS represents carrageenan-injected normal saline, C-I-DFS represents carrageenan-injected diclofenac sodium. Where; the* sign shows significance between untreated and treated groups while α and ss sign shows significance between carrageenan injury and other treatment groups. Where, ns is non-significant, * & α represents P<0.05, ** & ss represents P<0.001, *** & αss represents P<0.0001.
Figure 6 in Inflammation reduction potential of nanostructured lipid carriers encapsulated with rat's bone marrow cells' lysate
Figure 6. Percentage inhibition of inflammation at a time interval (hr) in carrageenan-induced rat's hind paw oedema model. The effect of different treatment groups, i.e., normal (N), normal rat paw injected with normal saline (N-NS), carrageenan injected group (C), carrageenan injected with normal saline group (C-NS), carrageenan injected with diclofenac sodium group (C-I-DFS), carrageenan injected with nanostructured lipid carriers group (C-I-NLC), carrageenan injected with bone marrow-derived mesenchymal stromal cells (BMSCs) lysate group (C-I-BMSCs-L) and Carrageenan injected with NLC loaded BMSCs lysate group (C-I-NLC-BMSCs-L); on hind paw oedema at different hours (0, 1 2, 3, 6 & 24 hours). Where; the* sign shows significance between normal and carrageenan-induced treated groups while α and ss sign shows significance between carrageenan injected and carrageenaninduced treatment groups. Where; ns is non-significant, ** & ss denotes P<0.001, *** & αss denotes P<0.0001.
Figure 4 in Inflammation reduction potential of nanostructured lipid carriers encapsulated with rat's bone marrow cells' lysate
Figure 4. (A) Expression analysis of angiogenesis marker vascular endothelial growth factor (VEGF) via immunocytochemistry; (B) Expression analysis of apoptotic marker p53 via immunocytochemistry; (C) Expression analysis of apoptotic marker p53 via immunocytochemistry. Where: Untreated (UT), H 2 O 2 injury (I-H 2 O 2), treated NLC (T-NLC), treated BMSCs lysate (T-BMSCs-L), and treated NLC loaded bone marrow-derived mesenchymal stromal cells lysate (T-NLC-BMSCs-L). Stained cells are red, and blue denotes the nuclei counterstained with 4′,6-diamidine-2′-phenylindole dihydrochloride (DAPI,) while arrows show the positive cells expressing the protein. Scale bar: 200µm.
Figure 2 in Inflammation reduction potential of nanostructured lipid carriers encapsulated with rat's bone marrow cells' lysate
Figure 2. Represents cytotoxicity analysis/percentage cell viability and standardized viability concentration (SVC) values of different treatment groups on NIH 3T3 Cells (A) Represents the percentage of NIH 3T3 cells viability treated with different concentrations of nanostructured lipid carriers (NLC), bone marrow-derived mesenchymal stromal cells lysate (BMSCs-L), and NLC loaded BMSCs lysate (NLC-BMSCs-L). N represents % age viability of normal cells that receive no treatment and no H 2O2 injury; (B) Cytotoxicity analysis of various concentrations (500µg/µL, 1mg/mL, 2mg/mL, and 3mg/mL) of BMSCs lysate (C) SVC of BMSCslysate on NIH 3T3 cells; (D) Cytotoxicity analysis of various concentrations (500µg/µL, 1mg/mL, 2mg/mL, and 3mg/mL) of NLC loaded BMSCs lysate (E) shows SVC of NLC loaded BMSCs lysate on cells. Where; ***P<0.0001, *shows significance between untreated and treated groups while α and ss sign shows significance between H 2O2 injury and other treatment groups, αss shows P<0.0001, and ns is non-significant.
Figure 1 in Inflammation reduction potential of nanostructured lipid carriers encapsulated with rat's bone marrow cells' lysate
Figure 1. (A) Scanning Electron Micrograph of NLC and (B) Scanning Electron Micrograph of NLC-BMSCs-L; (B) Characterization of nanostructured lipid carriers (NLC) loaded bone marrow-derived mesenchymal stromal cells (BMSCs) lysate via enzyme-linked immunosorbent assay (ELISA):vascular endothelial growth factor (VEGF) and interleukin-6 (IL-6) expression in NLC, BMSCs-L, and NLC loaded bone marrow-derived mesenchymal stromal cells lysate (NLC-BMSCs-L).Where; *P<0.05, **P<0.01, ***P<0.0001, ns is non-significant.
Function of RORA in mouse lung inflammation
<p>This dataset contains microscopy images of mice lung, RORA KO vs wildtype. The mice were infected with N. brasiliensis.</p>
The skin commensal yeast Malassezia triggers a Th17-response that coordinates anti-fungal immunity and exacerbates skin inflammation
<p>Data accompanying the publication: Sparber et al. The skin commensal yeast <em>Malassezia</em> triggers a Th17-response that coordinates anti-fungal immunity and exacerbates skin inflammation. 2019. Cell Host & Microbe. https://doi.org/10.1016/j.chom.2019.02.002</p>
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>
Inflammation-controlled anti-inflammatory hydrogels
<p>Dataset to the publication</p> <p><strong>Inflammation-controlled anti-inflammatory hydrogels</strong></p> <p>Tina Helmecke, Dominik Hahn, Nadine Matzke, Lisa Ferdinand, Lars Franke, Sebastian Kühn, Gunter Fischer, Carsten Werner, Manfred F. Maitz</p> <p>Advanced Science 2022, 2206412. <a href="https://doi.org/10.1002/advs.202206412">https://doi.org/10.1002/advs.202206412</a></p>
Data platform (genotyping data set) related to ERDF postdoctoral project No. 1.1.1.2/VIAA/4/20/718 "The role of vitamin D gene polymorphisms and its receptors in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis".
<p><strong>Data platform </strong><strong>(genotyping dataset)</strong> <strong>related to the ERDF postdoctoral project No. </strong><strong>1.1.1.2/VIAA/4/20/718</strong><strong> “</strong><strong>The role of vitamin D and its receptor gene polymorphisms in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis</strong><strong>”.</strong></p> <p><strong>About the project and gathered data:</strong></p> <p>The dataset contains genotyping data on 289 sex-balanced samples (approximately 60% women / 40% men)) were created at the the multiple sclerosis (MS) Clinic of the Latvian Maritime Medical Center (LMMC) in 2011 (disease duration of 1-51 years); the collection was updated within the framework of the ERDF MS project (2017-2020) and replenished during the ERDF postdoctoral project No. 1.1.1.2/VIAA/4/20/718 “The role of vitamin D and its receptor gene polymorphisms in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis” (2021-2023).</p> <p>For the <strong>Genotyping dataset </strong>relevant information for each patient from the MS disease cohort, referring to proteasomal gene genetic variations (microsatellites and SNPs): (HSMS006 <em>(PSMA6),</em> HSMS602 <em>(FAM177A1),</em> HSMS701 <em>(KIAA0391)</em>, HSMS702 <em>(KIAA0391)</em> HSMS801 <em>(KIAA0391)</em>, rs11543947<em>(PSMB5), </em>rs2277460 (mi110), rs1048990 (mi8)<em> (PSMA6),</em> rs1048990 (mi8)<em> (PSMA6),</em> rs2295826/rs2295827<em>(PSMC6),</em> rs2348071 <em>(PSMA3),</em> rs2071543, rs9357155 <em>(PSMB8),</em> rs17587<em>(PSMB9),</em> rs74421874 <em>(PSMD9); </em>rs9275596 from HLA region; vitamin D-related genes (VDR and GC) polymorphisms: rs2228570, rs1544410, rs7975232, rs731236 (<em>VDR</em>) and rs7041, rs4588 <em>(GC).</em></p>
ScienceDex guides
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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.