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Dataset results
178 results for “Ami”
Stent Placement and the Risk of New-onset AF in Patients With AMI
ClinicalTrials.gov study NCT03382990. IPD Sharing: NO. Countries: 0. Publications: 0.
Immunologic Analysis of Peripheral Blood and Aspirated Thromb In AMI Patients
ClinicalTrials.gov study NCT02704806. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Radiolabeled TSPO Targeted Molecular Probe in AMI
ClinicalTrials.gov study NCT07105982. IPD Sharing: Not stated. Countries: 0. Publications: 0.
A Study to Investigate the Procoagulant Effect of Tenecteplase (TNK-tPA), Alteplase (Rt-PA) and Streptokinase (SK) Administered to Patients With Acute Myocardial Infarction (AMI)
ClinicalTrials.gov study NCT02182011. IPD Sharing: Not stated. Countries: 0. Publications: 0.
HI-LNC RNA knockdown in EndoC-betaH1 cell using lentiviral ami-aRNAs
GEO Series GSE83619. Homo sapiens. 84 samples. Type: Expression profiling by array.
RNA-Seq of the BA9, BA40 and AMY for one typically developing individual #5407
GEO Series GSE63513. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
AMI Galactic Plane Survey 16-GHz Source Catalog
The Arcminute Microkelvin Imager (AMI) Galactic Plane Survey is a large-area survey of the outer Galactic plane to provide arcminute resolution (approximately 3 arcminutes) images at milli-Jansky (mJy) sensitivity in the centimeter-wave band. This table contains results from the first data release of the survey, consisting of 868 deg<sup>2</sup> of the Galactic plane, covering the area above 40 degrees Declination (corresponding to 76 to 170 degrees Galactic Longitude) between Galactic Latitudes of -5 to +5 degrees at a central frequency of 15.75 GHz (1.9 cm). The noise level in the survey is <~ 3mJy/beam away from bright sources. This table contains the source catalog of 3503 radio sources detected with peak flux densities at or greater than 5 sigma. In their paper, the authors describe in detail the drift-scan observations which have been used to construct the maps, including the techniques used for observing, mapping and source extraction, and summarize the properties of the finalized data sets. These observations constitute the most sensitive Galactic plane survey of large extent at centimeter-wave frequencies greater than 1.4 GHz. This table was originally ingested by the HEASARC in March 2013 based on the <a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/MNRAS/429/3330">CDS Catalog J/MNRAS/429/3330</a> file catdr1.dat. It was updated in September 2013 using the latest data file from the CDS, which provided positions with improved precision. This is a service provided by NASA HEASARC .
Epigenetic landscape of Human Brains by Single Nucleus DNA Methylation and Chromatin Conformation Profiling - pool69_h1930002_Amy
GEO Series GSE196603. Homo sapiens. 2996 samples. Type: Methylation profiling by high throughput sequencing.
Epigenetic landscape of Human Brains by Single Nucleus DNA Methylation and Chromatin Conformation Profiling - pool121_h1930004_AMY
GEO Series GSE211239. Homo sapiens. 3011 samples. Type: Methylation profiling by high throughput sequencing.
Epigenetic landscape of Human Brains by Single Nucleus DNA Methylation and Chromatin Conformation Profiling - m3c23_h1930002_AMY
GEO Series GSE221523. Homo sapiens. 3004 samples. Type: Methylation profiling by high throughput sequencing; Other.
TSd5.RDS - CD40 inhibiton in AMI on d5, seq on d7 and d14
<p>##### CD40 inhibiton in AMI on d5, seq on d7 and d14</p><p> </p><p> </p><p># Load necessary libraries for data manipulation, analysis, and visualization</p><p>library(dplyr)</p><p>library(Seurat)</p><p>library(patchwork)</p><p>library(plyr)</p><p> </p><p># Set the working directory to the folder containing the data</p><p>setwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/01_TS_d5_paper/03_CD40 inhibition on day 5, seq on day 7 and 14/938-2_cellranger_count/outs")</p><p> </p><p># Read the M0 dataset from the 10X Genomics format</p><p>pbmc.data <- Read10X(data.dir = "filtered_feature_bc_matrix/")</p><p> </p><p>RNA <- pbmc.data$`Gene Expression`</p><p> </p><p>ADT <- pbmc.data$`Antibody Capture`</p><p> </p><p>HST <- pbmc.data$`Multiplexing Capture`</p><p> </p><p> </p><p># Load the Matrix package</p><p>library(Matrix)</p><p> </p><p># Hashtag 1, 2 and 3 are marking the mouse replicates per condition</p><p># Subset the rows based on row names</p><p>subsetted_rows <- c("TotalSeq-B0301", "TotalSeq-B0302", "TotalSeq-B0303")</p><p>animals_data <- HST[subsetted_rows, , drop = FALSE]</p><p> </p><p># Hashtag 4, 5, 6, 7 are representing DMSO d7, TS d7, DMSO d14 and TS d14</p><p>subsetted_rows <- c(""TotalSeq-B0304", "TotalSeq-B0305", "TotalSeq-B0306", "TotalSeq-B0307")</p><p>treatment_data <- HST[subsetted_rows, , drop = FALSE]</p><p> </p><p>#Create a Seurat obeject and more assays to combine later</p><p>RNA <- CreateSeuratObject(counts = RNA)</p><p>ADT <- CreateAssayObject(counts = ADT)</p><p>Mice <- CreateAssayObject(counts = animals_data)</p><p>Treatment <- CreateAssayObject(counts = treatment_data)</p><p> </p><p> </p><p>seurat <- RNA</p><p> </p><p>#Add the Assays</p><p>seurat[["ADT"]] <- ADT</p><p> </p><p>seurat[["HST_Mice"]] <- Mice</p><p> </p><p>seurat[["HST_Treatment"]] <- Treatment</p><p> </p><p>#Check for AK Names</p><p>rownames(seurat[["ADT"]])</p><p> </p><p>#Cluster cells on the basis of their scRNA-seq profiles</p><p># perform visualization and clustering steps</p><p>DefaultAssay(seurat) <- "RNA"</p><p>seurat <- NormalizeData(seurat)</p><p>seurat <- FindVariableFeatures(seurat)</p><p>seurat <- ScaleData(seurat)</p><p>seurat <- RunPCA(seurat, verbose = FALSE)</p><p>seurat <- FindNeighbors(seurat, dims = 1:30)</p><p>seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)</p><p>seurat <- RunUMAP(seurat, dims = 1:30)</p><p>DimPlot(seurat, label = TRUE)</p><p> </p><p>FeaturePlot(seurat, features = "Col1a1", order = T)</p><p> </p><p># Normalize ADT data,</p><p>DefaultAssay(seurat) <- "ADT"</p><p>seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2)</p><p> </p><p>#Demultiplex cells based on Mouse_Hashtag Enrichment</p><p>seurat <- NormalizeData(seurat, assay = "HST_Mice", normalization.method = "CLR")</p><p>seurat <- HTODemux(seurat, assay = "HST_Mice", positive.quantile = 0.60)</p><p> </p><p> </p><p>#Visualize demultiplexing results</p><p># Global classification results</p><p>table(seurat$HST_Mice_classification.global)</p><p> </p><p>DimPlot(seurat, group.by = "HST_Mice_classification")</p><p> </p><p> </p><p>#Demultiplex cells based on Treatment_Hashtag Enrichment</p><p>seurat <- NormalizeData(seurat, assay = "HST_Treatment", normalization.method = "CLR")</p><p>seurat <- HTODemux(seurat, assay = "HST_Treatment", positive.quantile = 0.60)</p><p> </p><p> </p><p>#Visualize demultiplexing results</p><p># Global classification results</p><p>table(seurat$HST_Treatment_classification.global)</p><p> </p><p>DimPlot(seurat, group.by = "HST_Treatment_classification")</p><p> </p><p>Idents(seurat) <- seurat$HST_Treatment_classification</p><p>pbmc.singlet <- subset(seurat, idents = "Negative", invert = T)</p><p>Idents(pbmc.singlet) <- pbmc.singlet$HST_Mice_classification</p><p>pbmc.singlet <- subset(pbmc.singlet, idents = "Negative", invert = T)</p><p> </p><p>DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")</p><p> </p><p>#Redo the clssification to remove the doublettes</p><p>pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.99)</p><p>table(pbmc.singlet$HST_Treatment_classification.global)</p><p> </p><p>DimPlot(pbmc.singlet, group.by = "HST_Treatment_classification")</p><p>pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T)</p><p> </p><p> </p><p>pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.99)</p><p>table(pbmc.singlet$HST_Mice_classification.global)</p><p>pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T)</p><p> </p><p>pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.60)</p><p>pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.60)</p><p>DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")</p><p>DimPlot(pbmc.singlet, group.by = "HST_Mice_maxID")</p><p> </p><p> </p><p>seurat <- pbmc.singlet</p><p> </p><p>seurat$mice <- seurat$HST_Mice_maxID</p><p>seurat$treatment <- seurat$HST_Treatment_maxID</p><p> </p><p> </p><p>library(plyr)</p><p>seurat$treatment <- revalue(seurat$treatment, c(</p><p> "TotalSeq-B0304" = "DMSO_d7",</p><p> "TotalSeq-B0305" = "TS_d7",</p><p> "TotalSeq-B0306" = "DMSO_d14",</p><p> "TotalSeq-B0307" = "TS_d14"</p><p>))</p><p> </p><p>library(plyr)</p><p>seurat$mice <- revalue(seurat$mice, c(</p><p> "TotalSeq-B0301" = "1",</p><p> "TotalSeq-B0302" = "2",</p><p> "TotalSeq-B0303" = "3"</p><p>))</p><p> </p><p> </p><p>#Cluster cells on the basis of their scRNA-seq profiles without doublettes</p><p># perform visualization and clustering steps</p><p>DefaultAssay(seurat) <- "RNA"</p><p>seurat <- NormalizeData(seurat)</p><p>seurat <- FindVariableFeatures(seurat)</p><p>seurat <- ScaleData(seurat)</p><p>seurat <- RunPCA(seurat, verbose = FALSE)</p><p>seurat <- FindNeighbors(seurat, dims = 1:30)</p><p>seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)</p><p>seurat <- RunUMAP(seurat, dims = 1:30)</p><p>DimPlot(seurat, label = TRUE)</p><p> </p><p> </p><p>DefaultAssay(seurat) <- "ADT"</p><p>seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2)</p><p>setwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/01_TS_d5_paper/03_CD40 inhibition on day 5, seq on day 7 and 14/Analyse")</p><p> </p><p>saveRDS(seurat, file= "TSd5.v0.1.RDS")</p><p> </p><p> </p><p> </p><p> </p><p> </p>
Epigenetic landscape of Human Brains by Single Nucleus DNA Methylation and Chromatin Conformation Profiling - m3c41_h1930004_AMY
GEO Series GSE232006. Homo sapiens. 2970 samples. Type: Methylation profiling by high throughput sequencing.
Epigenetic landscape of Human Brains by Single Nucleus DNA Methylation and Chromatin Conformation Profiling - m3c22_h1930001_AMY
GEO Series GSE221503. Homo sapiens. 2910 samples. Type: Methylation profiling by high throughput sequencing; Other.
Human Plasma: Health vs. High risk vs. Stable Angina vs. AMI
GEO Series GSE66752. Homo sapiens. 35 samples. Type: Non-coding RNA profiling by array.
Epigenetic landscape of Human Brains by Single Nucleus DNA Methylation and Chromatin Conformation Profiling - pool68_h1930001_Amy
GEO Series GSE196577. Homo sapiens. 2935 samples. Type: Methylation profiling by high throughput sequencing.
Dataset related to the article "G-CSF for Extensive STEMI: Results From the STEM-AMI OUTCOME CMR Substudy"
<p>This record contains raw data related to the article "G-CSF for Extensive STEMI: Results From the STEM-AMI OUTCOME CMR Substudy".</p> <p><em>Rationale: </em>In the exploratory Phase II STEM-AMI (Stem Cells Mobilization in Acute Myocardial Infarction) trial,</p> <p>we reported that early administration of G-CSF (granulocyte colony-stimulating factor), in patients with anterior</p> <p>ST-segment–elevation myocardial infarction and left ventricular (LV) dysfunction after successful percutaneous</p> <p>coronary intervention, had the potential to significantly attenuate LV adverse remodeling in the long-term.</p> <p><em>Objective: </em>The STEM-AMI OUTCOME CMR (Stem Cells Mobilization in Acute Myocardial Infarction Outcome</p> <p>Cardiac Magnetic Resonance) Substudy was adequately powered to evaluate, in a population showing LV ejection</p> <p>fraction ≤45% after percutaneous coronary intervention for extensive ST-segment–elevation myocardial infarction,</p> <p>the effects of early administration of G-CSF in terms of LV remodeling and function, infarct size assessed by late</p> <p>gadolinium enhancement, and myocardial strain.</p> <p><em>Methods and Results: </em>Within the Italian, multicenter, prospective, randomized, Phase III STEM-AMI OUTCOME trial,</p> <p>161 ST-segment–elevation myocardial infarction patients were enrolled in the CMR Substudy and assigned to standard</p> <p>of care (SOC) plus G-CSF or SOC alone. In 119 patients (61 G-CSF and 58 SOC, respectively), CMR was available at</p> <p>baseline and 6-month follow-up. Paired imaging data were independently analyzed by 2 blinded experts in a core CMR</p> <p>lab. The 2 groups were similar for clinical characteristics, cardiovascular risk factors, and pharmacological treatment,</p> <p>except for a trend towards a larger infarct size and longer symptom-to-balloon time in G-CSF patients. ANCOVA showed</p> <p>that the improvement of LV ejection fraction from baseline to 6 months was 5.1% higher in G-CSF patients versus</p> <p>SOC (<em>P</em>=0.01); concurrently, there was a significant between-group difference of 6.7 mL/m2 in the change of indexed LV</p> <p>end-systolic volume in favor of G-CSF group (<em>P</em>=0.02). Indexed late gadolinium enhancement significantly decreased in</p> <p>G-CSF group only (<em>P</em>=0.04). Moreover, over time improvement of global longitudinal strain was 2.4% higher in G-CSF</p> <p>patients versus SOC (<em>P</em>=0.04). Global circumferential strain significantly improved in G-CSF group only (<em>P</em>=0.006).</p> <p><em>Conclusions: </em>Early administration of G-CSF exerted a beneficial effect on top of SOC in patients with LV</p> <p>dysfunction after extensive ST-segment–elevation myocardial infarction in terms of global systolic function,</p> <p>adverse remodeling, scar size, and myocardial strain.</p> <p> </p>
scRNAseq_Dataset Merge AMI d5 (CD45+Fibroblast) + AAA Kinetik + Cite-Seq_Dataset AG Gerdes
<p>Integration Skript:</p> <p> </p> <p>library(Seurat)<br> library(tidyverse)<br> library(Matrix)</p> <p>#cite <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Merge AAA mit Cite AAA/Cite_seq_v0.41.rds")<br> #CD45 <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper/CD45.rds")<br> AAA <- readRDS("C:/Users/alex/sciebo/AAA_Zhao_v4.rds")<br> cite <- readRDS("C:/Users/alex/sciebo/CITE_Seq_v0.5.rds")<br> all4 <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/Schrader_All4_Rohanalyse/all4_220228.rds")</p> <p>#fuse lists<br> c <- list(cite, all4, AAA)<br> names(c) <- c("cite", "all4", "AAA")</p> <p>pancreas.list <- c[c("cite", "all4", "AAA")]<br> for (i in 1:length(pancreas.list)) {<br> pancreas.list[[i]] <- SCTransform(pancreas.list[[i]], verbose = FALSE)<br> }</p> <p>pancreas.features <- SelectIntegrationFeatures(object.list = pancreas.list, nfeatures = 3000)<br> #options(future.globals.maxSize= 6091289600)<br> #pancreas.list <- PrepSCTIntegration(object.list = pancreas.list, anchor.features = pancreas.features,<br> #verbose = FALSE) #future.globals.maxsize was to low. changed it to options(future.globals.maxSize= 1091289600)<br> #identify anchors</p> <p>#alternative from tutorial (https://satijalab.org/seurat/articles/integration_introduction.html)<br> #memory.limit(9999999999)<br> features <- SelectIntegrationFeatures(object.list = pancreas.list, nfeatures = 3000)<br> pancreas.list <- PrepSCTIntegration(object.list = pancreas.list, anchor.features = features)<br> pancreas.anchors <- FindIntegrationAnchors(object.list = pancreas.list, normalization.method = "SCT", anchor.features = pancreas.features, verbose = FALSE)<br> pancreas.integrated <- IntegrateData(anchorset = pancreas.anchors, normalization.method = "SCT",<br> verbose = FALSE)</p> <p> </p> <p>setwd("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper")</p> <p>saveRDS(pancreas.integrated, file = "integrated_AAA_Cite_AMI.rds")</p> <p>saveRDS(cd45, file = "integrated_AAA_Cite_CD45.rds")</p> <p>seurat <- pancreas.integrated</p> <p>#seurat <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper/integrated_d5_cite.rds")</p> <p>DefaultAssay(object = seurat) <- "integrated"<br> seurat <- FindVariableFeatures(seurat, selection.method = "vst", nfeatures = 3000)<br> seurat <- ScaleData(seurat, verbose = FALSE)<br> seurat <- RunPCA(seurat, npcs = 30, verbose = FALSE)<br> seurat <- FindNeighbors(seurat, dims = 1:30)<br> seurat <- FindClusters(seurat, resolution = 0.5)<br> seurat <- RunUMAP(seurat, reduction = "pca", dims = 1:30)<br> DimPlot(seurat, reduction = "umap", split.by = "treatment") + NoLegend()</p> <p><br> DimPlot(seurat, label = T, repel = T) + NoLegend()</p> <p>DefaultAssay(object = seurat) <- "ADT"<br> adt_marker_integrated <- FindAllMarkers(seurat, logfc.threshold = 0.3)<br> write.csv(adt_marker_integrated, file = "adt_marker_all4_integrated.csv")</p> <p>DefaultAssay(object = seurat) <- "RNA"<br> RNA_marker_integrated <- FindAllMarkers(seurat, logfc.threshold = 0.5)<br> write.csv(RNA_marker_integrated, file = "RNA_marker_all4_integrated.csv")</p> <p>DimPlot(seurat, label = T, repel = T, split.by = "tissue") + NoLegend()</p> <p>FeaturePlot(seurat, features = "Cd40", order = T, label = T)<br> FeaturePlot(seurat, features = "Ms.CD40", order = T, label = T)</p> <p><br> #####<br> #leanup:<br> > seurat@meta.data[["sen_score1"]] <- NULL<br> > seurat@meta.data[["sen_score2"]] <- NULL<br> > seurat@meta.data[["sen_score3"]] <- NULL<br> > seurat@meta.data[["sen_score4"]] <- NULL<br> > seurat@meta.data[["sen_score5"]] <- NULL<br> > seurat@meta.data[["sen_score6"]] <- NULL<br> > seurat@meta.data[["sen_score7"]] <- NULL<br> > seurat@meta.data[["pANN_0.25_0.1_1211"]] <- NULL<br> > seurat@meta.data[["DF.classifications_0.25_0.1_1211"]] <- NULL<br> > seurat@meta.data[["DF.classifications_0.25_0.1_466"]] <- NULL<br> > seurat@assays[["prediction.score.celltype"]] <- NULL<br> > seurat@meta.data[["predicted.celltype"]] <- NULL<br> > seurat@meta.data[["DF.classifications_0.25_0.1_184"]] <- NULL<br> > seurat@meta.data[["DF.classifications_0.25_0.1_953"]] <- NULL<br> > seurat@meta.data[["integrated_snn_res.3"]] <- NULL<br> > seurat@meta.data[["RNA_snn_res.3"]] <- NULL<br> > seurat@meta.data[["SingleR"]] <- NULL<br> > seurat@meta.data[["SingleR_fine"]] <- NULL<br> > seurat@meta.data[["ImmGen"]] <- NULL<br> > seurat@meta.data[["ImmGen_fine"]] <- NULL<br> > seurat@meta.data[["percent.mt"]] <- NULL<br> > seurat@meta.data[["nCount_integrated"]] <- NULL<br> > seurat@meta.data[["nFeature_integrated"]] <- NULL<br> > seurat@meta.data[["S.Score"]] <- NULL<br> > seurat@meta.data[["G2M.Score"]] <- NULL<br> > seurat@meta.data[["Phase"]] <- NULL<br> > seurat@meta.data[["sen_score8"]] <- NULL<br> > seurat@meta.data[["sen_score9"]] <- NULL<br> > seurat@meta.data[["sen_score10"]] <- NULL<br> > seurat@meta.data[["sen_score11"]] <- NULL<br> > seurat@meta.data[["sen_score12"]] <- NULL<br> > seurat@meta.data[["sen_score13"]] <- NULL<br> > seurat@meta.data[["sen_score14"]] <- NULL<br> > seurat@meta.data[["sen_score15"]] <- NULL<br> > seurat@meta.data[["sen_score16"]] <- NULL<br> > seurat@meta.data[["sen_score17"]] <- NULL<br> > seurat@meta.data[["sen_score18"]] <- NULL<br> > seurat@meta.data[["sen_score19"]] <- NULL<br> seurat@meta.data[["pANN_0.25_0.1_184"]] <- NULL<br> seurat@meta.data[["pANN_0.25_0.1_953"]] <- NULL<br> seurat@meta.data[["pANN_0.25_0.1_466"]] <- NULL</p> <p> </p> <p> </p>
Postn and CD140 in TS766 d5 post AMI
<p>Postn and CD140 in TS766 d5 post AMI</p> <p> </p> <p>Merge Chanels:</p> <p>03, 00, 01, 02</p> <p>Range of Postn Signal: 260-420</p> <p> </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.