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1,604 results for “omics”
Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data
<p>We proposed DCCA for accurately dissecting the cellular heterogeneity on joint-profiling multi-omics data from the same individual cell by transferring representation between each other.</p>
Raw Data - Part 4 : Spatial multi-omic map of human myocardial infarction ---- Raw image
<p>We provide here the raw image for the visium data for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022</p>
Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma
<p><span>Glioblastomas are malignant tumors of the central nervous system hallmarked by subclonal diversity and dynamic adaptation amid developmental hierarchies </span><span>(Couturier et al., 2020; Neftel et al., 2019; Richards et al., 2021)</span><span>. The source of the dynamic reorganization within the spatial context of these tumors remains elusive. Here, we characterized glioblastomas in-depth by spatially resolved transcriptomics, metabolomics, and proteomics. By </span><span>deciphering regionally shared transcriptional programs across patients, </span><span>we infer that glioblastoma is organized by spatial segregation of lineage states and adapt to inflammatory and/or metabolic stimuli, </span><span>reminiscent </span><span>of the reactive transformation in</span> <span>mature astrocytes. Integration of metabolic imaging and imaging mass cytometry uncovered locoregional tumor-host interdependence, resulting in spatially exclusive adaptive transcriptional programs. Inferring copy-number alterations emphasizes a spatially cohesive organization of subclones associated with reactive transcriptional programs, confirming that environmental stress gives rise to selection pressure. A model of glioblastoma stem cells implanted into human and rodent neocortical tissue mimicking various environments confirmed that transcriptional states originate from dynamic adaptation to various environments.</span></p>
Multi-omic brain and behavioral correlates of cell-free fetal DNA methylation in macaque maternal obesity models (NMR datasets, maternal plasma and infant brain)
<p>Maternal obesity during pregnancy is associated with neurodevelopmental disorder (NDD) risk. We utilized integrative multi-omics to examine maternal obesity effects on offspring neurodevelopment in rhesus macaques by comparison to lean controls and two interventions. Differentially methylated regions (DMRs) from longitudinal maternal blood-derived cell-free fetal DNA (cffDNA) significantly overlapped with DMRs from infant brain. The DMRs were enriched for neurodevelopmental functions, methylation-sensitive developmental transcription factor motifs, and human NDD DMRs identified from brain and placenta. Brain and cffDNA methylation levels from a large region overlapping mir-663 correlated with maternal obesity, metabolic and immune markers, and infant behavior. A DUX4 hippocampal co-methylation network correlated with maternal obesity, infant behavior, infant hippocampal lipidomic and metabolomic profiles, and maternal blood measurements of DUX4 cffDNA methylation, cytokines, and metabolites. Ultimately, maternal obesity altered infant brain and behavior, and these differences were detectable in pregnancy through integrative analyses of cffDNA methylation with immune and metabolic factors. </p>
Data for: An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean
<p><span>Soybean [<em>Glycine max (L.) Merr.</em>] </span><span>serves as one of the most economically valuable crops globally, but it is sensitive to low temperatures during the crop growing season. Currently, agriculture around the world has faced more serious abiotic stresses due to climate change, so there is an urgent need to breed cold-tolerant cultivars to resist the changing environment. The cold-tolerant trait is a complex and quantitative trait controlled by multiple genes, environmental factors, and their interaction. A total of 56 soybean samples were used, including 28 resistant varieties and 28 susceptible varieties, in the field experiments. We selected 55 SNPs (which were mapped to 39 CTgenes) from the CTgenes for distinguishing cold-tolerant lines from cold-susceptible lines. The SNP data can be applied for soybean's cold-tolerant experiment, such as soybean marker-assisted selection, soybean varieties clustering, the systems biology analysis, and further validation. </span></p>
Leveraging omics data to boost the power of genome-wide association studies
<p>Summary-level GWAS data for 8 traits generated by models M0, M1 and M2 as presented in:</p> <p>Lin, Z., Knutson, K. A., & Pan, W. (2022). Leveraging omic data to boost the power of genome-wide association studies. <em>Human Genetics and Genomics Advances</em>, 100144.</p>
Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets
<p>The uploaded files are source datasets for the scMTNI algorithm. scMTNI is a multi-task learning framework that integrates the cell lineage structure, scRNA-seq and scATAC-seq measurements to enable joint inference of cell type-specific GRNs. See more details at Zhang, S., Pyne, S., Pietrzak, S. et al. Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets. Nat Commun 14, 3064 (2023). <a href="https://doi.org/10.1038/s41467-023-38637-9">https://doi.org/10.1038/s41467-023-38637-9</a></p> <p>The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/scMTNI_sourcedata.tar.gz?versionId=a19f69c1-5bb2-436e-9a30-a7efbf1ab3da">scMTNI_sourcedata.tar.gz</a> contains the following 3 parts:</p> <p>1) The cluster-specific scRNA-seq matrices and the prior networks for all three datasets and scMTNI inferred consensus networks.</p> <p>2) Gold standard human and mouse datasets for evaluation.</p> <p>3) Source data for scMTNI figures 2-8 and supplementary figures. The key for each figure and its corresponding file path is in SourceData_Key_v2.xlsx.</p> <p>The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/Buenrostro_Hematopoiesis.tar.gz">Buenrostro_Hematopoiesis.tar.gz</a> contains the scRNA-seq data for human hematopoietic differentiation downloaded from Data S2 of Buenrostro et al.</p> <p>The source data <a href="../api/files/5c75876b-c08a-4186-a08d-da9db78f64f3/RawMotifFiles.tar.gz">RawMotifFiles.tar.gz</a> contains the motif instance files and promoter files for human and mouse for generating prior networks using scATAC-seq data for scMTNI. Check <a href="https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md">https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md</a> for examples and scripts.</p> <p>The <a href="../api/records/11876980/draft/files/Buenrostro_priorNetwork_bamfiles.tar.gz/content">Buenrostro_priorNetwork_bamfiles.tar.gz</a> contains the raw bam files of scATAC-seq data for human hematopoietic differentiation downloaded from Buenrostro et al.</p>
Figure 2 in Omics in Weed Science: A Perspective from Genomics, Transcriptomics, and Metabolomics Approaches
Figure 2. Workflow of transcript analyses by RNA-Seq and qRT-PCR.
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
<p>This repository (and several sub-repositories) contains the data for the manuscript "His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models"</p> <p>The current repository contains the de-identified metadata of WSIs. Additionally, it contains Supplementary Data S1-S7.</p> <p>Due to the large size of WSIs, the archives have been divided into several parts to satisfy the size limit of Zenodo.</p> <p>HMU-C dataset:</p> <p>part a: <a href="../doi/10.5281/zenodo.12636965">https://zenodo.org/doi/10.5281/zenodo.12636965</a></p> <p>part b: <a href="../doi/10.5281/zenodo.12705912">https://zenodo.org/doi/10.5281/zenodo.12705912</a></p> <p>HMU-1st dataset:</p> <p>part a: <a href="../doi/10.5281/zenodo.12710399">https://zenodo.org/doi/10.5281/zenodo.12710399</a></p> <p>part b: <a href="../doi/10.5281/zenodo.12723825">https://zenodo.org/doi/10.5281/zenodo.12723825</a></p> <p>part c: <a href="../doi/10.5281/zenodo.12724507">https://zenodo.org/doi/10.5281/zenodo.12724507</a></p> <p>part d: <a href="../doi/10.5281/zenodo.12726785">https://zenodo.org/doi/10.5281/zenodo.12726785</a></p> <p>Please fully download all the parts and concatenate them before extraction.</p> <p>Supplementary Data S1-S7:</p> <p><a href="https://zenodo.org/doi/10.5281/zenodo.16763510">https://zenodo.org/doi/10.5281/zenodo.16763510</a></p>
Processed CODEX Datasets from - Discovery and Generalization of Tissue Structures from Spatial Omics Data
<p>This entry provides access to processed CODEX data files of four studies analyzed in the article "Discovery and Generalization of Tissue Structures from Spatial Omics Data". Details of datasets can be found in the STAR Methods section of the article.</p> <p>For each dataset, a zip file containing multiple comma-separated values (CSV) files is included.</p> <p>Each region is assigned an unique identifier (e.g., DKD_kidney_001), and its related data files are:</p> <ul> <li>`{region_id}.cell_data.csv`, a table containing three columns: "CELL_ID", "X", and "Y". This table provides centroid locations for all cells segmented in this region.</li> <li>`{region_id}.expression.csv`, a table containing multiple columns: "CELL_ID", "DAPI", "CD45", etc. This table provides detailed protein biomarker expression quantified for all cells in this region.</li> <li>`{region_id}.scgp_annotations.csv`, a table containing two columns: "CELL_ID" and "SCGP". This table provides SCGP/SCGP-Extension annotations for all cells in this region.</li> </ul> <p>Code base for SCGP is also included in this entry. Please refer to <a href="https://gitlab.com/enable-medicine-public/scgp">https://gitlab.com/enable-medicine-public/scgp</a> for the latest codes, questions, and/or issues. Raw CODEX data and images will be accessible through links posted at the code base. Raw data will also be available from lead contact (A.E.T.) upon request.</p>
Multi-omics analysis reveals regime shifts in the gastrointestinal ecosystem in chickens following anticoccidial vaccination and Eimeria tenella challenge
<p>A multi-omics study integrating gut microbiota and host metabolome to investigate the gastrointestinal health markers in broiler chickens (Cobb500) from an anti-coccidiosis vaccine trial.</p>
Benchmark cancer datasets for Clustering algorithms for Omics-based Patient Stratification (COPS)
<p>This repository contains seven multi-omic cancer datasets including several cancer types (breast, kidney, lung, ovary, prostate, and thyroid cancers as well as low grade gliomas) that were used for benchmarking several multi-view clustering algorithms implemented by COPS (https://github.com/UEFBiomedicalInformaticsLab/COPS). The datasets were originally compiled from The Cancer Genoma Atlas (TCGA) and downloaded using the <em>curatedTCGAData</em> R-package. The datasets include copy-number variations, methylomics as well as mRNA and miRNA transcriptomics. The methylomics data was mapped to genes by averaging methylation level of probes associated with the promoter regions of genes. Similarly the miRNA transcriptomics data was mapped to genes by using known and predicted miRNA -> gene interactions. Updated survival data was acquired from the Liu et al. 2018 paper. </p> <p>This repository also includes two sets of cancer associated pathway networks used by pathway-based multi-omic methods benchmarked in our study. NCI-PID pathways were downloaded using the <em>ndexr</em> R-package on December 22 2021. While KEGG pathways were downloaded using the <em>pathview</em> R-package on May 3 2022. </p> <p>More details on the processing can be found on the related publication.</p>
Rett Syndrome Omics Datasets integrated into RettDb
<p>This datasets collection is part of the web resource: RettDb, the Rett Syndrome Omics Database.</p> <p>RettDb is fully described in this paper submitted to DATABASE: Cillàri, N., Neri, G., Pisanti, N., Milazzo, P., Borello, U. "RettDb: the Rett Syndrome Omics Database to navigate the Rett Syndrome genomic landscape" </p> <p> </p> <p>The table below contains a brief description of the data</p> <p>For a full description of the data, refer to the web resourse or this publication</p> <p> </p> <table> <tbody> <tr> <td><strong>filenames</strong></td> <td><strong>description</strong></td> <td><strong>file-type</strong></td> </tr> <tr> <td>H3K4me3_wt_Chip_seq.bedgraph.gz</td> <td>H3K4me3 ChipSeq</td> <td>bedgraph </td> </tr> <tr> <td>H3K4me3_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H3K4me3 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>H3K9me3_wt_Chip_seq.bedgraph.gz</td> <td>H3K9me3 ChipSeq</td> <td>bedgraph </td> </tr> <tr> <td>H3K9me3_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H3K9me3 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>H3K27ac_wt_Chip_seq.bedgraph.gz</td> <td>H3K27ac ChipSeq</td> <td>bedgraph </td> </tr> <tr> <td>H3K27ac_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H3K27ac ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>H3K36me3_wt_Chip_seq.bedgraph.gz</td> <td>H3K36me3 ChipSeq</td> <td>bedgraph </td> </tr> <tr> <td>H3K36me3_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H3K36me3 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>H4K20me3_wt_Chip_seq.bedgraph.gz</td> <td>H4K20me3 ChipSeq</td> <td>bedgraph </td> </tr> <tr> <td>H4K20me3_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H4K20me3 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_1.bedgraph.gz</td> <td>Mecp2 ChipSeq</td> <td>bedgraph </td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_1.bedgraph.gz.tbi</td> <td>Mecp2 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_2.bedgraph.gz</td> <td>Mecp2 ChipSeq</td> <td>bedgraph </td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_2.bedgraph.gz.tbi</td> <td>Mecp2 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_3.bw.gz.bw</td> <td>Mecp2 ChipSeq</td> <td>bigwig</td> </tr> <tr> <td>Mecp2_RNA_seq_WT_vs_KO.bed.gz</td> <td>Mecp2 RNASeq </td> <td>bed</td> </tr> <tr> <td>Mecp2_RNA_seq_WT_vs_KO.bed.gz.tbi</td> <td>Mecp2 RNASeq Index</td> <td>bed index</td> </tr> <tr> <td>Mecp2_wt.hic</td> <td>Mecp2 WT chromatin conformation data</td> <td>hic</td> </tr> <tr> <td>Mecp2_ko.hic</td> <td>Mecp2 KO chromatin conformation data</td> <td>hic</td> </tr> <tr> <td>CpG_island_mm10.bed.gz</td> <td>CpG island</td> <td>bed </td> </tr> <tr> <td>CpG_island_mm10.bed.gz.tbi</td> <td>CpG island Index</td> <td>bed index</td> </tr> <tr> <td>FIMO_CISBP_TFBS_predictions.bed.gz</td> <td>FIMO CISBP TFBS Predictions</td> <td>bed </td> </tr> <tr> <td>FIMO_CISBP_TFBS_predictions.bed.gz.tbi</td> <td>FIMO CISBP TFBS Predictions Index</td> <td>bed index</td> </tr> <tr> <td>remap2022_CRMs.bb</td> <td>Remap2022 Cis Regulatory Modules</td> <td>bigbed</td> </tr> <tr> <td>remap2022_non_redundant_peak.bb</td> <td>Remap2022 Non Redundant Peaks</td> <td>bigbed</td> </tr> <tr> <td>hub.config.json</td> <td>Tracks configuration file for WashU</td> <td>json</td> </tr> </tbody> </table> <p> </p>
MangroveDB: A comprehensive online database for mangroves based on multi-omics data
<p><span>Mangroves are dominant flora of intertidal zones along tropical and subtropical coastline around the world that offer important ecological and economic value. Recently, the genomes of mangroves have been decoded, and massive omics data were generated and deposited in the public databases. Reanalysis of multi-omics data can provide new biological insights excluded in the original studies. However, the requirements for computational resource and lack of bioinformatics skill for experimental researchers limit the effective use of the original data. To fill this gap, we uniformly processed 942 transcriptome data, 386 whole-genome sequencing data, and provided 13 reference genomes and 40 reference transcriptomes for 53 mangroves. Finally, we built an interactive web-based database platform MangroveDB (https://github.com/Jasonxu0109/MangroveDB), which was designed to provide comprehensive gene expression datasets to </span><span>facilitate their exploration</span><span> and equipped with several online analysis tools, including principal components analysis, differential gene expression analysis, tissue-specific gene expression analysis, GO and KEGG enrichment analysis. MangroveDB not only provides query functions about genes annotation, but also supports some useful visualization functions for analysis results, such as volcano plot, heatmap, dotplot, PCA plot, bubble plot, population structure <em>etc</em>. In conclusion, MangroveDB is a valuable resource for the mangroves research community to efficiently use the massive public omics datasets.</span></p>
Pre-Processed Cancer Multi-Omic Data from TCGA and Synthetic Data
<p><strong>ABSTRACT </strong></p> <p>It contains the data of four omic profiles (CNV, mRNA, miRNA, and protein) obtained for BRCA, LGG, and LUAD obtained from the TCGA project. </p> <p>In addition, we provide synthetic data for a mixture of isotropic distributions.</p> <p><strong>Instructions: </strong></p> <p>Cancer data are identified by cancer type (LGG: low-grade glioma, BRCA: breast cancer, and LUAD: lung cancer). The data are scaled by using the minima and maxima of each column so that the values are between 0 and 1. In these files, the columns are the features and the rows correspond to the patients.</p> <p>The summary data contains only the numerical values. The columns are the features and the rows are the observations.</p> <p><strong>Inspiration:</strong></p> <p>This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON"</p> <p>https://cancer.ubrite.org/hackathon-2021/</p> <p><strong>Acknowledgements</strong></p> <p>Diego Salazar, June 20, 2021, "Pre-processed Cancer multi-omic data from TCGA and synthetic data", IEEE Dataport, doi: https://dx.doi.org/10.21227/pjb8-d090.</p> <p>https://ieee-dataport.org/documents/pre-processed-cancer-multi-omic-data-tcga-and-synthetic-data</p> <p><strong>U-BRITE last update date:</strong> 07/21/2021</p>
Data deposition of the article 'Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence'
<p>Data deposition of the article 'Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence'</p>
Multi-Omic Approach Associates Blood Methylome with Bronchodilator Drug Response in Pediatric Asthma: Summary statistics
<p>We conducted an epigenome-wide association study of bronchodilator drug response (BDR) in a discovery and validation design. The discovery phase was focused on 221 African American children with asthma. The association between DNA methylation and BDR was conducted using the limma package correcting for age, sex, ancestry, and tissue heterogeneity. Summary statistics include the output from toptable limma function and CpG annotation (based on Illumina EPIC Manifest file v 1.0 B4) organized in the following columns:</p> <ul> <li>Probe: Probe ID</li> <li>Chr: chromosome</li> <li>Pos: genomic position based on GRCh37/hg19</li> <li>Gene: Gene annotation based on Illumina EPIC Manifest file v 1.0 B4</li> <li>logFC: estimate of the log2-fold-change corresponding to the effect or contrast</li> <li>SE: standard error</li> <li>AveExpr: average log2-expression for the probe over all arrays and channels</li> <li>t: moderated t-statistic</li> <li>P.value: raw p-value</li> <li>FDR: adjusted p-value by false discovery rate</li> <li>B: log-odds that the gene is differentially expressed</li> <li>Problem: Flagged potentially problematic probes</li> </ul>
Montipora capitata multi-omics supplemental tables
<p>Supplemental tables for <em>Montipora capitata</em> multi-omics study.</p> <p><strong>Table S1.</strong> Read statistics for the microbiome 16S rRNA data from the four <em>M. capitata</em> colonies (n=3 per treatment/time point/colony).</p> <p><strong>Table S2.</strong> Protein and transcript FC values for genes identified in proteomic data. The 138 stress-response related genes are also highlighted.</p> <p><strong>Table S3.</strong> Major KEGG pathways used to filter proteins identified in the proteomic data.</p> <p><strong>Table S4.</strong> Results from the permutational MANOVA (PERMANOVA) tests run on the transcriptomic, proteomic, and metabolomic datasets.</p> <p><strong>Table S5.</strong> Results from the statistical tests run on the alpha- and beta-diversity metrics.</p>
High-resolution spatial multi-omics datasets
<p>Supplementary raw data. The raw microscopy data are not uploaded owing to their large size (6.8 Tb), but are available upon reasonable request (Long Cai: lcai@caltech.edu, Yodai Takei: ytakei@caltech.edu).</p> <p>These supplementary data contain additional files for RNA seqFISH+, DNA seqFISH+, and sequential immunofluorescence from cell culture and adult mouse cerebellum experiments.</p> <p>DNA seqFISH+ datasets (provided as a tar.gz folder for each replicate): Super-resolved DNA spot locations by DNA seqFISH+ along with sequential immunofluorescence intensity at the rounded voxel location.</p> <p>RNA seqFISH datasets (provided as a zip folder): Super-resolved mRNA or intron spot locations.</p> <p>Sequential immunofluorescence table (provided as a csv file): Mean voxel intensity of each immunofluorescence marker per nucleus for the adult mouse cerebellum datasets.</p> <p>Note that voxel sizes are 103 nm for x and y, and 250 nm for z in our experimental setting.</p> <p>Please find the uploaded readme.txt file for more details.</p>
Multi-omic integration of DNA methylation and gene expression data reveals molecular vulnerabilities in glioblastoma (processed data)
<p>Glioblastoma multiforme (GBM) is one of the most aggressive types of cancer and exhibits profound genetic and epigenetic heterogeneity, making the development of an effective treatment a major challenge. The recent incorporation of molecular features into the diagnosis of GBM patients has led to an improved categorisation into various tumour subtypes with different prognoses and disease management. In this work, we have exploited the benefits of genome-wide multi-omic approaches to identify potential molecular vulnerabilities existing in GBM patients. Integration of gene expression and DNA methylation data from both bulk GBM and patient-derived GBM stem cell lines has revealed the presence of major sources of GBM variability, pinpointing subtype-specific tumour vulnerabilities amenable to pharmacological interventions. In this sense, inhibition of the AP1, SMAD3 and RUNX1 / RUNX2 pathways, in combination or not with the chemotherapeutic agent temozolomide, led to the subtype-specific impairment of tumour growth, particularly in the context of the aggressive, mesenchymal-like subtype. These results emphasize the involvement of these molecular pathways in the development of GBM and have potential implications for the development of personalized therapeutic approaches.</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.