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11,105 results for “single cells”
Single cell RNA-seq Data - Dissecting the functional reprogramming of the microenvironment in bone marrow fibrosis at the single cell level
<p>We provide results regarding the bioinformatic analysis of scRNA-seq from distinct bone marrow fibrosis mouse models and human samples.</p> <p> </p> <p>These include:</p> <p>Robjects&Markdown - R markdown and R objects with QC statistics, UMAP and final scRNA-seq data sets.</p> <p>Markers - Excel tables with cluster specific marker genes.</p> <p>DE Genes - Excel table with DE genes when comparing cells in control vs. disease condition per cluster.</p> <p>GO Analysis - Gene enrichment analysis of either DE genes. These are divided by either UP or down regulated genes.</p>
EI Single-Cell RNA-Seq Workshop 2020
<p>Datasets to be used for the "Single-Cell RNA-Seq Workshop 2020" at the Earlham Institute, Norwich, UK.</p>
EpiScanpy: integrated single-cell epigenomic analysis
<p>All files below are in "h5ad" format, which can be opened by the Python package AnnData (see https://anndata.readthedocs.io/en/latest). The files are organized as:</p> <p><br> 1. Single-nucleus methylcytosine sequencing (snmC-seq) of neurons from the frontal cortex of young adult mouse brains from Luo et al., 2017. The files are the preprocessed input for EpiScanpy for multiple CG imputed feature spaces, 100k base pair windows, enhancers, gene bodies, promoters and CH gene bodies.<br> - processed_enhancers_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_genebodies_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_genebodies_CH_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_promoter_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_windows_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> </p> <p>2. Single cell ATAC sequencing (scATAC-seq) from 10X Genomics, preprocessed peak count matrix of Next GEM v1.1 10k Peripheral blood mononuclear cells (PBMCs) from a healthy donor. Peak matrices of Next GEM v1.1 10k PBMCs and whole blood fresh data (GEO:GSE129785) from Satpathy et al. 2019 (Greenleaf's lab). Concatenated 5000bp matrix of Fresh cortex from adult mouse brain (P50) from 10X Genomics and CEMBA180312_3B mouse brain sample from Fang et al. 2019.<br> - atac_pbmc_10k_nextgem_fragments_macs2_peaks_outter_all_chrom.h5ad<br> - atac_pbmc_10k_nextgem_fragments_merged_peaks_for_integration_greenleaf_outter_all_chrom.h5ad<br> - raw_greenleaf_pre_integration_oct_2020.h5ad (Satpathy et al. 2019)<br> - preprocessed_10x_genomics_5k_adulte_mouse_brain_Fang_et_al_2019_CEMBA180312_3B_5kb_windows.h5ad</p>
Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq
<p><strong><a href="https://gitlab.com/bu_cnio/Beyondcell">Beyondcell</a> </strong>is a methodology for the identification of drug vulnerabilities in single cell RNA-seq data. To this end, <strong>Beyondcell</strong> focuses on the analysis of drug-related commonalities between cells by classifying them into distinct therapeutic clusters. We have validated the tool in a population of MCF7-AA cells exposed to 500nM of bortezomib and collected at different time points: t0 (before treatment), t12, t48 and t96 (72h treatment followed by drug wash and 24h of recovery) obtained from <a href="https://www.nature.com/articles/s41586-018-0409-3"><strong><em>Ben-David U, et al., Nature, 2018</em></strong></a>. Here, you can find the integrated Seurat object obtained from this analysis. This object is meant to help users follow <strong>Beyondcell's</strong> <a href="https://gitlab.com/bu_cnio/Beyondcell/-/tree/master/tutorial/analysis_workflow">analysis workflow</a>.</p> <p> </p>
Set of loom files for three Ewing sarcoma cell lines CHLA9, CHLA10, TC71 profiled with scRNASeq at single cell level
<p>The raw sequence files were published in <a href="https://www.mdpi.com/2072-6694/12/4/948">Miller et al, 2020, Cancers</a> . The files were downloaded and processed using kallisto mapper.</p> <p>The loom files were used to build a <a href="https://doi.org/10.1101/2021.06.14.448414">model of cell cycle with switches</a> and in the development of <a href="https://github.com/csgroen/scycle">scycle Python package</a>.</p>
Supplementary Material: Fluorescent Protein‐Tagged Sindbis Virus E2 Glycoprotein Allows Single Particle Analysis of Virus Budding from Live Cells
<p>Supplementary Videos for <em>Viruses</em> <strong>2015</strong>, <em>7</em>(12), 6182-6199; doi:10.3390/v7122926, http://www.mdpi.com/1999-4915/7/12/2926:</p> <p><strong>Video S1A</strong> BHK cells infected with mCherry-E2 virus at 3 h p.i. Glycoprotein containing vesicles are transported to the PM from where individual virions bud out. White arrow point to budding virions. Overall amount of glycoproteins present on the PM and the number of virus particles budding out are relatively reduced compared to the late stage of infection. Images were acquired at a rate of 0.99 fps and 75 frames were acquired. Video was generated using these images and played at a rate of 5 fps. Image acquisition time is shown as Time: hour: minute: second: millisecond (h:min:sec:msec ) and the scale bar represents 10 μm.</p> <p><strong>Video S1B </strong>Enlarged area of video S1A showing budding virus particles from PM. White arrow indicates single particle post-budding moving away from the cell. Images were acquired at a rate of 0.99 fps and 75 frames were acquired. Video was generated using these images and played at a rate of 5 fps. Image acquisition time is shown as Time: hour: minute: second: millisecond (h:min:sec:msec ) and the scale bar represents 10 μm.</p> <p><strong>Video S2A</strong> Virus budding and single particle movement associated with filopodial extensions observed from mCherry-E2 virus-infected BHK cells at 6 h p.i. Glycoprotein containing vesicle transport to the PM is also observed. Budded virions travel along the periphery of filopodia and are released from filopodial extensions to the surrounding media. Image acquisition was at a rate of 1 fps and 285 frames were acquired. Movie was generated using these images and played at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S2B</strong> Enlarged area of video S2A showing budding virus particles from filopodia. White arrow indicates virus budding from filopodial extensions. Images were acquired at a rate of 1 fps and the acquired 285 frames were used to generate the video at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S3</strong> BHK cells transfected with RNA from a non-budding cdE2 mutant <sub>416</sub>CC<sub>417</sub>/A2 mCherry-E2 virus. This non-budding mutant is unable to release fluorescent virus particles from the infected cells. The video shows the absence of fluorescent virus particle budding from the PM at 6 h post transfection even though the PM and filopodial extensions contain mCherry-E2. Despite the transport of glycoproteins to the PM, no fluorescent particles were released into the media. Yellow arrows point toward filopodial extensions. For the video, 304 images were acquired at a rate of 0.98 fps and the video was generated using the acquired images at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S4</strong> BHK cells transfected with RNA from an E1 Fusion loop (G91D) mutant of mCherry-E2 virus at 6 h post transfection. This non-fusing mutant produces fluorescent virus particles at a slower rate compared to WT that are unable to fuse after entering a new cell. White arrow points to fluorescent particles that are releasing into the media from filopodial extensions. Yellow arrow represents a fluorescent particle that had entered an adjacent un-transfected cell. A total of 149 images were acquired at a rate of 0.98 fps. Video was generated using these images at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p> </p> <p><strong>Video S5A</strong> Glycoprotein E2 (mCherry-E2; red) colocalizing with Golgi stain (green) in BHK cells infected with mCherry-E2 virus and stained with BODIPY FL C5 ceramide at 5 h p.i. and imaged at 6 h p.i. Glycoprotein-containing red vesicles originate from Golgi as evidenced from the colocalization of red and green and these vesicles display anterograde transport to the PM and the virus particles are released by budding from the PM. Fluorescent particles are also seen budding from filopodial extensions (white arrows). Images were acquired at a rate of 0.13 fps for 295 seconds. Video was generated using these acquired images at a rate of 5 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S5B</strong> An enlarged area of the video S5A near the white arrow showing movement of particles on filopodial extensions between two cells. Movie was played at a rate of 5 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p> </p> <p> </p>
Direct chromosome-length haplotyping by single-cell sequencing.
<p>Selected Strand-seq libraries from PMID:27646535 study. Data were originally shared on the European Nucleotide Archive (http://www.ebi.ac.uk/ena) under the accession number: PRJEB14185</p>
Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells
<p>scRNA sequencing datasets used in the paper titled 'Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells' published in Nature Communications<br> <br> https://www.nature.com/articles/s41467-020-18155-8</p>
Single-cell datasets for cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2- breast tumor cells
<p>There are 7 files uploaded in the data.</p><p>tumor_preprocessed.h5ad: Full primary tumor dataset post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). AnnData object format. 14 cell cycle features, phase labels and other cell metadata, and two PHATE dimensions for manifold visualization.</p><p>T47D_preprocssed.h5ad: Full dataset of main text T47D dataset post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). AnnData object format. 14 cell cycle features, phase labels and other cell metadata, and two PHATE dimensions for manifold visualization.</p><p>sketched_integrated.h5ad: After downsample 6,000 (2,000 per condition) from T47D_preprocessed and tumor_preprocessed, we integrate the two datasets into one joint latent space using TRANSACT. Now included in the data are the consensus component columns ('0',..,'13'). AnnData object.</p><p>sketched_integrated_df.csv: sketched_integrated.h5ad in .csv format.</p><p>T47D_replicate_preprocessed: Replicate experimental dataset of T47D for supplementary analysis post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). 15 cell cycle features (same 14 but with CDK6).</p><p>sketched_rep.h5ad: Representative downsample of the T47D_replicate_preprocessed. Selecting 6,000 cells (2,000 for each of the three treatment conditions) using kernel herding sketching. AnnData object.</p><p>sketched_rep_df.csv: Same data as sketched_rep.h5ad in csv format.</p><p>T47D_triplicate_preprocessed.h5ad: T47D biological replicate sample collected in triplicate form (three wells for 0, 10, and 100 nM of palbociclib). Wells were joined and the data were sketched down to 20,000 per condition.</p><p>T47D_triplicate_preprocessed.h5ad: T47D triplicate in .csv form.</p><p>tumor_2_preprocessed.h5ad: An additional tumor sample from a new patient with the same treatment conditions of palbociclib. Sketched down to 2,000 cells per condition.</p><p>tumor_2_preprocessed.csv: The additional tumor sample in .csv form.</p><p> </p><p>Further description of sketched_integrated: This is the joint dataset between the T47D and primary tumor, after subsampling using kernel herding sketching. This is a dataset consisting of T47D and primary tumor cells resected from a consented patient. The samples were imaged using iterative indirect immunofluorescent imaging (4i) to get proteomic measurements on a single-cell level. The T47D and tumor samples were gathered, cultured, and imaged separately. Each sample was treated with three conditions of CDK4/6 inhibitor palbociclib (control, 10 nM, and 100 nM). Then, we used kernel sketching to representatively downsample each dataset, selecting 2,000 from each of the three treatment conditions (6,000 cells from each of the two sources). We used an integration method called TRANSACT to integrate the two datasets into one shared, latent space. The dataset here is consisting of these 12,000 cells. The columns ('0','1',...'13') are the principal vectors of the joint latent space. After that, there are the columns of the standardized proteomic measurements of different cell cycle effectors, and biological annotations of interest. The standardization is done for each data source separately. Well refers to the treatment condition. 'prb_ratio' is a marker of if a cell is still proliferating or arrested, found by selecting the upper modality of pRB/RB values. 'phase' are cell cycle phase labels found by unsupervised clustering done on a handful of known cell cycle markers.</p>
Single-cell mouse and PC9 data for "TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response through Chromosomal Instability"
<p>This repository includes the processed data (including copy number profiles and related analysis) for the E/EP mouse tumors and for the PC9 resistance cell lines for all the analyses of the manuscript "TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response through Chromosomal Instability".</p><p>The code for the related analyses is available in GitHub at https://github.com/zaccaria-lab/TP53loss_WGD</p>
Anabaena circadian clock behavior under nitrogen-poor conditions from single-cell measurements of fluorescence intensity
<p>Circadian clock arrays in multicellular filaments of the heterocyst-forming cyanobacterium Anabaena sp. strain PCC 7120 display remarkable spatio-temporal coherence under nitrogen-replete conditions. To shed light on the interplay between circadian clocks and the formation of developmental patterns, we followed the expression of a clock-controlled gene under nitrogen deprivation, at the level of individual cells. Our experiments showed that differentiation into heterocysts took place preferentially within a limited interval of the circadian clock cycle, that gene expression in different vegetative intervals along a developed filament was discoordinated, and that the circadian clock was active in individual heterocysts. Furthermore, Anabaena mutants lacking the kaiABC genes encoding the circadian clock core components produced heterocysts but failed in diazotrophy. Therefore, genes related to some aspect of nitrogen fixation, rather than early or mid-heterocyst differentiation genes, are likely affected by the absence of the clock. A bioinformatics analysis supports the notion that RpaA may play a role as master regulator of clock outputs in Anabaena, the temporal control of differentiation by the circadian clock and the involvement of the clock in proper diazotrophic growth. Together, these results suggest that under nitrogen-deficient conditions, the clock coherent unit in Anabaena is reduced from a full filament under nitrogen-rich conditions to the vegetative cell interval between heterocysts.</p>
Single cell analysis by Quantitative image-based cytometry (QIBC)
<p>Quantitative image-based cytometry (QIBC): Employing automated multichannel wild-field microscopy using the Olympus ScanR screening system. This system includes an inverted motorized Olympus IX83 microscope, a motorized stage, IR-laser hardware autofocus, a fast emission filter wheel with single band emission filters. </p> <p>Images were analyzed and processed using ScanR analysis software and TIBCOSpotfire software was used to plot total nuclear pixel intensities and mean (total pixel intensities divided by nuclear area) nuclear intensities.</p>
Molecular features of luminal breast cancer defined through spatial and single-cell transcriptomics (codes and data files)
<p>This dataset includes all the relevant codes and data files associated with the paper ("Molecular features of luminal breast cancer defined through spatial and single-cell transcriptomics") in Clinical and Translational Medicine journal.</p>
Raw differential gene expression data, data S1, from: Molecular cascades and cell type-specific signatures in ASD revealed by single cell genomics
<p>Genomic profiling in post-mortem brain from autistic individuals has consistently revealed convergent molecular changes. What drives these changes and how they relate to genetic susceptibility in this complex condition is not understood. We performed deep single nuclear RNA sequencing (snRNAseq) to examine cell composition and transcriptomics, identifying dysregulation of cell type-specific gene regulatory networks (GRNs) in autism, which we corroborated using snATAC-seq and spatial transcriptomics. Transcriptomic changes were primarily cell type-specific, involving multiple cell types, most prominently interhemispheric and callosal-projecting neurons, interneurons within superficial laminae, and distinct glial reactive states involving oligodendrocytes, microglia, and astrocytes. Autism-associated GRN drivers and their targets were enriched in rare and common genetic risk variants, connecting autism genetic susceptibility and cellular and circuit alterations in the human brain. This data is the raw differential gene expression comparing ASD versus CTL subjects for each cell cluster. </p>
Full-length, single-cell RNA-sequencing of human bone marrow subpopulations reveals hidden complexity
<p><a href="http://www.biorxiv.org/content/10.1101/2021.07.28.454226v2">Full-length, single-cell RNA-sequencing of human bone marrow subpopulations reveals hidden complexity</a></p> <p>Bone marrow progenitor cell differentiation has frequently been used as a model for studying cellular plasticity and cell-fate decisions. Recent analysis at the level of single-cells has expanded knowledge of the transcriptional landscape of human hematopoietic cell lineages. Using single-molecule real-time (SMRT) full-length RNA sequencing, we have previously shown that human bone marrow lineage-negative (Lin-neg) cell populations contain a surprisingly diverse set of mRNA isoforms. Here, we report from single cell, full-length RNA sequencing that this diversity is also reflected at the single-cell level. From fresh human bone marrow unselected and lineage-negative progenitor cells were isolated by droplet-based single-cell selection (10xGenomics). The single cell-derived mRNAs were analyzed by full-length SMRT and short-read sequencing. In both samples we detected an average of 8000 different genes using short-read sequencing. Differential expression analysis arranged the single-cells of the total bone marrow into only four clusters whereas the Lin-neg population was much more diverse with nine clusters. mRNA isoform analysis of the single-cell populations using full-length sequencing revealed that Lin-neg cells contain on average 24% more novel splice variants than the total bone marrow cells. Interestingly, among the most frequent genes expressing novel isoforms were members of the spliceosome, e.g. HNRNPs, DEAD box helicases and SRSFs. Mapping the isoforms from all genes to the cell type clusters revealed that total bone marrow cells express novel isoforms only in a small subset of clusters. On the other hand, lineage-negative progenitor cells expressing novel isoforms were present in nearly all subpopulations. In conclusion, on a single-cell level lineage-negative cells express a higher diversity of genes and more alternatively spliced novel isoforms suggesting that cells in this subpopulation are poised for different fates. </p> <p> </p>
Single-cell RNA-seq profiles of lung adenocarcinoma patients and tumor-bearing mice
<p>single-cell RNA sequencing (scRNA-seq) profiles from eight patients with lung adenocarcinoma (LUAD) and four samples of tumor tissues from tumor bearing mice were performed. By integrating other scRNA-seq data and clinical information, we identified activated adaptive immune responses in older patients, reflected by enriched dysfunctional T cell signature scores and immune checkpoint molecules. Our study shows increased efficacy of immune checkpoint blockade therapy in older patients, addressing the prominent role of age when considering immunotherapy.</p>
Multi-cell type deconvolution using a probabilistic model for single-molecule DNA methylation haplotypes
<p>Files required to run deconvolution with CelFIE-ISH and Epistate, in U250 regions from Loyfer et al. 2023, in both "pat" and "epiread" formats. </p>
Automatic monitoring of neural activity with single-cell resolution in behaving Hydra
<p>The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, we report coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian <em>Hydra vulgaris</em>. We imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic <em>Hydra </em>and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neuron's calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in <em>Hydra</em>, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal.</p>
Single-cell sequencing data of human umbilical cord and placental mesenchymal stem cells
<p>Expression matrix of umbilical cord and placenta single-cell sequencing data from the same donor.Table1 is the umbilical cord and Table2 is the placenta.</p>
Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance
<p>The below data are associated with our paper entitled "Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance."</p> <p>1) SNP-gene link predictions generated by pgBoost and existing methods SCENT (Sakaue et al. 2024 <em>Nat Genet</em>), Signac (Stuart et al. 2021 <em>Nat Methods</em>), ArchR (Granja et al. 2021 <em>Nat Genet</em>), and Cicero (Pliner et al. 2018 <em>Mol Cell</em>).</p> <p><strong>pgBoost_scores.tsv.gz </strong>contains linking predictions made by pgBoost.</p> <p><strong>constituent_method_scores.tsv.gz</strong> contains linking predictions made by constituent methods.</p> <p><em><span>**NOTE: promoters (+/- 1kb from TSS) and candidate links >500kb are excluded from linking predictions (see manuscript)**</span></em></p> <p>Linking scores and percentiles are reported for each method (pgBoost score, SCENT FDR, Signac correlation, ArchR correlation, Cicero co-accessibility). Rank percentiles are computed as: 1 - (rank / n). When multiple links receive the same score, they are assigned the percentile of the top rank. Links unscored by each method (denoted by zeros* in the linking score column) are assigned a percentile equivalent to the percent of links unscored by the focal method. See the Methods section of the paper for further details on computing linking scores and summarizing scores across cell types and data sets.</p> <p>*Candidate links tested and assigned a co-accessibility of zero by the Cicero method are given a score of 1e-100 in the "Cicero" column to distinguish between unscored candidate links and candidate links assigned a partial correlation of zero (see Pliner et al. 2018 <em>Mol Cell</em>).</p> <p><em>NOTE: The predictions associated with this release (version 2) were generated using an expanded set of data sets, an expanded training set, and corrected TSS coordinates.</em></p> <p>2) GWAS-derived evaluation SNP-gene link evaluation set.</p> <p><strong>gwas_evaluation.tsv</strong>: GWAS-derived evaluation SNP-gene link evaluation set. Column 1 provides SNP coordinates in the format <chr-start-end>. This evaluation framework was proposed by Weeks et al. 2024 <em>Nature Genetics</em> based on fine-mapping results from Kanai et al. <em>medrxiv</em> (see Methods: <em>Evaluation data sets</em> of Dorans et al.). "True" links (gold = 1) are non-coding variants fine-mapped to a focal trait (PIP > 0.1) with a coding variant for exactly one candidate gene within 1 Mb attaining PIP > 0.5 for the same trait. "False" links (gold = 0) are candidate SNP-gene pairs involving a SNP with a "true" link. This file of SNP-gene links was adapted from credible set-gene links <a href="https://github.com/Deylab999/GWAS_benchmark_IGVF/blob/bb91d08cc02d59cdd829eb1430569057ac26c5fe/V2G/ENCODE_E2G_2023/UKBiobank.ABCGene.anyabc.tsv">here</a> (the "truth" column defines true/false links) by identifying SNPs with PIP > 0.1 within each credible set-gene link.</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.