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679 results for “Gut microbiome”
Full-length and split homologs of human proteins in the gut microbiome
<p>These files were generated as part of the manuscript "Human xenobiotic metabolism proteins have full-length and split homologs in the gut microbiome" (submitted).</p> <p>The .tar file contains .ipc files that are tables of full-length (full_humcover3.ipc) and split homologs (part_humcover3.ipc) of human proteins in the gut microbiome, organized by alignment coverage threshold. For example, the directory `HumanUPR_0.67_src_20000_70` contains results obtained at a 67% alignment coverage threshold for the bacterial protein, and 70% for the human protein. Note that our pipeline collapses full-length alignments to the same UHGP-90 protein family into a single entry per species, with the number of genomes reported in the column nGenomes. Split homologs are not collapsed because genomic context is used to define them, and this context may differ across individual genomes.</p> <p>These files are in Arrow <a href="https://arrow.apache.org/docs/python/ipc.html#ipc">IPC</a> format, which provides compression and fast I/O for large tables. We recommend reading them using <a href="https://pola.rs/">pola.rs</a> or the <a href="https://arrow.apache.org/docs/r/">R Arrow</a> package. In particular, because the full-length homolog table is large, you may wish to work with it without loading it into memory, which can be accomplished using <a href="https://docs.pola.rs/api/python/dev/reference/api/polars.scan_ipc.html">scan_ipc</a> in pola.rs or <a href="https://arrow.apache.org/docs/r/reference/open_dataset.html">open_dataset</a> in R Arrow.</p> <p>We also provide gzipped .csv format datasets of full-length (pgkb_FH_drugs.csv.gz) and split (pgkb_SH_drugs.csv.gz) homologs, at the default 67% alignment coverage threshold for bacterial and 70% for human proteins, organized by their <a href="https://www.pharmgkb.org/">PharmGKB</a> annotations. For each drug annotated in PharmGKB as being metabolized by a human protein with full-length or split homologs, we provide the human protein(s) responsible, its xenobiotic enzyme class, the bacterial protein homolog(s), length and percent identity of the alignment, and either the specific genome (g, split homologs only) or the number of genomes (nGenomes, full homologs only). Xenobiotic enzyme classes are defined as in Figure 4 of the manuscript, with the additional classes "nucl" (nucleobase-containing metabolic proteins not annotated to any other class), "redox" (oxidoreductases not annotated to any other class), and "other" (all remaining proteins).</p>
Dietary fibers boost gut microbiome-produced B vitamin pool and alter host immune landscape
<p>This dataset contains fcs files of lymphocytes from the colonic lamina propria, lungs, and spleens of specific-pathogen-free (SPF), gnotobiotic (14-member synthetic microbiota, 14SM) or germ-free (GF) mice fed five distinct rodent diets (Standard chow 1, SC1; Standard chow 2, SC2; Fiber-supplemented diet, FS; Inulin-supplemented diet, IN; or Fiber-free diet, FF), analysed by mass cytometry. Three million cells per organ per animal were transferred into 15 mL conical tubes. For live/dead staining, cells were incubated with 5 μM cisplatin for 5 minutes. Cells were washed, and cell surface staining mix was added containing pre-conjugated antibodies for 30 minutes at room temperature. Samples were washed twice with FACS buffer, then fixed using the FoxP3 Fix/Perm kit (eBiosciences) for 45 minutes at 4°C, followed by permeabilization wash. Samples were then incubated with the intracellular staining mix for 30 minutes at room temperature. Cells were washed with FACS buffer twice, and pellets were resuspended in Cell-ID™ Intercalator-Ir (Fluidigm) in MaxPar fixation solution (Fluidigm, catalogue no. 201192B) and refrigerated overnight, or for up to five days. Prior to acquisition, samples were washed twice with 1X PBS, and then washed twice with deionized water. Cell pellets were further resuspended in deionized water at 0.5 × 10^6 cells/mL and topped up with 10% calibration beads (EQ Four Element Calibration Beads, Fluidigm). All samples were acquired on the Helios Mass Cytometer (Fluidigm). Effector immune populations and activated T cells in the gut accumulate in a microbiota-dependent manner. Shifts in the microbiome according to dietary fiber source and content result in altered concentrations of B vitamins available to the host, which is tied to distinct alterations in innate and adaptive immune populations. </p>
Experimental Factors Influence Diversity Metrics of the Gut Microbiome in Laboratory Mice
<p>Abstract<br> Introduction</p> <p>Gut microbiome studies often overlook experimental factors that could influence gut microbiome diversity and could impact findings. Large-scale studies investigating these experimental factors are lacking. Thus, we aimed to determine which experimental factors influence the gut microbiome diversity in pre-clinical animal model studies.</p> <p><br> Methods</p> <p>We extracted DNA and sequenced the V4 region of the 16S rRNA gene of a total of 538 samples from various sections of the gastrointestinal tract of 303 young and aged male and female C57BL/6J mice of three different genotypes on five diets from three animal house facilities. As a proof-of-concept in a disease model, some mice were treated with sham or angiotensin II, a commonly studied agent used as a hypertension model. Some samples were sequenced twice as a matched-comparison group.</p> <p>Results</p> <p>Using over 17 million sequencing reads, we found that experimental factors such as animal house facility, genotype, diet, age, sex, sampling site, and technical factor (i.e., sequencing batch) affected both α- and β-diversity (weighted and unweighted UniFrac), and were associated with compositional changes in the microbiome at varying magnitude, with diet and sampling site having the largest effect. After adjustment by these factors, treatment with angiotensin II had no impact on α-diversity and was only significant in unweighted UniFrac (presence/absence of bacteria) analyses.</p> <p><br> Conclusion</p> <p>Our data identified several key experimental and technical factors that affect the gut microbiome in laboratory mice. Our findings support that not accounting or adjusting for these factors may lead to false-positive discoveries and non-biologically relevant findings in the gut microbiome field.</p>
Animal Gut Microbiome (AGM) Data from 91 Published Studies for 224 Animal Species
Diversity and heterogeneity often are conflated but are fundamentally different. An aphorism proposed by Shavit and Ellison (2021; J. Phil. 118: 525–548) for distinguishing them is that “a zoo is diverse whereas an ecosystem is heterogeneous.” That is, a zookeeper measuring diversity simply enumerates the different types of animals; interactions are not expected to occur between animals separated by fences or other barriers. In contrast, measures of heterogeneity ought to include both interspecific interactions and relationships between species and their heterogeneous habitats. Here, we use cross-scale, dual scaling-law analyses of heterogeneity and diversity of animal gut microbiomes (AGMs) to address three objectives: (i) estimate the spatial heterogeneity and diversity of animal-gut microbiomes; (ii) analyze influences of phylogeny and diets on scaling of diversity and heterogeneity; (iii) explore mechanistic differences between diversity and heterogeneity in AGMs. From 4903 AGM samples collected from 318 animal species covering all six classes of vertebrates and four major classes of invertebrates, we estimated that ≈640,000 operational taxonomic units (OTUs or “species”) make up the pool of microbial species that could inhabit animal guts, among which ≈8000 are relatively common and ≈800 are dominant. The gut of any single animal, however, includes only 0.01–0.5% of the total species pool. We extended Ma’s diversity-area relationship for scaling diversity and extend Taylor’s Power Law and Luna et al.’s (2020; Diversity 12: 86) interaction diversity for scaling heterogeneity. At the community scale, phylogeny significantly influenced heterogeneity, but diets did not. Phylogeny and diets had limited influence on diversity at both community and landscape scales. Although two common measures of diversity—beta diversity and unevenness—commonly are synonymized with heterogeneity, our data lead us to conclude that diversity and heterogeneity measure two very different
Helminths, polyparasitism, and the gut microbiome in the Philippines
<p>Uploaded here are the raw FASTA files associated with this publication. Labelled P1-10, and 11-220.</p> <p><a href="https://zenodo.org/api/files/e3103a63-45d0-4b24-9786-d4f3a0f6534b/OTU%20tables%2C%20annotations%20and%20code%20book.zip">OTU tables, annotations and code book.zip</a> Annotations and OTU files for International Journal of Parasitology paper "Helminths, Polyparasitism, and the Gut Microbiome in the Philippines". Data set from human helminth infections from Palapag, the Philippines. Code book contains information on headings for the annotation file. OTUs generated by AGRF. Originally uploaded here; (<a href="http://dx.doi.org/10.17632/59j46prhvf.1">https://doi.org/10.17632/59j46prhvf.1</a>) </p> <p> </p> <p> </p>
Psoriasis is associated with elevated gut IL-1α and intestinal microbiome alterations
<p>Background: Psoriasis is a chronic inflammatory condition that predominantly affects the skin and is associated with extracutaneous disorders, such as inflammatory bowel disease and arthritis. Changes in gut immunology and microbiota are important drivers of proinflammatory disorders and could play a role in the pathogenesis of psoriasis. Therefore, we explored whether psoriasis in a Central Asian cohort is associated with alterations in select immunological markers and/or microbiota of the gut. Methods: We undertook a case-control study of stool samples collected from outpatients, aged 30-45 years, of a dermatology clinic in Kazakhstan presenting with plaque, guttate or palmoplantar psoriasis (n=20), and age-sex matched subjects without psoriasis (n=20). Stool supernatant was subjected to multiplex ELISA to assess the concentration of 47 cytokines and immunoglobulins and to 16S rRNA gene sequencing to characterize microbial diversity in both psoriasis participants and controls. Results: The psoriasis group tended to have higher concentrations of most analytes in stool (29/47=61.7%) and gut IL-1α was significantly elevated (4.19-fold, p=0.007) compared to controls. Levels of gut IL-1α in the psoriasis participants remained significantly unaltered up to three months after the first sampling (p=0.430). Psoriasis was associated with alterations in gut Firmicutes, including elevated Faecalibacterium and decreased Oscillibacter and Roseburia abundance, but no association was observed between gut microbial diversity or Firmicutes/Bacteroidetes ratios and disease status.<br> Conclusions: Psoriasis may be associated with gut inflammation and dysbiosis. Studies are warranted to explore the use of gut microbiome-focused therapies in the management of psoriasis in this under-studied population.</p>
Upcycling Human Excrement: The Gut Microbiome to Soil Microbiome Axis (supporting data)
<div> <div>This archive contains the supporting data and code for <a href="https://doi.org/10.1093/ismeco/ycaf089" target="_blank" rel="noopener">Meilander et al., 2024: <em>Upcycling Human Excrement: The Gut Microbiome to Soil Microbiome Axis</em></a>.</div> <div> </div> <div><strong>Clicking the links below will open the corresponding files using QIIME 2 View (<a href="https://view.qiime2.org" target="_blank" rel="noopener">https://view.qiime2.org</a>). </strong></div> <div> </div> <div> <div> <div>Summaries of master data files:</div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/asv-table.qzv/content" target="_blank" rel="noopener">Summary of master feature table (<code>asv-table.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/sample-metadata.qzv/content">Tabulated view of sample metadata (<code>sample-metadata.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/records/15390940/files/asv-seqs-ms10.qzv?download=1" target="_blank" rel="noopener">Summary of ASV sequences observed in at least 10 samples: (<code>asv-seqs-ms10.qzv</code>)</a></div> <div> </div> <div>PCoA plots:</div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/braycurtis.qzv/content" target="_blank" rel="noopener">Bray-Curtis Emperor plot (<code>braycurtis.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/jaccard.qzv/content" target="_blank" rel="noopener">Jaccard Emperor plot (<code>jaccard.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/unweighted_unifrac.qzv/content" target="_blank" rel="noopener">Unweighted UniFrac Emperor plot (<code>unweighted_unifrac.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/weighted_unifrac.qzv/content" target="_blank" rel="noopener">Weighted UniFrac Emperor plot (<code>weighted_unifrac.qzv</code>)</a></div> <div> </div> <div>Taxonomy barplots:</div> <div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/records/15390940/files/taxa-bar-plots-bucket2-gtdb-r214.1-weighted-stool-taxonomy.qzv?download=1">Taxonomy bar plot for Bucket 2 only (<code>taxa-bar-plots-bucket2-gtdb-r214.1-weighted-stool-taxonomy.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/records/15390940/files/taxa-bar-plots-bucket3-gtdb-r214.1-weighted-stool-taxonomy.qzv?download=1" target="_blank" rel="noopener">Taxonomy bar plot for Bucket 3 only (<code>taxa-bar-plots-bucket3-gtdb-r214.1-weighted-stool-taxonomy.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/taxa-bar-plots-gtdb-r214.1-weighted-stool-taxonomy.qzv/content" target="_blank" rel="noopener">Taxonomy bar plot for all samples (<code>taxa-bar-plots-gtdb-r214.1-weighted-stool-taxonomy.qzv</code></a>)</div> </div> <div> </div> <div>q2-fmt "raincloud plots":</div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/hec-raincloud.qzv/content">Raincloud plot (<code>hec-raincloud.qzv</code>)</a></div> <div> </div> </div> </div> <div> </div> <div>The linked <code>.qzv</code> files are also contained in the <code>gut-to-soil-qiime2.zip</code> zip file, along with all relevant data artifacts (<code>.qza</code> files).</div> <div>The <code>.qzv</code> files are also maintained outside of the <code>.zip</code> file to facilitate their viewing with QIIME 2 View.</div> </div> <div> </div> <div> <div>Code for generating figures 1 and 2 (and corresponding supplemental figures):</div> <div><code>gut-to-soil-manuscript-figures-main.zip</code> (also see: <a href="https://github.com/caporaso-lab/gut-to-soil-manuscript-figures" target="_blank" rel="noopener">https://github.com/caporaso-lab/gut-to-soil-manuscript-figures</a>)</div> <div> </div> <div>Code for generating ridgeline plots (Figure S6):</div> <div><code>gut-to-soil-ridgeline-plots-main.zip</code> (also see: <a href="https://github.com/caporaso-lab/gut-to-soil-ridgeline-plots" target="_blank" rel="noopener">https://github.com/caporaso-lab/gut-to-soil-ridgeline-plots</a>)</div> </div> <div> <div> </div> </div>
Altered infective proficiency of the gut microbiome following COVID-19
<p><strong>The effects of SARS-CoV-2 infections comprise of many heterogeneous symptoms including several involving the human gastrointestinal tract. We assess the effects of COVID-19 on the host microbiome</strong></p>
The effect of dietary bioactive on gut microbiome diversity (DIME) – a pilot study
<p>The DIME study consists of a randomised 2x2 cross-over human intervention where healthy participants (n = 20) are subjected to a diet high in bioactive-rich food for two weeks and a diet low in bioactive-rich food. There is a four-week washout between the two interventions. </p> <p>The continuous glucose monitoring was achieved using the Abbott freesylte libre flash glucose device. The baseline of the participants were determined 7 days before the start of the intervention, followed by the first arm and second arm. The period between the two arms (washout) was not recorded.</p> <p>We also included sleep data which consists of the amount of time spent in bed and during that time the amount of time spent in light, deep and rem in all 20 participants during the course of the dietary intervention, both the high and low bioactive diet. that was captured using Fitbit wearables during both stages of the dietary intervention,</p>
Reproductive hormones mediate changes in the gut microbiome during pregnancy and lactation in Phayre's leaf monkeys
Studies in multiple host species have shown that gut microbial diversity and composition change during pregnancy and lactation. However, the specific mechanisms underlying these shifts are not well understood. Here, we use longitudinal data from wild Phayre's leaf monkeys to test the hypothesis that fluctuations in reproductive hormone concentrations contribute to gut microbial shifts during pregnancy. We described the microbial taxonomic composition of 91 fecal samples from 15 females (n=16 cycling, n=36 pregnant, n=39 lactating) using 16S rRNA gene amplicon sequencing and assessed whether the resulting data were better explained by overall reproductive stage or by fecal estrogen (fE) and progesterone (fP) concentrations. Our results indicate that while overall reproductive stage affected gut microbiome composition, the observed patterns were driven by reproductive hormones. Females had lower gut microbial diversity during pregnancy and fP concentration was negatively correlated with diversity. Additionally, fP concentration predicted both unweighted and weighted UniFrac distances, while reproductive state only predicted unweighted UniFrac distances. Seasonality (rainfall and periods of phytoprogestin consumption) additionally influenced gut microbial diversity and composition. Our results indicate that reproductive hormones, specifically progestagens, contribute to the shifts in the gut microbiome during pregnancy and lactation.
MMGC: custom Kraken2/Bracken database for analysing the mouse gut microbiome
<p>Custom Kraken2/Bracken database built using the representative genomes for 1,021 microbial species from the mouse gut microbiota. Genomes include isolates and MAGs, but all are near-complete (>90% completeness; <5% contamination; maximum genome size ≤ 8 Mb; maximum contig count ≤ 500; N50 ≥ 10 kb; mean contig length ≥ 5 kb). This database achieved a mean read classification rate of 87.7% when benchmarked on 1,785 independent (i.e. non-contributory) mouse gut shotgun metagenome samples. An equivalent human database (UHGG) only attained classification rates of 36.6%.</p> <p>This database is a publicly available resource to facilitate more efficient/deeper analyses of mouse gut shotgun metagenomes.</p> <p>Find out more about the Mouse Microbial Genome Collection at our <a href="https://github.com/BenBeresfordJones/MMGC">GitHub repository</a>.</p>
Putative mobilized colistin resistance (mcr) genes co-occurring with other antibiotic resistance genes are widespread in the human gut microbiome
<p><strong>The dataset from the article </strong><strong>Putative mobilized colistin resistance (mcr) genes co-occurring with other antibiotic resistance genes are widespread in the human gut microbiome</strong></p>
Assembly-based analysis of the infant gut microbiome reveals novel ubiquitous plasmids
<p>Assembly-based plasmids found in the gut microbiome of 12 infants born in Norway (BabyBiome project).</p>
Maternal body condition affects the response of the gut microbiome to a widespread contaminant in larval spined toads
<p>Datasets (metadata and phyloseq object) </p> <p>Scripts used for the statistical analyses</p>
The gut microbiome reflects ancestry despite dietary shifts across a hybrid zone
<p>The microbiome is critical to an organism's phenotype, and its composition is shaped by, and a driver of, eco-evolutionary interactions. We investigated how host ancestry, habitat, and diet shape gut microbial composition in a mammalian hybrid zone that occurs across an ecotone between distinct vegetation communities. We found that habitat is the primary determinant of diet, while host genotype is the primary determinant of the gut microbiome—a finding further supported by intermediate microbiome composition in first generation hybrids. Despite these distinct primary drivers, microbial richness was correlated with diet richness, and individuals that maintained higher dietary richness had greater gut microbial community stability. Both relationships were stronger in the relative dietary generalist of the two parental species. Our findings show that host ancestry interacts with dietary habits to shape the microbiome, ultimately resulting in the organismal phenotypic plasticity that host-microbial interactions allow.</p>
Data from: Longitudinal gut microbiome dynamics in relation to age and senescence in a wild animal population
<p>In humans, gut microbiome (GM) differences are often correlated with, and sometimes causally implicated in, ageing. However, it is unclear how these findings translate in wild animal populations. Studies that investigate how GM dynamics change within individuals, and with declines in physiological condition, are needed to fully understand links between chronological age, senescence, and the GM, but have rarely been done. Here, we use longitudinal data collected from a closed population of Seychelles warblers (<em>Acrocephalus sechellensis</em>) to investigate how bacterial GM alpha diversity, composition, and stability are associated with host senescence. We hypothesised that GM diversity and composition will differ, and become more variable, in older adults, particularly in the terminal year prior to death, as the GM becomes increasingly dysregulated due to senescence. However, GM alpha diversity and composition remained largely invariable with respect to adult age and did not differ in an individual's terminal year. Furthermore, there was no evidence that the GM became more heterogenous in senescent age groups (individuals older than 6 years), or in the terminal year. Instead, environmental variables such as season, territory quality, and time of day, were the strongest predictors of GM variation in adult Seychelles warblers. These results contrast with studies on humans, captive animal populations, and some (but not all) studies on non-human primates, suggesting that GM deterioration may not be a universal hallmark of senescence in wild animal species. Further work is needed to disentangle the factors driving variation in GM-senescence relationships across different host taxa.</p>
Results of a Galaxy metagenomic analysis of bee gut microbiome data from PRJNA977416
<p>This dataset contains the outputs of a metagenomic Galaxy workflow run on the raw data of the project PRJNA977416, including the CSV file of associated metadata and the workflow.ga used for the analysis.</p> <p>Firstly, it has information on taxonomic assignment with :</p> <ul> <li>the reports of all samples for Kraken2, Bracken, and MetaPhlan taxonomic profilers. </li> <li>two tabular files obtained with Taxpasta, which merge samples and standardize taxonomic abundances.</li> <li>for the Bracken standardised abundance, a file with the measures of alpha diversity calculated </li> <li>two HTML files giving access to the Krona diagram for this taxonomic composition.</li> </ul> <p>Secondly, it contains functional informations with :</p> <ul> <li>a tabular file with the relative abundance of all GO terms for all samples</li> <li>a directory detailing pathways and genes families detected.</li> </ul>
Dog gut gene catalog. Supplemental data for "Similarity of the dog and human gut microbiomes in gene content and response to diet"
<p>Gene catalogue for the dog gut microbiome including</p> <ol> <li>FASTA file of nucleotide sequences (including padding, see coords file for exact coordinates)</li> <li>FASTA file of amino-acid sequences</li> <li>coords file (gene coordinates)</li> <li>Taxonomic predictions</li> <li>Functional predictions</li> </ol> <p>See the paper "<em>Similarity of the dog and human gut microbiomes in gene content and response to diet</em>" by Coelho et al. in Microbiome for details. We ask that you cite that publication when using this dataset in published literature</p>
bin3C - Cluster report and CheckM result for bin3C solution of the real human gut microbiome
<p>Supplementary data table S3 from the manuscript</p> <p>bin3C : Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs)</p> <p>A real human gut microbiome was deconvoluted using bin3C. Subsequently, bin3C produced a report detailing per-cluster statistics for the entire solution. The largest 296 clusters were then analyzed with CheckM and joined to the report.</p>
A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals (16S rRNA gene sequencing data)
<p>Microbiome data accompanying manuscript "A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals". Data is available for alpha- and beta- diversity, as well as for individual taxa both in binary and quantitative phenotypic representation. Data is available for 827 individuals that gave consent for their data to be shared outside of the Milieu intérieur consortium. </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.