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196 results for “16S rRNA”
Chemical composition, soil water content and 16S rRNA and ITS gene copy numbers of soil aggregates and bulk soil samples
<p>This repository contains all data to reproduce the analyses presented in "Distinct microbial communities are linked to organic matter properties in millimetre-sized soil aggregates", Simon et al 2024, <em>The ISME Journal </em>(DOI: 10.1093/ismejo/wrae156).</p>
DADA2 formatted 16S rRNA gene sequences for both bacteria & archaea
<p><strong><em>This version is to stay up to date with the improvements and increase in 16S rRNA gene sequences (SSU) added to the GTDB release 220. Please read this post for the stats on the updates. </em></strong><strong><em>https://gtdb.ecogenomic.org/stats/r220 </em></strong><strong><em>.</em></strong><strong><em> </em></strong></p> <p><strong><em>There has been no change to the RDP-RefSeq reference database please use previous versions.</em></strong></p> <p><strong><em>If anyone has concerns with MAG extracted 16S rRNA gene contamination concerns, then I suggest that they contact the curators of GTDB themselves because it is outside of my role with these resources designed for DADA2 usage only. </em></strong></p> <p><strong><em>Another concern that was raised was the orientation of the DB sequences, to get past this problem please use the tryRC = TRUE argument in the assignTaxonomy command within DADA2, this will search your ASVs in the reverse complement as well. </em></strong></p> <p>The bacterial and archaeal 16S rRNA gene sequence databases were collated from various sources and formatted to use the "assignTaxonomy" command within the DADA2 pipeline. The data was converted to suite DADA2 format by Alishum Ali.</p> <ol> <li>Genome Taxonomy Database (GTDB): The new version of our dada2 formatted GTDB reference sequences now contains 58102 bacteria and 3672 archaea full 16S rRNA gene sequences. If you wonder why there are fewer species with 16S rRNA, that is because some metagenomics-assembled genomes (MAGs) lack the 16S gene and thus cannot be extracted. The database was downloaded from <a href="https://data.ace.uq.edu.au/public/gtdb/data/releases/release95/">https://data.ace.uq.edu.au/public/gtdb/data/releases/</a> on 24/10/2024. Please read the release notes and file descriptions. </li> </ol> <p>The formatting to DADA2 was done using simple awk bash scripts. The script takes as input a fasta file and a tab-delimited taxonomy file (slightly edited to remove special characters) and then it outputs a fasta file with all 7 taxonomy ranks separated by ";" as required for DADA2 compatibility. Additionally, we have concatenated the unique sequence GTDB ID to the species entry (but replaced the "." with an " _". We see this as an important QC step to highlight the issues/confidence associated with short-read taxonomy assignment at the finer rank levels.</p> <p>Also, this update includes two other files that you can use with the assignTaxonomy and addSpecies commands in DADA2.</p>
DADA2 formatted eHOMD 16S rRNA gene sequences databse
<p>eHOMD Refseq database (V15.22) formated to be used with dada2 <em>i.e.</em>, dada2::assignTaxonomy(seqs, "eHOMD_RefSeq_dada2_V15.22.fasta.gz" ) and dada2::addSpecies(taxa, "eHOMD_RefSeq_dada2_assign_species_V15.22.fasta.gz", verbose=TRUE)</p> <p>Alternatively, you could use the metabaRpipe R package to directly update the taxonomy of a phyloseq object see: https://github.com/fconstancias/metabaRpipe#2-addingreplacing-taxonomical-table-in-a-phyloseq-object</p> <p>Example below:<br> source("https://raw.githubusercontent.com/fconstancias/metabaRpipe-source/master/Rscripts/functions.R")</p> <p>readRDS("dada2/phyloseq.RDS") %>%<br> phyloseq_dada2_tax(physeq = .,<br> threshold = 60,<br> db ="~/metabaRpipe/databases/eHOMD_RefSeq_dada2_V15.22.fasta.gz",<br> db_species ="~/metabaRpipe/databases/eHOMD_RefSeq_dada2_assign_species_V15.22.fasta.gz",<br> nthreads = 2,<br> full_return = FALSE) -> physeq_eHOMD_tax</p> <p> </p> <p> </p>
Catalog of GenBank sequence read archive (SRA) entries of 16S and 18S rRNA genes from bacterial and protistan planktonic communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2011-2013
Microbial communities in the coastal Arctic Ocean experience extreme variability in organic matter and inorganic nutrients driven by seasonal shifts in sea ice extent and freshwater inputs. Lagoons border more than half of the Beaufort Sea coast and provide important habitats for migratory fish and seabirds; yet, little is known about the planktonic food webs supporting these higher trophic levels. To investigate seasonal changes in bacterial and protistan planktonic communities, amplicon sequences of 16S and 18S rRNA genes were generated from samples collected during periods of ice-cover (April), ice break-up (June), and open water (August) from shallow lagoons along the eastern Alaska Beaufort Sea coast from 2011 through 2013. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA530074 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA530074. This data package is associated with the following publication: Kellogg CTE, McClelland JW, Dunton KH and Crump BC (2019) Strong Seasonality in Arctic Estuarine Microbial Food Webs. Front. Microbiol. 10:2628. doi: 10.3389/fmicb.2019.02628 Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provided site codes (column "site_name" here) and collection dates (column "collection_date" here) in each dataset. Note that the site codes in this package are without hyphens (e.g. JAA) while site codes in the above environmental data package have hyphens (e.g. JA-A). Instead of citing this package which is jus
Inventory of High-resolution phylogenetic profiles of the planktonic microbial communities (via 16S and 18S rRNA gene amplicons) from Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, 2017 - ongoing
Planktonic microbial communities mediate many vital biogeochemical processes in wetland ecosystems, yet compared to other aquatic ecosystems, like oceans, lakes, rivers, or estuaries, they remain relatively underexplored. Our study site, the Florida Everglades (USA)—a vast iconic wetland consisting of a slow-moving system of shallow rivers connecting freshwater marshes with coastal mangrove forests and seagrass meadows—is a highly threatened model ecosystem for studying salinity and nutrient gradients, as well as the effects of sea level rise and saltwater intrusion. This dataset provides the first high-resolution phylogenetic profiles of planktonic bacterial and eukaryotic microbial communities (using 16S and 18S rRNA gene amplicons) from these environments. The dataset contains 16S and 18S rRNA data from 2017, and contains 16S rRNA data for monthly (2019) and quarterly water samples (2020-ongoing). The 2017 data are published in Laas et al. 2022. A detailed list of sequence data and their accession numbers in GenBank is provided and will be updated as more data are published. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA525456 (at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA525456) and BioProject PRJNA1018945 (at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1018945). This data package is associated with the following publication: Laas, P., Ugarelli, K., Travieso, R., Stumpf, S., Gaiser, E. E., Kominoski, J. S., & Stingl, U. (2022). Water column microbial communities vary along salinity gradients in the Florida Coastal Everglades wetlands. Microorganisms, 10(2), 215. https://doi.org/10.3390/microorganisms10020215 Instead of citing this package, which is an inventory, please cite the original GenBank data or journal article, as appropriate. Citation guidance for the journal article is available on the respective publisher's website.
16S rRNA gene sequence accessions from discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (2013-2023, ongoing)
An important component of the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) project is monitoring spatial and temporal patterns in the biological composition of perennially ice-covered lakes in Antarctica’s McMurdo Dry Valleys. This data package contributes to this core research area by providing a curated table linking 16S rRNA gene sequence accession numbers archived in NCBI to MCM LTER limnological sampling campaigns conducted at specific depths along the water column of Lakes Fryxell, Hoare, Bonney, and Miers. These data enable integration of microbial community data with co-collected biological, chemical, and physical measurements.
Human intestinal Bacteria Collection (HiBC): 16S rRNA gene sequences
<p>The <a href="https://hibc.rwth-aachen.de/" target="_blank" rel="noopener">Human intestinal Bacteria Collection (HiBC)</a> is a collection of bacterial strains, isolated from the human gut for which 16S rRNA gene sequences, genome sequences and culture conditions are made available to the research community. In addition to previously described bacteria, we include strains that represent novel species which have been taxonomically described and validly named, or will be in the future. This collection will be updated regularly.</p> <p>This dataset includes the sequences of the 16S rRNA gene sequences of the isolates in the FASTA nucleotide format. Sequences ending in Sanger were obtained using the Sanger dideoxy sequencing technology. Sequences ending in Genome were obtained from the genome sequence using barrnap.</p>
EAW FASTQ files for bioinformatic courses (16S rRNA genes, 2018)
<p>Selection of 6 samples, with 6 Forward (R1) and 6 Reverse (R2) files, including primers. R1 and R2 reads are ca. 300 bp long, and were obtained from Illumina MiSeq technologies, at the FEM facility sequencing platform. The files refer to the16S rRNA gene reads obtained from the analyses carried out on the samples collected and filtered (Sterivex<sup>TM</sup> 0.22 µm) in different areas and depths of Lake Garda on September, 2018 (see EAW_2018_FASTQ_16S_description.docx).</p> <p>Sampling and analyses were carried out in the framework of the project Eco-AlpsWater (ASP569), funded by the Interreg Alpine Space program.</p> <p>A bioinformatic protocol for analyzing these data using DADA2 is available in Zenodo:</p> <pre>https://doi.org/10.5281/zenodo.5232772 </pre>
16S rRNA sequencing gene datasets for CRC data
<p>Used datasets: </p> <table> <thead> <tr> <th scope="col"> <table> <thead> <tr> <th>Dataset</th> <th>16S rRNA Region</th> <th>Control (n)</th> <th>Adenoma (n)</th> <th>CRC (n)</th> <th>Available metadata</th> </tr> </thead> <tbody> <tr> <td><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4823848/">Baxter</a></td> <td>V4</td> <td>171</td> <td>198</td> <td>120</td> <td>Gender, age, weight, height, BMI, country, race</td> </tr> <tr> <td><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4221363/">Zackular</a></td> <td>V4</td> <td>30</td> <td>30</td> <td>30</td> <td>Gender, age, weight, height, BMI, country, race, FOBT, medication</td> </tr> <tr> <td><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299606/">Zeller</a></td> <td>V4</td> <td>50</td> <td>38</td> <td>41</td> <td>Gender, age, BMI, country, FOBT</td> </tr> <tr> <td><strong>TOTAL</strong></td> <td>V4</td> <td>251</td> <td>266</td> <td>191</td> <td><em>All of the above</em></td> </tr> </tbody> </table> </th> </tr> </thead> <tbody> <tr> <td> </td> </tr> </tbody> </table> <p>Data processing & sharing</p> <p>All datasets were processed using <a href="https://docs.qiime2.org/2021.11/">qiime2</a> pipeline with <a href="https://benjjneb.github.io/dada2/">DADA2</a> for Sequence quality control and feature table construction and <a href="https://www.arb-silva.de/">SILVA</a> database for taxonomic assignment, and then a <em>phyloseq </em>object was constructed.</p> <ul> <li>Abundance table at genus level is in file <em>genus.csv</em> (Sample counts with NO filtering).</li> <li>Clean metadata is in <em>metadata.csv</em> file (Countries: CA - Canada. USA - United States of America. FRA - France.)</li> <li>Phyloseq object is in file <em>physeq.RDS</em> (Saved as an RDS object in R)</li> </ul> <p>More information is <a href="https://hackmd.io/nbsLqCLlSNSRFc5RBX9c5Q?view">here</a>. </p> <p> </p>
Inventory of soil prokaryotic microbiome (via 16S based on rRNA gene amplicons) in freshwater and brackish water marshes following saltwater intrusion along Shark River Slough boundary, Everglades National Park (FCE LTER), Florida, USA, September 2018
Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased episodic or chronic saltwater exposure, leading to shifts in microbial communities and related ecological services. Soil prokaryotes play a fundamental role in regulating important biogeochemical processes in coastal wetland ecosystem. Yet, it is still difficult to predict how soil prokaryotic communities respond to the saltwater exposure because of poorly understood prokaryotic sensitivity within complex wetland soil microbial communities, as well as the high heterogeneity of wetland soils and saltwater exposure. To address this, a four-year experimental simulation of saltwater intrusion in a pristine freshwater site and a previously saltwater-impacted site was conducted. The saltwater addition started in October 2014 on a monthly basis and continued through October 2018. The dataset contains amplicon sequencing date of 16S rRNA gene obtained from saltwater-exposed soils and unmanipulated native soils in both sites (collected in September 2018). The 2018 data are published in Zhao et al. 2023. A detailed list of sequence data and their accession numbers in GenBank is provided, and data collection is complete. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA804545 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804545). This data package is associated with the following publication: Zhao, J., Chakrabarti, S., Chambers, R., Weisenhorn, P., Travieso, R., Stumpf, S., Standen, E., Briceno, H., Troxler, T., Gaiser, E., Kominoski, J., Dhillon, B., & Martens-Habbena, W. (2023). Year-around survey and manipulation experiments reveal differential sensitivities of soil prokaryotic and fungal communities to saltwater intrusion in Florida Everglades wetlands. Science of The Total Environment, 858, 159865. https://doi.org/10.1016/j.scitotenv.2022.159865 Instead of citing this package, which is an
Inventory of soil prokaryotic and fungal microbiome (via 16S rRNA gene amplicons and ITS sequencing) from Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, February 2019 - October 2020
Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased saltwater exposure, leading to change in soil microbial communities and many important biogeochemical processes. Given the high spatial and temporal heterogeneity in coastal wetlands, especially in tropical or subtropical climates characterized by seasonal temperature, precipitation, and tidal fluctuations, it remains unclear which environmental factors influence the compositions of soil microbial communities in wetlands affected by varying degrees of sea-water intrusion. To address this, a two-year survey was conducted on microbial community structure in submerged surface soils from 14 wetland sites across the Florida Everglades, representing three major ecosystem types, i.e. freshwater marshes, mangrove forests, and seagrass meadows. Bulk surface soil samples of each site were collected from February 2019 to October 2020 to cover dry and wet seasons. In addition to bulk soil samples, soil cores were collected from each site in August 2020 to assess vertical gradients of microbial communities. The dataset contains amplicon sequencing data of 16S rRNA gene (both bulk soil and soil cores) and ITS gene (only the bulk soil). The 2019 to 2020 data are published in Zhao et al. 2023. A detailed list of sequence data and their accession numbers in GenBank is provided, and data collection is complete. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA804243 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804243), PRJNA804246 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804246), and PRJNA804228 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804228). This data package is associated with the following publication: Zhao, J., Chakrabarti, S., Chambers, R., Weisenhorn, P., Travieso, R., Stumpf, S., Standen, E., Briceno, H., Troxler, T., Gaiser, E., Kominoski, J., Dhillon, B., & Martens-H
Appendix. List of the 28S and 16S rRNA sequences recovered from GenBank. 28S = 28S rRNA GenBank accession number; 16S = 16S rRNA GenBank accession number. in Genetic and morphological evidence for cryptic species in Macrobrachium australe and resurrection of M. ustulatum (Crustacea, Palaemonidae)
Appendix. List of the 28S and 16S rRNA sequences recovered from GenBank. 28S = 28S rRNA GenBank accession number; 16S = 16S rRNA GenBank accession number.
Neighbor-joining phylogenetic tree based on 16S rRNA sequences.
<p><strong>Supplementary Figure (S1):</strong> Bayesian 50% majority rule phylogram of 16S ribosomal RNA region showing the phylogenetic relationships among the bacterial isolates in our study. The newly generated sequences are preceded by red circle. The GenBank sequences are preceded by blue squares. The GenBank accession number appears after the species name. Numbers above the branches represent Bayesian posterior probabilities (≥ 0.90), and the maximum parsimony bootstrap support values are given below the branches (≥70%). The out group used for tree construction preceded by empty circle.</p>
16S rRNA sequences data set of Dicronocephalus species in this study.
Explanation note: This 16S rRNA data includes 46 individual sequences of the examined Dicronocephalus species in this study.
The combined dataset of COI and 16S rRNA of Dicronocephalus species in this study.
Explanation note: There is the concatenated sequences of COI and 16S rRNA genes correspondence with each sample.
Comprehensive 16s rRNA sequencing and metabolomics to investigate the effect of anticancer bioactive peptides combined with oxaliplatin on gastric cancer
<p>背景: 胃癌的发生、发展与肠道菌群密切相关。既往研究发现抗癌生物活性肽(ACBP)与奥沙利铂(OXA)联合对胃癌有显着的治疗作用,但ACBP-OXA对肠道菌群的影响仍不清楚。</p><p><strong>Methods:</strong> We established a nude mouse model of ACBP-OXA combined therapy for gastric cancer, the diversity of gut microbiota and fecal metabolomics were studied, and the correlation between gut microbiota and metabolites was analyzed.</p><p><strong>Results: </strong>ACBP-OXA联合疗法对肠道菌群具有很强的调节作用。16s rRNA研究发现,在门中,ACBP-OXA处理后,厚壁菌门和拟杆菌门的相对丰度发生显着变化,厚壁菌门的相对丰度下降,拟杆菌门的相对丰度增加。属内,ACBP-OXA组中毛螺菌科NK4AB6组的相对丰度降低,odpribacter和拟杆菌属的相对丰度增加。ACBP组乳酸菌相对丰度增加,ACBP-OXA和OXA组葡萄球菌相对丰度下降。GO和KEGG研究发现联合治疗机制与代谢和免疫有关。通过代谢组学研究,本研究发现差异代谢物与Benzenoids、Ligans、neoligans、其中脂质和脂类大多参与酪氨酸代谢、不饱和脂肪酸生物合成、苯丙氨酸代谢α-生物过程。将代谢组学与16s rRNA长寿素相结合,发现氨基酸相关代谢物与Jetgalilicus、Staphylococcus、Proteiniphilum等细菌属相关。</p><p>结论: ACBP与ACBP-OXA联合治疗可能通过改变肠道菌群的分布多样性和菌群结构来改善和恢复胃癌裸鼠的肠道菌群,这可能是抑制胃癌发生、发展的关键。该研究为进一步研究ACBP-OXA在胃癌治疗中的应用提供了新的方向。</p>
Fig. 11. Bayesian inference trees. A. 16S rRNA dataset. B. Cytochrome oxidase I in Designation of a neotype for Myxicola infundibulum (Montagu, 1808) (Annelida: Sabellidae) and a new species from the UK
Fig. 11. Bayesian inference trees. A. 16S rRNA dataset. B. Cytochrome oxidase I gene dataset. The first value at each node represents maximum likelihood bootstrap support, the second the Bayesian posterior probabilities and the third the maximum parsimony bootstrap support.
16S rRNA Sequencing Data of Fecal Microbiota in an Italian Cohort of Patients with CDKL5 Deficiency Disorder
<h3>Summary of the study </h3> <p>CDKL5 deficiency disorder (CDD) is a neurodevelopmental condition characterized by global developmental delay, early-onset seizures, intellectual disability, visual and motor impairments, distinct from Rett Syndrome (RTT) due to the absence of a clear regression period. Gastrointestinal (GI) disturbances and signs of subclinical immune dysregulation are common in CDD patients, yet the underlying causes are unknown. Recent studies hint at a possible link between neurological disorders and gut microbiota, an unexplored area in CDD. In this groundbreaking study, we examined fecal microbiota in CDD patients and their healthy relatives, revealing differences in bacterial diversity and composition. We further investigated microbiota changes based on various factors, including the severity of GI issues, seizure frequency, sleep disorders, food intake type, neuro-behavioral features (assessed through the RTT Behaviour Questionnaire – RSBQ), and ambulation capacity. </p> <p>Our findings suggest a potential connection between CDD, microbiota, and symptom severity. This study represents the first exploration of the gut-microbiota-brain axis in CDD patients, contributing to the growing body of research on the role of gut microbiota in neurodevelopmental disorders. It opens doors to potential interventions targeting intestinal microbes to enhance the well-being of individuals with CDD.</p> <h3>Mehods</h3> <p>The Dataset represent the raw data (.fastq) obtained from the sequencing of the fecal samples from 17 Italian Patients with CDD, and 17 Healthy Relatives (i.e. siblings or mother), collected at a single time-point.</p> <p>Samples from Patients affected by CDD are called CDD, samples from Healthy Relatives are called HC-CDD (i.e. healthy controls of patients affected by CDD). For details about the sample names see the “Explanation Table”.</p> <p>Bacterial DNA was extracted from fecal samples using the QIAmp Powerfexal DNA Kit (Qiagen, Germany) following the manufacturer's protocol. The 16S rRNA sequencing and analysis was performed by a service offered by Zymo Research (Germany).</p> <p><em>Targeted Library Preparation</em>: The DNA samples were prepared for targeted sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research). The primer sets used were Quick-16S™ Primer Set V3-V4 (Zymo Research). The sequencing library was prepared using an innovative library preparation process in which PCR reactions were performed in real-time PCR machines to control cycles and therefore limit PCR chimera formation. The final PCR products were quantified with qPCR fluorescence readings and pooled together based on equal molarity. The final pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™, then quantified with TapeStation® (Agilent Technologies, Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA). </p> <p><em>Sequencing:</em> The final library was sequenced on Illumina® MiSeq™ with a v3 reagent kit (600 cycles). </p>
The processed clean data of 16S rRNA V4 amplicon sequecnces for the six stage of phenolic microbiome domestication
Open the record for dataset details and reuse information.
Bacteria and archaea of the Columbia and Willamette Rivers, 16S rRNA gene amplicon library metadata
<p>Bacterial and archaeal communities in the Columbia and Willamette Rivers in the Portland, OR, USA, region were characterized by 16S rRNA gene amplicon sequencing as part of the Lewis & Clark College spring 2022 Microbial Ecology course. Whole-water (>0.2 µm) samples were collected from: the Willamette River; the Columbia River above the confluence with the Willamette; and the Columbia River just downstream of the confluence with the Willamette.</p> <p>This dataset provides additional metadata to supplement the DNA sequences archived with the NCBI SRA at <a href="https://www.ncbi.nlm.nih.gov/sra/PRJNA865380">https://www.ncbi.nlm.nih.gov/sra/PRJNA865380</a></p>
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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.