Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
97
datasets available to search
ShareScore release 0.9.0
Dataset results
97 results for “quantitative trait loci”
Plasma circulating microRNA-expression quantitative trait loci (eQTLs) data in the Rotterdam Study
<p>The dataset contains GWAS summary statistics for 2,083 plasma circulating microRNAs, obtained from nearly 2,178 participants of the Rotterdam Study. The dataset includes three files, as outlined below:</p> <p><strong>File1: SNP_reference_file_maf0.01_Rsq0.7.txt</strong></p> <p>A reference file for SNPs with good imputation quality (Rsq > 0.7) and minor allele frequency > 0.01 among participants included in our GWAS in the Rotterdam Study (N=2,178). The headers are:</p> <p>SNP: rsID</p> <p>chr: chromosome number according to GRCh37</p> <p>bp: basepair position according to GRCh37</p> <p>effect_allele: effect allele</p> <p>other_allele: other allele</p> <p>eaf: effect allele frequency</p> <p><strong>File2: miReQTLs_1e-5_maf0.01_Rsq0.7.txt</strong></p> <p>Summary statistics for all SNPs significantly associated with 2083 miRNAs (p-value < 1e-5), filtered by minor allele frequency > 0.01 and Rsq > 0.7. The headers are:</p> <p>SNP: rsID</p> <p>beta: effect estimate</p> <p>se: standard error</p> <p>pval: p-value</p> <p>miRNA: miRNA ID</p> <p><strong>File3: miReQTLs_nominal_sig.csv.gz</strong></p> <p>Summary statistics for all SNPs nominally associated with 2083 miRNAs (p-value < 0.05). The headers are:</p> <p>RSID: SNP ID</p> <p>p-value: p-value</p> <p>phenotype: miRNA</p> <p>SE: standard error</p> <p>BETA: effect estimate</p> <p> </p> <p>The SNP allelic information and frequency can be found in the reference file (<strong>File1</strong>). </p> <p><br>For more information, please contact: m.ghanbari@erasmusmc.nl</p>
Phenotypic variation and quantitative trait loci for resistance to southern anthracnose and clover rot in red clover
<p>Red clover (<em>Trifolium pratense</em> L.) is an important forage legume of temperate regions, particularly valued for its high yield potential and its high forage quality. Despite substantial breeding progress during the last decades, continuous improvement of cultivars is crucial to ensure yield stability in view of newly emerging diseases or changing climatic conditions. The high amount of genetic diversity present in red clover ecotypes, landraces and cultivars provides an invaluable, but often unexploited resource for the improvement of key traits such as yield, quality, and resistance to biotic and abiotic stresses.</p> <p>A collection of 397 red clover accessions was genotyped using a pooled genotyping-by-sequencing approach with 200 plants per accession. Resistance to the two most pertinent diseases in red clover production, southern anthracnose caused by <em>Colletotrichum trifolii</em>, and clover rot caused by <em>Sclerotinia trifoliorum, </em>was assessed using spray inoculation. The mean survival rate for southern anthracnose was 22.9% and the mean resistance index for clover rot was 34.0%. Genome-wide association analysis revealed several loci significantly associated with resistance to southern anthracnose and clover rot. Most of these loci are in coding regions. One quantitative trait locus (QTL) on chromosome 1 explained 16.8% of the variation in resistance to southern anthracnose. For clover rot resistance we found eight QTL, explaining together 80.2% of the total phenotypic variation. The SNPs associated with these QTL provide, once validated, a promising resource for marker-assisted selection in existing breeding programs, facilitating the development of novel cultivars with increased resistance against two devastating fungal diseases of red clover.</p>
Hypergraph Factorisation Expression Quantitative Trait Loci
<p>Please cite:</p> <pre><code>Hypergraph factorisation for multi-tissue gene expression imputation. Vinas Torne, Ramon and Joshi, Chaitanya K. and Georgiev, Dobrik and Lin, Phillip and Dumitrascu, Bianca* and Gamazon, Eric* and Lio, Pietro*. *Co-corresponding authors. </code></pre>
Placental Expression Quantitative Trait Loci In An East Asian Population
<p>Analysis script, full eQTL summary statistics, and fine-mapping statistics of article "Placental Expression Quantitative Trait Loci In An East Asian Population". This data contains workflow and result of 102 East Asian placental expression quantitative trait loci analysis.</p><p>Genotype from 102 cord blood used in the analysis is also included. Variants with minor allele frequencies less than 0.01 was filtered out to prevent personnel identification.</p>
Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits
<p><strong>Introduction</strong></p> <p>These are the <em>Summary Level-data</em> as presented in:</p> <p>"Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits". Eur J Hum Genet. 2019 Jan 24. doi: 10.1038/s41431-018-0295-z.<em> [Epub ahead of print]</em></p> <p>If you use these data please cite the corresponding manuscript, which can be downloaded here: <a href="http://em.rdcu.be/wf/click?upn=lMZy1lernSJ7apc5DgYM8eFz0euOx0-2B13Abimi4Sb0A-3D_2NNavOiAD9A7CPFnsa04dGla3sU002fLfkDtL-2FhGlad0GuoM-2B3OlDb0C5GiEhwIvtH7ba4KKF45ipTOFodx6CqvVvoP2GQ992sPGoV9ZPWIe04tUd8-2BGWey0In0TXPII5zK-2Bfp8Wk9TpEqEcSd-2BEmywqZc8o5TW4xGPXZqmchfUH8chy3P4SEtpzHXMG1LwsIYrKfwegqTXG85RAJPr-2B21Tk9SobtpvFs0frMkJ4ekKsl33ryoZfFPk1byjQunJYn4-2BB0iqMgGs6cXv0AOgAxg-3D-3D">https://rdcu.be/bh8mu</a>. When you have any questions or comments regarding this study or these files, please contact me via:</p> <p>Jessica van Setten, PhD | <em>Department of Cardiology, University Medical Center Utrecht, Utrecht University</em> | j.vansetten [at] umcutrecht [dot] nl</p> <p> </p> <p><strong>Files and description</strong></p> <p>There are four files available:</p> <ol> <li>RR_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for RR interval</li> <li>PR_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for PR interval</li> <li>QT_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for QT interval</li> <li>QRS_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for QRS duration</li> </ol> <p>All these files have the same lay-out and are gzipped. The reference used for meta-analysis of GWAS was Genome of the Netherlands v4. </p> <ul> <li><em>SNP</em> - variantID (rsID), please note that few hundred variants do not have an rsID, but are NA instead. These can still be identified by chromosome and position.</li> <li><em>CHR</em> - chromosome numbers [1-22 and X].</li> <li><em>POS</em> - base pair position, hg19 / build37.</li> <li><em>CODED_ALLELE</em> - coded allele, <em>i.e.</em> the effect allele, as represented (and harmonized) across cohorts. Note that this is not necessarily the minor allele.</li> <li><em>NON_CODED_ALLELE</em> - the other allele, <em>i.e.</em> the non-effect allele.</li> <li><em>CODED_ALLELE_FREQ</em> - coded allele frequency, <em>i.e.</em> the effect allele frequency. Note that this is not necessarily the minor allele frequency.</li> <li><em>BETA</em> - beta from the fixed-effects model.</li> <li><em>SE </em>- standard error from the fixed-effects model.</li> <li><em>P </em>- P-value from the fixed-effects model.</li> <li><em>NEAREST_GENE</em> - the gene closest to the respective variant.</li> </ul> <p> </p>
Quantitative Trait Loci of Solanaceae species
<p>This archive contains experimental data on Quantitative Trait Loci (QTLs) mapped in <em>Solanacea</em> species (tomato and potato). QTLs were extracted from scientific literature using the QTLTableMiner++ tool. The resulting data are distributed in:</p> <ul> <li><a href="https://sqlite.org/">SQLite</a> database files (.db)</li> <li>CSV files (.csv)</li> <li><a href="https://www.w3.org/TR/turtle/">RDF/</a><a href="https://www.w3.org/TR/turtle/">Turle</a> files (gzip-ed .ttl)</li> </ul>
Expression quantitative trait loci influence DNA damage-induced apoptosis in cancer
<p><strong>Exposure expression quantitative trait loci (e2QTL)</strong></p> <p>The analysis of e2QTL allows for the identification of context-specific eQTL effects (Kim-Hellmuth et al. (2017), PMID: 28814792). To evaluate how inter-individual genetic variability influences the regulation of DNA damage-induced apoptosis, we performed e2QTL analysis of CD8+ T cells from 461 healthy European participants stimulated with high doses of 5 different carcinogens. These include Methyl-methanesulfonate (MMS), tert-butyl-hydroperoxide (TBOOH), benzo(a)pyrene-7,8-diol-9,10-epoxide (BPDE), 4-hydroxycyclophosphamide (HC) and UVC radiation.</p> <p><code>eQTL_DNA_damage_induced_apoptosis.csv:</code> eQTL data. FastQTL was used to analyze cis-eQTL within a 1 MB window of a gene’s transcription start site. Filtering and normalization of expression data was performed as described by the Genotype Tissue Expression (GTEx) project (The GTEx Consortium (2015), PMID: 25954001) including 60 PEER factors, top 3 genotype PCs and sex as covariates. Genotypes were filtered by PHWE > 10-6 and MAF > 5 %. Adjusted p-values were generated using 1,000 to 10,000 permutations. Variant IDs (CHR:POS:REF:ALT) are based on GRCh38.</p> <p><code>e2QTL_DNA_damage_induced_apoptosis.csv:</code> e2QTL data. The most significant variant for each analyzed gene was determined based on eQTL data to calculate e2QTL in a z-test that were corrected for multiple testing using Bonferroni correction as described by Kim-Hellmuth et al. (2017). Variant IDs (CHR:POS:REF:ALT) are based on GRCh38.</p> <p> </p>
Molecular mapping of quantitative trait loci (QTL) for resistance to early blight in tomato
<p>Molecular mapping of quantitative trait loci (QTL) for resistance to early blight in tomato (1135884)</p>
Dataset for Quantitative Trait Loci Associated with Lodging, Stem Strength, Yield, and Other Important Agronomic Traits in Dry Field Peas, 330 markers, sequences included
<p>Dataset for Quantitative Trait Loci Associated with Lodging, Stem Strength, Yield, and Other Important Agronomic Traits in Dry Field Peas, 330 markers, sequences included.</p>
Pleiotropic Expression Quantitative Trait Loci Are Enriched in Enhancers and Transcription Factor Binding Sites and Impact More Genes
<p>This dataset comprises two files that accompany the article (link to be added upon publication).</p> <h2>1. gwas2eqtl_colocalization_full.tar.gz</h2> <p>This file contains the complete colocalization dataset generated using the code from the following GitHub repository: gwas2eqtl. This dataset is used as input for the pleiotropic eQTL analysis available at gwas2eqtl_pleiotropy, which produces the figures in the article.</p> <p><strong>File structure:</strong></p> <blockquote> <p>.<br>└── gwas417<br> └── coloc<br> ├── ebi-a-GCST000998<br> │ └── pval_5e-08<br> │ └── r2_0.1<br> │ └── kb_1000<br> │ └── window_1000000<br> │ ├── Alasoo_2018_ge_macrophage_IFNg+Salmonella.tsv<br> │ ├── Alasoo_2018_ge_macrophage_IFNg.tsv<br>...</p> </blockquote> <p>Each TSV file contains the following columns:</p> <blockquote> <p>chrom pos rsid ref alt eqtl_gene_id gwas_beta gwas_pval gwas_id eqtl_beta eqtl_pval eqtl_id PP.H4.abf SNP.PP.H4 nsnps PP.H3.abf PP.H2.abf PP.H1.abf PP.H0.abf coloc_variant_id coloc_region<br>1 109272258 rs4970834 C T ENSG00000168765 -0.12874.25001047052626e-09 ebi-a-GCST000998 -0.250697 0.0893351 Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.0520205502224409 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>1 109274968 rs12740374 G T ENSG00000168765 -0.103341 1.63998546891446e-09 ebi-a-GCST000998 -0.197397 0.172673Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.0857585178966856 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>1 109275216 rs660240 T C ENSG00000168765 0.1044492.78997299740827e-09 ebi-a-GCST000998 0.214165 0.139318 Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.0557749486050279 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>1 109275684 rs629301 G T ENSG00000168765 0.1054716.129993302249e-10 ebi-a-GCST000998 0.197397 0.172673 Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.22229240584331 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>1 109278889 rs602633 T G ENSG00000168765 0.1034352.15998134341707e-09 ebi-a-GCST000998 0.226329 0.102673 Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.0782482718431504 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>...</p> </blockquote> <p> </p> <p>The dataset provides colocalization statistics for GWAS-eQTL pairs, including posterior probabilities and variant annotations.</p> <h2>2. gwas2eqtl0.1.3.tsv.gz</h2> <p>This file is a filtered version of the colocalization dataset, refined based on cutoffs of PP.H4.abf ≥ 0.75 and SNP.PP.H4 ≥ 0. This subset is utilized in the gwas2eqtl web application for data visualization.</p> <p>Sample Columns:</p> <blockquote> <p>chrom pos19 pos38 cytoband rsid ref alt gwas_trait gwas_class gwas_beta eqtl_gene_symbol eqtl_beta eqtl_id eqtl_gene_id gwas_id gwas_pval eqtl_pval pp_h4_abf snp_pp_h4 tophits_variant_id nsnps<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.175816 BrainSeq_ge_brain ENSG00000235098 ebi-a-GCST003043 2.85292266979231e-10 0.000841046 0.978425254116226 6.18454060493069e-12 1_1312114_T_C 3<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.175816 BrainSeq_ge_brain ENSG00000235098 ieu-a-294 2.85292266979231e-10 0.000841046 0.974019788384412 7.52286530905187e-12 1_1312114_T_C 4<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.293529 CommonMind_ge_DLPFC_naive ENSG00000235098 ebi-a-GCST003043 2.85292266979231e-10 2.30452e-06 0.953333690803618 6.9758581004380506e-15 1_1312114_T_C 6<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.293529 CommonMind_ge_DLPFC_naive ENSG00000235098 ieu-a-294 2.85292266979231e-10 2.30452e-06 0.951092499048109 7.0876793540547e-15 1_1312114_T_C 7<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.510549 FUSION_ge_adipose_naive ENSG00000235098 ebi-a-GCST003043 2.85292266979231e-10 1.2212e-06 0.974412793352836 1.70246427912903e-11 1_1312114_T_C 6</p> </blockquote> <p> </p> <p>This filtered dataset focuses on high-confidence colocalization events for functional exploration of genetic associations and regulatory mechanisms.</p>
Quantitative trait loci mapping in cichlid fishes: Aulonocara koningsi x Metriaclima mbenjii and Labidochromis caeruleus x Labeotropheus trewavasae
<p>Since the time of Darwin, biologists have sought to understand the evolution and origins of phenotypic variation. To understand the genetic and molecular sources of morphological differences, we capitalize on the cichlid fish system. Cichlids of the East African Rift Lakes have undergone an extensive adaptive radiation, including variation in body shape, head shape, and pigmentation. These morphological differences are often intimately linked to the ecology and behavior of these animals. Here, we investigate the genetic basis of these phenotypes using quantitative trait loci (QTL) mapping using four genera of Lake Malawi cichlids and two F<sub>2</sub> hybrid populations. The first hybrid cross is between <em>Aulonocara koningsi</em>, which lives in the open sandy region and feeds insects from the open sand, and <em>Metriaclima mbenjii</em>, an omnivore rock-dwelling fish. The second cross is between <em>Labidochromis caeruleus</em>, a suction-feeding insectivore that swims continuously searching for prey, and <em>Labeotropheus trewavasae</em>, which feeds by biting or scraping attached algae from the rocks in its benthic habitat. Such work can provide insights into the molecular basis of phenotypic adaptation, the genetic architecture of morphology, and the evolution of cichlid fishes.</p>
Data from: Genome-wide search for quantitative trait loci controlling important plant and flower traits in petunia using an interspecific recombinant inbred population of Petunia axillaris and Petunia exserta
A major bottleneck in plant breeding has been the much limited genetic base and much reduced genetic diversity in domesticated, cultivated germplasm. Identification and utilization of favorable gene loci or alleles from wild or progenitor species can serve as an effective approach to increasing genetic diversity and breaking this bottleneck in plant breeding. This study was conducted to identify quantitative trait loci (QTL) in wild or progenitor petunia species that can be used to improve important horticultural traits in garden petunia. An F7 recombinant inbred population derived between Petunia axillaris and P. exserta was phenotyped for plant height, plant spread, plant size, flower counts, flower diameter, flower length, and days to anthesis, in Florida in two consecutive years. Transgressive segregation was observed for all seven traits in both years. The broad-sense heritability estimates for the traits ranged from 0.20 (days to anthesis) to 0.62 (flower length). A genome-wide genetic linkage map consisting 368 single nucleotide polymorphism bins and extending over 277 cM was searched to identify QTL for these traits. Nineteen QTL were identified and localized to five linkage groups. Eleven of the loci were identified consistently in both years; several loci explained up to 34.0% and 24.1% of the phenotypic variance for flower length and flower diameter, respectively. Multiple loci controlling different traits are co-localized in four intervals in four linkage groups. These intervals contain desirable alleles that can be introgressed into commercial petunia germplasm to expand the genetic base and improve plant performance and flower characteristics in petunia.
Sol Genome Annotations and Quantitative Traits Loci
<p>RDF data graphs</p>
Identification of quantitative trait loci and associated candidate genes for pregnancy success in Angus – Brahman crossbred heifers
<p>Development of genomic tools to identify females with high genetic merit for reproductive function could increase the profitability and sustainability of beef production. Here, genome-wide association studies (GWAS) were performed on pregnancy outcome traits from a population of Angus – Brahman crossbred heifers. Furthermore, a validation GWAS was performed using data from another location. Heifers were genotyped with the Bovine GGP F250 array that contains ~250,000 SNPs. In the discovery population, heifers were bred in winter breeding seasons involving a single round of timed artificial insemination (AI) followed by natural mating for three months. Three phenotypes were analyzed: pregnancy outcome to first-service AI (PAI; n = 1481), pregnancy status at the end of the breeding season (PEBS; n = 1725), and pregnancy score (Pregscore where 1 = pregnant to first-service AI, 2 = pregnant to bull, 3 = not pregnant; n =1481). The heritability for PAI was estimated as 0.149. One large quantitative trait locus (QTL) that explained ~3% of the genetic variation for PAI was found on BTA7, in a region containing a cluster of γ-protocadherin genes and SLC25A2. Other QTLs explaining between 0.5-1% of the genetic variation were found on BTA12 and 25. The heritability of PEBS was estimated at 0.122. A large QTL on BTA7 was synonymous with the QTL for PAI, with minor QTL located on BTA5, 9, 10, 11, 19, and 20. Estimated heritability for Pregscore was 0.189. There was a large QTL on BTA7 synonymous with the other two traits as well as smaller QTLs on BTA1, 10, 15, 18, 19, and 20. The validation population for pregnancy status at the end of the breeding season were Angus-Brahman crossbred heifers bred by natural mating. In concordance with the discovery population, the large QTL on BTA7 and QTL on BTA10, 12 and 18 were identified. In summary, QTL and candidate SNPs associated with pregnancy outcomes in beef heifers were identified, including a large QTL associated with a group of protocadherin genes. Confirmation of these associations with larger populations could lead to the development of genomic estimates of reproductive function in beef cattle.</p>
Data from: Molecular mapping and identification of quantitative trait loci for domestication traits in field cress (Lepidium campestre L.) genome
Open the record for dataset details and reuse information.
Quantitative trait loci mapping in cichlid fishes: Aulonocara koningsi x Metriaclima mbenjii and Labidochromis caeruleus x Labeotropheus trewavasae
Open the record for dataset details and reuse information.
Data from: Large effect quantitative trait loci for salicinoid phenolic glycosides in Populus: implications for gene discovery
Open the record for dataset details and reuse information.
Identification of quantitative trait loci and associated candidate genes for pregnancy success in Angus – Brahman crossbred heifers
Open the record for dataset details and reuse information.
Data from: Genome-wide search for quantitative trait loci controlling important plant and flower traits in petunia using an interspecific recombinant inbred population of Petunia axillaris and Petunia exserta
Open the record for dataset details and reuse information.
Data from: Mining the stable quantitative trait loci for agronomic traits in wheat (Triticum aestivum L.) based on an introgression line population
<p><span><span><b>Background</b>: Human demand for wheat will continue to increase together with the continuous global population growth. Agronomic traits in wheat are susceptible to environmental conditions. Therefore, in breeding practice, priority is given to QTLs of agronomic traits that can be stably detected across multiple environments and over many years.</span></span></p> <p><span><span><b>Results: </b>In this study, QTL analysis was conducted for eight agronomic traits using an introgression line population across eight environments (drought stressed and well-watered) for five years. In total, 44 additive QTLs for the above agronomic traits were detected on 15 chromosomes. Among these, <i>qPH-6A</i>, <i>qHD-1A</i>, <i>qSL-2A</i>, <i>qHD-2D</i> and<i> qSL-6A</i> were detected across seven, six, five, five and four environments, respectively. The means in the phenotypic variation explained by these five QTLs were 12.26%, 9.51%, 7.77%, 7.23%, and 8.49%, respectively. </span></span></p> <p><b>Conclusions: </b>We identified five stable QTLs, which includes <i>qPH-6A</i>, <i>qHD-1A</i>, <i>qSL-2A</i>, <i>qHD-2D</i> and<i> qSL-6A</i>. They play a critical role in wheat agronomic traits. One of the dwarf genes<i> Rht14</i>, <i>Rht16</i>, <i>Rht18</i> and <i>Rht25</i> on chromosome 6A might be the candidate gene for <i>qPH-6A</i>. The <i>qHD-1A</i> and <i>qHD-2D</i> were novel stable QTLs for heading date and they differed from known vernalization genes, photoperiod genes and earliness per se genes.</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.