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data set related to article Broad phenotypic spectrum and genotype-phenotype correlations in GMPPB-related dystroglycanopathies: an Italian cross-sectional study
<p>This record contains raw data related to article Broad phenotypic spectrum and genotype-phenotype correlations in GMPPB-related dystroglycanopathies: an Italian cross-sectional study</p>
Distinguishing mutations and null alleles from genotyping errors using mother progeny comparisons in Brazilian pine (Araucaria angustifolia)
the rate of null alleles, mutations and genotyping errors in microsatellite loci, using Araucaria angustifolia, a threatened species, as a case study. We estimated the rates of the different types of genotyping deviations using mother-progeny genotype comparison from 50 seed-trees and their respective progeny (seeds). A total of 2336 A. angustifolia samples were genotyped, and we found that the rate of null alleles was 0.045. From the 1972 mother-progeny comparisons, the overall genotype deviation rate was 1.58%, consisting of 145 inconsistences (mutations), 339 null alleles and 210 genotyping errors. In terms of seed numbers, 128 (6.5%) showed inconsistencies in at least one locus, 118 (6.0%) null alleles, and 321 (16.3%) genotyping errors. This is the first study to describe the inconsistences (mutations) between mother-progeny genotypes for A. angustifolia, and the outcome makes it clear that an understanding of these genotyping deviations must be considered in assessing the accuracy of inferences made based on population genetics analyses.
Fig. 9 in Morphological description and multilocus genotyping of Onchocerca spp. in red deer (Cervus elaphus) in Switzerland
Fig. 9. Detail of Onchocerca flexuosa female: Uterine tubes (arrow) straightened inside curly body.
Fig. 1 in Linking phenotypic to genotypic metacestodes from Octopus maya of the Yucatan Peninsula
Fig. 1. Sampling localities where specimens of Octopus maya were collected in Yucatan, Mexico.
Fig. 1 in Prevalence and new genotypes of Enterocytozoon bieneusi in wild rhesus macaque (Macaca mulatta) in China: A zoonotic concern
Fig. 1. Specific locations at which specimens were collected in this study.
Fig. 2 in Molecular detection and characterization of a novel Theileria genotype in Dama Gazelle (Nanger dama)
Fig. 2. The clade credibility values of phylogram generated from bayesian analysis.
Fig. 2 in From wildlife to humans: The global distribution of Trichinella species and genotypes in wildlife and wildlife-associated human trichinellosis
Fig. 2. Global distribution of Trichinella spiralis in wildlife reported in this review.
Distribution of Chlamydia trachomatis ompA-genotypes over three decades (1990-2021) in Portugal - ompA sequence datasets
<p>This repository includes sequence data from the study: "<strong>Distribution of <em>Chlamydia trachomatis</em> <em>ompA</em>-genotypes over three decades (1990-2021) in Portugal</strong>”, conducted by the <strong>National Reference Laboratory (NRL) for Sexually Transmitted Infections (STI), National Institute of Health Doutor Ricardo Jorge (INSA), Portugal</strong>.</p> <p>The NRL performs molecular characterization (namely <em>ompA</em>-genotyping) of all <em>C. trachomatis</em> positive samples that receives. <em>C. trachomatis</em> <em>ompA</em>-genotyping technique was adapted from Lan, et al. [1]. Briefly, PCR and nested PCR were performed using primers NLO and NRO, and primers PCTM3 and SERO2A, respectively, as previously described [2]. Partial nucleotide sequencing of the ~1010 bp PCR product was performed with BigDye terminator v1.1 and capillary sequencing (3130XL Genetic Analyzer; Applied Biosystems), using either two primers, as described elsewhere [2,3,4], or one primer (since ~2018), as described elsewhere [5]. Until 2018, LaserGene (DNASTAR) and MEGA (http://www.megasoftware.net) software were applied for sequence curation, alignment and phylogenetic reconstructions involving multiple reference sequences, as previously described [3]. Since ~2018, <em>ompA</em> genotypes have been determined by BLASTn-based comparison (using the ABRIcate tool [6]) directly from raw Sanger (AB1 format) sequences, with a custom database enrolling reference and variant sequences of all main <em>ompA </em>genotypes (<a href="https://github.com/insapathogenomics/ReporType/blob/main/databases/c_trachomatis.fasta">https://github.com/insapathogenomics/ReporType/blob/main/databases/c_trachomatis.fasta</a>) [3, 5, 7, 8], as currently implemented in <strong>ReporType</strong> (<a href="https://github.com/insapathogenomics/ReporType">https://github.com/insapathogenomics/ReporType</a>) [8]. When needed, MEGA software is then applied for fine genotype confirmation, namely for L2/L2b discrimination and confirmation of the hybrid <em>ompA</em>-profile of the recombinant L2b/D-Da [5].</p> <p>This repository includes the following sequence datasets:</p> <ul> <li><strong>Dataset 1</strong> - <em>ompA</em> sequences (curated FASTA) of <em>C. trachomatis</em> positive samples collected between 1991 and ~2018, as described above.</li> <li><strong>Dataset 2</strong> - <em>ompA</em> sequences (raw Sanger sequences, converted from “ab1” format to FASTA) of <em>C. trachomatis</em> positive samples collected since ~2018 and 2021, as described above.</li> </ul> <p><em>Note: The associated metadata is described in the Supplementary table 2 of the manuscript. These sequence datasets do not cover genotyped samples for which the ompA sequences were lost over the three decades of the laboratory's historical collection.</em></p> <p> </p> <p>References</p> <p>1. Lan J, Ossewaarde JM, Walboomers JM, Meijer CJ, van den Brule AJ. Improved PCR sensitivity for direct genotyping of Chlamydia trachomatis serovars by using a nested PCR. <em>J Clin Microbiol</em>. 1994;32(2):528-530. doi:10.1128/jcm.32.2.528-530.1994;</p> <p>2. Gomes JP, Bruno WJ, Borrego MJ, Dean D. Recombination in the genome of <em>Chlamydia trachomatis </em>involving the polymorphic membrane protein C gene relative to <em>ompA </em>and evidence for horizontal gene transfer. <em>J Bacteriol </em>2004;186:4295–4306;</p> <p>3. Nunes A, Borrego MJ, Nunes B, Florindo C, Gomes JP. Evolutionary dynamics of <em>ompA</em>, the gene encoding the <em>Chlamydia trachomatis</em> key antigen. J Bacteriol. 2009 Dec;191(23):7182-92. doi: 10.1128/JB.00895-09. Epub 2009 Sep 25. PMID: 19783629; PMCID: PMC2786549;</p> <p>4. Nunes A, Nogueira PJ, Borrego MJ, Gomes JP. Adaptive evolution of the Chlamydia trachomatis dominant antigen reveals distinct evolutionary scenarios for B- and T-cell epitopes: worldwide survey. PLoS One. 2010 Oct 5;5(10):e13171. doi: 10.1371/journal.pone.0013171. PMID: 20957150; PMCID: PMC2950151.</p> <p>5. Borges V, Cordeiro D, Salas AI, et al. <em>Chlamydia trachomatis</em>: when the virulence-associated genome backbone imports a prevalence-associated major antigen signature. <em>Microb Genom</em>. 2019;5(11):e000313. doi:10.1099/mgen.0.000313;</p> <p>6. Seemann T. ABRIcate. <a href="https://github.com/tseemann/abricate">https://github.com/tseemann/abricate</a></p> <p>7 Nunes A, Nogueira PJ, Borrego MJ, Gomes JP. Adaptive evolution of the Chlamydia trachomatis dominant antigen reveals distinct evolutionary scenarios for B- and T-cell epitopes: worldwide survey. PLoS One. 2010 Oct 5;5(10):e13171. doi: 10.1371/journal.pone.0013171. PMID: 20957150; PMCID: PMC2950151.</p> <p>8. Cruz H, Pinheiro M, Borges V. ReporType: a flexible bioinformatics tool for targeted loci screening and typing of infectious agents (<a href="https://github.com/insapathogenomics/ReporType">https://github.com/insapathogenomics/ReporType</a>). Int J Mol Sci. 2024;25:3172. https://doi.org/10.3390/ijms25063172</p>
Table 1 in Prevalence and new genotypes of Enterocytozoon bieneusi in sheltered dogs and cats in Sichuan province, southwestern China
<p><b>Table 1.</b> Prevalence and genotypes of <i>E. bieneusi</i> in sheltered dogs from different cities and sources in Sichuan province, southwestern China.</p><table><tbody><tr><th>City</th><th>Source</th><th>No. examined</th><th>No. positive</th><th>Prevalence (%)</th><th>OR (95% CI)</th><th><i>p-</i> value</th><th>Genotypes (<i>n</i>)</th></tr></tbody><tbody><tr><th></th><td></td><td></td><td></td><td>(95% CI)</td><td></td><td></td><td></td></tr><tr><th>Chengdu</th><td>Shuangliu</td><td>158</td><td>16</td><td>10.1% (5.4–14.8)</td><td>Reference</td><td></td><td>CD9 (8); PtEb IX (7); SCD-1 (1)</td></tr><tr><th></th><td>Wenjiang</td><td>250</td><td>80</td><td>32.0% (26.2–37.8)</td><td>4.176</td><td>0.000</td><td>CD9 (79); PtEb IX (1)</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td>(2.336–7.468)</td><td></td><td></td></tr><tr><th>Ya’ an</th><td>Yucheng</td><td>228</td><td>36</td><td>15.8% (11.1–20.5)</td><td>1.664</td><td>0.112</td><td>CD9 (4); PtEb IX (32)</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td>(0.888–3.117)</td><td></td><td></td></tr><tr><th>Panzhihua</th><td>Dongqu</td><td>44</td><td>2</td><td>4.5% (<i>—</i> 1.6–10.7)</td><td>0.423</td><td>0.264</td><td>CD9 (1); PtEb IX (1)</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td>(0.093–1.913)</td><td></td><td></td></tr><tr><th>Guangyuan</th><td>Lizhou</td><td>44</td><td>2</td><td>4.5% (<i>—</i> 1.6–10.7)</td><td>0.423</td><td>0.264</td><td>Type IV (1); SCD-2 (1)</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td>(0.093–1.913)</td><td></td><td></td></tr><tr><th>Total</th><td></td><td>724</td><td>136</td><td>18.8% (15.9–21.6)</td><td></td><td></td><td>CD9 (92); PtEb IX (41);</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td></td><td>Type IV (1); SCD-1 (1); SCD-2 (1)</td></tr></tbody></table>
Table 2 in Prevalence and new genotypes of Enterocytozoon bieneusi in sheltered dogs and cats in Sichuan province, southwestern China
<p><b>Table 2.</b> Prevalence and genotypes of <i>E. bieneusi</i> in sheltered cats from different cities and sources in Sichuan province, southwestern China.</p><table><tbody><tr><th>City</th><th>Source</th><th>No. examined</th><th>No. positive</th><th>Prevalence</th><th>OR (95% CI)</th><th><i>p-</i> value</th><th>Genotypes (<i>n</i>)</th></tr></tbody><tbody><tr><th></th><td></td><td></td><td></td><td>(%) (95% CI)</td><td></td><td></td><td></td></tr><tr><th>Chengdu</th><td>Shuangliu</td><td>85</td><td>13</td><td>15.3% (7.6–22.9)</td><td>Reference</td><td></td><td>CD9 (10); D (2); PtEb IX (1)</td></tr><tr><th>Ya’ an</th><td>Yucheng</td><td>23</td><td>3</td><td>13.0% (<i>—</i> 0.7–26.8)</td><td>0.831 (0.215–3.203)</td><td>0.788</td><td>CD9 (1); D (2)</td></tr><tr><th>Panzhihua</th><td>Dongqu</td><td>48</td><td>6</td><td>12.5% (3.1–21.9)</td><td>0.791 (0.280–2.237)</td><td>0.791</td><td>Type IV (6)</td></tr><tr><th>Total</th><td></td><td>156</td><td>22</td><td>14.1% (8.6–19.6)</td><td></td><td></td><td>CD9 (11); Type IV (6);</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td></td><td>D (4); PtEb IX (1)</td></tr></tbody></table>
Genotypes of 180 soybean accessions
<p><span>The raw genotype file contains 180 soybean accessions and 52,041 SNPs in HapMap format. Genotyping was performed using the SoySNP50K Illumina Infinium BeadChip. The genotype data were used for a GWAS analysis to identify loci associated with soybean flowering and maturity. The results are presented in the article, </span><em><span>"Genome-Wide Association Study Revealed Some New Candidate Genes Associated with Flowering and Maturity Time of Soybean in Central and West Siberian Regions of Russia,"</span></em><span> published in the journal </span><em><span>Frontiers in Plant Science</span></em><span>. In the genotype file, the accessions are labeled with numbers, and Supplementary Table 1 in the article provides the correspondence between these numbers and the common names of the accessions.</span></p>
Impact of CYP3A5 Genotypes on Tacrolimus Pharmacokinetics in Colombian Liver Transplant Patients
<p><strong><span>Impact of CYP3A5 Genotypes on Tacrolimus Pharmacokinetics in Colombian Liver Transplant Patients</span></strong></p>
Diversity in CRISPR-based immunity protects susceptible genotypes by restricting phage spread and evolution
Diversity in host resistance often associates with reduced pathogen spread. This may result from ecological and evolutionary processes, likely with feedback between them. Theory and experiments on bacteria-phage interactions have shown that genetic diversity of the bacterial adaptive immune system can limit phage evolution to overcome resistance. Using the CRISPR-Cas bacterial immune system and lytic phage, we engineered a host-pathogen system where each bacterial host genotype could be infected by only one phage genotype. With this model system, we explored how CRISPR diversity impacts the spread of phage when they can overcome a resistance allele, how immune diversity affects the evolution of the phage to increase its host range, and if there was feedback between these processes. We show that increasing CRISPR diversity benefits susceptible bacteria via a dilution effect, which limits the spread of the phage. We suggest that this ecological effect impacts the evolution of novel phage genotypes, which then feeds back into phage population dynamics.
Data from: Host genotype and age shape the leaf and root microbiomes of a wild perennial plant
Bacteria living on and in leaves and roots influence many aspects of plant health, so the extent of a plant's genetic control over its microbiota is of great interest to crop breeders and evolutionary biologists. Laboratory-based studies, because they poorly simulate true environmental heterogeneity, may misestimate or totally miss the influence of certain host genes on the microbiome. Here we report a large-scale field experiment to disentangle the effects of genotype, environment, age and year of harvest on bacterial communities associated with leaves and roots of Boechera stricta (Brassicaceae), a perennial wild mustard. Host genetic control of the microbiome is evident in leaves but not roots, and varies substantially among sites. Microbiome composition also shifts as plants age. Furthermore, a large proportion of leaf bacterial groups are shared with roots, suggesting inoculation from soil. Our results demonstrate how genotype-by-environment interactions contribute to the complexity of microbiome assembly in natural environments.
Data from: RAD sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference
Restriction site-associated DNA sequencing (RADseq) provides researchers with the ability to record genetic polymorphism across thousands of loci for non-model organisms, potentially revolutionising the field of molecular ecology. However, as with other genotyping methods, RADseq is prone to a number of sources of error that may have consequential effects for population genetic inferences, and these have received only limited attention in terms of the estimation and reporting of genotyping error rates. Here we use individual sample replicates, under the expectation of identical genotypes, to quantify genotyping error in the absence of a reference genome. We then use sample replicates to (1) optimize de novo assembly parameters within the program Stacks, by minimizing error and maximizing the retrieval of informative loci, and; (2) quantify error rates for loci, alleles and SNPs. As an empirical example we use a double digest RAD dataset of a non-model plant species, Berberis alpina, collected from high altitude mountains in Mexico.
Data & Code from: Crop mixtures: does niche complementarity hold for belowground resources? an experimental test using rice genotypic pairs
<p>Data & Code for the study "Crop mixtures: does niche complementarity hold for belowground resources? an experimental test using rice genotypic pairs"</p> <p>Data:<br> "Rice_traits.csv": this file contains trait and productivity data measured at the individual plant level. It has one row per plant and one column per trait.</p> <p>Column headers:<br> "IDplant": unique plant identifier (1 to 200)<br> "IDpot": pot identifier with two plants per pot (1 to 100)<br> "Bloc": bloc identifier, with 20 pots per bloc (A, B, C, D, E)<br> "Treatment": P0 vs P+ = no P supply vs P supply<br> "Asso": pot type, either monoculture (M) or mixture (P)<br> "IDcouple": concatenation of the identifiers of the two genotypes in a pot (I64 = IR64, I64+=IR64 introgressed with QTL9, Pdi=Padi, Ktn=Ketan)<br> "IDgeno": focal genotype identifier (I64 = IR64, I64+=IR64 introgressed with QTL9, Pdi=Padi, Ktn=Ketan)<br> "IDnei": neighbour genotype identifier (I64 = IR64, I64+=IR64 introgressed with QTL9, Pdi=Padi, Ktn=Ketan)<br> "BIOM_above": aboveground biomass (g)<br> "Tillers": number of tillers<br> "PH": Plant height (cm)<br> "Biovolume": biovolume (m3)<br> "SLA": Specific Leaf Area (m2/kg)<br> "RB_top": Root biomass between 0 and 20 cm below the soil surface(g)<br> "RB_deep: Root biomass between 20 and 60 cm below the soil surface(g) (!!! Only measured at the pot-level)<br> "D_ad"/"D_bas": Mean root diameter (mm) of adventitious/basal roots, respectively<br> "SRL_ad"/"SRL_bas": Specific Root Length (m/g) of adventitious/basal roots, respectively<br> "RTD_ad"/"RTD_bas": Root Tissue Density (mg/cm3) of adventitious/basal roots, respectively<br> "RBI_ad"/"RBI_bas": Root Branching Intensity (nb tips/cm) of adventitious/basal roots, respectively<br> "PfR_ad"/"PfR_bas": Proportion of fine roots (diameter < 0.1 mm) (%) in adventitious/basal roots, respectively</p> <p>Code:<br> "Rice_mixtures_analysis.R": this file contains the main statisticl analysis presented in the study. It uses "Rice_traits.csv" as an input.</p> <p> </p>
Data from: Recent chapters of Neotropical history overlooked in phylogeography: shallow divergence explains phenotype and genotype uncoupling in Antilophia manakins
Establishing links between phenotypic and genotypic variation is a central goal of evolutionary biology, as they might provide important insights into evolutionary processes shaping genetic and species diversity in nature. One of the more intriguing possibilities is when no genetic divergence is found to be associated with conspicuous phenotypic divergence. In that case, speciation theory predicts that phenotypic divergence may still occur in the presence of significant gene flow—thereby resulting in little genomic divergence—when genetic loci underpinning phenotypes are under strong divergent selection. However, a finding of phenotypic distinctiveness with weak or no population genetic structure may simply result from low statistical power to detect shallow genetic divergences when small datasets are used. Here, we used a subgenomic dataset of 2386 ultraconserved elements to explore genome-wide divergence between two species of Antilophia manakins, which are phenotypically distinct yet evidently lack strong genetic differentiation according to previous studies based on a limited number of loci. Our results revealed clear population structure that matches the two phenotypes, supporting the idea that smaller datasets lacked the power to detect this recent divergence event (likely < 100 k ya). Indeed, we found little or no introgression between the species, as well as evidence of genome-wide divergence. One implication of our study is that the Araripe plateau may be a hotspot of cryptic-diverging forest Cerrado populations. Besides their use in biogeography, subgenomic datasets may help redefine local conservation programs by revealing cryptic population structure that may be key to population management.
Data from: CHIIMP: an automated high-throughput microsatellite genotyping approach reveals greater allelic diversity in wild chimpanzees
Short tandem repeats (STRs), also known as microsatellites, are commonly used to non-invasively genotype wild-living endangered species, including African apes. Until recently, capillary electrophoresis has been the method of choice to determine the length of polymorphic STR loci. However, this technique is labor intensive, difficult to compare across platforms, and notoriously imprecise. Here we developed a MiSeq-based approach and tested its performance using previously genotyped fecal samples from long-term studied chimpanzees in Gombe National Park, Tanzania. Using data from eight microsatellite loci as a reference, we designed a bioinformatics platform that converts raw MiSeq reads into locus-specific files and automatically calls alleles after filtering stutter sequences and other PCR artifacts. Applying this method to the entire Gombe population, we confirmed previously reported genotypes, but also identified 31 new alleles that had been missed due to sequence differences and size homoplasy. The new genotypes, which increased the allelic diversity and heterozygosity in Gombe by 61% and 8%, respectively, were validated by replicate amplification and pedigree analyses. This demonstrated inheritance and resolved one case of an ambiguous paternity. Using both singleplex and multiplex locus amplification, we also genotyped fecal samples from chimpanzees in the Greater Mahale Ecosystem in Tanzania, demonstrating the utility of the MiSeq-based approach for genotyping non-habituated populations and performing comparative analyses across field sites. The new automated high-throughput analysis platform (available at https://github.com/ShawHahnLab/chiimp) will allow biologists to more accurately and effectively determine wildlife population size and structure, and thus obtain information critical for conservation efforts.
Data from: A RAD-sequencing approach to genome-wide marker discovery, genotyping, and phylogenetic inference in a diverse radiation of primates
Until recently, most phylogenetic and population genetics studies of nonhuman primates have relied on mitochondrial DNA and/or a small number of nuclear DNA markers, which can limit our understanding of primate evolutionary and population history. Here, we describe a cost-effective reduced representation method (ddRAD-seq) for identifying and genotyping large numbers of SNP loci for taxa from across the New World monkeys, a diverse radiation of primates that shared a common ancestor ~20-26 mya. We also estimate, for the first time, the phylogenetic relationships among 15 of the 22 currently-recognized genera of New World monkeys using ddRAD-seq SNP data using both maximum likelihood and quartet-based coalescent methods. Our phylogenetic analyses robustly reconstructed three monophyletic clades corresponding to the three families of extant platyrrhines (Atelidae, Pitheciidae and Cebidae), with Pitheciidae as basal within the radiation. At the genus level, our results conformed well with previous phylogenetic studies and provide additional information relevant to the problematic position of the owl monkey (Aotus) within the family Cebidae, suggesting a need for further exploration of incomplete lineage sorting and other explanations for phylogenetic discordance, including introgression. Our study additionally provides one of the first applications of next-generation sequencing methods to the inference of phylogenetic history across an old, diverse radiation of mammals and highlights the broad promise and utility of ddRAD-seq data for molecular primatology.
Data from: Genotypic traits and tradeoffs of fast growth in silver birch, a pioneer tree
<p>Fast-growing and slow-growing plant species are suggested to show integrated economics spectrums and the tradeoffs of fast growth are predicted to emerge as susceptibility to herbivory and resource competition. We tested if these predictions also hold for fast-growing and slow-growing genotypes within a silver birch, <i>Betula pendula</i> population. We exposed cloned saplings of 17 genotypes with slow, medium or fast height growth to reduced insect herbivory, using an insecticide, and to increasing resource competition, using naturally varying field plot grass cover. We measured shoot and root growth, ectomycorrhizal (EM) fungal production using ergosterol analysis and soil N transfer to leaves using <sup>15</sup>N-labelled pulse of NH<sub>4</sub><sup>+</sup>. We found that fast-growing genotypes grew on average 78% faster, produced 56% and 16% more leaf mass and ergosterol, and showed 78% higher leaf N uptake than slow-growing genotypes. The insecticide decreased leaf damage by 83% and increased shoot growth, leaf growth and leaf N uptake by 38%, 52% and 76%, without differences between the responses of fast-growing and slow-growing genotypes, whereas root mass decreased with increasing grass cover. Shoot and leaf growth of fast-growing genotypes decreased and EM fungal production of slow-growing genotypes increased with increasing grass cover. Our results suggest that fast growth is genotypically associated with higher allocation to EM fungi, better soil N capture and greater leaf production, and that the tradeoff of fast growth is sensitivity to competition, but not to insect herbivory. EM fungi may have a dual role: to support growth of fast-growing genotypes under low grass competition and to maintain growth of slow-growing genotypes under intensifying competition.</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.