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FIGURE 2 in Genetic diversity and species limits in Veltheimia (Asparagaceae: Scilloideae): insights from noncoding cpDNA sequence data
FIGURE 2. Specimen distribution of species of Veltheimia. Red dots = known localities of V. bracteata, blue triangles = known locations of V. capensis (based on data from the BODATSA database of the National Herbarium, South Africa, http://posa.sanbi.org/). The inserted frame shows the Bayesian Inference phylogeny of the combined chloroplast non-coding data set (numbers shown below the branches indicate Posterior Probability values, the number above the red branch is the parsimony Bootstrap Support value). The branch with the thick red line indicates the "bracteata clade". Numbers preceding sample names link to specimens listed in Table 1, and are also (where the locality is known) indicated on the map. The Median Joining Network (MJN) is shown overlaid on the distribution to indicate the location of the samples (and haplotypes) used in the MJN analysis. The numbers in parentheses next to the lines linking the haplotypes indicate the number of mutational differences between the haplotypes, and the solid black circle indicate an un-sampled or hypothesised missing haplotype.
Co-GWAS unveils the genetic architecture of inter-individual epistasis affecting biomass and disease severity in wheat binary mixtures
<p>The repository contains all the data and scripts required to perform the analysis and generate the figures and tables presented in the article: <br>"Co-GWAS unveils the genetic architecture of inter-individual epistasis affecting biomass and disease severity in wheat binary mixtures".</p> <p>0. DATA</p> <p>FOLDER: 0_Data<br>This folder contains the raw phenotypic and genotypic data.</p> <p>I. FILE PREPARATION</p> <p>FOLDER: 1_FilePrep_Design_Fig<br>The R script prepares the phenotypic and genotypic data for analysis and generates the kinship matrix. It also creates a figure illustrating the pairs of phenotyped genotypes.</p> <p>II. PHENOTYPIC ANALYSIS </p> <p>FOLDER: 2_GeneticEffects_PhenoCorr_Residus<br>The R script tests the significance of genetic effects, calculates the proportion of phenotypic variance explained for each phenotype, and checks for correlations between phenotypes. It also controls the residuals in the models.</p> <p>III. A. DGE-BASED GWAS</p> <p>FOLDER: 3_DGE_GWAS<br>This folder contains three subfolders for each phenotype (GWAS_DGE_B, GWAS_DGE_N, GWAS_DGE_P).<br>For example, the pycnidia folder contains the script 3.A_GWAS_DGE_P_AsREML_cluster.R, which performs DGE-based GWAS for the pycnidia phenotype.</p> <p>III. B. Analysis of GWAS Results for DGE</p> <p>FOLDER: 3_DGE_GWAS<br>Within the same folder, the script 3.B_Results_GWAS_DGE_P.Rmd combines the result files and produces Manhattan plots and plots of significant SNPs.</p> <p>IV. A. IGE-BASED GWAS</p> <p>FOLDER: 4_IGE_GWAS<br>This folder contains three subfolders for each phenotype (GWAS_IGE_B, GWAS_IGE_N, GWAS_IGE_P).<br>For example, the pycnidia folder contains the script 4.A_GWAS_IGE_P_AsREML_cluster.R, which performs IGE-based GWAS for the pycnidia phenotype.</p> <p>IV. B. Analysis of GWAS Results for IGE</p> <p>FOLDER: 4_IGE_GWAS<br>Within the same folder, the script 4.B_Results_GWAS_IGE_P.Rmd combines the result files and produces Manhattan plots and plots of significant SNPs.</p> <p>V. PREPARATION OF FILES FOR CO-GWAS</p> <p>FOLDER: 5_FilePrep_coGWAS<br>The script 5.A_FilePrep_coGWAS.Rmd prepares the phenotypic and genotypic data for analysis after SNP pruning.<br>The script 5.B_FilePrep_SNP_Pruning.R performs SNP pruning.<br>The script 5.C_Plot_SNP_Pruning_position.R plots the positions of SNPs before and after pruning.</p> <p>VI. CO-GWAS</p> <p>FOLDER: 6_coGWAS<br>This folder contains three subfolders for each phenotype (coGWAS_B, coGWAS_N, and coGWAS_P). <br>For example, the pycnidia folder contains the script 6.A_coGWAS_DGEIGE_P_Sommer_cluster.R, which performs the co-GWAS for the pycnidia phenotype.<br>The other scripts in this folder combine the result files.</p> <p>VII. HEATMAPS AND QQPLOT OF CO-GWAS RESULTS</p> <p>FOLDER: 7_Heatmaps_qqplots_coGWAS<br>This folder contains three subfolders for each phenotype (Heatmaps_qqplots_B, Heatmaps_qqplots_N, and Heatmaps_qqplots_P).<br>For example, the pycnidia subfolder includes two scripts: 7.A_coGWAS_P_Heatmaps.R generates heatmaps for the pycnidia phenotype and 7.B_coGWAS_P_qqplots.R produces the QQ plot.</p> <p>VIII. BOXPLOTS - 3D PLOTS - PHYSICAL MAPS</p> <p>FOLDER: 8_3Dplots_PhysicalMaps_Boxplots_coGWAS<br>This folder contains three subfolders for each phenotype (Boxplots_PhysicalMaps_3Dplots_B, Boxplots_PhysicalMaps_3Dplots_N, and Boxplots_PhysicalMaps_3Dplots_P).<br>For example, the pycnidia folder contains the script 8_coGWAS_P_Boxplots_PhysicalMaps_3Dplots.R, which generates 3D plots, boxplots and physical maps for the significant interactions. </p> <p>IX. CIRCULAR PLOTS </p> <p>FOLDER: 9_CircularPlots_coGWAS<br>This folder contains three subfolders for each phenotype (CircularPlots_B, CircularPlots_N, and CircularPlots_P).<br>For example, the pycnidia folder contains the script 9_coGWAS_P_CircularPlot.R, which generates the necessary files to create the circular plot. <br>The script circos.conf creates the circular plot. </p> <p>X. GO ENRICHMENT ANALYSIS </p> <p>FOLDER: 10_GOterms_coGWAS<br>This folder contains three subfolders for each phenotype (GOenrichments_B, GOenrichments_N, and GOenrichments_P).<br>For example, the pycnidia folder contains the script 10_coGWAS_P_GOenrichments.Rmd, which generates GO enrichment plots.</p>
Supplemantal tables for "A Non-targeted Proteomics Newborn Screening Platform for Genetic Disorders".
<p>Table S1 (List of proteins identified in DBS samples analyzed in the study).</p> <p>The list includes identified proteins with peptide and protein false discovery rates < 1%. Proteins shown in black are those identified by unique peptide sequences that were clearly distinguishable from others. Proteins shown in blue represent grouped master proteins. Proteins shown in grey represent grouped proteins. A quantitative value of “0” indicates that the protein was detected, but did not reach a level sufficient for quantification.</p> <p>Table S2 (Proteins identified in DBS samples that are listed in OMIM)</p> <p>List of proteins identified in every healthy newborn sample that are included in the OMIM database. The list includes identified proteins with peptide and protein false discovery rates < 1%. AD, autosomal dominant; AR, autosomal recessive; ARLOF, autosomal recessive loss of function; XL, X-linked; IUIS2022, 2022 Classification of Human Inborn Errors of Immunity by the International Union of Immunological Societies Expert Committee.</p>
Figure 3 in Mitochondrial genetic variation in long-eared hedgehogs, Hemiechinus auritus, from the Anatolian Peninsula and Cyprus
Figure 3: Median-joining network constructed using cyt-b sequences of Hemiechinus auritus. The size of each circle is proportional to the frequency of the haplotype in the sample. Median vectors are indicated by blank circles. Each mutation between haplotypes is represented by a bar. For the geographical origins of the sequences, see Figure 1, Table 1 and Supplementary Table S1.
Figure 2 in Mitochondrial genetic variation in long-eared hedgehogs, Hemiechinus auritus, from the Anatolian Peninsula and Cyprus
Figure 2: Results of MP, ML and BI analyses combined on an ML tree based on cyt-b sequences of Hemiechinus auritus. Numbers at nodes indicate bootstrap support values (MP and ML)/posterior probabilities (BI). Bootstrap values ≥ 90 % and Bayesian posterior probabilities ≥0.90 are shown. For the geographical origins of the sequences, see Figure 1, Table 1 and Supplementary Table S1.
Figure 1 in Mitochondrial genetic variation in long-eared hedgehogs, Hemiechinus auritus, from the Anatolian Peninsula and Cyprus
Figure 1: Map showing the distribution range of the five main geographical lineages (I.–V. MGL) we discussed based on cyt-b data. It shows the match of the mitochondrial genetic structuring with named sub-species that have previously been described. Location numbers on the map for Hemiechinus auritus samples sequenced for the present study are listed in Table 1. For the geographical origins of the published sequences, see Supplementary Table S1.
Experimental data of genetic mapping, fine mapping or agronomic traits, related to Figures 1 and 3
<p>This dataset reports the experimental data of <span>g</span>enetic mapping<span>,</span> <span>f</span>ine mapping<span>, </span>agronomic, flowering time and yield-related traits for NIL<sup>DAN340</sup> and NIL<sup>K22</sup>, agronomic, flowering time and yield-related traits for knockout of <em>qKDR1</em>, agronomic, flowering time and yield-related traits for knockout of microRPG1<span>.</span></p>
Supplementary Figure S3 The leave-one-out analysis of the association between insomnia and MetS components genetically in the reverse MR analysis.
<p>This figure described the leave-one-out analysis of the association between insomnia and MetS components genetically in the reverse MR analysis.</p>
Supplementary Figure S2.The funnel plots of the association between insomnia and MetS components genetically in the reverse MR analysis.
<p>This figure described the funnel plots of the association between insomnia and MetS components genetically in the reverse MR analysis.</p>
MRBIGR: a versatile toolbox for genetic causal inference from population-scale multi-omics data
<p>MRBIGR is a multifunctional toolkit for pre-GWAS, GWAS and post-GWAS of both traditional and multi-omics data. MRBIGR provides all the components needed to build a complete GWAS pipeline, and integrates with rich post-GWAS analysis tools such as QTL annotation and haplotype analysis. In particular, Mendelian randomization (MR) analysis, MR-based network construction, module identification and gene ontology analysis are proposed for further genetic regulation studies. Additionally, it also produces rich plots for visualization of the analysis results and other formatted data.</p> <p>This dataset is used to generate images in MRBIGR papers and can also serve as an example to demonstrate how to use MRBIGR.</p>
Table 2. Genetic distances for mitochondrial DNA partial cytochrome c oxidase subunit I and cytochrome b in Molecular phylogeny of the Aplodactylidae (Perciformes: Cirrhitoidea), a group of Southern Hemisphere marine ® shes
<p>Table 2. Genetic distances for mitochondrial DNA partial cytochrome <i>c</i> oxidase subunit I and cytochrome <i>b</i> sequences when combined. Values are Kimura (1980) two-parameter percentage sequence divergences, obtained when using the optimum expected transition±transversion nucleotide substitution ratio of 3.0 from maximum likelihood analysis (fi gure 3).</p><table><tbody><tr><th></th><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th></tr></tbody><tbody><tr><th>1</th><td><i>Aplodactylus arctidens</i></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>2</th><td><i>Aplodactylus punctatus</i></td><td>6.1</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>3</th><td><i>Aplodactylus westralis</i></td><td>7.8</td><td>7.6</td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>4</th><td><i>Aplodactylus etheridgii</i></td><td>10.0</td><td>10.3</td><td>10.0</td><td></td><td></td><td></td><td></td></tr><tr><th>5</th><td><i>Aplodactylus lophodon</i></td><td>11.8</td><td>11.9</td><td>12.4</td><td>11.1</td><td></td><td></td><td></td></tr><tr><th>6</th><td><i>Chironemus marmoratus</i></td><td>20.0</td><td>18.7</td><td>18.3</td><td>19.3</td><td>19.5</td><td></td><td></td></tr><tr><th>7</th><td><i>Cheilodactylus fasciatus</i></td><td>21.8</td><td>21.0</td><td>20.5</td><td>22.6</td><td>20.2</td><td>21.2</td><td></td></tr><tr><th>8</th><td><i>Cirrhitus splendens</i></td><td>22.6</td><td>20.7</td><td>21.0</td><td>23.1</td><td>22.0</td><td>23.1</td><td>22.8</td></tr></tbody></table>
Data from: Scale-specific sex-biased dispersal in the Valais shrew unveiled by genetic variation on the Y chromosome, autosomes, and mitochondrial DNA
We investigated sex-specificities in the evolutionary processes shaping Y chromosome, autosomes and mitochondrial DNA patterns of genetic structure in the Valais shrew (Sorex antinorii), a mountain dwelling species with a hierarchical distribution. Both hierarchical analyses of variance and isolation-by-distance analyses revealed patterns of population structure that were not consistent across maternal, paternal and bi-parentally inherited markers. Differentiation on a Y microsatellite was lower than expected from the comparison with autosomal microsatellites and mtDNA, and it was mostly due to genetic variance among populations within valleys, while the opposite was observed on other markers. In addition, there was no pattern of isolation-by-distance for the Y, while there was strong isolation-by-distance on mtDNA and autosomes. We use a hierarchical island model of coancestry dynamics to discuss the relative roles of the micro-evolutionary forces that may induce such patterns. We conclude that sex-biased dispersal is the most important driver of the observed genetic structure, but with an intriguing twist: it seems that dispersal is strongly male-biased at large spatial scale, while it is mildly biased in favour of females at local scale. These results add to recent reports of scale-specific sex-biased dispersal patterns, and emphasize the usefulness of the Y chromosome in conjunction with mtDNA and autosomes to infer sex-specificities.
Data from: Non-equilibrium conditions explain spatial variability in genetic structuring of little penguin (Eudyptula minor)
Factors responsible for spatial structuring of population genetic variation are varied, and in many instances there may be no obvious explanations for genetic structuring observed, or those invoked may reflect spurious correlations. A study of little penguins (Eudyptula minor) in southeast Australia documented low spatial structuring of genetic variation with the exception of colonies at the western limit of sampling, and this distinction was attributed to an intervening oceanographic feature (Bonney Upwelling), differences in breeding phenology, or sea level change. Here, we conducted sampling across the entire Australian range, employing additional markers (12 microsatellites and mitochondrial DNA, 697 individuals, 17 colonies). The zone of elevated genetic structuring previously observed actually represents the eastern half of a genetic cline, within which structuring exists over much shorter spatial scales than elsewhere. Colonies separated by as little as 27 km in the zone are genetically distinguishable, while outside the zone, homogeneity cannot be rejected at scales of up to 1400 km. Given a lack of additional physical or environmental barriers to gene flow, the zone of elevated genetic structuring may reflect secondary contact of lineages (with or without selection against interbreeding), or recent colonization and expansion from this region. This study highlights the importance of sampling scale to reveal the cause of genetic structuring.
Data from: The many dimensions of diet breadth: phytochemical, genetic, behavioral, and physiological perspectives on the interaction between a native herbivore and an exotic host
From the perspective of an herbivorous insect, conspecific host plants are not identical, and intraspecific variation in host nutritional quality or defensive capacity might mediate spatially variable outcomes in plant-insect interactions. Here we explore this possibility in the context of an ongoing host breadth expansion of a native butterfly (the Melissa blue, Lycaeides melissa) onto an exotic host plant (alfalfa, Medicago sativa). We examine variation among seven alfalfa populations that differed in terms of colonization by L. melissa; specifically, we examined variation in phytochemistry, foliar protein, and plant population genetic structure, as well as responses of caterpillars and adult butterflies to foliage from the same populations. Regional patterns of alfalfa colonization by L. melissa were well predicted by phytochemical variation, and colonized patches of alfalfa showed a similar level of inter-individual phytochemical diversity. However, phytochemical variation was a poor predictor of larval performance, despite the fact that survival and weight gain differed dramatically among caterpillars reared on plants from different alfalfa populations. Moreover, we observed a mismatch between alfalfa supporting the best larval performance and alfalfa favored by ovipositing females. Thus, the axes of plant variation that mediate interactions with L. melissa depend upon herbivore life history stage, which raises important issues for our understanding of adaptation to novel resources by an organism with a complex life history.
Data from: Genetic erosion in wild populations makes resistance to a pathogen more costly
Populations that have suffered from genetic erosion are expected to exhibit reduced average trait values or decreased variation in adaptive traits when experiencing periodic or emergent stressors such as infectious disease. Genetic erosion may consequentially modify the ability of a potential host population to cope with infectious disease emergence. We experimentally investigate this relationship between genetic variability and host response to exposure to an infectious agent both in terms of susceptibility to infection and indirect parasite-mediated responses that also impact fitness. We hypothesized that the deleterious consequences of exposure to the pathogen (Batrachochytrium dendrobatidis) would be more severe for tadpoles descended from European tree frog (Hyla arborea) populations lacking genetic variability. Although all exposed tadpoles lacked detectable infection, we detected this relationship for some indirect host responses, predominantly in genetically depleted animals, as well as an interaction between genetic variability and pathogen dose on lifespan during the post-metamorphic period. Lack of infection and a decreased mass and post-metamorphic lifespan in low genetic diversity tadpoles lead us to conclude that genetic erosion, while not affecting the ability to mount effective resistance strategies, also erodes the capacity to invest in resistance, increased tadpole growth rate and metamorphosis relatively simultaneously.
Data from: Quantifying effects of environmental and geographical factors on patterns of genetic differentiation
Elucidating the factors influencing genetic differentiation is an important task in biology, and the relative contribution from natural selection and genetic drift has long been debated. In this study, we used a regression-based approach to simultaneously estimate the quantitative contributions of environmental adaptation and isolation by distance on genetic variation in Boechera stricta, a wild relative of Arabidopsis. Patterns of discrete and continuous genetic differentiation coexist within this species. For the discrete differentiation between two major genetic groups, environment has larger contribution than geography, and we also identified a significant environment-by-geography interaction effect. Elsewhere in the species range, we found a latitudinal cline of genetic variation reflecting only isolation by distance. To further confirm the effect of environmental selection on genetic divergence, we identified the specific environmental variables predicting local genotypes in allopatric and sympatric regions. Water availability was identified as the possible cause of differential local adaptation in both geographic regions, confirming the role of environmental adaptation in driving and maintaining genetic differentiation between the two major genetic groups. In addition, the environment-by-geography interaction is further confirmed by the finding that water availability is represented by different environmental factors in the allopatric and sympatric regions. In conclusion, this study found that geographical and environmental factors together created stronger and more discrete genetic differentiation than isolation by distance alone, which only produced a gradual, clinal pattern of genetic variation. These findings emphasize the importance of environmental selection in shaping patterns of species-wide genetic variation in the natural environment.
Data from: Population genetic structure and demographic history of Atrina pectinata based on mitochondrial DNA and microsatellite markers
The pen shell, Atrina pectinata, is one of the commercial bivalves in East Asia and thought to be recently affected by anthropogenic pressure (habitat destruction and/or fishing pressure). Information on its population genetic structure is crucial for the conservation of A. pectinata. Considering its long pelagic larval duration and iteroparity with high fecundity, the genetic structure for A. pectinata could be expected to be weak at a fine scale. However, the unusual oceanography in the coasts of China and Korea suggests potential for restricted dispersal of pelagic larvae and geographical differentiation. In addition, environmental changes associated with Pleistocene sea level fluctuations on the East China Sea continental shelf may also have strongly influenced historical population demography and genetic diversity of marine organisms. Here, partial sequences of the mitochondrial Cytochrome c oxidase subunit I (COI) gene and seven microsatellite loci were used to estimate population genetic structure and demographic history of seven samples from Northern China coast and one sample from North Korea coast. Despite high levels of genetic diversity within samples, there was no genetic differentiation among samples from Northern China coast and low but significant genetic differentiation between some of the Chinese samples and the North Korean sample. A late Pleistocene population expansion, probably after the Last Glacial Maximum, was also demonstrated for A. pectinata samples. No recent genetic bottleneck was detected in any of the eight samples. We concluded that both historical recolonization (through population range expansion and demographic expansion in the late Pleistocene) and current gene flow (through larval dispersal) were responsible for the weak level of genetic structure detected in A. pectinata.
Data from: Mutualism effectiveness and vertical transmission of symbiotic fungal endophytes in response to host genetic background
Certain species of the Pooideae subfamily develop stress tolerance and herbivory resistance through symbiosis with vertically-transmitted, asexual fungi. This symbiosis is specific, and genetic factors modulate compatibility between partners. Although gene flow is clearly a fitness trait in allogamous grasses, since it injects hybrid vigor and raw material for evolution, it could reduce compatibility and thus, mutualism effectiveness. To explore the importance of host genetic background in modulating the performance of symbiosis, Lolium multiflorum plants, infected and non-infected with Neotyphodium occultans, were crossed with genetically distant plants of isolines (susceptible and resistant to diclofop-methyl herbicide) bred from two cultivars, and exposed to stress. The endophyte improved seedling survival in genotypes susceptible to herbicide, while it had a negative effect on one of the genetically resistant crosses. Mutualism provided resistance to herbivory independently of the host genotype, but this effect vanished under stress. While no endophyte effect was observed on host reproductive success, it was increased by inter-population plant crosses. Neither gene flow nor herbicide had an important impact on endophyte transmission. Host fitness improvements due to gene flow do not appear to result in direct conflict with mutualism while this seems to be an important mechanism for the ecological and contemporary evolution of the symbiotum.
Data from: Population genetic structure of the giant cactus Echinopsis terscheckii in northwestern Argentina is shaped by patterns of vegetation cover
Species inhabiting drylands commonly depend on the surrounding vegetation for recruitment under stress, while competition may affect populations in moister environments. Our objective was to analyze how different climates and vegetation affect the fine-scale spatial genetic structure (SGS) of the columnar cactus Echinopsis terscheckii. At four sites we estimated vegetation cover by digitized patches and the normalized difference vegetation index (NDVI). We mapped 30 individuals per population and collected tissue for isozyme electrophoresis using 15 putative loci. Spatial autocorrelation between all possible genotype pairs and the number of genetically homogeneous groups and families were calculated for each population. Greater cover (66%) and average NDVI values were detected in the most humid habitat that consisted of fewer, larger, and more dispersed vegetation patches. All populations were genetically diverse and showed significant SGS. Positive correlations were found between the distance at which maximum autocorrelation and kinship values were reached and vegetation area and patch size. Also higher NDVI values were associated with lower number of patches. Populations exposed to higher precipitation and vegetation cover consisted of sparse individuals that clustered at larger distances whereas vegetation patches in arid climates produced groups of closely related genotypes at small distances. These results support the stress-gradient genetic hypothesis. Under water stress, facilitation promotes establishment underneath patchy vegetation resulting in fine-scale family structure. In moister xerophilous forests competition for resources, i.e. light, results in sparse individuals and thus coarse-scale neighborhoods. This information can guide conservation and/or restoration efforts, such as the spatial scale to be considered in germplasm collection.
Data from: Social and genetic population structure of free-ranging cheetah in Botswana: implications for conservation
Once widely distributed throughout Africa, cheetahs (Acinonyx jubatus) occur today within fragmented populations and are listed as vulnerable by the IUCN. Botswana currently hosts the second largest cheetah population throughout the species' range. This study initiated a molecular genetic survey of wild Botswana cheetah populations. It focused on the relatedness within presumed social groups using 14 microsatellite markers and revealed a higher proportion of unrelated male coalitions than was expected. Based on the unrelated cheetahs only, the estimation of the genetic variation corresponded with results from recent studies on different African populations. The analysis of unrelated individuals indicated limited genetic differentiation between cheetahs from different regions of Botswana. This suggests that the Botswana cheetah population might represent a unique panmictic population as long as sufficient levels of gene flow are maintained within the distribution range. This baseline information will now be incorporated to develop management strategies and set priorities for cheetah conservation in Botswana.
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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.