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5,805 results for “Data model”
Data from: An integrated model of phenotypic trait changes and site-specific sequence evolution
Recent years have seen a constant rise in the availability of trait data, including morphological features, ecological preferences, and life history characteristics. These phenotypic data provide means to associate genomic regions with phenotypic attributes, thus allowing the identification of phenotypic traits associated with the rate of genome and sequence evolution. However, inference methodologies that analyze sequence and phenotypic data in a unified statistical framework are still scarce. Here, we present TraitRateProp, a probabilistic method that allows testing whether the rate of sequence evolution is associated with a binary phenotypic character trait. The method further allows the detection of specific sequence sites whose evolutionary rate is most noticeably affected following the character transition, suggesting a shift in functional/structural constraints. TraitRateProp is first evaluated in simulations and then applied to study the evolutionary process of plastid plant genomes upon a transition to a heterotrophic lifestyle. To this end, we analyze 25 plastid genes across 85 orchid species, spanning different lifestyles and representing different genera in this large family of flowering plants. Our results indicate higher evolutionary rates following repeated transitions to a heterotrophic lifestyle in all but four of the loci analyzed.
Data from: Is saltmarsh restoration success constrained by matching natural environments or altered succession? a test using niche models
1.Restored habitats, such as saltmarsh created through managed realignment, sometimes fail to meet targets for biological equivalence with natural reference sites. Understanding why this happens is important in order to improve restoration outcomes. 2.Elevation in the tidal frame and sediment redox potential are major controls on the distribution of saltmarsh plants. We use niche models to characterize ten species' responses to these, and test whether differences in species occurrence between restored and natural saltmarshes in the UK result from failure to recreate adequate environmental conditions. 3.Six species occurred less frequently in recently restored marshes than natural marshes. Failure of restored marshes to achieve the elevation and redox conditions of natural marshes partially explained the underrepresentation of five of these species, but did not explain patterns of occurrence on older (> 50 years) restored marshes. 4.For all species, an effect of marsh age remained after controlling for differences in environmental conditions. This could be due to differences in successional mechanism between restored and natural marshes. In recently restored marshes, high-marsh species occurred lower in the tidal frame and low-marsh species occurred at higher elevations than in natural marshes. This supports the hypothesis that competition is initially weaker in restored marshes, because of the availability of bare sediment across the whole tidal frame. Species that establish outside their normal realized niche, such as Atriplex portulacoides, may inhibit subsequent colonization of other species that occurred less frequently than expected on older restored marshes. 5.Synthesis and applications. Niche models can be used to test whether abiotic differences between restored sites and their natural counterparts are responsible for discrepancies in species occurrence. In saltmarshes, simply replicating environmental conditions will not result in equivalent species occurrence.
Data from: Detecting hidden diversification shifts in models of trait-dependent speciation and extinction
The distribution of diversity can vary considerably from clade to clade. Attempts to understand these patterns often employ state-dependent speciation and extinction models to determine whether the evolution of a particular novel trait has increased speciation rates and/or decreased extinction rates. It is still unclear, however, whether these models are uncovering important drivers of diversification, or whether they are simply pointing to more complex patterns involving many unmeasured and co-distributed factors. Here we describe an extension to the popular state-dependent speciation and extinction models that specifically accounts for the presence of unmeasured factors that could impact diversification rates estimated for the states of any observed trait, addressing at least one major criticism of BiSSE (Binary State Speciation and Extinction) methods. Specifically, our model, which we refer to as HiSSE (Hidden State Speciation and Extinction), assumes that related to each observed state in the model are "hidden" states that exhibit potentially distinct diversification dynamics and transition rates than the observed states in isolation. We also demonstrate how our model can be used as character-independent diversification models that allow for a complex diversification process that is independent of the evolution of a character. Under rigorous simulation tests and when applied to empirical data, we find that HiSSE performs reasonably well, and can at least detect net diversification rate differences between observed and hidden states and detect when diversification rate differences do not correlate with the observed states. We discuss the remaining issues with state-dependent speciation and extinction models in general, and the important ways in which HiSSE provides a more nuanced understanding of trait-dependent diversification.
Data from: A multispecies coalescent model for quantitative traits
We present a multispecies coalescent model for quantitative traits that allows for evolutionary inferences at micro- and macroevolutionary scales. A major advantage of this model is its ability to incorporate genealogical discordance underlying a quantitative trait. We show that discordance causes a decrease in the expected trait covariance between more closely related species relative to more distantly related species. If unaccounted for, this outcome can lead to an overestimation of a trait's evolutionary rate, to a decrease in its phylogenetic signal, and to errors when examining shifts in mean trait values. The number of loci controlling a quantitative trait appears to be irrelevant to all trends reported, and discordance also affected discrete, threshold traits. Our model and analyses point to the conditions under which different methods should fare better or worse, in addition to indicating current and future approaches that can mitigate the effects of discordance.
Data from: Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models
Pairwise maximum-entropy models have been used in neuroscience to predict the activity of neuronal populations, given only the time-averaged correlations of the neuron activities. This paper provides evidence that the pairwise model, applied to experimental recordings, would produce a bimodal distribution for the population-averaged activity, and for some population sizes the second mode would peak at high activities, that experimentally would be equivalent to 90% of the neuron population active within time-windows of few milliseconds. Several problems are connected with this bimodality: 1. The presence of the high-activity mode is unrealistic in view of observed neuronal activity and on neurobiological grounds. 2. Boltzmann learning becomes non-ergodic, hence the pairwise maximum-entropy distribution cannot be found: in fact, Boltzmann learning would produce an incorrect distribution; similarly, common variants of mean-field approximations also produce an incorrect distribution. 3. The Glauber dynamics associated with the model is unrealistically bistable and cannot be used to generate realistic surrogate data. This bimodality problem is first demonstrated for an experimental dataset from 159 neurons in the motor cortex of macaque monkey. Evidence is then provided that this problem affects typical neural recordings of population sizes of a couple of hundreds or more neurons. The cause of the bimodality problem is identified as the inability of standard maximum-entropy distributions with a uniform reference measure to model neuronal inhibition. To eliminate this problem a modified maximum-entropy model is presented, which reflects a basic effect of inhibition in the form of a simple but non-uniform reference measure. This model does not lead to unrealistic bimodalities, can be found with Boltzmann learning, and has an associated Glauber dynamics which incorporates a minimal asymmetric inhibition.
Data from: A skull might lie: modelling ancestral ranges and diet from genes and shape of tree squirrels
Tropical forests of Central and South America represent hotspots of biological diversity. Tree squirrels of the tribe Sciurini are an excellent model system for the study of tropical biodiversity as these squirrels disperse exceptional distances, and after colonizing the tropics of the Central and South America, they have diversified rapidly. Here, we compare signals from DNA sequences with morphological signals using pictures of skulls and computational simulations. Phylogenetic analyses reveal step-wise geographic divergence across the Northern Hemisphere. In Central and South America, tree squirrels form two separate clades, which split from a common ancestor. Simulations of ancestral distributions show western Amazonia as the epicenter of speciation in South America. This finding suggests that wet tropical forests on the foothills of Andes possibly served as refugia of squirrel diversification during Pleistocene climatic oscillations. Comparison of phylogeny and morphology reveals one major discrepancy: Microsciurus species are a single clade morphologically but are polyphyletic genetically. Modeling of morphology–diet relationships shows that the only group of species with a direct link between skull shape and diet are the bark-gleaning insectivorous species of Microsciurus. This finding suggests that the current designation of Microsciurus as a genus is based on convergent ecologically driven changes in morphology.
Data from: Computational and experimental characterization of dVHL establish a Drosophila model of VHL syndrome
The von Hippel-Lindau (VHL) cancer syndrome is associated with mutations in the VHL gene. The pVHL protein is involved in response to changes in oxygen availability as part of an E3-ligase that targets the Hypoxia-Inducible Factor for degradation. pVHL has a molten globule configuration with marginal thermodynamic stability. The cancer-associated mutations further destabilize it. The Drosophila homolog, dVHL, has relatively low sequence similarity to pVHL, and is also involved in regulating HIF1-α. Using in silico, in vitro and in vivo approaches we demonstrate high similarity between the structure and function of dVHL and pVHL. These proteins have a similar fold, secondary and tertiary structures, as well as thermodynamic stability. Key functional residues in dVHL are evolutionary conserved. This structural homology underlies functional similarity of both proteins, evident by their ability to bind their reciprocal partner proteins, and by the observation that transgenic pVHL can fully maintain normal dVHL-HIF1-α downstream pathways in flies. This novel transgenic Drosophila model is thus useful for studying the VHL syndrome, and for testing drug candidates to treat it.
Data from: Using hidden Markov models to improve quantifying physical activity in accelerometer data – a simulation study
Introduction The use of accelerometers to objectively measure physical activity (PA) has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM) are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA. Methods:1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois]), the generalized Poisson distribution (HMM[GenPois]) and the Gaussian distribution (HMM[Gauss]) with regard to misclassification rate (MCR), bout detection, detection of the number of activities performed during the day and runtime. Results:The cutpoint method had a misclassification rate (MCR) of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint), 2.0 minutes (HMM[Gauss]) and 14.2 minutes (HMM[GenPois]). Conclusions: Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data.
Data from: A probabilistic metric for the validation of computational models
A new validation metric is proposed that combines the use of a threshold based on the uncertainty in the measurement data with a normalised relative error, and that is robust in the presence of large variations in the data. The outcome from the metric is the probability that a model's predictions are representative of the real world based on the specific conditions and confidence level pertaining to the experiment from which the measurements were acquired. Relative error metrics are traditionally designed for use with series of data values but orthogonal decomposition has been employed to reduce the dimensionality of data matrices to feature vectors so that the metric can be applied to fields of data. Three previously published case studies are employed to demonstrate the efficacy of this quantitative approach to the validation process in the discipline of structural analysis, for which historical data was available; however, the concept could be applied to a wide range of disciplines and sectors where modelling and simulation plays a pivotal role.
Data from: Phylogenetic relationship among horseshoe crab species: effect of substitution models on phylogenetic analyses
The horseshoe crabs, known as living fossils, have maintained their morphology almost unchanged for the past 150 million years. The little morphological differentiation among horseshoe crab lineages has resulted in substantial controversy concerning the phylogenetic relationship among the extant species of horseshoe crabs, especially among the three species in the Indo-Pacific region. Previous studies suggest that the three species constitute a phylogenetically unresolvable trichotomy, the result of a cladogenetic process leading to the formation of all three Indo-Pacific species in a short geological time. Data from two mitochondrial genes (for 16S ribosomal rRNA and cytochrome oxidase subunit I) and one nuclear gene (for coagulogen) in the four species of horseshoe crabs and outgroup species were used in a phylogenetic analysis with various substitution models. All three genes yield the same tree topology, with Tachypleus-gigas and Carcinoscorpius-rotundicauda grouped together as a monophyletic taxon. This topology is significantly better than all the alternatives when evaluated with the RELL (resampling estimated log-likelihood) method.
Data from: Modelling enrolment in Cambrian trilobites
Trilobites were capable of enroling in different ways based on the flexible articulation of thoracic segments and associated interlocking devices; the type of enrolment (spiral or sphaeroidal) is thought to have largely depended on the coaptative devices that each trilobite used to enclose the body. Based on X-ray microtomography scans of complete enrolled specimens from the Cambrian, we created three-dimensional (3D) computer models to assess the kinematics needed to achieve both enrolment types. We demonstrate that closely related trilobites with little morphological variation (Bailiaspis?, Conocoryphe and Parabailiella) developed different enrolment types as a result of small variations in the number of thoracic segments and the angle between adjacent segments. Moreover, our models indicate that sphaeroidal enrolment, which is associated with a smaller number of thoracic segments, enabled faster encapsulation. This supports the hypothesis that there was a trend in the evolution of trilobites towards reduction in the number of thoracic segments in phylogenetically derived taxa in order to enhance the efficiency of enrolment.
Data from: A Poissonian model of indel rate variation for phylogenetic tree inference
While indel rate variation has been observed and analyzed in detail, it is not taken into account by current indel-aware phylogenetic reconstruction methods. In this work, we introduce a continuous time stochastic process, the geometric Poisson indel process, that generalizes the Poisson indel process by allowing insertion and deletion rates to vary across sites. We design an efficient algorithm for computing the probability of a given multiple sequence alignment based on our new indel model. We describe a method to construct phylogeny estimates from a fixed alignment using neighbor joining. Using simulation studies, we show that ignoring indel rate variation may have a detrimental effect on the accuracy of the inferred phylogenies, and that our proposed method can sidestep this issue by inferring latent indel rate categories. We also show that our phylogenetic inference method may be more stable to taxa subsampling than methods that either ignore indels or indel rate variation.
Data from: Impaired hippocampal place cell dynamics in a mouse model of the 22q11.2 deletion
Hippocampal place cells represent the cellular substrate of episodic memory. Place cell ensembles reorganize to support learning but must also maintain stable representations to facilitate memory recall. Despite extensive research, the learning-related role of place cell dynamics in health and disease remains elusive. Using chronic two-photon Ca2+ imaging in hippocampal area CA1 of wild-type and Df(16)A+/− mice, an animal model of 22q11.2 deletion syndrome, one of the most common genetic risk factors for cognitive dysfunction and schizophrenia, we found that goal-oriented learning in wild-type mice was supported by stable spatial maps and robust remapping of place fields toward the goal location. Df(16)A+/− mice showed a significant learning deficit accompanied by reduced spatial map stability and the absence of goal-directed place cell reorganization. These results expand our understanding of the hippocampal ensemble dynamics supporting cognitive flexibility and demonstrate their importance in a model of 22q11.2-associated cognitive dysfunction.
Data from: Challenges and solutions for analyzing dual RNA-seq data for non-model host/pathogen systems
1. Dual RNA-seq simultaneously profiles the transcriptomes of a host and pathogen during infection and may reveal the mechanisms underlying host-pathogen interactions. Dual RNA-seq is inherently a mixture of transcripts from at least two species (host and pathogen), so this mixture must be computationally sorted into host and pathogen components. Sorting relies on aligning reads to respective reference genomes, which may be unavailable for both species in non-model host-pathogen pairs. This lack of genomic resources may present challenges to applying dual RNA-seq to non-model systems. 2. We assessed the accuracy of alignments of dual RNA-seq when using the genomic resources of a closely-related species to the species of interest by simulating datasets of mixed transcripts from a host and pathogen. Specifically, we compared how different aligners performed across different proportions of pathogen to host transcripts and across variation in the genetic distance between the pathogen genome and reference genome. We performed extensive analyses for a host plant with fungal pathogen, and then we extended the plant-fungus results by repeating key analyses in vertebrate (human)-fungus and vertebrate-bacterium systems. 3. Aligners that were able to map pathogen transcripts to the reference genome of a species closely related to the pathogen (a "related reference genome") also mismapped transcripts originating from the host to the pathogen's related reference genome, which results in regions where this occurred being quantified as overexpressed. If a host reference genome was available, we show that to minimize host transcript mismapping while retaining the ability to map pathogen transcripts, one could concatenate it with the pathogen's related reference genome, then map transcripts to the concatenated genomes. If a host genome was unavailable, assembling reads de novo prior to aligning substantially decreased host read mismapping, while retaining the ability to map pathogen transcripts to a related reference genome. 4. The application of dual RNA-seq to organisms without reference genomes is currently limited. We propose an analytical workflow that leverages the genomic resources of species closely related to species of interest to facilitate application of dual RNA-seq to reveal the mechanisms of host-pathogen interactions across a wider array of systems.
Data from: MatlabHTK: a simple interface for bioacoustic analyses using Hidden Markov models
1. Passive bioacoustic recording devices are now widely available and able to continuously record remotely located sites for extended periods, offering great potential for wildlife monitoring and management. Analysis of the huge datasets generated, in particular for specific biotic sound recognition, remains a critical bottleneck for widespread adoption of these technologies as current methods are labour intensive. 2. Several methods borrowed from speech processing frameworks, such as hidden Markov models, have been successful in analysing bioacoustic data but the software implementations can be expensive and difficult to use for non-specialists involved in wildlife conservation. To remedy this, we present a software interface to a popular speech recognition system making it possible for non-experts to implement hidden Markov models for bioacoustic signal processing. Octave/Matlab functions are used to simplify the set up and the definition of a bioacoustic signal recogniser as well as the analysis of the results. 3. We present the different functions as a workflow. To demonstrate how the package can be used we give the results of an analysis of a bioacoustic monitoring dataset to detect the nocturnal presence and behaviour of a cryptic seabird species, the common diving petrel Pelecanoides urinatrix urinatrix, from Northern New Zealand. 4. We show that the package matlabHTK can be used efficiently to reconstruct the daily patterns of colony activity in the common diving petrel.
Data from: Mutation screening of 1,237 cancer genes across six model cell lines of basal-like breast cancer
Basal-like breast cancer is an aggressive subtype generally characterized as poor prognosis and lacking the expression of the three most important clinical biomarkers, estrogen receptor, progesterone receptor, and HER2. Cell lines serve as useful model systems to study cancer biology in vitro and in vivo. We performed mutational profiling of six basal-like breast cancer cell lines (HCC38, HCC1143, HCC1187, HCC1395, HCC1954, and HCC1937) and their matched normal lymphocyte DNA using targeted capture and next-generation sequencing of 1,237 cancer-associated genes, including all exons, UTRs and upstream flanking regions. In total, 658 somatic variants were identified, of which 378 were non-silent (average 63 per cell line, range 37–146) and 315 were novel (not present in the Catalogue of Somatic Mutations in Cancer database; COSMIC). 125 novel mutations were confirmed by Sanger sequencing (59 exonic, 48 3'UTR and 10 5'UTR, 1 splicing), with a validation rate of 94% of high confidence variants. Of 36 mutations previously reported for these cell lines but not detected in our exome data, 36% could not be detected by Sanger sequencing. The base replacements C/G>A/T, C/G>G/C, C/G>T/A and A/T>G/C were significantly more frequent in the coding regions compared to the non-coding regions (OR 3.2, 95% CI 2.0–5.3, P<0.0001; OR 4.3, 95% CI 2.9–6.6, P<0.0001; OR 2.4, 95% CI 1.8–3.1, P<0.0001; OR 1.8, 95% CI 1.2–2.7, P = 0.024, respectively). The single nucleotide variants within the context of T[C]T/A[G]A and T[C]A/T[G]A were more frequent in the coding than in the non-coding regions (OR 3.7, 95% CI 2.2–6.1, P<0.0001; OR 3.8, 95% CI 2.0–7.2, P = 0.001, respectively). Copy number estimations were derived from the targeted regions and correlated well to Affymetrix SNP array copy number data (Pearson correlation 0.82 to 0.96 for all compared cell lines; P<0.0001). These mutation calls across 1,237 cancer-associated genes and identification of novel variants will aid in the design and interpretation of biological experiments using these six basal-like breast cancer cell lines.
Data from: Exploring the power of Bayesian birth-death skyline models to detect mass extinction events from phylogenies with only extant taxa
Mass extinction events (MEEs), defined as significant losses of species diversity in significantly short time periods, have attracted the attention of biologists because of their link to major environmental change. MEEs have traditionally been studied through the fossil record, but the development of birth-death models has made it possible to detect their signature based on extant-taxa phylogenies. Most birth-death models consider MEEs as instantaneous events where a high proportion of species are simultaneously removed from the tree ("single pulse" approach), in contrast to the paleontological record, where MEEs have a time-duration. Here, we explore the power of a Bayesian Birth-Death Skyline (BDSKY) model to detect the signature of MEEs through changes in extinction rates under a "time-slice" approach. In this approach, MEEs are time intervals where the extinction rate is greater than the speciation rate. Results showed BDSKY can detect and locate MEEs but that precision and accuracy depend on phylogenies size and MEE intensity. Comparisons of BDSKY with the single-pulse Bayesian model, CoMET, showed a similar frequency of Type II error and neither model exhibited Type I error. However, while CoMET performed better in detecting and locating MEEs for smaller phylogenies, BDSKY showed higher accuracy in estimating extinction and speciation rates.
Data from: Comparison of infinitesimal and finite locus models for long-term breeding simulations with direct and maternal effects at the example of honeybees
Stochastic simulation studies of animal breeding have mostly relied on either the infinitesimal genetic model or finite polygenic models. In this study, we investigated the long-term effects of the chosen model on honeybee breeding schemes. We implemented the infinitesimal model, as well as finite locus models, with 200 and 400 gene loci and simulated populations of 300 and 1000 colonies per year over the course of 100 years. The selection was of a directly and maternally influenced trait with maternal heritability of h²_m = 0.42, direct heritability of h² d = 0.27, and a negative correlation between the effects of r_md = −0.18. Another set of simulations was run with parameters h²_m = 0.53, h²_d = 0.34, and r_md = −0.53. All models showed similar behavior for the first 20 years. Throughout the study, we observed a higher genetic gain in the direct than in the maternal effects and a smaller gain with a stronger negative covariance. In thelong-term, however, only the infinitesimal model predicted sustainable linear genetic progress, while the finite locus models showed sublinear behavior and, after 100 years, only reached between 58% and 62% of the mean breeding values in the infinitesimal model. While the infinitesimal model suggested a reduction of genetic variance by 33% to 49% after 100 years, the finite locus models saw a more drastic loss of 76% to 92%. When designing sustainable breeding strategies, one should, therefore, not blindly trust the infinitesimal model as the predictions may be overly optimistic. Instead, the more conservative choice of the finite locus model should be favored.
Replication Data for: Interpretable machine learning prediction of fire emission and comparison with FireMIP process-based models
<p>The target and predictor variables used in the developed ML model.</p>
Data from: Inferring pathobiology from structural MRI in schizophrenia and bipolar disorder: modeling head motion and neuroanatomical specificity
Despite over 400 peer-reviewed structural MRI publications documenting neuroanatomic abnormalities in bipolar disorder and schizophrenia, the confounding effects of head motion and the regional specificity of these defects are unclear. Using a large cohort of individuals scanned on the same research dedicated MRI with broadly similar protocols, we observe reduced cortical thickness indices in both illnesses, though less pronounced in bipolar disorder. While schizophrenia (n = 226) was associated with wide-spread surface area reductions, bipolar disorder (n = 227) and healthy comparison subjects (n = 370) did not differ. We replicate earlier reports that head motion (estimated from time-series data) influences surface area and cortical thickness measurements and demonstrate that motion influences a portion, but not all, of the observed between-group structural differences. Although the effect sizes for these differences were small to medium, when global indices were covaried during vertex-level analyses, between-group effects became nonsignificant. This analysis raises doubts about the regional specificity of structural brain changes, possible in contrast to functional changes, in affective and psychotic illnesses as measured with current imaging technology. Given that both schizophrenia and bipolar disorder showed cortical thickness reductions, but only schizophrenia showed surface area changes, and assuming these measures are influenced by at least partially unique sets of biological factors, then our results could indicate some degree of specificity between bipolar disorder and schizophrenia.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.