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244 results for “coalescence”
Fig. 2 in A coalescent-based estimator of genetic drift, and acoustic divergence in the Pteronotus parnellii species complex
Fig. 2 Echolocation call frequency by island by sex and its relation to mass on the call frequency (F(1, 49) = 0.435, P value = 0.512). The body dimensions. a Boxplots of echolocation frequency (summarizing 95% HPD of population differences in means for body mass was 10 calls/individual). Bayesian 95% high-probability density (HPD) of 0.89–2.29 g. c Call frequency as a function of forearm length. Anathe difference in call frequency means between Puerto Rico and Hislyses of covariance support little influence of forearm length on the call paniola was 5.2–6.0 kHz. b Call frequency as a function of body mass. frequency (F(1, 52) = 2.851, P value = 0.097). The 95% HPD of Analyses of covariance support very different call frequency for island population differences in means for forearm lengths was −1.82, groups (F(1, 49) = 704.260, P value = 0.000), but no influence of body 0.422 mm
Fig. 1 Results from IMa2 in A coalescent-based estimator of genetic drift, and acoustic divergence in the Pteronotus parnellii species complex
Fig. 1 Results from IMa2 analyses of Pteronotus parnellii s.l. populations. a Joint posterior density of Ne estimates for island populations. b Divergence time estimates between Puerto Rican and Hispaniolan populations in thousands of years (Ka)
Fig. 3 in A coalescent-based estimator of genetic drift, and acoustic divergence in the Pteronotus parnellii species complex
Fig. 3 Densities of Bayesian posteriors for FST based on betweenpopulation migration rates, and PST for relevant phenotypic variables (Brommer et al. 2014). The lines show the 95th percentile for the corresponding FST, and the 5% percentile for the PST. The overlap between PST body mass and FST Hispaniola was 0.023, for FST Puerto Rico it was 0.084; between PST call frequency and FST Hispaniola was <0.001, for FST Puerto Rico it was 0.003; and between PST forearm length and FST Hispaniola was 0.049, for FST Puerto Rico it was 0.125
Supplementary material for: PhyloCoalSimulations: A simulator for network multispecies coalescent models, including a new extension for the inheritance of gene flow
<p>We consider the evolution of phylogenetic gene trees along phylogenetic species networks, according to the network multispecies coalescent process, and introduce a new network coalescent model with correlated inheritance of gene flow. This model generalizes two traditional versions of the network coalescent: with independent or common inheritance. At each reticulation, multiple lineages of a given locus are inherited from parental populations chosen at random, either independently across lineages, or with positive correlation according to a Dirichlet process. This process may account for locus-specific probabilities of inheritance, for example.</p> <p>We implemented the simulation of gene trees under these network coalescent models in the Julia package PhyloCoalSimulations, which depends on PhyloNetworks and its powerful network manipulation tools. Input species phylogenies can be read in extended Newick format, either in numbers of generations or in coalescent units. Simulated gene trees can be written in Newick format, and in a way that preserves information about their embedding within the species network. This embedding can be used for downstream purposes, such as to simulate species-specific processes like rate variation across species, or for other scenarios as illustrated in this note. This package should be useful for simulation studies and simulation-based inference methods. The software is available open source with documentation and a tutorial at <a href="https://github.com/cecileane/PhyloCoalSimulations.jl">https://github.com/cecileane/PhyloCoalSimulations.jl</a>.</p>
Hierarchical heuristic species delimitation under the multispecies coalescent model with migration
<p>The multispecies coalescent (MSC) model accommodates genealogical fluctuations across the genome and provides a natural framework for comparative analysis of genomic sequence data to infer the history of species divergence and gene flow. Given a set of populations, hypotheses of species delimitation (and species phylogeny) may be formulated as instances of MSC models (e.g., MSC for one species versus MSC for two species) and compared using Bayesian model selection. This approach, implemented in the program bpp, has been found to be prone to over-splitting. Alternatively, heuristic criteria based on population parameters under the MSC model (such as population/species divergence times, population sizes, and migration rates) estimated from genomic sequence data may be used to delimit species. Here we extend the approach of species delimitation using the genealogical divergence index (𝑔𝑑𝑖) to develop hierarchical merge and split algorithms for heuristic species delimitation and implement them in a python pipeline called hhsd. Applied to data simulated under a model of isolation by distance, the approach was able to recover the correct species delimitation, whereas model comparison by bpp failed. Analyses of empirical datasets suggest that the procedure may be less prone to over-splitting. We discuss possible strategies for accommodating paraphyletic species in the procedure, as well as the challenges of species delimitation based on heuristic criteria.</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Data behind the figures
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><br> <strong>Data behind the figures</strong></p> <p>This page contains the data behind various paper figures. The material for each figure is contained in a tar file. A short description can be found below. Figures not included here are associated with one of the other GWTC-3 data releases.</p> <p> </p> <p><strong>Figure 1</strong></p> <ul> <li>Figure01.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 1. This shows the number of candidates with probability of astrophysical origin > 50% as a function of surveyed time–volume.</p> <p>The dates for the first observing run (<a href="https://doi.org/10.1103/PhysRevX.6.041015">O1</a>) and second observing run (<a href="https://doi.org/10.1103/PhysRevX.9.031040">O2</a>) candidates are hard-coded into the script, and the dates for third observing run (<a href="https://arxiv.org/abs/2108.01045">O3a</a> and O3b) candidates are included in two text files. The effective binary neutron star time–volume (BNS-VT) for each observing run is stored in separate .csv files.</p> <p>Each .csv file contains two columns, the first is the GPS time and the second is the cumulative effective BNS VT in Mpc<sup>3</sup> kyr (this is converted to Gpc<sup>3</sup> yr in the included script).</p> <p>The included script reproduces Figure 1 from GWTC-3 using the supplied data.</p> <p> </p> <p><strong>Figure 2</strong></p> <ul> <li>Figure02.tar.gz</li> </ul> <p>Data and plotting scripts for GWTC-3: Figure 2. The figure shows sensitivity curves for LIGO Hanford, LIGO Livingston, and Virgo.</p> <p>The Python script reads the .txt files containing strain data for Hanford, Livingston, and Virgo and saves figures as PDF files.</p> <p>The sensitivity curves are representative of performance during O3b. Further examples of sensitivity curves across observing runs can be found from the <a href="https://www.gw-openscience.org/detector_status/">Gravitational Wave Open Science Center</a>.</p> <p> </p> <p><strong>Figure 3</strong></p> <ul> <li>Figure03.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 3. The left panel shows the <a href="https://doi.org/10.1088/1361-6382/abd594">binary neutron star inspiral range</a> of LIGO Hanford, LIGO Livingston, and Virgo versus time. The right panel shows histograms of the binary neutron star ranges for LIGO Hanford, LIGO Livingston, and Virgo.</p> <p>The Python script (figure_3.py) reads the range.txt files and the histogram.txt files to produce each panel and saves them as PDF files.</p> <p>Further summary information about the O3b run can be obtained from the <a href="https://www.gw-openscience.org/detector_status/O3b/">Gravitational Wave open Science Center</a>.</p> <p> </p> <p><strong>Figure 4</strong></p> <ul> <li>Figure04.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 4. This plot shows the rate of <a href="https://doi.org/10.1088/1361-6382/abfd85">non-Gaussian noise transients (glitches)</a> in the LIGO Hanford, LIGO Livingston and Virgo data across O3b. The recorded glitches are identified by the <a href="https://virgo.docs.ligo.org/virgoapp/Omicron/">Omicron</a> pipeline with signal-to-noise ratio of > 6.5. There is a reduction in the LIGO glitch rate after the introduction of <a href="https://doi.org/10.1088/1361-6382/abc906">reaction chain (RC) tracking</a>, which reduced the incidence of scattered light (slow scattering) glitches.</p> <p>The script glitch_rates_GWTC-3_Fig_4.py produces Figure 4 of GWTC-3 making use of the glitch rates stored in the glitch_rates_GWTC-3_Fig_4.h5 file. Run the script within an <a href="https://computing.docs.ligo.org/conda/environments/igwn-py37/">igwn-py37</a> or <a href="https://computing.docs.ligo.org/conda/environments/igwn-py38/">igwn-py38</a> Conda environment, paying attention to having the hdf5 file glitch_rates_GWTC-3_Fig_4.h5 in the same directory of the script. Pass the argument -v or --verbose for additional info about the rates.</p> <p> </p> <p><strong>Figure 5</strong></p> <ul> <li>Figure05.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 5. This figure illustrates the time–frequency structure of two common types of glitch seen in O3: <a href="https://doi.org/10.1088/1361-6382/abc906">slow scattering</a> and <a href="https://doi.org/10.1088/1361-6382/ac1ccb">fast scattering</a>. Both are caused by light scattering within the LIGO detectors.</p> <p>The Python script scattering_GWTC-3_Fig_5.py produces Figure 5 in the GWTC-3 Catalog paper using <a href="https://www.gw-openscience.org/O3/">open data</a>. The script saves the plot as a PDF file namely, scattering_GWTC-3_Fig_5.pdf and the data used to generate the plot in the files data_fast_scattering.txt and data_slow_scattering.txt.</p> <p>For further examples of the time–frequency structure of glitches, the community-science project <a href="https://gravityspy.org/">Gravity Spy</a> catalogs visualizations of glitches in gravitational-wave data.</p> <p> </p> <p><strong>Figure 12</strong></p> <ul> <li>Figure12.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 12. This plots results of the waveform consistency test (as does Figure 13), plotting the match between waveform templates and minimally modeled reconstructions. The on-source results are for the candidate signals, while the off-source results are for simulated signals with compatible properties.</p> <p>The waveform reconstructions are performed using <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> and <a href="https://gwburst.gitlab.io/">cWB</a>. The two pipelines select different sets of candidates to analyze.</p> <p>The Python script (figure_12.py) reads data from files FittingFactor.txt for Bayeswave and FittingFactor_C01.txt for cWB to produce the corresponding match–match plots (PDF files).</p> <p> </p> <p><strong>Figure 13</strong></p> <ul> <li>Figure13.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 13. This plots results of the waveform consistency test (as does Figure 12), plotting the p-values for the minimally modeled waveform reconstructions. The p-values are plotted in increasing order.</p> <p>The waveform reconstructions are performed using <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> and <a href="https://gwburst.gitlab.io/">cWB</a>. The two pipelines select different sets of candidates to analyze.</p> <p>The script (figure_13.py) reads data from files FittingFactor.txt for Bayeswave and FittingFactor_C01.txt for cWB (the same files used to produce Figure 12) to produce the corresponding p-value plots (PDF files).</p> <p> </p> <p><strong>Figure 14</strong></p> <ul> <li>Figure14.tar.gz</li> </ul> <p>Data and script to produce GWTC-3 Figure 14. This figure shows representative noise amplitude spectral densities for LIGO Hanford, LIGO Livingston, and Virgo during Observing Run 2 and Observing Run 3b.</p> <p>The script (figure_14.py) reads data from the .txt files included in the release to reproduce Figure 14 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 15</strong></p> <ul> <li>Figure15.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 15. This shows differences in the data used to analyze <a href="https://doi.org/10.7935/b024-1886">GW200115_042309</a>, with and without glitch subtraction. A low frequency cut (illustrated by the dotted white line) was used to remove the glitch in the <a href="https://doi.org/10.3847/2041-8213/ac082e">first analysis</a> of this candidate, whereas glitch subtraction is now used when performing parameter estimation. The curving orange line shows the approximate signal track.</p> <p>The script reads in the deglitched frame L-L1_HOFT_CLEAN_SUB60HZ_C01_T1700406_v4-1263095808-4096.gwf, downloaded from <a href="http://doi.org/10.5281/zenodo.5546679">an associated data release</a>, query raw <a href="https://www.gw-openscience.org/O3/">public LIGO Livingston data</a>, and reproduce Figure 15 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 16</strong></p> <ul> <li>Figure16.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 16. This figure illustrates the <a href="https://gwpy.github.io/docs/latest/examples/timeseries/qscan/">time–frequency structure</a> of data containing three O3 candidates identified only by <a href="https://gwburst.gitlab.io/">cWB</a> (the same as shown in Figure 17). Each shows evidence of instrumental origin. </p> <p>The script queries <a href="http://www.gw-openscience.org/O3/">public LIGO data</a> and reproduce Figure 16 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 17</strong></p> <ul> <li>Figure17.tar.gz</li> </ul> <p>Data and script for GWTC-3: Figure 17. This figure illustrates the <a href="https://doi.org/10.1088/1742-6596/363/1/012032">time–frequency structure</a> of candidate signals as reconstructed by <a href="https://gwburst.gitlab.io/">cWB</a> for three O3 candidates identified only by cWB (the same as shown in Figure 16). Each shows evidence of instrumental origin. For a compact binary coalescence signal, we would expect the signal to have a chirp structure, sweeping up from low to high frequencies.</p> <p>The script figs.py reads data (the reconstruction from cWB) from the .txt files to produce the corresponding time–frequency plots (PDF files). The script must be run three times to produce the panels of Figure 17: the event names are hardcoded into the script, which must be edited to produce the desired panel.</p> <p> </p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5571766 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave data analysis, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p> <p> </p>
Supplementary material for: PhyloCoalSimulations: A simulator for network multispecies coalescent models, including a new extension for the inheritance of gene flow
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Gene tree discord, simplex plots, and statistical tests under the coalescent
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Data for: PickMe: Sample selection for species tree reconstruction using coalescent weighted quartets
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Data from: The multispecies coalescent model outperforms concatenation across diverse phylogenomic
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Estimating waiting distances between genealogy changes under a multi-species extension of the sequentially Markov coalescent
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Hierarchical heuristic species delimitation under the multispecies coalescent model with migration
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Revisiting the multispecies coalescent model fit with an example from a complete molecular phylogeny of the Liolaemus wiegmannii species group (Squamata: Liolaemidae)
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ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes
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Data from: Cryptic patterns of speciation in cryptic primates: microendemic mouse lemurs and the multispecies coalescent
Species delimitation is ever more critical for assessing biodiversity in threatened regions of the world, especially when undescribed lineages may be at risk from habitat loss. Mouse lemurs (Microcebus) are an example of a rapid radiation of morphologically cryptic species that are distributed throughout Madagascar in its rapidly vanishing forested habitats. Here, we focus on two pairs of sister lineages that occur in a region in northeastern Madagascar that shows high levels of microendemism. We revisit previous hypotheses of species diversity by filling geographic sampling gaps and by generating new genomic data for three named species, as well as an undescribed lineage previously identified to be of interest due to its highly divergent mtDNA. We analyzed RADseq data with multiple species delimitation methods based on the multispecies coalescent (MSC) model while accounting for introgression. Non-sister lineages occur sympatrically in two instances, despite an estimated divergence time of less than 1 Ma, thus suggesting rapid evolution of reproductive isolation in the mouse lemur clade. We note, however, that the divergence time estimates reported here are based on the MSC and calibrated with pedigree-based primate mutation rates. These dates are considerably more recent than previous analyses that used traditional relaxed clock methods and distant fossil calibrations. One pair of sister lineages passed all species delimitation tests while the other pair failed most, largely due to differences in Ne between the two pairs of lineages. Nevertheless, delimitation results were also supported by differences in levels of gene flow and patterns of isolation-by-distance between the two pairs. We conclude that MSC-based species delimitation methods are valuable tools for evaluating cryptic species, even though these methods can be strongly affected by variable Ne. We suggest that this result has general implications for species delimitation studies of other recently diverged lineages.
Data from: The multilocus multispecies coalescent: a flexible new model of gene family evolution
<p>Incomplete lineage sorting (ILS), the interaction between coalescence and speciation, can generate incongruence between gene trees and species trees, as can gene duplication (D), transfer (T) and loss (L). These processes are usually modelled independently, but in reality, ILS can affect gene copy number polymorphism, i.e., interfere with DTL. This has been previously recognised, but not treated in a satisfactory way, mainly because DTL events are naturally modelled forward-in-time, while ILS is naturally modelled backwards-in-time with the coalescent. Here we consider the joint action of ILS and DTL on the gene tree/species tree problem in all its complexity. In particular, we show that the interaction between ILS and duplications/transfers (without losses) can result in patterns usually interpreted as resulting from gene loss, and that the realised rate of D, T and L becomes non-homogeneous in time when ILS is taken into account. We introduce algorithmic solutions to these problems. Our new model, the <em>multilocus multispecies coalescent</em> (MLMSC), which also accounts for any level of linkage between loci, generalises the multispecies coalescent model and offers a versatile, powerful framework for proper simulation and inference of gene family evolution.</p>
Phase resolution of heterozygous sites in diploid genomes is important to phylogenomic analysis under the multispecies coalescent model
<p>Genome sequencing projects routinely generate haploid consensus sequences from diploid genomes, which are effectively chimeric sequences with the phase at heterozygous sites resolved at random. The impact of phasing errors on phylogenomic analyses under the multispecies coalescent (MSC) model is largely unknown. Here we conduct a computer simulation to evaluate the performance of four phase-resolution strategies (the true phase resolution, the diploid analytical integration algorithm which averages over all phase resolutions, computational phase resolution using the program PHASE, and random resolution) on estimation of the species tree and evolutionary parameters in analysis of multi-locus genomic data under the MSC model. We found that species tree estimation is robust to phasing errors when species divergences were much older than average coalescent times but may be affected by phasing errors when the species tree is shallow. Estimation of parameters under the MSC model with and without introgression is affected by phasing errors. In particular, random phase resolution causes serious overestimation of population sizes for modern species and biased estimation of cross-species introgression probability. In general the impact of phasing errors is greater when the mutation rate is higher, the data include more samples per species, and the species tree is shallower with recent divergences. Use of phased sequences inferred by the PHASE program produced small biases in parameter estimates. We analyze two real datasets, one of East Asian brown frogs and another of Rocky Mountains chipmunks, to demonstrate that heterozygote phase-resolution strategies have similar impacts on practical data analyses. We suggest that genome sequencing projects should produce unphased diploid genotype sequences if fully phased data are too challenging to generate, and avoid haploid consensus sequences, which have heterozygous sites phased at random. In case the analytical integration algorithm is computationally unfeasible, computational phasing prior to population genomic analyses is an acceptable alternative. </p>
Using a coalescent approach to assess gene flow and effective population size of Acrocomia aculeata (Jacq.) Lodd. Ex Mart. in the Brazilian Atlantic Forest
<p><i>Acrocomia aculeata</i> is a tropical palm tree native to Central and South America that has significant economic, social, and environmental potential. However, land encroachment due to the expansion of agribusiness, and other factors such as urban sprawl, have resulted in the fragmentation and destruction of its habitat, leading to the loss of genes and genotypes in <i>A. aculeata</i> populations. In this context, the objective of this study was to characterize the genetic variability of <i>A. aculeata</i> populations by estimating gene flow and effective population size using an approach based on coalescent theory. Four populations located in the municipalities of Teodoro Sampaio (TSI and TSII), Rosana (RA), and Amparo (AP) in São Paulo State, Brazil, were genotyped with nine microsatellite markers. Gene flow and effective population size were estimated using a coalescent-based Bayesian inference implemented in the MIGRATE-N software. The effective population size (<i>N<sub>e</sub></i>) was obtained considering an assumed mutation rate of <a name="_Hlk6565387">5x10<sup>-5</sup>. </a>Gene flow (<i>Nm</i>) for pairwise populations ranged from 0.28 to 1.17, with higher levels of migration between the three geographically proximal locations (TSI, TSII, and RA). The estimates of effective population size (<i>N<sub>e</sub></i>) were 444, 835, 838, and 874 for AP, TSII, RA, and TSI, respectively, showing that the effects caused by genetic drift may be more pronounced when <i>N<sub>e</sub></i> is smaller. The coalescent-based results add to our understanding of <i>A. aculeata</i> population genetics and suggest that some traditional assessment methods may be ineffective in characterizing historical evolutionary processes.</p>
Data from: A coalescent-based estimator of genetic drift, and acoustic divergence in the Pteronotus parnellii species complex
Determining the processes responsible for phenotypic variation is one of the central tasks of evolutionary biology. While the importance of acoustic traits for foraging and communication in echolocating mammals suggests adaptation, the seldom-tested null hypothesis to explain trait divergence is genetic drift. Here we derive FST values from multi-locus coalescent isolation-with-migration models, and couple them with estimates of quantitative trait divergence, or PST, to test drift as the evolutionary process responsible for phenotypic divergence in island populations of the Pteronotus parnellii species complex. Compared to traditional comparisons of PST to FST, the migration-based estimates of FST are unidirectional instead of bidirectional, simultaneously integrate variation among loci and individuals, and posterior densities of PST and FST can be compared directly. We found the evolution of higher call frequencies is inconsistent with genetic drift for the Hispaniolan population, despite many generations of isolation from its Puerto Rican counterpart. While the Hispaniolan population displays dimorphism in call frequencies, the higher frequency of the females is incompatible with sexual selection. Instead, cultural drift toward higher frequencies among Hispaniolan females might explain the divergence. By integrating Bayesian coalescent and trait analyses, this study demonstrates a powerful approach to testing genetic drift as the default evolutionary mechanism of trait differentiation between populations.
Data from: ASTRAL: genome-scale coalescent-based species tree estimation
<p>Species trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions.</p> <p>Results: We present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy—improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees.</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.