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800 results for “mixtures”
Simulation of freely-diffusing smFRET data of a static mixture of 2 populations
<p>Simulation data created with PyBroMo smFRET simulation software.</p>
Increased accuracy of starch granule type quantification using mixture distributions
<p>This release accompanies the paper "Increased accuracy of starch granule type quantification using mixture distributions".</p>
Phase Diagram of Kob-Andersen-Type Binary Lennard-Jones Mixtures
<p>This data repository contains data related to the paper Phase Diagram of Kob-Andersen-Type Binary Lennard-Jones Mixtures, Phys. Rev. Lett. 120, 165501 (2018), DOI: <a href="https://doi.org/10.1103/PhysRevLett.120.165501">10.1103/PhysRevLett.120.165501 </a>by Ulf R. Pedersen, Thomas B. Schrøder, and Jeppe C. Dyre.</p><p> </p><p>Abstract of the paper:</p><p>The binary Kob-Andersen (KA) Lennard-Jones mixture is the standard model for computational studies of viscous liquids</p><p>and the glass transition. For very long simulations, the viscous KA system crystallizes, however, by phase separating</p><p>into a pure A particle phase forming a fcc crystal. We present the thermodynamic phase diagram for KA-type mixtures</p><p>consisting of up to 50% small (B) particles showing, in particular, that the melting temperature of the standard KA</p><p>system at liquid density 1.2 is 1.028(3) in A particle Lennard-Jones units. At large B particle concentrations, the</p><p>system crystallizes into the CsCl crystal structure. The eutectic corresponding to the fcc and CsCl structures is cutoff</p><p>in a narrow interval of B particle concentrations around 26% at which the bipyramidal orthorhombic PuBr3 structure is</p><p>the thermodynamically stable phase. The melting temperature's variation with B particle concentration at two constant</p><p>pressures, as well as at the constant density 1.2, is estimated from simulations at pressure 10.19 using isomorph</p><p>theory. Our data demonstrate approximate identity between the melting temperature and the onset temperature below which</p><p>viscous dynamics appears. Finally, the nature of the solid-liquid interface is briefly discussed.</p>
Predicted room temperature electrical conductivity of molecular mixtures
<p>In the associated manuscript, we propose the MolSets machine learning model for molecular mixture properties. Using the MolSets architecture, we train a model on a dataset curated by Bradford et al. (2023) to predict the room temperature (298 K) electrical conductivity of mixtures. Here, we report the model-predicted conductivities of all equal-weight binary mixtures among 28 types of small molecules, combined with 30 types of Li<sup>+</sup> salts (1 mol·kg<sup>-1</sup>), totaling 11,340 candidate lithium battery electrolytes. Note that the current model has a limitation of not taking salt solubility into account. This dataset is for demonstration purposes and should be used with caution.</p>
Fig. 2 in Assessing The Abundance Of Caucasian Salamander, Mertensiella Caucasica (Caudata, Salamandridae), With N-Mixture Model In Northeastern Anatolia
Fig. 2. The average abundance of Caucasian salamanders from the East Black Sea Region, Turkey. X-axis shows the sampling plots number in each city; Y-axis shows the estimated population size.
[DCASE2024 Task 3] Synthetic SELD mixtures for baseline training
<p><strong>DESCRIPTION:</strong><br><br>This audio dataset serves serves as supplementary material for the <a href="https://dcase.community/challenge2024/task-audio-and-audiovisual-sound-event-localization-and-detection-with-source-distance-estimation">DCASE2024 Challenge Task 3: Audio and Audiovisual Sound Event Localization and Detection with Distance Estimation</a>. The dataset consists of synthetic spatial audio mixtures of sound events spatialized for two different spatial formats using real measured room impulse responses (RIRs) measured in various spaces of Tampere University (TAU). The mixtures are generated using the same process as the one used to generate the recordings of the <a href="../record/5476980">TAU-NIGENS Spatial Sound Scenes 2021</a> dataset for the <a href="https://dcase.community/challenge2021/task-sound-event-localization-and-detection-results">DCASE2021 Challenge Task 3</a>. </p> <p>The SELD task setup in DCASE2024 is based on spatial recordings of real scenes, captured in the <a href="../records/7880637">STARS23</a> dataset. Since the task setup allows use of external data, these synthetic mixtures serve as additional training material for the <a href="https://github.com/partha2409/DCASE2024_seld_baseline">baseline model</a>. For more details on the task setup, please refer to the <a href="https://dcase.community/challenge2024/task-audio-and-audiovisual-sound-event-localization-and-detection-with-source-distance-estimation">task description</a>.</p> <p>Note that the generator code and the collection of room responses used to spatialize sound samples will be also be made available soon. For more details on the recording of RIRs, spatialization, and generation, see:</p> <ul> <li>Archontis Politis, Sharath Adavanne, Daniel Krause, Antoine Deleforge, Prerak Srivastava, Tuomas Virtanen (2021). A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2021)</em>, Barcelona, Spain.</li> </ul> <p>available <a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Politis_43.pdf">here</a>.</p> <p><strong>SPECIFICATIONS:</strong></p> <ul> <li><strong>13 target sound classes</strong> (see task description for details)</li> <li>The sound event samples are sources from the <strong><a href="../record/4060432">FSD50K</a></strong> dataset, based on affinity of the labels in that dataset to the target classes. The selection on distinguishing which labels in FSD50K corresponded to the target ones, then selecting samples that were tagged with only those labels, and additionally that they had annotator rating of Present and Predominant (see FSD50K for more details). The list of the selected files is included here.</li> <li><strong>1200</strong> 1-minute long spatial recordings</li> <li>Sampling rate of<strong> 24kHz</strong></li> <li>Two 4-channel recording formats, first-order Ambisonics (<strong>FOA</strong>) and tetrahedral microphone array (<strong>MIC</strong>)</li> <li>Spatial events spatialized in <strong>9 unique rooms</strong>, using measured RIRs for the two formats</li> <li>Maximum <strong>polyphony of 3</strong> (with possible same-class events overlapping)</li> <li>Even though the whole set is used for training of the baseline without distinction between the mixtures, we have included a <strong>separation into a training and testing split</strong>, in case on one needs to test the performance purely on those synthetic conditions (for example for comparisons with training on mixed synthetic-real data, fine-tuning on real data, or training on real data only).</li> <li>The training split is indicated as <strong>fold1</strong> in the dataset, contains 900 recordings spatialized on 6 rooms (150 recordings/room) and it is based on samples from the development set of FSD50K.</li> <li>The testing split is indicated as <strong>fold2</strong> in the dataset, contains 300 recordings spatialized on 3 rooms (100 recordings/room) and it is based on samples from the evaluation set of FSD50K.</li> <li>Common metadata files for both formats are provided. For the file naming and the metadata format, refer to the task setup.</li> </ul> <p> </p> <p><strong>DOWNLOAD INSTRUCTIONS:</strong></p> <p>Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:</p> <ol> <li>Combine the split archive to a single archive: <pre>zip -s 0 split.zip --out single.zip</pre> </li> <li>Extract the single archive using unzip: <pre>unzip single.zip</pre> </li> </ol>
Dataset in support of an AI-aided chronic mixture risk assessment along a small European river
<p>Here, we make available a dataset to perform an AI-aided multi-scenario chronic mixture risk assessment. In 2021, river-water samples were collected at six sampling sites along the Holtemme River in Central Germany using large-volume solid phase extraction. The extracts were analysed by target chemical analysis for contaminants of emerging concern. The dataset of the chemical analysis was already published and can be found at DOI: 10.5281/zenodo.10892038. Furthermore, a detailed description of the dataset can be found at DOI: 10.1016/j.dib.2024.110510.</p> <p> </p>
Supplementary material for: "Seeds adapted to mixed cropping increase yield and drought resistance of cereal-legume mixtures"
<p>Supplementary material for the research article: "Seeds adapted to mixed cropping increase yield and drought resistance of cereal-legume mixtures".</p> <ul> <li>Raw data</li> <li>R analysis code</li> <li>Statistical analysis info: ANOVA and Tukey comparisons tables</li> </ul> <p> </p> <p>Abstract:</p> <p>Cropland diversification through mixed cropping has the potential of achieving a more sustainable agriculture while securing food production. This is of special relevance with climate change and the expected drier growing conditions in the future. Seed adaptation to this cropping method is hypothesised to be a fundamental factor to maximise these benefits, as well as the particular species combined. In this study we compared the performance of four cereal-legume mixed crops (wheat and oat mixed with lupin and lentil in pairs) with their respective monocrops. Each crop was sown using seeds adapted to monoculture and mixed cropping, respectively. Moreover, they were grown under early-season and late-season drought treatments and under control conditions. We measured above-ground vegetative biomass, seed yield and harvest index to evaluate crop production, drought resistance and the effect of seed adaptation on each mixed and monocrop. Our results show that mixed cropping either had a beneficial or neutral effect on crop yield, depending on the species combination and drought conditions, but harvest index was generally higher in monocrops. We also confirmed that seed adaptation to a particular type of cropping is clearly a determining factor in its performance. In accordance with the insurance hypothesis, mixed cropping has the effect of protecting crop yields in the case of a sudden bad performance of one of the species, for example, caused by adverse environmental conditions. It is necessary to focus on effective species combinations which have the best responses to mixed cropping. We show for the first time that wheat-lentil mixtures performed poorly, while wheat-lupin showed the most promising results improving yield and drought resistance. Oat mixed crops did not show differences with the respective monocrops, so they can be a viable cropping option as well and benefit from advantages of crop diversity not measured in this study.</p>
Data for Spectral Induced Polarization of ZVI-AC-Sand Mixtures in Groundwater Remediation
<p>这是手稿“揭开地下水修复中 ZVI-AC-Sand 混合物的光谱诱导极化响应”的初始数据</p>
NIR/SWIR Spectral Library of Plastic-Substrate Mixtures
<p>NIR/SWIR spectra of substrate-plastic mixtures at varying concentrations</p> <p>Plastics include polyethylene (PE), polyethylene terephthalate (PET), polylactic acid (PLA), polypropylene (PP), polyvinyl chloride (PVC), and styrene-butadiene rubber (SBR)</p> <p>Substrates included 3 soils (Bu5, W6, TG), crushed cement (C), oak leaf powder (V), and water (DIW)</p> <p>Concentrations of 0% (pure substrate), 0.15%, 1.5%, 5%, 15%, 50%, and 100% (pure plastic)</p> <p>Datasets are in .csv format for reflectance spectra, absorbance spectra, absorbance 1st derivative, and absorbance 2nd derivative. Reflectance spectra are also included as .hdr and .sli for ease of importing into ENVI or other hyperspectral image processing software. Additionally, raw ASD spectra and the python script for processing are included for custom spectral processing or analyses.</p>
juan-duenas/NHESS: Soil conditioner mixtures as an agricultural management alternative to mitigate drought impacts: a proof-of-concept.
<p>The dataset and the R script have been enhanced and corrected, respectively. The main figures of the associated publication have been added in two different qualities.</p> <p>This data is associated to a paper that will appear in an special issue of the journal Natural Hazards and Earth System Sciences. https://nhess.copernicus.org/articles/special_issue1295.html</p>
Data & R Scripts - Jönander et al. (2022) Single substance and mixture toxicity of dibutyl-phthalate and sodium dodecyl sulphate to marine zooplankton. Ecotoxicol. Environ. Saf.
<p>Data and R scripts associated with:</p> <p>Jönander, C., Backhaus, T., Dahllöf, I. (2022) Single substance and mixture toxicity of dibutyl-phthalate and sodium dodecyl sulphate to marine zooplankton. Ecotoxicol. Environ. Saf.</p>
Conflict over the eukaryote root resides in strong outliers, mosaics and missing data sensitivity of site-specific (CAT) mixture models
Abstract Phylogenetic reconstruction using concatenated loci ("phylogenomics" or "supermatrix phylogeny") is a powerful tool for solving evolutionary splits that are poorly resolved in single gene/protein trees (SGTs). However, recent phylogenomic attempts to resolve the eukaryote root have yielded conflicting results, along with claims of various artefacts hidden in the data. We have investigated these conflicts using two new methods for assessing phylogenetic conflict. ConJak uses whole marker (gene or protein) jackknifing to assess deviation from a central mean for each individual sequence, while ConWin uses a sliding window to screen for incongruent protein fragments (mosaics). Both methods allow selective masking of individual sequences or sequence fragments in order to minimize missing data, an important consideration for resolving deep splits with limited data. Analyses focused on a set of 76 eukaryotic proteins of bacterial-ancestry previously used in various combinations to assess the branching order among the three major divisions of eukaryotes: Amorphea (mainly animals, fungi and Amoebozoa), Diaphoretickes (most other well-known eukaryotes and nearly all algae) and Excavata, represented here by Discoba (Jakobida, Heterolobosea, and Euglenozoa). ConJak analyses found strong outliers to be concentrated in under-sampled lineages, while ConWin analyses of Discoba, the most under-sampled of the major lineages, detected potentially incongruent fragments scattered throughout. Phylogenetic analyses of the full data using an LG-gamma model support a Discoba sister scenario (neozoan-excavate root), which rises to 99-100% bootstrap support with data masked according to either protocol. However, analyses with two site-specific (CAT) mixture models yielded widely inconsistent results and a striking sensitivity to missing data. The neozoan-excavate root places Amorphea and Diaphoretickes as more closely related to each other than either is to Discoba, a fundamental relationship that should remain unaffected by additional taxa.
Roadside turfgrass seed mixtures: models and figures
<div> <div> <div> <div> <p>Roadsides in urban areas are often seeded with turfgrass mixtures to provide ground cover and reduce weed abundance. Designing mixtures to withstand exposure to biotic and abiotic stress is challenging. Research from managed and natural ecosystems have shown that increasing plant species richness and diversity can increase groundcover and suppress weed cover, but it is unclear whether such relationships hold in roadside environments. Our objective was to determine the effect of seeded turfgrass species richness on ground cover and weed suppression alongside roadsides in diverse regions in Minnesota. We tested six turfgrass species in monocultures, two-way mixtures, some three-way mixtures, and a single six-way mixture at seven sites seeded in the fall of 2018, and seven sites seeded in the fall of 2019. Seeded turfgrass, weed, and bare soil coverage was measured at each site over two growing seasons. There was a positive relationship between turfgrass species richness and turfgrass cover, and this interaction effect increased over time. We found that increasing turfgrass species richness reduced bare soil coverage. Turfgrass cover was also more consistent across research sites (i.e., greater spatial stability) with increasing species richness. Our results show that positive relationships between plant species richness and groundcover hold in highly disturbed and managed roadside environments. These findings can improve the design of seed mixtures for roadsides and in other ecological contexts where vegetative cover is important.</p> </div> </div> </div> </div>
Trait functional diversity explains mixture effects on litter decomposition at the arid end of a climate gradient
<p><span>Litter decomposition is controlled by climate, litter quality and decomposer communities. Because the decomposition of specific litter types is also influenced by the properties of adjacent types, mixing litter types may result in non-additive effects on overall decomposition rates. The strength of these effects seems to depend on the litter functional diversity. However, it is unclear which functional traits or combination of traits explain litter mixture effects and if these depend on the range of trait values and the ecosystems involved. These uncertainties hamper our ability to predict decomposition in plant communities. </span></p> <p><span>We aimed at understanding whether and how functional diversity (measured as functional dispersion, FDis) influences litter decomposition, and how this influence varies among different climates and across decomposition stages. We calculated FDis based on litter traits related to nutrient concentrations or to litter recalcitrance, and tested whether these diversity measures and climatic parameters (soil moisture and temperature) explained litter mixture effects on decomposition. </span></p> <p><span>Additive mixture effects (i.e. decomposition of mixtures equalling the mean decomposition of the single litter types) were common in most of the evaluated climates. Non-additive, negative effects were mainly restricted to the driest and warmest sites, and decreased with time. Non-additive effects increased in magnitude with the mixtures' FDis, with positive effects being related to FDis in nutrient traits and negative effects being related to FDis in recalcitrance traits. </span></p> <p><span>Synthesis: Litter mixing did not have strong effects on decomposition rates across the studied climatic gradient overall, and the direction and intensity of the mixture effects were context-dependent. The effects were stronger and more negative in the dryer ecosystems. Where effects were found, functional diversity calculated from selected groups of traits (related to nutrients or litter recalcitrance) predicted mixture effects, especially where trait ranges were broad, though much of the variation remains unexplained. We propose that functional diversity metrics based on litter traits that are mechanistically relevant, applied to diverse site-specific litter mixtures in different climates, can help to better understand under which conditions and in which direction litter diversity affects decomposition.</span></p>
Performance of akaike information criterion and bayesian information criterion in selecting partition models and mixture models
<p>In molecular phylogenetics, partition models and mixture models provide different approaches to accommodating heterogeneity in genomic sequencing data. Both types of models generally give a superior fit to data than models that assume the process of sequence evolution is homogeneous across sites and lineages. The Akaike Information Criterion (AIC), an estimator of Kullback-Leibler divergence, and the Bayesian Information Criterion (BIC) are popular tools to select models in phylogenetics. Recent work suggests AIC should not be used for comparing mixture and partition models. In this work, we clarify that this difficulty is not fully explained by AIC misestimating the Kullback-Leibler divergence. We also investigate the performance of the AIC and BIC by comparing amongst mixture models and amongst partition models. We find that under non-standard conditions (i.e. when some edges have a small expected number of changes), AIC underestimates the expected Kullback-Leibler divergence. Under such conditions, AIC preferred the complex mixture models and BIC preferred the simpler mixture models. The mixture models selected by AIC had a better performance in estimating the edge length, while the simpler models selected by BIC performed better in estimating the base frequencies and substitution rate parameters. In contrast, AIC and BIC both prefer simpler partition models over more complex partition models under non-standard conditions, despite the fact that the more complex partition model was the generating model. We also investigated how mispartitioning (i.e. grouping sites that have not evolved under the same process) affects both the performance of partition models compared to mixture models and the model selection process. We found that as the level of mispartitioning increases, the bias of AIC in estimating the expected Kullback-Leibler divergence remains the same, and the branch lengths and evolutionary parameters estimated by partition models become less accurate. We recommend that researchers be cautious when using AIC and BIC to select among partition and mixture models; other alternatives, such as cross-validation and bootstrapping should be explored, but may suffer similar limitations.</p>
Satellite-derived chlorophyll-a concentrations for Lake Hume (Australia) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of Lake Hume (Australia) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
Satellite-derived chlorophyll-a concentrations for Western Water Treatment Plant (Melbourne, Australia) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of the Western Water Treatment Plant (Melbourne, Australia) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
North American Coastal Plain PRISMA Surface Reflectance and Mixture Residual Spectra
<p>The data available here include the training and validation spectra for creating the models for the currently unpublished manuscript “Classifying Plant Communities in the North American Coastal Plain with PRISMA Spaceborne Hyperspectral Imagery and the Spectral Mixture Residual." Spectral data contain both raw surface reflectance (SR) and spectral mixture residual spectra (MR) transformed with endmembers and code from Sousa et al.'s (2022) paper titled "The spectral mixture residual: A source of low‐variance information to enhance the explainability and accuracy of surface biology and geology retrievals." Spectra represent averaged 60 m x 60 m areas (2 x 2-pixel window) located in the Red Hills (RH), the Jones Ecological Research Center (JERC), the Ordway-Swisher Biological Station (OSBS), and the Disney Wilderness Preserve (DSNY). To maintain the confidentiality of private property information on behalf of landowners, the locations of the RH plots were intentionally obscured, considering the nature of the region. See manuscript for further details. </p>
The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is severely constrained by random peptide mixtures
<p><span>The prevalence of antibiotic-resistant pathogens has become a major threat to public health, requiring swift initiatives for discovering new strategies to control bacterial infections. Hence, antibiotic stewardship and rapid diagnostics, but also the development, and prudent use, of novel effective antimicrobial agents are paramount. Ideally, these agents should be less likely to select for resistance in pathogens than currently available conventional antimicrobials. The usage of antimicrobial Peptides (AMPs), key components of the innate immune response, and combination therapies, have been proposed as strategies to diminish the emergence of resistance.</span></p> <p><span>Herein, we investigated whether newly developed random antimicrobial peptide mixtures (RPMs) can significantly reduce the risk of resistance evolution <em>in vitro</em> to that of single sequence AMPs, using the ESKAPE pathogen <em>Pseudomonas aeruginosa</em> (<em>P. aeruginosa</em>) as a model Gram-negative bacterium. Infections of this pathogen are difficult to treat due the inherent resistance to many drug classes, enhanced by the capacity to</span><span> form biofilms. </span><em><span>P. aeruginosa</span></em><span> was experimentally evolved in the presence of AMPs or RPMs, subsequentially assessing the extent of resistance evolution and cross-resistance/collateral sensitivity between treatments. Furthermore, the fitness costs of resistance on bacterial growth were studied, and whole-genome sequencing used to investigate which mutations could be candidates for causing resistant phenotypes. Lastly, changes in the pharmacodynamics of the evolved bacterial strains were examined.</span></p> <p><span>Our findings suggest that using RPMs bears a much lower risk of resistance evolution compared to AMPs and mostly prevents cross-resistance development to other treatments, while maintaining (or even improving) drug sensitivity. This strengthens the case for using random cocktails of AMPs in favour of single AMPs, against which resistance evolved <em>in vitro</em>, providing an alternative to classic antibiotics worth pursuing.</span></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.