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217 results for “forest model”
Data from: Gross primary productivity from leaf-age-dependent light use efficiency (LA-LUE) model over pantropical evergreen broadleaved forests
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Data from: Forest tree breeding using genomic Markov causal models: A new approach to genomic tree breeding improvement
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The evolution, complexity and diversity of models of long-term forest dynamics
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Data from: Evaluating mechanisms of diversification in a Guineo-Congolian tropical forest frog using demographic model selection
The accumulation of biodiversity in tropical forests can occur through multiple allopatric and parapatric models of diversification, including forest refugia, riverine barriers and ecological gradients. Considerable debate surrounds the major diversification process, particularly in the West African Lower Guinea forests, which contain a complex geographic arrangement of topographic features and historical refugia. We used genomic data to investigate alternative mechanisms of diversification in the Gaboon forest frog, Scotobleps gabonicus, by first identifying population structure and then performing demographic model selection and spatially explicit analyses. We found that a majority of population divergences are best explained by allopatric models consistent with the forest refugia hypothesis and involve divergence in isolation with subsequent expansion and gene flow. These population divergences occurred simultaneously and conform to predictions based on climatically stable regions inferred through ecological niche modelling. Although forest refugia played a prominent role in the intraspecific diversification of S. gabonicus, we also find evidence for potential interactions between landscape features and historical refugia, including major rivers and elevational barriers such as the Cameroonian Volcanic Line. We outline the advantages of using genomewide variation in a model-testing framework to distinguish between alternative allopatric hypotheses, and the pitfalls of limited geographic and molecular sampling. Although phylogeographic patterns are often species-specific and related to life-history traits, additional comparative studies incorporating genomic data are necessary for separating shared historical processes from idiosyncratic responses to environmental, climatic and geological influences on diversification.
Data from: Demographic model selection using random forests and the site frequency spectrum
Phylogeographic data sets have grown from tens to thousands of loci in recent years, but extant statistical methods do not take full advantage of these large data sets. For example, approximate Bayesian computation (ABC) is a commonly used method for the explicit comparison of alternate demographic histories, but it is limited by the "curse of dimensionality" and issues related to the simulation and summarization of data when applied to next-generation sequencing (NGS) data sets. We implement here several improvements to overcome these difficulties. We use a Random Forest (RF) classifier for model selection to circumvent the curse of dimensionality and apply a binned representation of the multidimensional site frequency spectrum (mSFS) to address issues related to the simulation and summarization of large SNP data sets. We evaluate the performance of these improvements using simulation and find low overall error rates (~7%). We then apply the approach to data from Haplotrema vancouverense, a land snail endemic to the Pacific Northwest of North America. Fifteen demographic models were compared, and our results support a model of recent dispersal from coastal to inland rainforests. Our results demonstrate that binning is an effective strategy for the construction of a mSFS and imply that the statistical power of RF when applied to demographic model selection is at least comparable to traditional ABC algorithms. Importantly, by combining these strategies, large sets of models with differing numbers of populations can be evaluated.
Figure 2. - Bayesian (GTR+Γ+I and HKY+Γ models) and maximum likelihood 50% majority-rule consensus tree. Numbers in the nodes represent posterior probabilities (GTR+Γ+I and HKY+Γ, respectively), and bootstrap value for maximum likelihood and parsimony analyses, respectively. c1–Bragança, Pará; c2–Santa Maria do Pará, Pará; c3–National Forest of Amapá, Amapá; c4–Belém, Pará; i1–Solimões River, near Manaus, Amazonas; i2–Xingu River, Altamira, Pará; i3 and i4–Itacoatiara, Amazonas. MYBP–million years before present.
Figure 2. - Bayesian (GTR+Γ+I and HKY+Γ models) and maximum likelihood 50% majority-rule consensus tree. Numbers in the nodes represent posterior probabilities (GTR+Γ+I and HKY+Γ, respectively), and bootstrap value for maximum likelihood and parsimony analyses, respectively. c1–Bragança, Pará; c2–Santa Maria do Pará, Pará; c3–National Forest of Amapá, Amapá; c4–Belém, Pará; i1–Solimões River, near Manaus, Amazonas; i2–Xingu River, Altamira, Pará; i3 and i4–Itacoatiara, Amazonas. MYBP–million years before present.
Developing crown width model for mixed forests using soil, climate, and stand factors
<ol> <li>The tree crown is a useful measure of tree vigor and is highly relevant to a tree's environmental adaptability. Crown allometry depends on environmental and stand conditions. Several studies have focused on the effects of climate change and competitive intensity on the crown, but the regulatory role of soil resources and diversity on crown allometry and carbon allocation has been neglected.</li> <li>Data from 20,994 trees in 232 mixed forests collected between 2011 and 2019 was located near four major mountain ranges in northeast China. The proposed crown width model includes the stand developmental stage, soil, climate, competition intensity, species mixture, species diversity, structural diversity, and their interactions.</li> <li>We observed that the cross-species allometric scaling exponent does not conform to the universal scaling law. Our results showed that crown width increased with increasing soil bulk density, quadratic mean diameter, and coefficient of diameter variation but decreased with increasing de Martonne aridity index, basal area, Simpson index, and species mixture. The interaction of quadratic mean diameter and soil bulk density had a significant negative effect on crown width. The influence of a particular factor within the interaction term on crown width was modulated by the gradients of other factors. Furthermore, soil bulk density contributed more to crown width modeling than the aridity index, and structural diversity had a greater effect on crown width than species diversity.</li> <li> <em>Synthesis</em>. Our results provide new insights into the environmental variability of crown allometry in mixed forests under global change, which is critical for improving regional and global estimates of forest biomass and carbon stocks.</li> </ol>
tRIBS Model Scenarios: Forest Treatment Effects on Watershed Responses under Warming in the Beaver Creek (2003-2018)
<p>This dataset contains the model simulation setup and scenario results of the individual and combined effects of forest thinning and warming on the forest hydrologic response in the Beaver Creek watershed of central Arizona. The simulations were conducted with the Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS) model at a variable resolution of about 120 m, hourly resolution from 2003-2018 and aggregated daily values in this dataset. Raw model outputs at hourly resolution are available upon request from the authors, but not included here due to their excessive size. </p> <p>The model setup files are organized into the tar gzipped file: <strong>BCmodel.tar.gz</strong>. This contains the following files: (1) a series of input files (*.in) used for the model simulations, and (2) the ancillary data sets required for model execution (terrain model, soil map, land cover map, initialization file, data descriptor tables). </p> <p>The model rainfall forcing files are organized into the tar gzipped file: <strong>BCrain.tar.gz</strong>. This contains the following directories: (1) rainfall data from the bias-corrected NEXRAD product, and (2) rainfall data from the NLDAS-2 product.</p> <p>The model meteorological forcing files are organized into the tar gzipped file: <strong>BCweather.tar.gz</strong>. This contains the following data from bias-corrected, adjusted NLDAS-2: (1) atmospheric pressure, (2) wind speed, (3) air temperature, (4) incoming solar radiation, and (5) relative humidity. </p> <p>The daily values of the model outputs are stored in the file <strong>tRIBSModelScenarios_DailyOutputs.xlsx</strong>. This includes the scenarios (BC0, BC1, BC2, BC4, BC6, PT0, PT1, PT2, PT4, PT6) and the variables streamflow (Q), total evapotranspiration (ET), snowmelt (M), ground sublimation (Subg), and canopy sublimation (Subc). All values in mm/day over the period 10/01/2002 to 09/30/2018 (16 water years). A readme file is provided. </p> <p>More details can be found in the associated paper (this record will be updated when the paper is published):</p> <p>Cederstrom, C., Vivoni, E.R., Mascaro, G., and Svoma, B. 2024. Forest Treatment Effects on Watershed Responses under Warming.<em> Water Resources Research. (in revision)</em>.</p>
Converging findings of climate models and satellite observations on the positive impact of European forests on cloud cover
<p>Overview:<br>This repository hosts a comprehensive dataset resulting from a Space for Time (S4T) analysis (<em>Duveiller et al. 2018</em>). The dataset spans monthly data from 2004 to 2014, providing detailed insights into cloud cover dynamics and land cover characteristics. Leveraging observations from the Cloud CCI MODIS-Aqua dataset (<em>Stengel et al. 2017</em>) and RegCM5 (<em>Giorgi et al. 2023</em>) model outputs at 0.05 degrees resolution, it offers valuable resources for researchers studying atmospheric and terrestrial interactions.</p> <p>Contents:</p> <p>s4t_ESACCI.zip:<br>Output of the space-for-time algorithm applied to the Global MODIS-Aqua cloud cover data for low, medium, and high clouds.<br>s4t_RegCM5.zip:<br>Output of the space for time algorithm applied to the European RegCM5 cloud data for low, medium, and high clouds.<br>Variables:</p> <p>Cloud Area Fractions:<br>Includes low (cll), medium (clm), and high (clh) cloud area fractions, expressed as percentages.<br>Cloud layers are categorized based on cloud top pressure (CTP), following the convention of the International Satellite Cloud Climatology Project.</p>
Empirical data and model simulations of the effect of repeated hurricanes on soil carbon dynamics in a humid tropical forest
<p>Increasing hurricane frequency and intensity with climate change is likely to affect soil organic carbon (C) stocks in tropical forests. We examined the cycling of C between soil pools and with depth at the Luquillo Experimental Forest in Puerto Rico in soils over a 30-year period that spanned repeated hurricanes. We used a non-linear matrix model of soil C pools and fluxes ("soilR") and constrained the parameters with soil and litter survey data. Soil chemistry and stable and radiocarbon isotopes were measured from three soil depths across a topographic gradient in 1988 and 2018. Our results suggest that pulses and subsequent reduction of inputs caused by severe hurricanes in 1989, 1998, and two in 2017 led to faster mean transit times and younger mean ages of soil C in the particulate, occluded, and mineral-associated soil organic matter pools at 0–10 cm and 35–60 cm depths relative to a modeled control soil with constant inputs over the thirty years. Between 1988 and 2018, the occluded C stock increased, and d<sup>13</sup>C in all pools decreased, while changes in particulate and mineral-associated C were undetectable. The differences between 1988 and 2018 suggest that hurricane disturbance results in a dilution of the occluded light C pool with an influx of young, debris-deposited C, and possible microbial scavenging of old and young C in the particulate and mineral-associated pools. These effects led to a younger total soil C pool with faster mean transit times. Our results suggest that increasing frequency of intense hurricanes will speed up rates of C cycling in tropical forests, and eventually lead to net losses of C from tropical forest soils.</p>
Final products from "3D MODELING AS A CONSERVATION TOOL TO CHARACTERIZE ENDANGERED SEASONALLY FLOODED ECOSYSTEMS IN THE VOLTA GRANDE DO XINGU, AMAZON FOREST, PARÁ, BRAZIL"
<p>In these .zip folders, you will find the products generated from flight missions carried out between November 7th to 14th, 2021, in the Volta Grande do Xingu, Pará, Brazil. <br>These results are presented as an integral part of the article titled '3D MODELING AS A CONSERVATION TOOL TO CHARACTERIZE ENDANGERED SEASONALLY FLOODED ECOSYSTEMS IN THE VOLTA GRANDE DO XINGU, AMAZON FOREST, PARÁ, BRAZIL' published on the doctoral thesis "Characterization and monitoring of the flooding dynamics and seasonally flooded environments of the Volta Grande do Xingu through remote sensing", available at <a href="https://doi.org/10.11606/T.106.2023.tde-02022024-211517">https://doi.org/10.11606/T.106.2023.tde-02022024-211517</a>. To understand the data processing methodology that led to these results, please refer to the thesis.<br>Each folder represents a flight mission. They are named by date and flight number (DD_MM_YYYY_FLIGHT#).<br>Within each folder, there are six files resulting from the processed flights: The georeferenced orthophoto and Digital Surface Model, which are raster files (.TIFs), the dense point cloud (.las), and the files composing the 3D Model generated by Agisoft Metashape (extensions .OBJ, .MTL, and .JPEG).<br>The .OBJ file is the primary file for visualizing the model, including the three-dimensional mesh formed by the points of the point cloud and containing geometry, texture, and color information. <br>The .MTL file contains the material description associated with the OBJ file and includes information about the visual properties of the model, such as texture, reflections, and materials. <br>The .JPEG files are the texture images used on the Digital Surface Model to generate the 3D model. This information provides the model with its realistic properties.<br>The .OBJ, .MTL and .JPEG files need to be together in the same folder for a complete 3D Model visualization (i.e., shape, color, and texture).<br>When using this data, please cite Affonso, A. A. (2023). <em>Caracterização e monitoramento da dinâmica de alagamento e dos ambientes sazonalmente alagáveis da Volta Grande do Xingu através de sensoriamento remoto</em>. Tese de Doutorado, Instituto de Energia e Ambiente, Universidade de São Paulo, São Paulo. doi:10.11606/T.106.2023.tde-02022024-211517. Recuperado em 2024-04-14, de www.teses.usp.br</p>
Data for: A comprehensive dataset of forest above-ground biomass from field observations, machine learning and topographically augmented allometric models over the Kashmir Himalaya
<p>The repository contains observed Above Ground Biomass (AGB) estimates at about 275 sample plots chosen for AGB assessment in the forests of Kashmir Himalaya. The AGB is assessed as a fucntion of dbh using various allometric equations developed specifically for the region. It also contains the AGB for years 1978, 1990, 2000, 2010 and 2021 predicted using topographcally augmeneted multivariate regression model. The extent of forest, delineated using on-screen digitization using Landsat and Sentinel image collection at decadal scale is also provided for the years 1978, 1990, 2000, 2010 and 2021.</p>
Trained Random Forest Model and Scaler Parameters for (Phy+Man), 17 Feb, 2025
<p>This repository contains the trained models and scaler parameters. There are two trained random forest models. These models were trained on 5000 traces/class, The traces were of 40s (P-10, P+30), 110s (P-10, P+100) and 150s (P-50, P+100) bandpass filtered between 0.5-15 Hz, and resampled to 50 Hz. <br><br>The new version (17/02/2025) of the models were trained on 6000 traces per class. </p>
Random Forest model for PPI predictions
<p>The uploaded model contains the trained random forest model to predict protein-protein Interactions based on their amino acid sequence. The model is described in our preprint "ProteinPrompt: a webserver for predicting protein-protein interactions" which can be found on bioRxiv.org: https://doi.org/10.1101/2021.09.03.458859</p> <p>The model was trained with the scikit-learn package in version 0.20.3 under Python 3.7.12</p>
Model results of reduced wood harvest and forest protection scenarios using MAgPIE 4.3.5
<p>The files here contain MAgPIE 4.3.5 results of reduced wood harvest and forest protection scenarios.</p> <p>MAgPIE requires <em>GAMS</em> (<a href="https://www.gams.com/">https://www.gams.com/</a>) including licenses for the solvers <em>CONOPT</em> and (optionally) <em>CPLEX</em> for its core calculations. As the model benefits significantly from recent improvements in <em>GAMS</em> and <em>CONOPT4</em> it is recommended to work with the most recent versions of both.<br> <br> The results of the model run here have been cleaned up to avoid bulky uploads. The fulldata.gdx is the technical output of the GAMS optimization and contains all quantities that were used during the optimization in unchanged form. The mif-file is a CSV file of a specific format and is synthetized from the fulldata.gdx by post-processing scripts. It can be read in any text editor or spreadsheet program and is well suited for a brief look at the results and for further analysis.</p>
Random forest modelling of multi-scale, multi-species habitat associations within KAZA transfrontier conservation area using spoor data
<p>As landscape-scale conservation models grow in prominence, assessments of how wildlife utilise multiple-use landscapes are required to inform effective conservation and management planning. Such efforts should strive to incorporate multi-species perspectives to maximise value for conservation, and should account for scale to accurately capture species-environment relationships. We show that the random forest machine learning algorithm can be used to model large-scale sign-based data in a multi-scale framework. We used this method to investigate scale-dependent habitat associations for 16 mammal species of high conservation importance across the southern Kavango Zambezi (KAZA) Transfrontier Conservation Area in Botswana and Zimbabwe. Our findings revealed substantial variation in the factors shaping habitat use across species, and illustrate that different species often have divergent responses to the same environmental and anthropogenic factors, and differ in the scales at which they respond to them. For all variables across all species, scale optimisation most often selected our largest scale. Precipitation, soil nutrients, and vegetation appeared to be the most important factors determining mammal distributions, likely through their associations with food resources for herbivores and, in turn, prey availability for carnivores. Anthropogenic pressures also had an important influence on habitat use, with many species selecting against areas with high cattle density. The variety of relationships with human density indicated that species vary in their tolerance of humans. We found a consistent positive relationship with areas under high protection, and negative relationship with unprotected and less-strictly protected areas. Policy implications: This study highlights the importance of adopting a multi-scale, multi-species approach for critical decision-making processes that depend on understanding wildlife distributions and habitat associations, such as protected area, corridor, and buffer zone prioritisation. We use our findings to identify changing rainfall patterns and increasing livestock numbers as two emerging trends that may impact wildlife distributions, both within sub-Saharan Africa and on a global scale.</p>
Data from: Filtering ground noise from LiDAR returns produces inferior models of forest aboveground biomass in heterogenous landscapes
<p>Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering -- that is, excluding returns from LiDAR point clouds based on simple height thresholds -- is a common practice meant to improve the 'signal' content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. However, ground returns may be helpful for making accurate aboveground biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types.<br> <br> In this paper, we applied several ground noise filtering thresholds while mapping forest AGB across New York State (USA), a heterogenous landscape composed of both contiguously forested and highly fragmented areas with mixed land cover types. We fit random forest models to predictor sets derived from each filtering intensity threshold and compared model accuracies, paying attention to how changes in accuracy correlated with landscape structure. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling, with mean correlation (Spearman's $\rho$) between variables increasing from 0.183 to 0.266. We found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds, with RMSE increasing by up to 2.2 Mg ha^-1^ statewide. Although we only modeled AGB for forest cover types, models fit to predictors derived from filtered point clouds performed worse as landscape heterogeneity (as measured by patch density and edge density) increased, suggesting ground returns are particularly useful when modeling edge forests. Our results suggest that ground filtering should be a carefully considered decision when mapping forest AGB, particularly when mapping heterogeneous and highly fragmented landscapes, as ground returns are more likely to represent useful 'signal' than extraneous 'noise' in these cases.</p>
Model data to investigate wood frog abundance in 17-year post harvest variable retention mixed wood forests
<p>Variable retention forest harvesting aims to reduce negative effect of harvesting on forest biodiversity, but its effectiveness is not well understood for many taxa. To better understand the effects of variable retention forest management and environmental features on amphibians, we used pitfall traps to capture wood frogs (<em>Lithobates sylvaticus</em>) across 4 levels of retention harvest (clearcut [0%], 20%, 50%, and unharvested control [100%]), and 2 forest types (deciduous and coniferous), in 17-year post-harvest forests in northwest Alberta. We mapped breeding sites and used a terrain moisture index (Depth-to-Water) derived from airborne LiDAR to examine relationships between relative abundance, breeding site proximity and soil moisture. Retention level alone had no effect on relative abundance, but in late summer (July and August) there was a significant interaction between retention level and forest type: capture rates decreased with amount of retention for deciduous forests, but increased with amount of retention in conifer forests. During late summer, capture rates were higher in conifer forests than in deciduous forests, with soil moisture (lower Depth-to-Water) positively related to capture rates. Though timber retention may be beneficial to wood frogs in the short-term, any impacts of forest harvesting on wood frog abundance was undetectable in stands 17 years post-harvest. </p>
Comparing mixed models and Random Forest association tests using naturalGWAS and a Striped Bass SNP dataset
<p>In this study, we used the phenotype simulation package naturalGWAS to test the performance of Zhao's Random Forest method in comparison to an uncorrected Random Forest test, latent factor mixed models (LFMM), genome-wide efficient mixed models (GEMMA), and confounder adjusted linear regression (CATE). We created 400 sets of phenotypes, corresponding to five effect sizes and 2, 5, 15, or 30 causal loci, simulated from two empirical datasets containing SNPs from Striped Bass representing three and 13 populations. All association methods were evaluated for their ability to detect genotype-phenotype associations based on power, false discovery rates, and number of false positives. Genomic inflation was highest for uncorrected Random Forest and LFMM tests and lowest for Gemma and Zhao's Random Forest. All association tests had similar power to detect causal loci, and Zhao's Random Forest had the lowest false discovery rate in all scenarios. To measure the performance of association tests in small datasets with few loci surrounding a causal gene we also ran analyses again after removing causal loci from each dataset. All association tests were only able to find true positives, defined as loci located within 30k bp of a causal locus, in 3%–18% of simulations. In contrast, at least one false positive was found in 17%–44% of simulations. Zhao's Random Forest again identified the fewest false positives of all association tests studied. The ability to test the power of association tests for individual empirical datasets can be an extremely useful first step when designing a GWAS study.</p>
Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets
<p>Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets</p> <p>The impact of simulated rainfall on the soil surface roughness of different soil types with various initial surface states and the differences between their spectral characteristics were studied under laboratory conditions. The soil samples were collected from a horizon of fields near Poznań, western Poland. The physical and physicochemical properties of each soil sample were determined. Then, the part of the soil materials, consisting of natural aggregates, were used to form three soil surface roughness. </p> <p>An explanation of the table column names in the “soils properties.csv” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“sand” - Sand content in the soil sample in %.</p> </li> <li> <p>“silt” – Silt content in the soil sample in %.</p> </li> <li> <p>“clay” – Clay content in the soil sample in %.</p> </li> <li> <p>pHH2O” - The pH of the soil sample determined in water. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“pHKCl” – The pH of the soil sample determined in KCl. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“SOC” – Organic matter content in soil was determined by oxidation titration using K2Cr2O7 with H2SO4 on the block mineralization.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “rainfall doses.csv” file:</p> <p> </p> <ul> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p>“rainfall dose” - One-time amount of rainfall dose expressed in millimeters.</p> </li> <li> <p>“accumulated rainfall” – Summation of rainfall after each successive dose expressed in millimeters.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “soil measurements” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p> “reflectance” - The amount of radiation reflected from the soil surface under the influence of successive rainfalls and expressed in nanometres. </p> </li> <li> <p>“roughness state” - The size of the roughness: R1 is the lowest soil roughness state, R2 represents medium soil roughness, and R3 represents the greatest roughness.</p> </li> <li> <p>“T3D” - Tortuosity index is a surface roughness index. It was calculated from DEM (Digital Elevation Model). It expresses the ratio between the true surface of DEM and its flat horizontal area.</p> </li> <li> <p>“HSD” - Height Standard Deviation is the second surface roughness index. It was calculated from DEM and expressed in millimeters. </p> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><br> </p> <p> </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.