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3,472 results for “Balancing”
MPI load balancing simulation data sets (companion to IPDPS 2017)
<p>This package contains data sets and scripts (in an Org-mode file) related to our submission to IPDPS 2017, under the title "Using Simulation to Evaluate and Tune the Performance of Dynamic Load Balancing of an Over-decomposed Geophysics Application".</p> <p>The following contents are included:</p> <ul> <li><em>IPDPS2017.org :</em> Org mode (Emacs) file containing the shell (Bash) and R scripts used to: <ul> <li>run the load balancing simulation;</li> <li>process the traces of both real executions (Tau traces) and simulation (Pajé traces);</li> <li>generate the graphics.</li> </ul> </li> <li><em>lb_traces/:</em> this directory contains the raw traces from real executions and SMPI emulations of the Ondes3D application.</li> <li><em>processed_data/</em>: this directory contains the results of the processing of the traces in the form of CSV format data files which are be used to generate the graphics.</li> <li>i<em>mg</em>/: this directory contains the generate graphics, in PNG format.</li> </ul> <p> </p>
Effect of hip abduction assistance on metabolic cost and balance during human walking
<p>The use of wearable robots to provide walking assistance has rapidly grown over the last decade, with significant advances made in robot design and control methods toward reducing physical effort while performing an activity. The reduction in the walking effort has mainly been achieved by assisting forward progression in the sagittal plane. Human gait, however, is a complex movement that combines motions in three planes, not only the sagittal but also the transverse and frontal planes. In the frontal plane, the hip joint plays a key role in gait, including balance. However, wearable robots targeting this motion have rarely been investigated. In this study, we developed a hip abduction assistance wearable robot by formulating the hypothesis that assistance that mimics the biological hip abduction moment or power could reduce the metabolic cost of walking and affect the dynamic balance. We found that hip abduction assistance with a biological moment second peak mimic profile reduced the metabolic cost of walking by 11.6% compared to the normal walking condition (p=0.009). The assistance also influenced balance-related parameters, including the margin of stability. Hip abduction assistance influenced the center-of-mass movement in the mediolateral direction. When the robot assistance was applied as the center of mass moved toward the opposite leg, the assistance replaced some of the efforts that would have otherwise been provided by a human. This indicates that hip abduction assistance can reduce physical effort during human walking while influencing balance.</p>
Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses
<p>This repository contains extracted data features and all questionnaires from our study "Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses". </p><p> </p><p>Data_FinalFeatureSet.xlsx contains data for the 42 dyads who completed the study protocol. Rows represent individual participants, with the two participants in the same dyad always on consecutive rows. Columns consist of:</p><ul><li>Participant gender and age.</li><li>Group that dyads were assigned to. PosInit/NeutInit/NegInit represent positive, neutral or negative initial prompts. Devil1st/NoEmot1st represent which of the two secret prompts was presented first ("devil's advocate" or "no emotion").</li><li>A column stating which of the two participants was given the secret prompts (participant on left or right).</li><li>A column stating whether the participants had already known each other before the session (Y/N).</li><li>Extracted physiological features for 12 intervals: the first baseline (interval 1), 10 conversation intervals (intervals 2-11), and the second baseline (interval 12). Individual features are present for all individual participants while synchrony features exist for dyads (not individuals) and are thus present for only one row of a dyad.</li><li>Raw data from three personality questionnaires: the Brief Fear of Negative Evaluation Scale (BFNES), the Questionnaire of Cognitive and Affective Empathy (QCAE) and the Center for Epidemiologic Studies Depression Scale (CESD).</li><li>Self-reported results of the Self-Assessment Manikin (SAM) for the 10 conversation intervals, with the three columns in each interval corresponding to valence, arousal and balance.</li></ul><p>Note that one dyad's physiological data were corrupted and that dyad was not used for further analysis. Their demographics and questionnaire data are included, but no physiological features were calculated.</p><p> </p><p>Questionnaire files include the BFNES, QCAE and CESD as well as three versions of our modified SAM: one with no secret prompts, one with secret prompts for participants who saw the "devil's advocate" prompt first, and one with secret prompts for participants who saw the "no emotion" prompt first.</p>
Modelling the carbon balance in bryophytes and lichens: Presentation of PoiCarb 1.0, a new model for explaining distribution patterns and predicting climate-change effects
<p><strong>Premise </strong></p> <p>Bryophytes and lichens have important functional roles in many ecosystems. Insight into how their CO<sub>2</sub> exchange responds to climatic conditions is essential for understanding current and predicting future productivity and biomass patterns, but responses are hard to quantify at time-scales beyond instantaneous measurements. We present PoiCarb 1.0, a model to study how CO<sub>2</sub> exchange rates of these poikilohydric organisms change through time as a function of weather conditions.</p> <p><strong>Methods</strong></p> <p>PoiCarb simulates diel fluctuations of CO<sub>2</sub> exchange and estimates long-term carbon balances, identifying optimal and limiting climatic patterns. Modelled processes are net photosynthesis, dark respiration, evaporation and water uptake. Measured CO<sub>2</sub>-exchange responses to light, temperature, atmospheric CO<sub>2</sub> concentration, and thallus water content (calculated in a separate module) are used to parameterise the model's carbon module. We validated the model by comparing modelled diel courses of net CO<sub>2</sub> exchange to such courses from field measurements on the tropical lichen <em>Crocodia aurata</em>. To demonstrate the model's usefulness, we simulated potential climate-change effects.</p> <p><strong>Results </strong></p> <p>Diel patterns were reproduced well and modelled and observed diel carbon balances were strongly positively correlated. Simulated warming effects via changes in metabolic rates were consistently negative, while effects via faster drying were variable, depending on the timing of hydration.</p> <p><strong>Conclusions</strong></p> <p>Being able to reproduce the weather-dependent variation in diel carbon balances is a clear improvement compared to simple extrapolations of short-term measurements or potential photosynthetic rates. Apart from predicting climate-change effects, future uses of PoiCarb include testing hypotheses about distribution patterns of poikilohydric organisms and guiding species' conservation.</p>
Dataset for Hydroclimate risk to electricity balancing throughout the U.S. paper
<p>Data and working scripts for publication "Hydroclimate risk to electricity balancing throughout the U.S." </p>
Climatic mass balance of the Cordillera Darwin Icefield (2000-2023), Tierra del Fuego, Chile
<p>This dataset contains the annual averages of the climatological input and the modelled climatic mass balance of the Cordillera Darwin Icefield, Tierra del Fuego. The climatological input is calculated by statistically downscaling ERA5 reanalysis data to weather stations across the Cordillera Darwin via Quantile Mapping. The precipitation is simulated with an orographic precipitation model. Global radiation is simulated with a radiation model. The applied climatic mass balance model is the coupled snowpack and ice surface energy and mass balance model (COSIPY).</p>
balancing alpha for tada-entity
<p>This contains a list of figures which represents the accuracy of alpha prediction using different datasets and settings.</p>
Two Source Energy Balance Model Inputs and Outputs from Drone Surveys at Majadas de Tietar in May 2021
<p><strong>MONSOON PROJECT SURVEY DATA OUTPUTS: Majadas de Tietar Tree-Grass Savanna Ecosystem 05/05/2021-20/05/2021</strong></p> <p>Here we make available high resolution (0.82 cm) energy and water flux maps from unmanned aerial system (UAS) data collected using a Micasense Altum in May 2021. We use the Two Source Energy Balance Model (via pyTSEB) and include model inputs and outputs. We use the Priestley Taylor (TSEB hereafter) and Dual Time Difference (DTD hereafter) methods in pyTSEB, the details of which can be found here pyTSEB https://pytseb.readthedocs.io/en/latest/index.html. The data collection method largely follows https://www.mdpi.com/2072-4292/13/7/1286, however a new paper detailing these surveys in Majadas is under review (as of November 2021). </p> <p>This upload includes the following gridded datasets:</p> <p><strong>Model inputs</strong></p> <p>Zipfiles are named according to their collection date (<strong>DDMMYYYY.7z</strong>). Within each zipfile are the datasets corresponding to different flight times UTC +2 (<strong>hhmm_DDMMYY</strong>). Within each survey folder are rasters with descriptive filenames using the following format:</p> <p><em>Product type_Resolution_survey area_date_flight time.tif</em></p> <p>The following prefixes denote the Product types:</p> <ul> <li>CHM_... = Canopy Height Model (m)</li> <li>GFrac2_... = Green Fraction (0-1)</li> <li>MSpec_... = Raw multispectral dataset from Altum (Blue, Green, Red, NIR, Rededge, LWIR)</li> <li>TEmpK_... = Radiometric Surface Temperature (empirical calibration, K)</li> <li>TRawK_... = Radiometric Surface Temperature (no calibration, K)</li> <li>LST2_... = Radiometric Surface Temperature (calibrated using methods outlined here https://www.mdpi.com/2072-4292/12/7/1075, K)</li> <li>Grass_... = grass vegetation mask</li> <li>Tree_... = tree vegetation mask</li> </ul> <p>We also supply the config files used to generate TSEB and DTD. To run these you will need to edit the filepaths according to your own system. </p> <p><strong>Model Outputs</strong></p> <p><strong>Majadas_TSEB_EMP_outputs.7z</strong> = Two Source Energy Balance (pyTSEB) model outputs (using the Priestley-Taylor method), using radiometric temperature datasets calibrated empirically. </p> <p><strong>Majadas_DTD_EMP_outputs.7z</strong> = TSEB Dual Time Difference model outputs (from pyTSEB) using radiometric temperature datasets calibrated empirically. </p> <p><strong>DTD_ET.7z</strong> = Evapotranspiration rasters (calculated using DTD latent heat data) in g m<sup>-2</sup> s<sup>-1</sup></p> <p><strong>File names are descriptive</strong>:</p> <p><em>Model type_radiometric temperature method_vegetation type_survey area_date_flighttime.tif</em></p> <p>Model type = DTD or TSEB</p> <ul> <li>Radiometric temperature method = always empirical calibration here</li> <li>vegetation type = grass, tree, or merge (which is both tree and grass)</li> <li>Survey area = N (north, or Nitrogen fertiliser treatment), C (central, or Control fertiliser treatment), S (south, or Nitrogen and Phosphorus fertiliser treatment)</li> <li>date = in DDMMYY format</li> <li>flight time = takeoff time for the drone (hhmm) (UTC+2)</li> </ul> <p>To find the exact local time of survey times, please see the table in flight_data3.csv</p>
Glaciological data (point mass balance, SWE, snow depth, bulk snow density, modelled runoff) from Werenskioldbreen (Svabard) 2009-2020
<p>This repository contains supporting data associated to the manuscript to <em>Earth System Science Data: </em></p> <p><strong>Ignatiuk D., Błaszczyk M., Budzik T., Grabiec M., Jania J., Kondracka M., Laska M., Małarzewski Ł., Stachnik Ł. A decade of glaciological and meteorological observations in the High Arctic (Werenskioldbreen, Svalbard)</strong></p> <p>In 2009-2020, 9 ablation stakes were installed on the Werenskioldbreen.<strong> </strong>Based on the data collected, the following glaciological variables are available for Werenskioldbreen: annual and seasonal point ablation and accumulation, snow cover depth, bulk snow density and SWE (snow water equivalent) at the measuring points and modelled total runoff from the surface ablation. </p>
Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design
<p>Dataset related to the article: Virtanen, E.A., Lappalainen, J., Nurmi, M., Viitasalo, M., Tikanmäki, M., Heinonen, J., Atlaskin, E., Kallasvuo, M., Tikkanen, H., Moilanen, A. (2022) Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design. Renewable and Sustainable Energy Reviews 158, 112087.</p> <p>Dataset includes suitability maps for offshore windfarms, where priority values are scaled between 0-1 (note the reversed value scale): analysis solution (A) economy, (B) society, (C) biodiversity, (D) restrictions, (E) A+B+C without restrictions and (F) A+B+C with restrictions. Dataset includes also the conflict map (and R script), where each three main solutions (A, B, C) are mapped onto an RGB color composite map. </p> <p>Additional details can be found from the published article: <a href="https://doi.org/10.1016/j.rser.2022.112087">https://doi.org/10.1016/j.rser.2022.112087</a></p>
The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long-term Streamflow Components
<p>The excel file <em>Attributes_manuscript.csv</em> contains the mean annual variables and the catchments' attributes used in the manuscript: "<strong>The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long-term Streamflow Components</strong>". Details of each attributes are indicated in the .txt file <em>Attributes_description.txt</em>.</p>
Shifting balances in the weighting of sensory modalities are predicted by divergence in brain morphology in incipient species of Heliconius butterflies
<p>Integrating and weighting sensory perception across modalities is crucial to how animals adapt to their environment. Divergence in brain structure is often in sensory processing regions, suggesting that investment reflects ecological needs. Here, we use two parapatric closely related species, <em>Heliconius erato cyrbia</em> and <em>Heliconius himera</em>, to test the hypothesis that divergence in sensory brain regions affects foraging decisions. These butterflies are isolated across an ecological gradient, which is linked to differences in brain morphology, with <em>H. e. cyrbia </em>investing more in visual centres and <em>H. himera</em> investing in olfactory centres. Here, we demonstrate that these two species vary in how they associate visual and olfactory cues with positive food rewards. We found that when individuals were trained on paired olfactory and visual stimuli, then presented with these stimuli in conflict, they showed distinct behavioural responses. <em>Heliconius himera</em> was more likely to favour positive olfactory cues than <em>H. e. cyrbia</em>, which favoured visual cues regardless of the paired stimulus. This suggests that these species have diverged in the emphasis placed on these different sensory domains during foraging, consistent with observed differences in brain morphology. This result strengthens evidence that speciation initiated by local adaptation is partly facilitated by changes in the neural basis of key behavioural functions.</p>
A low-dimensional, mechanistic water balance model for piñon pine-juniper woodlands in southern Nevada, USA
<p>This is a low-dimensional water balance model developed for pinon pine-juniper woodlands in southern Nevada. It may require extensive revision for use in other similar or dissimilar woodland ecosystems. The model will require parameterization before use in any capacity.</p> <p>This model is mechanistic, but is driven from randomized precipitation. This framework allows the user to estimate the mean and standard deviation of water balance variables by simulating the same average meteorological year 1000s of times, each with randomized precipitation. This technique approximates a normal distribution of precipitation, and will need to be modified for locations/sites that have a non-normal precipitation distribution. It is not recommended to use the model in any other way without first testing and validating its output.</p> <p>I have provided documentation throughout the wrapper, model and parameter files to assist with your use of the model. You are encouraged to learn from, build on, and improve this model. You will need to set your own pathways and build your own meteorological inputs and site parameterizations. You may need to update, revise and add/remove different submodules depending on your intended use.</p>
Supplementary Data & Software for "Balancing the marine sulfur cycle", Johnson & Adkins (submitted)
<p>These files are the supplementary data and software for the manuscript "Balancing the marine sulfur cycle" by Johnson & Adkins (submitted).</p> <p>The MATLAB workspace file "DeepSeaSO4_ClusterAnalysisData.mat" contains organized data structures with DSDP, ODP, and IODP site metadata, porosity data, pore water sulfate concentration data, methane concentration data, total organic carbon wt% data, and calcium carbonate wt% data + their corresponding depths. These data served as the basis for the cluster analysis performed by the included MATLAB script "SO4_conc_cluster_analysis.m" in the study.</p> <p>The "DatasetS1.xlsx" spreadsheet file contains all compiled pyrite sulfur isotopic data, pyrite abundance data, and total sulfur abundance data included within the literature compilation discussed in the manuscript. Pyrite sulfur isotopic data references are listed and are cited in the main text. DOI URLs for the total sulfur abundance data from the PANGAEA repository are also included in the spreadsheet. These data have additionally been saved into the MATLAB workspace files "Pyrite_d34S_Comp.mat" and "Pyrite Abundance_Comp.mat" for import into MATLAB by other scripts.</p> <p>The MATLAB workspace file "POROSITY.mat" includes vectors of latitude (in degrees), longitude (in degrees), water depth (in mbsl), sediment depth (in mbsf), porosity (in vol%), and corresponding PANGAEA DOI URLs along with the data tables from which this information was extracted. This file is called by "PorosityPlotter.m" to extract initial porosity values, compile coring methods, and make plots of initial porosity as a function of water depth for each coring method.</p> <p>The MATLAB script "hypsometry.m" loads the global topographic data included in "topography.mat", calculates associated surface areas across different ocean depth intervals, and determines average porosity and sedimentary pyrite parameters across those intervals in tandem with "S_Iso_Mass.m". "S_Iso_Mass.m" uses the averages to calculate the steady-state sulfur isotopic composition of seawater.</p> <p>The script "phi_waterdepth_analysis.m" loads the cluster analysis data and uses the corresponding porosity data to calculate linear regressions between initial porosity and water depth for use by other scripts; the workspace files "phi_waterdepth_fit.mat" and "phi_waterdepth_trimmed_fit.mat" are saved versions of the regressions used in the manuscript and called by "hypsometry.m" for calculating averages. </p> <p>The additional included files "PyriteSAbundanceCompilationSources.pdf" and "PorosityCompilationSources.pdf" files include full citations to the data included in the pyrite S abundance and porosity compilations (respectively).</p>
Balancing carnivore conservation and sustainable hunting of a key prey species: a case study on the Florida panther and white-tailed deer
<p>1. Large carnivore restoration programs are often promoted as capable of providing ecosystem services. However, these programs rarely measure effects of successful restoration on other economically and ecologically important species. In South Florida, while the endangered Florida panther (Puma concolor coryi) population has increased in recent years due to conservation efforts, the population of its main prey, the white-tailed deer (Odocoileus virginianus), has declined in some regions. The extent to which panther predation has affected deer populations has been difficult to assess because several other factors have changed during this period, including hydrology and hunting regulations.</p> <p>2. We collected known-fate survival data on 241 GPS-collared adult deer (156 females and 85 males) from 2015 to 2018 in the Florida Panther National Wildlife Refuge and the Big Cypress National Preserve in Florida, USA, to assess effects of panther predation on the deer population, while also evaluating the impacts of hunting and hydrology.</p> <p>3. Predation was the primary cause of death (110 of 134 mortalities), and 87% of predation events were attributed to panthers, a much greater rate than reported by studies conducted before the panther genetic restoration effort initiated in 1995. One deer was legally harvested, and two were likely killed by poachers. Increasing water depth decreased female survival but had little impact on male survival, and drowning was never a cause of mortality.</p> <p>4. Females had greater survival probability than males, except during fawning season. From 2015 to 2018, annual survival rates increased from 0.61 (0.52-0.70) to 0.86 (0.79-0.91) for females, and from 0.45 (95% CI: 0.33-0.58) to 0.79 (0.69-0.86) for males.</p> <p>5. Synthesis and applications – High predation rates, coupled with previous evidence of low recruitment of deer in South Florida, suggest that it will be challenging to meet society's competing demands for large predator restoration and sustainable deer harvest. Deer hunting in the area must remain tightly controlled, for now, if it is to be sustainable, and managers should seek to mitigate effects of high waters and improve deer habitat quality to increase deer population viability. Future work should closely monitor the deer population to assess if management actions can increase vital rates and abundance in the context of high predation rates.</p>
Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
<p>The potential for statistical complexity in species distribution models (SDMs) has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data, but can be difficult to interpret, reducing their practical impact. Founding model complexity in ecological theory can improve insight gained from SDMs. </p> <p>Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane (<em>Grus grus</em>) in a spatio-temporal species distribution model. We discuss the role of model components and their impacts on predictions, in comparison with a simpler binomial presence/absence approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in SDMs. </p> <p>Using the marked point process approach, crane distribution was predicted to be dependent on the density of suitable habitat patches, as well as close to observations of the existing population. This demonstrates the advantage of complex model components in accounting for spatio-temporal population dynamics (such as habitat preferences and dispersal limitations) that are not explained by environmental variables. However, including an AR1 temporal correlation structure in the models resulted in unrealistic predictions of species distribution; highlighting the need for careful consideration when determining the level of model complexity.</p> <p>Increasing model complexity, with careful evaluation of the effects of additional model components, can provide a more realistic representation of a system, which is of particular importance for a practical and impact-focused discipline such as ecology (though these methods extend to applications for a wide range of systems). Founding complexity in contextual theory is not only fundamental to maintaining model interpretability, but can be a useful approach to improving insight gained from model outputs. </p>
Thermal adaptation of pelage in desert rodents balances cooling and insulation
<p>Phenotypic convergence across distantly related taxa can be driven by similar selective pressures from the environment or intrinsic constraints. The roles of these processes on physiological strategies, such as homeothermy, are poorly understood. We studied the evolution of thermal properties of mammalian pelage in a diverse community of rodents inhabiting the Mojave Desert, U.S.A. We used a heat flux device to measure the thermal insulation of museum specimens and determined whether thermal properties were associated with habitat preferences while assessing phylogenetic dependence. Species that prefer arid habitats exhibited significantly lower conductivity and thinner pelage relative to species with other habitat preferences. Despite having thinner pelage, the low conductivity imparted comparable insulation to species with other habitat preferences. Thus, arid species retain insulative pelage while simultaneously benefitting from thin pelage that promotes heat loss via convective cooling. We found no evidence of intrinsic constraints or phylogenetic dependence. Heat flux models designed to simulate body temperature regulation demonstrated that arid specialists saved 14.5% of their annual energy required for homeothermy by evolving lower conductivity, providing support for adaptive evolution of pelage. Our study indicates that selection for lower energetic requirements of homeothermy has shaped evolution of pelage thermal properties.</p>
Asynchronous Workload Balancing through Persistent Work-Stealing and Offloading for a Distributed Actor Model Library
<p>With dynamic imbalances caused by both software and ever more complex hardware, applications and runtime systems must adapt to dynamic load imbalances. We present a diffusion-based, reactive, fully asynchronous, and decentralized dynamic load balancer for a distributed actor library. With the asynchronous execution model, features such as remote procedure calls, and support for serialization of arbitrary types, UPC++ is especially feasible for the implementation of the actor model. While providing a substantial speedup for small- to medium-sized jobs with both predictable and unpredictable workload imbalances, the scalability of the diffusion-based approaches remains below expectations in most presented test cases.</p> <p>Actor-UPCXX is a high-performance computing library based on the actor model to enable the use of the actor model for HPC simulations. The source code can be found at: https://github.com/TUM-I5/Actor-UPCXX</p>
Data sets for the Simulated AMPI (SAMPI) load balancing simulation workflow and Ondes3D performance analysis (Companion to CCPE - Euro-Par 2017 special issue)
<p>This package contains data sets and scripts (in an Org-mode file) related to our submission to the special Euro-Par 2017 issue of the journal "Concurrency and Computation: Practice and Experience", under the title "Performance Modeling of a Geophysics Application to Accelerate Over-decomposition Parameter Tuning through Simulation".</p>
Mixed salt systems in the built environment - charge balance calculations
<p>A large database of salt mixtures found in the built environment and a charge balance calculations toolkit. Output of the <a href="http://predict.kikirpa.be/">PREDICT</a> project predominantly including measurements of Belgian historic sites collected by KIK-IRPA Monuments Lab.</p> <p>Contents:</p> <ul> <li>Full integrated database and charge balance calculation sheet, including raw ion concentrations and balanced outputs (.xlsx)</li> <li>Sample integrated database (25 mixtures) and charge balance calculation sheet, including raw ion concentrations and balanced outputs (.xlsx)</li> <li>R scripts for charge balance calculations (.R)</li> <li>Full set of raw ion concentrations for the R scripts (.txt)</li> <li>Sample of 25 raw ion concentrations for the R scripts (.txt)</li> <li>Full set of balanced outputs from the R script (.txt)</li> <li>Sample of 25 balanced outputs from the R script (.txt)</li> </ul>
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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.