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648 results for “uncertainties”
Intrusive and Non-Intrusive Uncertainty Quantification Methodologies for Pyrolysis Modeling
<p>This repository contains python scripts and results for uncertainty analysis of Arrhenius equation with kinetic parameters as uncertain. The data set contains folders for each PMMA variant (1,2 and 3) used for uncertainty quantification (UQ) and 'Misc' folder containing miscellaneous files and scripts used in the study. </p> <p>Each PMMA variant folder has further sub-folders for the UQ methods implemented:</p> <ol> <li>Intrusive polynomial chaos (IPC)</li> <li>Monte Carlo (MC)</li> <li>Non-intrusive polynomial chaos (NIPC)</li> </ol> <p>Additionally convergence and comparison for the UQ methods is available in sub-folders of the same name respectively.</p> <p>The python file 'UoWu_noLatex.mplstyle' for plotting style used by us is attached to ease the rerunning of the scripts.</p>
Bi-fidelity Variational Auto-encoder for Uncertainty Quantification: Experimental Data
<p>Quantifying the uncertainty of quantities of interest (QoIs) from physical systems is a primary objective in model validation. However, achieving this goal entails balancing the need for computational efficiency with the requirement for numerical accuracy. To address this trade-off, we propose a novel bi-fidelity formulation of variational auto-encoders (BF-VAE) designed to estimate the uncertainty associated with a QoI from low-fidelity (LF) and high-fidelity (HF) samples of the QoI. This model allows for the approximation of the statistics of the HF QoI by leveraging information derived from its LF counterpart. Specifically, we design a bi-fidelity auto-regressive model in the latent space that is integrated within the VAE's probabilistic encoder-decoder structure. An effective algorithm is proposed to maximize the variational lower bound of the HF log-likelihood in the presence of limited HF data, resulting in the synthesis of HF realizations with a reduced computational cost. Additionally, we introduce the concept of the bi-fidelity information bottleneck (BF-IB) to provide an information-theoretic interpretation of the proposed BF-VAE model. Our numerical results demonstrate that BF-VAE leads to considerably improved accuracy, as compared to a VAE trained using only HF data, when limited HF data is available.</p>
Design for Measurement Uncertainty software and 1D instrument combination evaluations
Open the record for dataset details and reuse information.
Data from: navigating uncertainty: managing herbivore communities enhances savanna ecosystem resilience under climate change
<p>Savannas are characterized by water scarcity and degradation, making them highly vulnerable to increased uncertainties in water availability resulting from climate change. This poses a significant threat to ecosystem services and rural livelihoods that depend on them. In addition, the lack of consensus among climate models on precipitation change makes it difficult for land managers to plan for the future. Therefore, savanna rangeland management needs to develop strategies that can sustain savanna resilience and avoid tipping points under an uncertain future climate. Our study aims to analyze the impacts of climate change and rangeland management on degradation in savanna ecosystems of southern Africa, providing insights for the management of semi-arid savannas under uncertain conditions worldwide. To achieve this, we simulated the effects of projected changes in temperature and precipitation, as predicted by ten global climate models, on water resources and vegetation (cover, functional diversity, tipping points (transition from grass-dominated to shrub-dominated vegetation)). We simulated three different rangeland management options (herbivore community dominated by grazers, by browser and by mixed-feeders), each with low and high animal densities using the ecohydrological model EcoHyD. Our results identified intensive grazing as the primary contributor to the increased risk of degradation in response to changing climatic conditions across all climate change scenarios. This degradation encompassed a reduction in available water for plant growth within the context of predicted climate change. It also entails a decline in the overall vegetation cover, the loss of functionally important plant species, and the inefficient utilization of available water resources, leading to earlier tipping points. Our findings underscore that in the face of climate uncertainty, farmers' most effective strategy for securing their livelihoods and ecosystem stability is to integrate browsers and apply management of mixed herbivore communities. This management approach not only significantly delays or averts tipping points but also maintained greater plant functional diversity, fostering a more robust and resilient ecosystem that acts as a vital buffer against adverse climatic conditions.</p>
Ancient introgression in mouse lemurs (Microcebus:Cheirogaleidae) explains 20 years of phylogenetic uncertainty
<p>Mouse lemurs (genus <em>Microcebus</em>) are a clade of approximately 26 named species of small, nocturnal primates endemic to Madagascar. The genus radiated one and ten million years ago and is morphologically cryptic, with most species having been named within the past 20 years largely based on phylogenetic analysis of short fragments of mitochondrial data. More recent work has been focused on revisiting species designations with autosomal nuclear data using more sophisticated statistical approaches. The order of speciation events in <em>Microcebus </em>remains contentious, particularly with regard to the placement of the <em>M. ravelobensis </em>clade. We investigated support for previous phylogenetic hypotheses based on available whole-genome assemblies from six species and an outgroup. We recovered over 4,000 one-to-one orthologs from these assemblies and used concatenation and coalescent species tree methods to<em> </em>evaluate if differences between previous studies were due to methodological differences or to limitations from too few loci. Observed gene tree discordance was high with patterns inconsistent with incomplete lineage sorting alone. Therefore, we estimated phylogenetic networks to investigate ancient introgression events that may explain observed gene tree distributions and previous phylogenetic conflicts. A network model, invoking some role for introgressive hybridization in the early evolution of <em>Microcebus</em>, better characterizes phylogenetic relationships than does any binary species tree. Our results provide insights into the biogeographic history of a threatened and diverse group of primates while also highlighting an important role for phylogenetic network methods in resolving cases of phylogenetic uncertainty.</p>
Data from Uncertainty Ensembles of the MIT EPPA Model
<p>This data repository is created by the MIT Joint Program on the Science and Policy of Global Change (https://globalchange.mit.edu/) and makes available data resulting from ensembles of simulations of the MIT Economic Projection and Policy Analysis (EPPA) Model that were designed to quantify socio-economic uncertainties. </p>
Segmentation Uncertainty
<p>This data includes: </p> <ul> <li><strong>Torso.zip </strong>including torso geometry and electrode positions</li> <li><strong>vent-1..10.vtk </strong>the original segmentations corresponding to one single patient</li> <li><strong>shape-models.zip</strong> 261 shape models derived from the original segmentations<strong> </strong></li> <li><strong>StimProtocols</strong> coordinates of the stimulation sites</li> <li><strong>EikonalSim.zip </strong>eikonal simulations on shape models % stimProtocol</li> <li><strong>pseudoECGs_ShapeModels.zip </strong>computed body surface potentials for each shape model % stimProtocol</li> </ul>
Figure 7 in The death adder Acanthophis antarcticus (Shaw & Nodder, 1802) in Victoria: historical records and contemporary uncertainty
Figure 7. First edition of the Dangerous Snakes of Victoria poster, produced in 1877.
Figure 4 in The death adder Acanthophis antarcticus (Shaw & Nodder, 1802) in Victoria: historical records and contemporary uncertainty
Figure 4. Dorsal perspective of the head and neck (including neck wound) of specimen D4349.
Research data for "Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes"
<h1>Data publication for the paper "Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes"</h1> <div>Contains scripts for performing fabrication uncertainty guided design optimization, example scripts, research data (raw data), cleanup), additional information on models, convergence plots, and field exports.</div> <h2>Funding</h2> <div> <div> <div>We acknowledge funding by the German Federal Ministry of Education and Research (BMBF project siMLopt number 05M20ZAA and BMBF Forschungscampus MODAL number 05M20ZBM) as well as funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -- The Berlin Mathematics Research Center MATH+ (EXC-2046/1, project ID: 390685689). This project (20FUN05 SEQUME) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme. This project is co-financed by the European Regional Development Fund (EFRD, application no. 10184206, QD-Sense).</div> </div> </div> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: Accounting for uncertainty in marine ecosystem service predictions for spatial prioritisation
<p>Spatial assessments of Ecosystem Services (ES) are increasingly used in environmental management and spatial planning, but rarely provide information on the accuracy of predictions. Uncertainty estimates are essential to allow for confidence in the quality and credibility of ES assessments to enable informed decision-making. In marine environments, the need for uncertainty assessments for ES is unparalleled as they are data scarce, poorly (spatially) defined, with complex interconnectivity of seascapes. This study illustrates the uncertainty associated with a principle-based method for ES modelling by accounting for model variability, data coverage, and uncertainty in thresholds and parameters. A sensitivity analysis was applied on ES models for marine bivalves (<em>Austrovenus stutchburyi</em> and <em>Paphies australis</em>) and their contribution to <em>Food provision, Water quality regulation, Nitrogen removal,</em> and <em>Sediment stabilisation</em>.<em> </em>ES estimates from the sensitivity analysis were compared against baseline ES predictions. Spatial uncertainty patterns were analysed for individual ES through bi-plots and multiple ES through spatial prioritisation using Zonation. Results showed spatially explicit differences in uncertainty patterns for ES and between species. <em>Food</em><em> provision</em> had highest maximum uncertainty (>5 points) but also the largest area of high ES and high certainty conditions. Zonation analysis conducted on baseline and conservative ES values showed overall robust outcomes of top 30% area, but important nuances through shifts in top 10% and top 5% area that allowed for a consistently better representation of ES when accounting for uncertainty. The spatial prioritisation in combination with the ES uncertainty biplots provide tools for spatial planning of individual and multiple ES to focus on area of highest value with highest certainty and can thereby help reduce risk and aid informed decision-making at acceptable confidence levels. This type of information is urgently needed in marine ES assessments and their management, but likewise extends to other environments to improve transparency. </p>
Uncertainty Analysis of MSL's CCT-K7.2021 Key Comparison Measurements Using Uncertain Numbers
<p>This dataset is associated with a publication of the same name (currently submitted to Metrologia). It contains Python modules and text files and can be used to repeat the analysis described in the article. The GUM Tree Calculator (GTC) Python software package is required (version 1.4.0, or above: https://github.com/MSLNZ/GTC).</p>
Uncertainties of Predictions from Temperature Replica Exchange Simulations
<p>Parallel tempering molecular dynamics simulation, also known as temperature replica exchange simulation, is a popular enhanced sampling method used to study biomolecular systems. This method makes it possible to calculate the free energy differences between states of the system for a series of temperatures. We developed a method to easily calculate errors (standard errors or confidence intervals) of these predictions using a modified version of our recently introduced JumpCount method. The number of transitions between states (e.g. protein folding events) is counted for each temperature. This number of transitions, together with the temperature, fully determines the value of standard error or the confidence interval of the free energy difference. We also address the issue of convergence in the situation where all replicas start from one state by developing an estimator of the equilibrium constant from simulations that are not fully equilibrated. The prerequisite of the method is the Markovianity of the process studied.</p>
Dataset related to the publication "Procedure for automated low uncertainty assessment of empty cavity mode frequencies in Fabry-Pérot cavity based refractometry"
<p>The data set consists of; The published paper, all figures that present measurement or simulation data in .png and .fig format and the underlying data plotted in the figures in text format. The published plots were generated from the fig files. The text files were generated by reading the plotted data from the fig files. The files are named Fig_XX were XX corresponds to the figure number in the publication. The format of the text file is as follows. Before every data set there is a header consisting of; The number of the subplot where the data is plotted (Plot: XX), the number of the data set in the sub plot (DataSet: XX), and the color of the line or marker in the plot (Color: XX). The description of what each color represents can be found in the publication.</p>
Dataset related to the publication "An Invar-based dual Fabry–Perot cavity refractometer for assessment of pressure with a pressure independent uncertainty in the sub-mPa region"
<p>The data set consists of; The published paper, all figures that present measurement or simulation data in .png and .fig format and the underlying data plotted in the figures in text format.<span> </span>The published plots were generated from the fig files. The text files were generated by reading the plotted data from the fig files. The files are named Fig_XX were XX corresponds to the figure number in the publication.<span> </span>The format of the text file is as follows. Before every data set there is a header consisting of; The number of the subplot where the data is plotted (Plot: XX), the number of the data set in the sub plot (DataSet: XX), and the color of the line or marker in the plot (Color: XX). The description of what each color represents can be found in the publication.</p>
Estimation of Unfactorizable Systematic Uncertainties
<p>Dataset for the "Efficient Estimation of Unfactorizable Systematic Uncertainties" paper. </p> <p>The dataset consists of 30,000 simulated three-jet events. </p> <p>Keys and datasets:</p> <ul> <li>j1_threeM: Dataset of shape (30000, 3) containing the three-momenta of the hardest jets in the events.</li> <li>j2_threeM: Dataset of shape (30000, 3) containing the three-momenta of the second hardest jets in the events.</li> <li>j3_threeM: Dataset of shape (30000, 3) containing the three-momenta of the softest jets in the events.</li> </ul>
Results Discharge Observation Uncertainty and Model Performance Pub
<p><br>Dataset for Publication:<br>On the Importance of Discharge Observation Uncertainty When Interpreting Hydrological Model Performance</p> <p>Authors and Affiliations:<br>- Jerom P. M. Aerts<br> Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands<br>- Jannis M. Hoch<br> Department of Physical Geography, Utrecht University, Utrecht, the Netherlands<br> Fathom, Bristol, United Kingdom<br>- Gemma Coxon<br> Geographical Sciences, University of Bristol, Bristol, United Kingdom<br>- Nick C. van de Giesen<br> Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands<br>- Rolf W. Hut<br> Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands</p> <p>Correspondence:<br>For inquiries about the dataset or publication, contact:<br>Jerom P. M. Aerts<br>Email: j.p.m.aerts@tudelft.nl</p> <p>---</p> <p>Overview:<br>This dataset supports the research article, "On the Importance of Discharge Observation Uncertainty When Interpreting Hydrological Model Performance."<br>It includes outputs and analyses from hydrological models and observational data across multiple flow categories and performance metrics. The dataset enables replication of the analyses and insights into the impacts of discharge observation uncertainty on model evaluation.</p> <p>---</p> <p>Dataset Structure:</p> <p>Folder: Model_Results<br>Contains results from hydrological model runs and associated analyses.</p> <p>Subfolders:</p> <p>1. MARRMoT_Models<br> - Contains results for six hydrological models implemented using the MARRMoT framework.</p> <p>2. PCR-GLOBWB<br> - Includes results for the PCR-GLOBWB hydrological model.</p> <p>3. wflow_sbm<br> - Contains results for the wflow_sbm hydrological model.</p> <p>Each Subfolder Contains:</p> <p>- Flow Categories<br> - Results categorized by flow regime:<br> - Low_Flow<br> - Average_Flow<br> - High_Flow</p> <p>- Gumboot Temporal Sampling Analysis<br> - Results of temporal sampling analyses conducted using the Gumboot method.</p> <p>- Objective Functions<br> - Files containing model performance metrics based on different objective functions.</p> <p>- Observations<br> - Observed discharge time series data used for model calibration and validation.</p> <p>- Simulation Time Series<br> - Simulated discharge time series generated by the models.</p> <p>---</p> <p>Data Description:</p> <p>- File Formats<br> - All data files are in CSV format with UTF-8 encoding.</p> <p>- Variable Descriptions<br> - Date: YYYY-MM-DD<br> - Discharge Values: Cubic meters per second (m³/s).<br> - Flow Categories: Indicates if data corresponds to low, average, or high flow conditions.</p> <p>- Performance Metrics:<br> - Includes common hydrological metrics (e.g., Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency) computed for each model and flow category.</p> <p>---</p> <p>Usage Guidelines:</p> <p>Citation:<br>If you use this dataset in your research, please cite the publication as follows:<br>> Aerts, J. P. M., Hoch, J. M., Coxon, G., van de Giesen, N. C., & Hut, R. W. (2024). On the importance of discharge observation uncertainty when interpreting hydrological model performance. [Journal Name, Volume, Pages]. DOI: [Insert DOI].</p> <p>Licensing:<br>This dataset is made available under the [Insert License, e.g., Creative Commons Attribution 4.0 International License (CC BY 4.0)].</p> <p>---</p> <p>Contact:<br>For further questions, feel free to contact:<br>Jerom P. M. Aerts<br>j.p.m.aerts@tudelft.nl</p>
Best Worst Scale under uncertainty
<p><span>Dataset from a questionnaire that included a Best Worst Scaling task as follows “Imagine you are in your favourite wine shop to buy a bottle of wine to drink at home with family and/or friends. You will then be shown a series of tables with 6 attributes or characteristics of the wine in each table. Please select the characteristic that would influence you the most and the characteristic that would influence you the least when considering the purchase of a bottle of wine in the shop. Some attributes or characteristics shown may not be common but please consider all the characteristics as if they were possible”. The Excel data contains two worksheets associated with the BWS experiments (control and treatment with follow up question on preference uncertainty) and socio-demographic characteristics (gender, income age, education level). </span></p>
Evaluating UCE data adequacy and integrating uncertainty in a comprehensive phylogeny of ants
<p>Data from manuscript "Evaluating UCE data adequacy and integrating uncertainty in a comprehensive phylogeny of ants". Includes assembled contigs, unaligned and aligned ultraconserved element loci (UCE) and concatenated matrices, input and output of phylogenetic analyses, custom scripts used.</p>
Data and models for "Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery" by Lagerquist et al.
<p><span><span><span>The file geocenter_models.tar contains all models comprising the GeoCenter ensemble: 3 convolutional neural networks (CNN), 3 isotonic-regression files (one for correcting each CNN’s mean estimate), and 3 more isotonic-regression files (one for correcting each CNN’s ensemble spread). Every model is found in a subdirectory whose names indicate which infrared (IR) wavelengths are used as input to the CNN. For example:</span></span></span></p> <ul> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/model.weights.h5: An HDF5 file containing the trained CNN that uses data from bands 7, 10, 16 (corresponding to 3.9, 7.34, and 13.3 microns on the GOES ABI imager). The trained CNN can always be read by neural_net_utils.read_model() in the ml4tccf library (https://doi.org/10.5281/zenodo.15116854).</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/model_metadata.p: A Pickle file containing metadata for the trained CNN. This file is needed to read the CNN itself with neural_net_utils.read_model(). Otherwise, you will probably never need to access this metafile directly.</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/isotonic_regression/isotonic_regression.dill: A Dill file </span></span></span><span><span><span>containing isotonic-regression models used to bias-correct the ensemble mean from the same CNN. </span></span></span><span><span><span> The trained isotonic-regression models can always be read by scalar_isotonic_regression.read_file() in the ml4tccf library. Note that there are technically two isotonic-regression models for every CNN’</span></span></span><span><span><span>s ensemble mean</span></span></span><span><span><span>: one that bias-corrects the </span></span></span><em><span><span><span>x</span></span></span></em><span><span><span>-coordinate of the TC-center, another that bias-corrects the </span></span></span><em><span><span><span>y</span></span></span></em><span><span><span>-coordinate.</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/</span></span></span><span><span><span>uncertainty_calibration</span></span></span><span><span><span>/</span></span></span><span><span><span>uncertainty_calibration.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble spread from the same CNN. In the ml4tccf code, I make a distinction between “isotonic_regression” (correcting the ensemble mean) and “uncertainty_calibration” (correcting the ensemble spread), but note that both models are isotonic regression and use the sklearn.isotonic.IsotonicRegression class. The trained uncertainty-calibration models can always be read by scalar_uncertainty_calibration.read_file() in the ml4tccf library. Again, note that there are technically two uncertainty-calibration models per CNN: one for spread in the </span></span></span><span><span><span><em>x</em></span></span></span><span><span><span>-coordinate, one for spread in the </span></span></span><span><span><span><em>y</em></span></span></span><span><span><span>-coordinate.</span></span></span></p> </li> </ul> <p><span> </span></p> <p><span><span><span>As mentioned above, every trained CNN can be read by neural_net_utils.read_model(). Also, every trained CNN can be applied to new data (inference mode) by neural_net_utils.apply_model(). The input argument model_object should be the object returned by neural_net_utils.read_model(), and I suggest setting num_examples_per_batch = 10 to avoid out-of-memory errors. The only other input argument is predictor_matrices, which is a list of two numpy arrays. The first numpy array contains IR imagery centered at the first-guess TC center, and the second numpy array contains ATCF scalars. The first numpy array should have dimensions S (number of TC samples) x </span></span></span><span><span><span>3</span></span></span><span><span><span>00 (grid rows) x </span></span></span><span><span><span>3</span></span></span><span><span><span>00 (grid columns) x </span></span></span><span><span><span>9</span></span></span><span><span><span> (lag times) x 3 (wavelengths). Lag times should be in the following order: </span></span></span><span><span><span>240, 210, </span></span></span><span><span><span>180, 150, 120, 90, 60, 30, 0 min ago. Wavelengths should be in the order indicated by the subdirectory name. The numpy array itself should contain </span></span></span><em><span><span><span>normalized</span></span></span></em><span><span><span> brightness temperatures at the given lag times and wavelengths, following the grid specifications laid out in the journal paper (a </span></span></span><em><span><span><span>plate carrée</span></span></span></em><span><span><span> grid with 2-km spacing). The original IR data (brightness temperatures) must be normalized to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-scores using the same normalization parameters as in the journal paper, </span></span></span><em><span><span><span>i.e.,</span></span></span></em><span><span><span> those based on the training data. See details below. The second numpy array in predictor_matrices should have dimensions S (number of TC samples) x 9 (variables). The variables must in the order: absolute latitude, cosine of longitude, sine of longitude, TC intensity, minimum central pressure, tropical flag, subtropical flag, extratropical flag, disturbance flag. The journal paper contains details on all these variables in one table. These variables must come from A-deck files at the </span></span></span><span><span><span>second-</span></span></span><span><span><span>most recent synoptic time. Like the IR data, these ATCF scalars must be normalized to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-scores using the same normalization parameters as in the journal paper. See details below.</span></span></span></p> <p> </p> <p><span><span><span>Once you have predictions (estimated TC-center locations) from a CNN, you can bias-correct these predictions. To read the isotonic-regression model for the given CNN’s ensemble mean, use scalar_isotonic_regression.read_file() in the ml4tccf library. To apply the same model, use scalar_isotonic_regression.apply_models(). For the CNN’s ensemble spread, use scalar_uncertainty_calibration.read_file() and scalar_uncertainty_calibration.apply_models().</span></span></span></p> <p> </p> <p><span><span><span>To normalize the IR data, you will need the file ir_satellite_normalization_params.tar included with this dataset. Within the tar file is a single zarr file. You can read the zarr file with normalization.read_file() in the ml4tccf library; then you can normalize new data with normalization.normalize_data().</span></span></span></p> <p> </p> <p><span><span><span>To normalize the ATCF data, you will need the file a_deck_normalization_params.nc included with this dataset. This is a NetCDF file, containing the full set of training values for all 5 ATCF variables that are normalized (the binary storm-type flags are not normalized). You can read this file using any of the standard Python methods for reading NetCDF files, such as xarray.open_dataset(). To normalize new ATCF data, you can use the method normalization._normalize_one_variable(), where the argument actual_values_training is the list of training values from a_deck_normalization_params.nc for the given variable, while actual_values_new is the list of values to be normalized (currently in physical units, to be converted to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-score units).</span></span></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.