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648 results for “uncertainties”
Dataset for the uncertainty assessment of confocal measurements of industrial samples
<p>These original measurement data relate to the publication: J. Paredes, G. Kortaberria: Towards task-specific uncertainty assessment for imaging confocal microscopes, <a href="https://www.euspen.eu/knowledge-base/ICE23191.pdf">ICE23191.pdf (euspen.eu)</a>. Please refer to this open access publication for a detailed description of the measurement setup and procedure.</p><p>All data are in ASCII-format. Each file contains 2 columns, where they are the measured <i>x/y</i> and <i>z</i>-coordinates of the 2D profile extracted from the surface. All the coordinates are recorded in micrometers. The 0/90 at the end of the file names represent the orientation of the extracted profile being<i> x</i> and <i>y</i> direction respectively.</p><p>The topoghraphy files contain 3 columns, they are the measured<i> x</i>, <i>y</i>, and <i>z</i>-coordinates of the surface.</p><p><strong>Acknowledgement</strong></p><p>This project 20IND07 TracOptic 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. Funder name: European Metrology Programme for Innovation and Research (EMPIR).</p>
A Global Sensitivity Analysis of Parameter Uncertainty in the CLASSIC Model
<p>Input scripts, datasets and outputs used for the GSA methods. Please read the README and workflow files.</p>
Figure 6 in The death adder Acanthophis antarcticus (Shaw & Nodder, 1802) in Victoria: historical records and contemporary uncertainty
Figure 6. Ventral view of the head scales of Death Adder specimens from Melbourne Museum, and close up of Gerard Krefft's illustration of the ventral head scales of the Death Adder collected at Lake Boga in 1857 (bottom right). Top left is specimen D3579. Top right is D51857. Bottom left is D4349.
Figure 1 in The death adder Acanthophis antarcticus (Shaw & Nodder, 1802) in Victoria: historical records and contemporary uncertainty
Figure 1. South-eastern Australia, showing records of the death adder Acanthophis antarcticus (black dots; Atlas of Living Australia, year) and key localities discussed in the text.
Figure 2. Specimen D4349, a in The death adder Acanthophis antarcticus (Shaw & Nodder, 1802) in Victoria: historical records and contemporary uncertainty
Figure 2. Specimen D4349, a death adder Acanthophis antarcticus in the collection of Museums Victoria.
Figure 5 in The death adder Acanthophis antarcticus (Shaw & Nodder, 1802) in Victoria: historical records and contemporary uncertainty
Figure 5. Illustration by Gerard Krefft of the death adder collected at Lake Boga in north-western Victoria on the 8 March 1857. (Photograph by Rebecca Carland; Museum of Natural History Berlin. Historical collection of pictures and writings. [Sigel: MfN, HBSB.] Bestand: Zool. Mus. Signatur: B VIII/56.)
Dataset - Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties
<p>Data needed to reproduce the results from the manuscript “Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties" by L. Miskovic, J. Beal, M. Moret, and V. Hatzimanikatis</p> <p>1. Data generated with the ORACLE workflow that was used in the iSCHRUNK training:</p> <ul> <li>Classification label vectors for the three analyzed metabolic concentration cases: <ul> <li>Reference case: class_vector_train_ref.mat</li> <li>Extreme1 case: class_vector_train_ex1.mat</li> <li>Extreme2 case: class_vector_train_ex2.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: training_set_ref.mat</li> <li>Extreme1 case: training_set_ex1.mat</li> <li>Extreme2 case: training_set_ex2.mat</li> </ul> </li> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ref.mat</li> <li>Extreme1 case: ccXTR_ex1.mat</li> <li>Extreme2 case: ccXTR_ex2.mat</li> </ul> </li> <li>Thermodynamics-based Flux Analysis (TFA) models for the three cases: <ul> <li>Reference case: tfa_ref.mat</li> <li>Extreme1 case: tfa_ex1.mat</li> <li>Extreme2 case: tfa_ex2.mat</li> </ul> </li> <li>Parameter names identical for the three cases <ul> <li>parameterNames.mat</li> </ul> </li> </ul> <p>2. Validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Figure 4).</p> <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>ccXTR_ValidNeg.mat</li> </ul> </li> <li>Parameter sets used in validation <ul> <li>validation_set_neg.mat</li> </ul> </li> </ul> <p>3. Validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Table 3).</p> <ul> <li>Negative control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ValidRef_neg_agg.mat</li> <li>Extreme1 case: ccXTR_ValidEx1_neg_agg.mat</li> <li>Extreme2 case: ccXTR_ValidEx2_neg_agg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_ref_neg_agg.mat</li> <li>Extreme1 case: validation_set_ref_neg_agg.mat</li> <li>Extreme2 case: tvalidation_set_ref_neg_agg.mat</li> </ul> </li> </ul> </li> </ul> <ul> <li>Positive control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ValidRef_pos_agg.mat</li> <li>Extreme1 case: ccXTR_ValidEx1_pos_agg.mat</li> <li>Extreme2 case: ccXTR_ValidEx2_pos_agg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_ref_pos_agg.mat</li> <li>Extreme1 case: validation_set_ex1_pos_agg.mat</li> <li>Extreme2 case: validation_set_ex2_pos_agg.mat</li> </ul> </li> </ul> </li> </ul> <p>4. Reassignment study: validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Figure 6 and Table 4).</p> <ul> <li>Negative control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_Valid_reassignment_neg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_neg_reassignment.mat</li> </ul> </li> </ul> </li> <li>Positive control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_Valid_reassignment_pos.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_pos_reassignment.mat</li> </ul> </li> </ul> </li> </ul> <p> </p> <p> </p>
Data and code repository for the "Uncertainties in cloud-radiative heating within an idealized extratropical cyclone"
<p><strong>Author:</strong> Behrooz Keshtgar, behrooz.keshtgar@kit.edu</p> <p>This archive contains the post-processed data used to generate the figures and the code repository for the publication "Uncertainties in cloud-radiative heating within an idealized extratropical cyclone" by Behrooz Keshtgar, Aiko Voigt, Bernhard Mayer and Corinna Hoose.</p> <p>Description of the <strong>data</strong>:</p> <p>figure1.nc: precipitation rate, cloud cover, surface pressure, and cloud classes on day 4.5 of the ICON-NWP baroclinic life cycle simulation.</p> <p>figure2.nc: spatially and temporally averaged profiles of cloud water, ice mass content, and cloud fractions from ICON-LEM simulations.</p> <p>figure4.nc: spatially and temporally averaged cloud-radiative heating profiles from ICON-LEM simulations and offline radiation calculations for each LEM domain.</p> <p>figure5.nc: cross-section of radiative heating rates for 3D and 1D radiative transfer calculations in the shallow cumulus domain.</p> <p>figure6.nc: spatially averaged cloud-radiative heating profiles from 3D and 1D radiation calculations for each LEM domain.</p> <p>figure7.nc: cross-section of cloud-radiative heating calculated with the ice optics of Fu and Baum_ghm in the WCB ascent region.</p> <p>figure8.nc: spatially and temporally averaged profiles of cloud-radiative heating from 1D radiation calculations with different ice optics for each LEM domain.</p> <p>figure9.nc: spatially and temporally averaged profiles of cloud-radiative heating from 1D radiation calculations with LEM and NWP clouds for each LEM domain.</p> <p>figure10.nc: spatially and temporally averaged density and cloud-radiative heating profiles from different offline radiation calculations for each LEM domain.</p> <p>figure11.nc: profiles of the mean absolute difference of cloud-radiative heating from different offline radiation calculations at different resolutions for each LEM domain.</p> <p> </p> <p>The <strong>keshtgar-etal-2024-cyclone-crh-uncertainties-main.zip</strong> is the copy of the published git repository for the model run and analysis scripts. The repository contains</p> <p>- Scripts for the ICON model simulations</p> <p>- Scripts for the offline radiative transfer calculations with LibRadTran and the post-processing routine</p> <p>- Python scripts and Jupyter Notebooks for the analysis in the paper</p>
Figure 2 in The problem of taxonomic uncertainty in biosecurity: South African mite interceptions as an example
Figure 2 Images of slide-mounted specimens, showing features which could clearly distinguish the intercepted unknownBrevipalpus species,
Figure 1 in The problem of taxonomic uncertainty in biosecurity: South African mite interceptions as an example
Figure 1 South African biosecurity personnel inspecting imported kiwifruit for insects and mites. Images have been edited to remove sensitive information, including identity of personnel.
Data supporting "A comprehensive analysis of air-sea CO2 flux uncertainties constructed from surface ocean data products"
<p>Changelog</p> <p>v2: Fixes an identified issue in FluxEngine v4.0.7 that affects the calculation of fCO2atm. Fluxes have been recalculated using FluxEngine v4.0.9.1, and the analysis regenerated. The intergrated air-sea CO2 flux (or ocean sink) has reduced by ~0.2-0.3Pg C yr-1 but uncertainties are unchanged. </p> <p>v1: Initial dataset released along with the supporting manuscript</p> <p> </p> <p>Data included in this repository supports the manuscript "A comprehensive analysis of air-sea CO<sub>2</sub> flux uncertainties constructed from surface ocean data products".</p> <p>Two files are present:</p> <ol> <li>A Python config file used to run the software developed for the analysis (Ford et al., 2024)</li> <li>A ZIP file containing the input, neural network, and output files for the analysis.</li> </ol> <p>Within the ZIP file, multiple folders are present:</p> <ol> <li>Decorrelation contains .csv files that contain the annual estimates of the decorrelation lengths for the parameters requiring these (SST, sea ice, wind, fCO<sub>2</sub> and fCO<sub>2</sub> network).</li> <li>Flux contains the individual FluxEngine output files that provide all the flux calculations, and auxillary data to the flux calculations.</li> <li>Fluxengine_input contains the input files to FluxEngine, which specifies the fCO<sub>2 (sw), </sub>xCO<sub>2 (atm)</sub> and the temperature, salinities for the skin and subskin layers.</li> <li>Inputs contains all the monthly 1 degree input data used. Many of the data used are not native monthly 1 deg, and so these are generated from the higher resolution data. These are all combined into the neural_network_input.nc file, so a single file can be distributed with all the inputs used.</li> <li>Networks contains the TensorFlow neural network (FNN) files, where each province has 10 folders (one for each ensemble).</li> <li>Plots contains output plots for debugging and final plots of uncertainties</li> <li>Scalars contains the scalars used to normalise the data before input into the neural network. These are saved as Python pickle files, as they are needed if the neural network is used on other data.</li> <li>Unc_lut contains the look up tables to generate the parameter uncertainty as described in the manuscript. These are Python pickle files.</li> <li>Validation contains a csv file with the independent test RMSD, along with Python Pickle files of the validation data.</li> </ol> <p>In the main folder, three files are present:</p> <ol> <li>Annual_flux.csv contains the annual air-sea CO<sub>2</sub> flux (or ocean sink estimate) estimated from the fCO<sub>2 (sw)</sub> fields. This also contains the annual integrated uncertainties for each component in the uncertainty flow chart in the manuscript.</li> <li>Output.nc contrains the gridded global fields of the fCO<sub>2 (sw)</sub>, the air-sea CO<sub>2</sub> flux, and the uncertainties for all the individual components. Metadata within the file should provide all the information required.</li> <li>Training.tsv contains the training/validation data alongside the input parameters for neural network training</li> </ol> <p> </p> <p>Please contact Daniel J. Ford (<a href="mailto:d.ford@exeter.ac.uk">d.ford@exeter.ac.uk</a>) if you have any questions.</p> <p><strong>Acknowledgements</strong></p> <p>This work was funded by the Convex Seascape Survey (https://convexseascapesurvey.com/) and the European Union under grant agreement no. 101083922 (OceanICU; https://ocean-icu.eu/) and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10054454, 10063673, 10064020, 10059241, 10079684, 10059012, 10048179]. The views, opinions and practices used to produce this dataset/software are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p> </p> <p><strong>References</strong></p> <p>Ford, D. J., Blannin, J., Watts, J., Watson, A. J., Landschutzer, P., Jersild, A., & Shutler, J. D. (2024, June 30). OceanICU Neural Network Framework with per pixel uncertainty propagation (v1.1) (Version v1.1). Zenodo. https://doi.org/10.5281/ZENODO.12597803</p>
Technology pathways could help drive the U.S. West Coast grid's exposure to hydrometeorological uncertainty (Figure Data)
<p>Data used to create figures for:</p> <p>Wessel, J., Kern, J.D., Voisin, N., Oikonomou, K., Haas, J. (2021). “Technology pathways could drive the U.S. West Coast grid's exposure to hydrometeorological uncertainty”.</p> <p>California and West Coast Power System (CAPOW) model is Python based. The model was built to simulate the operations of the major markets comprising the West Coast bulk electric power system: the Mid-Columbia (Mid-C) market, and the California Independent System Operator (CAISO). This version adds future technology pathways, EV adoption, and battery storage.</p> <p>See https://github.com/jawessel/CAPOW_pathways (v1.0 release) for version of CAPOW model used.</p>
Data and codes related to the article: Horner et al. Streamflow uncertainty due to the limited sensitivity of controls at hydrometric stations
<p>The data files and R code files are related to the article Horner et al. "Streamflow uncertainty due to the limited sensitivity of controls at hydrometric stations" published in Hydrological Processes.</p> <p><strong>Data: </strong></p> <p>1/ Q_Craponne.txt</p> <p>Original streamflow time series of Craponne hydrometric station which was used to generate the synthetic stage time series using the theoretical equations of the 5 fictive hydrometric stations.</p> <p>Column separator: semi-colon (;)<br> Column 1 ==> Time (%Y-%m-%d %H:%M)<br> Column 2 ==> Streamflow (in m3/s)</p> <p>2/ h_Mercier.txt</p> <p>Original stage time series for Mercier hydrometric station</p> <p>Column separator: semi-colon (;)<br> Column 1 ==> Time (%Y-%m-%d %H:%M)<br> Column 2 ==> Stage(in mm)<br> Missing value code: NA</p> <p>3/ Gaugings_Mercier.txt</p> <p>Gaugings (date, stage, streamflow and associated uncertainty) of the Mercier hydrometric station before and after hydraulic control change which occured in November 2013.</p> <p>Column separator: semi-colon (;)<br> Column 1 ==> Date (%Y-%m-%d)<br> Column 2 ==> Stage_m (in m)<br> Column 3 ==> Streamflow_m3_per_s (in m3/s)<br> Column 3 ==> Uncertainty (unitless)</p> <p><strong>R codes:</strong></p> <p>0/ other ressources:<br> All the ressources related to the bayesian estimation of the rating curve can be found on github: <a href="https://github.com/BaM-tools">https://github.com/BaM-tools</a><br> Time aggregation of time series was done using the tAgg R package available on github: <a href="https://github.com/IvanHeriver/tAgg">https://github.com/IvanHeriver/tAgg</a></p> <p>1/ functions.R<br> This files contains several functions. Comments within the file explains each of the function:</p> <ul> <li>hydraulicEquations(): returns a list of the theoretical equations for the rating curves of the five fictive cases</li> <li>hydraulicEquationsInverter(): inverts of a theoretical rating curve (given a function Q=f(h), it returns a function h=f(Q))</li> <li>computeAM30(): computes the AM30</li> <li>generate_nonsyst_errors(): generates a matrix of non systematic errors</li> <li>generate_syst_errors(): generates a matrix of systematic errors</li> <li>get_resampling_indices_from_periodicity(): returns the indices of the resampling time steps used to generate systematic errors given a time vector and a periodicity</li> </ul> <p>Some of the code require the following packages: dplyr and RcppRoll</p> <p>2/ examples.R<br> This file contains some code illustrating the usage of the functions in the "functions.R" file.</p> <p> </p>
Dataset - Controlled release experiment to investigate uncertainties in UAV-based emission quantification for methane point sources
<p>This dataset was created by Randulph Morales (randulph.morales@empa.ch) and was used for Morales et al. (2021) AMT publication (amt-2021-314). A short description of the files is written in <strong>readme.txt</strong></p> <p>The dataset contains:</p> <ul> <li>QCLAS methane measurement</li> <li>Active AirCore methane measurement</li> <li>Meteorology files</li> </ul>
Data and code for: Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data
<p>UAV images used for SfM model generation and all images (both SI and OM), in which we measured grain sizes. The code used for image processing and uncertainty estimation of grain size distributions as python files and executable jupyter notebooks, where the latter also serve as documentation.</p>
Assessment of uncertainty in weather forecasts
<p>Weather data from the Ebro River Basin Hydrographic Demarcation to train machine learning models to evaluate uncertainty in weather forecasts in real time.</p> <p>The dataset is divided into two parts. To see it and download it completely without splitting, here it is published:</p> <ul> <li><strong><a href="https://open.scayle.es/dataset/assessment-of-uncertainty-in-weather-forecasts">https://open.scayle.es/dataset/assessment-of-uncertainty-in-weather-forecasts</a></strong></li> </ul>
Data for Microkinetic modeling of the transient CO2 methanation with DFT-based uncertainties in a Berty reactor
<p>Dataset and scripts for the manuscript "Microkinetic modeling of the transient CO2 methanation with DFT-based uncertainties in a Berty reactor", which has been submitted for review. The file contains all the raw data and the evaluation of the experiments. Additionally, all scripts for the microkinetic model are provided to perform transient simulations with all 5000 methanation mechanisms investigated in the manuscript.</p>
New calibrated models for the TRGB luminosity and a thorough analysis of theoretical uncertainties
<p>The files contain the grid models for TRGB stars based on <strong>Saltas & Tognelli, 2022, MNRAS, in press (arXiv: 2203.02499)</strong>. See the paper for details on the physics, and the attached readme file for a description of the structure of the attached files. If you use these data in your research please cite the above article. <br> </p>
Code: The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems
<p>This is the code archive for the publication "The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems" in Earth's Future.</p> <p>Abstract:</p> <p>Studies have recently focused on using coupled natural–human systems (CNHS) to inform policymaking. However, model uncertainty can increase with model complexity and affect the variance of the model outcomes. Therefore, this study explores an uncertainty analysis of coupled hydrological and human decision models to better evaluate CNHS modeling properties. Five coupled models are proposed with different model complexities for human behavior settings (i.e., model structure and the number of calibrated parameters): one static, two adaptive, and two learning adaptive. Learning adaptive models (the most complex) have both a learning component (capturing long-term trends) and an adaptive component (capturing short-term variations), while adaptive models omit the learning component. The static model is the simplest, without learning or adaptive components. Applying the law of total variance, the model output uncertainty is decomposed into three sources: (1) climate change scenario uncertainty, (2) climate internal variability, and (3) different model configurations with parameter sets or model structures that are equally capable of producing similar outcomes. Our exploratory analysis demonstrated that model uncertainty would likely increase with model complexity given uncertain input data (e.g., climate forcing) and different model configurations; the inclusion of a learning mechanism in the human system can potentially offset the impact of the natural system on uncertainty through coupling natural and human systems. We also discuss other uncertainty sources, such as assumptions about model structure due to incomplete knowledge and metrics for calibration target selection for future studies.</p>
Know what you don't know: Embracing state uncertainty in disease-structured multistate models
<p>Hidden Markov models (HMMs) are broadly applicable hierarchical models that derive their utility from separating state processes from observation processes yielding the data. Multistate models such as mark-recapture and dynamic multistate occupancy models are examples of HMMs that are frequently used in ecology. In their early formulations, states, such as pathogen infection status, were assumed to be perfectly observed without ambiguity in state assignment. However, state uncertainty is a pervasive feature of many ecological systems, and multievent models were developed to explicitly account for it.</p> <p>We developed a novel extended multievent mark-recapture model that incorporates state uncertainty at multiple levels of detection. Using a disease-structured example, both false-negative and false-positive state assignment errors are modeled at two levels of state assignment---the pathogen sampling process and the diagnostic process that samples are subjected to. We additionally describe methods to jointly model infection intensity to integrate heterogeneity in ecological parameters, such as survival, and the pathogen detection processes. We provide code to simulate and analyze datasets with various underlying ecological processes and fit our model to a mark-recapture dataset of <em>Mixophyes fleayi</em> (Fleay's barred frog) infected with the amphibian chytrid fungus (<em>Batrachochytrium dendrobatidis</em>, <em>Bd</em>).</p> <p>In our case study, we found evidence for various state assignment errors: the sampling protocol performed poorly in detecting <em>Bd</em>, pathogen detection was highly dependent on infection intensity, and false-positives were non-negligible. Incorporating state uncertainty yielded significantly higher estimates of infection prevalence and 4--5 times lower rates of infection state transitions compared to those obtained from a traditional multistate model.</p> <p>Our results highlight that incorporating state assignment errors improves inference on the ecological state process, especially when sensitivity and specificity of the state assignment processes are low. The general model structure can be applied to other HMMs, providing a foundation for modeling state uncertainty in a range of related models. --</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.