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567 results for “Simulated datasets”
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes, simulation dataset 1% spike-in contaminant sequences
<p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p> <p> </p> <p>Simulation Dataset 1% contaminant spike-in.</p>
An analogue based forecasting system for Mediterranean marine litter concentration - Code and simulations dataset
<p>This dataset contains the files and codes required to perform the computations described in "An analogue based forecasting system for Mediterranean marine litter concentration" by Gabriel Jordà and Javier Soto-Navarro, to be published in Ocean Science 2022</p> <p><strong>Summary</strong></p> <p>This dataset comprises the files and code necessary to implement a statistical forecasting system for marine litter (ML) concentration in the Mediterranean Sea based on the analogues method. The system uses a historical database of ML concentration simulated by a high resolution realistic model and is trained to identify meteorological situations in the past that are similar to the forecasted ones. Then, the corresponding ML concentrations of the past analog days are used to construct the ML concentration forecast. Due to the scarcity of observations, the forecasting system has been validated against a synthetic reality (i.e. the outputs from a ML modelling system). Different approaches can be tested to refine the system. The analysis of the results show that using integral definitions for the similarity function, based on the history of the meteorological situation, improves the system performance. The system accuracy depends on the region of application being better for larger regions. The method performs well to capture the spatial patterns but performs worse to capture the temporal variability, specially the extreme values. Despite the inherent limitations of using a synthetic reality to validate the system.</p> <p><strong>Dataset</strong></p> <p>The dataset is developed in Matlab format, and is comprised by:</p> <ul> <li>Matlab files with ML concentration maps derived from the dispersion simulations performed by Soto-Navarro et al. (2020). See the reference for a detailed description of the model and simulations.</li> <li>Matlab files with atmospheric fields from ERA5 dataset (sea level pressure (SLP) and wind speed (U<sub>10</sub>, V<sub>10. </sub><a href="https://climate.copernicus.eu/climate-reanalysis">https://climate.copernicus.eu/climate-reanalysis</a>).</li> <li>Matlab files with the grid information for the different regions and sub-regions of the Mediterranean Sea analyzed.</li> <li>Matlab scripts needed for the implementation and running of the analogs based model.</li> <li>A document describing the implementation procedure.</li> </ul> <p><strong>References</strong></p> <p>Soto-Navarro, J., Jordà, G., Deudero, S., Alomar, C., Amores, Á., and Compa, M.: 3D hotspots of marine litter in the Mediterranean: A modeling study, Mar. Pollut. Bull., 155, 111159, https://doi.org/10.1016/j.marpolbul.2020.111159, 2020.</p>
Dataset of "Vertical-Wind-Induced Cloud Opacity Variation in Low Latitudes Simulated by a Venus GCM"
<p>This dataset contains the GrADS data of Venus GCM results used for figures in the paper "Vertical-Wind-Induced Cloud Opacity Variation in Low Latitudes Simulated by a Venus GCM" by H. Karyu et al. (2022). </p> <p>The file 'dataset_day1' contains the three-dimensional (X: longitude, Y: latitude, Z:altitude (km)) data of temperature (unit: K), zonal wind velocity (unit: m/s), meridional wind velocity (unit: m/s), vertical wind velocity (unit: m/s), geopotential height (unit: m), cloud mass mixing ratio of mode 1, 2, 2', 3 particles, mass mixing ratio of sulfuric acid, air density (unit: kg/m<sup>3</sup>), cloud mass mixing ratio changing rate of mode 1, 2, 2', 3 particles (unit: 1/s), in snapshots of every 3 hours for the periods of the first Venusian days (117 Earth days). The file 'dataset_day2' contains the same for the second Venusian days.</p> <p>The file 'cloudtau-wc' contains three-dimensional (X: longitude, Y: latitude, Z:altitude (km)) data of column-integrated optical depth (COD) of mode 1, 2, 2' 3 particles and column mass abundance of mode 1, 2, 2' 3 particles (unit: kg/m<sup>2</sup>), in snapshots of every 3 hours for the periods of 2 Venusian days (234 Earth days). The COD at each altitude corresponds to the integrated value from the top of the atmosphere, and the column mass abundance of each altitude corresponds to the integrated value from the bottom of the atmosphere. The COD is calculated with the cloud mass mixing ratio stored in the file ‘dataset’ and extinction efficiency shown in the paper.</p> <p>The file 'stf-wc' contains two-dimensional (Y: latitude, Z:altitude (km)) data of mass stream function (unit: kg/s) and residual mass stream function (unit: kg/s), in snapshots of every 3 hours for the periods of 2 Venusian days (234 Earth days). One should refer to Holton (2004) for the definition of the (residual) mass stream function.</p> <p>The files 'dataset_comp' and 'taudataset_comp' are composite mean data of 'dataset' and 'cloudtau-wc', respectively, in snapshots of every 3 hours for the period of 30 days starting from day 86 of the simulation (Earth day). The composite mean is calculated by averaging atmospheric parameters with respect to the frame moving at the rotation period of 7.1-day.</p> <p>The file ‘scripts’ contains FORTRAN scripts and some additional files to derive the atmospheric parameters stored in 'cloudtau-wc’, 'stf-wc’, 'dataset_comp' and ‘taudataset_comp' from the GCM output file ‘dataset’. Please refer to the ‘README.txt’ contained in ‘scripts’ for how to use FORTRAN scripts, required input files and their output.</p> <p>The .tar.xz files can be extracted in Linux with 'tar Jxvf' command, and .grd and .ctl files with the same stem are generated.</p> <p> </p>
A Method for Generating a Quasi-Linear Convective System Suitable for Observing System Simulation Experiments: Dataset
<p>This repository provides data files to quickly run the QLCS observing system simulation experiments. The data file includes assimilated observations prepared for the data assimilation research testbed system (./data/obs/). The file also contains a restart files to initialize the nature run simulation (./data/nature_run/) and the initial prior ensemble at the time of the first data assimilation cycle (./data/initial_fcst_ensemble/).</p>
Estimation of axial loads in tie-rods: Dataset generated from Finite Element simulations for training Artificial Neural Network
<p>Dataset employed for training the Artificial Neural Networks (ANNs) presented in the cited journal article. The trained ANNs were used to estimate the tensile force in tie-rods installed in a historical structure (the church of the monastery of Sant Cugat close to Barcelona) from dynamic parameters obtained from vibration testing.</p> <p>The dataset consists of input-otput data generated using finite element (FE) simulations. A blank column has been used to separate input data from output data.</p> <p>More details on the nature of the data and how it was employed can be found in the following journal article, which is supplemented by this upload:<br> <em><strong>Makoond N, Pelà L, Molins C. Robust estimation of axial loads sustained by tie-rods in historical structures using Artificial Neural Networks. Structural Health Monitoring. 2022;0(0). doi:</strong></em><strong><a href="https://doi.org/10.1177/14759217221123326">10.1177/14759217221123326</a></strong></p> <p><a href="https://www.researchgate.net/publication/364098652_Robust_estimation_of_axial_loads_sustained_by_tie-rods_in_historical_structures_using_Artificial_Neural_Networks">Link to author's version of accepted manuscript</a></p> <p>This work was supported by the Servei del Patrimoni Arquitectònic of the Generalitat de Catalunya through a project (managed by the City Council of Sant Cugat) aimed at monitoring the church of the Monastery of Sant Cugat (grant number C-10764). Financial support is also acknowledged from the Ministry of Science, Innovation and Universities of the Spanish Government and the ERDF (European Regional Development Fund) through the SEVERUS project (Multilevel evaluation of seismic vulnerability and risk mitigation of masonry buildings in resilient historical urban centres) (grant number RTI2018-099589-B-I00).</p>
D7.3 Dataset of noise produced by Hog-Jaeren wind park (simulation)
<p>This dataset was used in Deliverable 7.3 of Upwards.</p> <p>For further explanations refer to Work Package 4.</p> <p>Contained are results of the noise simulation for the Hog-Jaeren wind park with south-east wind direction.</p> <p>The yaw misalignment of the turbines in the freestream was varied as indicated by the folder names. Here 270 corresponds to a yaw misalignment of 0° while 260 corresponds to -10°.</p> <p>There are several types of 2D plots available:</p> <ul> <li>OASPL: Shows the OASPL in dB(A) in the area of the park</li> <li>noise_regulation: Contains the OASPL in dB(A) in the neghborhood of the park as well as lines indicating distance and noise based regulations by the Norwegian government.</li> <li>annoyance_level: Based on the OASPL the percentage of people being annoyed by the noise is presented.</li> </ul> <p>The file 'OASPL_at_observer_in_dBA.txt' contains the coordinates of an observer as well as the OASPL at that point.</p> <p> </p>
Datasets containings simulation results
<p>These are the datasets containings the simulation results presented in "Approximate Bayes factors: flexible and likelihood-free model comparison with ABrox".</p>
Correcting for population stratification reduces false positive and false negative results in joint analyses of host and pathogen genomes [G2G-Simulator: Simulated dataset]
<p>Data associated with the paper 'Correcting for population stratification reduces false positive and false negative results in joint analyses of host and pathogen genomes'.</p> <p>It contains the raw simulated data from the 'G2G-Simulator' program. </p> <p>Those data need to be loaded in a R environment .</p> <p>You can reproduce plots present in the paper by parsing the R object using the script 'parse_paper_data.R' present in the G2G-Simulator GitHub repository (https://github.com/onaret/G2G-Simulator/paper/parse_paper_dataset.R). </p>
Dataset and Model Weights for Plasma Sheet Model Graph Network Simulator
<p>This repository contains the simulation data and pre-trained Graph Neural Network (GNN) models produced in [1].</p> <p>Two *.zip files are provided:</p> <ul> <li>data.zip - contains the datasets of train/test simulations produced using the Sheet Model algorithm [1, 2]</li> <li>models.zip - contains the GNN model weights (<em>*.</em>pkl<em>) </em>+ relevant training information and model parameters <em>(*.</em>yml<em> and *</em>.txt)</li> </ul> <p>Dataset subfolders are named according to dataset/{'train' or 'test'}/{number of sheets}/{boundary condition}/. Each subfolder contains multiple simulations and a single info.yml file with relevant information regarding the overall setup. For each i-th simulation the following files are provided:</p> <ul> <li>x_{i}.npy - array with sheet trajectories (#time-steps, #sheets)</li> <li>v_{i}.npy - array with sheet velocities (#time-steps, #sheets)</li> <li>x_eq_{i}.npy - array with sheet equilibrium positions (#time-steps, #sheets)</li> </ul> <p> Model sub-folders are named according to :</p> <ul> <li>models/{time step}/{seed} - default architecture (preferred)</li> <li>models/{time step}/{'collisions', 'nosent' or 'equivariant'}/{seed} - alternative (less performing) architectures mentioned in the paper appendices.</li> </ul> <p>For each model we provide:</p> <ul> <li>params_best.pkl - model weights that performed the best during training on the validation set</li> <li>params_final.pkl - model weights at the end of training</li> <li>model_cfg.yml - GNN architecture metadata</li> <li>train_cfg.yml - training configuration metadata</li> <li>train_data.yml - training dataset metadata</li> <li>loss.txt - training and validation loss per epoch</li> <li>loss_i.txt - training loss per gradient update step</li> </ul> <h3>Source Code</h3> <p>The source code used to produce the data, train, and test the models can be found at: <a href="https://github.com/diogodcarvalho/gns-sheet-model">https://github.com/diogodcarvalho/gns-sheet-model</a></p> <h3>References</h3> <p>[1] D. D. Carvalho, D. R. Ferreira, L. O. Silva, "Learning the dynamics of a one-dimensional plasma model with graph neural networks<em>", Mach. Learn.: Sci. Technol. 5 025048 </em>(2024)</p> <p>[2] J. Dawson, "One‐Dimensional Plasma Model"<em>, The Physics of Fluids</em> 5.4 (1962): 445-459.</p> <p> </p>
lr-kallisto Simulation Dataset
<p>NanoSim pretrained model human_NA12878_dRNA_Bham1_guppy was used to generate transcriptomic reads, stored in file human_NA12878_dRNA_Bham1_guppy_reads.fastq.gz.</p> <p>The modified uLTRA simulators, included in https://github.com/pachterlab/LSRRSRLFKOTWMWMP_2024 Figures folder SupplementaryFigure4, were used to generate:</p> <p>ultra_sim_ONT_homo_2M.fq.gz (2 million ONT InDel Profile reads at 1% sequencing error),<br>ultra_sim_ONT_homo_2M.02.fq.gz (2 million ONT InDel Profile reads at 2% sequencing error),<br>ultra_sim_ONT_homo_2M.04.fq.gz (2 million ONT InDel Profile reads at 4% sequencing error),</p> <p>ultra_sim_PB_homo_2M.001.fq.gz (2 million ONT InDel Profile reads at 0.1% sequencing error),<br>ultra_sim_PB_homo_2M.005.fq.gz (2 million ONT InDel Profile reads at 0.5% sequencing error),<br>ultra_sim_PB_homo_2M.015.fq.gz (2 million ONT InDel Profile reads at 1.5% sequencing error), and<br>ultra_sim_PB_homo_2M.02.fq.gz (2 million ONT InDel Profile reads at 2% sequencing error).</p> <p> </p> <p>Lastly, 2 simulation files were not uploaded (due to size of files): Mouse.ONT.R10.4.simulated.shuffled.fastq.gz and human_NA12878_cDNA_Bham1_guppy_reads.fastq.gz. Mouse.PB.simulated.shuffled.fastq.gz and Mouse.ONT.R10.4.simulated.shuffled.fastq.gz are the simulations performed in Prjibelski et al. 2023 (https://doi.org/10.1038/s41587-022-01565-y) that were converted from bams to fastqs with samtools and shuffled with bbtools. NanoSim pretrained model human_NA12878_cDNA_Bham1_guppy was used to generate 10 million transcriptomic reads stored in file human_NA12878_cDNA_Bham1_guppy_reads.fastq.gz.</p>
Dataset of 4D conserved tracers for convection simulated by large eddy model and cloud resolving model
<p>There are conserved tracers and active flag for convection used for diagnosis of bulk entrainment rate for four convection cases in this dataset. Total water and moist static energy are selected as tracer for shallow convection (BOMEX and RICO) and deep convection (GATE and KWAJEX), respectively. The two variables simulated by large eddy model for shallow convection and cloud resolving model for deep convection are four-dimension variables with horizontal scales, vertical altitude, and time. </p> <p>The size of domain simulated for BOMEX and RICO is 6.4 km with horizontal grid spacing of 100 m, and that GATE and KWAJEX is 256 km with horizontal grid spacing of 1 km. Besides, vertical layers in the simulation are 75 levels with spacing of 40m for BOMEX and 100 levels with spacing of 40m for RICO. For KWAJEX and GATE, the model was set up with 64 levels vertically, which gradually increases from 75 m at the surface to a spacing of 400 m through the troposphere and a larger spacing of 1 km in the Newtonian damping region. The model is integrated for 6 hours for BOMEX, 24 hours for RICO, 52.25 days for KWAJEX, and 20 days for GATE. Here, the range of time in these variables The four-dimension variables are saved every 3 seconds for shallow convection, and every 6 minutes for deep convection for two consecutive days.</p>
An SI-traceable protocol for the validation of radiative transfer model-based reflectance simulation: datasets
<p>This data record contains datasets used in the study "An SI-traceable protocol for the validation of radiative transfer model-based reflectance simulation":</p> <ul> <li>The <code><span>final_design.ply</span></code> file contains the mesh corresponding to the final artefact design.</li> <li>The <code><span>material_measurements.nc</span></code> file contains goniophotometer records for the material reflectance.</li> <li>The <code><span>artefact_measurements.nc</span></code> file contains goniophotometer records for the artefact reflectance.</li> </ul>
Evaluation datasets and results of the paper "A Framework for Measuring the Quality of Business Process Simulation Models"
<p>Datasets and files used in the evaluation of the publication entitled "A Framework for Measuring the Quality of Business Process Simulation Models", where:</p> <ul> <li><strong><em>BPS-models/</em></strong>: folder containing the BPS models used in the evaluation (the BPS models discovered by ServiceMiner are not included due to privacy reasons). <ul> <li>The BPS models discovered by SIMOD are composed of <em>i)</em> a BPMN file with the process model structure, and <em>ii)</em> a JSON file with the parameters of the simulation. These files correspond to the format of Prosimos simulation engine (<a href="https://prosimos.cloud.ut.ee/">https://prosimos.cloud.ut.ee/</a>).</li> <li>The BPS models of the Loan Application and Procure to Pay processes are composed of a BPMN file with both the process model structure and parameters, corresponding to the format of the BIMP simulator used in APROMORE (<a href="https://apromore.com/">https://apromore.com/</a>).</li> </ul> </li> <li><em><strong>measures/</strong></em>: folder containing the distance values of each measure reported in the paper.</li> <li><em><strong>original-event-logs/</strong></em>: folder containing the (train and test) event logs used in the evaluation.</li> <li><em><strong>simulated-logs/</strong></em>: folder containing the simulated logs evaluated in the paper (synthetic, SIMOD, and ServiceMiner).</li> <li><em><strong>ComputeLogDistance.py</strong></em>: script to compute the distance measures proposed in the paper.</li> </ul> <p> </p> <p>To evaluate the distance measures of a set of simulated event logs in the folder <em>simulated_logs/</em> against the test log <em>test_event_log.csv.gz</em>, run:<br><em> python ComputeLogDistance.py -cfld test_event_log.csv.gz simulated_logs/</em></p> <p>*The flag <em>-cfld</em> is optional, due to the high computational complexity of the CFLD measure.</p> <p><strong>WARNING</strong>: set the column names of each log accordingly (where <em>log_1_ids</em> are the IDs of the test log, and <em>log_2_ids</em> the IDs of the simulated logs). Examples:</p> <pre><code># Column IDs for the (train/test) real-life logs, and the SIMOD simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='start_time', end_time='end_time', resource='resource' ) # Column IDs for the Loan Application and Procure to Pay simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='Start_Time', end_time='End_Time', resource='resource' ) # Column IDs for the ServiceMiner simulated logs. EventLogIDs( case='case_id', activity='Activity', start_time='start_time', end_time='end_time', resource='Resource' )</code></pre> <p> </p>
Dataset: Simulations Plus, Inc. (SLP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Leaf and wood classification framework for terrestrial LiDAR point clouds: Simulated data validation dataset
<p>Set of 200 3D point clouds used in the validation of "Leaf and wood classification framework for terrestrial LiDAR point clouds". This dataset is a collection of point clouds simulated by a Monte-Carlo ray tracing (librat) using four 3D tree models from the fourth phase RAMI exercise (Widlowski et al, 2015).</p>
Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory: Dataset
<p>This dataset collects the unprocessed (= outputs from calculations) and processed (= plots, average values for ionization energies) results discussed in the paper titled "Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory", by Yassine Bouchafra, Avijit Shee, Florent Réal, Valérie Vallet and André Severo Pereira Gomes.</p> <p>In each archive file there is a README explaining how to use the bundled scripts to process the data.</p>
Dataset of Paper "Novel procedure for the numerical simulation of solar water disinfection processes in flow reactors" (DOI: 10.1016/j.cej.2018.10.131)
<p>Datasets of Paper "Novel procedure for the numerical simulation of solar water disinfection processes in flow reactors".</p> <p>DOI: 10.1016/j.cej.2018.10.131</p> <p>Data of the velocity profiles at different distances from the inlet of a solar rainwater reactor.</p> <p>Data of the simulated radiation field inside of a solar rainwater reactor as a function of the location, date, time and CPC inclination.</p> <p>Data of the disinfection efficiency versus illumination time in a solar reactor under simulated and natural sunlight.</p>
Dataset of 100 simulated root images, with different resolutions
<p>All images were analysed with WinRhizo<sup>TM</sup> 2013, Regent method (Régent Instruments Inc 2013) and ImageJ (1.51j8, Schneider, Rasband, & Eliceiri, 2012) with the macro IJ_Rhizo (IJ_Rhizo_v0beta). We conducted batch analyses with the grey level thresholds set to automatic at all four resolutions (200, 400, 800 and 1200 DPI). We further analysed the images at 800 dpi and 1200 dpi with the threshold value manually set to 144. We considered this value suitable after visual inspection of 10 images at different thresholds using the threshold slider of WinRhizo<sup>TM</sup>. Since no debris was simulated, we used no automatic debris correction. For WinRhizo<sup>TM</sup>, the boundaries of diameter classes were set from 0.1 to 1.9 mm (with an increment of 0.1 mm).</p> <p>The dataset contains:</p> <p>- the root images in jpg format (images.zip)</p> <p>- the root architecture data in rsml format (rsml.zip)</p> <p>- the ground-truth value for each image (all_root_data_2D.csv)</p> <p>- the ground-truth value for each root in each image (ground_truth_data_2D.csv)</p> <p>- the estimation given by WinRhizo and IJ_Rhizo (all_analyses_2D.txt and all_analyses_fixed.txt)</p> <p>- the different codes used for the analysis (*.R)</p> <p>Dataset used in : Accuracy of image analysis tools for functional root traits: A comment on Delory et al., 2017 from Rose and Lobet (2019), Methods in Ecology and Evolution. </p> <p>The images in this dataset are a subset of the dataset here: https://doi.org/10.5281/zenodo.208214</p>
Histone tail dynamics in partially disassembled nucleosomes during chromatin remodeling: Simulation dataset
<p>Dataset of molecular dynamics simulations of partially disassembled nucleosomes.</p> <p>- Input: Parameters and initial structures</p> <p>- Output: Trajectories</p> <p>NAMD 2.12 (multi-core with CUDA) was used for the simulations.</p>
Traffic forecasting with Virtual Induction Loops - SUMO simulation dataset
<p>This repository is associated with my doctoral dissertation titled, "<strong>Smartphone based applications for Road Traffic Telematics</strong>". In particular this repository serves as the basis of Chapter 7 titled, "<strong>Traffic forecasting with Virtual Induction Loops (VIL)</strong>". The basic idea is to validate a traffic forecasting system which uses machine learning techniques on the simulation of real traffic flows on a real intersection in the City of Turin. This dataset contains simulation output from SUMO software for 56 real days between the months of October-2017 to April-2018. Details about these days are available in my thesis. For each day, 3 output files are available. Here is the description and naming convention:</p> <ol> <li>M1_100seed_100pr_dump.csv (This is the data dump file from SUMO. It contains flows of every single vehicle that was simulated. Naming convention is day_seed_vilPenetrationRate_dump.csv)</li> <li>M1_100seed_ilNorth_100pr.xml (This is the output from a simulated induction loop for Northbound traffic. Naming convention is day_seed_ilNorth_vilPenetrationRate.xml)</li> <li>M1_100seed_ilSouth_100pr.xml (This is the output from a simulated induction loop for Southbound traffic. Naming convention is day_seed_ilSouth_vilPenetrationRate.xml)</li> </ol> <p>For further details, please refer to my thesis.</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.