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5,805 results for “Data model”
Marine time domain electromagnetic data and true model for 2.5D inversion
<p>Dataset contains the description of complex 3D geoelectric model (with bathymetry, curved surfaces of geoelectric layers, target bodies simulated HC deposits, and background inhomogeneities) and marine time domain electromagnetic data calculated via finite element modeling. Noised data sets have been used for geometric 2.5D inversion.</p>
Data set from Fischertechnik Smart Factory Model at University of St.Gallen
<p>This is the data set of IoT data from the Fischertechnik Smart Factory Model deployed at the Institute of Computer Science at the University of St.Gallen. It is used as basis for the interactive identification of process activity executions from the IoT data. The corresponding publication can be found here:</p> <p>Seiger, R., Franceschetti, M., & Weber, B. (2023). An Interactive Method for Detection of Process Activity Executions from IoT Data. <em>Future Internet</em>, <em>15</em>(2), 77.<br> <a href="https://doi.org/10.3390/fi15020077">https://doi.org/10.3390/fi15020077</a></p> <p>The data set contains:</p> <ul> <li><strong>cps_log.txt:</strong> A file of all sensor and actuator readings (in JSON format) from the smart factory during the execution of 3 instances of the storage process and 3 instances of the production process. For visualization, it can be fed line-by-line into an <a href="https://www.influxdata.com/">Influx</a> database and <a href="https://grafana.com/">Grafana</a> can then be used to create visualizations of the data.</li> <li><strong>wfms_log.txt:</strong> A file containing the corresponding event log (in JSON format) recorded and extracted from the <a href="https://camunda.com/">Camunda Platform</a> workflow management system during the execution of the process instances. For visualization, it can be fed line-by-line into an <a href="https://www.influxdata.com/">Influx</a> database and <a href="https://grafana.com/">Grafana</a> can then be used to create visualizations of the data.</li> <li><strong>storage_process.bpmn:</strong> Executable BPMN 2.0 model of the storage process executed in the smart factory model.</li> <li><strong>production_process.bpmn:</strong> Executable BPMN 2.0 model of the storage process executed in the smart factory model.</li> </ul> <p>More details on the systems architecture used to execute the processes and record the data from the smart factory can be found in the follow publication:</p> <p>Ronny Seiger, Lukas Malburg, Barbara Weber, Ralph Bergmann,<br> Integrating process management and event processing in smart factories: A systems architecture and use cases,<br> Journal of Manufacturing Systems, Volume 63, 2022, Pages 575-592, ISSN 0278-6125,<br> <a href="https://doi.org/10.1016/j.jmsy.2022.05.012">https://doi.org/10.1016/j.jmsy.2022.05.012</a></p>
Data for: The structure of evolutionary model space for proteins across the tree of life
<p>Supporting data for "The structure of evolutionary model space for proteins across the tree of life," submitted by GE Scolaro and EL Braun. The data files correspond to three gzipped tarballs including protein multiple sequence alignments, PAML format models of protein evolution, and model fit data; see included README for details.</p>
Model input data for the FACETS downscaling simulation with the CAM-MPAS model
<p>The archived file contains input data necessary to reproduce the set of simulations described in Sakaguchi et al., submitted to GWD, "Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM-MPAS variable-resolution model", using the experimental CAM-MPAS code further modified by Sakaguchi and Harrop (2022) for long-term AMIP-type simulations.</p>
Accompanying data for the open-source book Modeling of Hydrological Systems in Semi-Arid Central Asia
<p>This data set is used to reproduce examples in the open-source book <a href="https://hydrosolutions.github.io/caham_book/">"Modeling of Hydrological Systems in Semi-Arid Central Asia"</a> which is part of a free course on hydrological modeling in Central Asia. The course teaches how to use publicly available data to implement a hydrological model for climate impact studies (Marti et al., 2023). </p> <p>To use the data set to reproduce the examples in the book: Download the book from https://doi.org/10.5281/zenodo.6350042 and this data set to the same hierarchical level in your file system: </p> <p>|- caham_book<br> |- caham_data<br> |- AmuDarya<br> |- central_asia_domain<br> |- student_case_study_basins<br> |- SyrDarya</p> <p>You will need a working installation of R (https://www.r-project.org/) and a GUI (e.g. Posit, formerly RStudio https://posit.co/) to reproduce the scripted examples in the book. Once your software is set up, you can proceed to run the examples. </p> <p> </p>
3-D model data used to investigate the role of K-feldspar and quartz in global ice nucleation by mineral dust in mixed-phase clouds
<p>These simulations were run by Chemical Transport Model TM4-ECPL covering the years 2009-01 to 2016-12 and are used for the bellow publication:</p> <p>Chatziparaschos, M., Daskalakis, N., Myriokefalitakis, S., Kalivitis, N., Nenes, A.,<br> Gonçalves Ageitos, M., Costa-Surós, M., Pérez García-Pando, C., Zanoli, M., Vrekoussis,<br> M., and Kanakidou, M.: Role of K-feldspar and quartz in global ice nucleation by mineral dust in mixed-phase clouds,<br> Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-551, in press 2023.</p> <p>Laboratory: Environmental Chemical Processes Laboratory (EPCL), Department of Chemistry, University of Crete, Heraklion.<br> contact: Kanakidou Maria <mariak@uoc.gr></p> <p>Model resolution: 2x3<br> Model Levels: 25</p> <p>Data info:</p> <p>DU_m2m(time, lev, lat, lon)<br> short_name :DU_m2m<br> long_name : Dust mode 2 mass accumulation</p> <p>DU_m3m(time, lev, lat, lon)<br> short_name : DU_m3m<br> long_name : Dust mode 3 mass coarse</p> <p>qua2_acc(time, lev, lat, lon)<br> short_name :qua2_acc<br> long_name :Quartz – accumulation mode</p> <p>qua2_coa(time, lev, lat, lon)<br> short_name :qua2_coa<br> long_name :Quartz – coarse mode</p> <p>FEL_acc(time, lev, lat, lon)<br> short_name :FEL_acc<br> long_name : K-Feldspar – accumulation mode</p> <p>FEL_coa(time, lev, lat, lon)<br> short_name :FEL_coa<br> long_name : K-Feldspar – coarse mode</p> <p>INP_QUA(time, lev, lat, lon)<br> short_name :INP_QUA<br> long_name :Ice Nucleating Particles derived form Quartz</p> <p>INP_FELD(time, lev, lat, lon)<br> short_name :INP_FELD<br> long_name :Ice Nucleating Particles derived form K-Feldpsar</p> <p> </p>
Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"
<p>This dataset is associated with the following publication:</p> <p>Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2.</p> <p>The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error.</p> <p>The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Detecting coarse beach sediment using remotely sensed imagery at the FRF, Duck, NC, USA: Labeled images, deep learning model, testing data, and predictions.
<p>This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA. All data is from 2015-2021</p> <p>The `training_data.zip` contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021).</p> <p>The `model.zip` file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an `.npz` file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format. All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022).</p> <p>The `test_data_c6.zip` file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `test_data_c1.zip` file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `predictions.zip` file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).</p>
Raw data files associated with the paper "Beyond generalists: the Brassicaceae pollen specialist Osmia brevicornis as a prospective model organism when exploring pesticide risk to bees"
<p>These are the raw data CSV files associated with the results described in the paper "Beyond generalists: the Brassicaceae pollen specialist Osmia brevicornis as a prospective model organism when exploring pesticide risk to bees".</p> <p>By Sara Hellström, Verena Strobl, Lars Straub, Wilhelm H. A. Osterman, Robert J. Paxton, Julia Osterman</p>
Development of a machine learning model for river bedload - Data, Model, and Scripts
<p>This repository for “Development of a machine learning model for river bedload” Hosseiny et. Al (in review at Earth Surface Dynamics) contains the following assets. These assets may need to be modified for your purposes. You are responsible for inspecting these assets and making adjustments as necessary.</p> <p>Assets:</p> <p>1) The trained ANN model described in Hosseiny et al. (in review) as a .zip file named ‘Hosseiny_et_al_trained_ANN.zip’. This contains a folder (BEDLOAD_MODEL_FINAL) which contains the trained ANN Model (saved_model.pb) and associated information related to the input variables and variable weights.</p> <p>2) An accompanying Jupyter notebook named “bedload_ann_example.ipynb” that provides a step-by-step guide for implemented the trained ANN model (Asset 1).</p> <p>3) A .xls file named “Hosseiny et al_Supplemental_Data_Tables.xlsx” which provides the original observations that the model was trained and tested on, the summary statistics of the original input data as a compilation and for individual sites, the model errors associated with training and validation steps, the bedload calculations from the four uncalibrated existing bedload transport models described in the original study and for the ANN for the test data population, associated summary statistics with model output, and additional site-specific calculations of model error.</p>
SEMAFORA Semantic Reference Data Models
<p>To support the aim of the Semafora project, a series of Semantic Reference Data Models were created to provide a target semantic structure for the integration of standard archaeological survey data. </p> <p> </p> <p>The following models constitute the Semafora SRDM package:</p> <p> </p> <p>Place: This model is used to document any places associated with the archaeological survey.</p> <p> </p> <p>Institution: This model is used to document any institution associated with the survey.</p> <p> </p> <p>Period: This model is used to document the generic historical period assigned to the production of artefacts, existence of sites or other observable archaeological and historical events.</p> <p> </p> <p>Feature: This model is used to document any physical features, such as walls and other human-made structures observable on the field.</p> <p> </p> <p>Project: This model is used to document the overarching project, a part of which is the archaeological survey. Some projects may involve surveys, excavations, and other archaeological activities.</p> <p> </p> <p>Site: This model is used to document a site declared as archaeological as a result of the survey process.</p> <p> </p> <p>Digital Object: This model is used to document any type of digital asset associated with the survey.</p> <p> </p> <p>Survey Unit: This model is used to document a defined survey unit where the survey activity happens. It has both the properties of a place with dimensions and coordinates and of a physical thing from which samples can be collected.</p> <p> </p> <p>Collection: This model is used to document a collection of physical things, usually artefacts, collected while surveying.</p> <p> </p> <p>Artefact: This model is used to document individual artifacts collected from while surveying as a part of a larger collection of material things or as a singular artefact collection or documentation.</p> <p> </p> <p>Image: This model is used to document any image representing components of the archaeological survey, such as artefacts, features, places, people, etc.</p> <p> </p> <p>Observation: This model is used to document the act of observation usually associated with archaeological sites or survey units and the properties assigned to those as a result of the observation.</p> <p> </p> <p>Bibliography: This model is used to document any textual object associated with the survey or any components of it.</p> <p> </p> <p>Sample: This model is used to document a material sample of the survey unit. It partially overlaps with collection but acts as a parent sample that may contain other physical things besides human-made objects.</p> <p> </p> <p>Person: This model is used to document an individual person (alive or dead) involved in some way in the survey process.</p> <p> </p> <p>These models are intended to be used in order to guide semantic data mapping processes as well as to provide instructions for the creation of a target data semantic data management system.</p> <p> </p> <p>Each model’s semantic reference data model description is stored here as a csv. The ongoing curation and updating of these SRDMs is undertaken using the Zellij system and can be accessed here:</p> <p> </p> <p><a href="https://zellij.pythonanywhere.com/docs/list/appaCfYmUmH78z85c">https://zellij.pythonanywhere.com/docs/list/appaCfYmUmH78z85c</a></p>
Data for "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion"
<p>Data used in generating results for the paper <a href="https://doi.org/10.1029/2023JG007457">"Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion."</a></p> <ol> <li>VIIRS_MOD16_MOD17_tower_site_drivers_v9.h5</li> <li>MOD17_5km_global_simulation.zip</li> <li>VNP17_5km_global_simulation.zip</li> </ol> <p>[1] is an HDF5 file containing surface meteorological drivers, MODIS/ VIIRS vegetation fPAR and LAI, and other data necessary for calibrating and validating the MOD17 and VNP17 GPP models at FLUXNET towers. It also contains driver data and field-based NPP data for calibrating and validating MOD17/ VNP17 NPP models.</p> <p>[2] and [3] are the global, 5-km GPP and NPP simulations using the updated MOD17 parameters and new VNP17 model parameters. Other than their 5-km resolution, these global, annual GeoTIFF files are formatted the same as MOD17A3H data; <a href="https://lpdaac.usgs.gov/products/mod17a3hgfv061/">see the User Guide</a> for more information. The same scale factors apply to recover geophysical units: multiply the values by 0.0001 to obtain [kg C m-2 year-1].</p> <p>Please cite the peer-reviewed paper:</p> <blockquote> <p>Endsley, K.A., M. Zhao, J.S. Kimball, S. Devadiga. 2023. "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion." <em>Journal of Geophysical Research: Biogeosciences</em> 128(9).</p> </blockquote>
Data from the OPERAS business models survey on open access books
<p>OPERAS (the European Research Infrastructure for the development of open scholarly communication in the social sciences and humanities) has conducted a survey of publishing organisations throughout Europe to identify and better understand existing and potential business models to support the Open Access publication of research monographs. The results of the survey are used to inform the formulation of recommendations about how to create a sustainable open access book publishing ecosystem within Europe.</p> <p>The survey was designed to serve two core aims: <br> 1. To further, better or improve our understanding of the scholarly publishing landscape and of the challenges that publishers face in the context of publishing OA monographs;<br> 2. To identify main trends (including opportunities and challenges) and the knowledge of collaborative funding and infrastructure models in OA publishing in SSH. </p> <p>The survey was open between 16 February and 14 April 2021.</p> <p>The results are presented in two versions of the white paper of the Open Access Business Models Special Interest Group: Stone, Graham, Błaszczyńska, Marta, Lebon, Chloé, Morka, Agata, Mosterd, Tom, Mounier, Pierre, Proudman, Vanessa, Speicher, Lara, & Melinščak Zlodi, Iva. (2021). Collaborative models for OA book publishers (1.0). Zenodo. https://doi.org/10.5281/zenodo.5494731 and the second version to be published in Spring 2023.</p>
Data, code and software to reproduce the article entitled "Modeling soil-plant functioning of intercrops using comprehensive and generic formalisms implemented in the STICS model"
<p>This is the data, code and software to reproduce the article entitled " Modeling soil-plant functioning of intercrops using comprehensive and generic formalisms implemented in the STICS model". Here is a summary of the paper:</p> <p>The growing demand for sustainable agriculture is raising interest in intercropping for its multiple potential benefits to avoid or limit the use of chemical inputs or increase the production per surface unit. Predicting the existence and magnitude of those benefits remains a challenge given the numerous interactions between interspecific plant-plant relationships, their environment and the agricultural practices. Soil-crop models are critical in understanding these interactions in dynamics during the whole growing season, but few models are capable of accurately simulating intercropping systems.</p> <p>In this study, we propose a set of simple and generic formalisms for simulating key interactions in intercropping systems that can be readily included into existing dynamic crop models. This requires simulating important processes such as development, light interception, plant growth, N and water balance, and yield formation in response to management practices, soil conditions, and climate. These formalisms were integrated into the STICS soil-crop model and evaluated using observed data of intercropping systems of cereal and legumes mixtures, including Faba bean-Wheat, Pea-Barley, Sunflower-Soybean, and Wheat-Pea mixtures. We demonstrate that the proposed formalisms provide a comprehensive simulation of soil-plant interactions in various types of bispecific intercrops. The model was found consistent and generic under a range of spring and winter intercrops (nRMSE = 25% for maximum leaf area index, 23% for shoot biomass at harvest, and 18% for yield).</p> <p>This is the first time a complete set of formalisms has been developed and published for simulating intercropping systems and integrated into a soil-crop model. With its emphasis on being generic, sufficiently accurate, simple, and easy to parameterize, STICS is well-suited to help researchers designing <em>in silico</em> the agroecological transition by virtually pre-screening sustainable, manageable intercrop systems adapted to local conditions.</p> <p> </p> <p> </p> <p> </p>
Data-driven plasma modelling: Fluorocarbon ICP data set
<p>This is an open source dataset of optical emission spectra and optical images in fluorocarbon plasmas, along with associated tool logs, captured from a Oxford Intruments Plasma Technology PP100 ICP etcher. The dataset consists of Ar, O<sub>2</sub>, Ar/O<sub>2</sub>, CF<sub>4</sub>/O<sub>2</sub> and SF<sub>6</sub>/O<sub>2</sub> gas mixtures etching Si wafers.</p> <p>The data has been split into chunks for uploading to Zenodo, to reconstruct them:</p> <p>$ cat generative_model-fluorocarbon_data_set.tar.xz* > generative_model-fluorocarbon_data_set.tar.xz </p> <p>$ tar -xvf generative_model-fluorocarbon_data_set.tar.xz </p> <p>You can find the code to train an autoencoder model using the optical emission spectra and optical images at our github repo, https://github.com/gregdaly/generative_modelling_for_optical_plasma_diagnostics </p>
Supplementary Data: Global fits if simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator
<p>This record contains the YAML files, data files, and some of the plotting scripts for: "Global fits of simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator".</p> <p>Samples have been created using GAMBIT and figures can be reproduced with pippi. Plotting scripts (*.pip) are designed to work with either the original version of pippi 2.1 or the forked unreleased version. The provided scripts do not reproduce all the figures in the paper exactly.</p> <p>To save storage space, all samples have been compressed using <code>tar</code>. To inflate each dataset after downloading run <code>tar -zxvf <samples>.hdf5.gz</code>.</p> <p>To facilitate uploading to Zenodo, several of the data files have been thinned to only include enough points to reproduce plots.</p>
Processed data for "Model identification of neural encoding (MINE)" publication
<p>This dataset contains mouse and zebrafish data processed by MINE. These datafiles were used to generate the publication figures for the mouse cortical dataset [m<em>usall.hdf5</em>] (Figure 5) and the zebrafish whole-brain [<em>main_analysis.hdf5</em>] (Figures 6 and 7) and reticulospinal datasets [r<em>s_analysis.hdf5</em>] (Figure 6).</p> <p> </p> <p><em>Musall.hdf5 </em>contains reordered data from "Musall, S., Kaufman, M.T., Juavinett, A.L. <em>et al.</em> Single-trial neural dynamics are dominated by richly varied movements. <em>Nat Neurosci</em> <strong>22</strong>, 1677–1686 (2019)."</p> <p>The contents of each dataset are described in <em>DataContent_xxx.pdf</em></p>
ArrayCGH microarray images for 'Autoencoder and NCA based neural network model to estimate survival prognosis in multiple myeloma using arrayCGH data'
<p>ArrayCGH microarray images for 'Autoencoder and NCA based neural network model to estimate survival prognosis in multiple myeloma using arrayCGH data'</p>
Data for Accident Severity Prediction Modelling for Indian Highways Case Study
<p>Accident Data: Road accidents data is of Indian Highways sections Pune-Solapur and Bengal (BAEL) Section. For the Pune-Solapur Section of NH-9, which is located between Km.144/400 and Km. 249/000 in the state of Maharashtra, accident dates from 2013 to 2018. For the Six-Laning of Barwa-Adda-Panagarh Section of NH-2, which includes Panagarh Bypass and is located in the States of Jharkhand and West Bengal Stretch, accident dates from 2015 to 2019 for the stretch between km 398.240 and km 521.120. </p> <p>The data is sorted and analyzed using Random Forest Machine Learning for Accident Severity Prediction Modelling.</p> <p>Acknowledgement: We highly acknowledge the two organizations 1. National Highways Authority of India, 2. IL&FS Engineering and Construction Company for making the raw data available.</p> <p>Source: 1. National Highways Authority of India, 2. IL&FS Engineering and Construction Company.</p> <p> </p>
Supplementary data for the paper "Visual integration of omics data to improve 3D models of fungal chromosomes"
<ul> <li>13 parameter files (*.YML) used by the 3DGB workflow to produce models of 3D genomes.</li> <li>13 3D genomes structures (*.PDB).</li> <li>4 animated GIF of representative structures.</li> <li>1 XLSX file that lists raw (Hi-C and ChIP-seq) data used in this study and the associated analysis.</li> </ul>
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.