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1,956 results for “test data”
Laser Beam Characterization Data Related to Testing Different Near-Infrared Laser Therapy Devices Used in Sports Medicine
<p>This dataset contains laser beam characterization data collected for different near-infrared laser therapy devices that are used in sports medicine. Data were collected between September 2021 and December 2022 at the Department of Anatomy II (Neuroanatomy) of the Ludwig-Maximilians University of Munich, Germany. Laser therapy devices were characterized using a thermal power sensor, a photodiode, and a beam profiling camera (for details, see [1]). Two different laser therapy devices were investigated (Dolorclast High Power Laser, Electro Medical Systems; Cube 4 Med, Eltech K-laser s.r.l.). The measurements were repeated for three units of each laser therapy device (EMS-1, EMS-2, EMS3; K-1, K-2, K-3). The dataset contains raw data files from each sensor that include text (TXT) files, comma-separated-values (CSV) formatted files, and raw camera recordings in binary format (BINARY.BGDATA). The data was used to create figures that were submitted to the journal MDPI Biomedicines [1]. The figures in [1] were created with Python in a Jupyter Notebook, which is also included in this dataset.</p> <p><strong>2023-02-17 Update: The manuscirpt has been published in MDPI Biomedicines [2]. If you make any use of this dataset, please cite [2].</strong></p> <p>[1] Kaub, L.; Schmitz, C. Spread of the Optical Power Emission of Several Units of the Same Laser Therapy Devices Used in Sports Medicine, Which Cannot Be Assessed by the Users, Shown by Means of High-Fidelity Laser Measurement Technology. <em>Preprints</em> <strong>2023</strong>, <em>2023010251</em>, https://doi.org/10.20944/preprints202301.0251.v1.</p> <p>[2] Kaub, L.; Schmitz, C. Spread of the Optical Power Emission of Three Units Each of Two Different Laser Therapy Devices Used in Sports Medicine, Which Cannot Be Assessed by the Users, Shown by Means of High-Fidelity Laser Measurement Technology. <em>Biomedicines</em> <strong>2023</strong>, <em>11</em>, 585. https://doi.org/10.3390/biomedicines11020585</p>
Training, Validation and Test Sets for paper 'A Little Data goes a Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano with Transfer Learning'
<p>Training, Validation and Test Data for model presented in paper 'A Little Data Goes A Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano with Transfer Learning', submitted to Journal of Geophysical Research: Solid Earth.</p> <p>Files:</p> <p>- train_events_2498.h5 = training set of seismic waveforms (events with P-/S-wave labelled arrivals only, i.e., no noise waveforms)</p> <p>- train_events_2498.pkl = event training set metadata (UTC P-/S-wave phase arrival times)</p> <p>- train_noise_2498.h5 = training set of seismic waveforms (noise sections only, i.e., no event waveforms)</p> <p>- train_noise_2498.pkl = noise training set metadata (UTC time for training noise waveforms)</p> <p>- val_events.h5 = validation set of seismic waveforms (events with P-/S-wave labelled arrivals only, i.e., no noise waveforms)</p> <p>- val_events.pkl = event validation set metadata (UTC P-/S-wave phase arrival times)</p> <p>- val_noise.h5 = validation set of seismic waveforms (noise sections only, i.e., no event waveforms)</p> <p>- val_noise.pkl = noise validation set metadata (UTC time for validation noise waveforms)</p> <p>- test.h5 = test set of seismic waveforms (events and noise)</p> <p>- test_events.pkl = event test set metadata (UTC P-/S-wave phase arrival times for test event waveforms)</p> <p>- test_noise.pkl = noise test set metadata (UTC time for test noise waveforms)</p> <p>- nabro_2011-247.mseed = 24 hours seismic data from Nabro Urgency Array (2011-09-04), saved in mseed format (e.g., can be read with obspy)</p> <p>- nabro_2011-269.mseed = 24 hours seismic data from Nabro Urgency Array (2011-09-26), saved in mseed format (e.g., can be read with obspy)</p> <p> </p> <p>Further details and code for reading and using these files can be found at the GitHub repo for this paper: <a href="https://github.com/sachalapins/U-GPD">https://github.com/sachalapins/U-GPD</a></p> <p> </p>
Sample, test, and validation data for findmycells
<p>findmycells is an open source python package, developed to foster the use of deep-learning based python tools for bioimage analysis, specifically for researchers with limited python coding experience. It is developed and maintained in the following GitHub repository: https://github.com/Defense-Circuits-Lab/findmycells</p> <p><strong>Disclaimer: All data (including the model ensemble) uploaded here serve solely as a test dataset for findmycells and are not intended for any other purposes.</strong></p> <p>For instance, the group, subgroup, or subject IDs don´t refer to the actual experimental conditions. Likewise, also the included ROI-files were only created to allow the testing of findmycells and may not live up to scientific standards. Furthermore, the image data represents a subset of a dataset that is already published here:</p> <blockquote> <p>Segebarth, Dennis et al. (2020), Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses, Dryad, Dataset, <a href="https://doi.org/10.5061/dryad.4b8gtht9d">https://doi.org/10.5061/dryad.4b8gtht9d</a></p> </blockquote> <p>The model ensemble (cfos_ensemble.zip) was trained using deepflash2 (v 0.1.7)</p> <blockquote> <p>Griebel, M., Segebarth, D., Stein, N., Schukraft, N., Tovote, P., Blum, R., & Flath, C. M. (2021). Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2. <em>arXiv preprint arXiv:2111.06693</em>.</p> </blockquote> <p>The training was performed on a subset of the "lab-wue1" training dataset, using only the 27 images with IDs 0000 - 0099 (cfos_training_images.zip) and the corresponding est. GT masks (cfos_training_masks.zip). The images used in "cfos_fmc_test_project.zip" for the actual testing of findmycells are the images with the IDs 0100, 0106, 0149, and 0152 of the aforementioned "lab-wue1" training dataset. They were randomly distributed to the made-up subject folders and renamed to "dentate_gyrus_01" or "dentate_gyrus_02".</p>
Supplementary data for "Testing the efficacy of different molecular tools for parasite conservation genetics: a case study using horsehair worms (Phylum Nematomorpha)"
<p>Supplementary data for "Testing the efficacy of different molecular tools for parasite conservation genetics: a case study using horsehair worms (Phylum Nematomorpha)"</p> <p>alignments: alignments used for BEAST ("bayes") and PopArt ("popart"). The "popart" folder also has a traits file per each species.</p> <p>bayesian_plots: TSVs ("tsv") and PDF files ("ogs") generated by BEAST. The "tsv" folder also has the scripts for plotting the results in R.</p> <p>easysfs: scripts, population file and results from the VCF to SFS conversione done by easySFS.</p> <p>fineRADstructure: fineRADstructure input files and output PDF plots ("plots") for <em>C. formosanus</em> ipyrad and Stacks ("stacks") data. </p> <p>logs: logs for ipyrad, ModelTest, PGDspider, PopArt ("popart") and Stacks ("stacks"). The "popart" folder also have the generated networks in a TXT file. The "stacks" folder also has ODS files for calculating the amount of loci per each M/n fixed value.</p> <p>snapclust: STR files used with R for snapclust. Scripts included.</p> <p>stairway_plot: input (blueprint files) and outputs for Stairway Plot 2 analyses. The <em>C. formosanus</em> folder ("chordodes") also has scripts for R plotting.</p> <p>vcfs: VCF and HDF5 files used in this study. Also scripts for filtering/converting data and plotting the PCA with ipyrad (activate python first!) for <em>C. formosanus</em>.</p> <p>"acutogordius" = <em>A. taiwanensis</em><br> "chordodes" = <em>C. formosanus</em><br> "gordius" = <em>G. chiashanus</em></p>
Data for: Nest material preferences in wild hazel dormice Muscardinus avellanarius: Testing predictions from optimal foraging theory
<p class="MsoNormal">Obtaining nesting material presents an optimal foraging problem, collection of materials incurs a cost in terms of risk of predation and energy spent, and individuals must balance these costs with the benefits of using that material in the nest. The hazel dormouse, <em>Muscardinus avellanarius</em> is an endangered British mammal in which both sexes build nests. However, whether material used in their construction follows the predictions of optimal foraging theory is unknown. Here, we analyse the use of nesting materials in forty two breeding nests from six locations in Southwest England. Nests were characterised in terms of which plants were used, the relative amount of each plant, and how far away the nearest source was. We find that dormice exhibit a preference for plants closer to the nest, but that the distance they are prepared to travel depends on the plant species. Dormice travelled further to collect honeysuckle <em>Lonicera periclymenum</em>, oak <em>Quercus robur</em>, and beech <em>Fagus sylvatica</em> than any other plants. Distance did not affect the relative amount used, although the proportion of honeysuckle in nests was highest, and more effort was expended collecting honeysuckle, beech, bramble <em>Rubus fruticosus</em> and oak compared to other plants. Our results suggest that not all aspects of optimal foraging theory apply to nest material collection. However, optimal foraging theory is a useful model to examine nest material collection, providing testable predictions. As found previously honeysuckle is important as a nesting material, and should be taken account when assessing suitability of sites for dormice.</p>
Supporting Data Set for Paper "Using GUI Test Videos to Obtain Stakeholders' Feedback"
<p>This data set is a supporting material for an accepted paper "Using GUI Test Videos to Obtain Stakeholders’ Feedback" on <a href="https://conf.researchr.org/track/icssp-2023/">ICSSP 2023</a>.</p> <p>This dataset consists of</p> <ul> <li>Questionnaires of control and experimental groups <ul> <li> <p>Questionnaire-Control Group (German).pdf -> Original quetionnaire for control group (in German)</p> </li> <li> <p>Questionnaire-Control Group (translated)-v03.pdf -> Translated quetionnaire for control group (in English)</p> </li> <li> <p>Questionnaire-Experimental Group (German).pdf -> Original quetionnaire for experimental group (in German)</p> </li> <li> <p>Questionnaire-Experimental Group (translated)-v03.pdf -> Translated quetionnaire for experimental group (in English)</p> </li> </ul> </li> <li>Survey-Data-v25.ods -> Collected data through questionnaires</li> <li>Calculate-Mann-Whitney-U-Test-v07.ods -> Detailed calculation of Mann Whitney U Test</li> <li>The videos of the ten scenarios in this study are also available on OneDrive <a href="https://1drv.ms/f/s!AtqkJ5cB802BoABI7w_Bft9H8Psi?e=upToep">https://1drv.ms/f/s!AtqkJ5cB802BoABI7w_Bft9H8Psi?e=upToep</a> , where you can play them directly in browsers.</li> </ul>
Data for: Who defines the "personal utility" of genetic and genomic testing?
<div> <div> <div> <p><strong>Importance</strong>: Expansion in the clinical use of genetic and genomic testing has led to a recognition that these tests provide personal as well as clinical utility to patients and families. It is essential to ensure that members of diverse sociodemographic backgrounds are included in defining and measuring personal utility.</p> <p><strong>Objective</strong>: To determine the demographics of participants contributing to the development of a definition of personal utility for genetic and genomic testing.</p> <p><strong>Evidence</strong> <strong>Review</strong>: We searched PubMed, Scopus, Web of Science, and Embase for peer-reviewed literature published between 2003 and January 2022 on the personal utility of genetic or genomic sequencing. Our review included both qualitative and quantitative studies with samples that included patients, their family members, or the general public. Eligible studies could examine any clinical genetic or genomic test and required the use of the term "utility." Authors extracted and reviewed study and participant characteristics including number of participants, study location (U.S. or international), primary methodology (qualitative or quantitative), race and ethnicity, gender, income, and education data.</p> <p><strong>Findings</strong>: Our final review included 53 studies and 13,315 total participants. Gender was provided for 95.6% of participants (n=12,724), of whom 61.5% were female (n=7,823). Race and/or ethnicity was provided for 83.0% of participants (n=11,048), of whom 82.2% (n=9,083) were White. The remaining participants were identified as Hispanic/Latinx (5.5%, n=607), Asian American and Pacific Islander (3.8%, n=421), Black (3.5%, n=387), multiracial (0.2%, n=27), and various other racial or ethnic categories (3.7%, n=412). Educational attainment was reported for 82.6% of participants. Among these participants, 71.2% (n=7,830) had a bachelor's degree or higher. Income was reported for 66.5% of participants (n=8,857), and 66% of these participants (n=5,831) reported income above the U.S. median.</p> <p><strong>Conclusions and Relevance</strong>: Our results suggest that the concept of personal utility in genetic and genomic testing in the U.S. is disproportionately defined by the perspectives of a narrow subset of the population – specifically non-Hispanic White, well-educated women with above-average household incomes. If we are to provide equitable care in the areas of genomics and genetics, we will need to expand research to include more diverse and representative samples.</p> </div> </div> </div>
DeliCS Testing Data + DL Checkpoints - SPI-TGAS-MRF+GRE
<p>This data set consists of raw MRI k-space data from 3 healthy volunteers (train_case000, test_case000, and test_case001) and 3 patients (test_case002, test_case003, and test_case004). The data were acquired on a 3T Premier MRI scanners (GE Healthcare, Waukesha, WI) with 48-channel head receiver-coils. The raw data was saved as numpy-arrays to remove any potentially identifying meta-data, and to work in the reconstruction pipeline presented in [1]. </p> <p>Each case tarball contains three files: <strong>raw_mrf.npy, gre_mrf.npy, noise.npy</strong></p> <p>SPI-TGAS-MRF (files named <strong>raw_mrf.npy</strong>):</p> <p>The acquisition consists of an initial adiabatic inversion pulse followed by a 500 TR long readout train (TI/TE/TR = 20/0.7/12ms) with varying flip angles (10 to 75 degrees) and a rotating 3D center-out spiral trajectory. 48 repeats of the TR train are used for a 6 min acquisition. Details available in [2]. The data shape is: (2000, 48, 24000) = (data along spiral readout, number of receive channels, number of spirals across 500 TR's and 48 repeats)</p> <p>GRE (files named <strong>raw_gre.npy</strong>):</p> <p>A 20 second, low resolution (6.9 mm isotropic) gradient echo (GRE) pre-scan with a large FOV of 440x440x440mm^3. The data shape is: (64, 48, 4096) = (data along readout, number of receive channels, number of phase encode lines (64x64))</p> <p>Noise estimation (files named <strong>noise.npy</strong>):</p> <p>Data from a noise scan acquired using all receive channels to calculate the noise coherence matrix. The data shape is: (48, 4096) = (number of receive channels, noise measurement points)</p> <ul> </ul> <p>Finally, <strong>checkpoints.tar.gz</strong> contains the pre-trained weights used for the deliCS network.</p> <p> </p> <p>[1] Iyer S, Schauman S, Sandino C, et al. Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction. <em>BioRxiv: </em><a href="https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1">https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1</a></p> <p>[2] Cao, X, Liao, C, Iyer, SS, et al. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. <em>Magn Reson Med</em>. 2022; 88: 133- 150. doi:<a href="https://doi.org/10.1002/mrm.29194">10.1002/mrm.29194</a></p>
Mooring Tests Data
<p>The shared folder contains the laboratory test data for the mooring lines. It includes the configurations carried out for the two types of lines: ALL-CHAIN configuration (regular, irregular and tension-deformation subfolders) and CHAIN-NYLON configuration (regular and tension-deformation subfolders). It also contains a “readme” file as a schematic explanation of the data. </p> <p>This data are an open experimental database for numerical modelling and future research. The context of this data is within the “corewind” project (D5.4). For further information check Deliverable 5.2: “Mooring and cable dynamic testing report” of the Corewind project.</p>
Hifieval test data
<p>Hifieval test dataset.</p> <p>The <em>E. coli </em>reference genome is from https://www.ncbi.nlm.nih.gov/assembly/GCF_000005845.2/. The HiFi reads data is simulated by PBSIM2.</p>
Asphalt profile temperatures and weather data of CyPaTs test track
<p>This dataset includes asphalt temperature measurements and weather data of CyPaTs test track located at Campus Groenenborger, University of Antwerp, Antwerp, Belgium. The recorded asphalt temperature measurements and weather data is between March 17<sup>th</sup>, 2021, and March 14<sup>th</sup>, 2022. There are five sheets in each excel file, containing asphalt temperatures at various depths (near-surface, 4cm, 7cm and 10 below asphalt surface), and corresponding weather data. The total number of data points is 371707, with each of these data points including information on weather parameters and 45 sensors embedded in different layers of the asphalt pavement.</p> <ol> <li>Date (time): time of the recorded data</li> <li>TC_x-x: label of the temperature sensor embedded in asphalt pavement, in °C</li> <li>Ta: ambient air temperature, in °C</li> <li>RH: relative humidity, in %</li> <li>FF: wind speed, in m/s</li> <li>SR: solar radiation, in W/m<sup>2</sup></li> </ol>
TDMS: System Test Data
<p>TDMS (the Time Domain Maxwell Solver) is a tool for solving Maxwell's equations to simulate light propagation through a medium. Visit the <a href="https://github.com/UCL/TDMS">project on GitHub</a> to find out more.</p> <p>This dataset contains multiple sets of input data, and the corresponding expected output data, of the TDMS executable. These datasets are used in the system testing of the TDMS executable.</p> <p>This version is intended for use with TDMS versions v1.0.0 or later.</p>
Trans-eQTL effects on risk of type 1 diabetes: a test of the sparse effector (omnigenic) hypothesis of complex trait genetics (supplementary data)
<p>This repository contains summary-level data generated by performing <a href="https://github.com/molepi-precmed/trans-qtls">Genomewide aggregated trans- effects (GATE) analysis</a> in case-control study of Type 1 Diabetes (T1D).</p>
Male Xiphophorus multilineatus behavioral, brain weight, and testes weight data
<p class="MsoNormal"><span>Behavioural plasticity may require energetically expensive sensory and neural adaptations to detect, process, and respond to social cues. These costs could lead to selection against behavioural plasticity and its eventual loss. We show that males from the behavioural plastic alternative reproductive tactic (ART) in the swordtail fish <em>Xiphophorus multilineatus</em> have relatively larger brains, in addition to a trade off with testes size, that is not detected in the males from the behaviourally fixed ART. Given these costs, we consider the hypothesis that plasticity in mating behaviours is maintained due to intralocus tactical conflict, where a shared genome can constrain one or both ARTs from evolving to their optima. When we reduced any potential for intralocus tactical conflict by removing the behaviourally fixed ART from long-term breeding mesocosms, the males from the behaviourally plastic ART were less plastic and had smaller brains as compared to their counterpart from control mesocosms (both male ARTs). We also detected evidence for a genetic correlation between the ARTs for behaviour, which is required for intralocus conflict. Our findings suggest that intralocus tactical conflict could be maintaining behavioural plasticity, in which case behavioural plasticity may not be adaptative in some cases.</span></p>
Training data and test data sets for simultaneous inversion of velocity density based on U-T
<p>Here are the training and testing data sets involved in the numerical experiments in the article that has been submitted to the journal “Journal of Geophysical Research: Solid Earth”, named “Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density”: Marmousi model. Each dataset consists of two parts: a training dataset and a testing dataset. Both training and testing data sets contain three parts: seismic data, velocity model and density model.</p>
U-T training and test data for LayerFault model
<p>Here are the training and testing data sets involved in the numerical experiments in the article that has been submitted to the journal “Journal of Geophysical Research: Solid Earth”, named “Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density”: LayerFault model. Each dataset consists of two parts: a training dataset and a testing dataset. Both training and testing data sets contain three parts: seismic data, velocity model and density model.</p>
Data From: Laisk measurements in the non-steady-state: tests in plants exposed to warming and variable CO2 concentrations
<p>Light respiration (<em>R</em><sub>L</sub>) is an important component of plant carbon balance and a key parameter in photosynthesis models. <em>R</em><sub>L</sub><em> </em>is often measured using the Laisk method, a gas exchange technique that is traditionally employed under steady-state conditions. However, a non-steady-state dynamic assimilation technique (DAT) may allow for more rapid Laisk measurements. In two studies, we examined the efficacy of DAT for estimating <em>R</em><sub>L</sub> and the parameter <em>C</em><sub>i</sub>*<em> </em>(the intercellular CO<sub>2</sub> concentration where rubisco's oxygenation velocity is twice its carboxylation velocity), which is also derived from the Laisk technique. In the first study, we compared DAT and steady-state <em>R</em><sub>L</sub> and <em>C</em><sub>i</sub>* estimates in paper birch (<em>Betula papyrifera</em>) growing under control and elevated temperature and CO<sub>2</sub> concentrations. In the second, we compared DAT-estimated <em>R</em><sub>L</sub> and <em>C</em><sub>i</sub>* in hybrid poplar (<em>Populus nigra L. x P. maximowiczii</em> A. Henry 'NM6') exposed to high or low CO<sub>2</sub> concentration pre-treatments. The DAT and steady-state methods provided similar <em>R</em><sub>L</sub> estimates in <em>B</em>. <em>papyrifera</em>, and we found little acclimation of <em>R</em><sub>L</sub> to temperature or CO<sub>2</sub>; however, <em>C</em><sub>i</sub>* was higher when measured with DAT compared to steady-state methods. These <em>C</em><sub>i</sub>* differences were amplified by the high or low CO<sub>2</sub> pre-treatments. We propose that changes in the export of glycine from photorespiration may explain these apparent differences in <em>C</em><sub>i</sub>*.</p>
CyTOF test data for BinaryClust2
<p>PBMC samples from 11 Myeloproliferative neoplasms(MPN) patients were subject to a 36-marker panel and aquired by mass cytometry for immune surveillance, which represent a test dataset for BinaryClust2 pipeline(https://github.com/JingAnyaSun/BinaryClust2). </p> <p>Enclosed are raw data including fcs files after clean-up, sample metadata('metadata2.xlsx') and panel metadata('panel_metadata2'), and the defined R objects which comprises panel.RData, md.RData and MPN_sce.RData. </p>
A decade of cumulative radiocesium testing data for foodstuffs throughout Japan after the 2011 Fukushima Daiichi Nuclear Power Plant accident
<p>Updated dataset.</p>
Data from: A test of the green wave hypothesis in omnivorous brown bears across North America
<p>Herbivorous animals tend to seek out plants at intermediate phenological states to improve energy intake while minimizing consumption of fibrous material. In some ecosystems, the timing of green-up is heterogeneous and propagates across space in a wave-like pattern, known as the green wave. Tracking the green wave allows individuals to prolong access to higher-quality forage. While there is a plethora of empirical support for such behavior in herbivorous taxa, the green wave hypothesis (GWH) is nuanced based on factors such as body morphometrics and digestive capacity. Furthermore, little is known about whether other taxa, such as omnivores, track the green wave. Our objective was to assess whether the GWH can be extended to explain the movements of omnivores. Using GPS collar data from seven populations (n = 127 individuals) of brown bears (<em>Ursus</em> <em>arctos</em>) across their entire North American range, we first tested whether bears tracked the green wave. Using conditional resource selection functions, we found that variation in proxies of vegetative forage quality better-explained movement and habitat selection than proxies of forage biomass in over half of the bears in our study, providing evidence of green wave tracking. Second, we assess factors that explained variation in green wave tracking using linear mixed-effects models. Green wave tracking in brown bears was explained by the variation in availability of green-up within spring home ranges, and how green-up transitioned across those home ranges. Our results demonstrate that the GWH can partially explain movement of a non-migratory omnivorous species, extending the generality of the GWH as a broad predictor of animal space use. The green wave is another resource wave brown bears track, and our findings help predict brown bear space use, which can be used to guide conservation and habitat restoration efforts.</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.