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1,904 results for “Breathing”
Count data of air-breathing fauna from visual transect surveys including water temperature, time, sea and weather conditions in Shark Bay Marine Park, Western Australia from February 2008 to July 2014
This dataset provides information on the relative abundances of air breathing fauna (dugongs, dolphins, sea snakes, marine birds, and sea turtles) in the study area of the Eastern Gulf of Shark Bay, Western Australia. The dataset comprises transects that quantify animal abundances in three microhabitats (shallow seagrass banks, seagrass bank edges, and deep sandy channels). These microhabitats vary in their food supply as well as their potential to facilitate or inhibit detection and escape from predators, mainly the tiger shark (Galeocerdo cuvier). As a result these data have been used to examine risk-specific habitat use behaviors of these fauna, in addition to general abundance estimates.
Dataset for "Large scale patterns and drivers of the diving behavior of gill-breathing large pelagic predators"
<p>This dataset includes all supporting data and scritps to generate figure panels in the paper "Large scale patterns and drivers of the diving behavior of gill-breathing large pelagic predators" (A. Nuno, J. Guiet, B. Baranek and D. Bianchi)</p>
Eye tracking videos and raw data of breathing recognition attempts in simulated out-of-hospital cardiac arrest
<div> <div> <div> <p>This dataset comprises eye tracking videos and raw data documenting attempts to recognize breathing in simulated out-of-hospital cardiac arrest scenarios.</p> <p>The data were recorded using an Ergoneers Dikablis head-mounted eye tracker.</p> <p>Our analysis of this data resulted in the publication of two studies: Study 1, available at <a href="https://doi.org/10.1097/SIH.0000000000000617" target="_blank" rel="noopener">https://doi.org/10.1097/SIH.0000000000000617</a>, and Study 2, accessible at <a href="https://doi.org/10.25894/ijfae.2307" target="_blank" rel="noopener">https://doi.org/10.25894/ijfae.2307</a></p> <p> </p> <p>Version 2 is up-to-date.</p> <p>In Version 1:</p> <ul> <li>the doi for Study 2 was incorrect</li> <li>data for participant #51 of Study 1 were missing</li> </ul> </div> </div> </div>
Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" (Data set and materials used for human-robot interaction experiment)
<p>Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" authored by Troels Aske Klausen, Ulrich Farhadi, Evgenios Vlachos, and Jonas Jørgensen.</p> <p>Contents of set 2:<br> - Data set and materials used for the human-robot interaction experiment and for data analysis</p> <p>Files:<br> - "Questionnaire.pdf": Questionnaire used for data collection.<br> - "Video links.txt": Weblinks to stimuli videos used.<br> - "Data set.xls": Collected raw data.<br> - "Matlab_DataAnalysis.mlx": Matlab script used to analyze raw data.<br> - "Linear_Arousal.png": Linear fit between the scoring of arousal and BPM.<br> - "Linear_Dominance.png": Linear fit between the scoring of dominance and BPM.<br> - "Linear_Pleasure.png": Linear fit between the scoring of pleasure and BPM.</p> <p>The experiment procedure is described in the paper.<br> The soft robot used for the experiment is open source and can be manufactured using design files available on Zenodo: 10.5281/zenodo.5565201</p>
A Ligand Field Molecular Mechanics Study of CO2 Induced Breathing in the metal-organic framework DUT-8(Ni)
<p>Raw Data, scripts and processed data for the publication "A Ligand Field Molecular Mechanics Study of CO2 Induced Breathing in metal-organic framework DUT-8(Ni)"</p>
Britain Breathing 2016-2019 Air Quality and Meteorological Regional Estimates Dataset
<p>This data set is a collection of estimated daily mean and maximum values for a range of air quality and meterological measurements and model forecasts for the <em>UK and crown dependencies</em> postcode districts (e.g. 'AB') for the years 2016-2019, inclusive.</p> <p>The paper describing this dataset is available here: <a href="https://www.nature.com/articles/s41597-022-01135-6">https://www.nature.com/articles/s41597-022-01135-6</a></p> <p>The data uses a 'concentric regions' method to estimate the measurement for all regions, as follows. If measurements exist within the region, the mean of those measurements is used, if not, then a ring of neighbouring postcode regions are selected, and the mean of their measurement values used. If no measurement sites/data are found in the first ring, the process continues, taking the next ring of postcode district regions, working outwards until one or more sensors are found in a ring. As well as the measurement estimations, the number of rings required to find site data and make the estimations is also published. <strong>As a result, please note that estimations with higher ring counts ('rings') are likely to be calculated from more distant sensors. This distance depends upon the size of the postcode regions surrounding the location being estimated. Please use the ring count ('rings') to limit/filter estimations based on your required level of confidence.</strong><br> <br> The meteorological, pollen and air quality measurement data used to make the regional estimations can be found at <a href="https://zenodo.org/record/4416028#.YABxNnX7RhF">this Zenodo archive</a>. The data there contains Temperature, Relative Humidity, and Pressure data, downloaded from the Met Office MIDAS archives via the MEDMI server (https://www.data-mashup.org.uk/). Also downloaded from the MEDMI server are daily pollen measurements for the UK. PM10, PM2.5, NO2, NOx (as NO2), O3, and SO2 measurements from the DEFRA AURN network, and also model forecasts of the same made using the EMEP model.</p> <p>The code used to make the estimations is available at <a href="https://zenodo.org/record/4518866">this Zenodo archive</a>.</p> <p>The postcode data in postcode_district_data.csv are collated from several sources: </p> <ul> <li><a href="https://www.doogal.co.uk/UKPostcodes.php">https://www.doogal.co.uk/UKPostcodes.php</a> (population figures for the UK (UK Census 2011))</li> <li><a href="https://www.freemaptools.com/download-uk-postcode-outcode-boundaries.htm">https://www.freemaptools.com/download-uk-postcode-outcode-boundaries.htm</a> (postcode boundary polygons for UK and crown dependancies)</li> <li><a href="https://www.gov.gg/population">https://www.gov.gg/population</a> (Guernsey (GY) population data for end June 2020) </li> <li><a href="https://www.gov.je/Government/JerseyInFigures/Population/Pages/Population.aspx">https://www.gov.je/Government/JerseyInFigures/Population/Pages/Population.aspx</a> (Jersey (JE) population data for end 2019) </li> <li><a href="https://www.gov.im/media/1369690/isle-of-man-in-numbers-july-2020.pdf">https://www.gov.im/media/1369690/isle-of-man-in-numbers-july-2020.pdf</a> (Isle of Man (IM) population data for April 2016)</li> </ul> <p>The data-set is presented in CSV format, as six files:</p> <ol> <li>postcode_district_data.csv: location metadata (region_id, geometry, description, population, country)</li> <li>regional_site_counts.csv: a table showing the number of sites for each measurement (columns), for each region_id (rows). region_id's match those in the postcode_district_data.csv file.</li> <li>turing_regional_estimates_aq_daily_met_pollen_pollution_imputed_data.csv: uses imputed site data (timestamp, region_id, ...[measurement name, rings]) ('rings' is the number of rings required to make the estimation)</li> <li>turing_regional_estimates_aq_daily_met_pollen_pollution_original_data.csv: uses original site data (timestamp, region_id, ...[measurement name, rings]) ('rings' is the number of rings required to make the estimation)</li> <li>turing_regional_estimates_aq_loc_type_daily_imputed_data.csv: uses imputed site data. Air quality regional estimates are calculated using specific AQ site location types* separately. (To prevent, for example, 'Traffic Urban' type sites being used to estimate 'non-traffic' or rural regions.)</li> <li>turing_regional_estimates_aq_loc_type_daily_original_data.csv: uses original data. Air quality regional estimates are calculated using specific AQ site location types* separately. (To prevent, for example, 'Traffic Urban' type sites being used to estimate 'non-traffic' or rural regions.)</li> </ol> <p>* Air quality site types: </p> <ul> <li>Industrial: comprises 'urban industrial' (9 sites) and suburban industrial (2 sites)</li> <li>'Rural background' (14 sites)</li> <li>'Urban background' (48 sites)</li> <li>'Urban traffic' (47 sites)</li> </ul>
Britain Breathing 2020 Air Quality and Meteorological Regional Estimates Dataset
<p>This data set is a collection of estimated daily mean and maximum values for a range of air quality and meterological measurements and model forecasts for UK postcode districts (e.g. 'AB') for the year 2020.</p> <p>The data uses a 'concentric regions' method to estimate the measurement for all regions, as follows. If measurements exist within the region, the mean of those measurements is used, if not, then a ring of neighbouring postcode regions are selected, and the mean of their measurement values used. If no measurement sites/data are found in the first ring, the process continues, taking the next ring of postcode district regions, working outwards until one or more sensors are found in a ring. As well as the measurement estimations, the number of rings required to find site data and make the estimations is also published. <strong>As a result, please note that estimations with higher ring counts ('rings') are likely to be calculated from more distant sensors. This distance depends upon the size of the postcode regions surrounding the location being estimated. Please use the ring count ('rings') to limit/filter estimations based on your required level of confidence.</strong></p> <p>The meteorological, pollen and air quality measurement data used to make the regional estimations can be found at <a href="https://zenodo.org/record/4740965#.YPWJf3VKhhF">this Zenodo archive</a>. The data there contains Temperature, Relative Humidity, and Pressure data, downloaded from the Met Office MIDAS archives via the MEDMI server (https://www.data-mashup.org.uk/). Also downloaded from the MEDMI server are daily pollen measurements for the UK. PM10, PM2.5, NO2, NOx (as NO2), O3, and SO2 measurements from the DEFRA AURN network, and also model forecasts of the same made using the EMEP model.</p> <p>The code used to make the estimations is available at <a href="https://zenodo.org/record/4518866">this Zenodo archive</a>.</p> <p>The data-set is presented in CSV format, as two files:</p> <ol> <li>turing_regional_estimates_aq_daily_met_pollen_pollution_original_data.csv: uses original site data (timestamp, region_id, ...[measurement name, rings]) ('rings' is the number of rings required to make the estimation)</li> <li>turing_regional_estimates_aq_loc_type_daily_original_data.csv: uses original data. Air quality regional estimates are calculated using specific AQ site location types* separately. (To prevent, for example, 'Traffic Urban' type sites being used to estimate 'non-traffic' or rural regions.)</li> </ol> <p>* Air quality site types: </p> <ul> <li>Industrial: comprises 'urban industrial' (9 sites) and suburban industrial (2 sites)</li> <li>'Rural background' (14 sites)</li> <li>'Urban background' (48 sites)</li> <li>'Urban traffic' (47 sites)</li> </ul>
Sustaining Cities, Naturally Webinar: Education Session - Breathe Respirar Programme - CONEXUS schools
<p>Poorly planned urbanisation can lead to societal challenges as social deprivation, climate change, deteriorating health and increasing pressure on urban nature. Urban ecosystem restoration can contribute to lessen these challenges, e.g. through implementing nature-based solutions (NBS). </p> <p>This pitch was made as part of the online webinar ‘Sustaining cities, Naturally: Urban ecosystem restoration in Europe, China and Latin America’, which took place as an official side-event of the European Week of Regions and Cities 2022 on 13th and 14th October 2023. The webinar was jointly organised by the projects: INTERLACE, CONEXUS, Regreen and CLEARING HOUSE. </p> <p>The webinar illustrated how Horizon 2020 projects support international cooperation in knowledge creation and knowledge exchange between local authorities and researchers to promote urban ecosystem restoration in Europe, China and Latin America and brought together cities, regions and local authorities, city network representatives, policy makers, researchers, civil society and experts on nature-based solutions and urban ecosystem restoration from Europe, China and Latin America. </p>
Data from: Oxygen limited thermal tolerance is seen in a plastron breathing insect, and can be induced in a bimodal gas exchanger
<p>Dataset on respiration and ctmax in two freshwater bugs, associated with the paper:<br> <strong>Verberk WCEP & Bilton DT (2015) </strong>Oxygen limited thermal tolerance is seen in a plastron breathing insect, and can be induced in a bimodal gas exchanger. <em>Journal of Experimental Biology </em>218: 2083-2088. doi: 10.1242/jeb.119560</p>
Free Breathing Lung MRI Dataset at 3T
<p> This is a free breathing lung MRI dataset. Data is acquired with 3D ultra-short te (UTE) radial sequence.</p>
Free-Breathing Self-Gated 4D-Lung MRI using wave-CAIPI
<p>MRI raw data set of a volunteer examination for the project "Free-Breathing Self-Gated 4D-Lung MRI using wave-CAIPI". The upload includes the measured k-space for 8 different breathing phases, the respective image reconstructions and the coil sensitivity maps required for the Conjugate Gradient SENSE reconstruction.</p> <p>C++ source code for image reconstruction can be downloaded at <a href="https://github.com/expRad/4d_lung">https://github.com/expRad/4d_lung</a>.</p> <p> </p>
BreathBase: Intra-Speech Breathing Dataset
<p>BreathBase contains 5070 breath instances detected on the recordings of 20 participants reading pre-prepared random pseudo texts in 5 different postures with 4 different microphones, simultaneously.</p> <p>It is recorded in a studio with a maximum background noise of 40 dB SPL and with professional recording equipment. It also provides tagging for 5 different postures and 4 different channels as different recording conditions for data variety.</p> <p>More than 90% of the recordings is shorter than 600 milliseconds. The minimum number of breath instances per participant is 89, the maximum number of instances is 710 and the average for all participants is 253.5 breath instances.</p>
Cine and real-time free-breathing CMR at rest and under exercise stress of healthy volunteers
<p>The dataset consists of cine and real-time images from 15 healthy volunteers (7 males; 8 females). All images were acquired in supine position using a 32-channel cardiac surface receiver coil at 3 T (Skyra, Siemens Healthineers, Germany).</p> <p>Conventional imaging at rest included a balanced steady-state free precession (bSSFP) ECG-gated cine sequence to create a short-axis stack covering the entire heart including both ventricles and atria. Real-time CMR data acquisition was performed during free-breathing and without ECG-synchronization at rest and under two different levels of exercise stress.</p> <p>The dataset includes automatically created contours (comDL) using Medis (version 4.0.56.4, QMass® 8.1, Medical Imaging Systems, Leiden, Netherlands) for all images, as well as manually corrected (mc) contours based on the comDL contours for all cine and real-time measurements at rest and under exercise stress for end-diastolic (ED) and end-systolic phases (ES).</p> <p>The dataset also includes segmentation masks in NIfTI format for cine and real-time CMR at rest and under exercise stress created with nnU-Net (DOI:10.1038/s41592-020-01008-z) with freely available weights based trained on the dataset of the cardiac segmentation challenge "Automated Cardiac Segmentation Challenge" (ACDC) (DOI:10.1109/TMI.2018.2837502).</p> <p>To minimize the influence of respiratory motion on clinical measures, images in the ED and ES phase of the cardiac cycle during end-expiration were manually selected for each slice. The dataset includes indices for these images for real-time CMR measurements at rest and under exercise stress. For intra-observer variability, manually corrected contours for the derivation of the clinical measures were created three to six months after the initial segmentation. For inter-observer variability, manually corrected contours for the derivation of the clinical parameters were created for the first five volunteers by a second reader with experience in cardiac segmentation. Single images in the ED and ES phase during end-expiration were once again chosen from each slice.</p> <p>Image data is provided in a file format used by the BART toolbox. <br>DOI:10.5281/zenodo.7110562</p>
Cardiopulmonary excercise breath-by-breath data during locomotion at variable speed in 28 healthy young subject
<p>Ergospyrometric evaluations are useful in physio-mechanics of locomotion.</p> <p>This dataset includes the raw ergospyrometric data of 28 young subjects during locomotion at variable speed, walk and run on a treadmill at different speeds and gaits.</p> <p>Characteristics of the experimental group:</p> <p>- gender: 28 males<br> - age: 32. 53 (10.99SD)<br> - height: 175.0 cm (0.008 SD)<br> - weight: 72.96 kg (9.51 SD)</p> <p>Equipments:<br> - Cosmed K5 wearable metabolic analyzer<br> - Software Cosmed Omnia v.1.6.5</p> <p>Experimental design:<br> -The Walking Run Transtition Speed(W-R Ts) were experimentally determined. Each subject was asked to perform 3 trials on a treadmill (GE T2100, General Electric, USA), with a staircase protocol of increasing speeds. The ramp was designed to start at a comfortable walking gait (3.0 km h-1), and to increase the speed by 0.5 km.h-1 each 15 s. When the subject started to run, the ramp was stopped and the speed marked down on a worksheet. The average or the modal transition speed was taken as the Ts of the subject. All the treadmill trials were performed in the Biomechanics Research and Movement Analyses Laboratory (LIBiAM) of the Universidad de la República in Paysandú (Uruguay), at a controlled temperature of 25ºC.</p> <p>The theoretical transition speed tTs was computed according to the Froude number equation (Alexander. 1976): v = (nFr g LL)0.5, where v is the theoretical speed, g is gravity, LL is the leg length and nFr the Froude number, which was set to the constant value of 0.5, corresponding to the W-R transition (Alexander & Jayes, 1983; Alexander, 2003; Bona et al., 2019).</p> <p>Experimental speed ramp:</p> <p>-A personalized ascending and descending speed ramp was designed, centered on the transition speed and ranged from (Ts= Transition speed) Ts-20% to Ts+20%, each step lasting 5 s. Each ramp cycle lasted 50 s, and was repeated 5 times, for a total trial time of 250 s. The trial was repeated twice.</p> <p>Note: not all the subject performed the entire protocol. In particular some data are lacking in track.</p> <p>Cost of Transport Analysis:</p> <p>- The resting O2 (RO2) consumption was computed as the averaged VO2 (ml/min/kg) of the 5' in orthostasis. - The trial O2 (TO2) consumption was computed as the averaged VO2 (ml/min/kg) of the last 2' of each speed trial - The exercise O2 (EO2) consumption was computed as TO2 - RO2 - The trial respiratory quotient (RQ) was computed as the averaged RQ (VCO2/VO2) of the last 2' of each speed trial - The RQ based Energetic Equivalent (EE) to transform mlO2 in Joules was derived from Di Prampero (2015). - The metabolic power (W/kg) was computed as (EO2 * EE) / 60 (remember that W = J/s) - The Cost of transport (J/kg/m) was computed by dividing the metabolic power for the speed (m/s) (Saibene and Minetti, 2003).</p> <p> All the participants signed an informed consent. The protocol was approved by the Ethical Committee of the University (#311170-000921-19).<br> </p> <p>Dataset legend.</p> <p><br> Filename:<br> - Subject ID (S1, S2...)<br> - Contents (Orthostasis, Walk, Run, Skip and speed)<br> - Mode (CPET Breath by breath)<br> - Date and time<br> <br> Columns A to I<br> - General info (sensitive data were deleted)<br> - Speed and gait of the trial<br> - When resting in orthostasis was included, it was marked in green<br> Columns of interest (for the other columns please refer to the Cosmed K5 / Cosmed Omnia manuals)<br> <br> J = Time in sec.<br> O = VO2 oxygen consumption in ml/min<br> P = CO2 carbon dioxide production in ml/min<br> Q = RQ respiratory quotient (VCO2/VO2)<br> V = VO2/kg oxygen consumption per kg (ml/min/kg)<br> AI = Marker: Begin and End of each trial and of the resting in orthostasis have been marked</p> <p>Marked row are in yellow or green</p> <p>AJ-AN = Environmental data<br> BA-BF = GPS data</p> <p><br> </p>
Exhaled breath condensate samples (mzML files) analysed by uHPLC-ESI-OrbitrapMS
<p>LC-MS raw data files<strong> </strong>were converted to mzML using MSConvert (Version: 3.0.20279, ProteoWizard)</p> <p>- QC_pos_xxx: QC samples analysed in positive polarity (11 files)<br> - ACOS_XXX_pos_01 to 03: Asthmatic patients (3 replicates) randomly injected (15 files)<br> - Ctrl_XXX_pos_01 to 03: Control (15 files)<br> - DPOC_XXX_pos_01 to 03: COPD (15 files)</p> <p> </p> <p><strong>EBC Samples and Clinical Assessment</strong></p> <p>EBC samples were collected from 15 individuals randomly selected (five controls, five with asthma medical diagnosis, and five with COPD medical diagnosis, as assessed by the OLDER Study – Obstructive Lung Diseases in Elders). The Ethics Committee of Nova Medical School approved this study.</p> <p>Asthma was assigned when the patient reported respiratory symptoms, was a nonsmoker, and presented a positive reversibility test (FEV1 > 12% and 200 mL). COPD disease was attributed to those who also reported being current smokers, had a post-bronchodilator FEV1/FVC < 0.70, and had a negative reversibility test.</p> <p><strong>LC–MS Analysis</strong></p> <p>Samples were analyzed in triplicate by LC–MS using an Orbitrap Q Exactive Focus (Thermo Scientific) coupled to an Ultimate 3000 UHPLC (Thermo Scientific). A pooled quality control (QC) sample was used to compensate for any possible time-dependent batch effects. The QC samples were created using a small aliquot from each sample. The QC samples were reinjected at regular intervals to bracket the samples. The separation was performed using a Waters XBridge column C18 (2.1 × 150 mm, 3.5 μm particle size, P/N 186003023). The mobile phase A was water with 0.1% formic acid (v/v), and mobile phase B was acetonitrile with 0.1% formic acid (v/v) (Optima LC–MS Grade, Fisher Scientific). The gradient program was as follows: 1 min at 1% B; 1–13 min from 1 to 99% B, 13–15 min at 99% B, 15–16 min from 99 to 1% B, and 4 min at 1% B. The column temperature was maintained at 30 °C, and a flow rate of 400 μL/min was used.</p> <p>The Q Exactive Focus MS method consisted of several cycles of full MS scan (<em>R</em> = 70000) followed by three ddMS2 scans (<em>R</em> = 17500), with a (N)CE of 30 and in positive mode. External calibration was performed using LTQ ESI Positive Ion Calibration Solution (Thermo Scientific) and the lock mass enabled internal calibration. Data were obtained using the Xcalibur software v.4.0.27.19 (Thermo Scientific). The raw MS files, as recorded by the instrument, are available from the corresponding author upon reasonable request.</p>
Text formatted plethysmography recordings for Breathe Easy vignette
<p>This dataset includes text file exports of 16 plethysmography recordings that were originally recorded in LabChart in .adicht format. These text exports are the input for the first part of our Breathe Easy software. Breathe Easy is our automated waveform analysis pipeline. For more information on the pipeline, see our publication. For more information on using this dataset in Breathe Easy, refer back to our user manual.</p>
DataPad selection exports for Breathe Easy vignette
<p>These text files should be used for a manual run of our software, Breathe Easy. These text files are all manual selections added to the Datapad in LabChart for each of the files in the example dataset. If you are attempting to run a practice manual run of Breathe Easy, then these text files are required.</p>
FIGURE 7 in A New Species of Air-breathing Catfish (Clariidae: Clarias) from Salonga National Park, Democratic Republic of the Congo
FIGURE 7. Species of the Clarioides subgenus from the Congo River basin: A, AMNH 250869 Clarias angolensis; B, AMNH 227571 Clarias buthupogon, marbled variant; C, AMNH 274802 Clarias buthupogon, dark variant; and D, AMNH 268382 Clarias gabonensis. Scale bars = 1 cm.
FIGURE 8 in A New Species of Air-breathing Catfish (Clariidae: Clarias) from Salonga National Park, Democratic Republic of the Congo
FIGURE 8. Maxillary barbel length plotted against standard length for Clarias monsembulai (open circles), Clarias angolensis (shaded circles), and two type specimens of Clarias angolensis macronema (diamonds).
FIGURE 6 in A New Species of Air-breathing Catfish (Clariidae: Clarias) from Salonga National Park, Democratic Republic of the Congo
FIGURE 6. Collection localities of Clarias monsembulai within the Congo River Basin (upper inset). Coded regions of the map (lower inset) correspond to the freshwater ecoregions of the world obtained from www. feow.org.
ScienceDex guides
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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.