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19 results for “floating wind”
Hydroelastic response of the scaled model of a floating offshore wind turbine platform in waves: HELOFOW Project Database
<p>This dataset contains the data measured during the <strong>HELOFOW </strong>model test campaign, performed at the Ocean and Hydrodynamic Engineering wave tank of Ecole Centrale Nantes (ECN): decay tests, regular wave tests and irregular waves tests. The preprocessed measured data is contained in MAT files.</p> <p>The model, the measurements and the tests are described in the appended Excel files. A Matlab(R) function is given as a short example to show how the MAT files are structured and how data may be handled for a plot. </p> <p>As stated in the reference paper (Leroy et al., <em>Ocean Engineering</em>, 2022):</p> <p>"As the size of floating wind turbines continues to increase, floating platforms reach dimensions that make their elastic and hydro-elastic behaviour significant. Several works in connection with the numerical modelling of the elastic behaviour of these wind turbines have been carried out but few validation data are available. This study focuses on the hydro-elastic response of a large floating wind turbine, in regular waves and severe sea-states. A new experimental wind turbine model has been designed to represent a 1:40 Froude-scaled spar platform carrying the DTU 10 MW turbine. The main challenge is here to reproduce a 1st bending mode frequency and hydrodynamic loads representative of a realistic large floating wind turbine. The platform model is made of a flexible backbone, reproducing the correct flexibility, and light floaters fixed on it provide the correctly scaled geometry. This experimental model is tested in various conditions including regular waves of several periods and steepness, and irregular waves of various intensity, including extreme 50-year return period conditions."</p> <p> </p> <p>This work was carried out within the framework of the WEAMEC, West Atlantic Marine Energy Community, and with funding from the Pays de la Loire Region and Europe (European Regional Development Fund). <br><br>HELOFOW project on <a href="https://www.weamec.fr/en/projects/helofow/">the WEAMEC website</a>. </p>
Reference site conditions for floating wind arrays: dataset of reference sites
<h1>IEA Task 49: Integrated Design of Floating Wind Arrays</h1> <h3>This dataset contains atmospheric and oceanographic data of 11 locations around the globe of future floating offshore wind farms.<br>Each dataset was used to perform a metocean analysis for preliminary design, published in the Work Package 1 Report of IEA Task 49.</h3> <div> <div> <div><a name="_msocom_1"></a></div> </div> </div> <table> <tbody> <tr> <td><strong>4COffshore ID</strong></td> <td><strong>Name</strong></td> <td><strong>Latitude [deg]</strong></td> <td><strong>Longitude [deg]</strong></td> <td><strong>Water Depth [m] (GEBCO)</strong></td> <td><strong>Distance from shore [m]</strong></td> <td><strong>Country</strong></td> <td><strong>Dataset curated by</strong></td> </tr> <tr> <td>IT95 </td> <td>Hannibal </td> <td> <div>37.842</div> </td> <td> <div>12.0722</div> </td> <td> <div> -353</div> </td> <td> <div> 35</div> </td> <td>Italy </td> <td>RSE</td> </tr> <tr> <td>US0W </td> <td> Humboldt</td> <td> <div> 40.928</div> </td> <td> <div>-124.708</div> </td> <td> <div> -707</div> </td> <td> <div> 43.8</div> </td> <td>USA</td> <td>NREL</td> </tr> <tr> <td>KR0R </td> <td>Ulsan </td> <td> <div> 35.449</div> </td> <td> <div> 129.949</div> </td> <td> <div> -188</div> </td> <td> <div> 32</div> </td> <td>South Korea </td> <td>UOU</td> </tr> <tr> <td>IE34 </td> <td>Moneypoint One</td> <td> <div> 52.519</div> </td> <td> <div>-10.276</div> </td> <td> <div> -1<a>02</a> </div> </td> <td> <div> 23.4</div> </td> <td>Ireland </td> <td>GDG</td> </tr> <tr> <td>UK6L </td> <td>Havbredey </td> <td> <div>58.862</div> </td> <td> <div> <div> <p>-5.54</p> </div> </div> </td> <td> <div> <div> <p> -91</p> </div> </div> </td> <td> <div> <div> <p> 41.6</p> </div> </div> </td> <td>Scotland </td> <td>DHI</td> </tr> <tr> <td>JP06 </td> <td>Fukushima</td> <td> <div>37.311</div> </td> <td> <div>141.251</div> </td> <td> <div> -90</div> </td> <td> <div> 19.4</div> </td> <td>Japan </td> <td>AIT</td> </tr> <tr> <td>NO44 </td> <td>Utsira nord<strong>*</strong></td> <td> <div>59.276</div> </td> <td> <div>4.541</div> </td> <td> <div> -273</div> </td> <td> <div> 42.4</div> </td> <td>Norway </td> <td>4subsea / UiS,UiB*</td> </tr> <tr> <td>USZ3 </td> <td>Gulf of Maine</td> <td> <div>43.25</div> </td> <td> <div>-69.5</div> </td> <td> <div> -148</div> </td> <td> <div> 138</div> </td> <td>USA </td> <td>NREL</td> </tr> <tr> <td>KR88 </td> <td>Geomundo<strong>**</strong></td> <td> <div>34.039</div> </td> <td> <div>126.901</div> </td> <td> <div> -70</div> </td> <td> <div> 47</div> </td> <td>South Korea </td> <td>IAE**</td> </tr> <tr> <td>FR87 </td> <td>Sud de la Bretagne II</td> <td> <div>47.325</div> </td> <td> <div>-3.659</div> </td> <td> <div> -94</div> </td> <td> <div> 30.7</div> </td> <td>France </td> <td>UiB</td> </tr> <tr> <td>NO66</td> <td>Sørlige Nordsjø II<strong>***</strong></td> <td> <div> 56.78 </div> </td> <td> <div> 4.92 </div> </td> <td> <div> -60</div> </td> <td> <div> 180</div> </td> <td>Norway</td> <td>4subsea / UiS,UiB***</td> </tr> </tbody> </table> <p>* Suplementary dataset published at: https://doi.org/10.5281/zenodo.10048048</p> <p>** Dataset is confidential, for details of usage reach out to the contact person.</p> <p>*** Suplementary dataset published at: https://doi.org/10.5281/zenodo.7057407</p>
Datasets of the work named Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform
<pre>- 1_Motion_Simulator/ - IMU_results/ - 20211028101756.csv - 20220114101543.csv - 20220117000000.csv - Rotary_Table/ - 15/ - rover_20220105.nav - rover_20220105.obs - solution_20220105_CAS.log - 360/ - rover_20211221.nav - rover_20211221.obs - solution_20211221_CAS.log - 360-15/ - rover_20220202_CAS.nav - rover_20220202_CAS.obs - solution_20220202_CAS.log - Static_Tests/ - solution_SSRA00CAS0 - solution_SSRA00WHU0 - 2_GNSS_Signal_Simulator/ - platformmov_C1.xtd - platformmov_C2.xtd - platformmov_C3.xtd - TestBetaNoneMov_C1 - TestBetaNoneMov_C1.nav - TestBetaNoneMov_C1.obs - TestBetaNoneMov_C1.ubx - TestBetaNoneMov_C2 - TestBetaNoneMov_C2.nav - TestBetaNoneMov_C2.obs - TestBetaNoneMov_C2.ubx - TestBetaNoneMov_C3 - TestBetaNoneMov_C3.nav - TestBetaNoneMov_C3.obs - TestBetaNoneMov_C3.ubx - 3_Test_Sea/ - 20220503000000.xlsx - solution_28.nav - solution_28.obs - solution_28.ubx Background: {Journal Article using this dataset} 'Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform' Paper DOI: <a href="https://doi.org/10.3390/s23020925">https://doi.org/10.3390/s23020925</a> Abstract: a low-cost smart sensor GNSS system has been developed to provide accurate real-time position and orientation measurements on a floating offshore wind platform. The approach chosen to offer a viable and reliable solution for this application is based on the use of the well-known advantages of the GNSS system as the main driver for enhancing the accuracy of positioning. For this purpose, the data reported in this work are captured through a GNSS receiver operating over multiple frequency bands (L1, L2, L5) and combining signals from different constellations of navigation satellites (GPS, Galileo, and GLONASS), and they are processed through the precise point positioning (PPP) and real-time kinematic (RTK) techniques. Furthermore, aiming to improve global positioning, the processing unit fuses the results obtained with the data acquired through an inertial measurement unit (IMU), reaching final accuracy of a few centimeters. To validate the system designed and developed in this proposal, three different sets of tests were carried out in a (i) rotary table at the laboratory, (ii) GNSS simulator, and (iii) real conditions in an oceanic buoy at sea. The real-time positioning solution was compared to solutions obtained by post-processing techniques in these three scenarios and similar results were satisfactorily achieved. </pre>
Wind spacecraft floating potential measurements
<p><strong>Quick Summary:</strong></p> <p>The ASCII files herein are a dataset of spacecraft electric potential for the <em>Wind</em> spacecraft between January 1, 2005 and January 1, 2022. The data is thoroughly described in the publication "Spacecraft floating potential measurements for the <em>Wind</em> spacecraft," <em>The Astrophysical Journal Supplement Series</em>.</p> <p><strong><em>Wind</em> Spacecraft:</strong></p> <p>The <em>Wind</em> spacecraft (<a href="https://wind.nasa.gov">https://wind.nasa.gov </a>and <a href="https://doi.org/10.1029/2020RG000714">https://doi.org/10.1029/2020RG000714</a>) was launched on November 1, 1994 and currently is in a halo orbit about the first Sun-Earth Lagrange point. It holds a suite of instruments from gamma ray detectors to quasi-static magnetic field instruments, Bo. The instruments used in this study and these datasets are the fluxgate magnetometer (MFI), the radio receivers (WAVES), ion Faraday cups (SWE), and the electron and ion electrostatic analyzers (3DP). The MFI measures 3-vector <strong>B</strong><sub>o</sub> at ~11 samples per second (sps); the SWE measures reduced velocity distribution functions (VDFs) of the thermal proton and alpha-particle populations from which velocity moments are derived and used herein; WAVES observes electromagnetic radiation from ~4 kHz to >12 MHz which provides an observation of the upper hybrid line (also called the plasma line) used to define the total electron density; and 3DP observes full 4π steradian VDFs of electrons and ions from a few eV to ~30 keV which provide both ion velocity moments and the electron VDFs modeled herein.</p> <p><strong>Brief Method Description:</strong></p> <p>The spacecraft potential, <span class="math-tex">\(\phi_{sc}\)</span>, was found using four methods. Three of these methods return a range of values while the fourth returns a single value. The methods rely on examining the shape of the electron energy distribution function (EDF), f(E) versus energy, E, for three different pitch-angles (parallel, perpendicular, and anti-parallel with respect to the quasi-static magnetic field, <strong>B</strong><sub>o</sub>). The instrument has a physical lower energy threshold, E<sub>min</sub>, below which no data are measured. We impose an upper energy threshold, E<sub>max</sub>, allowed when searching for <span class="math-tex">\(\phi_{sc}\)</span> based on empirical evidence. The methods are as follows:</p> <p>Method 1: find the range of energies where d<sup>2</sup>f/dE<sup>2</sup> > 0, also referred to as the positive curvature region;<br> Method 2: find the range of energies where df/dE transitions from negative to positive, i.e., the local minimum point of f(E);<br> Method 3: find the range of energies bounding the minimum and maximum values of d<sup>2</sup>f/dE<sup>2</sup>, i.e., region of minimum to maximum curvature; and<br> Method 4: find the local minimum between E<sub>min</sub> and E<sub>max</sub></p> <p>There are some additional constraints imposed in the software, available at <a href="https://github.com/lynnbwilsoniii/wind_3dp_pros">https://github.com/lynnbwilsoniii/wind_3dp_pros</a> (<a href="https://doi.org/10.5281/zenodo.6141586">https://doi.org/10.5281/zenodo.6141586</a>). We found four basic shapes for the EDFs (see paper for example figures), two (i.e., Types A and B) of which satisfy E<sub>min</sub> < <span class="math-tex">\(\phi_{sc}\)</span> and thus are good. The other two shapes (i.e., Types C1 and C2) satisfy E<sub>min</sub> > <span class="math-tex">\(\phi_{sc}\)</span>, and thus we cannot determine <span class="math-tex">\(\phi_{sc}\)</span> from the EDF. We can only know that it has an upper bound of E<sub>min</sub>. All Type A EDFs are given a quality flag (QF) of 4 (i.e., the best), all Type Bs are given a QF of 2 (i.e., still okay and useable), and all Type Cs are given a QF of 0 (i.e., do not use these).</p> <p><strong>ASCII File Description:</strong></p> <p>Each ASCII file contains one year of data. There is summary information contained in the header of each file. The first two columns are the start and end times (UTC) of the EDF (format 'YYYY-MM-DD/hh:mm:ss.xxx'). After the times, Methods 1-3 have six columns and Method 4 has three columns. The first(second) three columns for Methods 1-3 correspond to the lower(upper) bound on the range of <span class="math-tex">\(\phi_{sc}\)</span> [eV] solutions. Method 4 only has one set of three-column solutions. Each three-column set corresponds to the parallel, perpendicular, and anti-parallel pitch-angle solutions. All of these in total comprise 21 columns. The <span class="math-tex">\(\phi_{sc}\)</span> solutions are followed by a column for E<sub>min</sub> [eV] and E<sub>max</sub> [eV]. The last two columns are the EDF label or type (i.e., A, B, C1, or C2) and the quality flag (i.e., 4, 2, or 0).</p> <p>Note that NaNs have been replaced with -10<sup>30</sup> fill values</p>
SparBOFWEC Spar Buoy for Offshore Floating Wind Energy Conversion - Data Storage Report
<p>The present work describes the experiences gained from the design methodology and operation of a 3D physical model experiment aimed to investigate the dynamic behaviour of a spar buoy (SB) off-shore floating wind turbine (WT) under different wind and wave conditions. The physical model tests have been performed at Danish Hydraulic Institute (DHI) off-shore wave basin within the European Union-Hydralab+ Initiative, in April 2019. The floating WT model has been subjected to a combination of regular and irregular wave attacks and wind loads.</p>
Bearings damage dataset for the 5 MW reference drivetrain on spar type floating wind turbine
<p>This dataset contains simulated acceleration measurements for the 5MW reference drivetrain model installed on a spar-type floating wind turbine. Measurements are one-hour simulations with a sample rate of 200 Hz. See Data description file for details.</p> <p>How to cite: Dibaj, Ali, & Nejad, Amir. (2023). Bearings damage dataset for the 5 MW reference drivetrain on spar type floating wind turbine [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7674842</p>
Aerodynamics of a Floating Wind Turbine Scale Model with Active Control
<p>This dataset is about the aerodynamic response of a wind turbine scale model subjected to prescribed platform pitch motion, as it would occur during normal operation of floating wind turbines. The wind turbine has active control functionalities representative of those of utility-scale turbines. The turbine controller is the Reference Open Source Controller (ROSCO), which has been implemented in MATLAB Simulink for wind tunnel testing and co-simulation with OpenFAST. The dataset contains an OpenFAST model of the scaled turbine and its controller. </p>
Experimental Database of deterministic wave prediction built from synchronous measurements from an X-band pulse radar and met-ocean sensors deployed on the Floatgen floating wind turbine and its vicinity on SEM-REV test site.
<p>This dataset is a deliverable of the FLOATECH project, funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101007142.<br> The aim of this dataset is a result of the field experiments carried out at the Floatgen FOWT located at the SEM-REV test site.</p>
Vertical wake deflection for floating wind turbines by differential ballast control
<pre>This paper presents a feasibility analysis of vertical wake steering for floating turbines by differential ballast control. This new concept is based on the idea of pitching the floater with respect to the watersurface, thereby achieving a desired tilt of the turbine rotor disk. The pitch attitude is controlled by moving water ballast among the columns of the floater. This study considers the application of differential ballast control to a conceptual 10~MW wind turbine installed on two platforms, differing in size, weight and geometry. The analysis considers: a) the aerodynamic effects caused by rotor tilt on the power capture of the wake-steering turbine and at various downstream distances in its wake; b) the effects of tilting on fatigue and ultimate loads, limitedly to one of the two turbine-platform layouts; and c) for both configurations, the necessary amount of water movement, the time to achieve a desired attitude and the associated energy expenditure. Results indicate that (in accordance with previous research) steering the wake towards the sea surface leads to larger power gains than steering it towards the sky. Limitedly to the structural analysis conducted on one of the turbine-platform configurations, it appears that these gains can be obtained with only minor effects on loads, assuming a cautious application of vertical steering only in benign ambient conditions. Additionally, it is found that rotor tilt can be achieved in the order of minutes for the lighter of the two configurations, with reasonable water ballast movements.Although the analysis is preliminary and limited to the specific cases considered here, results seem to suggest that the concept is not unrealistic, and should be further investigated as a possible means to achieve variable tilt control for vertical wake steering in floating turbines.</pre>
Multidirectional load multipliers: application for shared anchors for floating wind turbines
<div> <h1>Multidirectional load multipliers</h1> </div> <p>The load multipliers presented in this dataset are based on the multiline anchor loads calculated for a spar floating wind turbine presented by:</p> <p>Balakrishnan, K., Arwade, S.R., DeGroot, D.J., Fontana, C., Landon, M., Aubeny, C.P., 2020. <em>Comparison of multiline anchors for offshore wind turbines with spar and with semisubmersible</em>. Journal of Physics: Conference Series, <strong>1452</strong>, 012032. <a href="https://doi.org/10.1088/1742-6596/1452/1/012032" rel="nofollow">doi:10.1088/1742-6596/1452/1/012032</a>.</p> <div> <h2>Files</h2> </div> <p>Load multipliers for three load cases grouped in Wind Wave and Current directions. Those are the loads arriving at a multiline anchor.</p> <div> <h3>DLC 0WWC: Wind Wave and Current at 0 degrees</h3> </div> <ul> <li>DLC_0deg_push.txt</li> <li>DLC_0deg_push2.txt</li> <li>DLC_0deg_push3.txt</li> <li>DLC_0deg_step.txt</li> <li>DLC_0deg_step2.txt</li> <li>DLC_0deg_step3.txt</li> </ul> <div> <h3>DLC 30WWC: Wind Wave and Current at 30 degrees</h3> </div> <ul> <li>DLC_30deg_push.txt</li> <li>DLC_30deg_push2.txt</li> <li>DLC_30deg_push3.txt</li> <li>DLC_30deg_step.txt</li> <li>DLC_30deg_step2.txt</li> <li>DLC_30deg_step3.txt</li> </ul> <div> <h3>DLC 60WWC: Wind Wave and Current at 60 degrees</h3> </div> <ul> <li>DLC_60deg_push.txt</li> <li>DLC_60deg_push2.txt</li> <li>DLC_60deg_push3.txt</li> <li>DLC_60deg_step.txt</li> <li>DLC_60deg_step2.txt</li> <li>DLC_60deg_step3.txt</li> </ul>
Dataset for the paper "Parameter study and optimization of floating wind-wave co-generation system based on the Taguchi method"
<p>The Dataset is the result files for the paper "Parameter study and optimization of floating wind-wave co-generation system based on the Taguchi method"</p>
NETTUNO Experiment 1 – Wake Development in Floating Wind Turbines
<p>This dataset, collected as part of the NETTUNO research project, includes measurements from wind tunnel tests on a 1:75 scale model wind turbine. The primary focus of the experiment was to analyze how platform motion in different directions affects the aerodynamics of the wind turbine rotor and the development of its wake. The dataset consists of two components:<br>• Measurements of the aerodynamic forces and moments experienced by the rotor under various platform motion conditions.<br>• Wind speed measurements collected at multiple downstream distances from the rotor, capturing the velocity profiles and turbulence characteristics within the turbine's wake.<br>The dataset is designed to serve as a comprehensive benchmark for researchers and engineers working on floating wind turbine aerodynamics, offering valuable insights for optimizing wind farm layouts and for developing simulation tools.</p>
Dataset for An efficient multivariate deep learning model for monitoring mooring line tension of floating wind turbine
<p>Reference data needed for mooring line tensions prediction of a 15 MW wind turbine.<br> <br> This dataset contains OpenFAST outputfiles for different design load cases used in the paper.</p> <p>These data files are designed to be used together with the python code, which is available publicly on https://github.com/ramisetti/ 3SDLMooringPrediction</p>
Data from: The design of an intelligent fault-tolerant control for floating offshore wind turbine with blade faults
Open the record for dataset details and reuse information.
Lorentzian filter correction of turbulence measurements on oscillating floating platforms: impact on wind spectra and eddy covariance fluxes
<p class="MsoTitle">Turbulence and eddy covariance measurements on a floating platform over water surfaces can be contaminated by platform oscillations, which may affect the calculated air–water exchange. The conventional method for decontamination of the platform oscillations from the wind velocity measurements requires the installation of an additional sensitive, and often costly, motion sensor. This paper examines a new mathematical decontamination method, termed Lorentzian filter, which avoids the need for such an instrument. The method, based on the Lorentzian function, capitalizes on the pseudo-harmonic behavior of the platform oscillations and reduces the amplitude of turbulent wind velocity data detected as artifacts at the specific natural frequencies of the platform. The Lorentzian filter was applied to wind velocity data measured by sonic anemometer and eddy covariance system over the Dead Sea, Israel, for 30 days. We examined three approaches of dealing with motion contamination: Lorentzian filter decontamination, motion sensor decontamination, and non-filtered raw wind velocity. Using the 3D wind velocity series, we examined the wind spectra, the co-spectra of water vapor concentration and horizontal wind speed with vertical wind speed and H2O and momentum fluxes. The Lorentzian filter performed very well in decontaminating the wind spectrum, meaning that it efficiently identified the contamination in the natural oscillation frequency and returned a decontaminated wind velocity time series. The co-spectra and fluxes were less prone to the contamination of platform oscillations, presumably due to low correlations between the spurious wind velocity components and other measured scalars, such as water vapor.</p>
Site-specific Design Load Cases for floating offshore wind turbine applications I : Historical data
<p>This document includes a brief description of the <a href="https://leopard.tu-braunschweig.de/receive/dbbs_mods_00077703" target="_blank" rel="noopener">first database</a> on the site-specific Design Load Cases (DLCs) based on historical metocean data. The dataset includes metocean data, statistical analysis and site-specific DLCs across the three areas of study defined in the INF4INiTY project: Scottish Sea, Baltic Sea and Adriatic Sea. In addition to the dataset, this deliverable includes a Graphical User Interface (GUI) for the analysis of specific locations within these three areas and the generation of the site-specific DLCs.<br>The aim of this initial version of the database is to provide a first characterisation of the areas of interest in order to use the DLCs on the design of the different innovations planned in various work packages (WPs) INF4INiTY. As the project proceeds, the second database will extend the site-specific DLCs including forecasted data for different horizons and under diverse climate change scenarios.<br>The deliverable is divided into six brief sections describing the (i) the GUI, (ii) characteristics of the data, (iii) the three areas of study and technological requirements, (iv) historical metocean data, (v) site-specific statistical analysis and reporting, and (vi) site-specific DLCs.</p>
On the characteristics of the wake of a wind turbine undergoing large motions caused by a floating structure: an insight based on experiments and multi-fidelity simulations from the OC6 Phase III Project - Supplementary material
<p>This link stores the supplemenary material for the paper: "On the characteristics of the wake of a wind turbine undergoing large motions caused by a floating structure: an insight based on experiments and multi-fidelity simulations from the OC6 Phase III Project" published on Wind Energy Science. The pdf file contains the plots for all the investigated metrics analysed in this work, which could not be reported in the manuscript due to space constraints. Further details about these results can be found in the original publication. </p>
Lorentzian filter correction of turbulence measurements on oscillating floating platforms: impact on wind spectra and eddy covariance fluxes
Open the record for dataset details and reuse information.
Figures and graphics used in "Optimizing Strength of Shared Anchors in an Array of Floating Offshore Wind Turbines"
<p>All figures and graphics used in tables are included in PDF format here. Instructions on how to reproduce the MATLAB-generated plots (or similar plots) are included in the README of the software package, also cited in the paper.</p>
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.