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175 results for “wind turbine”
Supplemental Material to Article "A practical approach for the peel stress prediction in the trailing-edge adhesive joint of wind turbine blades"
<p>This set supplements the figure data to the article "A practical approach for the peel stress prediction in the trailing-edge adhesive joint of wind turbine blades", DOI: .</p>
Wind Turbine SCADA Data For Early Fault Detection
<p>This dataset is published together with the <a href="https://doi.org/10.3390/data9120138">paper</a> "CARE to Compare: A real-world dataset for anomaly detection in wind turbine data" which explains the dataset in detail and defines the CARE score that can be used to evaluate anomaly detection algorithms on this dataset. When referring to this dataset, please cite the paper mentioned in the related work section. </p> <p>The data consists of 95 datasets, containing 89 years of SCADA time series distributed across 36 different wind turbines<br>from the three wind farms A, B and C. The number of features depends on the wind farm; Wind farm A has 86 features, wind farm B has 257 features and wind farm C has 957 features. </p> <p>The overall dataset is balanced, as 45 out the 95 datasets contain a labeled anomaly event that leads up to a turbine fault and the other 50 datasets represent normal behavior. Additionally, the quality of training data is ensured by turbine-status-based labels for each data point and further information about some of the given turbine faults are included.</p> <p>The data for Wind farm A is based on data from the EDP open data platform (https://www.edp.com/en/innovation/open-data/data), <br>and consists of 5 wind turbines of an onshore wind farm in Portugal. <br>It contains SCADA data and information derived by a given fault logbook which defines start timestamps for specified faults. <br>From this data 22 datasets were selected to be included in this data collection. <br>The other two wind farms are offshore wind farms located in Germany. All three datasets were anonymized due to confidentiality reasons for the wind farms B and C.<br>Each dataset is provided in form of a csv-file with columns defining the features and rows representing the data points of the time series. Files</p> <p>More detailed information can be found in the included README-file.</p> <p><strong>Notes</strong></p> <p>In wind farm A status_type_id labels can be ignored while evaluating prediction time frames of error events with metrics like the CARE-score since the status_type_id is of wind farm A is based on the EDP failure logbook and it is intended to be used for filtering of the training data.</p> <p><strong>Version Changes:</strong></p> <p><em>Version 5 -> 6:</em></p> <ul> <li>Changed unit of sensor_40 and sensor_61 for wind farm C to hPa instead of bar. This unit error became obvious when looking at the data and comparing it to the standard air pressure.</li> <li>Edited event_description of events 34, 7 and 19 to high temperature in transformer cell.</li> <li>Changed date in event description of event 44 since it was not affected by the change in the date anonymization procedure from version 2.</li> <li>Changed date in event description of event 47 since it was not affected by the change in the date anonymization procedure from version 2 and edited the description text</li> <li> Changed date format in event_info files to match the date format in the dataset files.</li> <li>Fixed typo in Readme</li> <li>Re-added Readme files</li> </ul> <p><em>Version</em> 4->5:</p> <p>Corrections to labels were made:</p> <ul> <li>Previously missing status_type_id 4 labels were added to datasets in Wind Farm A. </li> <li>Event 51 from Wind Farm A was wrongly labeled as a normal event. With the newly added status_type_id 4 occurences, it is to be considered an anomaly event due to a gearbox bearing damage within the prediction data.</li> <li>Wind Farm A no longer contains status_type_id 5. All occurences of status_type_id 5 have been changed to 0 and are considered normal time stamps. This change is done, because status_type_id 5 was set as a result of a wind speed and power analysis, flagging potential anomalous data. This is not based on a fixed ground truth, so status_type_id 5 was removed. For Wind Farms B and C status_type_id 5 is still valid since it is based on real SCADA-status codes.</li> <li>The event_info.csv files now contain an additional column 'asset_id'.</li> </ul> <p><em>Version 3->4:<br></em></p> <ul> <li>The change of the timestamp anonymization lead to duplicate timestamps when transitioning from a leap year to 2022. This is now fixed in Version 4.</li> </ul> <p><em>Version 2->3:</em></p> <ul> <li>In version 2 timestamp changes were not consistent with the timestamps in the event-info-files. Version 3 fixes this.</li> </ul> <p><em>Version 1->2:<br></em></p> <ul> <li>Version 2 contains one deviation from version 1 regarding the anonymization procedure. Instead of shifting the timestamps of each sub-dataset by a random number of years, the size of the time shift is now determined to be the number of years so that each sub-dataset starts in 2022. This change is made to make the timestamp anonymization more consistent and to avoid future timestamps being present within the data.</li> </ul>
Simulation data and surrogate model for the DTU 10MW reference wind turbine including down-regulation, power boosting and individual blade control
<p>This contribution provides the simulated data and surrogate models for the DTU 10 MW reference wind turbine in an onshore configuration simulated with FAST v8.16.00. The dimensions include mean wind speed, turbulence intensity, and power level, as well as the application of an individual blade control (IBC) loop. Down-regulation up to 50% is considered using two controller trajectories. The <em>constTSR</em> trajectory considers only pitching for down-regulation, maintaining a constant tip speed ratio, and the <em>lin70</em> trajectory considers both pitch and rotational speed reduction to achieve down-regulation. Power boosting is performed up to 130% power level by following the optimal Cp trajectory until the requested power level is reached.</p> <p>The regression is done with two methods: a spline-based interpolation and a Gaussian Process Regression (GPR). The raw data, smoothened data, and the trained GPR models are provided along with scripts for generating the surrogate model's predictions with both methods. A short description of the simulation parameters and variables considered is given in the supplementary pdf file.</p> <p>The dataset is part of the doctoral thesis 'Wind Turbine Operational Optimization Considering Revenue and Fatigue Objectives' by Vasilis Pettas at the University of Stuttgart (<a href="http://dx.doi.org/10.18419/opus-13959">http://dx.doi.org/10.18419/opus-13959</a>) and the journal publication 'Surrogate Modeling and Aeroelastic Analysis of a Wind Turbine with Down-Regulation, Power Boosting, and IBC Capabilities' <a href="https://doi.org/10.3390/en17061284">(https://doi.org/10.3390/en17061284</a>). Detailed analysis of the controller design and validation of the surrogate models can be found in these publications. </p>
Forest gaps around wind turbines attract bat species with high collision risk
<p><span>Globally, renewable energy is highly demanded, leading to a recent increase in the number of wind turbines at forested sites. For the deployment of turbines, forest areas must be cleared, which results in significant habitat changes. To assess the ecological consequences of these changes for forest-associated bats, we quantified the activity of three bat foraging guilds at turbine clearings, adjacent forest edges, and nearby forest canopies. Specifically, open-space and edge-space foraging bats were more active at turbine clearings and forest edges than at forest canopies. Narrow-space foragers were also more often recorded at turbine clearings than at forest canopies. An increased activity of open- and edge-space foragers at turbine clearings may increase the risk for casualties. </span><span>Therefore, if turbines need to be placed in forests, strict curtailment schemes should be practiced to mitigate collision risk for these bats. This may impair the efficacy of wind energy production at forested sites.</span></p>
Forest bat activity declines with increasing wind speed in the proximity of operating wind turbines
<p>The increasing use of onshore wind energy is leading to an increased deployment of wind turbines in structurally rich habitats such as forests. Forest-affiliated bats, in turn, are at risk of colliding with the rotor blades. Due to the legal protection of bats in Europe, it is imperative to restrict the operation of wind turbines to periods of low bat activity to avoid collisions. However, bats have also been observed to avoid wind turbines over several hundred meters distance, indicating a displacement that cannot solely be explained by modifications to the habitat. This avoidance suggests a displacement of bats by indirect factors related to wind turbine operation, e.g., wake turbulences and noise emissions. Therefore, we investigated whether the activity of forest-affiliated bats is influenced by operation mode (on/off) under variable wind conditions along transects from 80 to 450 m distance to wind turbines. We divided recordings by foraging guild, i.e., either narrow-space (<em>Myotis</em>, <em>Plecotus)</em>, edge-space (<em>Pipistrellus, Barbastella</em>), or open-space foraging bats (<em>Nyctalus, Eptesicus, Vespertilio</em>), and analyzed the effects of wind turbine operation and wind speed on the recorded bat guild activity with mixed effects models. The acoustic activity of narrow-space foraging bats decreased by 91% with increasing wind speed when wind turbines were operating, while bat activity remained unaffected by wind speed when turbines were not operating. This was neither observed for open-space foraging bats nor for edge-space foraging bats, and neither wind turbine operation nor wind speed (ranging between 0 – 4 m/s at 10 m height above ground) were found to affect bat activity when considered alone. Wind turbine noise emissions are known to increase with rotor speed and consequently, wind speed, thus presenting a likely explanation for the interactive negative effect of turbine operation and wind speed specifically on noise-sensitive narrow-space foraging bats. To understand potential ecological long-term consequences for bat populations in forest areas with wind turbines and to design effective conservation measures, future research should focus on disentangling the effects of different disturbances related to turbine operation.</p>
Velocity field of "Toward ultra-efficient high fidelity predictions of wind turbine wakes"
<p>This data collection contains the velocity field obtained from VFS-Wind LES simulations, FLORIS v3.4 GCH-model and the new ML model.</p>
Supplemental Material to Doctoral Thesis "Engineering approach for predicting tunneling crack initiation in trailing-edge adhesive joints of wind turbine blades under mechanical fatigue and thermal residual stresses"
<p>This set supplements the figure data to the doctoral thesis "Engineering approach for predicting tunneling crack initiation in trailing-edge adhesive joints of wind turbine blades under mechanical fatigue and thermal residual stresses", DOI: <a href="https://doi.org/10.14279/depositonce-19144">10.14279/depositonce-19144</a></p>
META-DATA for IEA Wind Task 46 report: Atmospheric drivers of wind turbine blade leading edge erosion: Hydrometeors
<p>The objectives of the work summarized in the report that accompanies this dataset are to:</p> <ul> <li>Describe crucial meteorological parameters for wind turbine blade leading edge erosion</li> <li>Describe technologies appropriate to measurement of hydroclimates and specifically hydrometeor size distributions and phase</li> <li>Identify available data sets that are available to describe hydrometeor size distributions and phase and generate meta-data for data sets available for use in mapping wind turbine blade leading edge erosion potential. This dataset summarizes those meta-data. </li> <li>Identify priority geographic areas for geospatial mapping of wind turbine blade leading edge erosion potential <p> </p> </li> </ul>
Data Supplement for 'Curled Wake Development of a Yawed Wind Turbine at Turbulent and Sheared Inflow' - Wind Energy Science Journal
<p>Data Supplement for 'Curled Wake Development of a Yawed Wind Turbine at Turbulent and Sheared Inflow' - Wind Energy Science Journal</p> <p>This database contains the measurement using a model wind turbine with 0.6m diameter(D) in a wind tunnel. A short-range Lidar WindScanner facilitated mapping the wake with a high spatial and temporal resolution in vertical, cross-stream planes at different downstream locations and in a horizontal plane at hub height.</p> <p>The measurement campaign was conducted in the large wind tunnel at ForWind-University of Oldenburg. The wind tunnel has a test section cross-section with the dimensions of 3m x3m. For this study three movable test section elements of 6m length were attached for a total enclosed length of 18m. The roof of the test section was adjusted to compensate for boundary layer growth to achieve a zero pressure gradient for the target wind speed of the experiments, nominally 7.5m/s, with an empty tunnel with no grid or turbine installed. The three-bladed MoWiTO 0.6 wind turbine model(Schottler et al.(2016)), with a hub height (h) of 0.77m and a diameter of 0.58m was placed at a distance of 2.4D downstream of the test section inlet, where the distance was measured to the centre of the rotor. In addition, the distance between the rotor center and the tower center is 110mm.<br> The flow blockage, based on rotor swept area and tower flow-facing area, was 2.7%. The wind turbine controller is based on the torque of the generator (Petrovi ́c et al. (2018)) leading to a tip speed ratio of 5.7 at the operational point during non-misaligned cases with no grid. More information can be found in the paper.</p> <p>The folder contains 12 unique .mat files each containing a matlab structure. The matlab structure conatins the vertical and horizontal scan for each inflow and operational condition:<br> With the upstream turbine installed:<br> - Yaw0_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal<br> - Yaw30_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal<br> - Yawneg30_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal</p> <p> - Yaw0_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yaw30_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yawneg30_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal </p> <p> - Yaw0_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yaw30_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yawneg30_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal </p> <p>Without the upstream turbine installed:<br> - NoTurbine_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D<br> - NoTurbine_Uniform_PassiveGrid<br> - 0D, 1D, 2D, 3D, 5D, 7D, 10D<br> - NoTurbine_BoundaryLayer_PassiveGrid<br> - 0D, 1D, 2D, 3D, 5D, 7D, 10D </p> <p>Within each substructure the following parameters are provided:<br> - v_los [m/s] ----------> Line of sight velocity<br> - sigma [m/s] ----------> Spectrum width<br> - x_Global_frame [m] ---> x-coordinate referenced at the lower grid midpoint<br> - y_Global_frame [m] ---> y-coordinate referenced at the lower grid midpoint<br> - z_Global_frame [m] ---> z-coordinate referenced at the lower grid midpoint<br> - xx [m] ---------------> Grid of the x-coordinate referenced at the lower grid midpoint<br> - yy [m] ---------------> Grid of the y-coordinate referenced at the lower grid midpoint<br> - zz [m] ---------------> Grid of the z-coordinate referenced at the lower grid midpoint<br> - uu [m/s] -------------> Horizontal wind speed at the position of the gridded coordinates, these data have been interpolated onto the grid<br> </p> <p>When using this database please reference to the journal paper.</p> <p>All data has been included without warranty, express or implied.</p> <p>For further questions, please contact the corresponding author.<br> </p>
Data generated for study of simultaneous design of wind turbines and cable layout in offshore wind
<p>This set of files contains the results of the models proposed in the manuscript: "Pérez-Rúa, J.-A. and Cutululis, N. A.: A Framework for Simultaneous Design of Wind Turbines and Cable Layout in Offshore Wind, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-47, in review, 2021."</p>
Figures data from papers "Breakdown of the velocity and turbulence in the wake of a wind turbine", parts 1 and 2
<p>This python file makeFigure.py along with the .json files in folder Data/ allows to draw pictures corresponding to the articles "Breakdown of the velocity and turbulence in the wake of a wind turbine" - Part 1 and Part 2 published in Wind Energy Science. The .sh file makeFolders selects and separate the figures needed for the two parts.</p> <p>Slightly more informations are available in the data compared to the article, due to lack of space. In particular, one can found all the planes data from 1 to 8D downstream (instead of only 1D, 5D and 8D) and some data for the unstable and stable cases that were only shown for the neutral case in the paper.</p> <p><br> Do not hesitate to contact me if more informations are needed.</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>
Activity of forest specialist bats decreases towards wind turbines at forest sites
<p><span>Worldwide, wind turbines are increasingly being built at forest sites to meet the goals of national climate strategies. Yet, the impact on forest ecosystems and biodiversity is barely understood. Bats may be heavily affected by wind turbines in forests, because many species depend on forest ecosystems for roosting and hunting and can experience high fatality rates at wind turbines. <br></span></p> <p><span>We performed acoustic surveys in 24 temperate forests in the low mountain ranges of Central Germany to monitor changes in the acoustic activity of bats in relation to wind turbine proximity, rotor size, vegetation structure and season. Call sequences were identified and assigned to one of three functional guilds: open-space, edge-space and narrow-space foragers, the latter being mainly forest specialists. <br></span></p> <p><span>Based on the response behaviour of bats towards wind turbines in open landscapes, we predicted decreasing bat activity towards wind turbines at forest sites, especially for narrow-space foragers. <br></span></p> <p><span>Vertical vegetation heterogeneity had a strong positive effect on all bats, yet responses to wind turbines in forests varied across foraging guilds. Activity of narrow-space foragers decreased towards turbines over distances of several hundred meters, especially towards turbines with large rotors and during midsummer months. The activity of edge-space foragers did not change with distance to turbines or season, whereas the activity of open-space foragers increased close to turbines only in late summer. </span><span><br></span></p> <p><span><em>Synthesis and applications</em>: We show that narrow-space foragers avoid wind turbines in forests over a spatial scale of several hundred meters. This response was most apparent towards turbines with large rotors. Since forests are an important habitat for this guild, we advise to exclude forests with diverse vegetation structure as potential wind turbine sites and to consider compensation measures to account for habitat degradation associated with the operation of wind turbines in forests.</span></p>
Confirmation that eagle fatalities can be reduced by automated curtailment of wind turbines
<p>1. Automated curtailment is potentially a powerful technique to reduce collision mortality of wildlife with wind turbines. Previously, we used a before-after-control-impact framework to demonstrate that eagle fatalities declined after automated curtailment was implemented with the IdentiFlight system at a wind power facility in Wyoming, USA. We received substantial interest and feedback regarding our study and, here, we implement several analytical suggestions and include more recent data that strengthen the inference we draw from our results.</p> <p>2. The five main analytical suggestions we received were to 1) exclude from analysis data that were collected during the period when automated curtailment was only partially implemented; 2) only analyze data from a single make and model of turbine; 3) evaluate changes in the rate of fatality, instead of the yearly numbers of fatalities that result from fluctuations around that rate; 4) calculate the standard measure determining effects of a treatment in a before-after-control-impact study; and, 5) examine yearly fluctuations of the fatality rate during the before period.</p> <p>3. After incorporating these suggestions and including additional data collected since the prior paper was published, our results confirm prior work. We demonstrate that eagle fatalities were reduced by 85% (95% highest density interval = 12%, 100%) after implementation of automated curtailment. Rate of fatalities declined by 2.85 eagles per year (-0.67, 5.70) between before and after periods at the treatment site and increased by 2.26 eagles per year (-7.37, 1.77) at the control site. Overall, the fatality rate declined by 4.91 (-0.27, 11.27) more eagles per year at the treatment site than at the control site. The probability that the fatality rate declined at the treatment site relative to the control site was 0.97.</p> <p>4. Our re-analysis strengthens our inference by using more robust analyses and data to support the conclusions of the prior study suggesting that automated curtailment was effective at reducing eagle fatalities at our treatment site. Because of the site- and species-specific nature of our work, future research should examine the efficacy of automated curtailment at other sites, with other species, and under different curtailment regimes.</p>
Global offshore wind turbine analysis with Sentinel-1 - supplementary data
<p>Gloabl offshore wind turbine analysis with Sentinel-1 - supplementary data</p> <p>The files are supplementary data of the publication:</p> <p>Global dynamics of the offshore wind energy sector monitored with Sentinel-1: Turbine count, installed capacity and site specifications</p> <p>which is currently under review in the International Journal of Applied Earth Observation and Geoinformation</p> <p>supplementary_data_B_OWT_height_capacity.csv holds 50 pairs of offshore wind turbine hub heights and the corresponding installed capacities along with the offshore wind farm project name, the number of turbines of this wind farm, and the source the information originates from.</p> <p>supplementary_data_B_DeepOWT_1_21_2_plus.geojson is the extended version of the DeepOWT data set (https://zenodo.org/record/5933967) with all of the derived attributes in the respective publication e.g. OWT hub height and installed capacity.</p>
Supplemental Material to Journal Article "Determination of as-built properties of fiber reinforced polymers in a wind turbine blade using scanning electron and high-resolution X-ray microscopy"
<p>This set supplements the figure data to the article "Determination of as-built properties of fiber reinforced polymers in a wind turbine blade using scanning electron and high-resolution X-ray microscopy", DOI: <a href="https://doi.org/10.1016/j.jcomc.2022.100310">https://doi.org/10.1016/j.jcomc.2022.100310</a></p>
Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models
<p>Dataset of the paper "Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models" published on Energies [1].</p> <p>[1] Lin, M., & Porté-Agel, F. (2019). Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models. <em>Energies</em>, <em>12</em>(23), 4574.</p>
Datasets used in the Paper of "Analysis of leading edge protection application on wind turbine performance through energy and power decomposition approaches"
<p>These are the datasets used in the <em>Wind Energy</em> paper "Analysis of leading edge protection application on wind turbine performance through energy and power decomposition approaches." The paper can be accessed <a href="https://onlinelibrary.wiley.com/doi/10.1002/we.2722">here</a>. The computer code used to produce the results in the paper can be found <a href="../records/6321157">here</a>.</p>
Hovione Wind Turbine: bird survey datasets, March 2023 - March 2024
<p>Bird surveys carried out to inform the Environmental Impact Assessment Report and Natura Impact Statement for a proosed wind turbine at the Hovione Cork site, Ringaskiddy, Co. Cork, Ireland</p>
Data-driven surrogate model for wind turbine damage equivalent load
<p>There are four zip files in this data set:</p> <ul> <li>PythonCode_OpenFAST: The code used to generate 32768 OpenFAST fst files to build the database.</li> <li>ML_TrainingCode: The code that used to train the TCN-FCNN and FCNN models for both free stream and wake</li> <li>Trained_Models: All the trained models are saved in Keras format. The models with max in their filenames were trained on maximum values. The models with XY in their naming were trained on wind in the X and Y directions.</li> <li>data: It includes all the CSV files for training and testing.</li> </ul>
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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.