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607 results for “wind data”
Spectral data used in the paper "Intense Zonal Wind in the Martian Mesosphere During the 2018 Planet-Encircling Dust Event Observed by Ground-based IR Heterodyne Spectroscopy"
<p>This data contains the spectral data used in the paper "Intense Zonal Wind in the Martian Mesosphere During the 2018 Planet-Encircling Dust Event Observed by Ground-based IR Heterodyne Spectroscopy". </p> <p>"MILAHI_2018PEDE_Spectral_Data" is the spectral data and you can find the detailed information of the data in "README".</p> <p> </p> <p> </p>
The effect of midnight temperature maximum winds on post-midnight equatorial spread F: data used for this study
<p>Supporting Information for “The effect of midnight temperature maximum winds on post-midnight equatorial spread F”</p> <p>J. Krall 1 , D. Hickey 2 , J. D. Huba 3 , and P. B. Dandenault 4<br> 1 Plasma Physics Division, Naval Research Laboratory, Washington, District of Columbia, USA<br> 2 Space Science Division, Naval Research Laboratory, Washington, District of Columbia, USA<br> 3 Syntek Technologies, Fairfax, VA, USA<br> 4 Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA</p> <p>Contents</p> <p>1. Text-formatted data for Figure 1; this data also appears in Figures 2, 3 and 6.<br> 2. Text-formatted data for Figure 3; this data also appears in Figure 6.<br> 3. Text-formatted data for Figure 4.<br> 4. Text-formatted data for Figure 5.<br> 5. Text-formatted data for Figure 7.<br> 6. Text-formatted data for Figure 8.<br> 7. Text-formatted data for Figure 9.<br> 8. nation_2013_361_windfield_fit.txt, wind data from NATION<br> 9. MENTAT_WINDS_LON-85LAT30_TO_30.zip, MENTAT output</p> <p>This supplemental information is included to satisfy data-availability requirements.<br> Each file is formatted as ASCII text. Each file contains an explanatory header.<br> In all cases save ds01.txt and nation_2013_361_windfield_fit.txt, the “data” is model output as described in the main article.</p>
Data from: The influence of wind selectivity on migratory behavioral strategies
Air and water currents affect the timing and energy expenditure of many migratory animals, and therefore selection of favorable currents is important for optimal migratory performance. However, waiting for favorable currents also incurs costs. Here we conduct an optimality analysis to determine how wind selectivity affects three migratory currencies: time, energy, and risk. To describe variation in these metrics under varying degrees of selectivity, we constructed an individual-based model to simulate fall migration of passerines across eastern North America, allowing birds to use different thresholds of wind profit as the criterion for daily departure. A gradient of thresholds were tested across a range of realistic wind currents, from initiating flights only on nights when winds were directed in their preferred migratory direction (highly selective), to flying under most wind conditions (low selectivity). Our analysis indicated that relative mortality risk was lowest at intermediate selectivity; energy expended during flight was least for the most selective individuals; and of those that successfully completed migration, time spent on migration was lowest for the least selective birds. We solved for the optimal range of wind selectivity and show that this departure criterion alone can produce a tradeoff between time and energy that has been seen in many other behavioral contexts. While we solved for optima using some conditions specific to eastern North America, we show that variation in wind selectivity at departure can produce migratory behaviors that mimic the classic "time-minimizer" and "energy-minimizer" strategies developed from measurements of wild birds across multiple continents.
Summary raw wind data from the Southern Ocean collected on board the Antarctic Circumnavigation Expedition (ACE) during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>A Vaisala MAWS240 meteorological station was installed on the R/V Akademik Tryoshnikov during a circumnavigation of Antarctica in the austral summer season of 2016/2017. This dataset contains the raw wind parameter data that have been extracted from the original raw text data files. Data coverage is from 17th November 2016 until 11th April 2017, with gaps where the ship was in port.</p> <p>True and relative wind speed and direction parameters were recorded with a resolution of three seconds.</p> <p>Datetime should be combined with TIMEDIFF to convert it to UTC.</p> <p>Data from this dataset have been corrected and quality-checked in another published dataset. We recommend these data for further use (Landwehr et al., 2019; DOI 10.5281/zenodo.3379590).</p> <p><strong>Dataset contents</strong></p> <ul> <li>metdata_wind_YYYYMMDD_YYYYMMDD.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>ace_meteorology_raw_wind_summary_change_log.txt</li> </ul> <p>Data files contain data for each leg of the Antarctic Circumnavigation Expedition (ACE). Dates included in the file name are the start and end dates of the legs and therefore the data within the files as well.</p> <p><strong>Change log</strong></p> <p><strong>v1.2</strong> - Added missing data from 2017-02-05 - 2017-02-08 inclusive. Updated this change log file.</p> <p><strong>v1.1</strong> - Added additional data coverage from 2016-11-17 - 2016-11-22 inclusive, into the first data file. Updated README.txt with information about data coverage. Added this change_log file.</p> <p><strong>v1.0</strong> - Initial release of raw summary meteorological data.</p> <p><strong>Dataset license</strong></p> <p>This raw meteorological dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
SARWIND LG-Mod (v4.01) output data with reference to the Sentinel-1A (2014/12/07, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ the additional SKYRON reference wind)
<p>A zipped folder containing SARWIND LG-Mod (v4.01) output data obtained by processing the Sentinel-1A (2014/12/07, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ the additional SKYRON reference wind).</p> <p>Pre-processed data derived from the Sentinel-1 SAR image have been obtained by using the Sentinel Application Platform (SNAP), ver. 3.0.3, distributed by the European Space Agency (ESA). The reference wind components have been provided by the Numerical Weather Model (NWM) SKYRON.</p> <p>These Earth Observation (EO) data have been used as inputs to the SARWIND LGMod algorithm, ver. 4.01, developed by Rana Fabio Michele (2014-2016) for the sea surface wind field retrieval.</p> <p>The available Sentinel-1, Extra Wide Swath Mode, Ground Range, Multi-look, Detected, Medium Resolution, Vertical polarisation on transmit, Vertical polarisation on receive, Descending orbit, C-band SAR image, has been acquired on the 7th Dec 2014, 05:51:54.</p>
Sentinel-1A (2014/12/07, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ additional SKYRON reference wind) for SARWIND LG-Mod (v4.01)
<p>A zipped folder containing pre-processed data from a Sentinel-1 SAR image by using the Sentinel Application Platform (SNAP), ver. 3.0.3, distributed by the European Space Agency (ESA). The reference wind components provided by the Numerical Weather Model (NWM) SKYRON is also added in the folder.</p> <p>These Earth Observation (EO) data can be used as inputs to the SARWIND LGMod algorithm, ver. 4.01, developed by Rana Fabio Michele (2014-2016), with the aim at retrieving the sea surface wind field by exploiting the single co-polarized Sentinel-1 image available.</p> <p>In particular, the latter is the Sentinel-1, Extra Wide Swath Mode, Ground Range, Multi-look, Detected, Medium Resolution, Vertical polarisation on transmit, Vertical polarisation on receive, Descending orbit, C-band SAR image, acquired on the 7th Dec 2014, 05:51:54</p>
Sentinel-1A (2014/12/31, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ additional SKYRON reference wind) for SARWIND LG-Mod (v4.01)
<p>A zipped folder containing pre-processed data from a Sentinel-1 SAR image by using the Sentinel Application Platform (SNAP), ver. 3.0.3, distributed by the European Space Agency (ESA). The reference wind components provided by the Numerical Weather Model (NWM) SKYRON is also added in the folder.</p> <p>These Earth Observation (EO) data can be used as inputs to the SARWIND LGMod algorithm, ver. 4.01, developed by Rana Fabio Michele (2014-2016), with the aim at retrieving the sea surface wind field by exploiting the single co-polarized Sentinel-1 image available.</p> <p>In particular, the latter is the Sentinel-1, Extra Wide Swath Mode, Ground Range, Multi-look, Detected, Medium Resolution, Vertical polarisation on transmit, Vertical polarisation on receive, Descending orbit, C-band SAR image, acquired on the 31st Dec 2014, 05:51:53.</p>
SARWIND LG-Mod (v4.01) output data with reference to the Sentinel-1A (2014/12/31, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ the additional SKYRON reference wind)
<p>A zipped folder containing SARWIND LG-Mod (v4.01) output data obtained by processing the Sentinel-1A (2014/12/31, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ the additional SKYRON reference wind).</p> <p>Pre-processed data derived from the Sentinel-1 SAR image have been obtained by using the Sentinel Application Platform (SNAP), ver. 3.0.3, distributed by the European Space Agency (ESA). The reference wind components have been provided by the Numerical Weather Model (NWM) SKYRON.</p> <p>These Earth Observation (EO) data have been used as inputs to the SARWIND LGMod algorithm, ver. 4.01, developed by Rana Fabio Michele (2014-2016) for the sea surface wind field retrieval.</p> <p>The available Sentinel-1, Extra Wide Swath Mode, Ground Range, Multi-look, Detected, Medium Resolution, Vertical polarisation on transmit, Vertical polarisation on receive, Descending orbit, C-band SAR image, has been acquired on the 31st Dec 2014, 05:51:53.</p>
European Wind Atlas data disk
<p>This data set is a ZIP archive of the European Wind Atlas data disk; data set is described in Appendix D of the atlas:</p> <p>The main results of the European Wind Atlas analysis – the regionally representative wind statistics for each station – are furnished on a disk at the back of the Atlas. The disk furthermore contains the wind speed data in the form of histograms. The disk is divided into a number of subdirectories corresponding to the EC12 countries. The subdirectories are named as the country codes given below:</p> <p>B – Belgium</p> <p>DK – Denmark</p> <p>F – France</p> <p>D – Germany (FRG)</p> <p>GR – Greece</p> <p>I – Italy</p> <p>EI – Ireland</p> <p>L – Luxembourg</p> <p>NL – Netherlands</p> <p>P – Portugal</p> <p>E – Spain</p> <p>GB – United Kingdom</p> <p>Radiosonde statistics for all the countries are in a separate subdirectory with the name RS.</p> <p>The Wind Atlas data are stored as sequential ASCII files with the file name extension LIB, and contain 48 lines/records of information. The contents of a file are shown schematically in Table D.1 of the European Wind Atlas.</p> <p>The raw data are stored as sequential ASCII files with the file name extension TAB. The contents of a histogram file are shown schematically in Table D.2 of the European Wind Atlas.</p>
Data for Wind Energy Science paper "Modal dynamics of structures with bladed isotropic rotors and its complexity for 2-bladed rotors"
<p>The files are data files with model input and Matlab files with model parameters and function that sets up the block matrices of the dynamic model used in the paper. The Matlab script "test_repo.m" shows how to this function with all input.</p>
Data and results related to "Fattori et al. 2017 - High Solar Photovoltaic Penetration in the Absence of Substantial Wind Capacity: Storage Requirements and Effects on Capacity Adequacy - Energy"
<p>The file includes data used for the analysis and results coming from the study (which was focused on the Italian "Nord" bidding zone). In particular:</p> <p>(i) Series of hourly load data [MW], from 01.01.2006 to 31.12.2015. The data come from elaborations based on ENTSO-E (https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package) and Terna S.p.A. (http://www.terna.it/en-gb/sistemaelettrico/transparencyreport/load/actualload.aspx). All the elaborations are described in details on the paper.</p> <p>(ii) Data related to the penetration of PV. Installed capacity of PV is assumed to increase from zero up to the capacity needed so that the average annual PV generation (based on the years 1986-2015) potentially equals the average annual demand (based on the years 2006-2015).</p> <p>(iii) Synthesis of the results about: residual load (with and w/o storage), ramps (with and w/o storage), excess energy (with and w/o storage), storage requirements</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>
Data for: "Hydrogen for harvesting the potential of offshore wind: A North Sea case study"
<p>Supply and demand data for hydrogen and electricity for article "Hydrogen for harvesting the potential of offshore wind: A North Sea case study".</p><p> </p><p>The data is based on the following work:</p><p>G. Durakovic, P. C. del Granado, A. Tomasgard, Powering Europe with North Sea offshore wind: The impact of hydrogen investments on grid infrastructure and power prices, Energy 263 (2023) 125654. doi:10.1016/j.energy.2022.125654.</p><p>The data is obtained from the EMPIRE model found at https://github.com/Goggien/EMPIRE-Public.</p><p>The EMPIRE model is developed at NTNU in The Department of Industrial Economics and Technology Management.</p>
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>
Stratosphere-Troposphere wind profiler radar data
<p>Horizontal wind profiles from ST radar data at Cochin (10.04N, 76.33 E) during mosoon seasons (June to September) for three years (2019-2021)</p>
Data from: Geographic source of bats killed at wind-energy facilities in the eastern United States
<p>Bats subject to high rates of fatalities at wind-energy facilities are of conservation concern, but the impact on broader bat populations is difficult to assess. One reason is the poor understanding of the geographic source of individual fatalities and whether they constitute local resident individuals or migrants. Here, we used stable hydrogen isotopes, trace elements and species distribution models to determine the summer geographic origins of three different bat species (<em>Lasiurus borealis</em>, <em>L. cinereus</em>, and <em>Lasionycteris noctivagans</em>) killed at wind-energy facilities in Ohio and Maryland in the eastern United States. In Ohio, 58.4%, 78.7%, and 97.8% of all individuals of <em>L. borealis</em>, <em>L. cinereus</em>, and <em>L. noctivagans</em>, respectively, lacked evidence of movement and were likely residents. In contrast, in Maryland 22.7%, 62.9% and 72.7% of these same species were classified as residents. Our results suggest that a substantial portion of bats killed at a given wind facility are likely derived from resident populations. Finally, there is variation in the proportion of residents killed between seasons for some species and evidence of philopatry to summer roosts. Overall, these results indicate that impact of wind-energy facilities on resident bat populations may be greater than previously appreciated, but this impact is likely to vary across species and sites. Similar studies should be conducted across a boarder geographic scale to understand the impacts on bat populations from wind-energy facilities.</p>
Data from: Interplay between wind-driven advection and mixing of salt and dissolved oxygen in a microtidal estuary
<p>Most work on how estuarine dynamics impact dissolved oxygen (DO) distributions has focused on tides as the primary mixing mechanism, but in shallow estuaries with large fetch or small tides, wind can be the primary mixing agent and also drives advection. To investigate how these processes interact and affect DO distributions, an observational study was conducted in the shallow, micro-tidal Neuse Estuary (NRE). Salinity, DO, and velocity profiles were measured at multiple positions along and across the estuary over a 6-month period. A one-dimensional model (General Ocean Turbulence Model) provided additional insight into the response of salinity and DO to wind. Salinity and oxygen conservation equation terms were calculated from observations and simulations to investigate the roles of advection and mixing under different conditions. Cross-estuary wind drove lateral circulations and tilted the isohalines, reducing stratification; lateral advection and enhanced vertical mixing reduced vertical gradients and increased the bottom DO. Down-estuary wind tended to increase the exchange flow and increase stratification, but concurrently the wind-driven surface turbulent boundary layer deepened over time. The balance of these processes determined if the water column became fully mixed or remained stratified, and the depth of the pycnocline and oxycline. Up-estuary wind inhibited the exchange flow and ultimately the combination of advection and vertical mixing homogenized the water column. While these patterns generally held for purely across- or along-channel wind, the response was often more complex because the wind vector could have any orientation and wind speed and direction varied continuously with time.</p>
CNN-Based Forecasting of Pitch Angle-Resolved Energetic Electron Flux at MEO Using Solar Wind and Geomagnetic Data
<div> <p> CNN-Based Forecasting of Pitch Angle-Resolved Energetic Electron Flux at MEO Using Solar Wind and Geomagnetic Data. The model and test set data are provided here. Data used for training, validating, and testing the 1.8MeV channel model is also offered as an example.</p> </div> <p><strong>initial_data: </strong>The test set data has been normalized and can be used as model input.</p> <p><strong>norm para: </strong>The normalization parameters used for data processing</p> <p><strong>model_test_dataset_performance.py: </strong>The script to obtain the outputs of the models at different energy levels on the test set. Before running it, unzip “initial_data.rar” and "norm_para.rar"</p> <p><strong>full_dataset_for_rept_ch0: </strong>Data used for training, validating, and testing the 1.8MeV channel model. It's not essential for model_test_dataset_performance.py</p>
WindSightNet: Catalogue of wind speed and direction data from NASA InSight lander on Mars using seismic data
<p>Dataset associated with the publication "WindSightNet: the inter-annual variability of Martian winds retrieved from InSight's seismic data with machine learning" submitted to JGR: Planets.</p> <p>Authors:</p> <p>A. E. Stott, R. F. Garcia, N. Murdoch, D. Mimoun, M. Drilleau, C. Newman, A. Spiga, D. Banfield, M. Lemmon, S. Navarro, L. Mora-Sotomayor, C. Charalambous, W. T. Pike, P. Lognonné, W. B .Banerdt</p> <p>Files containing catalogue of winds produced from the seismic data on the NASA InSight mission using machine learning algorithm produced in above publication. Please refer to this publication for technical details.</p> <p> </p> <p>Contents:</p> <p>WindSightNet.csv - file containing wind speed and direction produced from the WindSightNet neural network based on seismic data</p> <p>TWINS.csv - comparitive wind speed and direction from TWINS wind sensor when available. </p> <p>TWINS data originally available from:</p> <p>J A Manfredi, Insight Auxiliary Payload Sensor Subsystem (APSS) Temperatures and Wind Sensor for Insight (TWINS) Archive Bundle, (2019), https://doi.org/10.17189/1518950</p> <p> </p> <p>Each file contains values for:</p> <p>Wind Speed</p> <p>Wind dir.</p> <p>Sol - number of sol of InSight mission </p> <p>UTC - Coordinated Universal Time of sample</p> <p>LTST - Local True Solar Time of sample</p> <p>L_s - Solar longitude value of sample</p> <p>Time - seconds since UNIX epoch</p> <p>Data is considered to be sampled at a rate of 0.01 Hz when there are no gaps.</p> <p> </p> <p>Example code for plotting paper figures can be found:</p> <p>https://doi.org/10.5281/zenodo.14267939</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>
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.