Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,855
datasets available to search
ShareScore release 0.7.1
Dataset results
1,855 results for “winds”
Figures and data: Combining wake redirection and derating strategies in a load-constrained wind farm power maximization
<p><strong>Figures from the publication <em>Combining wake redirection and derating strategies in a load-constrained wind farm power maximization.</em></strong></p> <p> </p> <p>*.fig files can be opened in <code>Matlab</code></p> <p>*.csv files can be opened through a standard text editor (e.g.,<code> Notepad++</code>), imported and visualized in <code>Matlab</code> through the functions <code>>>readmatrix()</code> and <code>>>readtable()</code></p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for wind power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>wind</span><span> power </span><span>generation</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Wind energy supplies 7% of global electricity, and production has grown three-fold in the decade to 2022.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>1506</span></span><span><span> datapoints from </span></span><span><span>28</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span><span> It is designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span><span> </span>Technoeconomic data on utility-scale wind energy was collected from websites, reports, academic articles and databases of national and international organisations.</p>
The impact of solar wind magnetic field fluctuations on the magnetospheric energetics
<p>This dataset provides the results and analysis tools of the manuscript by Ala-Laht et al. "The impact of solar wind magnetic field fluctuations on the magnetospheric energetics". In addition, relevant SWMF simulation input files are included. See ReadMe.txt for information.</p>
Infrared thermography of turbulence patterns of operational wind turbine rotor blades supported with high-resolution photography: KI-VISIR Dataset
<h2>Abstract</h2> <p><span>With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading-edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie – Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTP) that result from such surface contamination or damage. To compliment the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise the turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.</span></p>
Supplementary material for "In-flight reactions of nocturnally migrating birds to winds"
<p><strong>Abstract</strong></p> <p>Available knowledge on in-flight reactions of nocturnal bird migrants to winds is reviewed, with emphasis on the challenging topographical and meteorological conditions in Western Europe, and differences from the situation in North America discussed. Conclusions drawn are used for a new approach: using individual radar tracks of nocturnal migrants (mainly passerines) as well as winds measured at their flight altitudes, we defined the basic direction (BD=average flight direction of all migrants tracked under negligible wind influence) as a reference. For two altitudinal zones above a radar site near Nuremberg, we modelled the deviations of tracks and headings from BD for increasing wind from six 60° sectors. A comparison of birds’ air speeds Va with winds from four 90°-sectors confirmed that Va increased with opposing winds from ~11 to 13 (14) m/s; a similar increase occurred with side winds. An expected, slight decrease of Va with increasing following winds was only indicated for high-flying, not for low-flying birds. A predicted increase in average Va due to decreasing air density with increasing height was not observed; possible explanations (birds climbing to high altitudes in following, but not in strong opposing winds) are discussed. Over the whole autumn migration season, headings were concentrated in a sector of ±30° around 230° in both altitudinal zones. Prevailing winds from 230 to 320° (SW–NW, i.e. opposing from right) led to widely scattered tracks primarily between 190° and 270°, but additional ones in the SE sector (mainly 100°–170°). The analysis of tracks and headings relative to BD revealed the following features. (1) Overcompensation was frequently observed at low wind speeds (<3 m/s); (2) under all wind conditions, but particularly with opposing winds and at low flight levels, tracks were widely scattered, including birds deviating more than 90° from BD. (3) Under opposing and side winds from the right compensatory efforts led to partial drift compensation up to wind speeds of ~8–10 m/s. Because efforts to compensate drift dwindled with increasing wind speeds, birds were fully drifted. Many even shifted their heading to due south and, hence, overdrifted. (4) Opposing and side winds from the left induced partial compensation at low flight levels and full drift above 1500 m asl. (5) The lateral components of the rare and weak following winds led to tracks close to expected minimal drift (without important compensation needed). In general, migrants compensated less for deviations by wind force than expected. The tendency of birds to maintain headings close to BD under opposing winds was so strong that many individuals continued migration with minimal progress over ground or even with retrograde migration as an extreme. On the other hand, there was an omnipresent fraction of birds with tracks far from seasonally favourable directions, including reverse migration.</p>
CONDOR Meteor Radar Horizontal Wind
<p><a href="http://alo.erau.edu/instrument/mr/index.php">CONDOR</a> is a multi-static meteor radar system with the main station at Cerro Pachón, Chile, next to the Andes Lidar Observatory (ALO) and two remote stations at <a href="https://www.lco.cl/">Las Campanas Observatory</a> (LCO) 137 km to the north and <a href="https://astroturismochile.travel/observatorio-cruz-del-sur/">Southern Cross Observatory</a> (SCO) near Combarbalá to the south. The system was built and installed by <a href="https://www.atrad.com.au/">ATRAD, Inc.</a> and funded by the U.S. National Science Foundation. It has been in operation since the summer of 2019. </p> <p>This dataset is the horizontal wind from <a href="http://alo.erau.edu/instrument/mr/index.php">CONDOR</a> meteor radar system at 1-hr temporal resolution from all three stations. It is one of the standard products of this meteor radar system. The data is converted from ATRAD native format into Matlab format that is self-explanatory when loaded into Matlab.</p>
Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power »
<p>This dataset contains data provided alongside the paper "An all-Africa dataset of energy model “supply regions” for solar PV and wind power" by Sterl et al. (2022).</p> <p>It concerns a novel representative subset of attractive sites for solar PV and onshore wind power for the entire African continent. We refer to these sites as “Model Supply Regions” (MSRs). This MSR dataset was created from an in-depth analysis of various existing datasets on resource potential, grid infrastructure, land use, topography and others (see Methods), and achieves hourly temporal resolution and kilometre-scale spatial resolution. This dataset fills an important research need by closing the gap between comprehensive datasets on African VRE potential (such as the Global Solar Atlas and Global Wind Atlas) on the one hand, and the input needed to run cost-optimisation models on the other. It also allows a detailed analysis of the trade-offs involved in exploiting excellent, but far-from-grid resources as compared to mediocre but more accessible resources, which is a crucial component of power systems planning to be elaborated for many African countries.</p> <p>Five separate datasets are included:</p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 2, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Algeria<br>Angola<br>Benin<br>Botswana<br>Burkina Faso<br>Burundi<br>Cameroon<br>Central African Republic<br>Chad<br>Congo Republic<br>Democratic Republic of the Congo<br>Djibouti<br>Egypt<br>Equatorial Guinea<br>Eritrea<br>Eswatini<br>Ethiopia<br>Gabon<br>The Gambia<br>Ghana<br>Guinea<br>Guiné-Bissau<br>Côte d'Ivoire<br>Kenya<br>Lesotho<br>Liberia<br>Libya<br>Madagascar<br>Malawi<br>Mali<br>Mauritania<br>Morocco<br>Mozambique<br>Namibia<br>Niger<br>Nigeria<br>Rwanda<br>Senegal<br>Sierra Leone<br>Somalia<br>South Africa<br>South Sudan<br>Sudan<br>Togo<br>Tunisia<br>Uganda<br>Tanzania<br>Zambia<br>Zimbabwe</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <span><a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></span></p> <p><strong>See also</strong></p> <p>Sterl, S. (2024). Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10650822" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10650822</a></p>
AIMS - Earth Observation Satellite Data of Wave and Wind in the Tyrrhenian Sea
<p>This dataset is part of the AIMS project (Artificial Intelligence to Monitor our Seas), which has the vision to develop and validate novel Artificial Intelligence (AI) algorithms to unlock the true potential of remote monitoring and enable a faster transition to a climate neutral society and economy: the AI algorithms will leverage the advantages of usual monitoring methodologies of the features of waves and offshore wind, and eventually overcome their intrinsic limitations. The value and resolution of sparse measurements of satellites and unevenly-distributed in-situ instruments will be increased, hence leading to a significant reduction of the cost and execution time of data collection, ultimately making knowledge wider and more accessible.</p> <p>In particular, this dataset aggregates earth observation satellite data from 10 different satellites, measureing the significant wave height and the wind speed at 10 meters above sea leavel in the Tyrrhenian Sea, from January 2021 to May 2024.</p>
Aeroelastic simulations of wind turbines affected by leading edge erosion: datasets for multivariate time-series classification
<p>This repository contains data generated and used for classification in the publication:<br> Duthé, G.; Abdallah, I.; Barber, S.; Chatzi, E. Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 7262. https://doi.org/10.3390/en14217262</p> <p>The data is generated via OpenFAST aeroelastic simulations coupled with a Non-Homogeneous Compound Poisson Process for degradation modelling and was used to train a Transformer deep learning model.</p> <p>One degradation run generates 1200 samples (1 sample every 6 days corresponding to a 20 year degradation period). In total 20 degradation runs are made available (20x1200 = 24'000 multivariate time-series samples). This repo can serve to benchmark long multivariate time-series classification algorithms. There are 10 possible classes of erosion severity.</p> <p>Each sample is a multivariate time-series of length 60'000, with the following 4 channels extracted from the simulations for a section at the tip of the blade:</p> <ul> <li>Inflow velocity</li> <li>Angle of attack</li> <li>Lift coefficient</li> <li>Drag coefficient</li> </ul> <p>Please see the publication above for more information as well as the included readme for information about the data and an example of how to load it into to PyTorch.</p> <p> </p>
Gradient winds and neutral flow dawn-dusk asymmetry in the auroral oval during geomagnetically disturbed conditions (data files)
<p>Data files with wind profiles used to generate figures in the paper entitled "Gradient winds and neutral flow dawn-dusk asymmetry in the auroral oval during geomagnetically disturbed conditions"</p>
Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design
<p>Dataset related to the article: Virtanen, E.A., Lappalainen, J., Nurmi, M., Viitasalo, M., Tikanmäki, M., Heinonen, J., Atlaskin, E., Kallasvuo, M., Tikkanen, H., Moilanen, A. (2022) Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design. Renewable and Sustainable Energy Reviews 158, 112087.</p> <p>Dataset includes suitability maps for offshore windfarms, where priority values are scaled between 0-1 (note the reversed value scale): analysis solution (A) economy, (B) society, (C) biodiversity, (D) restrictions, (E) A+B+C without restrictions and (F) A+B+C with restrictions. Dataset includes also the conflict map (and R script), where each three main solutions (A, B, C) are mapped onto an RGB color composite map. </p> <p>Additional details can be found from the published article: <a href="https://doi.org/10.1016/j.rser.2022.112087">https://doi.org/10.1016/j.rser.2022.112087</a></p>
Wall Resolved Fluid-Structure Interaction Numerical Simulation of a Modern Wind Turbine Blade
<p>Wall-resolved fluid-structure interaction (FSI) numerical simulations of the NREL 5 MW wind turbine blade<br> are compared using two FSI approaches. The first method is based on high-fidelity Nektar++/SHARPy FSI framework,<br> where the fluid governing equations are solved using high-order spectral/hp element method and the turbulent flow is<br> resolved using Large Eddy Simulation (LES) on thick strips, while large-deformation dynamics of the structure are mod-<br> elled using a geometrically exact nonlinear composite beam finite-element model. Thick strip method for the fluid reduces<br> the computational cost by considering a series of smaller domains, each of which has a finite thickness in the spanwise<br> direction. Hence, the overall flow over the blade is treated with a sectional approach, where in each of these sections,<br> strips, the 3D flow is reconstructed locally. Tip-loss correction is used to compensate for the sectional approach over the<br> blade. The second FSI approach is based on OpenFoam/Calculix coupling, where the second-order unstructured finite<br> volume method approach is used for solving the three-dimensional flow equations and the flow turbulence is captured us-<br> ing the k-ω SST model. The structural dynamics are modeled via second-order finite element method using standard solid<br> elements. Effects of the solution fidelity on the prediction of aerodynamic forces as well as on the full three-dimensional<br> flow modelling over the blade versus sectional representation of flow over the blade while incorporating the local three-<br> dimensionality in each section and tip-correction are discussed. Further, significance of two approaches on modelling<br> the slender blade, one using the beam mode and the other utilizing the full 3D solution of structure is addressed. Finally,<br> assessment of computational cost and scalability of the two approaches are presented and discussed.</p>
Assessment of future wind speed and wind power changes over South Greenland using the MAR regional climate model : MAR ouptuts and KATABATA weather stations timeseries
<p>Daliy MARv3.12 outputs and KATABATA weather stations timeseries used in :</p> <p>Lambin, C., Fettweis, X., Kittel, C., Fonder, M., & Ernst, D. (2022).Assessment of future wind speed and wind power changes over South Greenland using the Modèle Atmosphérique Régional regional climate model. <em>International Journal of Climatology</em>, 43(1),558–574. https://doi.org/10.1002/joc.7795574 </p> <p> </p>
Data set from long-term wind and acceleration monitoring of the Gjemnessund Bridge
<p>The Gjemnessund Bridge has been monitored by accelerometers and anemometers for almost ten years. The data collected between 2013 and 2018 are now available in this open-access research entry, for free access and download. The data is collected in two h5-files (hierachical data format), with sampling rates 2 Hz and 10 Hz, downsampled from the raw sampling rate of 200 Hz. Some minimal signal processing is applied to the data in line with that applied to the Hardanger Bridge data described in Fenerci et al. (2021) and the Bergsøysund Bridge data described in Kvåle et al. (2022). The structure of the data is identical to that of the latter reference, which is described in a preprint appended to that research entry. The Python package opyndata available on GitHub contains useful tools compatible with the format of the dataset, for data import, processing and visualization.</p>
List of wind power projects in Vietnam
<p>Historical inventory of wind power projects in Vietnam updated 2022-05-04 "post FIT race"</p> <p>This release is complete for projects with COD or PPA at the end of the FIT period.</p> <p>The number of records is 548, of which 360 refer to active projects phases, and 215 have investment costs.</p> <p>Each record describes a wind project phase. The table includes projects at all stages of the lifecycle. Some have been mentioned years ago and are now of historical interest only. Some have just a signed exploration MoU. All records are referenced to one or several public sources.</p> <p>Fields: Record status, Project stage, Project name, Owner, Location (hamlet, commune, province, region, longitude, latitude), Location type (onshore/nearshore/offshore), Capacity (MWp), Connection plan, Turbines, Investment (VND or USD),</p> <p>dates (MoU, Investment Licence,, Groundbreaking), Note and sources.</p> <p>The dataset also include:</p> <ul> <li>Descriptive data paper.</li> <li>A CSV export of the table.</li> <li>Python module to read the data from the CSV and python script to print the descriptive statistics.</li> </ul>
Plume and wind data from ROMEO campaign on 17.10.2019.
<p>Measurements from the oil well 1474 in Darmanesti, Parhova County, Romania. Dataset contains plume transect measurements taken by the team from TNO during the ROMEO measurement campaign. Included in the dataset are the measurements from a tracer (N2O), emitted next to the oil well. The wind data measured on-site with a 3D sonic is also added as 1 min averages of 25 Hz measurements of the three wind components.</p>
VisMetHack2022: Visualizing winds and surface variables from the ECMWF IFS 1-km nature run
<p><strong>Overview</strong></p> <p>This data collection was contributed to the <a href="https://events.ecmwf.int/event/305/">Visualisation Hackathon 2022</a> (#VisMetHack2022), in conjunction with the Using <a href="https://events.ecmwf.int/event/296/">ECMWF's Forecasts (UEF2022</a>) workshop.</p> <p>The European Center for Medium-Range Weather Forecasts (ECMWF) and the Oak Ridge National Laboratory (ORNL) are pleased to announce access to the data collection from global 1-km nature run (NR) simulations using the Integrated Forecast System (IFS) with explicit convection. We invite you to join us in exploring this precursor to a digital twin of the earth!</p> <p>The NR simulations reveal unprecedented detail of the earth’s atmosphere [1], and the then outgoing Editor-in-Chief of AGU JAMES commended the project as one of “stunning ambitions,” enabled by computational capacity at scale [2]. The project also won the <em>2020 HPCwire Readers Choice Award </em>for Best Use of HPC in Physical Sciences. </p> <p>A set of two NR seasonal simulations have been completed, one corresponding to the northern hemispheric winter months (NDJF) and the other for the North Atlantic tropical cyclone season (ASO). The project used the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). The simulations were facilitated with an INCITE award from the US Department of Energy Office of Science. </p> <p>For the first seasonal run of four months (NDJF), the hydrostatic IFS model was initialized at 00Z on 1 November 2018. The NR for the TC season (AS) was initialized at 00Z on 1 August 2019. The NR simulations were constrained only by sea surface temperatures (SST) at the lower boundary. The IFS output was saved every 3 hours.</p> <p>After feedback and interest from the scientific community, the simulations were rerun for four specific extreme events, with output every 15 minutes. The special cases include a tropical cycle and three severe storm events over the continental USA.</p> <p><strong>NR Data for visualizing winds</strong></p> <p>A small subset from the 1-km IFS NR collection is make available for #VisMetHack22. This subset is extracted from the tropical cyclone area in the North Atlantic from the ASO simulations. The 912 model time steps correspond to 97935 to 111600 in minutes since the NR reference time 2019-08-01 00:00:00. The time increment is 15 minutes, corresponding to the output frequency.</p> <p>The following variables are provided for #VisMetHack2022:<br> </p> <table> <tbody> <tr> <td> <p>Short Name</p> </td> <td> <p>Parameter ID</p> </td> <td> <p>Units</p> </td> <td> <p>Long Name</p> </td> </tr> <tr> <td> <p>10u</p> </td> <td> <p>165</p> </td> <td> <p>m/s</p> </td> <td> <p>10 metre U wind component</p> </td> </tr> <tr> <td> <p>10v</p> </td> <td> <p>166</p> </td> <td> <p>m/s</p> </td> <td> <p>10 metre V wind component</p> </td> </tr> <tr> <td> <p>2t</p> </td> <td> <p>167</p> </td> <td> <p>K</p> </td> <td> <p>2 metre temperature</p> </td> </tr> <tr> <td> <p>i10fg</p> </td> <td> <p>228029</p> </td> <td> <p>m/s</p> </td> <td> <p>Instantaneous 10 metre wind gust</p> </td> </tr> <tr> <td> <p>msl</p> </td> <td> <p>151</p> </td> <td> <p>Pa</p> </td> <td> <p>Mean sea level pressure</p> </td> </tr> <tr> <td> <p>xtprate</p> </td> <td> <p>99999</p> </td> <td> <p>kg m**-2 s**-1</p> </td> <td> <p>Total instantaneous precipitation rate. Summation of convective and large scale rain and snowfall rates. </p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Data processing</strong></p> <ol> <li> <p>The native model output was in the form of data objects consisting of GRIB1/2 16-bit AEC compressed messages. The messages were extracted from the FDB database instances into one or more files.</p> </li> <li> <p>The files containing the GRIB messages were interpolated to 0.02 x 0.02 a regular latitude-longitude grid using ECMWF Meteorological Interpolation and Regridding (MIR), and then written out to files as GRIB messages.</p> </li> <li> <p>The MIR output files were extracted to the area of interest (AOI) from global fields, and converted to Netcdf-4 (NC).</p> </li> <li> <p>The metadata in NC4 files were selectively edited or added.</p> </li> <li> <p>Finally, the NC4 files were compressed to reduce volume using the ncks utility from Netcdf Operators (NCO), with lossless L1 compression.</p> </li> <li> <p>The variable ‘xtprate’ was calculated by a summation of instantaneous and large scape rainfall and snowfall rates.</p> </li> </ol> <p><strong>Contact</strong></p> <p>Valentine Anantharaj <<a href="mailto:vga@ornl.gov">vga@ornl.gov</a>> or <vga1.ornl@gmail.com> </p> <p>Samuel Hatfield <<a href="mailto:Samuel.Hatfield@ecmwf.int">Samuel.Hatfield@ecmwf.int</a>></p> <p> </p> <p><strong>Citation and references</strong></p> <p>Please cite the following manuscript as well as the DOI provided by Zenodo:</p> <p>[1] Wedi, N. P., Polichtchouk, I., Dueben, P., Anantharaj, V. G., Bauer, P., Boussetta, S., et al. (2020). A baseline for global weather and climate simulations at 1 km resolution. Journal of Advances in Modeling Earth Systems, 12, e2020MS002192. <a href="https://doi.org/10.1029/2020MS002192">https://doi.org/10.1029/2020MS002192</a></p> <p>[2] Anantharaj, V., Hatfield, S. and Vukovic, Milana (2022). VisMetHack2022: Visualizing winds and surface variables from the ECMWF IFS 1-km nature run. https://doi.org/10.5281/zenodo.6633929</p> <p><strong>Acknowledgements</strong></p> <p>This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725.</p> <p>ECMWF also benefited from collaborations funded via ESCAPE-2 (No. 800897), MAESTRO (No. 801101), EuroEXA (No. 754337), and ESiWACE-2 (No. 823988) projects funded by the European Union's Horizon 2020 future and emerging technologies and the research and innovation programmes. </p>
Doppler lidar wind profiles from Granada
<p>This is data set includes Doppler wind lidar quantities which were calculated from measurements performed between 2016 and 2020 at <em>Andalusian Global Observatory of the Atmosphere</em>, AGORA, in particular, at the UGR station, Andalusian Institute for Earth System Research (IISTA-CEAMA) in Granada, Spain (37.16ºN, 3.61ºW, 680 m a.s.l.).</p> <p>The system is a Doppler lidar Stream Line (Halo Photonics), which is part of ACTRIS-Cloudnet (Illingworth et al., 2007). The system laser emits at 1.5 μm and the detector is heterodyne using fiber-optic technology. The measurements for this data set consisted of conical scans with constant elevation of 75° and 12 equidistant azimuth points performed every 10 min. A more detailed description of the instrument can be found in (Ortiz-Amezcua et al., 2022)</p>
SuperDARN meteor wind data for January 2019
<p>SuperDARN meteor wind data</p> <p>*.m.* - meridional</p> <p>*.z.* - zonal</p> <p>X/Y are in radar coordinates - most users can disregard. </p> <p> </p> <p>Supported by NSF #1934973</p> <p>Collaborative Research: Super Dual Auroral Radar Network (SuperDARN) Operations, Research and Community Support</p> <p> </p> <p> </p> <p>SuperDARN is an international collaboration operating high frequency (HF) radars deployed in the northern and southern hemispheres to measure ionospheric plasma circulation. Each partner institution secures funding and manages operations for their own facilities. The continued availability of SuperDARN data depends on the proper acknowledgment of data by its users. Guidelines for data acknowledgment are as follows:</p> <p>When data from an individual radar or radars are used, users must contact the principal investigator(s) of those radar(s) to obtain the appropriate acknowledgement information and to offer collaboration, where appropriate. Contact information is available in the README file for this collection.</p> <p>For all usage of SuperDARN data, users are asked to include the following standard acknowledgment text: “The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.”</p> <p>While SuperDARN has an open data use policy, i.e., prior permission to access and analyse the data is not required, the data user is strongly encouraged to establish early contact with any Principal Investigator whose data are involved in the project to discuss the intended usage and collaboration. Data can be subject to limitations that are not immediately evident to users. In addition, some data are embargoed for use by designated Principal Investigators for a period of one year. SuperDARN and the organizations that contributed data must be acknowledged in all reports and publications that use SuperDARN data.</p> <p>The SuperDARN Executive Council (see list in the README) must be notified before data are redistributed through another database. The data are not to be used for commercial purposes. If you have any questions about appropriate use of these data, contact any SuperDARN Principal Investigator.</p> <p> </p>
Data depository - "Quantifying the effect of wind on volcanic plumes: implications for plume modelling"
<p>This depository contains all data to understand, evaluate, and build upon the research reported in the manuscript: "Quantifying the effect of wind on volcanic plumes: implications for plume modelling", submitted to Journal of Geophysical Research.</p> <p><strong>Abstract</strong></p> <p>The considerable effects that wind can have on estimates of mass eruption rates (MERs) in explosive eruptions based on volcanic plume height are well known but difficult to quantify rigorously. Many explicitly wind-affected plume models have the additional difficulty that they require the use of centerline heights of bent-over plumes, a parameter not easily obtained directly from observational data. We tested two such models by using the time series of varying plume heights and wind speeds of the 2010 Eyjafjallajökull eruption. The mapped fallout and photos taken during this eruption allow us to estimate the plume geometry and to empirically constrain input parameters for the two models tested. Two strategies are presented to correct the difference in maximum plume height and centerline height: (i) based on plume radius, and (ii) by using the plume type parameter ∏, which quantifies the relative influence of buoyancy and cross-wind on the plume dynamics, to discriminate weak, intermediate and strong plumes. The results indicate that it may be more appropriate to classify plumes as either wind-dominated, intermediate or buoyancy-dominated, where the relative effects of both wind and MER define the type. The analysis of the Eyjafjallajökull data shows that the MER estimates from both models are considerably improved when a plume-type dependent centreline-correction is applied and the wind entrainment coefficient <em>β</em> is refined. For this particular eruption, we find that the best value for <em>β</em> lies between 0.22 and 0.34, unlike previous suggestions that set this parameter to 0.50.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.