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607 results for “wind data”
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>
Data for the manuscript named 'Soft X-ray imaging of the magnetosheath and cusps under different solar wind conditions: MHD simulations'
<p> This is the data used by the manuscript named 'Soft X-ray imaging of the magnetosheath and cusps under different solar wind conditions: MHD simulations'.</p> <p> The uploaded data is the X-ray intensity data for all the five cases studied in the manuscritpt. 'Casen' (n=1, 2, 3, 4, and 5) in the name of each data file indicates the case number, and 'sat pointX' (X=A, B, C, D) show the satellite positions analyzed in the manuscript. </p> <p> The data can be read by IDL using the following program statments:</p> <p>openr,lun,datai,/get_lun<br> xgse=0. & ygse=0. & zgse=0.<br> readf,lun,xgse,ygse,zgse ;;;;(satellite position in the GSE coordinate)<br> xsat=0. & ysat=0. & zsat=0.<br> readf,lun,xsat,ysat,zsat ;;;;(satellite position in the GSM coordinate)<br> xpoint=0. & ypoint=0. & zpoint=0.<br> readf,lun,xpoint,ypoint,zpoint ;;;;(satellite pointing of SXI, aim point)<br> nthtmax=0L & nphimax=0L<br> readf,lun,nthtmax,nphimax ;;;;(number of the tht and phi grids)<br> thti=fltarr(nthtmax) & phii=fltarr(nphimax)<br> readf,lun,thti,format='(e14.6)' ;;;;(the tht grids)<br> readf,lun,phii,format='(e14.6)' ;;;;(the phi grids)<br> Pxraytp=fltarr(nthtmax,nphimax)<br> readf,lun,Pxraytp,format='(e14.6)' ;;;;(X-ray intensity)<br> close,lun<br> free_lun,lun</p>
Data for "Near Surface Maximum Winds during the Landfall of Hurricane Harvey"
<p>This archive corresponds to the data described in Alford et al. (2018) to be published in <em>Geophysical Research Letters</em>. Please see the included readme.txt file for details about each data file.</p> <p>Citation for paper: Alford, A. A., M. I. Biggerstaff, G. D. Carrie, J. L. Schroeder, B. D. Hirth, and S. M. Saugh, 2018: Near-surface maximum winds during the landfall of Hurricane Harvey. <em>Geophysical Research Letters</em>, <strong>46</strong>. https://doi.org/10.1029/2018GL080013.</p> <p> </p>
Tidal wind data at Syowa
<p>This data set contains information on the tidal wind obtained during Jan. 2004 and July 2018 at Syowa Station in the Antarctica. Please contact the author for more information when needed.</p>
Data Intervals To Study Statistics of Whistler Waves in the Solar Wind
<p>This dataset contains a list of intervals that were used to study the statistics of whistler waves in the solar wind. It is created in companion to a manuscript submitted to ApJ.</p>
Wind Technology Network data
<p>This dataset contains nodes and edges of the wind technology diffusion network inferred in the paper:</p> <p> </p> <p>S. Halleck-Vega, A. Mandel & K. Millock, (2018). "Accelerating diffusion of climate-friendly technologies: A network perspective." Ecological Economics, Vol.152, pp 235-245.</p>
Cluster merged fluxgate/search coil data for a solar wind interval occuring on 15/02/2015 21:25:00-22:40:00
<p>Cluster merged fluxgate/search coil data for a solar wind interval occuring on 15/02/2015 21:25:00-22:40:00</p>
Data Supporting "Air-Sea Momentum Exchange with Explicit Wind-Wave-Current Coupling and Effects on Hurricane Structure and Impacts"
<p>This dataset produced the figures for the Air-Sea Momentum Exchange with Explicit Wind-Wave-Current Coupling and Effects on Hurricane Structure and Impacts Manusrcipt.</p>
Venus Express IMA solar wind data for VeRa radio science observations
<p>The Venus Express (VEX) Analyser of Space Plasmas and Energetic Atoms (ASPERA-4)<br>ion mass spectrometer (IMA) observed the the pristine solar wind parameters<br>at Venus between 2006 and 2014. Details can be found in<br>Barabash et al. (2007) Planetary and Space Science, Vol. 55 (12), pp. 1772-1792<br>The Analyser of Space Plasmas and Energetic Atoms (ASPERA-4) for the <br>Venus Express mission.</p> <p>This file is based on the VEX_IMA_SW_NVP_20060101.cef data file provided<br>by M. Fränz (MPI for Solar System Research, Goettingen, Germany) and contains only those ASPERA-4<br>solar wind observations used in the publication Peter et al. (2024) Icarus<br>The variability of the topside ionospheres of Venus and Mars<br>as seen by recent radio science observations (Appendix A4 Fig. 2).</p> <p>Data are based on hourly average IMA spectra obtained when VEX was at larger distance than<br>1 Venus radius from nominal bow shock (Martinecz 2009, PhD). Density and total velocity<br>are obtained by integration over the IMA_EXTRA proton spectrum. Dynamic pressure<br>by product of density and velocity. This file contains only orbits used for VERA analyis.</p>
Solar and Wind Energy Drought Data for 15 BAs in the CONUS
<p><strong>Solar and wind energy drought data for 15 BAs in the CONUS</strong></p> <p>This dataset has 2 components, (1) physically consistent wind, solar and load data for 15 Balancing Authorities (BAs) in the CONUS and (2) pre-computed BA-level energy droughts for a variety of time scales from 1 hour to 5 days. The generation and load data is aggregated from plant level data based on EIA-860 2020 infrastructure. </p> <p>For more information please refer to Bracken et al. 2023, Standardized Benchmark of Historical Compound Wind and Solar Energy Droughts Across the Continental United States, in prep, or refer to the Github repository https://github.com/GODEEEP/energy-droughts</p> <p><strong>Wind, solar and load data</strong></p> <p>The data is broken up with one csv file per time scale, the available time scales are 1-hour, 4-hour, 12-hour, 1-day, 2-day, 3-day, and 5-day. Each file has the following columns</p> <ul> <li>ba - Abbreviated name for the BA </li> <li>year - The current year as an integer</li> <li>period - A unique integer for the current time step</li> <li>solar_gen_mwh - Aggregated solar generation in units of MWh</li> <li>solar_capacity_mwh - Aggregated solar plant capacity expresed as MWh </li> <li>wind_gen_mwh - Aggregated wind generation in units of MWh</li> <li>wind_capacity_mwh - Aggregated wind plant capacity expresed as MWh </li> <li>load_mwh - BA load in MWh</li> <li>load_max_mwh - The maximum BA load over the entire historical period</li> <li>datetime_utc - Time stamp for the current time step, in UTC, All time stamps are beginning of period. </li> <li>timezone - The predominant time zone for the BA</li> <li>wind_cf - Wind capacity factor, wind_gen_mwh/wind_capacity_mwh</li> <li>solar_cf - Solar capacity factor, wind_gen_mwh/wind_capacity_mwh </li> <li>load_cf - Load "capacity factor", expresed as a fraction of the maximum BA load, load_mwh/load_max_mwh </li> </ul> <p><strong>Energy drought data</strong></p> <p>Several kinds of energy droughts are available</p> <ul> <li><strong>lws</strong> - Load, wind, and solar compound droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>rl</strong> - Residual load (load minus wind and solar gen) droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>solar</strong> - Solar only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>solar_fixed</strong> - Solar only droughts defined using a single 10th percentile threshold </li> <li><strong>wind</strong> - Wind only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>wind_fixed</strong> - Wind only droughts defined using a single 10th percentile threshold </li> <li><strong>ws</strong> - Wind only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>ws_fixed</strong> - Wind and solar droughts defined using a single 10th percentile threshold </li> </ul> <p>Each drought type and time scale is in a csv file with the following columns (not all columns are available for every drought type)</p> <ul> <li>ba - Abbreviated name for the BA </li> <li>run_id - unique id for each drought event</li> <li>datetime_utc - Date stamp for the start of the drought, in UTC</li> <li>timezone - The predominant time zone for the BA</li> <li>run_length - The length of a drought in time steps</li> <li>run_length_days - The length of the drought in days</li> <li>severity_ws - Drought severity for wind and solar droughts, computed using the compound drought magnitude metric</li> <li>severity_lws - Drought severity load, wind, and solar droughts, computed using the compound drought magnitude metric</li> <li>severity_mwh - Drought severity expressed as MWh</li> <li>zero_prob - For solar, this value indicates if the timestep has zero probability of solar production, i.e. night time</li> <li>year - The year of the timestep</li> <li>month - The month of the timestep </li> <li>hour - The hour of the timestep </li> <li>wind_cf - Wind capacity factor for the drought</li> <li>solar_cf - Solar capacity factor for the drought </li> <li>srepi_solar - Standardized renewable energy production index for solar</li> <li>srepi_wind - Standardized renewable energy production index for wind</li> </ul> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p> </p>
Data and Code for "Study of Solar Wind and Interplanetary Magnetic Field Features Associated with Geomagnetic Storms: The Cross Wavelet Approach"
<p>These files are the supplementary information, including dataset, codes and plots for the research work entitled "Study of Solar Wind and Interplanetary Magnetic Field Features Associated with Geomagnetic Storms: The Cross Wavelet Approach".</p>
Towards FAIR phytolith data: first steps down a long and winding road.
<p>This is a video recording of a presentation given at the International Meeting of Phytolith Research on 7th September 2021. It introduces the FAIR phytoliths project that strives to improve the FAIRness of phytolith data for the phytolith community. It starts by explaining what FAIR is, what constraints there currently are on data sharing and what the benefits of implementing FAIR for phytolith data would be. It then goes on to explain the project - what has been done so far, our plan over the next year and also how the phytolith community can get involved. </p> <p>A pdf and powerpoint with a script of the talk can be found here: <a href="http://zenodo.org/record/5336872#.YTZUWp30lPY">10.5281/zenodo.5336872</a></p>
Supplementary data for: Does Phobos reflect solar wind protons? Mars Express special flyby operations with and without the presence of Phobos
<p>Supplementary data to reproduce figures for "Does Phobos reflect solar wind protons? Mars Express special flyby operations with and without the presence of Phobos".</p>
Data from: Mechanisms of dispersal and colonisation in a wind-borne cereal pest, the haplodiploid wheat curl mite
<p><strong>Filename: </strong>1_Allele_freq</p> <p>Variables:</p> <p>1. marker_id - Name of the microsatellite marker analysed<br> 2. regime - Experimental regime (low heterozygosity - LH; medium heterozygosity - MH; high heterozygosity - HH)<br> 3. allele frequency - The frequency of alleles occurrence in the tested populations</p> <p><strong>Filename: </strong>2_Pattern_of_plants_infestation</p> <p>Variables:</p> <p>1. regime - Experimental regime (low heterozygosity - LH; medium heterozygosity - MH; high heterozygosity - HH)<br> 2. rep.id - Repetition ID<br> 3. mites.no - Number of mites found on one plant<br> 4. plant.no - Number of plant examinated<br> </p> <p><strong>Filename: </strong>3_Dispersal_colonisation_data</p> <p>Variables:</p> <p>1. regime - Experimental regime (low heterozygosity - LH; medium heterozygosity - MH; high heterozygosity - HH)<br> 2. rep.no - Number of repetition within regime<br> 3. rep.id - Repetition ID<br> 4. mite.id - Individual mite ID<br> 5. plant.no - Number of plant examinated<br> 6. dds - Developmental stage of dispersing individual<br> 7. egg.no - Number of eggs laid<br> 8. F1_f.sex - Number of individuals developing into females<br> 9. F1_m.sex - Number of individuals developing into males</p> <p> </p> <p><strong>Filename: </strong>4_Sex_ratio</p> <p>Variables:</p> <p>1. regime - Experimental regime (low heterozygosity - LH; medium heterozygosity - MH; high heterozygosity - HH)<br> 2. source - The source of the tested population (predisperal or postdispersal)<br> 3. rep.id - Repetition ID<br> 4. females.no - Number of females found in a population<br> 5. males.no - Number of males found in a population</p>
Data from: Influence of topography and the underlying surface of the Bohai Sea on wind and gust forecasts
<p class="MsoNormal"><span>Accurate gust forecasts can reduce potential threats to people's lives and properties, but we need more reliable forecasting methods and models. A recent development is the meteorologically stratified gust factor (MSGF) model, which is more accurate in forecasting gusts than the previous gust factor model. The regional terrain and underlying surface both have crucial effects on the gust factor. We therefore combined observations from the China Meteorological Administration over the ocean surface and along the coast with the MSGF model to explore the influence of topography and the underlying surface on wind and gust forecasts. The regional terrain and underlying surface affected the peak gust climatologies, the mean wind speed, the mean prevailing wind direction and the gust factors. The topography and the underlying surface had different impacts in different ranges of the mean wind speed. The strong turbulence that causes changes in the gust factor under light winds is not initiated over rough underlying surfaces. When the mean wind speed is >2.5 m s<sup>−1</sup>, the underlying surface influences both the wind speed and the gust factor. A rough underlying surface stimulates stronger turbulence and increases the gust speed and gust factor, whereas a smooth underlying surface directly increases the mean wind speed and the gust speed by different magnitudes to reduce the difference between them, thus decreasing the gust factor. We evaluated the ability of the MSGF model to forecast gusts and verified a method combining the products of a numerical model and the MSGF model in gust forecasts.</span></p>
Data for: Wind speed that can effect increasing COVID-19
<p>Several nations are currently experiencing a significant increase in coronavirus (COVID-19), including Indonesia. A total of 34,874,744 confirmed cases with 1,097,497 deaths (case fatality rate (CFR) 3.1%) were reported in 216 countries based on data from World Health Organization. COVID-19 remains public health problem around the world. It is possible the climate could affect the transmission of COVID-19. The wind is one of the climate factors besides temperature, humidity, and rainfall. Wind speed data can be used to study the spread of COVID-19 cases.</p>
Data for: The influence of vegetation structure on secondary diaspore dispersal by wind
<p><span>The role of vegetation structure in relation to wind speed and diaspore attributes on secondary diaspore dispersal by wind has not </span><span>been empirically studied</span><span>. </span><span>Here, we investigated secondary dispersal by wind of diaspores placed in </span><span>12</span><span> different kinds of vegetation</span><span> and bare land</span><span>. The experiments were conducted in a wind tunnel using a range of wind speeds and diaspores that differed in mass and kind of appendages. </span><span>The explanations of wind speed, diaspore attribute</span><span>s</span><span>, vegetation coverage, life-form, vertical </span><span>pattern </span><span>and horizontal pattern for diaspore dispersal capacity were 6.67~10.40%, 16.13~20.53%, </span><span>6.227~</span><span>24.64%, 0.10%, 0.74%, and 0.10%, respectively. </span><span>Compared with wind speed and diaspore attributes, vegetation coverage contributed the most to diaspore dispersal capacity when vegetation coverage was low (</span><span><10% in our study). However</span><span>, but with a high (10-30%) coverage, vegetation coverage was the least influential factor in secondary diaspore dispersal by wind. V</span><span>egetation coverage </span><span>significantly </span><span>interact</span><span>ed with</span> <span>vegetation life-form, horizontal pattern and vertical pattern </span><span>on affecting</span><span> diaspore dispersal capacity. </span><span>Thus,</span><span> the most influential factor determining secondary diaspore dispersal by wind is vegetation coverage.</span></p>
Data for the manuscript named 'Altitude-dependence response near cusp region after solar wind variations'
<p>Data for the manuscript named 'Altitude-dependence response near cusp region after solar wind variations</p> <p>1) cluster data, observation from cluster mission</p> <p>2) MAD6400_2001-10-12_tau1a_59.4@vhfa_041292, EISCAT radar data</p> <p>3) OMNI: solar wind and IMF data</p> <p>4) p_shell_5.7Re_011012 is thermal pressure at 5.7 Re-shell, which was carried out in real-time solar wind conditions during 08:30-10:00UT (real-time-density run)</p> <p>5) p_shell_5.7Re_011012cd is thermal pressure at 5.7 Re-shell, which was carried out in real-time solar wind conditions except for solar wind density during 08:30-10:00UT (no-density-increasing run) </p> <p>“1_5.7Rshell_out.txt”time_latitude_out</p> <p>“1P_shell_5.7Re.csv”time_shell_5.7Re</p> <p>data range: MLT 08:00~16:00, latitude -90°~ -30°</p>
Data from: Sensitivity analysis of collision risk at wind turbines based on flight altitude of migratory waterbirds
<p>This dataset contains information on the distribution of geese and swans and the three-dimensional flight trajectories. The former was obtained through vehicle field surveys, interviews, and a literature review. The latter was obtained using ornithodolites.</p>
META-DATA for IEA Wind Task 46. WP2: Atmospheric drivers of wind turbine blade leading edge erosion: Ancillary variables
<p>Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) created Task 46 to undertake cooperative research in the key topic of blade erosion.</p> <p>This report is a product of WorkPackage 2 <strong>Climatic conditions driving blade erosion. </strong></p> <p>The objectives of the work summarized in this report are to:</p> <ul> <li>Summarize efforts to elucidate critical atmospheric co-stressors that may accelerate leading edge erosion and hence for which meta-data regarding observations should be collated.</li> <li>Briefly describe and summarize additional data pertaining to those LEE co-stressors from sites that were the focus of analyses of hydrometeors in the report “Atmospheric drivers of wind turbine blade leading edge erosion: Hydrometeors” (Pryor et al. 2021)</li> </ul> <p>Accompanying this report is a detailed spreadsheet that summarizes the meta-data regarding these co-stressor variables.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.