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274 results for “wakes”
"Wind turbine wakes on escarpments: A wind-tunnel study"
<p>Dar, Arslan Salim, and Fernando Porté-Agel. "Wind turbine wakes on escarpments: A wind-tunnel study." <em>Renewable Energy</em> 181 (2022): 1258-1275.</p>
Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words
<p>Hi,KIA dataset is a shared short Wakeup Word database focusing on perceived emotion in speech The dataset contains <strong>488 </strong>Wakeup Word speech. </p> <p>For more detailed information about the dataset, please refer to our paper: Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words</p> <p><strong>File Description</strong></p> <ul> <li><em><strong>wav/</strong></em>: wav files. <ul> <li>Filename f`{gender}_{pid}_{scene}_{trial}_{emotion}.wav` The first letter was used to express emotion.<br> </li> </ul> </li> <li><em><strong>annotation/</strong></em>: Information related to annotation and human validation of the entire speech</li> <li> <p><em><strong>split</strong></em>: 8fold data split with {train, valid, test}.csv </p> </li> <li> <p><em><strong>handcraft:</strong></em> Features used for data EDA and baseline performance</p> </li> <li> <p><em><strong>best_weights:</strong></em> wav2vec2.0 context network finetuning weights for re-implementation. Due to file size, we attach only fold M1, F5</p> </li> </ul> <p> </p> <p><strong>Reference</strong></p> <ul> </ul> <p>Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words [[ArXiv](https://arxiv.org/abs/2211.03371)]</p> <p>```<br> @inproceedings{kim2022hi,<br> title={Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words},<br> author={Taesu Kim, SeungHeon Doh, Gyunpyo Lee, Hyung seok Jun, Juhan Nam, Hyeon-Jeong Suk},<br> booktitle={Proceedings of the 14th Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)},<br> year={2022}<br> }<br> ```</p>
Tohoku Tsunami, March 11 2011: GoogleEarth-Screenshot of Wake tide gauge station at 10:07 hours CET
<p>GoogleEarth-Screenshot of Wake tide gauge station on March 11 at 10:07 hours CET. Tide gauge station live data provided from Marine Obs by Program - National Data Buoy Center - NOAA (<a href="https://deref-gmx.net/mail/client/_W-lmx-gCeY/dereferrer/?redirectUrl=http%3A%2F%2Fwww.ndbc.noaa.gov%2Fkml%2Fmarineobs_by_pgm.kml">www.ndbc.noaa.gov/kml/marineobs_by_pgm.kml</a>).</p>
Wake data documentation for a wind turbine rotor with winglets
<p>This is the documentation of data, measured in a experimental campaign, in which the effects of winglets<br> on a model wind turbine rotor were investigated</p>
Parameter uncertainty quantification of wake models to analyze effects of wake superposition: data and code
<p>Codebase for wake deficit, wake superposition, and wake-added turbulence modeling within Markov-chain Monte Carlo framework. Data for results and figures in associated paper is also included.</p>
Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment
<p>Selection of the data in the following paper:<br> Sengers, B. A. M., Steinfeld, G., Hulsman, P., & Kuehn, M. (2023). Validation of an interpretable data-driven wake model using lidar measurements from a free-field wake steering experiment. Wind Energy Science Discussions, 1-32.</p> <p>This data subset provides input parameters commonly used in wake models, as well as ten-minuted averaged cross sections of the flow field at 4 rotor diameters downstream, as measured by a nacelle-mounted lidar. </p> <p>Cite this as:<br> B.A.M. Sengers (2023). Dataset: Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment. https://doi.org/10.5281/zenodo.7741395</p>
Figures: Vortex model of the aerodynamic wake of airborne wind energy systems
<p>Figures in .pdf, .png and .fig format.</p><p>Figures in .fig format can be opened with MATLAB or other open source programming languages (e.g., Python thought the command scipy.io.loadmat or Octave)</p><p>Figures were updated after: Trevisi, F., Croce, A., and Riboldi, C. E. D.: Corrigendum to "Vortex model of the aerodynamic wake of airborne wind energy systems", published in Wind Energ. Sci., 8, 999–1016, 2023, https://doi.org/10.5194/wes-8-999-2023-corrigendum"</p>
Fish can use coordinated fin motions to recapture their own vortex wake energy
<p>This data repository includes all the fish swimming data and metadata used to investigate how fish can recapture their own wake to improve swimming efficiency. Custom Matlab scripts developed to compute kinematics, pressure fields, and forces are available here.</p> <p><strong>Matlab scripts:</strong> The custom Matlab scripts developed to compute and analyze kinematics, velocimetry, pressure, and force data are in separate folders, including the master script and secondary functions. Pressure calculations from PIV data use the queen2 Matlab package available at ( <a href="http://dabirilab.com/software">http://dabirilab.com/software</a>).</p> <p><strong>Respirometry data (supplementary data): </strong>We provide the oxygen consumption data used in statistical analysis of the COT of each specimen in separate sheets in a single xlsx file. In addition to raw oxygen consumption data, each sheet includes metadata relative to experimental conditions and the magnitude of the relative flow speeds in body length per second tested during steady swimming trials. We also provide the rpm to flow velocity calibration used with our flume. Scaled original pictures of the fish used for respirometry experiments are available in .jpg format and include morphological information such as standard body length (BL) and fork length.</p> <p><strong>PIV data:</strong> High-resolution videos and velocity fields computed using DaVis are sorted by fish specimen and swimming speed. Additional metadata relative to the fish and experimental parameters (i.e., salinity, temperature) are included. Scaled original pictures of the fish used to conduct top-down PIV experiments are available in .jpg format and include morphological information such as standard body length (BL) and fork length.</p>
Data from: Scn2a insufficiency alters spontaneous neuronal Ca2+ activity in somatosensory cortex during wakefulness
<p class="MsoNormal">SCN2A protein-truncating variants (PTV) can result in neurological disorders such as autism spectrum disorder and intellectual disability, but they are less likely to cause epilepsy in comparison to missense variants. While<em> <span>i</span>n vitro </em>studies showed PTV reduce action potential firing, consequences at <em>in vivo</em> network level remain elusive. Here, we generated a mouse model of Scn2a insufficiency using antisense oligonucleotides (Scn2a ASO mice), which recapitulated key clinical feature of SCN2A PTV disorders. Simultaneous two-photon <span>Ca<sup>2+</sup></span> imaging and electrocorticography (ECoG) in awake mice showed that spontaneous <span>Ca<sup>2+</sup></span> transients in somatosensory cortical neurons, as well as their pairwise co-activities were generally decreased in Scn2a ASO mice during spontaneous awake state and induced seizure state. The reduction of neuronal activities and paired co-activity are mechanisms associated with motor, social and cognitive deficits observed in our mouse model of severe Scn2a insufficiency, indicating these are likely mechanisms driving SCN2A PTV pathology.</p>
Interstitial cortisol measurements aligned by wake time, healthy volunteers
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Cortical astrocyte histamine-1-receptors regulate intracellular calcium and extracellular adenosine dynamics across sleep and wake
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Long-term multichannel recordings in Drosophila flies reveal altered predictive processing during sleep compared with wake
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Data from: Scn2a insufficiency alters spontaneous neuronal Ca2+ activity in somatosensory cortex during wakefulness
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Mesolimbic dopamine neurons drive infradian rhythms in sleep-wake and heightened activity state
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Functional networks in the infant brain during sleep and wake states
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Experimental and theoretical study of wind turbine wakes in yawed conditions
<p>Hub-height horizontal-plane PIV measurement data of the wake behind a stand-alone yawed wind turbine.</p> <p>HDF5 data structure:</p> <ul> <li>Yaw_0_Lambda_o: yaw angle (0, 10, 20, 30 degree) case at the optimal tip-speed ratio <ul> <li>u_avg: mean of u (v, w) component.</li> <li>u_std: standard deviation of the u (v, w) component.</li> <li>x: x grid point.</li> <li>y: y grid point.</li> </ul> </li> </ul> <p>Reference</p> <p>Bastankhah, Majid, and Fernando Porté-Agel. "Experimental and theoretical study of wind turbine wakes in yawed conditions." <em>Journal of Fluid Mechanics</em> 806 (2016): 506-541.</p> <p> </p>
Wikidata Dump wake_dump_rus
<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> <br> <a href="https://tools.wmflabs.org/wdumps/dump/445">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>
Dataset supporting - On the Problem of Modeling the Boat Wake Climate; the Florida Intracoastal Waterway - by Forlini et al., submitted to JGR-Ocean
<p>This dataset comprises of 9 .txt file containing water levels data (m) necessary to reconstruct the wakes observations for all the instruments (Acoustic Doppler Velocimeter) deployed during the field experiment.</p> <p> </p>
Aerodynamics code used in Wind Energy Science paper "Comparison of a coupled near- and far-wake model with a free-wake vortex code"
<p>This research code has been developed from the start of my PhD as a first step before the HAWC2 implementation of the near wake model.</p> <p>It can be used to make aerodynamic computations of a stiff wind turbine rotor, and it includes</p> <ul> <li>A BEM and far wake model implementation based on the one in HAWC2</li> <li>An attached flow unsteady airfoil aerodynamics model including the modifications described in the WES article</li> <li>Most importantly a near wake model implementation including all major modifications except the recent stand still extension presented at TORQUE 2016</li> </ul> <p>All the data files need to be in a subfolder 'NREL_5MW' located in the same folder as the compiled source code.</p> <p>With the present (hardcoded) settings, the program will simulate the NREL 5 MW reference turbine for 650 seconds, with blade vibrations according to different prescribed mode shapes after steady state is reached. The aerodynamics model is a coupled near and far wake model. The integrated aerodynamic work during 1 period of the different prescribed vibrations will be output in the file 'aerowork.out' .</p> <p>The NREL 5 MW turbine is described in:</p> <p>Jonkman, J., Butterfield, S., Musial,W., and Scott, G.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development, National Renewable Energy Laboratory, 2009.</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>
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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)
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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.