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121 results for “Surface Wave”
Surface-wave instability without inertia in shear-thickening suspensions
<p>All data plotted in the figures of the article "Surface-wave instability without inertia in shear-thickening suspensions".</p> <p> </p>
Ground magnetic perturbations associated with "Ultra-Fast Kelvin Wave (UFKW) Variations in the Surface Magnetic Field"
<p>The data set is based on the thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM) simulation results and a post-processing 3D ionospheric current and magnetic perturbation calculation, which is used in the publication "Ultra-Fast Kelvin Wave (UFKW) Variations in the Surface Magnetic Field" by Forbes et al. Focus is on an eastward-propagating ultra-fast Kelvin wave (UFKW) packet with periods between 2-4 days and zonal wavenumber s = -1 during DOY 266-281, 2009. Two sets of simulations are provided with different lower boundary (97km) conditions: 1. forced by daily and zonal mean (S0) 2. forced by S0 and 2-7days with s=-1 perturbations with the planetary wave amplitudes increased by a factor of 1.5. For these simulations the solar flux and geomagnetic forcing is held constant. Hourly output is provided of the neutral wind, neutral temperature, and ground magnetic perturbations.</p>
Global increase in tropical cyclone ocean surface waves
<p>The data and codes in this repository can be used to support the main conclusion in the manuscript "Global increase in tropical cyclone ocean surface waves" by Shi et al., submitted to Nature Communications.</p>
Equatorial waves for: A prediction attempt using equatorial waves for tropical sea surface temperature anomaly by Atlantic zonal mode
<p>The dataset is the wave-induced geopotential output from linear ocean models and potential energy flux by a group-velocity-based wave energy flux scheme in the period (1992–2016), which is involved in building a lightweight model, as well as showing a simple instance of utilizing the wave energy transfer for the prediction of Atlantic Niño/Niñas.</p>
Active and passive surface-wave recordings along high-speed lines
<p>Seismic uniform linear arrays (ULAs) with a length of 23.75 m and composed by 96 geophones spaced at intervals of 25 cm were installed in 3 different sites (sites 1, 2 and 3). The ULAs were positioned on the cess (i.e. the track side) along the railway, at approximately 2 m from the rails, on track 1 for site 1 and track 2 for sites 2 and 3. Active seismic data was acquired by vertically striking a metal plate placed in-line, 12.5 cm away from the first (direct shot) and last (inverse shot) geophones.The impact was achieved with a hammer (1.5 kg) and waves propagation along the ULA was recorded for 2 s with a sampling interval of 0.5 ms (i.e. sampling rate of 2 kHz) and a pre-triggering delay of -0.02 s. Six direct and inverse shots were stacked in the time domain to enhance signal-to-noise ratio.HST passages on tracks 1 and 2 were also recorded with the exact same ULAs for durations of 120 s with a sampling interval of 2 ms (i.e. sampling rate of 500 Hz). The recordings were initiated manually upon visual confirmation of approaching trains at the site.</p>
Material parameter influence on solitary wave induced surface dilation: restart files, output files, videos and input scripts
<p>Restart files, output files, videos and input scripts for Material parameter influence on the expression of Solitary-Wave-Induced Surface Dilation (Frizzell and Hartzell, 2024). Input files can be find at the linked repository (https://github.com/efrizz-umd/SID_sensitivity). Restart files and output files were made with LIGGGHTS (https://www.cfdem.com/liggghtsr-open-source-discrete-element-method-particle-simulation-code) and videos were made with OVITO (https://www.ovito.org/).</p>
Data for Paper: Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions
<p>Laboratory measurements of wind, waves, and airside static pressure under low to moderate wind forcing (U10 ~ 6 -16 m/s) collected in Oct 2022 in the SUSTAIN wind-wave facility at the University of Miami.</p> <p>This dataset includes 11 runs, all of which contain monochromatic waves generated by the wave paddles with various wind forcing exerted above. All data is in ".mat" formate readable via MATLAB.</p> <p>Experiment set up and positions of instruments are documented in more details in the manuscript Tan et al (2024): Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions.</p> <p> Fig_3: time series static pressure p sampled at 100 Hz and horizontal/vertical wind speed (u/w) sampled at 1000 Hz</p> <p>Fig_4: Frictional velocity u_star_Rn* obtained at differenet heights (h) using frictional velocity</p> <p>Fig_5: a folder that containes the phase-averaged, spline-interpolated static pressure (p2_total), X-coordinate (long-wave phase), Y coordinate (heights above the stationary water) and the u/w at respective heights to generate airflow streamlines</p> <p>Fig_6 and 7: NSS-based phase-averaged, spline-interpolated pressure (delta_P_new).</p> <p>Fig_8: phase-averaged form stress based on measurements and NSS for all 11 runs</p> <p>Fig_9: NSS-based form stress deviation from measured form stress (NSS miscal) against wind-steepness and wave age;</p> <p>Fig_10 and 11: wave growth rate (gamma) against wave age (Cp/ustar) and two other parameterization from Fig.10</p> <p>(The revised version contains the projection of Donelan (1999) and Yang et al. (2013)'s data to the U10/Cp parameterization in panel (b) per reviewer's suggestion);</p> <p>Fig_12: form stress values (tau_form) and form stress to total stress (tau_tot) ratio.</p> <p>(The revised version contains U10 per reviewer's suggesion).</p> <p>This project was Funded in part by Office of Naval Research/Naval Research Laboratory base program unit 73-1Y91.</p> <p>Please cite our JGR: Oceans paper "Wind-wave momentum flux in steep, strongly forced,1 surface gravity wave conditions" if you were to use our dataset.</p> <p>Contact: Peisen Tan <pxt254@miami.edu> for different levels of raw data collected in this experiment.</p> <p>We kindly ask the readers who use our dataset to cite our paper:</p> <p><span>Tan, P.</span><span>, </span><span>Savelyev, I.</span><span>, </span><span>Laxague, N. J. M.</span><span>, </span><span>Haus, B. K.</span><span>, </span><span>Curcic, M.</span><span>, </span><span>Matt, S.</span><span>, et al. (</span><span>2025</span><span>). </span><span>Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions</span><span>. </span><em>Journal of Geophysical Research: Oceans</em><span>, </span><span>130</span><span>, e2024JC021616. </span><a href="https://doi.org/10.1029/2024JC021616">https://doi.org/10.1029/2024JC021616</a></p> <p>We would also appreciate if you can send us a copy of your manuscript if you have used our data. Thank you!</p>
Observed and Simulated Surface Wave and Roller Dissipation with Ground Truth Data at the West Coast of Sylt on Sep 27 - Oct 2, 2016
<p>This dataset contains post-processed Doppler marine radar observations and numerical simulations of surface wave and roller dissipation as well as wave energy flux across the surf zone of a double-barred sandy beach at the island of Sylt, Germany.</p> <p>The methodology to obtain dissipation from coherent marine radar data is described in the following article:</p> <p>Streßer, M., Horstmann J., Baschek, B. (2022): Surface Wave and Roller Dissipation Observed with Shore-based Doppler Marine Radar. Manuscript in preparation.</p> <p>The simulations were realized with with the <a href="https://github.com/mstresser/SimpleWaves1D">SimpleWaves1D</a> model using the <a href="https://linkinghub.elsevier.com/retrieve/pii/S0378383907000580">Janssen and Battjes (2007)</a> wave breaking parameterization. The wave buoy data used to force the model was recorded as part of the <a href="https://codm.hzg.de/codm/">COSYNA</a> observation system. The bathymetry transect was generated using the echosounder data of <a href="https://doi.pangaea.de/10.1594/PANGAEA.898407">Cysewski et al. (2019)</a></p> <p>Radar data and and simulation results are re-gridded to a common hourly time grid using nearest neighbor interpolation with the dimension [time x range].</p> <p>Structure of radar observations:</p> <pre><code>CMRgridded = struct with fields: t: [109×1 double] time as Matlab datenum r: [435×1 double] range, i.e. distance from radar antenna [m] cg: [435×109 double] wave group velocity [m/s] cp: [435×109 double] wave phase velocity [m/s] d: [435×109 double] local water depth [m] H_rms_roller: [435×109 double] energy wave height estimated using the roller concept [m] k: [435×109 double] local wave number [rad/m] w: [435×109 double] wave frequency [rad/s] Dr: [435×109 double] roller dissipation [W/m^2] Er: [435×109 double] roller energy [Nm/m^2] Fr: [435×109 double] flux of roller energy [W/m] Dw: [435×109 double] wave dissipation [W/m^2]</code></pre> <p>Structure of the simulation results:</p> <pre><code>SWgridded = struct with fields: t: [109×1 double] timestamp as Matlab datenum r: [435×1 double] range, i.e. distance from radar antenna [m] cg: [435×109 double] wave group velocity [m/s] cp: [435×109 double] wave phase velocity [m/s] d: [435×109 double] local water depth [m] H_rms: [435×109 double] energy wave height [m] E: [435×109 double] wave energy [Nm/m^2] Dr: [435×109 double] roller dissipation [W/m^2] Dw: [435×109 double] wave dissipation [W/m^2] Er: [435×109 double] roller energy [Nm/m^2] Qb: [435×109 double] fraction of breaking waves [-]</code></pre> <p> </p> <p>Ground truth data is available from two bottom mounted pressure wave gauges (PG) located at r=127.5 m (PG<sub>A</sub>) and r=180 m (PG<sub>B</sub>) and a wave rider buoy located at r=1100 m. </p> <p>Structure of the ground truth data;</p> <pre><code>PG = struct with fields: t: [209×1 double] timestamp as Matlab datenum Hs_pg_A: [209×1 double] significant wave height at the pressure gauge A Hs_pg_B: [209×1 double] significant wave height at the pressure gauge B WR = struct with fields: t: [1344×1 double] timestamp as Matlab datenum Hs: [1344×1 double] significant wave height [m] Dirp: [1344×1 double] wave direction at the peak frequency [°] Tp: [1344×1 double] peak period [s] Tmean: [1344×1 double] mean period, or Tm(0,1) [s] Tcross: [1344×1 double] zero-upcross period [s] Sprp: [1344×1 double] directional spread at the peak frequency [°] </code></pre> <p> </p> <p>The data is stored in Matlab<sup>®</sup> v7.3 format. Timestamps are given as Matlab<sup>®</sup> datenum, i.e. whole and fractional number of days from January 0, 0000.</p> <p> </p>
Synthetic seismograms for "Resolving continental magma reservoirs with 3D surface wave tomography"
<p>This repository contains the Specfem3D input files required to reproduce the results shown in Maguire et al., (2022), G-Cubed. Each model directory (M1, M2, and M3), containes subdirectories for each virtual source. Within a virtual source directory, there is a "DATA" directory, containing the Specfem3D input files, and an "OUTPUT_FILES" directory, containing the output files produced from a simulation, including the ascii seismograms.</p> <p>The Specfem3D source code is not included, but can be downloaded at https://github.com/geodynamics/specfem3d</p>
Coupled lithospheric deformation in the Qinling Orogen, central China: Insights from seismic reflection and surface-wave tomography
<p><strong>Data of geochronology of intrusive plutons and selected zircon Hf values shown in Figure S1 and the references cited</strong></p>
Less-well-developed crustal flow in the central Tibetan Plateau revealed by receiver function and surface wave joint inversion
<p>A crustal flow model has been previously used to explain the crustal extension of the Tibetan Plateau. However, the existence of massive crustal flow in the central plateau is still controversial. We conducted a joint inversion of receiver functions and surface wave dispersions from the 2-D broadband seismic array SANDWICH deployed in the central plateau. The crustal S-wave velocity structure with high vertical resolution shows a low-velocity layer (LVL) in the middle-lower crust beneath most stations. The S-wave velocity of this LVL is mostly within 3.0-3.4 km/s, reflecting a melt volume percentage (MVP) ≤ 7%, except at two stations. Our study suggests that there is not a high enough melt volume in central Tibet to develop crustal flow, which requires an MVP ≥ 7% to decrease rock strength. The formation of extensional structures in the central plateau may contribute to ductile deformation in the middle-lower crust but not crustal flow.</p>
Dataset used in Sea surface wind structure observed by wave gliders during tropical cyclones
<p>Sea surface wind vector observed by three wave gliders deployed in the Western Pacific Ocean. The observation level is 1.2 meter.</p>
Wave field in a wind and paddle tank: effect of a thin surface layer of fish oil
<p>Video showing the surface elevation field in a wind- and paddle-wave tank in clean water condition (tap water) and in water covered with a thin layer of fish oil. Reference wind speed 8 m/s and JONSWAP-like paddle spectrum (Hs = 0.062 cm, Tp =1.0 s) Experiments carried out in the flume of the First Institute of Oceanography (Qingdao, P.R. China).</p>
Wave field in a wind tank: effect of a thin surface layer of fish oil.
<p>Video showing the surface elevation field in a wind-wave tank in clean water condition (tap water) and in water covered with a thin layer of fish oil. Reference wind speed 6 m/s. Experiments carried out in the flume of the First Institute of Oceanography (Qingdao, P.R. China).</p>
Roles of Wind-Driven Currents and Surface Waves in Sediment Resuspension and Transport During a Tropical Storm
<p>Roles of Wind-Driven Currents and Surface Waves in Sediment Resuspension and Transport During a Tropical Storm</p>
Free surface evolution from numerical wave tank simulations - Experiment W6N5D5
<p>An ensemble of two-dimensional numerical wave tank (NWT) simulations of breaking and non-breaking wave packets. The NWT uses the Gerris software package, a two-phase Navier-Stokes solver that utilises the volume-of-fluid method and explicitly models viscosity and surface tension effects. It is configured in non-dimensional coordinates scaled by the length and time characteristics of a deep-water wave with wavelength 1 m. This dataset contains simulations from experiment W6N5D5 (wind forcing speed equal to 6 times the wave speed, chirped wave packet with 5 waves in the packet signal, deep water) and forms part of an ensemble of experiments available <a href="https://doi.org/10.5281/zenodo.12797829" target="_blank" rel="noopener">here</a>. A full description of the NWT is provided in:</p> <p><a href="https://doi.org/10.1017/jfm.2023.134" target="_blank" rel="noopener">Boettger, D. G., Keating, S. R., Banner, M. L., Morison, R. P., & Barthelemy, X. (2023). An energetic signature for breaking inception in surface gravity waves. Journal of Fluid Mechanics, 959, A33.</a></p> <p><a href="https://doi.org/10.1103/PhysRevFluids.9.054803" target="_blank" rel="noopener">Boettger, D. G., Keating, S. R., Banner, M. L., Morison, R. P., & Barthelemy, X. (2024). Energetic inception of breaking in surface gravity waves under wind forcing. Physical Review Fluids, 9 (5), 054803.</a></p>
Free surface evolution from numerical wave tank simulations - Experiment W0N5D2
<p>An ensemble of two-dimensional numerical wave tank (NWT) simulations of breaking and non-breaking wave packets. The NWT uses the Gerris software package, a two-phase Navier-Stokes solver that utilises the volume-of-fluid method and explicitly models viscosity and surface tension effects. It is configured in non-dimensional coordinates scaled by the length and time characteristics of a deep-water wave with wavelength 1 m. This dataset contains simulations from experiment W0N5D2 (Zero wind forcing, chirped wave packet with 5 waves in the packet signal, intermediate water depth) and forms part of an ensemble of experiments available <a href="https://doi.org/10.5281/zenodo.12797829" target="_blank" rel="noopener">here</a>. A full description of the NWT is provided in:</p> <p><a href="https://doi.org/10.1017/jfm.2023.134" target="_blank" rel="noopener">Boettger, D. G., Keating, S. R., Banner, M. L., Morison, R. P., & Barthelemy, X. (2023). An energetic signature for breaking inception in surface gravity waves. Journal of Fluid Mechanics, 959, A33.</a></p> <p><a href="https://doi.org/10.1103/PhysRevFluids.9.054803" target="_blank" rel="noopener">Boettger, D. G., Keating, S. R., Banner, M. L., Morison, R. P., & Barthelemy, X. (2024). Energetic inception of breaking in surface gravity waves under wind forcing. Physical Review Fluids, 9 (5), 054803.</a></p>
Free surface evolution from numerical wave tank simulations - Experiment W0N5D5
<p>An ensemble of two-dimensional numerical wave tank (NWT) simulations of breaking and non-breaking wave packets. The NWT uses the Gerris software package, a two-phase Navier-Stokes solver that utilises the volume-of-fluid method and explicitly models viscosity and surface tension effects. It is configured in non-dimensional coordinates scaled by the length and time characteristics of a deep-water wave with wavelength 1 m. This dataset contains simulations from experiment W0N5D5 (Zero wind forcing, chirped wave packet with 5 waves in the packet signal, deep water) and forms part of an ensemble of experiments available <a href="https://doi.org/10.5281/zenodo.12797829" target="_blank" rel="noopener">here</a>. A full description of the NWT is provided in:</p> <p><a href="https://doi.org/10.1017/jfm.2023.134" target="_blank" rel="noopener">Boettger, D. G., Keating, S. R., Banner, M. L., Morison, R. P., & Barthelemy, X. (2023). An energetic signature for breaking inception in surface gravity waves. Journal of Fluid Mechanics, 959, A33.</a></p> <p><a href="https://doi.org/10.1103/PhysRevFluids.9.054803" target="_blank" rel="noopener">Boettger, D. G., Keating, S. R., Banner, M. L., Morison, R. P., & Barthelemy, X. (2024). Energetic inception of breaking in surface gravity waves under wind forcing. Physical Review Fluids, 9 (5), 054803.</a></p>
Free surface evolution from numerical wave tank simulations - Experiment W0N9D5
<p>An ensemble of two-dimensional numerical wave tank (NWT) simulations of breaking and non-breaking wave packets. The NWT uses the Gerris software package, a two-phase Navier-Stokes solver that utilises the volume-of-fluid method and explicitly models viscosity and surface tension effects. It is configured in non-dimensional coordinates scaled by the length and time characteristics of a deep-water wave with wavelength 1 m. This dataset contains simulations from experiment W0N9D5 (Zero wind forcing, chirped wave packet with 9 waves in the packet signal, intermediate water depth) and forms part of an ensemble of experiments available <a href="https://doi.org/10.5281/zenodo.12797829" target="_blank" rel="noopener">here</a>. A full description of the NWT is provided in:</p> <p><a href="https://doi.org/10.1017/jfm.2023.134" target="_blank" rel="noopener">Boettger, D. G., Keating, S. R., Banner, M. L., Morison, R. P., & Barthelemy, X. (2023). An energetic signature for breaking inception in surface gravity waves. Journal of Fluid Mechanics, 959, A33.</a></p> <p><a href="https://doi.org/10.1103/PhysRevFluids.9.054803" target="_blank" rel="noopener">Boettger, D. G., Keating, S. R., Banner, M. L., Morison, R. P., & Barthelemy, X. (2024). Energetic inception of breaking in surface gravity waves under wind forcing. Physical Review Fluids, 9 (5), 054803.</a></p>
Free surface evolution from numerical wave tank simulations - Experiment W1N5D5
<p>An ensemble of two-dimensional numerical wave tank (NWT) simulations of breaking and non-breaking wave packets. The NWT uses the Gerris software package, a two-phase Navier-Stokes solver that utilises the volume-of-fluid method and explicitly models viscosity and surface tension effects. It is configured in non-dimensional coordinates scaled by the length and time characteristics of a deep-water wave with wavelength 1 m. This dataset contains simulations from experiment W1N5D5 (wind forcing speed equal to wave speed, chirped wave packet with 5 waves in the packet signal, deep water) and forms part of an ensemble of experiments available <a href="https://doi.org/10.5281/zenodo.12797829" target="_blank" rel="noopener">here</a>. A full description of the NWT is provided in:</p> <p><a href="https://doi.org/10.1017/jfm.2023.134" target="_blank" rel="noopener">Boettger, D. G., Keating, S. R., Banner, M. L., Morison, R. P., & Barthelemy, X. (2023). An energetic signature for breaking inception in surface gravity waves. Journal of Fluid Mechanics, 959, A33.</a></p> <p><a href="https://doi.org/10.1103/PhysRevFluids.9.054803" target="_blank" rel="noopener">Boettger, D. G., Keating, S. R., Banner, M. L., Morison, R. P., & Barthelemy, X. (2024). Energetic inception of breaking in surface gravity waves under wind forcing. Physical Review Fluids, 9 (5), 054803.</a></p>
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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.