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969 results for “velocity”

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zenodo40/100

Instantaneous, three-dimensional velocity fields past a bio-prosthetic aortic valve measured in-vitro with tomographic particle image velocimetry.

<p>Each folder contains&nbsp;instantaneous, three-dimensional velocity vector data obtained in a simplified model of the&nbsp;aortic&nbsp;root with a distinct size and geometry (small, medium, large, and sinus-less). The specific geometry of each aortic root model is contained in the corresponding folder.</p> <p>The velocity data is structured in the following way: Two separate&nbsp;folders for&nbsp;velocity data in the &quot;ascending aorta&quot; domain (AAo) and in the &quot;sinus of Valsalva&quot; domain (SOV). Each domain contains velocity datasets for instances t=0.00, 0.03, 0.06, ..., 0.39 s (t000, t003, t006, ..., t039). Each velocity dataset&nbsp;contains&nbsp;N=16 phase-locked instantaneous 3D velocity fields.</p> <p>The data was acquired using tomographic particle image velocimetry and a custom built hydraulic setup capable of replicating normal physiological flow conditions in the human aorta (heart rate = 72 bpm, cardiac output = 4.8 l)</p> <p>Data format:</p> <p>- aortic root geometry: STL (the geometry is provided with respect to the&nbsp;reference frame of&nbsp;the velocity data)</p> <p>- velocity data: NPY (NumPy), shape= (N_nodes, 6), columns contain X, Y, Z, U, V, W data, where U, V, W are the X, Y, Z components of the instantaneous vector field</p>

opencc-by-4.0Jan 2018View details →
zenodo40/100

STRATIFICATION EFFECTS ON FLOW HYDRODYNAMICS AND MIXING AT A CONFLUENCE WITH A HIGHLY DISCORDANT BED AND A RELATIVELY LOW VELOCITY RATIO

<p>The effects of temperature induced stratification on flow hydrodynamics, thermal mixing and the capacity of the flow to entrain sediment at a medium-size stream confluence with a highly discordant bed are investigated. To isolate the effects due to differences in the temperature/density of the incoming streams, two simulations were conducted with identical flow conditions (mean velocity ratio VR=2.44, temperature difference between the two streams &Delta;T=4.7<sup>0&nbsp;</sup>C). In the first case the Richardson number was Ri=0 (no coupling between the temperature and the momentum equations via the Boussinesq approximation), while in the second simulation Ri=0.67. Even in the Ri=0 case the structure of the mixing interface (MI) was different from the one expected for concordant bed confluences with a similar confluence angle and VR. The MI contained only co-rotating eddies shed in the shear layer forming on the fast speed side of the confluence apex. In the Ri = 0.67 case no wake region was present but a large recirculation eddy formed not far from the confluence apex. In both cases, the flow near the upstream part of the MI was found to be highly 3D and to allow the passage of particles from one side of the confluence to the other. While in the Ri = 0 case mixing was driven by the MI eddies, in the Ri = 0.67 case mixing was controlled by large near-bed intrusions of heavier fluid from the tributary containing colder water and also by the fluid advected in and out of the recirculation eddy.</p>

opencc-by-4.0Mar 2018View details →
zenodo40/100

Time series of detonation velocity for Fickett's model for various values of activation energy

<p>This dataset contains several time series of detonation velocity for Fickett&#39;s model.</p> <p>Parameters are: q=4, resolution per unit lenth is 1280.</p> <p>Activation energies (theta) are 0.95, 1, 1.004, 1.055, 1.065, 1.089.</p>

opencc-by-4.0Jun 2018View details →
zenodo40/100

Velocity-based macrorefugia for boreal passerine birds

<p>Velocity-based macrorefugia for boreal passerine birds&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Citation for dataset&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> --------------------&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Stralberg, D. Velocity-based macrorefugia for boreal passerine birds. Boreal Avian Modelling Project. Edmonton, Alberta, Canada. DOI: 10.5281/zenodo.1299880&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> https://doi.org/10.5281/zenodo.1299880&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Data layers &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> -----------------&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Refugia layers represent mid-century (2041-2070) and end-of-century (2071-2100) conditions for the SRES A2 emissions scenario at 4-km resolution&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> -----------------&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Combined index for 53 species (clipped to Brandt&#39;s boreal region): &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> _refbrandt53_YYYYZZZZ&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Species-specific indices:&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> XXXX_refYYYY&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> where:&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> YYYY = Time period (2050s or 2080s)&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> ZZZZ = weighted or unweighted&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> XXXX = Songbird Species Code (see Birdlookup.csv)&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Percentile values of refugia indices for mapping purposes&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;&nbsp; &nbsp;0.01&nbsp;&nbsp; &nbsp;0.1&nbsp;&nbsp; &nbsp;0.25&nbsp;&nbsp; &nbsp;0.5&nbsp;&nbsp; &nbsp;0.75&nbsp;&nbsp; &nbsp;0.9&nbsp;&nbsp; &nbsp;0.99<br> &quot;2050s, weighted &quot;&nbsp;&nbsp; &nbsp;0.032&nbsp;&nbsp; &nbsp;0.243&nbsp;&nbsp; &nbsp;0.317&nbsp;&nbsp; &nbsp;0.399&nbsp;&nbsp; &nbsp;0.484&nbsp;&nbsp; &nbsp;0.589&nbsp;&nbsp; &nbsp;0.779<br> &quot;2080s, weighted&quot;&nbsp;&nbsp; &nbsp;0.002&nbsp;&nbsp; &nbsp;0.09&nbsp;&nbsp; &nbsp;0.137&nbsp;&nbsp; &nbsp;0.2&nbsp;&nbsp; &nbsp;0.281&nbsp;&nbsp; &nbsp;0.386&nbsp;&nbsp; &nbsp;0.675<br> &quot;2050s, unweighted&quot;&nbsp;&nbsp; &nbsp;0.006&nbsp;&nbsp; &nbsp;0.108&nbsp;&nbsp; &nbsp;0.159&nbsp;&nbsp; &nbsp;0.218&nbsp;&nbsp; &nbsp;0.292&nbsp;&nbsp; &nbsp;0.358&nbsp;&nbsp; &nbsp;0.421<br> &quot;2080s, unweighted&quot;&nbsp;&nbsp; &nbsp;0.001&nbsp;&nbsp; &nbsp;0.055&nbsp;&nbsp; &nbsp;0.083&nbsp;&nbsp; &nbsp;0.123&nbsp;&nbsp; &nbsp;0.185&nbsp;&nbsp; &nbsp;0.241&nbsp;&nbsp; &nbsp;0.297<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Projection information&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> -------------------&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> &quot;&quot;&quot;+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs&quot;&quot;&quot;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> -------------------&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Projection &nbsp; &nbsp;LAMBERT&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Spheroid &nbsp; &nbsp; &nbsp;GRS80&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Units &nbsp; &nbsp; &nbsp; &nbsp; METERS&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Zunits &nbsp; &nbsp; &nbsp; &nbsp;NO&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Xshift &nbsp; &nbsp; &nbsp; &nbsp;0.0&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Yshift &nbsp; &nbsp; &nbsp; &nbsp;0.0&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> Parameters &nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp; 49 &nbsp;0 &nbsp;0.0 /* 1st standard parallel&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp; 77 &nbsp;0 &nbsp;0.0 /* 2nd standard parallel&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp;-95 &nbsp;0 &nbsp;0.0 /* central meridian&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> &nbsp; &nbsp;0 &nbsp;0 &nbsp;0.0 /* latitude of projection&#39;s origin&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> 0.0 /* false easting (meters)&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> 0.0 /* false northing (meters)&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;</p>

opencc-by-4.0Jun 2018View details →
zenodo40/100

High Mountain Asia glacier velocities 2013-2015 (Landsat 8)

<p>This dataset contains the median glacier surface velocity for the Pamir-Karakoram-Himalaya for the years 2013-2015 (Landsat 8 only). The velocities has been obtained by feature-tracking of Landsat images spaced 1 year apart.</p> <p>The folder contains the following fields at 120 m resolution in GeoTiff format:</p> <p>- the velocity magnitude &#39;vel&#39; (meters per year)</p> <p>- the x/y velocity components x_vel/y_vel (meters per year)</p> <p>- the associated errors err, x_err, y_err (meters per year)</p> <p>- the standard deviation of all the merged velocities &#39;std&#39; (meters per year)</p> <p>- the number of image pairs that have been merged in the median</p> <p>&nbsp;</p> <p>I recommend filtering data with error larger than 10 m/yr.</p> <p>For more information and any use of the data, please refer to Dehecq, A., Gourmelen, N., Trouve, E., 2015. Deriving large-scale glacier velocities from a complete satellite archive: Application to the Pamir&ndash;Karakoram&ndash;Himalaya. Remote Sensing of Environment 162, 55&ndash;66. <a href="https://doi.org/10.1016/j.rse.2015.01.031">https://doi.org/10.1016/j.rse.2015.01.031 </a></p>

opencc-by-4.0Feb 2019View details →
zenodo40/100

High Mountain Asia glacier velocities 1999-2003 (Landsat 7)

<p>This dataset contains the median glacier surface velocity for the Pamir-Karakoram-Himalaya for the years 1999-2003 (Landsat 7 SLC-ON only). The velocities has been obtained by feature-tracking of Landsat images spaced 1 year apart.</p> <p>The folder contains the following fields at 120 m resolution in GeoTiff format:</p> <p>- the velocity magnitude &#39;vel&#39; (meters per year)</p> <p>- the x/y velocity components x_vel/y_vel (meters per year)</p> <p>- the associated errors err, x_err, y_err (meters per year)</p> <p>- the standard deviation of all the merged velocities &#39;std&#39; (meters per year)</p> <p>- the number of image pairs that have been merged in the median</p> <p>&nbsp;</p> <p>I recommend filtering data with error larger than 10 m/yr.</p> <p>For more information and any use of the data, please refer to Dehecq, A., Gourmelen, N., Trouve, E., 2015. Deriving large-scale glacier velocities from a complete satellite archive: Application to the Pamir&ndash;Karakoram&ndash;Himalaya. Remote Sensing of Environment 162, 55&ndash;66. <a href="https://doi.org/10.1016/j.rse.2015.01.031">https://doi.org/10.1016/j.rse.2015.01.031 </a></p>

opencc-by-4.0Feb 2019View details →
zenodo40/100

Measurements of Pn velocity and anisotropy in Northwest Pacific region

<p>Measurements of Pn velocity and anisotropy in Northwest Pacific region. The eight numbers in each line are the longitude, latitude, Pn velocity, velocity error, magnitude of Pn anisotropy, magnitude error, direction of the fastest wave propagation, and direction error at each grid.</p>

opencc-by-4.0Mar 2019View details →
zenodo40/100

Cyclostrophic corrections of AVISO/DUACS surface velocities and its application to mesoscale eddies in the Mediterranean Sea

<p>We apply an optimised iterative method to retrieve with best accuracy the cyclogeostrophic corrections on fifteen years (2000-2015) of surface geostrophic velocity fields provided by AVISO/DUACS for the Mediterranean Sea. The initial gridded altimeter products were produced by SSALTO/DUACS and distributed by the Copernicus Marine Environment Monitoring Service (marine.copernicus.eu).&nbsp;</p> <p>Each netCFD file corresponds to the two cyclogeostrophic velocity components zonal u and meridional&nbsp; v.&nbsp;</p> <p>(ssu_adt_DYNED_MED_cyclo_2000_2015.nc &amp;&nbsp;ssv_adt_DYNED_MED_cyclo_2000_2015.nc)</p> <p>&nbsp;</p>

opencc-by-4.0Sep 2019View details →
zenodo40/100

Spatial distribution of random velocity inhomogeneities in the southern Aegean from inversion of S‐wave peak delay times - Dataset

<p>This dataset contains supplementary files uploaded as part of the above journal article.</p> <p>5 sub-datasets have been uploaded separately. The first sub-dataset contains the peak delay times data. A few<br> records contain negative peak delay times due to change in waveform shape from filtering, these<br> were excluded during further calculations. The other 4 sub-datasets contain files as well as the script<br> to generate the results of &Delta;log <em>t<sub>p</sub></em> , &kappa;, &epsilon;<sub>param</sub> and P(<em>m<sub>l </sub></em>) as shown in figures 7, 8, 9 and 10 respectively<br> of the main article.</p> <p>Sub-Dataset S1: File &ldquo;ds01.csv&rdquo; contains the list of peak delay times (<em>t<sub>p</sub> </em>) in 2-4 Hz, 4-8 Hz, 8-16 Hz<br> and 16-32 Hz bands for the waveforms used in this study. The columns in the file represent<br> origin time (in year-month-day&rsquo;T&rsquo;hour:minute:seconds.microseconds format), event latitude,<br> event longitude, event depth, station code, station latitude, station longitude, <em>t<sub>p</sub></em> in 2-4 Hz, <em>t<sub>p</sub></em> in 4-<br> 8 Hz, <em>t<sub>p</sub></em> in 8-16 Hz and <em>t<sub>p</sub></em> in 16-32 Hz in a sequential manner.</p> <p><br> Sub-Dataset S2: File &ldquo;ds02.zip&rdquo; contains four text files (nodes_24e.txt, nodes_48e.txt, and<br> nodes_816e.txt) and one GMT (Generic Mapping Tools) script file (plot_final_comb.gmt)<br> written in BASH. The text files contain &Delta;log <em>t<sub>p</sub></em> values in 2-4 Hz, 4-8 Hz and 8-16 Hz bands<br> respectively. The columns in the text files represent node index, node latitude, node longitude,<br> node depth and &Delta;log <em>t<sub>p</sub></em> value in a sequential manner. The GMT script uses GSHHG coastline<br> data which is freely available for download from http://www.soest.hawaii.edu/wessel/gshhg/ .<br> Once downloaded and extracted its path can be added to the variable &ldquo;GDIR&rdquo; at the beginning of<br> the script. The GMT script file can be run to see the spatial distribution of &Delta;log t p using GMT-5<br> (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S3: File &ldquo;ds03.zip&rdquo; contains four text files (kappa_f10.txt, kappa_f30.txt,<br> kappa_f50.txt, kappa_f70.txt) and one GMT script file (inv_kappa.gmt) written in BASH. The<br> text files contain &kappa; values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively.<br> The columns in the text files represent node latitude, node longitude and &kappa; value of the node<br> sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the<br> script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution<br> of &kappa; using GMT-5 (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S4: File &ldquo;ds04.zip&rdquo; contains four text files (aetal_f10.txt, aetal_f30.txt, aetal_f50.txt,<br> aetal_f70.txt) and one GMT script file (inv_aetal.gmt) written in BASH. The text files contain<br> &epsilon;<sub>param</sub> values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The columns in<br> the text files represent node latitude, node longitude and &epsilon;<sub>param</sub> value of the node sequentially.<br> This GMT script also uses GSHHG coastline data whose path can be added to the script, same as<br> in data set S2 case. The GMT script file can be run to see the spatial distribution of &epsilon;<sub>param</sub> using<br> GMT-5 (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S5: File &ldquo;ds05.zip&rdquo; contains four text files (psdf_f10.txt, psdf_f30.txt, psdf_f50.txt,<br> psdf_f70.txt) and one GMT script file (inv_psdf.gmt) written in BASH. The text files contain<br> psdf (P(<em>m<sub>l</sub></em><sub> </sub>)) values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The<br> columns in the text files represent node latitude, node longitude and P(<em>m<sub>l</sub></em> ) value of the node<br> sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the<br> script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution<br> of P(<em>m</em><sub><em>l </em></sub>) using GMT-5 (Wessel et al., 2013) and above.</p>

opencc-by-4.0Oct 2019View details →
zenodo40/100

A model of P-wave velocity beneath the greater Alpine region from teleseismic full P-waveform inversion

<p>The dataset provides values of P-wave velocity in a 3D spherical chunk beneath the greater Alpine region as they resulted from a teleseismic full waveform inversion of AlpArray data.&nbsp;</p> <p>Please find a description of the dataset in the accompanying README file.</p>

opencc-by-4.0Aug 2024View details →
zenodo40/100

Flow velocity measurement data for the two desanding chambers of HPP Susasca before and after modification of tranquilizing racks in 2019 and 2021

<p>This dataset includes the flow velocity measurements in the sand trap of HPP Susasca before and after the modification of the tranquilizing racks, respectively. The study was conducted by the Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich.</p>

opencc-by-4.0Jul 2024View details →
zenodo40/100

Velocity from the shelf at Station CSI 6 in 2020

<div>Title: Velocity from the shelf at Station CSI 6 in 2020</div> <div>&nbsp;</div> <div>Velocity Data from ADCP deployed at CSI 6 (~ 20 m)</div> <div>Time - days counted from Jan. 1, 2020 (UTC) - Jan. 1 is Day 1.</div> <div>u, v, horizontal velocity in east and north direction in m/s</div> <div>Zmab (bin depth meter above bottom)</div> <div>Invalid data is indicated by a large velocity of -32.768 m/s</div> <div>PI: Chunyan Li at Louisiana State University</div> <div>Project: NSF 1736713&nbsp;</div> <div>File name: Velocity_from_shelf_CSI6_2020A.dat</div> <div>&nbsp;</div>

opencc-by-4.0Sep 2024View details →
zenodo40/100

Velocity and concentration measurements of passive tracer above gravelly seabeds under the wave influence

<p><strong>General description:</strong><br>This dataset was compiled and generated by Helena Stirnwei&szlig; as part of a series of Particle Image Velocimetry (PIV) and Laser-Induced Fluorescence (LIF) experiments conducted at the Institute of Fluid Mechanics at the University of Rostock. All data included herein is original and was acquired under controlled laboratory conditions.&nbsp;</p> <p>The experiments were conducted for 21 individual configurations of 3 different wave scenarios (description can be found in 'overview_wavescenarios.csv') and 7 seabed models (description can be found in 'overview_seabedmodels.csv'). The folders where the data is stored are named accordingly '[name of seabed model]_[name of wave scenario]'.</p> <p>Horizontal (u [m/s]) and vertical (w [m/s]) velocities were measured simultaneously to the concentration (c [l/l]) of a tracer fluid released from the seabed. The collected data was analyzed and phase averages and phase-resolved covariances were derived and are given for the field of view of each configuration as .npy-files in the respective folder.&nbsp;<br>Time-averaged and horizontally averaged profiles were determined for the concentration and all covariances in dependence on the bottom distance. The profiles are stored as .npy-files in the respective Folders.&nbsp;</p> <p>Mass mixing length (l_c) and Eddy diffusivity (D_t) profiles were derived for each configuration from the measured data as described in the corresponding article. The variables are given in dependence on the bottom distance as .npy-files in the respective folder. Slopes of the vertical mass mixing length profiles and Eddy diffusivity profiles from linear regression are given in 'slopes_turbmodels.csv'.&nbsp;</p> <p>TIME-RESOLVED DATA IS NOT PROVIDED IN THIS DATASET DUE TO EXTENSIVE DATA SIZE but will be shared upon request. Please contact the creators.</p> <p>&nbsp;</p> <p><strong>Description of .npy files in .zip-folders:</strong></p> <p>The time-averaged, horizontally averaged profiles (named '[c/RS/TF/TKE]_[optional: names of covariates]_time_averaged_[name of seabed model]_[name of wave scenario].npy') are given in each folder.<br>All time-averaged data is stored in the following format.</p> <p>import numpy as np</p> <p>data = np.load(filename.npy, allow_pickle=True)<br>#data[0] -&gt; z-dimensions in mm<br>##data[0][z]</p> <p>#data[1] -&gt; respective quantity (c, Reynolds stresses (RS), turbulent fluxes (TF), turbulent kinetic energy (TKE))<br>##data[1][z]</p> <p>&nbsp;</p> <p>The phase-averages (named '[u/w/c]_phase_averaged_[name of seabed model]_[name of wave scenario].npy'), phase-resolved covariances of the fluctuations (named '[RS/TF]_[names of the covariates]_[name of seabed model]_[name of wave scenario].npy', note: RS stands for Reynolds stresses, TF stands for turbulent fluxes), and the turbulent kinetic energy (named 'TKE_[name of seabed model]_[name of wave scenario].npy') are given in each folder.<br>All phase-resolved data is stored in the following format.</p> <p>import numpy as np</p> <p>data = np.load(filename.npy, allow_pickle=True)<br>#data[0] -&gt; x-dimensions in mm<br>##data[0][z, x]</p> <p>#data[1] -&gt; z-dimensions in mm<br>##data[1][z, x]</p> <p>#data[2] -&gt; respective quantity (c, u, w, Reynolds stresses (RS), turbulent fluxes (TF), turbulent kinetic energy (TKE))<br>##data[2][phi_idx, z, x]</p> <p>#(phase-averaging is performed with 100 phase bins -&gt; phi_idx ranges from 0 to 99)</p> <p>&nbsp;</p> <p>Mass mixing length (lc) and eddy diffusivity (Dt) profiles were derived as described in the corresponding article and are given in each folder under '[lc/Dt]_prof_[name of seabed model]_[name of wave scenario].npy' in the following format:<br>data = np.load(filename.npy, allow_pickle=True)<br>#data[0] -&gt; z-dimensions in mm<br>##data[0][z]</p> <p>#data[1] -&gt; respective quantity (l_c in mm, D_t in m^2/s)<br>##data[1][z]</p>

opencc-by-4.0Aug 2024View details →
zenodo40/100

A High-Performance Code for Analyzing Loss Transport Equations in High-Fidelity Simulations - velocity field data

<p>Velocity field to be processed by the methodology described in&nbsp;</p> <p>Biassoni, D, Russo, M, Viviani, P, Vitali, G, &amp; Lengani, D. "A High-Performance Code for Analyzing Loss Transport Equations in High-Fidelity Simulations." <em>Proceedings of the ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition</em>. <em>Volume 12C: Turbomachinery &mdash; Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions</em>. London, United Kingdom. June 24&ndash;28, 2024. V12CT32A039. ASME. <a href="https://doi.org/10.1115/GT2024-127953" target="_blank" rel="noopener">https://doi.org/10.1115/GT2024-127953</a></p> <p>and with the code provided at&nbsp;</p> <p><a href="https://gitlab.linksfoundation.com/across-public/aeronautics-workflows/asme-turboexpo-2024">https://gitlab.linksfoundation.com/across-public/aeronautics-workflows/asme-turboexpo-2024</a></p> <h3>&nbsp;</h3>

openmit-licenseOct 2024View details →
zenodo40/100

HF Radar surface current velocity dataset in the Eastern Australia (2012-2023): version 1.0

<p>This dataset contains the HF radar surface current velocity collected along the eastern Australian coast from 2012 to 2023. It includes data from two radar sites: Coff Harbour (COF, 153&deg;9'E 30&deg;18'S) and Newcastle (NEWC, 151&deg;49'E 32&deg;55'S). The spatial resolutions of the data are 1.5km upstream (COF radar) and 6km downstream (NEWC), and they cover an area approximately 150km from the coast. The dataset covers the period from 2012 to 2020 for the upstream radar (COF) and from 2018 to 2024 for the downstream radar (NEWC). The dataset was quality-checked and gap-filled by the variational approach (2dVar). Two-dimensional variables: Sea-surface current velocity in zonal (UCUR) and meridional (VCUR) directions.&nbsp;&nbsp;</p>

opencc-by-4.0Oct 2024View details →
zenodo40/100

The velocity boundary conditions of the model, the static Coulomb stress, displacement and the direction of the maximum principal stressin in Weiyuan area,China

<p>The velocity boundary conditions of the model,&nbsp; the displacement, the static coulomb stress&nbsp; and the direction of the maximum principal stress data in Weiyuan area, Sichuan Province, China, calculated by numerical simulation method. The calculation time is 10 years and 50 years after fracturing, respectively. The data include longitude, latitude, depth and corresponding calculation results. The parameters are: fracture volume-to-model volume ratio (&theta;) , the fractures distributed radius (&gamma;) around the wells.&nbsp;</p>

opencc-by-4.0Nov 2024View details →
zenodo40/100

Oscillations of Offshore Wind Turbines undergoing Installation II: Filtered and Integrated data - acceleration, velocity, displacement

<p>This is dataset is based on the raw measurement data from <a href="https://zenodo.org/record/5009061">https://zenodo.org/record/5009061</a></p> <p>The data included in the archives are the resampled and high-pass filtered accelerations as well as the velocity and displacement data.</p>

opencc-by-4.0Jun 2021View details →
zenodo40/100

2D P-wave velocity model for Bay of Plenty, New Zealand

<p>The files contain the geographic projection, P-wave velocities, and earth layer model for the Bay of Plenty, New Zealand. The methods and data are presented in the article: Seismic Evidence of Magmatic Rifting in the Offshore Taupo Volcanic Zone, New Zealand, Gase et al. 2019&nbsp; (<a href="https://doi.org/10.1029/2019GL085269">https://doi.org/10.1029/2019GL085269</a>).</p>

opencc-by-4.0Aug 2021View details →
zenodo40/100

3D crustal velocity model for the wider Zagreb area

<p>The 3D structural model covers 60 km by 80 km area around the city of Zagreb, Croatia, and extends to the depth of 60 km. It describes seismologically relevant parameters, density, P- and S-wave velocity, on a working grid of 125 m in UTM (zone 33N) coordinate system. The model is represented by four main layers: sediments, upper crust, lower crust and mantle. The format of the model is suitable for simulations obtained using software package SPECFEM3D Cartesian (<a href="http://geodynamics.org/cig/software/specfem3d/">geodynamics.org/cig/software/specfem3d/</a>; accessed Aug 2021).<br> Description of the formatting of the file can be found on the SPECFEM3D &#39;engCartesian package documentation site:<br> <a href="https://specfem3d.readthedocs.io/en/latest/13_changing_the_model/#using-external-tomographic-earth-models">https://specfem3d.readthedocs.io/en/latest/13_changing_the_model/#using-external-tomographic-earth-models</a><br> (accessed Aug 2021).</p> <p>3D seismic model for the wider Zagreb area was assembled using publicly available geological and geophysical data. It describes in detail main structures observed in the uppermost part of the crust (e.g. sedimentary basins and high-velocity structures) and is embedded within the regional EPcrust crustal model (<a href="https://doi.org/10.1111/j.1365-246X.2011.04940.x">https://doi.org/10.1111/j.1365-246X.2011.04940.x</a>). The performance of<br> the model was tested by simulating ground motion for several moderate earthquakes. Results show that the 3D model is able to reproduce main characteristics of the ground motion, primarily shaking duration and amplification effects. Therefore, it is suited for simulation of shaking scenarios in the wider Zagreb area, mostly for T &gt; 1 s.</p>

opencc-by-4.0Aug 2021View details →
zenodo40/100

Synthetic automotive LiDAR dataset with radial velocity additional feature - (x,y,z,v)

<p>The synthetic dataset was generated using KITTI-like specifications and annotations format. It is comprised by the KITTI&nbsp;standard&nbsp; folders: label_2, image_2 and calib. Furthermore, there is a velodyne file for each of the following use cases:</p> <ul> <li>Point cloud 1: (x,y,z, (Bool)Is_Object):&nbsp;In this point cloud, the best performance of the Deep Learning model is expected as ground truth information is provided as the additional feature of each point. <ul> <li>Point cloud 1A: (x,y,z, (Bool)Is_Car):&nbsp;the additional feature of each point that belongs to an object of the &rsquo;Car&rsquo; type has a Boolean 1.0 value; contrariwise, the 0.0 value was used. File:&nbsp;velodyne_1A_isCar;</li> <li>Point cloud 1B: (x,y,z, (Bool)Is_Ped):&nbsp;the additional feature of each point that belongs to an object of the &rsquo;Pedestrian&rsquo; type has a Boolean 1.0 value; contrariwise, the value 0.0 was used.&nbsp; File:&nbsp;velodyne_1A_isPed.</li> </ul> </li> <li>Point cloud 2: (x,y,z, (Float)Radial_Velocity): this point cloud has the relative radial velocity as an additional feature for each point. File:&nbsp;velodyne_2_radial_velocity;</li> <li>Point cloud 3: (x,y,z,(Float)Absolute_Speed): in this point cloud, every point has the absolute speed of the object as the additional feature.&nbsp;File:&nbsp;velodyne_3_abs_speed;</li> <li>Point cloud 4:&nbsp;(x,y,z,(Bool)Is_Moving):&nbsp;the additional feature of this point cloud is a Boolean value that is set to 1.0 if the object is moving; contrariwise, it is set to 0.0 for static objects. File:&nbsp;velodyne_4_is_moving;</li> <li>Point cloud 5:&nbsp;(x,y,z,0): no additional feature information. If desired, requires post-processing to convert to (x,y,z) or changing the toolbox point cloud configuration to not consider the additional feature.&nbsp;File:&nbsp;velodyne_5_xyz;</li> </ul> <p>Additionally, the label split for testing and training sets&nbsp;used can be found at file: Labels_split.</p> <p>This work was made as part of a master thesis. For further details, please check the dataset generation source code [1]. Any further questions please contact Leandro Alexandrino (l.alexandrino@ua.pt).</p> <p>&nbsp;</p> <p>[1] -&nbsp;Fork deepgtav-presil - leandro alexandrino, https://github.com/leandroalexandrino1995/DeepGTAVPreSIL.</p>

opencc-by-4.0Oct 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record