Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
48
datasets available to search
ShareScore release 0.9.0
Dataset results
48 results for “velocity field”
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>
Mesh and velocity fields for "Resolving Pore-scale Concentration Gradients for Transverse Mixing and Reaction in Porous Media"
<p>This dataset contains the data needed to reproduce the results in the manuscript "Resolving Pore-scale Concentration Gradients for Transverse Mixing and Reaction in Porous Media". The three folders refer to (1) uniform flow, (2) flow through a bead pack, and (3) flow through a Berea sample (from the 11 sandstones repository, Digital Rocks Portal). The meshes and the velocity fields (in the subfolder "velocity") are in a HDF5 format. The meshes and velocity fields can be read using FEniCS version 2019.2.0. The velocity fields need 2nd order Lagrange tetrahedral elements.</p>
Effect of impeller rotational phase on the FDA blood pump velocity fields
Open the record for dataset details and reuse information.
Ambient noise data and velocity model from DEEPEN array in Hengill geothermal field, Iceland
<p>This repository contains the data and velocity model associated with the manuscript entitled "<em>Crustal characterization of the Hengill geothermal fields: Insights from isotropic and anisotropic seismic noise imaging using a 500-node array</em>" by Wu et al. (2024), to be published in <em>Journal of Geophysical Research: Solid Earth</em>. </p> <p>The dataset is the nine component cross-correlation functions (ZZ, ZN, ZE, NZ, NN, NE, EZ, EN, EE) after stacking over seismic array deployment time period (summer 2021) and after spatial averaging (bin stacking). The bin locations are provided in "bin_locations.txt".</p> <p>The derived VOIGT velocity and radial anisotropy model can be found in "Hengill_Voigt_Aniso_DEEPEN_4share.txt".</p> <p> </p>
Dataset of velocity and density fields from numerical simulations
<p>This dataset contains the outcomes of numerical simulations conducted for a research paper titled "Transformation of internal solitary waves at the edge of ice cover" with the use a non-hydrostatic model (Maderich et al., 2012) and code of the non-hydrostatic model. <br>Folder FIG3 contains a *.zip archive with data corresponding to the following parameters: x-coordinate length (m), z-coordinate depth (m) and module of horizontal velocity field (m/s). Folders FIG4 and FIG6 within the archive contain information on the following parameters: x-coordinate length (m), z-coordinate depth (m) and density field (kg/m^3).<br>Folder MODEL contains fortran source code files, input files, and a short model description.</p>
Dataset files for 'Tan et al., Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'
<p>These files are the data and result files for the manuscript entitled<strong> 'Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'</strong> by Tan et al., including</p> <p>catalog.dat : the seismic phase catalog used in seismic tomography</p> <p>station.dat : the station coordinates of the local seismic network</p> <p>relocation.dat : the earthquake relocations obtained by double-difference seismic tomography</p> <p>1-D Vs.xlsx : the 1-D Vs model in the shale gas field</p> <p>3-D Vp.dat: the 3-D Vp model obtained by DD seismic tomography</p> <p>3-D Vs.dat: the 3-D Vs model obtained by DD seismic tomography</p> <p>3-D VpVs.sgy: the 3-D Vp/Vs model obtained by DD seismic tomography (3-5 km)</p> <p>3-D pressure.sgy: the 3-D pore pressure field model obtained by focal mechanism tomography (3-5 km)</p>
Dataset of "Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes"
<p>Dataset of the article <em>Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes </em>(<a href="https://doi.org/10.1016/j.expthermflusci.2022.110647">https://doi.org/10.1016/j.expthermflusci.2022.110647</a>). A data-driven method is applied to combine non-time-resolved velocity field and fast probe data, and achieve time-resolved velocity and pressure field.</p> <p>The codes processing data here are on https://github.com/erc-nextflow/Data-driven-pressure-estimation-with-EPOD.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085, NEXTFLOW).</p>
Dataset files for 'Tan et al., Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'
<p>These files are the data and result files for the manuscript entitled<strong> 'Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'</strong> by Tan et al., including</p> <p><strong>station.dat</strong> : the station coordinates of the local seismic network (including the station ID, longitude, latitude, elevation(negative)/depth(positive), X, Y)</p> <p><strong>catalog.dat</strong> : the seismic phase catalog used in double-difference (DD) seismic tomography</p> <p><strong>relocation.dat </strong>: the earthquake relocations obtained by DD tomography</p> <p><strong>1-D Vs.xlsx</strong> : the 1-D Vs model in the shale gas field</p> <p><strong>3-D Vp.dat</strong>: the 3-D Vp model obtained by DD tomography</p> <p><strong>3-D Vs.dat</strong>: the 3-D Vs model obtained by DD tomography</p> <p><strong>3-D VpVs.sgy</strong>: the 3-D Vp/Vs model (interpolated, within 3-5 km)</p> <p><strong>3-D pressure.sgy</strong>: the 3-D pore pressure field model (interpolated, within 3-5 km)</p>
TEAMx-PC22 (TEAMx pre-campaign 2022) - Animations of radial velocity and coplanar-retrieved horizontal wind fields from KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159
<p><strong>Abstract</strong></p> <p>This data set was collected during the TEAMx pre-campaign in summer 2022 (TEAMx-PC22) in the Inn Valley Target Area, Austria.</p> <p><strong>Data Description</strong></p> <p>This data set is comprised of 64 daily .mp4 files showing:</p> <ul> <li>post-processed radial velocity fields sampled by KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159</li> <li>their coplanar-retrieved horizontal wind field output</li> <li>additionally, horizontal wind speed and direction sampled by the KITcube Vaisala Windcube v2.1 (WLS7-1489) at 60-m above ground level is added as an independent measurement for subjective validation of the coplanar-retrieved wind</li> </ul> <p>The wind fields shown in the animations originate from an accompanying Zenodo data set (DOI: 10.5281/zenodo.7212801), where complete information concerning lidar locations, scan parameters, and post-processing may be found.</p>
Figure 2. Velocity field U, t in Reconstruction of a passive tracer boundary source in an open water area
Figure 2. Velocity field U, t = 0.
Velocity and strain rate fields of the Southern Tibetan Plateau
<p>This data set contains velocity and strain rate fields over the southern Tibetan Plateau, which are derived from Sentinel-1A and -1B synthetic aperture radar satellite data (SAR) and NETCDF (.grd) formats.</p> <p>This repository contains:</p> <p>(1) asc.grd : the InSAR LOS velocity field in the ascending tracks in a resolution of ~1000 m.</p> <p>(2) desc.grd : the InSAR LOS velocity field in the descending tracks in a resolution of ~1000 m.</p> <p>(3) dilatation_strain_rate.grd : the dilatational strain rate calculated from the interpolated GNSS Vn and InSAR-derived Ve.</p> <p>(4) second_invariant_horizontal_strain_rate.grd: the second invariant horizontal strain rate calculated from the interpolated GNSS Vn and InSAR-derived Ve.</p>
Eurasia-fixed GNSS-derived velocity field for Turkey
<p>The dataset and the analysis is described in the <a href="http://journals.tubitak.gov.tr/cgi/viewcontent.cgi?article=1844&context=earth">paper</a> and presented <a href="https://meetingorganizer.copernicus.org/EGU23/EGU23-12258.html">here</a>. The data fields are: longitude, latitude, E velocity, N velocity, sigma E, sigma N, rho, U velocity, sigma U, station. You can use the following code snippet to load your data to python:</p> <pre><code class="language-python">fields = ['longitude', 'latitude', 've', 'vn', 'se', 'sn', 'rho', 'vu', 'su', 'station'] df = pd.read_csv('Kurt_etal_2023.csv', header=None, names=fields)</code></pre> <p> </p>
Dataset of ``Plasma Distribution Solver: A Model for Field-Aligned Plasma Profiles Based on Spatial Variation of Velocity Distribution Functions"
<p>This dataset contains the plasma distribution data in the Jupiter–Io system, calculated from the Plasma Distribution Solver and used for figures in the paper “Plasma Distribution Solver: A model for field-aligned plasma profiles based on spatial variation of velocity distribution functions” by K. Saito et al. (2023).</p> <p> </p> <p>The contents of files ‘all_Case_1.csv’ and ‘all_Case_2.csv’ are as follows:</p> <ul> <li>Position along the magnetic field line (0 at the magnetic equator) [m] (column 1)</li> <li>Distance from the Jovian center [km] (column 2)</li> <li>Magnetic latitude [rad]([degree]) (column 3(4))</li> <li>Magnetic flux density [T] (column 5)</li> <li>The initial condition of electrostatic potential [V] (column 6)</li> <li>The result of electrostatic potential [V] (column 7)</li> <li>Number density profiles [m<sup>-3</sup>] (columns 8-17)</li> <li>Charge density profiles obtained from the integration of velocity distribution functions [C m<sup>-3</sup>] (column 18)</li> <li>Charge density profiles obtained from Poisson’s equation [C m<sup>-3</sup>] (column 19)</li> <li>Convergence value (column 20)</li> <li>Particle flux density [m<sup>-2</sup> s<sup>-1</sup>] (columns 21-30)</li> <li>Mean flow velocity parallel to the field line [m s<sup>-1</sup>] (columns 31-40)</li> <li>Plasma pressure perpendicular to the field line [Pa] (columns 41-50)</li> <li>Plasma pressure parallel to the field line [Pa] (columns 51-60)</li> <li>Plasma dynamic pressure [Pa] (columns 61-70)</li> <li>Perpendicular temperature [J] (columns 71-80)</li> <li>Parallel temperature [J] (columns 81-90)</li> <li>Alfvén speed considering the displacement current term in Ampère’s law [m s<sup>-1</sup>] (column 91)</li> <li>Alfvén speed per the speed of light (column 92)</li> <li>Ion inertial length using averaged mass [m] (column 93)</li> <li>Electron inertial length [m] (column 94)</li> <li>Ion Larmor radius using averaged mass [m] (column 95)</li> <li>Ion acoustic gyroradius using averaged mass [m] (column 96)</li> <li>Electron Larmor radius [m] (column 97)</li> <li>Current density [A m<sup>-2</sup>] (column 98)</li> </ul> <p>The Python codes ‘plot_all.py,’ ‘plot_plasma_beta_comparison.py,’ and ‘plot_Alfven_speed_comparison.py’ can plot Figures 5, 6, 7, and 9 of the paper using the above CSV files.</p> <p> </p> <p>The files ‘boundary_conditions_Case_1.csv’ and ‘boundary_conditions_Case_2.csv’ contain the boundary conditions for Cases 1 and 2.</p> <p> </p> <p>The zip files ‘probability_density_function_Case_1_H_Io.zip’ and ‘probability_density_function_Case_1_H_Jupiter_North.zip’ are zipped CSV files with the same name. The contents of these files are as follows:</p> <ul> <li>Magnetic latitude [degree] (column 1)</li> <li>Perpendicular velocity at the particle position [m s<sup>-1</sup>] (column 2)</li> <li>Parallel velocity at the particle position [m s<sup>-1</sup>] (column 3)</li> <li>Perpendicular velocity at the boundary [m s<sup>-1</sup>] (column 4)</li> <li>Parallel velocity at the boundary [m s<sup>-1</sup>] (column 5)</li> <li>Probability density function [s<sup>3</sup> m<sup>-3</sup>] (column 6)</li> <li>Differential flux per number density [cm<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> keV<sup>-1</sup>] (column 7)</li> </ul> <p>The Python code ‘plot_velocity_distribution_function.py’ can plot Figure 8 of the paper using this CSV file.</p>
Velocity field of Jupiter's Great Red Spot in December 2016
Open the record for dataset details and reuse information.
Supplemental Data for Cell Reports Physical Science article "Probing transference and field-induced polymer velocity in block copolymer electrolytes"
<p>The data and Jupyter notebook uploaded here is Supplemental Information for the article:</p><p><strong>Probing transference and field-induced polymer velocity in block copolymer electrolytes </strong></p><p>Coauthored by:</p><p>Michael D. Galluzzo, Hans-Georg Steinrück, Christopher J. Takacs, Aashutosh Mistry, Lorena S. Grundy, Chuntian Cao, Suresh Narayanan, Eric M. Dufresne, Qingteng Zhang, Venkat Srinivasan, Michael F. Toney, and Nitash P. Balsara.</p><p>Journal: Cell Reports Physical Science</p><p>Notes:</p><ul><li>This depository includes the experimental data used in Figure 2, 3, and 4 of the main text and an additional data set.</li><li>The Jupyter notebook "velocity_Echem_Data.ipynb' can be used to visualize the data in the .csv files provided in the folder 'echem' and 'XPCS_fits'.</li><li>The folder 'echem' contains the raw electrochemical data obtained from the two XPCS experiments discussed in the main text and an additional experiment set.</li><li>The folder 'XPCS_fits' contains the results of fitting the autocorrelation functions at each spatial position in the cell at each time point for the two XPCS experiments discussed in the main text and an additional experiment set. </li><li>The additional experiment included here (reffered to as Cell P in the Jupyter notebook) is not discussed in the main text, however it demonstrates that the second 'hump' in velocity (see Figure 3 and S5) that is observed after switching the direction of polarization was replicated in a separate experiment.</li></ul><p> </p>
Clustering Data for Passive Tracers in Random Velocity Fields
<p>Contains the post-processed model output. All detected clusters from each individual model run used to compile statistics are included as well as python scripts to manipulate cluster objects and create figures.</p>
An integrated GNSS velocity field in the Pamir, Central Asia
<p>GPS velocity field in the Pamir region in the Eurasian-fixed frame compiled from our latest results and previous studies. Site with upperscript c is a continuous GPS staiton.</p>
The Black Sea velocity fields calculated by GETM/GOTM model.
<p>Example of the Black Sea velocity fields to be used by the Lagrangian model LTRANS-Zlev (https://github.com/inogs/LTRANS_Zlev). The dataset consists of 4 zip-fies, each containing daily NETCDF files with 4-hour mean velocity records. Velocity are calculated by using the GETM (http://www.getm.eu/) and General Ocean Turbulence Model (GOTM) (Miladinova, S., Stips, A., Garcia-Gorriz, E., Macias Moy, D., 2017. Black Sea thermohaline properties: Long-term trends and variations. J. Geophys. Res. Oceans 122,<br> 5624–5644. https://doi.org/10.1002/2016JC012644). The Stokes drift is added to the horizontal velocities at the surface (BLKSEA_MULTIYEAR _WAV_007_006, <a href="https://marine.copernicus.eu/">https://marine.copernicus.eu</a>).</p>
A sea state dependent gas transfer velocity for CO$_2$ unifying theory, model and field data
<p>Dataset for "A sea state dependent gas transfer velocity for CO2 unifying theory, model and field data"</p> <p>WaveWatch III simulated significant wave height (Hs, unit:m), volume of entrained air ('wva', unit m/s), 10-meter wind vector ( 'uwnd','vwnd', unit, m/s) for 9 datasets from 11 cruises.</p> <p>The information of dataset is shown in name of each file.</p>
Data from: Reconstruction of velocity fields in electromagnetic flow tomography
Electromagnetic flow meters (EMFMs) are the gold standard in measuring flow velocity in process industry. The flow meters can measure the mean flow velocity of conductive liquids and slurries. A drawback of this approach is that the velocity field cannot be determined. Asymmetric axial flows, often encountered in multiphase flows, pipe elbows and T-junctions, are problematic and can lead to serious systematic errors. Recently, electromagnetic flow tomography (EMFT) has been proposed for measuring velocity fields using several coils and a set of electrodes attached to the surface of the pipe. In this work, a velocity field reconstruction method for EMFT is proposed. The method uses a previously developed finite-element-based computational forward model for computing boundary voltages and a Bayesian framework for inverse problems. In the approach, the vz-component of the velocity field along the longitudinal axis of the pipe is estimated on the pipe cross section. Different asymmetric velocity fields encountered near pipe elbows, solids-in-water flows in inclined pipes and in stratified or multiphase flows are tested. The results suggest that the proposed reconstruction method could be used to estimate velocity fields in complicated pipe flows in which the conventional EMFMs have limited accuracy.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.