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86 results for “velocity measurement”
Temperature, Salinity, Sound Velocity, Location, Depth, Heading, and Velocity measured aboard the R/V Nanuq for the Northern Gulf of Alaska LTER site, 2020-2021
This dataset describes measurements from a thermosalinograph and navigation device used aboard the R/V Nanuq during cruises in Resurrection Bay and the Gulf of Alaska. Thermosalinograph data includes temperature, salinity, conductivity, and sound speed measurements every five seconds while the instrument was in use. Navigation data describes latitude, longitude, depth, heading, course over ground, and speed over ground. Data are collected on R/V Nanuq using the ship's GPS devices and a Seabird Electronics SBE-45 thermosalinograph (TSG) that samples uncontaminated pumped seawater. Temperature and conductivity data are sampled every 5 seconds and salinity and sound velocity are derived parameters. No data quality control has been applied to this data, so users should be cautious that conductivity, salinity and sound speed dropouts to due bubbles are common when the ship is plowing through large waves. Data are collected by a variety of projects, including from the NSF-funded Northern Gulf of Alaska Long Term Ecological Research (NGA LTER) program, the Exxon Valdez Oil Spill Trustee Council (EVOSTC) Gulf Watch Alaska GAK1 project, the UAF Sub-Arctic Oceanography Field Course, the Alaska Ocean Observing System (AOOS) glider program, and others.
High resolution pond velocity measurements, Idaho21
<p>These scientific data were obtained by Jeffrey Nielson and Stephen Henderson of Washington State University, working in collaboration with Sandra Mayne, Caren Goldberg and Jeffrey Manning. High-resolution current meters were used to obtain detailed measurements of water velocity, with supporting measurements of wind velocity and water temperature profiles. Overview of observations in referenced Henderson et al. (2024) L&O paper, more details in included files. </p>
Mean current velocity sections along 11°S, 5°S, 35°W, and 23°W from shipboard measurements used in "Transports and pathways of the tropical AMOC return flow from Argo data and shipboard velocity measurements"
<p>This data set contains current velocity measurements used in the study "Transports and pathways of the tropical AMOC return flow from Argo data and shipboard velocity measurements“ by <em>Tuchen et al. (2022)</em> published at <em>Journal of Geophysical Research: Oceans</em>.</p> <p>For the meridional mean sections along 35°W and 23°W, and for the quasi-zonal sections along 11°S and 5°S, one ".mat" file is provided for each of the sections. Please note that the section along 11°S consists of a zonal part (east of 34.2°W) and a cross-shore part closer to the coast. The meridional velocities along the cross-shore part of the 11°S-section are rotated clockwise by 36° in order to derive along-shore velocities.</p> <ul> <li>11°S: meridional velocity / alongshore velocity (V), neutral density (gamma_n), longitude (LON), depth (Z)</li> <li>5°S: meridional velocity (V), neutral density (gamma_n), longitude (LON), depth (Z)</li> <li>35°W: zonal velocity (U), neutral density (gamma_n), latitude (LAT), depth (Z)</li> <li>23°W: zonal velocity (U), neutral density (gamma_n), latitude (LAT), depth (Z)</li> </ul> <p>Mean velocity data in the upper 10 m are replaced by the gridded mean surface current velocities at 1/4° horizontal resolution derived from satellite-tracked surface drifting buoys (<em>Laurindo et al. 2017</em>) that were horizontally interpolated to the resolution of the individual ship sections.</p>
MEaSUREs ITS_LIVE Sentinel-1 Image-Pair Glacier and Ice Sheet Surface Velocities: Version 2 (Greenland Sample Products)
<p>We provide 21 sample products of MEaSUREs ITS_LIVE Sentinel-1 Image-Pair Glacier and Ice Sheet Surface Velocities: Version 2 in three test regions of Greenland Ice Sheet. The full archive of version 2 ITS_LIVE products (including image pair maps, data cubes and mosaics) from Sentinel-1 as well as other optical sensors (Landsat-4/5/6/7/8 and Sentinel-2) can be found at the ITS_LIVE project website: <a href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov</a>.</p> <p><strong>Sensor</strong>: Sentinel-1A/B</p> <p><strong>Processor</strong>: <a href="https://github.com/isce-framework/isce2">ISCE</a>v2.4.1 (topsApp -> <a href="https://github.com/leiyangleon/Geogrid">Geogrid</a>v1.4.0 -> <a href="https://github.com/nasa-jpl/autoRIFT">autoRIFT</a>v1.4.0)</p> <p><strong>Project</strong>: NASA MEaSUREs project <a href="https://its-live.jpl.nasa.gov">ITS_LIVE</a></p> <p><strong>Region 1</strong> (69.13N, 50.88W; Jakobshavn Isbræ Glacier): 7 ascending image pairs</p> <p><strong>Region 2</strong> (77.61N, 42.79W; central north of interior Greenland): 3 ascending image pairs</p> <p><strong>Region 3</strong> (72.48N, 35.87W; central south of interior Greenland): 10 descending image pairs and 1 ascending image pair</p> <p>This serves as a supplementary dataset for the companion journal article submitted to Earth System Science Data (to appear).</p> <p> </p> <p><strong>Acknowledgement</strong>: This effort was funded by the NASA MEaSUREs program in contribution to the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project (<a href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov/</a>) and through Alex Gardner’s participation in the NASA NISAR Science Team.</p>
[Dataset] Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy
<p>Research data for the purpose of reproducing the results presented in the journal publication titled "Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy"</p>
Data sets used for: Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry
<p>Original videos and reference bulk velocity and water depth data sets used to develop the study: <em>Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry.</em></p> <p>The reference bulk velocity and water depth data sets were obtained with the Nivus OFR Radar and Nivus NivuCompact sensors, respectively.</p>
Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities</em>”</strong> in Optics Express (<a href="https://doi.org/10.1364/OE.456139">doi.org/10.1364/OE.456139</a><em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 5-10 minutes.</p> <p>For ideal scanning alignment each dataset includes diffusion, focus (beam waist) calibration, and flow measurements (using both M-scan and B-scan methods) for all used sample lengths. The names “M-scan” and “A-scan” are used interchangeably. The analysis process is as follows: firstly, the diffusion coefficient is determined for every sample size (time series length) to be analyzed using the script ‘Diffusion.py’. Secondly, the beam waist (focus) calibration is performed using the script ‘Beam Waist.py’. Since the beam waist should be constant for each dataset, choose the value obtained from the file with a largest time series length for minimizing the statistical uncertainty and fix it for a given dataset. Beam scanning for our setup is not exactly perpendicular to the optical axis. Therefore, for B-scan Doppler flow measurements the calibration parameter v_d, quantifying the axial scan bias, must be used. This calibration parameter varies with time series length and needs to be obtained for each sample size. This is done with the same script as the beam waist calibration. Thirdly, the Doppler angle is determined using M-scan measurement with the lowest discharge rate using the script ‘Angle.py’. Finally, the flow profiles are obtained both for M-scan and B-scan methods with predetermined calibration parameters using the script ‘Flow.py’. All file names are sufficiently descriptive, showing sample size, scan mode, measurement type and discharge rate. The number on the file name represents the time series length.</p> <p>For arbitrary scanning alignment, the dataset includes one diffusion and one focus (beam waist) measurements for calibration purposes. The diffusion measurement is used only for the beam waist calibration and not for flow measurements. It also contains several B-scan flow measurements (with different scan speeds) for every discharge rate. The analysis process is same as before but without the angle calibration step. Use the script ‘Omnidirectional.py’ for this step.</p> <p>The table below summarizes all datasets and Python scripts uploaded to this repository.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Applicability</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Dataset, 12-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 0.39 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 16-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 0.94 deg and alignment angle of 0.94 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 20-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 1.58 deg and alignment angle of 2.26 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 18-05-2021.zip</p> </td> <td> <p>Arbitrary alignment</p> </td> <td> <p>Dataset for alignment angle of 2.7 deg.</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>Both methods</p> </td> <td> <p>File containing k-interpolation data</p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> </tr> <tr> <td> <p>DataProcessing.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This module contains all analysis and processing routines.</p> </td> </tr> <tr> <td> <p>Diffusion.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This script determines diffusion coefficient from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Beam Waist.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This script determines focus beam waist from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Angle.py</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>This script determines Doppler angle from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Flow.py</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>This script determines M-scan and B-scan flow profiles from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Omnidirectional.py</p> </td> <td> <p>Arbitrary alignment</p> </td> <td> <p>This script determines flow profiles for arbitrary scan alignment.</p> </td> </tr> </tbody> </table>
Eddy Duck Data: Measured Three-Dimensional Structure of Surfzone Velocities
<p>This archive contains data from the 2011 EddyDuck experiment. Three-dimensional structure of surfzone velocity fields was measured using 12 horizontally-spaced Nortek Aquadopp current meters, each instrument measuring vertical profiles of water velocity. The experiment was conducted by folks from Washington State University and Oregon State University, with help from staff of the USACE's Duck Field Research Facility.</p> <p>Funding from the US National Sciences Foundation, Award OCE-1061692</p> <p>An in-review JGR-Oceans manuscript “Depth-Dependence of Nearshore Currents and Eddies” discusses the observations.</p> <p> </p> <p>Data are contained in two zip files.</p> <p>File summary.zip unzips to give a folder containing summary data in matlab 2015b format, and a pdf readme file explaining details.</p> <p>Folder full.zip unzips to give a folder containing full time-resolution data in ASCII format, and a readme file explaining details.</p> <p> </p> <p> </p> <p>Questions to steve_henderson@wsu.edu</p>
Instantaneous, three-dimensional velocity fields past a bio-prosthetic aortic valve measured in-vitro with tomographic particle image velocimetry.
<p>Each folder contains instantaneous, three-dimensional velocity vector data obtained in a simplified model of the aortic 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 folders for velocity data in the "ascending aorta" domain (AAo) and in the "sinus of Valsalva" 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 contains 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 reference frame of 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>
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>
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>
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 Stirnweiß 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. </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. <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. </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'. </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> </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] -> z-dimensions in mm<br>##data[0][z]</p> <p>#data[1] -> respective quantity (c, Reynolds stresses (RS), turbulent fluxes (TF), turbulent kinetic energy (TKE))<br>##data[1][z]</p> <p> </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] -> x-dimensions in mm<br>##data[0][z, x]</p> <p>#data[1] -> z-dimensions in mm<br>##data[1][z, x]</p> <p>#data[2] -> 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 -> phi_idx ranges from 0 to 99)</p> <p> </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] -> z-dimensions in mm<br>##data[0][z]</p> <p>#data[1] -> respective quantity (l_c in mm, D_t in m^2/s)<br>##data[1][z]</p>
Eddy flux measurements and transfer velocities of momentum, water vapor, and sulfur dioxide over the coastal Atlantic ocean
Open the record for dataset details and reuse information.
Air-sea gas transfer velocities measured at wind speeds up to 85m/s in fresh water and seawater
<p>This data set contains gas transfer velocities of 12 tracers (CF4, He, SF6, He, Kr, Pentafluoroethane, Xe, Acetylene, Hexafluorobenzene, Difluoromethane, 1,4-Difluorobenzene, Dimethyl Sulfide, Methyl Acetate) measured in the Kyoto High Speed Wind-Wave tank with fresh water and modeled seawater and the Miami SUSTAIN wind-wave tank with seawater at wind speeds up to 85m/s.</p> <p> </p>
VELOCITY VECTOR FIELDS, MEASURED AT THE OUTFLOW FROM TWO HINGE MODELS OF A BILEAFLET MECHANICAL HEART VALVE, USING 2-COMPONENT PARTICLE IMAGE VELOCIMETRY TECHNIQUE
<p>VELOCITY VECTOR FIELDS, MEASURED AT THE OUTFLOW FROM TWO HINGE MODELS OF A BILEAFLET MECHANICAL HEART VALVE, USING 2-COMPONENT PARTICLE IMAGE VELOCIMETRY TECHNIQUE</p>
Velocity measurements of a bench scale buoyant plume applying particle image velocimetry
<p>2D PIV velocity field data from the open plume experiment run 2015 at the <br> Forschungszentrum Juelich. An electrically heated copper block placed in an enclosure <br> creates a buoyancy driven plume.</p> <p><strong>IMPORTANT: </strong>The data set is provided on the following webpage<br> https://www.fz-juelich.de/ias/ias-7/EN/Research/Fire_Dynamics/Data/2017_openplume/_node.html</p> <p> </p> <p> </p>
Wind tunnel measurements of concentration and velocity in urban geometries with trees
<p><span>This dataset contains concentration and velocity mesurements performed in the aerodynamic wind tunnel of the Ecole Centrale de Lyon (France). <br>An idealized urban district was simulated by an array of blocks, and two rows of model trees were arranged inside a street. <br>Reduced scale trees were chosen to mimic a realistic shape and aerodynamic behaviour.<br>Three different spacings between the trees were considered: "zero" (no trees), "half" (14 cm distance between the tree trunks) and "full" (7 cm distance between the tree trunks).</span></p> <p><span>The dataset includes: <strong> </strong></span></p> <ul> <li><span>concentration and velocity measurements performed within a street canyon, under various geometries and wind directions; </span></li> <li><span>the characterization of the boundary layer above the buildings;</span></li> <li><span>the characterization of the tree drag.</span></li> </ul> <p><span>The detailed description of the dataset is contained in the document <code>Info_dataset.pdf</code></span></p> <p> </p> <p> </p>
Ultrasonic velocity measurements of lunar regolith simulant at low confining pressures with variable ice content
<p>This dataset was created by Christopher Chance Amos during completion of a PhD degree in Space Resources</p><p>at Colorado School of Mines. This data was collected during Spring 2023.</p><p> </p><p>This dataset includes compressional and shear raw collected waveforms as well as interpreted velocities</p><p>from first-break picking. See the README files in subdirectories for explanations of individual files.</p><p> </p><p>The purpose of this dataset is to serve as a foundation and calibration for seismic modeling of the lunar</p><p>near-surface. These models will be used to determine if seismic methods are feasible for characterizing</p><p>the quantity and form of lunar subsurface ice deposits.</p>
Adult male baseball pitchers anthropometric measurements and pitching velocity in tryout settings
<p>In this study, we adopted field tests executed using affordable equipment in a tryout event for a professional baseball team in Taiwan, 2019. We have only half day to test 64 players, and the result of measurement are used to develop a model for predicting pitching velocity of amateur adult pitchers (age: 23.9 ± 2.8 years; height: 180.3 ± 5.9 cm; weight: 81.4 ± 10.9 kg) . The measurements and tests in tryout settings should be easy to implement, take short time, do not need high skill levels, and correlate to the pitching velocity. The outcome measures included maximum external shoulder rotation, maximum internal shoulder rotation, countermovement jump (CMJ) height, 20-kg loaded CMJ height, 30-m sprint time, height, age, and weight tests. </p>
A high pressure, high temperature gas medium apparatus to measure acoustic velocities during deformation of rock: supporting information
<p>Processed mechanical data and raw transmitted waveforms.</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)
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.