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969 results for “velocity”
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>
Vapor bubbles and velocity control on the cooling rates of lava and pyroclasts during submarine eruptions
<p>These dataset contains videos (.mp4) of experiments and the time-temperature data (.txt) measured during the experiments. Each video starts when the sample is completely submerged in the water. The temperature data represents the full experimental duration, where a spike in water temperature data to about 27 deg C on average indicates the synchronization process between the video and the data. </p>
VESPA ASL: VElocity and SPAtially Selective Arterial Spin Labeling
<p>This repository contains the simulation code, in vivo data, preprocessing code, and analysis code used in the Magnetic Resonance in Medicine article titled "VESPA ASL: VElocity and SPAtially Selective Arterial Spin Labeling" (<a href="https://doi.org/10.1002/mrm.29159">https://doi.org/10.1002/mrm.29159</a>). Please cite this article and this repository (<a href="https://doi.org/10.5281/zenodo.6870683">https://doi.org/10.5281/zenodo.6870683</a>) if you use any of this code or data in your work.</p> <p>Software requirements: BASH, FSL6, and MATLAB (tested with MATLAB versions 2021a and 2021b; requires the Optimization Toolbox and the Statistics and Machine Learning Toolbox). Preprocessing steps tested on macOS 12.4 (Monterey) and 10.14 (Mojave).</p> <p>This updated version (V2.0) simplifies and streamlines the setup of command line and MATLAB paths, making it easier to re-run the preprocessing and analysis steps.</p>
Code and Velocity Data for Sustained indentation in 2D models of continental collision involving whole mantle subduction
<p>Contains the Python code for all models and resolution tests using the <a href="https://www.underworldcode.org/intro-to-underworld">underworld geodynamics code</a> in "Sustained indentation in 2D models of continental collision involving whole mantle subduction" submitted to GJI</p>
IODP Expedition 352 P-wave velocity logger (whole round)
<p>P-wave velocity data were measured on whole-round sections on the Whole-Round Multisensor Logger (WRMSL) using pairs of piezoelectric transducers mounted on a caliper system. Measurements may be affected by degassing of pore fluid and microfracturing during core recovery. Report includes P-wave velocity in x-y plane and distance and traveltime between transducers.</p>
Seismic noise interferometry and Distributed Acoustic Sensing (DAS): Inverting for the firn layer S-velocity structure on Rutford Ice Stream, Antarctica
<p>This dataset contains files including continuous DAS and geophone data and a refracted P wave travel time data collected on Rutford Ice Stream, Antarctica. The seismic data is used to perform seismic noise interferometry. The travel time data is used to perform refraction inversion to get the P wave velocity profile.</p> <p><br> 1. 7 hours of continuous DAS data (100 Hz sampling): 2020-01-14T00:00:19.598000Zoffset_****.mseed, with offset referring to the distance from the DAS channel to the interrogator.</p> <p>2. Corresponding 7 hours of vertical component continuous geophone (A000, located at DAS channel offset 570 m) data.</p> <p>3. Refraction P wave travel time from a geophone array refraction survey.</p>
IODP Expedition 351 P-wave velocity logger (whole round)
<p>P-wave velocity data were measured on whole-round sections on the Whole-Round Multisensor Logger (WRMSL) using pairs of piezoelectric transducers mounted on a caliper system. Measurements may be affected by degassing of pore fluid and microfracturing during core recovery. Report includes P-wave velocity in x-y plane and distance and traveltime between transducers.</p>
IODP Expedition 351 P-wave velocity bayonet (section)
<p>P-wave velocity data were measured on undisturbed section halves using pairs of piezoelectric transducers mounted in bayonets that are inserted into soft sediment along the JRSO-defined y-axis and/or z-axis. Report includes P-wave velocity in y and/or z direction, bayonet separation, traveltime between transducers, and first arrival picks.</p>
Full Inverse Velocity Fields for "Transport of Antarctic Bottom Water entering the Brazil Basin in a Planetary Geostrophic Inverse Model"
<p>Velocity fields on all approximate neutral surfaces from the inverse model presented in "Transport of Antarctic Bottom Water entering the Brazil Basin in a Planetary Geostrophic Inverse Model". The pressure of the approximate neutral surface is contoured in the background. The number in the title represents the pressure of the approximate neutral surface at the reference station in the Hunter Channel.</p>
Dataset for "Correlation between sonic pulse velocity and flat-jack tests for the estimation of the elastic properties of unreinforced brick masonry"
<p>This repository contains data from the sonic test experimental campaign carried out in eight structures at various location in Croatia.</p> <p>The data set is structured in 3 levels of folders:</p> <p>- At first level, the 8 folders correspond to the 8 tested buildings.</p> <p>- At second level, for each building, each folder corresponds to a different test location within the building, e.g. "Data FJ1".</p> <p>- At third level, for each location, each folder corresponds to a different setup (i.e. distance and location of hammer and accelerometer), , e.g. "FJ1 1-2".</p> <p>Sonic data are presented in .txt files in three columns corresponding to the time, the hammer (emitter) and the accelerometer (receptor), respectively.</p> <p>Please cite the following related publication:</p> <p>Ortega J, Stepinac M, Lulic L, Nuñez Garcia M, Saloustros S, Aranha C, Greco F, Correlation between sonic pulse velocity and flat-jack tests for the estimation of the elastic properties of unreinforced brick masonry, under review (2022)</p>
TEAMx-PC22 (TEAMx pre-campaign 2022) - 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 a single TAR file containing 1536 hourly NetCDF files. Within these, radial velocities from KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159 Doppler wind lidars, as well as coplanar-retrieved horizontal wind speed components in their common scanning plane are stored. </p> <p>The time period is 29 June 2022, 00:00 UTC - 31 August 2022, 23:58 UTC.</p> <p>More details about the variables, lidar locations, scan details, as well as post-processing can be found in the NetCDF metadata. The wind fields stored in the NetCDF files are also available in daily animation form under an accompanying Zenodo Video/Audio data set (DOI: 10.5281/zenodo.7212837).</p>
Data for "Sound velocity of hexagonal close-packed iron to the Earth's inner core pressure"
<p>This file is the dataset used in the article "Sound velocity of hexagonal close-packed iron to the Earth's inner core pressure", Nat. Commun. 13, 7211 (2022). https://doi.org/10.1038/s41467-022-34789-2</p>
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>
Regional velocity anomalies and slip distribution of the 2023 Kahramanmaraş, Türkiye Earthquakes
<p>dvp.dat contains columns of longitude, latitude, depth (km), and dVp (%) relative to the 1-D reference model used in https://www.mdpi.com/2076-3263/11/2/91.</p> <p>dvs.dat contains columns of longitude, latitude, depth (km) and dVs (%) relative to the 1-D reference model used in https://www.mdpi.com/2076-3263/11/2/91.</p> <p>slip_model.csv contains columns of subfault node ID, UTM Zone 37 easting (m), UTM Zone 37 northing (m), depth (m), strike (o), dip (o), strike slip (m),dip slip (m) and associated primary fault (i.e. <span>Southern East Anatolian fault (SEAF) and Savrun-Çardak-Sürgü fault (SCSF)). A</span><span> positive strike slip refers to a right-lateral slip and a positive dip slip refers to a normal slip.</span></p>
"Monthly velocity and seasonal variations of the Mont Blanc glaciers derived from Sentinel-2 between 2016-2024" - supplementary materials
<p>The repository contains the supplementary materials to be downloaded relative to the research article:</p> <p>“Monthly velocity and seasonal variations of the Mont Blanc glaciers derived from Sentinel-2 between 2016-2024” </p> <p>https://doi.org/10.5194/egusphere-2023-2771</p> <p>The available files are:</p> <p>-92 raster maps of monthly velocity of the study area.</p> <p>-Shapefiles whith the glacier outlines of the 30 studied glaciers.</p> <p>-Shapefiles of the velocity time series extraction areas.</p> <p>-Velocity time series 2016-2024 of the 30 glaciers from the study. </p>
Figure 2. Jump distance versus the maximum horizontal velocity component for 13 in Jumping Performance in Flightless Hawaiian Grasshopper Moths (Xyloryctidae: Thyrocopa spp.)
Figure 2. Jump distance versus the maximum horizontal velocity component for 13 total jumps of male T. apatela. The regression is given by: y = 1.01 x + 16.39, r2=0.57, P<0.004.
Turkish Straits System - Vertical Velocity
<p>Vertical Velocity daily mean estimates from a six-year simulation of Turkish Straits System (TSS) using high-resolution unstructured triangular mesh ocean model FESOM between 2008-2013. Other variables are provided separately.</p> <p>The mesh files are appended to the dataset for processing purposes.</p> <p>Aydogdu, A., Pinardi, N., Ozsoy, E., Danabasoglu, G., Gurses, O., and Karspeck, A.: Circulation of the Turkish Straits System under interannual atmospheric forcing, Ocean Sci., 14, 999-1019, doi:10.5194/os-14-999-2018, 2018.'</p>
Turkish Straits System - Meridional Velocity
<p>Meridional Velocity daily mean estimates from a six-year simulation of Turkish Straits System (TSS) using high-resolution unstructured triangular mesh ocean model FESOM between 2008-2013. Other variables are provided separately.</p> <p>The mesh files are appended to the dataset for processing purposes.</p> <p>Aydogdu, A., Pinardi, N., Ozsoy, E., Danabasoglu, G., Gurses, O., and Karspeck, A.: Circulation of the Turkish Straits System under interannual atmospheric forcing, Ocean Sci., 14, 999-1019, doi:10.5194/os-14-999-2018, 2018.'</p>
Turkish Straits System - Zonal Velocity
<p>Zonal Velocity daily mean estimates from a six-year simulation of Turkish Straits System (TSS) using high-resolution unstructured triangular mesh ocean model FESOM between 2008-2013. Other variables are provided separately.</p> <p>The mesh files are appended to the dataset for processing purposes.</p> <p>Aydogdu, A., Pinardi, N., Ozsoy, E., Danabasoglu, G., Gurses, O., and Karspeck, A.: Circulation of the Turkish Straits System under interannual atmospheric forcing, Ocean Sci., 14, 999-1019, doi:10.5194/os-14-999-2018, 2018.'</p>
VELOCE I. High-precision Radial Velocities of Cepheids
<p>The first data release of the VELOcities of CEpheids project (VELOCE DR1, Anderson et al. 2024, A&A in press, arXiv: 2404.12280, doi: <a href="https://doi.org/10.1051/0004-6361/202348400">10.1051/0004-6361/202348400</a>) comprises 18,225 radial velocity measurements (RVs) of 258 bona-fide classical Cepheids as well as 1161 RVs of 164 additional targets, most of which were previously misclassified as Cepheids. The observations were collected mainly between 2010 and 2022 using two 1.2m telescopes: Euler (Coralie spectrograph) at ESO La Silla Observatory in Chile, and Mercator (Hermes spectrograph) at Roque de los Muchachos Observatory on La Palma, Canary Islands, Spain. </p> <p>Here, we publish the FITS files as described in appendix C of VELOCE paper I (Anderson et al. 2024). A total of 422 FITS files -- one per star -- are compressed together using tar and gzip as they would otherwise exceed the limit of 100 files in Zenodo. The FITS files contain all 19,386 individual RV measurements, as well as the per-epoch template fit residuals used to measure zero-point offsets and investigate long-term orbital motion. </p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.