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69 results for “Flow Velocity”
One-minute average horizontal wind velocity data (not corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains the one-minute average horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4. The data has been filtered for spurious observations and the true wind correction has been redone using the quality checked one-minute ship track velocity data. This data set has not been corrected for air-flow distortion, which was caused by the ship's super structure. The flow-distortion corrected data should be used for studies interested in the actual true wind speed near the ship's location.</p> <p><strong>Dataset contents</strong></p> <ul> <li>wind-observations-stbd-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>wind-observations-port-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</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>
Water flow velocity data, Shark River Slough (SRS) near Black Hammock island, Everglades National Park (FCE LTER), South Florida from October 2003 to August 2005
Water velocity data measured every 5 or 15 minutes in Shark River Slough beside Black Hammock tree island, Everglades National Park using Sontek Agronaut water flow sampler.
Water flow velocity data, Shark River Slough (SRS) near Chekika tree island, Everglades National Park (FCE LTER) from January 2006 to March 2021
Water velocity data measured every 5 or 15 minutes in Shark River Slough beside Chekika tree island, Everglades National Park, using Sontek Agronaut water flow sampler. Data collection is complete.
Water flow velocity data, Shark River Slough (SRS) near Frog City, south of US 41, Everglades National Park (FCE LTER) from October 2006 to July 2009
Water velocity data measured every 5 or 15 minutes in Shark River Slough near Frog City jetty, Everglades National Park, using Sontek Agronaut water flow sampler.
Water flow velocity data, Shark River Slough (SRS) near Gumbo Limbo Island, Everglades National Park (FCE) from October 2003 - December 2018
Water velocity data measured every 5 or 15 minutes in Shark River Slough near Gumbo Limbo Island, Everglades National Park, using Sontek Agronaut water flow sampler. Data collection is complete.
Water flow velocity data, Shark River Slough (SRS) near Satinleaf Island, Everglades National Park (FCE LTER) from July 2003 to December 2005
Water velocity data measured every 5 or 15 minutes in Shark River Slough near Satinleaf tree island, Everglades National Park, using Sontek Agronaut water flow sampler.
Extreme to phenomenal storm wave impacts on a steep rocky coast, north Mayo, Ireland: video data, image analysis, runup and flow velocity calculations for waves of storms Fionn and Gareth.
<p>The primary data are video (.mp4) files of extreme storm wave impacts on the sites of high elevation (>=20m above high water mark) coastal boulder deposits, recorded during storms Fionn (16/01/2018) and Gareth (12/03/2019), at (54.320355, -9.569633) on the north Mayo coast of Ireland, while the significant wave height was in the range [11m,14m]. There are also .png and .jpg files derived from frames of some of the videos, relating to the analysis of the impacting wave kinematics (runup/landward propagation and flow velocities), together with physical measurements for scale determination and runup/velocity/measurement uncertainty calculations in Excel. The files EventX.mp4 are the primary data for the wave impacts EventX. The files EventX_Frame_Y.jpg are frames sampled from EventX.mp4 at constant time intervals in the temporal vicinity of the impact. The files EventX_Edges_Y.png are the edges derived from the frames with the Canny edge detector. The files EventX_Registration_Y.jpg are the impacting wavefront edges with topographical edges registered on the file ReferenceImage.jpg The files EventX.jpg are the stacked registrations for all Y, from which the impact kinematics are derived. The file Scale_Registration_Position_Velocity_Measurements_AndUncertainty.xlsx contains physical measurements for scale determination, measurements of registration error, and the calculations of impact runup/landward displacement and flow velocities, with their uncertainties. The files JetX_Leacht_a_Chúil.mp4/g are videos of large jet-producing impacts at another site.</p> <p>The files DSCN0066.MP4-DSC0085.MP4 are the raw video observations of Storm Gareth, recorded from 15:35-18:41 UT on 12 March 2019 with a Nikon Coolpix W100, while the significant wave height increased from 12m to in excess of 14m (the timestamp of these videos in Properties->Details->Media Created is one hour later than the UT of creation, because the camera's clock was set to Irish Summer Time). The file GPO15366.MP4 is an example of the GoPro (Hero 5) videos recorded simultaneously.</p>
High Temporal Resolution Records of Hansbreen Ice Flow Velocity for Years 2006-2019
<p>This repository contains the datasets of the positions of 16 mass balance stakes, horizontal velocity (m/yr) and accuracy of velocity (m/yr) for Hansbreen, a tidewater glacier in southern Svalbard. Data were derived from GNSS measurements conducted in the period 2006-2019. Stake positions are given in UTM zone 33X, and elevation in geoidal height (EGM96). Additionally, we provide files with annual, summer and winter velocities (m/yr) with a standard deviation of velocity, estimated for the hydrological year. The file „Hansbreen_preprocessing_code_stakes.zip” contains the code used for the velocity estimation.</p>
Cilia density and flow velocity affect alignment of motile cilia from brain cells
<p>Here we store the supplementary Materials and Methods for the publication Cilia density and flow velocity affect alignment of motile cilia from brain cells.</p> <p>In the Supplementary methods we included additional information on the hydrodynamic simulations. </p> <p>Video1 and Video2 are videos referenced in the main text of the paper</p> <p>In the archive 'raw data and code.tar' , we provide raw images and codes to support the article.The complete dataset of raw images is more than 1 Tb. Here we are limited to 50Gb. The full dataset is available upon request.<br> <br> We choose to provide a full dataset of two culture at DIV 16, one treated with shear flow and a control without flow.</p> <p>For each of the two cultures, the videos with propelled particles are in the directory FL,<br> The bright field images without particles are stored in BF. Unfortunately we uploaded only few videos because of their large size. The results of the analysis of this dataset is reported in the directory analysis (available for each culture).</p> <p>Moreover we provide the code to analyse these data.<br> The analysis routine:</p> <p>Step 1: for each field of view (fov) getting the cilia beating direction from the FL images. This is done with PIV. The code is Step1_PIVanalysis.mat</p> <p>Step 2: for each fov getting ciliated cell position and CBF from the BF movies. Gather the cilia beating direction and cilia posion and frequency in a unique figure and matlab class (Res.mat). This is done in Step2_gatherResults.mat</p> <p>The results of these analysis are stored in the analysis folder for each culture.</p> <p>These routines are repeated for each experiment and results are then plotted to get trends. In the folder code4figures we report the code that we used to make the figures in the papers starting from a matlab file "all_results*.mat", where are gathered all the analysis.</p> <p>The code may improve in the future with more comments. please check Nicola's github page for the latest update. Please contact us for any problem. https://github.com/NicolaPellicciotta/Code4-Cilia-density-and-flow-velocity-affect-alignment-of-motile-cilia-from-brain-cells</p> <p>All the raw videos and code are in the archive.</p> <p> </p>
Laboratory Open Channel Flow: Video, Waterlevel and Surface Velocity
<p>Video footage of an open channel flow in a laboratory setting, associated with the surface velocity and water level.</p> <p><br> - Video footage was collected using a Raspberry Pi Camera Module v2 attached to a Raspberry Pi 4 at 25fps from three positions and split into roughly 15s chunks.<br> - A "mic+35/IU/TC" ultrasonic sensor (accuracy: ±1%) measured the water level<br> - A "Nortek Vectrino" (accuracy: ±1% ±1mm/s) velocimeter measured the velocity at the surface</p> <p> </p> <p>- The video files can be found in the folders position1, position2 and position3, each file name contains the initial timestamp to match frames to the measurements<br> - The file "waterlevel.csv" contains the timestamps, Waterlevel [mm] and Percentage Full [%]. The waterlevel column is reversed, as the distance between the sensor and the surface was measured. This means, that lower values correspond to higher water levels.<br> - The file "velocity.csv" contains the timestamps and surface velocity measurements [m/s]</p>
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae- in New Findings Of White Clawed Crayfish, Austropotamobius Pallipes (Decapoda, Astacidae), And Peculiarities Of Its Spatial Distribution In Neretvica (Bosnia And Herzegovina)
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae-
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>
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 ΔT=4.7<sup>0 </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>
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>
Low-frequency oscillations of blood flow velocity
<p>figS4a-d.h5 - Intensity profiles obtained by scanning along a line (described by the below attribute) placed along a FITCdextran stained vessel. Record features traces of individual red blood cells from which the blood flow velocity in figure S4 was obtained.<br> File contains single dataset:<br> 1 - Imaging data_200807_111309 -> voltage from PMT collecting FITCdextran fluorescence signal<br> The dataset features two attributes:<br> 1 - Display Time -> hologram dwell time in microseconds<br> 2 - Image Dimentions -> dimensions of the full resolution image used prior the line-scans has been selected <br> 3 - ROI_XY -> coordinates of individual pixels of the scanning trajectory</p> <p> </p>
Dataset accompanying the publication "Transport and retention of micro-Polystyrene in coarse riverbed sediments: Effects of flow velocity, particle and sediment sizes"
<p>The dataset in this repository is accompanying the publication "Transport and retention of micro-Polystyrene in coarse riverbed sediments: Effects of flow velocity, particle and sediment sizes" (in Microplastics and Nanoplastics, 2023, submitted 09.06.2023)</p> <p>The repository contains the raw image files of all sample filters which were scanned using the fluorescence imaging system ChemiDoc and used to analyse the infiltration behaviour of microplastic polystyrene in the manuscript. In addition, we provide the resulting data from the particle identification and geometric analysis which were derived from the raw data using ImageJ in tabular excel format. The data is structured in folders following the naming of the columns from the manuscript.</p>
Flume 3D Flow Velocities - Otemma Outdoor Flume Experiment (2021)
<p><strong>Flume 3D Flow Velocities - Otemma Outdoor Flume Experiment (2021)</strong></p> <p>We collected the 3D flow velocities with an Acoustic Doppler Velocimeter (ADV), the Nortek Vectrino (VCN9421), supported by a sliding aluminum structure that allowed us to relocate the ADV precisely within the flumes. In each flume, we sampled the 3D velocities of 45 points, and we did this for the near-bed layer at 1 cm from the flume bottom. The sampling points were divided in three parallel lines (15 points each), located at the center of the flume and sufficiently away from the flume walls to avoid wall hydraulic interference. Each sampling point was measured for 60 seconds at a sampling rate of 25 Hz.</p> <p> </p> <p>Data format and information:</p> <ul> <li>Flume A: mmdd_FA_nD or mmdd_FA_nE or mmdd_FA_nF (n is the number of sampling point, from 1 to 15)</li> <li>Flume B: mmdd_FB_nA or mmdd_FB_nB or mmdd_FB_nC (n is the number of sampling point, from 1 to 15)</li> <li>Data are in .dat format</li> <li>File headers are provided (Header_A and Header_B), and are meant to explain the structures of the .dat matrices</li> </ul>
Five-minute average horizontal wind velocity data combined from both sensors (which has been corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.
<p><strong>Dataset abstract</strong></p> <p>The horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4 has been corrected for air-flow distortion. The measurements from both the port and starboad side anemometer were averaged to five-minute resolution and have been combined via vector averaging of the data. The ten meter neutral wind speed (U10N) has been estimated using ERA-5 surface heat fluxes, which were interpolated onto the ship's track, and the COARE 3.5 drag coefficient. This data set provides a continous and high-resolution record of the wind speed and direction near to the ship's location.</p> <p><strong>Dataset contents</strong></p> <ul> <li>wind-observations-port-stbd-corrected-combined-5min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This five-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
One-minute average horizontal wind velocity data (which has been corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.
<p><strong>Dataset abstract</strong></p> <p>One-minute average horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4. The data has been filtered for spurious observations and the true wind correction has been redone using the quality checked one-minute ship track velocity data. The data has been corrected for air-flow distortion, which was caused by the ship's super structure.</p> <p><strong>Dataset contents</strong></p> <ul> <li>wind-observations-stbd-corrected-1min-legs0-4.csv, data file, comma-separated values</li> <li>wind-observations-port-corrected-1min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
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International Brain Laboratory public data
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OpenNeuro
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