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69 results for “Flow Velocity”
Recherchebreen delta formation and glacier flow velocity data
<p>Data supporting our study on the rapid delta formation connected to glacier surge. The dataset contains positions of delta shoreline and centreline length (2020-2022) together with glacier flow velocity derived from Sentinel-1. Delta-related data produced by Jan Kavan, glacier velocity data by Adrian Luckman.</p> <p> </p> <p>This study is a contribution to the National Science Centre project ‘GLAVE’ (Award No. UMO-2020/38/E/ST10/00042).</p>
Optical Flow FLDAS Climate Velocity for 2001-2021
<p><span>Climate velocity estimated using an optical flow method using global surface temperature data of the NASA FLDAS model at 0.1</span><span>×</span><span>0.1-degree grid for 2000-2021</span></p>
Surface flow velocity from Pulmanki, Koita and Sävar Rivers 2020-2022
<p>Data description:<br>Surface flow velocity dataset was created using Hydro-STIV software (Hydro-STIV v.1.2.2, Hydro Technology Institute co.), which uses Space-Time Image Velocimetry (STIV) for velocity estimation, a method derived from Large-Scale Particle Image Velocimetry (LSPIV) developed by Fujita in 2007 (Fujita et al., 2007). The videos were georeferenced using known GCPs and the software performed orthorectification and calibration. The data has been used for publication "Surface flow and ice rafting velocities during freezing and thawing periods in Nordic rivers". Data consists of videos and daily stil images from Pulmanki, Koita and Sävar Rivers from Autumn freezing and Spring thawing periods. The data from Koita River is from 2020-2021 and from Pulmanki and Sävar Rivers from 2021-2022. The original raw data based on which the STIV analysis was performed was collected with Burrel time-lapse RGB cameras. </p> <p> </p> <p>Acknowledgements:<br><span>The river-ice related measurements were initiated at Pulmankijoki River in 2014 under the post-doctoral research project of Dr Lotsari, funded by the Research Council of Finland (ExRIVER: grant number 267345), and this study is a continuum in the series of these winter season studies. The work for this study was financially supported by four other projects funded by the Research Council of Finland (DefrostingRivers: 338480; HYDRO-RDI-Network: 337394; Digital Waters [DIWA] Flagship;359248). In addition, the work was funded by The European Union – NextGenerationEU Recovery instrument (RRF) through Research Council of Finland projects Hydro RI Platform (346167) and Green-Digi-Basin (347703). The Department of Geographical and Historical Studies, University of Eastern Finland, supported financially the field work done at Koita River. The work by Dr Lina Polvi-Sjöberg at the Sävar River was financed by a grant (2023-01513) from the Swedish Research Council Formas.</span></p>
Flow velocity measurements over a migrating train of dunes in a flume in the laboratory
<p>Acoustic Doppler Velocimeter (ADV) measurements conducted over a migrating train of dunes in a flume in the laboratory. The files with an .ntk extension are the raw data as measured and recorded by the instrument (Nortek Vectrino Profiler) and those with an extension .mat are the raw data as exported from the original software into a MatLab readable file format. <br> The data was used to create a streamwise flow velocity profile in the publication associated with this dataset. <br> File names have a nominal distance to the bed in mm expressed by the numbers at the end of the name. For example, 00_10 indicates measurements from 0 to 10 mm. However, as the measurements were conducted over a migrating train of dunes, those numbers are not as precise. However, the instrument records the distance to the bed and it is available inside the files. The distance inside the files is the one used to create the figure for the publication. <br> <br> In the upcoming publication the data was used to plot figure 4(d)<br> <br> The figure is available as 4D in the preprint found in this link: https://www.researchsquare.com/article/rs-1370465/v1</p> <p> </p>
Synthetic velocity profiles for simulations of blood flow in the aorta
<p>Synthetic dataset of aortic velocity profiles, suitable to be used for numerical simulations of blood flow.</p> <p>Please refer to the profile number ID when using it.</p>
Flow velocity measured from MacKay River Estuary, Georgia, USA
<p><strong>Title: </strong>Flow velocity measured from MacKay River Estuary, Georgia, USA</p> <p><strong>Author: </strong>Li, Chunyan</p> <p><strong>Contact/PI: </strong>Li, C. (cli@lsu.edu)</p> <p><strong>Description:</strong></p> <p>The data provided here are measured flow velocity profiles from a moving vessel in the MacKay River Estuary in Georgia, USA. The survey was done on March 31 16, 2003. Time is UTC.</p> <p>There is only 1 file. The instrument was a 1200 kHz RDI ADCP. The file is in ASCII format. It is output from the RDI’s program WinRiver II. The file name is:</p> <p>March31_MackayRiver002_ASC.TXT</p>
Experimental recordings of cross-flow vortex-induced vibrations on slender free-clamped ends cylinder and cylinder with a pair of branches in constant uniform flow for different reduced velocities.
<p>Dataset of the experiments done on a cylinder (N=0) and a coral model (N=1) 3D-printed with SLS out of TPE material. Cross-flow VIV are recorder with a GoPro in a test section of a water tunnel for different reduced velocities (Ur). </p> <p>This was made for our publication "Modelling vortex-induced vibrations of branched structures by coupling a 3D-corotational frame finite element formulation with wake-oscillators" in the Journal of Fluids and Structures.</p>
Subglacial Drainage Evolution Modulates Seasonal Ice Flow Variability of Three Tidewater Glaciers in Southwest Greenland - velocity and plume data
<p>This file contains four datasets:</p> <p>- ice velocity estimates of the area surrounding and including Kangiata Nunata Sermia, southwest Greenland, derived from Landsat imagery and from Sentinel 1 imagery</p> <p>- plume observations in this area</p> <p>- subglacial discharge at the terminus</p> <p>- glacier terminus positions</p> <p>A more complete description of these datasets will be provided in the publication with the same name (currently in review). The methods used to measure glacier terminus positions are described in https://doi.org/10.5194/esurf-6-551-2018 </p>
Representation model of wind velocity fluctuations and saltation sand transport in aeolian sand flow
<p>The data of the figures in the article.</p>
Data from: The role of flow velocity combined with habitat complexity as a top–down regulator in seagrass meadows
Large‐scale losses of seagrass areas have been associated with eutrophication events, which have led to an overproduction of photosynthetic organisms including epiphytes. Grazers that feed on epiphytes can exert a significant top–down control in the system, but the effects of physical factors on grazing activity and feeding behaviour have been rarely examined. We addressed the combination of hydrodynamic regime and seagrass shoot density can alter the feeding and foraging behaviours of mesograzers. A full factorial experiment, with flow velocity (high, medium and low) and shoot density (high versus low) as main factors, was conducted in a racetrack flume using artificial seagrass plots. The results showed that when high flow velocity conditions were combined with low shoot density, consumption of epiphytes by mesograzers was strongly reduced. In contrast, when flow velocity was low or shoot density was high, mesograzers exhibited high feeding rates and vigorous swimming behaviour. These results clearly indicate that hydrodynamic stress reduces the time that mesograzers can spend feeding, since it inhibits their swimming behaviour, and thus indirectly affecting to the density of epiphytes. Therefore, the triggering of trophic cascade effects in seagrass communities under these experimental conditions depended on the interrelationship and feedbacks among shoot density, abiotic (flow velocity) and biotic (epiphytes and mesograzers) compartments, with flow velocity exerting a top–down control on seagrass ecosystems.
Calibration of a movable heat pulse probe in borehole for measuring horizontal groundwater flow velocity in deep aquifers
<ol> <li>The first file is the flow profile data calculated by analytical solution in the steady-state flow. The calculation region is 30cm×30cm.</li> <li>The secend file is the temperature response data calculated by finite difference method. The simulated temperature response data corresponding to all heat pulse experiments are calculated and listed here.</li> </ol>
Calibration of a movable heat pulse probe in borehole for measuring horizontal groundwater flow velocity in deep aquifers
<ul> <li>In the laboratory, 42 heat pulse experiments with 7 different Darcy’s velocities and 6 different heating powers had been done. The results are summarized in a relationship of temperature increase with time.</li> <li>The temperature responses were continuously monitored over a 30min period. Data were collected every 2s throughout the recording period. In order to strictly monitor the actual power to the heater, the voltage and current delivered to the heater were also recorded simultaneously with the temperature.</li> <li>The heater was switched on and lasted for 1 min to generate a heat pulse. The time when the heater was switched on was selected as the initial time of data analysis.</li> </ul>
Stress effects on wave velocities of rocks: Contribution of crack closure, squirt flow and acoustoelasticity
<p>These data are the P- and S-wave waveform data obtained by Ba et al. (2019-2022) through ultrasonic experimental measurements on rock samples at different pressures.</p> <p>In the package of data, each folder of Sample A1, Sample A2, Sample A3, Sample A4, Sample A5, Sample A6, Sample B1, Sample B2, Sample B3, Sample B4, Sample B5, and Sample B6 indicates sample number. For each sample with different pore fluids, waveforms measured at different pressures are saved as different TXT files. For the title of each TXT file, it includes the sample number, saturation condition, temperature, differential pressure value and wave type. For instance, “Sample A1_fullwater_20℃_15MPa_Pwave” denotes that P-wave waveform of sample A1 with the full water saturation is measured at 20℃ and the differential pressure of 15MPa, while “Sample B2_dry_130℃_20MPa_Swave” denotes that S-wave waveform of dry sample B2 is measured at 130℃ and the differential pressure of 20 MPa. The package also includes the TXT file of sample lengths.</p> <p>In the TXT files of Samples A1-A6, the columns give wave amplitudes. The time interval is 0.05 μs. The travel times of P-wave and S-wave in the measured system are 8.4 μs and 14.45 μs, respectively. In the TXT files of Samples B1-B6, the first column gives time and the second column gives wave amplitude.</p>
Data for Individual and combined impacts of carbon dioxide enrichment, heatwaves, flow velocity variability and fine sediment deposition on stream invertebrate communities
Open the record for dataset details and reuse information.
Debris Flow Dataset for Debris Flow Velocity Inversion based on Farneback Optical Flow
<p>A velocity inventory of large-scale debris flow flume experimental data, published by USGS (Logan, 2018), was generated using the Debris Flow Velocity Inversion Method based on optical flow model (Farnebäck<span>, 2003</span>). This dataset includes raw data from three debris flow experiments conducted in 2007, 2015, and 2017. Each dataset corresponds to three relevant results: perspective transformation, optical flow analysis, and front position detection.</p>
Quantifying flow velocities in river deltas via remotely sensed suspended sediment concentration
<p><strong>bathymetry and model velocity field</strong></p>
Experimental data of Magnetic resonance imaging of analog lava flows: Velocity and rheology
<p>Experimental data of two- and three-phases suspension analog lava flow experiments to accompany "Magnetic resonance imaging of analog lava flows: Velocity and rheology"</p>
Dataset of Flow Velocity Prediction in Vegetated Alluvial Channels Comparing Empirical and State-of-the-art Hybrid Machine Learning Models
<p>We compiled 447 datasets from different sources and lab- and field-based measurements. These datasets included Einstein and Banks (1950), Fenzl (1962), Kouwen et al. (1969), Ree and Crow (1977), Murota (1984), Tsujimoto and Kitamura (1990), Tsujimoto (1991), Tsujimoto (1993), Shimizu (1994), Dunn et al. (1996), Ikeda and Kanazawa (1996), Meijer (1998), Jarvela (2002), Rowinski and Kubrak (2002), Stone and Shen (2002), Poggi et al. (2004), Carollo et al. (2005), and Murphy et al. (2007).</p>
Dataset of velocities of dry granular flows in a partially obstructed tilted chute
<p>The dataset presented here corresponds to data collected in an experimental campaign on dry granular flows, in which one experiment was repeated 31 times. The experimental campaign was performed in a 1.5 m length facility sloping at 20 degrees, where a volume of granular material was released from an upstream gate and trapped through a vertical obstruction in the downstream area of the channel, simulating slit dam conditions.</p> <p>The experiments carried out presented the following characteristics: a) uni-sized polystyrene particles (d=1.8mm); b) 3 litres volume of particles and c) the obstruction used had a double distance from the chute lateral walls of twice the diameter of the particles. Images from the experiments were collected by means of one high-speed camera located at the downstream part of the channel with a target frame rate of 300 frames per second and an exposure time of 200µs. </p> <p>The collected data was processed and filtered by means of Matlab algorithms aiming the assembly of a along-chute (u) and wall-normal (w) velocity ensemble database for the total time evaluated of 437 frames.</p> <p> </p>
Stress and frequency dependence of wave velocities in saturated rocks: an acoustoelasticity-squirt flow model
<p>These data are the P- and S-wave velocities obtained by Ba et al. through ultrasonic experimental measurements and the seismic measurements on a rock sample at different pressures.</p> <p>In the package of data, wave velocities measured at different pore fluids and frequencies are saved as different XLSX files. The title of each XLSX file includes the sample number, saturation condition and measurement frequency. For instance, “Sample D_Seismic_Brine” denotes that wave velocities of sample D measured with the full brine saturation are measured at seismic frequency, while “Sample D_Ultrasonic_Gas” denotes that wave velocities of gas-saturated sample D are measured at ultrasonic frequency. The package also includes the TXT file of sample properties.</p> <p>In the TXT files of seismic measurements, the first column gives the differential pressure values, the second column gives frequency values, the third and fourth column gives P- and S-wave velocities. In the TXT files of ultrasonic measurements, the first column gives the differential pressure values, the second and third column gives P- and S-wave velocities.</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.