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86 results for “velocity measurement”
Relationship Between Blood Pressure and Pulse Wave Velocity Measurements in Peritoneal Dialysis
ClinicalTrials.gov study NCT03607747. IPD Sharing: UNDECIDED. Countries: 1. Publications: 36.
Rosuvastatin Effect on Serial Echocardiographic Measurement of Coronary Flow Velocity Reserve
ClinicalTrials.gov study NCT01490398. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Reliability of Minimally Trained Operator's Velocity-Time Integral Measurement Guided by Artificial Intelligence VTI
ClinicalTrials.gov study NCT06486467. IPD Sharing: YES. Countries: 1. Publications: 7.
Simplified Pulse Wave Velocity Measurement, Validation Study of the pOpmètre in Children
ClinicalTrials.gov study NCT02991703. IPD Sharing: Not stated. Countries: 0. Publications: 8.
Photoplethysmographic Measurements of Pulse Wave Velocity (PWV) and Blood Pressure (BP)
ClinicalTrials.gov study NCT05393401. IPD Sharing: NO. Countries: 1. Publications: 4.
Microstructure turbulence, conductivity-temperature-depth, and current velocity measurements of a submesoscale eddy in Terra Nova Bay (2018-2019)
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Supplemental dataset from: Initial acoustoelastic measurements in olivine: Investigating the effect of stress on P- and S-wave velocities
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Velocity vector files from PIV measurements of the wake behind a flying beetle
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Improving measurements of the falling trajectory and terminal velocity of wind-dispersed seeds
<p>1. Seed dispersal by wind is one of the most important dispersal mechanisms in plants. The key seed trait affecting seed dispersal by wind is the effective terminal velocity (hereafter "terminal velocity", Vt), the maximum falling speed of a seed in still air. Accurate estimates of Vt are crucial for predicting intra- and interspecific variation in seed dispersal ability. However, existing methods produce biased estimates of Vt for slow- or fast-falling seeds, fragile seeds, and seeds with complex falling trajectories.</p> <p>2. We present a new video-based method that estimates the falling trajectory and Vt of wind-dispersed seeds. The design involves a mirror that enables a camera to simultaneously record a falling seed from two perspectives. Automated image analysis then determines three-dimensional seed trajectories at high temporal resolution. To these trajectories, we fit a physical model of free fall with air resistance to estimate Vt. We validated this method by comparing the estimated Vt of spheres of different diameters and materials to theoretical expectations, and by comparing the estimated Vt of seeds to measurements in a vertical wind tunnel.</p> <p>3. Vt estimates closely match theoretical expectations for spheres and vertical wind tunnel measurements for seeds. However, our Vt estimates for fast-falling seeds are markedly higher than those in an existing trait database. This discrepancy seems to arise because previous estimates inadequately accounted for seed acceleration. </p> <p>4. The presented method yields accurate, efficient and affordable estimates of the three-dimensional falling trajectory and terminal velocity for a wide range of seed types. The method should thus advance the understanding and prediction of wind-driven seed dispersal.</p>
Offset-Controlled Localized Velocity Inversion in the $\tau-p$ Domain Using Acoustic Traveltimes: Modeling and Application in Borehole Measurements
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Seawater velocities measured by autonomous sailbuoy Adcp in the Barents Sea from May to July 2021 on campaign the PolarFront project
<p>This dataset contains ocean currents measured by sailing autonomous surface vehicle <em>Adcp</em>, while on campaign at 70–77N in the Barents Sea for the <a href="https://github.com/akvaplan-niva/polarfront">PolarFront</a> research project, from 16 May to 25 July 2021 (Daase 2021). The <em>Adcp</em> is equipped with a <a href="https://www.aanderaa.com/">Aanderaa</a> DCPS 5400 operating at 600 kHz, mounted in the keel at –0.2 m.</p> <p><strong>Space/time coverage</strong></p> <ul> <li>Time: 2021-05-16T13:45:00Z/2021-07-25T13:15:00Z</li> <li>Space: [20.5,70.0,33.0,76.8] ([W,S,E,N])</li> </ul> <p><strong>Data distributions</strong></p> <p><em>Datalogger (text files)</em></p> <p>The primary distribution consists of 2 datalogger text files retrieved post-mission from sailbuoy internal storage. The files are the original full scale original data records, as captured by the internal sofware of manufacturer <a href="http://www.sailbuoy.no/">Offshore Sensing</a>.</p> <ul> <li>DATA.TXT: Position and sensor metadata</li> <li>DCPS.TXT: Ocean currents</li> </ul> <p><em>GeoJSON</em></p> <p>For increased data interoperability, a <a href="https://www.rfc-editor.org/info/rfc7946"><strong>GeoJSON</strong></a> file is also included, consisting of Point features like the following:</p> <p>{ "geometry": { "coordinates": [30.11165, 76.438894], "type": "Point" }, "properties": { "time": "2021-05-19T22:45:00Z", "TTFF": 11, "Count": 24, "Commands": 0, "TxTries": 1, "ONT": 635, "DiskUsed": 0.783123, "Files": 0, "I": 0.01564, "V": 13.195122, "Temperature": 4.06502, "DCPSStatus": 0, "DCPSOnMin": 10, "DCPSSpeed": 0.329999, "DCPSDirection": 233.233643 }, "type": "Feature" }</p> <p>The GeoJSON file is derived from DATA.TXT and contains the full position and DCPS metadata log in a standard/machine-parsable format, including coordinated universal time following <a href="https://datatracker.ietf.org/doc/html/rfc3339">RFC 3339</a>/<a href="https://www.iso.org/obp/ui#iso:std:iso:8601:-1:ed-1:v1:en">ISO 8601</a>.</p> <p><strong>Data formats and interoperability</strong></p> <p>Both of the datalogger text files are in non-standard vendor formats. The DCPS text format is described in the <a href="https://www.aanderaa.com/media/pdfs/td304-manual-dcps.pdf">vendor manual.</a></p> <p><strong>Related publications</strong></p> <p>For a recent discussion on measuring ocean currents from sailbuoys compared to other platforms, see <a href="https://doi.org/10.3390/s22155553">Wullenweber et al., 2022</a>.</p> <p>See also <a href="https://doi.org/10.1029/2019GL086649">Swart et al.,2020</a>; <a href="https://doi.org/10.3390/s21206752">Camus et al., 2021</a> for further discussion on near-surface data collection with sailbuoys and other autonomous vehicles.</p> <p><strong>References</strong></p> <p>Camus L, Andrade H, Aniceto AS, Aune M, Bandara K, Basedow SL, Christensen KH, Cook J, Daase M, Dunlop K, Falk-Petersen S, Fietzek P, Fonnes G, Ghaffari P, Gramvik G, Graves I, Hayes D, Langeland T, Lura H, Kristiansen T, Nøst OA, Peddie D, Pederick J, Pedersen G, Sperrevik AK, Sørensen K, Tassara L, Tjøstheim S, Tverberg V, Dahle S (2021). Autonomous Surface and Underwater Vehicles as Effective Ecosystem Monitoring and Research Platforms in the Arctic—The Glider Project. Sensors 21(20):6752. <a href="https://doi.org/10.3390/s21206752">https://doi.org/10.3390/s21206752</a></p> <p>Daase M (ed.). (2021). ARCTOS Barents Sea Polar Front May 2021 Cruise Report (1.1.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7384076">https://doi.org/10.5281/zenodo.7384076</a></p> <p>Edholm J. (2022). Sailbuoy DCPS. Zenodo. <a href="https://doi.org/10.5281/zenodo.6798056">https://doi.org/10.5281/zenodo.6798056</a></p> <p>Swart S, du Plessis MD, Thompson AF, Biddle LC, Giddy I, Linders T, Mohrmann M, Nicholson S (2020). Submesoscale fronts in the Antarctic marginal ice zone and their response to wind forcing. Geophysical Research Letters, 47, e2019GL086649. <a href="https://doi.org/10.1029/2019GL086649">https://doi.org/10.1029/2019GL086649</a></p> <p>Wullenweber N, Hole LR, Ghaffari P, Graves I, Tholo H, Camus L (2022). SailBuoy Ocean Currents: Low-Cost Upper-Layer Ocean Current Measurements. Sensors. 22(15):5553. <a href="https://doi.org/10.3390/s22155553">https://doi.org/10.3390/s22155553</a></p>
Particle tracking algorithm and additional data for "Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 µm)"
<p>This repository provides additional files for in the publication "Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 µm)" by Stefan Dittmar, Aki Sebastian Ruhl and Martin Jekel (DOI: <a href="https://www.doi.org/10.1021/acsestwater.3c00457">10.1021/acsestwater.3c00457</a>) </p><p>It contains:</p><p>- image processing routine for particle tracking written in Python (<strong>1_particle_tracking_algorithm.zip</strong>)<br>- single particle raw data from settling experiments (<strong>2_settling_data.zip</strong>)<br>- single particle data from appyling empirical model for interactions between settling particles (<strong>3_model_results_data.zip</strong>)<br>- additional video & animated graph referenced in publication or SI (<strong>4_videos.zip</strong>)</p>
Validation of Optical Device for Aortic Pulse Wave Velocity Measurement
ClinicalTrials.gov study NCT05400421. IPD Sharing: NO. Countries: 0. Publications: 10.
Data from: Nuclear magnetic resonance measurements of velocity distributions in an ultrasonically vibrated granular bed
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Improving measurements of the falling trajectory and terminal velocity of wind-dispersed seeds
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supplementary data to 'Mercury's Interior Structure constrained by Density and P-wave Velocity Measurements of Liquid Fe-Si-C Alloys'
<p>This zipfile contain raw experimental data, the matlab code used for data-analysis, and the interior structure models of Mercury that are used in the paper 'Mercury’s Interior Structure constrained by Density and P-wave Velocity Measurements of Liquid Fe-Si-C Alloys', which is authored by Knibbe et al.</p>
Validation of Pulse Wave Doppler Demodulation Algorithm for the Continuous, Non-invasive Measurement of Blood Flow Velocity
ClinicalTrials.gov study NCT01750125. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Respiratory Variability in Aortic Blood Velocity Measured by Suprasternal View as an Indicator of Fluid Responsiveness
ClinicalTrials.gov study NCT02791984. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Reliability and Validity of the Vicorder Device When Measuring Pulse Wave Velocity Within Chronic Stroke Patients
ClinicalTrials.gov study NCT04342143. IPD Sharing: NO. Countries: 1. Publications: 0.
Diagnostic Performance of the Mitral Annulus Velocity Variation Measured by Tissue Doppler to Evaluate the Fluid Responsiveness During the Initial Management of Shock in Patients Admitted to the Emerg
ClinicalTrials.gov study NCT05888974. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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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.