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969 results for “velocity”
Accuracy of the Group Velocity of Love Waves Extracted from Ambient Seismic Noise
<p>Love wave waveforms derived from the empirical Green's functions and Ground Truth earthquake in my manuscript submitted to Journal of Geophysical Research: Solid Earth.</p>
IODP Expedition 366 P-wave velocity caliper (section/discrete)
<p>P-wave velocity data were measured on undisturbed section halves (JRSO-defined x-axis) and/or discrete cube and cylinder samples (x, y, or z-axis) using pairs of piezoelectric transducers mounted on a caliper system. Report includes P-wave velocity in x, y, and/or z-direction, caliper separation, traveltime between transucers, and first arrival picks.</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>
USGSG16AP00094: Developing a seismic velocity model of the central valley, northern California: models SSJDOPHW95, SSJDGRANW95, SSJDFRAN95, and SSJDFRANG16
<p>Data and Figures for final technical report for:<br> USGS16AP00094: Developing a seismic velocity model the Central Valley, Northern California<br> Justin Lindeman, Donna Eberhart-Phillips, Louise H. Kellogg, and Lorraine J. Hwang<br> University of California, Davis</p> <p>Data for the final velocity model SSJD2016 may be found here:<br> <br> Eberhart-Phillips, Donna. (2017). USGSG16AP00094: Developing a seismic velocity model of the central valley, northern California: model SSJD2016 [Data set]. Zenodo. http://doi.org/10.5281/zenodo.556605</p> <p><br> Data for the shallow velocity models can be found in this repository.</p> <p>DATA FILES</p> <p>SSJDOPHW95.out, SSJDGRANW95.out, and SSJDFRANW95.out are crustal velocity models from velocity vs depth relation (Aagaard et al., 2010) and Wentworth et al., 1995 basement surface contours.</p> <p>SSJDFRANG16.out is the crustal velocity model from velocity-vs-depth relations (Aagaard et al., 2010) and <br> Graymer (written communication) 2016 basement surface contours.</p> <p>FIGURES</p> <p>All figures in the final technical report are included here.</p> <p>Map view slices of Vp (All*VP.pdf) and cross sections (All*crosssectionscomp.pdf) are also included in this package.</p> <p>SCRIPTS<br> shallowvelocityFRAN.m, shallowvelocitytygran.m, and shallowvelocityOPH.m are the matlab scripts used to create the shallow velocity models.</p>
Plots of De Rham's function and its fractional velocity
<p>Plots of De Rham's function and its fractional velocity. Examples can be run in Maxima. Dataset complements the paper "Characterization of strongly non-linear and singular functions by scale space analysis", Chaos, Solitons & Fractals, 2016, 93, 14-19.</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>
The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities
<p>Velocity field for the India-Eurasia collision zone from Sentinel-1 InSAR and GNSS data</p> <p>Citations:</p> <p>[1] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2023). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10053499</p> <p>[2] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2024). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities, Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2023JB028571</p> <p>More details about the methodology to generate the velocity field can be found in Wright et al. (2023):</p> <p>[3] Tim J Wright, Greg Houseman, Jin Fang, Yasser Maghsoudi, Andy Hooper, John Elliott, Lynn Evans, Milan Lazecky, Qi Ou, Barry Parsons, Chris Rollins, Lin Shen, Hua Wang (2023). High-resolution geodetic strain rate field reveals dynamics of the India-Eurasia collision, submitted to Science, preprint available at https://doi.org/10.31223/X5G95R.</p>
Radial velocities and broadening functions for AI Hya
<p>Radial velocity and broadening function measurements from CORALIE and HIDES spectra.</p>
Reprocessing of Three-Decade GNSS Observations: Millimeter-Level Global Velocity Field and Plate Motion Model Refinement
<p>The accurate and reliable Terrestrial Reference Frame (TRF) functions as a unified spatiotemporal datum crucial for solid earth research, encompassing disciplines such as geodesy and geodynamics. High-precision GNSS velocity field products stand out as pivotal foundational data for the maintenance of the TRF. This dataset encapsulates the outcomes of two GNSS velocity field refinement products: Global GNSS Velocity Model 2020 (GGVM2020) and the Global Interpolation Velocity Model 2020 (GIVM2020). GGVM2020 comprises velocity values and formal errors derived from over 3000 GNSS sites worldwide. And GIVM2020 incorporates speed values from approximately 2000 grid points on land globally, with a grid spacing of 3 degrees.</p>
Climate velocities and species tracking in global mountain regions
<p>Mountain ranges harbor high concentrations of endemic species and are indispensable refugia for lowland species under anthropogenic climate change<sup>1,2</sup>. Forecasting biodiversity redistribution hinges on assessing whether species can track shifting isotherms as climate warms<sup>3,4</sup>. However, a global analysis of isotherm shift velocities along elevation gradients is hindered by the scarcity of weather stations in mountainous regions<sup>5</sup>. We address this by mapping the lapse rate of temperature (LRT) across mountain regions globally using satellite data (SLRT) and laws of thermodynamics to account for water vapour<sup>6</sup> (i.e., the moist adiabatic lapse rate: MALRT). Dividing the rate of surface warming from 1971 to 2020 by either the SLRT or MALRT, we provide the first maps of vertical isotherm shift velocities. We identify 17 mountain regions with exceptionally high vertical isotherm shift velocities (> 11.67 m/yr for the SLRT, > 8.25 m/yr for the MALRT), predominantly in dry areas but also in wet regions with shallow lapse rates like Northern Sumatra, the Brazilian Highlands, and Southern Africa. By linking these velocities to species range shift velocities, we report instances of close tracking in mountains with lower climate velocities. However, many species lag behind, suggesting persisting range shift dynamics even if we manage to curb climate change trajectories. Our findings are vital for devising global conservation strategies, particularly in the 17 high-velocity mountain regions we identified.</p> <p><strong>References</strong></p> <ol> <li>Rahbek, C.<em> et al.</em> Building mountain biodiversity: Geological and evolutionary processes. <em>Science</em> 365, 1114-1119 (2019).</li> <li>Rahbek, C.<em> et al.</em> Humboldt's enigma: What causes global patterns of mountain biodiversity? <em>Science</em> 365, 1108-1113 (2019).</li> <li>Chen, I. C., Hill, J. K., Ohlemuller, R., Roy, D. B. & Thomas, C. D. Rapid Range Shifts of Species Associated with High Levels of Climate Warming. <em>Science</em> 333, 1024-1026 (2011).</li> <li>Lenoir, J.<em> et al.</em> Species better track climate warming in the oceans than on land. <em>Nature Ecology & Evolution</em>, 1-16 (2020).</li> <li>Pepin, N.<em> et al.</em> Elevation-dependent warming in mountain regions of the world. <em>Nat Clim Change</em> 5, 424-430 (2015).</li> <li>Holton, J. R. & Hakim, G. J. <em>An introduction to dynamic meteorology</em>. Vol. 88 (Academic press, 2012).</li> </ol>
Velocity field of "Toward ultra-efficient high fidelity predictions of wind turbine wakes"
<p>This data collection contains the velocity field obtained from VFS-Wind LES simulations, FLORIS v3.4 GCH-model and the new ML model.</p>
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>
Mesh and velocity fields for "Resolving Pore-scale Concentration Gradients for Transverse Mixing and Reaction in Porous Media"
<p>This dataset contains the data needed to reproduce the results in the manuscript "Resolving Pore-scale Concentration Gradients for Transverse Mixing and Reaction in Porous Media". The three folders refer to (1) uniform flow, (2) flow through a bead pack, and (3) flow through a Berea sample (from the 11 sandstones repository, Digital Rocks Portal). The meshes and the velocity fields (in the subfolder "velocity") are in a HDF5 format. The meshes and velocity fields can be read using FEniCS version 2019.2.0. The velocity fields need 2nd order Lagrange tetrahedral elements.</p>
Effect of impeller rotational phase on the FDA blood pump velocity fields
Open the record for dataset details and reuse information.
Cumulative Cost Climate-Analogue FLDAS Climate Velocity for 2001-2021
<p><span>Climate velocity estimated using an cumulative cost climate-analogue method using global surface temperature data of the NASA FLDAS model at 0.1</span>×<span>0.1-degree grid for 2000-2021.</span></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>
Ambient noise data and velocity model from DEEPEN array in Hengill geothermal field, Iceland
<p>This repository contains the data and velocity model associated with the manuscript entitled "<em>Crustal characterization of the Hengill geothermal fields: Insights from isotropic and anisotropic seismic noise imaging using a 500-node array</em>" by Wu et al. (2024), to be published in <em>Journal of Geophysical Research: Solid Earth</em>. </p> <p>The dataset is the nine component cross-correlation functions (ZZ, ZN, ZE, NZ, NN, NE, EZ, EN, EE) after stacking over seismic array deployment time period (summer 2021) and after spatial averaging (bin stacking). The bin locations are provided in "bin_locations.txt".</p> <p>The derived VOIGT velocity and radial anisotropy model can be found in "Hengill_Voigt_Aniso_DEEPEN_4share.txt".</p> <p> </p>
Dataset of velocity and density fields from numerical simulations
<p>This dataset contains the outcomes of numerical simulations conducted for a research paper titled "Transformation of internal solitary waves at the edge of ice cover" with the use a non-hydrostatic model (Maderich et al., 2012) and code of the non-hydrostatic model. <br>Folder FIG3 contains a *.zip archive with data corresponding to the following parameters: x-coordinate length (m), z-coordinate depth (m) and module of horizontal velocity field (m/s). Folders FIG4 and FIG6 within the archive contain information on the following parameters: x-coordinate length (m), z-coordinate depth (m) and density field (kg/m^3).<br>Folder MODEL contains fortran source code files, input files, and a short model description.</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.