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48 results for “velocity field”
3-D velocity field of the Tibetan Plateau due to land water loading
<h3>Basic information:</h3> <p>This dataset includes a series of 3-D loading deformation velocity fields, which are supplements to the GRL paper entitled "Present-Day Three-Dimensional Crustal Deformation Velocity of the Tibetan Plateau Due to Multi-Component Land Water Loading" [<a href="https://doi.org/10.1029/2024GL108684">https://doi.org/10.1029/2024GL108684</a>]. The deformation velocities are fitted using long time span data during 2000-2020, and the detailed description of the data processing and calculation methods can be found through the GRL paper. There are results of three different grid resolutions (0.5x0.5, 0.25x0.25, 0.1x0.1), and for distinction, different file naming suffixes are used. For example, '0point5grids' indicates the results are in 0.5-degree grid resolutions (0.5x0.5), and so forth.</p> <h3>Application scenario:</h3> <p>The velocity fields here can be directly used for the analysis of crustal deformation or used for the separation of land water-induced loading deformation within geodetic deformation velocity fields over Tibetan Plateau. There are results of all the six main land water components, including soil moisture (SM) [Table S1], snow water equivalent (SWE) [Table S2], glacier [Table S3], lake [Table S4], permafrost (PM) [Table S5] and groundwater storage (GWS) [Table S6], thus users can choose one or some they focus on, or directly choose the sum of all the six main components (i.e., GRACE-inferred total terrestrial water storage [Table S7]).</p> <h3>Citation: </h3> <p>When using this dataset, please cite the GRL paper: Jiao, J., Pan, Y., Ren, D., & Zhang, X. (2024). Present-day three-dimensional crustal deformation velocity of the Tibetan Plateau due to multi-component land water loading. <em>Geophysical Research Letters</em>, 51, e2024GL108684. <a href="https://doi.org/10.1029/2024GL108684">https://doi.org/10.1029/2024GL108684</a></p> <h3>Contents:</h3> <p>Table S1. 3-D velocity field of the Tibetan Plateau due to the loading of soil moisture (SM).</p> <p>Table S2. 3-D velocity field of the Tibetan Plateau due to the loading of snow water equivalent (SWE).</p> <p>Table S3. 3-D velocity field of the Tibetan Plateau due to the loading of glacier.</p> <p>Table S4. 3-D velocity field of the Tibetan Plateau due to the loading of lake.</p> <p>Table S5. 3-D velocity field of the Tibetan Plateau due to the loading of permafrost (PM).</p> <p>Table S6. 3-D velocity field of the Tibetan Plateau due to the loading of groundwater storage (GWS).<br>Table S7. 3-D velocity field of the Tibetan Plateau due to the loading of GRACE-inferred total terrestrial water storage (TWS).</p>
Gas exchange velocities (k600), gas exchange rates (K600), and hydraulic geometries for streams and rivers derived from the NEON Reaeration field and lab collection data product (DP1.20190.001)
This dataset contains estimates of gas exchange velocity, gas exchange rate, and hydraulic parameters for streams calculated from tracer-gas experiments and conservative tracer injections collected by the National Ecological Observatory Network (NEON). All input data were collected by NEON and is available on the NEON data portal at https://data.neonscience.org. Specifically, the NEON Reaeration field and lab collection data product (DP1.20190.001) was used to calculate these estimates. Gas exchange was estimated in two ways: first, following an unpooled frequentist approach and second, following a partially pooled Bayesian approach. In addition, a salt-correction was applied to gas exchange estimates for sites where it was possible and necessary. All estimates of gas exchange are included in the file gasExchange_ds.csv. A recommended selection of these estimates is included in the dataset (best_k600_mPerDay and best_K600_mPerDay). The stanfit objects used for the partially pooled Bayesian approach are also included as site-specific model objects for gas exchange velocities and rates. In addition, water velocity was calculated from conservative tracer injections, and mean water depth was calculated from these water velocity estimates and measurements of wetted width and water discharge. All hydraulic parameters are included in the file hydraulics_ds.csv. All processing code is available in the reaRates R package. NEON is sponsored by the National Science Foundation (NSF) and operated under cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON Program.
Data supporting 'Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers'
<p><strong>Note: An updated dataset covering the majority of Greenland's marine-terminating glaciers is available as part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) project through the National Snow and Ice Data Center (NSIDC) at <a href="https://doi.org/10.5067/B28FM2QVVYWY">https://doi.org/10.5067/B28FM2QVVYWY</a>. </strong></p> <p>Data supporting the paper:</p> <blockquote> <p>Chudley, T. R., Howat, I. M., Yadav, B. N., & Noh, M. J. (2022). Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers. <em>The Cryosphere. </em>16, 2629–2642, https://doi.org/10.5194/tc-16-2629-2022</p> </blockquote> <p>Dataset consists of four netCDF files containing stacked Sentinel-2 velocity data of four Greenlandic outlet glaciers (Helheim Glacier, Jakobshavn Isbræ, Store Glacier, and Kangerlussuaq) between 2017 and 2021. Velocity data are derived and corrected following the methods outlined in Chudley <em>et al.</em> (2022). </p> <p>NetCDF files are created by, and tested to be readable by, Python's xarray package.</p> <p>The dimensions of the netCDF file are as follows:</p> <ul> <li><strong>X</strong> - <em>x </em>coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li><strong>Y</strong> - <em>y</em> coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li><strong>time</strong> - temporal midpoint of velocity field.</li> </ul> <p>The variables of the netCDF file are as follows:</p> <ul> <li><strong>dmag</strong> - the absolute magnitude of the velocity, in metres per day.</li> <li><strong>dx</strong> - the velocity in the <em>x</em> direction, in metres per day.</li> <li><strong>dy</strong> - the velocity in the <em>y</em> direction, in metres per day.</li> <li><strong>date1</strong> - the date and time of the first scene acquisition.</li> <li><strong>date2</strong> - the date and time of the second scene acquisition.</li> <li><strong>baseline</strong> - the temporal baseline, in days, between scene acquisitions.</li> <li><strong>orbit_pair</strong> - the combination of orbital pathways in the string format 'RXXX_RYYY', where XXX is relative orbit number of the first scene and YYY the relative orbit number of the second scene.</li> <li><strong>mag_rmse</strong> - the root mean square error of the absolute velocity of the off-ice area. </li> <li><strong>dx_mean</strong> - the mean velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dx_sd</strong> - the standard deviation of the velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dy_mean</strong> - the mean velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dy_sd</strong> - the standard deviation of the velocity of the off-ice area in the <em>y</em> direction.</li> </ul>
Vertical velocity field from the JCOPE-T-NEDO simulation
<p>Vertical velocity field from the "NEDO" version of the "JCOPE-T" ocean general circulation model (Varlamov et al 2015; Wang et al 2024).</p> <p><a href="../api/records/13132471/draft/files/wzm-2014.11.01-2014.11.11.nc.gz/content">wzm-2014.11.01-2014.11.11.nc.gz</a> contains the hourly-mean vertical velocity "wzm" from 133.3°E to 148°E, from 24°N to 28.3°N, and from 2014-11-01T00:00:00Z to 2014-11-11T00:00:00Z. The model uses the sigma coordinates and the data file contains the variable zed(x,y,sigma) that indicates the depth at which "wzm" is defined for each x and y.</p>
Buoyancy versus local stress field control on the velocity of magma propagation: insight from analog and numerical modelling, Supporting Data
<p>Experimental data and numerical codes used in the manuscript "Buoyancy versus local stress field control on the velocity of magma propagation: insight from analog and numerical modelling" by V. Pinel, S. Furst, F. Maccaferri and D. Smittarello.</p>
Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields (data sets)
<p>Supplementary dataset for Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields. Output functionals of interest from finite element simulations together with postprocessing source code. </p>
Observations of Diurnal Coastal-Trapped Waves with a Thermocline-Intensified Velocity Field
<p>Repository for the data presented in the JPO 2019 article: "Observations of Diurnal Coastal-Trapped Waves with a Thermocline-Intensified Velocity Field". Observations were compared against output from Ken Brink's model. The model is available from: <a href="https://darchive.mblwhoilibrary.org/handle/1912/10527">https://darchive.mblwhoilibrary.org/handle/1912/10527</a>.</p>
3D S-wave velocity profiles with random fields
<p>This dataset contains 100,000 3D matrices representing S-wave velocity profiles with random fields. The spatial resolution corresponds to a grid of 9.6 x 9.6 x 9.6km with a spatial step of 300m (matrices are of size 33 x 33 x 33). Velocities go from 1,071 to 4,500m/s.</p> <p>Each profile contains a 1.8km-thick bottom layer at constant velocity 4500m/s. Above, the profile contains between 1 and 6 layers of random thickness. In each layer, the mean velocity is chosen uniformly in [1,785; 3,214m/s] and the coefficient of variation follows a Gaussian distribution N(0.2; 0.1). Then, a correlation length is chosen randomly among 1.5, 3, 4.5, 6 km in all three directions for each layer.</p> <p>The random fields' kernel has a von Karman correlation with a Hurst exponent of 0.1 and log-normal marginal distributions.</p>
One meter integrated depth velocity (total, ageostrophy, Ekman and Stokes components) fields for the Mediterranean Sea each 6 hours
<p>One meter integrated depth surface velocity fields for the Mediterranean Sea calculated following the methodology in Morales-Marquez et al.(2020). This dataset provides the total velocity field, the ageostrophic, Ekman and Stokes components.</p>
Microstructure and velocity data collected near Velasco Reef during the June 2016 FLEAT field program
<p>Data used in Wynne-Cattanach et al paper, "Measurements of turbulence generated by wake eddies near a steep headland"</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>
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>
Instantaneous, three-dimensional velocity fields past a bio-prosthetic aortic valve measured in-vitro with tomographic particle image velocimetry.
<p>Each folder contains instantaneous, three-dimensional velocity vector data obtained in a simplified model of the aortic root with a distinct size and geometry (small, medium, large, and sinus-less). The specific geometry of each aortic root model is contained in the corresponding folder.</p> <p>The velocity data is structured in the following way: Two separate folders for velocity data in the "ascending aorta" domain (AAo) and in the "sinus of Valsalva" domain (SOV). Each domain contains velocity datasets for instances t=0.00, 0.03, 0.06, ..., 0.39 s (t000, t003, t006, ..., t039). Each velocity dataset contains N=16 phase-locked instantaneous 3D velocity fields.</p> <p>The data was acquired using tomographic particle image velocimetry and a custom built hydraulic setup capable of replicating normal physiological flow conditions in the human aorta (heart rate = 72 bpm, cardiac output = 4.8 l)</p> <p>Data format:</p> <p>- aortic root geometry: STL (the geometry is provided with respect to the reference frame of the velocity data)</p> <p>- velocity data: NPY (NumPy), shape= (N_nodes, 6), columns contain X, Y, Z, U, V, W data, where U, V, W are the X, Y, Z components of the instantaneous vector field</p>
A High-Performance Code for Analyzing Loss Transport Equations in High-Fidelity Simulations - velocity field data
<p>Velocity field to be processed by the methodology described in </p> <p>Biassoni, D, Russo, M, Viviani, P, Vitali, G, & Lengani, D. "A High-Performance Code for Analyzing Loss Transport Equations in High-Fidelity Simulations." <em>Proceedings of the ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition</em>. <em>Volume 12C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions</em>. London, United Kingdom. June 24–28, 2024. V12CT32A039. ASME. <a href="https://doi.org/10.1115/GT2024-127953" target="_blank" rel="noopener">https://doi.org/10.1115/GT2024-127953</a></p> <p>and with the code provided at </p> <p><a href="https://gitlab.linksfoundation.com/across-public/aeronautics-workflows/asme-turboexpo-2024">https://gitlab.linksfoundation.com/across-public/aeronautics-workflows/asme-turboexpo-2024</a></p> <h3> </h3>
GNSS velocity fields in China and its vicinity under the Eurasian reference
<p>The dataset illustrates the GNSS velocities in China and its vicinity under the Eurasian reference used for calculating the strain rate field.</p>
One meter integrated depth velocity (total, ageostrophy, Ekman and Stokes components) fields for the Mediterranean Sea each 6 hours (from 2007 to 2018)
<p>One meter integrated depth surface velocity fields for the Mediterranean Sea from January 2007 to June 2018 calculated following the methodology in Morales-Marquez et al.(2020). This dataset provides the total velocity field, the ageostrophic, Ekman and Stokes components.</p>
One meter integrated depth velocity (total, ageostrophy, Ekman and Stokes components) fields for the Mediterranean Sea each 6 hours (from 2005 to 2006)
<p>One meter integrated depth surface velocity fields for the Mediterranean Sea from January 2005 to January 2006 calculated following the methodology in Morales-Marquez et al.(2020). This dataset provides the total velocity field, the ageostrophic, Ekman and Stokes components.</p>
GPS velocity field in the Pamir region in the Eurasian-fixed frame complied from our own results and previous studies
<p>The data file includes the GPS velocities with respect to the Eurasian frame in the Pamir, Tien Shan region.</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>
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>
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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)
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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.