Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
35
datasets available to search
ShareScore release 0.9.0
Dataset results
35 results for “Surface velocity”
Spatially and temporally continuous reconstruction of Antarctic Amundsen Sea sector ice sheet surface velocities: 1996-2018
<p>Spatially and temporally continuous reconstruction of ice sheet surface velocities for the Amundson Sea Sector of the Antarctica. The reconstruction is derived from the synthesis of annual published InSAR (R14: Rignot et al. 2014) and optical (G18: Gardner et al., 2018 & Gardner et al., 2022) surface velocities. Data are posted on a uniform 240 m by 240 m grid in Antarctic Polar Stereographic (EPSG:3031) coordinates. The temporal posting is every 2.4 months or 1/5 of a year.</p> <p>R14 and G18 annual velocity data have large errors and data gaps in both space and time that make the data challenging to work with. For this reason, a Spatially and temporally continuous reconstruction was made. These are the preprocessing steps that were applied to create the reconstruction:</p> <ol> <li>R14 component velocities [vx/vy] are mapped to the same 240-m grid as G18 for the Amundson Sea sector.</li> <li>Velocities falling outside of mapped ice extents (see Paolo et al., 2022) are set to no data values.</li> <li>A reference velocity is defined as the 1996 velocity field or the earliest valid measurement thereafter. The average of both velocities is taken if multiple observations exist for the first year of data.</li> <li>For areas moving faster than 200 m/yr., the percentage anomalies are calculated for all years relative the reference velocity. This was done for both G18 and R14 velocities separately.</li> <li>Annual velocity anomalies are then filter with a 5-km windowed moving median.</li> <li>G18 and R14 filtered anomalies are merge by taking the mean of each year. Years with less than 30% coverage for fast moving ice (>= 200 m/yr.) were discarded.</li> <li>If missing annual values were within 25 km of a valid datapoint they are filled using natural neighbor interpolation, otherwise anomalies were set to zero.</li> <li>Outside of fast-moving areas, annual anomalies are tapered to zero using a 10-km cosine taper.</li> <li>Merged and filled annual anomalies are then smoothed one last time using a 5-km windowed moving mean.</li> <li>To create a continuous record of velocity, annual anomalies are interpolated in time to every 1/5 of a year for every 240 m pixel using a spline interpolant and multiplied by the reference velocity.</li> </ol> <p>All x and y component velocities [vx/vy] and velocity magnitudes [v] are stored as individual geotiff files and are contained in the .zip included file. A visualization of the velocity magnitudes is included as an animated gif. </p> <p> </p> <p>References:</p> <p>Gardner, A., M. Fahnestock, and T. Scambos. (2022). MEaSUREs ITS_LIVE Regional Glacier and Ice Sheet Surface Velocities, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/6II6VW8LLWJ7. Date Accessed 04-07-2019.<br> <br> Gardner, A. S., Moholdt, G., Scambos, T., Fahnstock, M., Ligtenberg, S., van den Broeke, M., & Nilsson, J. (2018). Increased West Antarctic and unchanged East Antarctic ice discharge over the last 7 years. <em>The Cryosphere</em>, <em>12</em>(2), 521–547. https://doi.org/10.5194/tc-12-521-2018</p> <p>Paolo, F., Gardner, A., Greene, C., Nilsson, J., Schodlok, M., Schlegel, N., & Fricker, H. (2022). Widespread slowdown in thinning rates of West Antarctic Ice Shelves. <em>EGUsphere</em>, <em>2022</em>, 1–45. https://doi.org/10.5194/egusphere-2022-1128</p> <p>Rignot, E., J. Mouginot, and B. Scheuchl. (2014). MEaSUREs InSAR-Based Ice Velocity of the Amundsen Sea Embayment, Antarctica, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0545.001. Date Accessed 04-07-2019.</p>
Average decomposed InSAR-GNSS surface velocities for Iran
<p>Average East and vertical InSAR-GNSS surface velocities for Iran at a 0.005° pixel spacing (approximately 500 m), derived from COMET-LiCS interferograms (https://comet.nerc.ac.uk/COMET-LiCS-portal/) through the use of LiCSBAS (https://github.com/yumorishita/LiCSBAS) and "decompose_insar_velocities" (https://github.com/andwatson/decompose_insar_velocities).</p> <p>File details:</p> <ul> <li>vE and vU refer to the East and vertical velocities, respectively.</li> <li>sE and sU refer to the East and vertical 1-standard deviation uncertainties, respectively.</li> <li>A positive velocity in vE signifies motion to the East, while a positive velocity in vU signifies upward motion.</li> <li>All four files are stored as geotiffs, created using the Matlab "geotiffwrite" function.</li> </ul>
Data from: Validation of surface wave spectral measurements from velocity profiling floats
Open the record for dataset details and reuse information.
Controlling propagation velocity in Al/Ni reactive multilayer systems by periodic 2D surface structuring
Open the record for dataset details and reuse information.
Changes in the Kuroshio path, surface velocity and transport during the last 35,000 years
<p>The major data for “Changes in the Kuroshio path, surface velocity and transport during the last 35,000 years” are listed as follows:</p> <p>1) The annual mean surface current (0-100m, m/s) in 5 cases. Data are separately saved in MATLAB files (surface_velocity_0ka.mat, surface_velocity_6ka.mat, surface_velocity_lgm.mat, surface_velocity_30ka.mat and surface_velocity_35ka.mat) with variables of longitude, latitude, u and v.</p> <p>2) The Kuroshio paths (longitude and latitude) in 5 cases are saved in MATLAB file of “data_pathxy.mat”.</p> <p>3) Distributions of upper 1000-m integrated volume-transport stream function from model results in 5 cases. Data is saved in the MATLAB file of “VT_model.mat” with variables of longitude, latitude, VT_model and casename.</p> <p>3) Distributions of wind-driven Sverdrup transport stream function calculated by wind stresses in 5 cases. Data is saved in the MATLAB file of “ST_wind.mat” with variables of longitude, latitude, ST_wind and casename.</p>
Data for "Different Martian Crustal Seismic Velocities across the Dichotomy Boundary from Multi-Orbiting Surface Waves"
<p>Data for "Different Martian Crustal Seismic Velocities across the Dichotomy Boundary from Multi-Orbiting Surface Waves"</p>
Dataset and Software for An anisotropic shear velocity model of the Earth's mantle using normal modes, body waves, surface waves and long-period waveforms
<p><strong>What is the nature of flow in the mantle?</strong><br> <strong>How fast do waves travel anywhere on Earth?</strong><br> <strong>Where can radial anisotropy be robustly detected?</strong><br> <strong>Can we reconcile a broad spectrum of seismic data? What are the benefits?</strong></p> <p>We use normal-mode splitting functions in addition to surface-wave phase anomalies, body-wave travel times and long-period waveforms to construct a three-dimensional model of anisotropic shear-wave velocity in the Earth's mantle. This is the <strong>first tomographic study</strong> to exploit the sensitivity of mode-splitting data to constrain radial anisotropy in the Earth's mantle jointly with several other types of data. Our modeling approach inverts for mantle velocity and anisotropy as well as transition-zone discontinuity topographies, and incorporates new crustal corrections for the splitting functions that are consistent with the nonlinear corrections we employ for the waveforms. Our preferred anisotropic model, S362ANI+M, is an update to the earlier model S362ANI, which did not include normal-mode splitting functions in its derivation.</p> <p><strong>Feedback/Questions?</strong> Please contact Raj Moulik (<a href="https://rajmoulik.com">rajmoulik.com</a>) at <a href="mailto:moulik@caa.columbia.edu?subject=Query%20from%20Zenodo">moulik@caa.columbia.edu</a> </p> <p><strong>Reference:</strong></p> <p><em>Please cite the following work if you use this data or software.</em></p> <ul> <li>Moulik, P. & Ekström, G., 2014. An anisotropic shear velocity model of the Earth's mantle using normal modes, body waves, surface waves and long-period waveforms, <em>Geophys. J. Int.</em>, <strong>199</strong>(3), 1713-1738, doi: <a href="http://dx.doi.org/10.1093/gji/ggu356">10.1093/gji/ggu356</a>. <em><a href="https://rajmoulik.com/Publications/MoulikEkstrom_GJI2014.pdf">pdf</a></em></li> </ul> <p><em>You can also cite the dataset and software from this Zenodo page (Optional).</em></p> <ul> <li> <p>Moulik, P. & Ekström, G., 2014. Dataset and Software for An anisotropic shear velocity model of the Earth's mantle using normal modes, body waves, surface waves and long-period waveforms. In Geophys. J. Int. (v1.0, Vol. 199, pp. 1713–1738). Zenodo. doi: <a href="https://doi.org/10.5281/zenodo.8357379">10.5281/zenodo.8357379</a></p> </li> </ul> <p><strong>Data Products:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/S362ANIplusM_Figures.tar.gz"><strong>S362ANIplusM_Figures.tar.gz</strong></a> - contains all figures from the paper in .png format</li> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/S362ANI%2BM_MapViewsCrossSections.pdf"><strong>S362ANI+M_MapViewsCrossSections.pdf</strong></a> - Some cross sections and map views at various depths in the mantle</li> <li><strong><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/S362ANI%2BM">S362ANI+M</a> - </strong>Coefficients of the spline basis functions for each parameter. Refer c<sub>ij</sub> in equation 11. This is our preferred global model of shear-wave velocity. In this model, radial anisotropy is confined to the uppermost mantle (that is, since the anisotropy is parameterized with only the four uppermost splines, it becomes very small below a depth of 250 km, and vanishes at 410 km). This is an updated version of S362ANI (Kustowski et al., 2008) which did not include normal modes in its derivation. Please note the stronger isotropic shear velocity anomalies in the transition zone.</li> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/STW105"><strong>STW105</strong></a> - reference model used in S362ANI+M. Described in Kustowski et al. (2008)</li> <li><strong><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/setup.cfg">setup.cfg</a> - </strong>Some configuration metadata relevant to this model for reproducibility.</li> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/epix.tar.gz"><strong>epix.tar.gz</strong></a> - Perturbations in horizontally (vsh) and vertically polarized shear velocity (vsv), Voigt-average isotropic velocity (vs), ansotropy (as) and topography of the internal boundaries. This is calculated from the spline coefficients at every 1 by 1 degree cell-centered pixel and at every ~25 km depth region from Moho to the core-mantle boundary and stored in extended pixel format (.epix) ASCII files. </li> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/S362ANI%2BM.BOX25km_PIX1X1.avni.nc4"><strong>S362ANI+M.BOX25km_PIX1X1.avni.nc4</strong></a> - The perturbations in a standard AVNI format that utilizes the NETCDF4 container format. This file can be read in Python using either xarray or AVNI libraries. For example, to plot vs perturbations at 24.4-50 km depth range <ul> <li><em>import xarray as xr</em></li> <li><em>ds = xr.open_dataset('S362ANI+M.BOX25km_PIX1X1.avni.nc4')</em></li> <li><em>ds.vs[0].plot()</em></li> </ul> </li> <li><strong>Fortran Code</strong> <ul> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/readme"><strong>readme</strong></a> - contains a description of all files in the folder below</li> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/PROGRAMS.tar.gz"><strong>PROGRAMS.tar.gz</strong></a> - tools for obtaining model values at specific locations and some GMT plotting tools</li> </ul> </li> <li><a href="https://zenodo.org/api/files/a15ed123-5262-4c4b-9806-1b50e7f8ee82/GRD.tar.gz"><strong>GRD.tar.gz</strong></a> - longitude-latitide-velocity files with vsh, vsv, and Voigt average in km/s evaluated on a grid of points at many depths in the mantle</li> </ul>
Inverted S-wave velocity model for "Anomalous radial anisotropy and its implications for upper mantle dynamics beneath South China from multimode surface wave tomography"
<p># Instructions for the South China velocity model dataset from multi-mode surface-wave inversion.</p> <p> </p> <p>1. The detailas can be found at the paper: Tang Q, Sun W, Yoshizawa K, et al. Anomalous radial anisotropy and its implications for upper mantle dynamics beneath South China from multimode surface wave tomography. Journal of Geophysical Research: Solid Earth, 2022, 127(8): e2021JB023485.</p> <p> </p> <p>2. The relative SV-wave and SH-wave velocity models at different depths (from 0 to 300 km) are stored in the "data" foder. Each file follows the name convention: shear_{SV/SH}.{depth}.dat representing SV or SH velocity at a given depth.</p> <p> </p> <p>3. In each velocity file, each line have three columns: longitude (deg) latitude (deg) relative_velocity (%)</p> <p> </p> <p>4. The reference velocity can be found in the two files "SV_velocity" and "SH_velocity", depth and velocity.</p> <p> </p> <p>5. The relative velocity is calculated from (absolute-reference)/reference*100%. Thus one can have the absolute velocity via absolute = relative * (1+relative/100).</p>
2D solar surface velocities
Open the record for dataset details and reuse information.
Dataset to accompany the manuscript "Drifter observations reveal intense vertical velocity in a surface ocean front"
<p>This dataset has been used in the publication "Drifter observations reveal intense vertical velocity in a surface ocean front"</p> <p>The dataset contains data from the following platforms<br> · Drifters<br> · Underway-CTD</p>
A Predictive Model for the Streamwise Velocity in the Near-neutral Atmospheric Surface Layer
<p>A Predictive Model for the Streamwise Velocity in the Near-neutral Atmospheric Surface Layer</p>
Mixed layer velocity and surface wind stress from numerical simulation of Super Typhoon Mangkhut
<p>Mixed layer velocities and surface wind stress from a WRF-HYCOM coupled simulations of Super Typhoon Mangkhut.</p>
MEaSUREs ITS_LIVE Regional Glacier and Ice Sheet Surface Velocities V001
This data set from the Inter-Mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project, part of NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of regionally compiled, mean annual surface velocities for major glacier-covered regions, derived from Landsat 4, 5, 7, and 8 imagery. Data are available from 1985 to 2018. Data availability is significantly limited prior to 2013 due to image scarcity and low image quality; however annual coverage is nearly complete for the years following the Landsat 8 launch in 2013.
MEaSUREs ITS_LIVE Landsat Image-Pair Glacier and Ice Sheet Surface Velocities V001
This data set, part of NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of ice velocities at 240 m resolution, generated from Landsat 4, 5, 7, and 8 optical image pairs. Velocities were derived using the autonomous Repeat Image Feature Tracking algorithm (autoRIFT) processing chain. Data are available for all land ice areas larger than 5 square km, spanning the period from 1985 to 2018 (subject to image availability and quality). Data scarcity and/or low radiometric quality are significant limiting factors for many regions during the earlier years of the data record. Annual, global coverage is nearly complete after the 2013 launch of Landsat 8.
High Mountain Asia 2 m DEM, Surface Velocity, and Lagrangian Surface Mass Balance for Select Debris Covered Glaciers V001
This High Mountain Asia data set contains 2 m resolution digital elevation models (DEMs), surface velocities, surface mass balance (SMB) rates, and SMB uncertainties for six debris-covered glaciers in Nepal. SMB rate is estimated by applying a Lagrangian specification to DEMs derived from very-high-resolution optical stereo imagery acquired by Maxar Technologies satellites WorldView-1, WorldView-2, WorldView-3, and GeoEye-1. This data set was granted permission for public release on 1 March 2024 under the National Reconnaissance Office (NRO) Electro-Optical Commercial Layer (EOCL) program.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.