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969 results for “velocity”
GPS velocities in North China from a combination of published results
<p>GPS velocities in North China from a combination of published results ( e.g., Wang & Shen, 2020,<a href="https://doi.org/10.1029/2019JB018774">https://doi.org/10.1029/2019JB018774</a> ; Hao et al., 2021, <a href="https://doi.org/10.1029/2020GL091008">https://doi.org/10.1029/2020GL091008</a>)</p>
Data for "Evolutionary velocity with protein languge models"
<p>Data tar ball for "Evolutionary velocity with protein language models"; more information can be found here: https://github.com/brianhie/evolocity#data.</p>
Distribution of halo peculiar velocities
<p>Example of the distribution of halo peculiar velocities in one reference (solid black histogram) and one BAM (solid blue and filled histogram) halo catalog, in different cosmic-web types.</p>
Three-dimensional anisotropic phase velocity and S-wave velocity models in Northeastern Tibet
<p>This supplementary material contains three parts.</p> <p>The file named "COR_XM2_all.tar.gz" contains the ambient noise cross-correlation waveforms of the Rayleigh wave.</p> <p>The file named "phase_velocity.rar" contains the anisotropic phase velocity using double beamforming tomography (DBF) method.</p> <p>The file named "Anisotropic_Vs_model.rar" contains the anisotropic S-wave velocity models.</p>
Sentinel-1 InSAR LOS velocity map over the Bay Area (Descending track 42, 2015-2022)
<p>Sentinel-1 InSAR LOS velocity map over the Bay Area</p> <p>(Descending track 42, 2015-2022)</p> <p>Please cite the following paper If you find the data useful: </p> <p><strong>Li, Y.</strong>, Bürgmann, R., & Taira, T. (2023). Spatiotemporal variations of surface deformation, shallow creep rate, and slip partitioning between the San Andreas and southern Calaveras Fault. <em>Journal of Geophysical Research: Solid Earth</em>, 128, e2022JB025363. <a href="https://doi.org/10.1029/2022JB025363">https://doi.org/10.1029/2022JB025363</a></p>
Seawater velocities from ship acoustic Doppler current profiler during PolarFront 2022-05 cruise
<p>Seawater velocities captured by Ocean Surveyor ADCP, RD Instruments, were recorded (blanking distance 8 m, bottom track on) during our entire study from 18-26 May.</p>
Cross-spectra used in "Retrieval and precise phase-velocity estimation of Rayleigh waves by the spatial autocorrelation method between distributed acoustic sensing and seismometer data"
<p>Cross-spectra used in "Retrieval and precise phase-velocity estimation of Rayleigh waves by the spatial autocorrelation method between distributed acoustic sensing and seismometer data</p> <p>", by Shun Fukushima, Masanao Shinohara, Kiwamu Nishida, Akiko Takeo, Tomoaki Yamada, and Kiyoshi Yomogida </p> <p>For more information, please contact Shun Fukushima (s-fuku@eri.u-tokyo.ac.jp)</p>
GNSS position time series (.neu files) and seismic velocity strcture (Profil_lat2822_Vp_Vs.dat) used in the manuscript
<p>These are the raw GNSS time series files (in .neu format) and seismic velocity strcture file (Profil_lat2822_Vp_Vs.dat) we used in the manuscript. These data are not allowed to use before the manuscript is accepted.</p>
SUMOylation of NaV1.2 channels regulates the velocity of backpropagating action potentials in cortical pyramidal neurons
<p>Voltage-gated sodium channels located in axon initial segments (AIS) trigger action potentials (AP) and play pivotal roles in the excitability of cortical pyramidal neurons. The differential electrophysiological properties and distributions of Na<sub>V</sub>1.2 and Na<sub>V</sub>1.6 channels lead to distinct contributions to AP initiation and backpropagation. While Na<sub>V</sub>1.6 at the distal AIS promotes AP initiation and forward propagation, Na<sub>V</sub>1.2 at the proximal AIS promotes backpropagation of APs to the soma. Here, we show the Small Ubiquitin-like Modifier (SUMO) pathway modulates persistent sodium current (I<sub>NaP</sub>) generation at the AIS to increase neuronal gain and the speed of backpropagation. Since SUMO does not affect Na<sub>V</sub>1.6, these effects were attributed to SUMOylation of Na<sub>V</sub>1.2. Moreover, SUMO effects were absent in a mouse engineered to express Na<sub>V</sub>1.2-Lys38Gln channels that lack the site for SUMO linkage. Thus, SUMOylation of Na<sub>V</sub>1.2 exclusively controls I<sub>NaP</sub> generation and AP backpropagation, thereby playing a prominent role in synaptic integration and plasticity.</p>
High-rate GNSS Raw Doppler Positive Impact on Cascading Filter-based Approach for Improving Real-time Transient Coseismic Velocities Modeling
<p>The high-rate GNSS average and instantaneous coseismic velocity waveforms for the 2016 Mw 6.6 Norcia earthquake and the 2011 Mw 9.1 Tohoku earthquake are included in this repository.</p>
Computational results and python files for the work "Divergence-conforming velocity and vorticity approximations for incompressible fluids obtained with minimal facet coupling"
<p><br> This repository contains data accompanying the paper "Divergence-conforming velocity and vorticity approximations for incompressible fluids obtained with minimal facet coupling".</p> <p>The implementation is based on the python-interface of the NGSolve open source Finite Element library (ngsolve.org).</p> <p>The file solve_problem_allione.py represents a minimum working example where the proposed MCS/HDG (set the use_MCS flag) method is used to solve the problem from the numerics section of the paper.</p> <p>The files FlowTemplates.py and krylovspace_extension.py contain a somewhat larger and more modular implementation of the proposed method that also features preconditioned iterative solvers, including support for the NgsAMG NGSolve extension library as well as the NGSolve-PETSc interface.</p> <p>The files errors_hdg.pickle, errors_mcs.pickle and kappas.pickle contain the raw data the tables and pictures in the paper were generated from.</p> <p>This data was generated with the scripts conv3d_hdg.py, conv3d_mcs.py and calc_kappas.py which use the FlowTemplates.py infrastructure.</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>
Velocity models from "Investigation of Martian regional crustal structure near the dichotomy using S1222a surface-wave group velocities"
<p>The isotropic velocity models from joint inversion of Rayleigh- and Love-wave group-velocity measurements of S1222a. The details about these models and the joint inversion are in "Investigation of Martian regional crustal structure near the dichotomy using S1222a surface-wave group velocities" which is submitted to GRL.</p>
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>
Sn velocity model and original catalogue data for essay "Uppermost mantle structure of the Japan subduction zone from Sn tomography"
<p>Sn velocity model and original catalogue data for essay “Uppermost mantle structure of the Japan subduction zone from Sn tomography”</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>
Setup of a 3D printed wind tunnel: application for calibrating bi-directional velocity probes used in Fire Engineering Applications
<p>The research presented here focuses on the development of a 3D printed wind tunnel and the relevant equipment to be used for calibrating bi-directional velocity probes (BDVP). BDVP are equipment to be used for measuring velocity flow by determining the pressure difference of hot gases generated during fires. The manufactured probes require calibration to determine the calibration factor to achieve precise measurement. The calibration is usually performed in wind tunnels which can be difficult to access due to costs, complexity and the various pieces of equipment required. The aim of the current study is to develop and assemble an inexpensive and easy-to-build bench-scale wind tunnel, with a data-logging system and fan control functionalities for fast and effective calibration of BDVP. A 3D printer with a PET-G filament is used, able to produce parts for the wind tunnel system which are durable and easy to handle and assemble. The system additionally includes an Arduino-based measuring unit with a hot-wire anemometer and temperature correction: Rev. P. This takes precise measurements; continuously logging data on a computer through a USB interface and capable of saving data on an SD card. This design provides users with parameters of velocity flow up to 4 m/s with standard deviation of 1.2 % and turbulence intensity of 1 %. The main advantages of this wind tunnel are its simplicity to build and portability.</p> <p>The dataset contains design files for 3D printing of the wind tunnel, BOM and wiring of electronic components.</p> <p>The project is prototype under development and authors are not responsible for any damages or injuries caused by inappropriate construction or operation. This source is distributed WITHOUT ANY EXPRESS OR IMPLIED WARRANTY, INCLUDING OF MERCHANTABILITY, SATISFACTORY QUALITY AND FITNESS FOR A PARTICULAR PURPOSE.</p>
Eurasia-fixed GNSS-derived velocity field for Turkey
<p>The dataset and the analysis is described in the <a href="http://journals.tubitak.gov.tr/cgi/viewcontent.cgi?article=1844&context=earth">paper</a> and presented <a href="https://meetingorganizer.copernicus.org/EGU23/EGU23-12258.html">here</a>. The data fields are: longitude, latitude, E velocity, N velocity, sigma E, sigma N, rho, U velocity, sigma U, station. You can use the following code snippet to load your data to python:</p> <pre><code class="language-python">fields = ['longitude', 'latitude', 've', 'vn', 'se', 'sn', 'rho', 'vu', 'su', 'station'] df = pd.read_csv('Kurt_etal_2023.csv', header=None, names=fields)</code></pre> <p> </p>
Cone Glacier velocity and climate data from November 2017 to January 2022
<p>Glacier velocity (m/yr) data obtained using Sentinel-2 near-infrared band 8 satellite images and the Glacier Image Velocimetry (GIV) open-source toolkit (Van Wyk de Vries & Wickert, 2021; https://doi.org/10.5194/tc-15-2115-2021). Precipitation (mm) and temperature (degrees centigrade) data obtained from gridded ERA5 reanalysis data (Hersbach et al., 2020; https://doi.org/10.1002/qj.3803) for the 0.25° x 0.25° grid square within which Cone Glacier sits. (2023-05-09)</p>
Turbulent dissipation rate and velocity spectrum at two radar wind profiler sites in North China
<p>This dataset includes the monthly mean velocity spectrum width and turbulence dissipation rate from 0900 local standard time (LST) to 1700 LST in 2021, which are retrieved from the radar wind profiler measurements at Baoding (urban) and Zhangbei (plateau) stations. Each data file is stored in xlsx format and contains two sheets, and each sheet is a two-dimensional data, including velocity spectrum width and turbulence dissipation rate. These two variables are stored in a matrix of 30 rows and 9 columns. The row refers to the height at an interval of 120 meters, and the columns refers to time which corresponds to 0900 to 1700 LST. </p>
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.