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969 results for “velocity”
Velocity and temperature dependence of steady-state friction of natural gouge controlled by competing healing mechanisms
<p>The empirical rate- and state-dependent friction law is widely used to explain the frictional resistance of rocks. However, the constitutive parameters vary with temperature and sliding velocity, preventing extrapolation of laboratory results to natural conditions. Here, we explain the frictional properties of natural gouge from the San Andreas Fault, Alpine Fault, and the Nankai Trough from room temperature to $\sim300^\circ$C for a wide range of slip-rates with constant constitutive parameters by invoking the competition between two healing mechanisms with different thermodynamic properties. A transition from velocity-strengthening to velocity-weakening at steady-state can be attained either by decreasing the slip-rate or by increasing temperature. Our study provides a framework to understand the physics underlying the slip-rate and state dependence of friction and the dependence of frictional properties on ambient physical conditions. </p>
Surface flow velocity from Pulmanki, Koita and Sävar Rivers 2020-2022
<p>Data description:<br>Surface flow velocity dataset was created using Hydro-STIV software (Hydro-STIV v.1.2.2, Hydro Technology Institute co.), which uses Space-Time Image Velocimetry (STIV) for velocity estimation, a method derived from Large-Scale Particle Image Velocimetry (LSPIV) developed by Fujita in 2007 (Fujita et al., 2007). The videos were georeferenced using known GCPs and the software performed orthorectification and calibration. The data has been used for publication "Surface flow and ice rafting velocities during freezing and thawing periods in Nordic rivers". Data consists of videos and daily stil images from Pulmanki, Koita and Sävar Rivers from Autumn freezing and Spring thawing periods. The data from Koita River is from 2020-2021 and from Pulmanki and Sävar Rivers from 2021-2022. The original raw data based on which the STIV analysis was performed was collected with Burrel time-lapse RGB cameras. </p> <p> </p> <p>Acknowledgements:<br><span>The river-ice related measurements were initiated at Pulmankijoki River in 2014 under the post-doctoral research project of Dr Lotsari, funded by the Research Council of Finland (ExRIVER: grant number 267345), and this study is a continuum in the series of these winter season studies. The work for this study was financially supported by four other projects funded by the Research Council of Finland (DefrostingRivers: 338480; HYDRO-RDI-Network: 337394; Digital Waters [DIWA] Flagship;359248). In addition, the work was funded by The European Union – NextGenerationEU Recovery instrument (RRF) through Research Council of Finland projects Hydro RI Platform (346167) and Green-Digi-Basin (347703). The Department of Geographical and Historical Studies, University of Eastern Finland, supported financially the field work done at Koita River. The work by Dr Lina Polvi-Sjöberg at the Sävar River was financed by a grant (2023-01513) from the Swedish Research Council Formas.</span></p>
No sustained mean velocity in the boundary region of plane thermal convection
<p>The dataset can be used to generate the figures from the publication titled <a href="https://doi.org/10.1017/jfm.2024.853">"<em>No sustained mean velocity in the boundary region of plane thermal convection</em>"</a>. Along with the data, the relevant Python scripts are also supplied for easy reproduction of the figures presented in this work.</p>
Dataset of relative seismic velocity variations (dv/v) of R66E3 Raspberry Shake and groundwater level variations of BSS002NNZL borehole from 2022/04/28 to 2025/02/04
<p>Dataset of relative seismic velocity variations (dv/v) of R66E3 Raspberry Shake and groundwater level variations of BSS002NNZL borehole from 2022/04/28 to 2025/02/04.</p> <p>A Raspberry Shake RS3D, a three-components geophone (station R66E3 and network code AM) with a natural frequency of 4.5 Hz (electronically extended to 0.5 Hz), is installed in the technical room located 8 m from the BSS002NNZL borehole (a groundwater monitoring borehole), in the town of La Trinité, Martinique. </p> <p>Seismic recordings for station R66E3 (network AM: <a href="https://urldefense.com/v3/__https:/doi.org/10.7914/SN/AM__;!!KbSiYrE!iv8kuFqjRdPI_Ko8AW4XCYuOXVn1VnpNP__Nya_VHhUPUgY40Hpa99szk_9JYls8gw61SbHCjstW1a3C0w$">https://doi.org/10.7914/SN/AM</a>) are collected from the Raspberry Shake data center (<a href="https://urldefense.com/v3/__https:/data.raspberryshake.org/fdsnws/__;!!KbSiYrE!iv8kuFqjRdPI_Ko8AW4XCYuOXVn1VnpNP__Nya_VHhUPUgY40Hpa99szk_9JYls8gw61SbHCjsumhGvdYA$">https://data.raspberryshake.org/fdsnws/</a>). The raw continuous seismic recordings and relative velocity variations are processed by using MATLAB (<a href="https://urldefense.com/v3/__https:/mathworks.com__;!!KbSiYrE!iv8kuFqjRdPI_Ko8AW4XCYuOXVn1VnpNP__Nya_VHhUPUgY40Hpa99szk_9JYls8gw61SbHCjss0LIo2Pw$">https://mathworks.com</a>).</p> <p>This dataset contains the resulting 2-3 Hz frequency range relative velocity variations data. </p> <p>The dataset also contains daily mean groundwater level data (in altitude and pressure) of BSS002NNZL borehole. The borehole is equipped with a PARATRONIC SNP pressure sensor with data recording every minutes. The probe resolution is ±1 mm.</p> <p>Rainfall and air temperature are available close to the site thanks to the French climatic network operated by Météo-France (<a href="https://meteo.data.gouv.fr/">meteo.data.gouv.fr</a>). Daily rainfall (from Morne des Esses station) and daily air temperature data (from Spoutourne station) are also provided in the dataset.</p>
The Repository for the Manuscript "Temperature and Precipitation Dominate Seasonal Variations in Seismic Velocity and Attenuation in Deserts"
<p><strong><span>Overview</span></strong></p> <p><span>This dataset contains the essential code and data for calculating the Horizontal-to-Vertical Spectral Ratio (HVSR), analyzing vehicle-generated seismic events, retrieving Q-values, and comparing them with meteorological data. It also includes waveform data from 20 seismic events.</span></p> <p><span>The seismic data originate from a temporary broadband seismic array deployed in the Tarim Basin, from July 2017 to October 2019 (Zuo et al., 2022). This dataset focuses on three seismic stations: T12, T52, and T23. Stations T12 and T23 recorded data from July 2017 to October 2019, while station T52 recorded from November 2018 to October 2019.</span></p> <p><span> </span></p> <p><strong><span>Code</span></strong></p> <p><span>The dataset includes Python scripts for calculating HVSR and retrieving Q-values. The HVSR calculation follows Li et al., (2023), while forward modeling is based on Antonio García-Jerez et al., (2016).</span></p> <p><span>The codes for Q-value estimation are stored in ‘Retrieving Q-value’ folder. The Q-value estimation process, demonstrated for station T12 in Jupyter Notebook, involves extracting single vehicle signals from continuous data, time-frequency spectrogram calculations, two-dimensional correlation coefficient of their time-frequency amplitude calculations, using hierarchical clustering algorithm to classify vehicle signals, vehicle speed estimation, and performing Q-value inversion.</span></p> <p><span> </span></p> <p><strong><span>Dataset </span></strong></p> <p><span>HVSR variations over time for three stations are calculated from continuous seismic recordings and are stored in the <em>‘HVSR’</em> folder under each station directory. </span></p> <p><span>Time-frequency spectrograms for Q-value estimation are stored in the <em>‘Spectrogram’</em> folder, with filenames indicating the record time of each vehicle signal. The Q-value is inverted using these signals, and for stability, we stacked every 100 individual results, which are stored in the 'Q-values' folder under the corresponding station name folder. Due to interference from wind and other sources, Q-value inversion using vehicle signals was unreliable for T23, so Q-values are only provided for T12 and T52.</span></p> <p><span>Meteorological data (temperature and soil water content) are stored in the <em>‘temperature’</em> and <em>‘soil water content’</em> folders under each station directory.</span></p> <p><span>Seismic event waveforms for 20 selected strong earthquakes are stored in the <em>‘events’</em> folder, with filenames indicating the start and end times of the events.</span></p> <p><span> </span></p>
Sea ice velocity and area flux in the Fram Strait based on an improved algorithm
<p>These data are related to the paper on the sea ice velocity improvement in the Fram Strait. I provide the daily sea ice velocity and area flux in the Fram Strait. Besides, the data used for validation with buoy ice velocity are also included.</p>
Adult male baseball pitchers anthropometric measurements and pitching velocity in tryout settings
<p>In this study, we adopted field tests executed using affordable equipment in a tryout event for a professional baseball team in Taiwan, 2019. We have only half day to test 64 players, and the result of measurement are used to develop a model for predicting pitching velocity of amateur adult pitchers (age: 23.9 ± 2.8 years; height: 180.3 ± 5.9 cm; weight: 81.4 ± 10.9 kg) . The measurements and tests in tryout settings should be easy to implement, take short time, do not need high skill levels, and correlate to the pitching velocity. The outcome measures included maximum external shoulder rotation, maximum internal shoulder rotation, countermovement jump (CMJ) height, 20-kg loaded CMJ height, 30-m sprint time, height, age, and weight tests. </p>
Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan by a new analysis method for distributed acoustic sensing data using a seafloor cable and seismic interferometry"
<p>Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan, derived from distributed acoustic sensing data collected using a seafloor cable with seismic interferometry", 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>
The exact and approximate solutions of phase velocities and reflection coefficients
<p>These are the scripts of the exact and approximate solutions of phase velocities and reflection coefficients in the paper "Approximate equations of PP-, PS1- and PS2-wave reflection coefficients in fluid-filled monoclinic media".</p>
Model of Pn velocity and anisotropy in Hainan Island and surrounding areas
<p>This file is the model of Pn velocity and anisotropy in Hainan Island and the surrounding areas. It is only used for scientific research.</p>
Sentinel-1 InSAR time-series and velocity map over the Bay Area (Descending track 42, 2015-2020)
<p>Supplemental material for <em>"Spatiotemporal variations of surface deformation, shallow creep rate, and slip partitioning between the San Andreas and southern Calaveras Fault"</em> at JGR-Solid Earth</p> <p>Citation: <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>
Grain size distribution of Amazon river sediment samples collected over the period 2005-2008; and ADCP water velocity profiles collected on the major tributaries of the Amazon in Bolivia and Peru, 2007-2008
<p>This dataset contains two items:</p> <p>- The grain size distribution of river sediment samples collected along the Amazon River and its tributaries during four sampling campaigns performed in June 2005 (lower Amazon, Brazil), March 2006 (lower Amazon, Brazil), May 2007 (Upper Madeira, Bolivia), and April 2008 (Upper Solimões-Amazonas, Peru). [spreadsheet "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_data.xlsx"].</p> <p>- River water velocity profiles derived from Acoustic Doppler Current Profiler (ADCP) measurements performed on the major tributaries of the Amazon in May 2007 (Upper Madeira, Bolivia) and April 2008 (Upper Solimões-Amazonas, Peru) [folder "ADCP_dataset_Amazon_2007-2008_Bouchez_data"].</p> <p>The dataset description and the relevant references are provided in the text files "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_description.docx" and "ADCP_dataset_Amazon_2007-2008_Bouchez_description.docx" .</p> <p>These data were acquired thanks to the support of the French National Service for Observation "HYBAM" ("Hydrogeochemistry of the Amazon Basin"), part of the CNRS National Infrastructure "OZCAR" ("Critical Zone Observatories: Applications and Research").</p>
Shallow three-dimensional shear wave velocity model of Volcán de Colima
<p><strong>Velocity model presented in the paper:</strong></p> <p><strong>De Plaen, R.S.M., Mordret, A., Arámbula-Mendoza, R., Vargas-Bracamontes, D., Márquez-Ramírez, V.H., Lecocq, T., Ramírez Vázquez, C.A., González Amezcua, M. The shallow three-dimensional structure of Volcán de Colima revealed by ambient seismic noise tomography</strong></p> <p>Please cite the paper above when using this VS model.</p> <p><em>Description:</em></p> <ul> <li><strong>Colima_ANT_VSmodel.mat:</strong> Shallow 3D VS model of Volcán de Colima along with the corresponding velocity anomaly, error, and radial anisotropy. This version of the model has no vertical smoothing and no topographic correction.</li> <li><strong>Colima_AverageVSmodel.csv:</strong> Simple 1D VS model averaging over the entire study area.</li> <li><strong>Colima_CraterVSmodel.csv:</strong> Simple 1D VS model under the crater of Volcán de Colima.</li> <li><strong>3d plot.ipynb:</strong> Small jupyter notebook (in python) to plot, and understand the structure of the VS model.</li> </ul>
Dataset files for 'Tan et al., Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'
<p>These files are the data and result files for the manuscript entitled<strong> 'Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'</strong> by Tan et al., including</p> <p>catalog.dat : the seismic phase catalog used in seismic tomography</p> <p>station.dat : the station coordinates of the local seismic network</p> <p>relocation.dat : the earthquake relocations obtained by double-difference seismic tomography</p> <p>1-D Vs.xlsx : the 1-D Vs model in the shale gas field</p> <p>3-D Vp.dat: the 3-D Vp model obtained by DD seismic tomography</p> <p>3-D Vs.dat: the 3-D Vs model obtained by DD seismic tomography</p> <p>3-D VpVs.sgy: the 3-D Vp/Vs model obtained by DD seismic tomography (3-5 km)</p> <p>3-D pressure.sgy: the 3-D pore pressure field model obtained by focal mechanism tomography (3-5 km)</p>
Dataset of "Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes"
<p>Dataset of the article <em>Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes </em>(<a href="https://doi.org/10.1016/j.expthermflusci.2022.110647">https://doi.org/10.1016/j.expthermflusci.2022.110647</a>). A data-driven method is applied to combine non-time-resolved velocity field and fast probe data, and achieve time-resolved velocity and pressure field.</p> <p>The codes processing data here are on https://github.com/erc-nextflow/Data-driven-pressure-estimation-with-EPOD.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085, NEXTFLOW).</p>
A high pressure, high temperature gas medium apparatus to measure acoustic velocities during deformation of rock: supporting information
<p>Processed mechanical data and raw transmitted waveforms.</p>
IODP Expedition 372A P-wave velocity logger (whole round)
<p>P-wave velocity data were measured on whole-round sections on the Whole-Round Multisensor Logger (WRMSL) using pairs of piezoelectric transducers mounted on a caliper system. Measurements may be affected by degassing of pore fluid and microfracturing during core recovery. Report includes P-wave velocity in x-y plane and distance and traveltime between transducers.</p>
Dataset files for 'Tan et al., Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'
<p>These files are the data and result files for the manuscript entitled<strong> 'Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'</strong> by Tan et al., including</p> <p><strong>station.dat</strong> : the station coordinates of the local seismic network (including the station ID, longitude, latitude, elevation(negative)/depth(positive), X, Y)</p> <p><strong>catalog.dat</strong> : the seismic phase catalog used in double-difference (DD) seismic tomography</p> <p><strong>relocation.dat </strong>: the earthquake relocations obtained by DD tomography</p> <p><strong>1-D Vs.xlsx</strong> : the 1-D Vs model in the shale gas field</p> <p><strong>3-D Vp.dat</strong>: the 3-D Vp model obtained by DD tomography</p> <p><strong>3-D Vs.dat</strong>: the 3-D Vs model obtained by DD tomography</p> <p><strong>3-D VpVs.sgy</strong>: the 3-D Vp/Vs model (interpolated, within 3-5 km)</p> <p><strong>3-D pressure.sgy</strong>: the 3-D pore pressure field model (interpolated, within 3-5 km)</p>
Figures data from papers "Breakdown of the velocity and turbulence in the wake of a wind turbine", parts 1 and 2
<p>This python file makeFigure.py along with the .json files in folder Data/ allows to draw pictures corresponding to the articles "Breakdown of the velocity and turbulence in the wake of a wind turbine" - Part 1 and Part 2 published in Wind Energy Science. The .sh file makeFolders selects and separate the figures needed for the two parts.</p> <p>Slightly more informations are available in the data compared to the article, due to lack of space. In particular, one can found all the planes data from 1 to 8D downstream (instead of only 1D, 5D and 8D) and some data for the unstable and stable cases that were only shown for the neutral case in the paper.</p> <p><br> Do not hesitate to contact me if more informations are needed.</p>
High Rate GNSS Velocities for Earthquake Strong Motion Signals
<p>This zipped dataset consists of directories by earthquake of 5Hz GNSS velocity time series.</p> <p>── comcat_earthquake_code</p> <p>└── velocities_4char_DOY_YYYY.txt</p> <p> </p> <p>References:</p> <ul> <li><a href="https://earthquake.usgs.gov/data/comcat/">USGS Comcat Catalog</a> </li> <li><a href="https://www.unavco.org/data/gps-gnss/gps-gnss.html">UNAVCO Archive 4char station codes</a></li> <li><a href="http:// https://github.com/crowellbw/SNIVEL">SNIVEL processing software repo</a></li> </ul>
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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)
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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.