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969 results for “velocity”
High Temporal Resolution Records of Hansbreen Ice Flow Velocity for Years 2006-2019
<p>This repository contains the datasets of the positions of 16 mass balance stakes, horizontal velocity (m/yr) and accuracy of velocity (m/yr) for Hansbreen, a tidewater glacier in southern Svalbard. Data were derived from GNSS measurements conducted in the period 2006-2019. Stake positions are given in UTM zone 33X, and elevation in geoidal height (EGM96). Additionally, we provide files with annual, summer and winter velocities (m/yr) with a standard deviation of velocity, estimated for the hydrological year. The file „Hansbreen_preprocessing_code_stakes.zip” contains the code used for the velocity estimation.</p>
Dataset and Software for The Relationships Between Large-scale Variations in Shear Velocity, Density, and Compressional Velocity in the Earth's Mantle
<p><strong>Is there a chemically distinct reservoir in the Earth?</strong><br><strong>Do superplumes overly denser-than-average material?</strong><br><strong>Can we detect these anomalies with seismic data?</strong><br><strong>Can we evaluate statistical significance of the features in tomography?</strong></p><p>This study presents the <strong>strongest evidence</strong> to date (ca. 2015) of <strong>large-scale thermo-chemical heterogeneities in the lowermost mantle</strong> using the full spectrum of seismic data. A large data set of surface-wave phase anomalies, body-wave travel times, normal-mode splitting functions and long-period waveforms is used to investigate the scaling between shear velocity, density and compressional velocity in the Earth's mantle (ϱ=dln ρ/dln vS, ν=dln vS/dln vP). Our preferred joint model consists of denser-than-average anomalies (∼1% peak-to-peak) at the base of the mantle roughly coincident with the low-velocity superplumes. The relative variation of shear velocity, density and compressional velocity in our study disfavors a purely thermal contribution to heterogeneity in the lowermost mantle, with implications for the long-term stability and evolution of superplumes.</p><p><strong>Note on Odd Degree Structure:</strong></p><p>Since the self-coupled normal-mode splitting observations constrain only even-degree density variations, all inversions strongly disfavored even-degree vS-ρ correlation (R2 ~ –0.46 to –0.25) in the lowermost mantle, which also disfavors a purely thermal contribution to heterogeneity in this region. However, the starting assumptions on positive vS-ρ correlation persisted in the remaining regions and for odd degree variations. In viscosity inversions with the geoid, opposing sign of the correlation of the longest wavelength even-versus odd-degree structure maps into a region of reduced viscosity in the lower mantle (Rudolph et al., 2020, doi:10.1029/2020gc009335). While important for such dynamical implications, <strong>odd-degree density variations in the lowermost mantle are poorly constrained in this study and should not be interpreted</strong>. We therefore used even-degree variations up to degree 6 for our inferences on thermo-chemical variations in the lowermost mantle (Figure 14), and provide those values in the files below.</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><i>Please cite the following work if you use this data or software.</i></p><ul><li>Moulik, P. & Ekström, G., 2016. The relationships between large-scale variations in shear velocity, density and compressional velocity in the Earth's mantle, <i>J. Geophys. Res.</i>, <strong>121</strong>, doi: <a href="http://dx.doi.org/10.1002/2015JB012679">10.1002/2015JB012679</a>. <a href="https://rajmoulik.com/Publications/MoulikEkstrom_JGR2016.pdf"><i>pdf</i></a></li></ul><p><i>You can also cite the dataset and software from this Zenodo page (Optional).</i></p><p>Moulik, P. & Ekström, G. (2016). Dataset and Software for The Relationships Between Large-scale Variations in Shear Velocity, Density, and Compressional Velocity in the Earth's Mantle. In J. Geophys. Res. Solid Earth (v1.0, Vol. 121, pp. 2737–2771). Zenodo. doi: <a href="https://doi.org/10.5281/zenodo.8356540">10.5281/zenodo.8356540</a></p><p><strong>Data Products:</strong></p><ul><li><strong>ME16_Figures(</strong><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/ME16_Figures.tar.gz"><strong>.tar.gz</strong></a><strong> or </strong><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/ME16_Figures.pdf"><strong>.pdf</strong></a><strong>)</strong> - contains all figures from the paper in .png format</li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/ME16"><strong>ME16</strong></a><strong> - </strong>Coefficients of the spline basis functions for each parameter. Refer cij in equation 3. This is our preferred global model of anisotropic elastic parameters and density. Density variations are allowed to deviate from a constant scaling with shear-velocity variations in the lowermost mantle, which is required to fit the longest-period normal modes (e.g. 0S2). 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+M (Moulik and Ekström, 2014) which did not solve independently for density and compressional-wave velocity variations and imposed a constant scaling throughout the mantle instead (ϱ=0, ν=1/0.55).</li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/STW105"><strong>STW105</strong></a> - reference model used in ME16. Described in Kustowski et al. (2008)</li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/setup.cfg"><strong>setup.cfg</strong></a><strong> - </strong>Some configuration metadata relevant to this model for reproducibility.</li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/epix.tar.gz"><strong>epix.tar.gz</strong></a> - Perturbations in horizontally (<i>vsh</i>) and vertically polarized shear velocity (<i>vsv</i>), Voigt-average isotropic shear-wave (<i>vs</i>) and compressional-wave velocity (<i>vp</i>), density (<i>rho</i>). anisotropy (<i>as</i>) 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. Even-degree variations up to degree 6 are provided for density (<i>rho_even6)</i> and isotropic shear-wave velocity (<i>vs_even6</i>), which should be used for density inferences on thermochemical structure (See note above).</li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/ME16.BOX25km_PIX1X1.avni.nc4"><strong>ME16.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 even-degree variations up to degree 6 in Voigt-averaged shear velocity perturbations at the bottom of the mantle (2875-2891 km depth)<ul><li><i>import xarray as xr</i></li><li><i>ds = xr.open_dataset('ME16.BOX25km_PIX1X1.avni.nc4')</i></li><li><i>ds['vs_even6'][-1].plot()</i></li></ul></li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/PROGRAMS.tar.gz"><strong>PROGRAMS.tar.gz</strong></a> - Fortran tools for obtaining model values at specific locations. After creating the executables from source code in the <i>src</i> folder, the <i>readme</i> script generates most of the epix files provided in epix.tar.gz above<strong>.</strong></li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/profilescaling.txt"><strong>profilescaling.txt</strong></a> - contains the median scaling ratios as used in Figure 15(a).</li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/scaling3D_MoulikJGR16.tar.gz"><strong>scaling3D_MoulikJGR16.tar.gz</strong></a> - contains the scaling ratios and poisson ratio calculated from the joint model, as used in Figure 15(b).</li></ul>
Atlantic sand fiddler differences in antimony, running velocity, and behavior, Sapelo Island, Georgia: 2021
Running velocities and behavior assay data of 42 crabs (21 male and 21 female) from Sapelo Island, Georgia. Each crab had three run trials and four for the behavior assay. Crabs were run on a 1m track and behavior assays timed how long it took for crabs to reemerge from burrows. These data were used to determine major influences on crab behavior.
Lagrangian Water Age trajectories initiated from the coastal 500m isobath and derived from surface velocities obtained from satellite observations
We conduct a Lagrangian particle trajectory analysis of surface velocities. We define an “offshore water age” as the time taken by a water parcel to be advected backward in time from its current position along its trajectory until it crosses the 500 m isobath. The rationale of this diagnostic is to detect filaments of coastal water advected offshore by horizontal transport and to estimate the time for water parcels in the filament o leave the coastal area. For example, a value of “20 days” assigned to a pixel means that the water parcel in that area was in the coastal area approximately 20 days before, where it was likely enriched in nutrients.
SRH01 Seed release height and terminal velocity of forb species at Konza Prairie
Height of seed release values recorded at Konza Prairie Biological Station (2022-2024) and terminal velocity measurements of seeds collected from those same species. Intended to for seed dispersal estimates.
IODP Expedition 362 P-wave velocity bayonet (section)
<p>P-wave velocity data were measured on undisturbed section halves using pairs of piezoelectric transducers mounted in bayonets that are inserted into soft sediment along the JRSO-defined y-axis and/or z-axis. Report includes P-wave velocity in y and/or z direction, bayonet separation, traveltime between transducers, and first arrival picks.</p>
IODP Expedition 362 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>
CONUS crustal vertical velocities 2007-2017
<p>This dataset includes vertical velocity fields derived from Global Positioning System (GPS) daily positions from 2007-2017, in the IGS08 reference frame. Residual velocities after removing glacial isostatic adjustment model (ICE6GD), elastic deformation due to GRACE-derived hydrologic loading, and Earth's geocenter motion are also presented. Description of data, methods, and results are documented in the following peer-reviewed publication:</p> <p>Lau, N., Borsa, A.A., & Becker, T.W. (2020). Present-day crustal vertical velocity field for the contiguous United States. Journal of Geophysical Research: Solid Earth, 125, e2020JB020066. https://doi.org/10.1029/2020JB020066</p> <p>This NETCDF file contains the following nine subsets:</p> <ol> <li>'lat', latitude, centerd in 0.25-degree grid cell.</li> <li>'lon', longitude, centered in 0.25-degree grid cell.</li> <li>'gps_vu', gps derived vertical velocities, in millimeters.</li> <li>'gps_vu_smooth', gps derived vertical velocities smoothed by 300-km Gaussian filter; in millimeters.</li> <li>'gps_uncertainties', one-sigma uncertainty estimates for 'gps_vu'; in millimeters.</li> <li>'gps_vu_poro', gps derived vertical velocities, including stations strongly affected by poroelastic effect; in millimeters.</li> <li>'grace_vu', velocities due to hydrologic elastic loading estimated by GRACE; in millimeters.</li> <li>'net_vu', velocities after removing GRACE hydrolog, ICE6GD glacial isostatic adjustment model, and Earth's geocenter motion from 'gps_vu'; in millimeters.</li> <li>'net_vu_smooth',velocities after removing GRACE hydrolog, ICE6GD glacial isostatic adjustment model, and Earth's geocenter motion from 'gps_vu_smooth'; in millimeters.</li> </ol> <p> </p>
Surface velocities of the Müller ice cap
<p>A median surface velocity map of the Müller ice cap on Axel Heiberg Island in the period of 2014 to 2019. The surface velocity maps are made using feature tracking of optical Landsat 8 images using the panchromatic band. The velocity map is on a 900 meters grid.<br> <br> ListOFLandsat8scenes.xlsx provides a list of all of the scenes used in the feature tracking process. The feature tracking is done using two scenes from the same row and path with approximately one year in between. The median of all velocity maps has been made and is presented here.</p> <p>Citation:</p> <p>Ann-Sofie P. Zinck, Surface velocity and ice thickness of the Müller ice cap, Axel Heiberg Island, Master thesis, University of Copenhagen, Copenhagen, 2020</p>
Cilia density and flow velocity affect alignment of motile cilia from brain cells
<p>Here we store the supplementary Materials and Methods for the publication Cilia density and flow velocity affect alignment of motile cilia from brain cells.</p> <p>In the Supplementary methods we included additional information on the hydrodynamic simulations. </p> <p>Video1 and Video2 are videos referenced in the main text of the paper</p> <p>In the archive 'raw data and code.tar' , we provide raw images and codes to support the article.The complete dataset of raw images is more than 1 Tb. Here we are limited to 50Gb. The full dataset is available upon request.<br> <br> We choose to provide a full dataset of two culture at DIV 16, one treated with shear flow and a control without flow.</p> <p>For each of the two cultures, the videos with propelled particles are in the directory FL,<br> The bright field images without particles are stored in BF. Unfortunately we uploaded only few videos because of their large size. The results of the analysis of this dataset is reported in the directory analysis (available for each culture).</p> <p>Moreover we provide the code to analyse these data.<br> The analysis routine:</p> <p>Step 1: for each field of view (fov) getting the cilia beating direction from the FL images. This is done with PIV. The code is Step1_PIVanalysis.mat</p> <p>Step 2: for each fov getting ciliated cell position and CBF from the BF movies. Gather the cilia beating direction and cilia posion and frequency in a unique figure and matlab class (Res.mat). This is done in Step2_gatherResults.mat</p> <p>The results of these analysis are stored in the analysis folder for each culture.</p> <p>These routines are repeated for each experiment and results are then plotted to get trends. In the folder code4figures we report the code that we used to make the figures in the papers starting from a matlab file "all_results*.mat", where are gathered all the analysis.</p> <p>The code may improve in the future with more comments. please check Nicola's github page for the latest update. Please contact us for any problem. https://github.com/NicolaPellicciotta/Code4-Cilia-density-and-flow-velocity-affect-alignment-of-motile-cilia-from-brain-cells</p> <p>All the raw videos and code are in the archive.</p> <p> </p>
Example Datasets for Iliski - Neuronal calcium, RBC velocities and fUS responses to odorant stimuli in the mouse olfactory bulb
<p>Dataset containing 2 HDF5 files, one per mouse. It is intended to be used to test Iliski, a Transfer Function computation software. Iliski is available on GitLab (<a href="https://gitlab.com/AliK_A/iliski">https://gitlab.com/AliK_A/iliski</a>) along with the User Manual. Refer to the User Manual and to the ReadMe file for more details on Iliski. Data were already published on Zenodo (<a href="https://doi.org/10.5281/zenodo.3773863">https://doi.org/10.5281/zenodo.3773863</a>), but along an old version of the software. This upload is made for clarity purposes.</p> <p>Each file contains acquisitions of responses to odorant stimuli in the olfactory bulb made with :</p> <ul> <li>two-photon linescan microscopy (for Ca2+ and RBC velocity);</li> <li>functional ultrafast ultrasound, acquired from a coronal plane.</li> </ul> <p>HDF5 files tree is as follows :</p> <ul> <li>Data type <ul> <li>Raw : straight out of our extraction software, no specific treatment applied;</li> <li>Aligned : every acquisition has been aligned so that the odor delivery matches the 10 s mark. Acquisitions have also been interpolated to be meaned;</li> <li>Delta : aligned acquisitions are subtracted with the baseline value (between 5 and 10 s);</li> <li>DetaOverBSL : aligned acquisitions are subtracted and then divided with the baseline value.</li> </ul> </li> <li>Data source <ul> <li>Ca : calcium data from GCamP6f expressed in the mitral cells dendritic tufts;</li> <li>RBC : RBC velocities in a capillary near the calcium recording site, simultaneously acquired;</li> <li>FUS : fUS data, coronal plane. Only in FUS folder is two different folders then : High and Lowspeed, corresponding to different filter for fUS treatment, > 80Hz and 10-30Hz respectively.</li> </ul> </li> <li>Stimulation type : Odorant_Quantity_Duration <ul> <li>Odorant type, either Iso Amyl Acetate (AA) or Ethyl tiglate (ET);</li> <li>Odor quantity : measured and calibrated in volt with a photo-ionizator;</li> <li>Odor duration : from 5 s down to 120 ms, a single sniff for a mouse.</li> </ul> </li> </ul> <p>Ca2+ : Calcium</p> <p>fUS : functional ultrafast ultrasound</p> <p>RBC : Red Blood Cell</p>
IODP Expedition 368X 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>
IODP Expedition 366 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>
Bandung (Indonesia) area InSAR mean velocity maps
<p>Bandung is the capital of the West Java province of Indonesia. The larger metropolitan area of Bandung has a population of more than 8 million people, and a strong exposure to a variety of geohazards. Satellite SAR data provide information on ground deformation, needed to monitor and model the various sources of these hazards and to perform multi-hazard risk analysis.</p> <p>We show the results of an ALOS-1 and COSMO-SkyMed SAR data investigation over the Bandung metropolitan area retrieved by means of InSAR multi temporal technique aimed mainly at mapping urban subsidence.</p>
velocity
<p>This dataset uses the CK OO metrics.</p> <p>More information at http://openscience.us/repo/defect/ck/velocity.html</p>
USGSG16AP00094: Developing a seismic velocity model of the central valley, northern California: model SSJD2016
<p>Seismic velocity model SSJD2016 uses earthquake travel-time, ambient noise group velocity and gravity data to update Thurber NC2009, for northern California.</p>
Next-generation 3D object detection and tracking for self-driving vehicles using object velocity
<p>The synthetic dataset was generated using KITTI-like specifications and annotations format. It is comprised by the training and testing sets, that include KITTI standard folders: label_2, image_2 and calib. Furthermore, there is a velodyne file for each of the following use cases:</p><ul><li>Point cloud 1: (x,y,z, (Float)Radial_Velocity): this point cloud has the relative radial velocity as an additional feature for each point. File: velodyne_radial_velocity;</li><li>Point cloud 2: (x,y,z,(Float)Absolute_Speed): in this point cloud, every point has the absolute speed of the object as the additional feature. File: velodyne_abs_speed;</li><li>Point cloud 3: (x,y,z,(Bool)Is_Moving): the additional feature of this point cloud is a Boolean value that is set to 1.0 if the object is moving; contrariwise, it is set to 0.0 for static objects. File: velodyne_is_moving;</li><li>Point cloud 4: (x,y,z,0): no additional feature information. If desired, requires post-processing to convert to (x,y,z) or changing the toolbox point cloud configuration to not consider the additional feature. File: velodyne_xyz;</li></ul><p>Additionally, the detections generated with the OpenPCDet toolbox and Second-IoU model are provided.</p><p>This work was made as part of a master thesis of Informatics Engineering in the University of Aveiro.</p>
CI23: a 3D radially anisotropic velocity model of Central Apennines lithosphere
<p>We retrieve the 3D radially anisotropic model of Central Apennines lithosphere implementing Full-Waveform Inversion (FWI). </p> <p>The model has the following parameterization: VPH, VPV, VSH, VSV. It resolves P- and S-waves velocities in the period range 8 - 50s (0.02 - 0.125 Hz). For each point in the mesh (LAT1: 40.0°, LAT2: 45.0°, LON1: 11.0°, LON2: 16.0°), the model returns velocity values in units of m/s.</p> <p>The CI_23 model is available in multiple formats:</p> <ul> <li> <p>A <code>.vtk</code> version is hosted on Zenodo</p> </li> <li> <p>An <code>.h5</code> version can be accessed via Google Drive <a href="https://drive.google.com/drive/folders/18mHH6WRGOTJIaBGB8wn3FcrqYBMwVJyZ?usp=drive_link" target="_blank" rel="noopener">here</a></p> </li> <li> <p>A version interpolated onto a structured grid (in <code>.netCDF</code> format) is available through <a href="https://doi.org/10.17611/dp/emc.2025.ci23stallone.1" target="_blank" rel="noopener">IRIS-EMC </a></p> </li> </ul> <p> </p>
Freshwater thickness, pycnocline depth, and depth averaged velocities from 17 model scenarios in Admiralty Bay, Antarctica.
<p>The dataset contains model results from the Admiralty Bay hydrodynamic model calculated using the Delft3D Flow. It contains freshwater thickness (FWT), pycnocline depth, and depth-averaged velocities mean values from period 1.1.2022-28.01.2022, from 17 model scenarios.</p><p>Scenarios details:</p><p>1–14 scenarios with increasing glacial influx (m^3/s per ~1 km of ice/water boundary) spread homogenously across glacial fronts with 0 m/s initial velocity: 1 - 0; 2 - 0.15 3 - 0.3; 4 - 0.6; 5 - 0.9; 6 -1.7; 7 - 3.0; 8 - 4.5; 9 - 6.0 (also described as test run H0); 10 - 8.0; 11 -11.0; 12 - 14.0; 13 - 28.0 ; 14 - 60.0;</p><p>15 - H2 test run with glacial water discharged from all glaciers, homogenously through the entirety of glacial front, with an initial velocity of 2 m/s</p><p>16 - S0 test run with glacial water discharged from all glaciers subglacially, with zero initial velocity</p><p>17 - S2 test run with glacial water discharged from all glaciers subglacially, with an initial velocity of 2 m/s</p>
Radar high resolution vertical velocity
<p>High temporal resolution vertical velocity profiles from 205 MHz wind profiler radar at Cochin university of science and technology</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.