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424 results for “gravity”
Crustal thicknesses, Moho depths and 3-D density anomaly model for GJI paper: Crustal structure of onshore-offshore Atlantic Canada and environs from constrained 3-D gravity inversion using variable mesh depths by J. Kim Welford
<p>The files are provided as ascii text files in terms of both latitudes/longitudes and eastings/northings. For the 3-D density anomaly model, it is provided with columns of x, y, z, and absolute density. The conversions from latitudes/longitudes to eastings/northings for all of the models and maps in this work are computed with ellipsoid WGS-84 and UTM zone 19 using Generic Mapping Tools.</p>
Kappmmesser М-1937/I (German gravity knife)
German gunsmiths managed to create a simple, compact and reliable knife, which was destined not only to survive the Third Reich, but also to become one of the most famous army knives in the world, which is still in service with the Bundeswehr and some NATO countries. In total, five different modifications of this knife are known, of which the first two were produced during the period of the existence of Nazi Germany, the other three - in the post-war period. The first model of sling cutter "M-1937", or Type I FKm was produced in the period from 1937 to 1941 in Solingen, at the enterprises of the German companies "Paul Weyersberg & Co" and "SMF" (Solinger Metallwaffenfabrik Stoecker & Co).  * [Additional screenshots](http://www.artstation.com/artwork/q9gwVP) * [Second type of knife (M-1937/II)](http://skfb.ly/opEZx) * [English experimental knife](http://skfb.ly/opHt6) Source: Objaverse 1.0 / Sketchfab
Data and codes for "Chapter 2: Particle-laden gravity currents: the lock-release slumping regime at the laboratory scale"
<div> <div> <h1>PALAGRAM Monograph</h1> </div> <p>This repository contains the data used in the book chapter:</p> <blockquote> <p>Gadal, C., Schneider, J., Bonamy, C., Chauchat, J., Dossmann, Y., Kiesgen de Richter, S., Mercier, M.J., Naaim-Bouvet, F., Rastello, M. and Lacaze, L. (2025). <strong>Particle-laden Gravity Currents: The Lock-release Slumping Regime at the Laboratory Scale.</strong> In Particulate Gravity Currents. <a href="https://doi.org/10.1002/9781394216727.ch2">10.1002/9781394216727.ch2</a></p> </blockquote> <div> <h2>Repository organization</h2> </div> <div> <pre><code> palagram_monograph │ └───data: data are stored here │ └───input_data: input data as sent by everyone │ └─── ... : NETCDF files │ └───output_data: processed data output by analysis.py (also contains input_data) │ └─── ... : NETCDF files └───analysis: └───analysis.py: analysis code, that reads input_data and writes output_data └───paper: contains source files for article │ └───figures: contains source figures │ └─── ... : PDF files │ └─── figure_scripts: contains figure scripts that reads data in data/output_data and writes figures in paper/figures │ └─── *.py : python scripts for figures │ └─── ... : various files (.tex, .bib, ...) │ └─── main.pdf : article preprint </code></pre> <div> </div> </div> <div> <h2>Data organization</h2> </div> <p>The CSV file <code>dataset_summary.csv</code> offers a summary of all runs and corresponding experimental parameters, allowing for easier access to the data.</p> <p>The folder <code>data/output_data</code> contains 287 netcdf4 files corresponding to each experimental run used in the paper. For each run, the structure of the NetCDF file is the following:</p> <ul> <li> <p>attributes:</p> <ul> <li>particle_type: particle type used (silica sand, glass beads, etc..)</li> <li>label: filename</li> <li>lab: lab where this run has been performed</li> <li>run_oldID: Old filename, corresponding to the experimental notebook</li> <li>author: author(s) that acquired this run</li> <li>setup: setup used to acquire the data. See article.</li> <li>dataset: Dataset classification of this run, See paper.</li> </ul> </li> <li> <p>dimensions(sizes): time(n)</p> </li> <li> <p>variables(dimensions):</p> <ul> <li>At(): Atwood number</li> <li>Fr(): Froude number (adi. initial current velocity)</li> <li>H0(): initial heavy fluid height inside the lock</li> <li>H_a(): ambient fluid height outside the lock</li> <li>L0(): streamwise lock length</li> <li>L_1(): streamwise tank length after the lock</li> <li>Re(): Reynolds number</li> <li>S(): Settling number</li> <li>St(): Stokes number</li> <li>T_a(): ambient temperature</li> <li>T_f(): heavy fluid temperature inside the lock</li> <li>W0(): crossstream lock width</li> <li>a(): lock aspect ratio</li> <li>alpha(): bottom slope</li> <li>d(): particle diameter</li> <li>gprime(): specific gravity</li> <li>lamb(): adi. attenuation parameter</li> <li>nu_a(): ambient viscosity</li> <li>nu_f(): heavy fluid lock viscosity</li> <li>phi(): initial particle volume fraction inside the lock</li> <li>rho_a(): ambient fluid density</li> <li>rho_c(): heavy fluid mix density inside the lock</li> <li>rho_f():</li> <li>rho_p(): particle density</li> <li>t('time',): time vector</li> <li>t0(): characteristic timescale, t0 = L0/u0</li> <li>u0(): characteristic velocity scale, u0 = sqrt(gprime*H0)</li> <li>vs(): particle Stokes velocity</li> <li>x_front('time',): front position vector</li> </ul> </li> </ul> <p>Variables can sometimes possess the following attributes:</p> <ul> <li>unit: corresponding unit</li> <li>std: error(s) on the given quantity, calculated by error propagation from measurement uncertainties using the <code>uncertainties</code> module (<a href="https://pythonhosted.org/uncertainties/" rel="nofollow">https://pythonhosted.org/uncertainties/</a>) in Python.</li> <li>comments: comments on the given quantity (definition, formulas, etc ..)</li> </ul> <div> <h2>Related works</h2> </div> <ul> <li> <p>Gadal, C., Schneider, J., Bonamy, C., Chauchat, J., Dossmann, Y., Kiesgen de Richter, S., Mercier, M.J., Naaim-Bouvet, F., Rastello, M. and Lacaze, L. (2025). Particle-laden Gravity Currents: The Lock-release Slumping Regime at the Laboratory Scale. In Particulate Gravity Currents. <a href="https://doi.org/10.1002/9781394216727.ch2">10.1002/9781394216727.ch2</a></p> </li> <li>Gadal, C., Mercier, M. J., Rastello, M., & Lacaze, L. (2023). Slumping regime in lock-release turbidity currents. <em>Journal of Fluid Mechanics</em>, 974, A4. <a href="https://doi.org/10.1017/jfm.2023.762" rel="nofollow">doi:10.1017/jfm.2023.762</a></li> <li> <p>Gadal, C., Mercier, M., Rastello, M., & Lacaze, L. (2023). Data used in 'Slumping regime in lock-release turbidity currents' [Data set]. In Journal of Fluid Mechanics (Vol. 974, p. A4). <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.10058946" rel="nofollow">https://doi.org/10.5281/zenodo.10058946</a></p> </li> <li> <p>Schneider, J., Dossmann, Y., Farges, O. et al. Investigation of particle laden gravity currents using the light attenuation technique. <em>Exp Fluids</em>, 64, 23 (2023). <a href="https://doi.org/10.1007/s00348-022-03562-y" rel="nofollow">doi:10.1007/s00348-022-03562-y</a></p> </li> <li> <p>Chauchat, J., Cheng, Z., Nagel, T., Bonamy, C., and Hsu, T.-J. (2017) SedFoam-2.0: a 3-D two-phase flow numerical model for sediment transport, <em>Geosci. Model Dev.</em>, 10, 4367-4392, <a href="https://doi.org/10.5194/gmd-10-4367-2017" rel="nofollow">doi:10.5194/gmd-10-4367-2017</a> and <a href="https://github.com/sedfoam/sedfoam">github</a></p> </li> </ul> </div>
Influence of low orbit design and strategies for gravity field recovery of Europa
<p>Contains the simulated data that were used for the generation of the results presented in Desprats, W., Bertone, S., Arnold, D., Jäggi, A., & Blanc, M. (2023). Influence of low orbit design and strategies for gravity field recovery of Europa. <em>Planetary and Space Science</em>, <em>226</em>, 105631, https://doi.org/10.1016/j.pss.2022.105631.</p>
SABER gravity wave absolute momentum fluxes for the global monsoon seasons 2016-2020
<p>This is a near-global dataset of gravity wave absolute momentum fluxes derived from SABER satellite observations using the method described in Ern et al. (2018). It covers the months of JJA and DJF, starting with December 2016, and ending with August 2020.</p> <p>Dimensions: 4 vertical levels, 37 longitudes, and 37 latitudes.</p> <p>Variables: gravity wave absolute momentum flux</p>
Gravity data collected during volcanic unrest period of the Svartsengi geothermal field in Iceland
<p>The file contains free-air corrected data collected during the one-year unrest at the Svartsengi geothermal field in Iceland as a precursor the Fagradalsfjall eruption in 2021.</p> <p>Column 1: Name of the measurement site</p> <p>Column 2 and 3: Geographical coordinates of measurement sites</p> <p>Column 4-6. Change in elevation, free-air correction and free air gravity change from January 28-29th to April 22-28th 2020</p> <p>Column 7-9. Change in elevation, free-air correction and free air gravity change from April 22-28th to October 5-6th 2020</p> <p>Column 10-12. Change in elevation, free-air correction and free air gravity change from October 5-6th 2020 to February 17-18th 2021</p>
SABER gravity wave temperature variances for the global monsoon seasons 2016-2020
<p>This is a near-global dataset of gravity wave temperature variances at 50km altitude derived from SABER satellite observations using the method described in Ern et al. (2018). It covers the months of JJA and DJF, starting with December 2016, and ending with August 2020.</p> <p>Dimensions: 1 vertical level (z=50km), 37 longitudes, and 37 latitudes.</p> <p>Variables: gravity wave temperature variances</p>
Ultimate load of randomly sampled stainless steel frames under gravity plus wind loads
<p>Data was generated using the general purpose finite element software ABAQUS and performing advanced nonlinear analyses. The database is comprised of ultimate load factors corresponding to different random samples of six different nominal stainless steel frames under gravity and wind load combinations. The values of the random variable assignments are given for each case.</p> <p>The full details of the finite element model can be found in: Arrayago, I.; Rasmussen, K.J.R.; Zhang, H. System-based reliability analysis of stainless steel frames subjected to wind loads. "Structural Safety", July 2022, vol. 97, art. No. 102211.</p> <p>DOI: https://doi.org/10.1016/j.strusafe.2022.102211</p>
Gravity data used in the paper "Imaging the volcanic structures beneath Gran Canaria Island using new gravity data"
<p>This file includes the values of gravity and complete Bouguer gravity anomaly calculated for the land gravity stations in Gran Canaria Island (Canary Islands, Spain). Each station has the corresponding UTM coordinates (Zone 28N) in metres (Datum WGS84). </p> <p>This gravity data set has been used in the paper:</p> <p>Montesinos, F. G., Arnoso, J., Gómez-Ortiz, D., Benavent, M., Blanco-Montenegro, I., Vélez, E., et al. (2022). Imaging the volcanic structures beneath Gran Canaria Island using new gravity data. <em>Journal of Geophysical Research: Solid Earth</em>, 127, e2022JB024863. <a href="https://doi.org/10.1029/2022JB024863">https://doi.org/10.1029/2022JB024863</a></p> <p> </p> <p> </p> <p> </p>
Data for gravity and magnetic property inversion with tetrahedral grids
<p>These are the matlab code and data to analyzing the accuracy of gravity and magnetic property inversion with tetrahedral grids.</p>
Gravity data from AQG-B03 and iGrav#16
<p>Gravity data collected by AQG-B03 and iGrav#16 at MT. Etna, during 31/07 to 03/12/2020.</p>
SDUST2021GRA: Global marine gravity anomaly model recovered from Ka-band and Ku-band satellite altimeter data
<p>SDUST2021GRA is the global marine gravity anomaly model on a grid of 1′×1′, which is established from the altimeter data of <strong> </strong>Ka-band and Ku-band altimetry satellite including HY-2A. Its spatial coverage is 80°S-80°N. Assessed by the shipborne gravity data, the accuracy of SDUST2021GRA in the global is 2.37 mGal, and that in the open ocean is about 1.5 mGal.</p>
Data for Estimation of 3D Moho depths beneath Southern Indian Shield by inverting seismic constraint gravity anomalies
<p>This is a help file for a description of all Data used for the implementation of our present paper<br> 'Estimation of 3D Moho depths beneath Southern Indian Shield by inverting seismic constraint gravity anomalies.' </p> <p> </p>
Quasi-Armored Mud Clasts as Indicators of Transport Processes of Subaqueous Sediment Gravity-Flow
<p>Fig. S1 Quasi-armored mud clasts in different case studying. (A) Quasi-armored mud clasts in the H1 division of Hybrid event bed, Eocene Liushagang Formation; (B) Quasi-armored mud clasts in massive sandstone, Early Cretaceous Lingshandao Formation; (C) Quasi-armored mud clasts in massive sandstone, Early Cretaceous, North Falkland Basin (Dodd et al., 2018); (D) Quasi-armored mud clasts in the upper part of massive sandstone, Middle Eocene, Ainsa System (Pickering et al., 2015); (E) and (F) Quasi-armored mud clasts in massive sandstone, Upper Jurassic, North Sea rift system (Jackson et al., 2011); (G) Quasi-armored mud clasts in the H3 division, Late Jurassic, in the northern North Sea (Haughton et al., 2003) ; (H) and (I) Quasi-armored mud clasts in massive sandstone, Late Jurassic, in the northern North Sea (Williams, 2015).</p> <p>Fig. S2 (A) Location of Beibuwan Basin in China. (B) Location of Weixi’nan depression in the Beibuwan Basin. (C) Sedimentary facies distribution of the upper member of the Liushagang Formation in the Weixi’nan depression. (D) Sedimentary sequence and stratigraphic framework of the Weixinan Depression, Beibuwan Basin (Huang et al., 2013).</p> <p>Fig. S3 The lateral distribution of gravity-flow deposits in the Upper of the Liushagang Formation in the research area from proximal to distal. For the location of the section, see Fig. S2 C.</p> <p>Fig. S4 Facies analysis of Well WZ11-7-1 (in relatively distal), dominated by hybrid event beds with common quasi-armored mud clasts in different units.</p> <p>Fig. S5 Facies analysis of Well WZ11-7-4 (in relatively proximal), dominated by high-density turbidities beds with occasional quasi-armored mud clasts. For the legend of facies analysis, see Fig. S4.</p>
Data for "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations"
<p>These data are shown in the figures included with the article "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations," submitted to Atmospheric Chemistry and Physics.</p>
Surface Height Displacements and Time-Variable Gravity From Changes in the Seasonal Polar Cap on Mars
<p>This repository is the location at which the data created for Wagner et al. 2024 is located. Info about specific files is included in the README.</p>
Studying the scale selection of mixed Rossby-gravity waves: Idealized simulations with the TIGAR model
<p>Mixed Rossby-gravity waves peak in two atmospheric regions in reanalysis: the upper troposphere and the upper stratosphere. The scales of MRG waves are different in these two regions, which can be seen e.g. on real-time MRG wave vertical profiles (https://modes.cen.uni-hamburg.de/products#MRG). In order to understand the MRG wave scale selection in these regions, we run idealized simulations with the TIGAR model (Vasylkevych and Zagar, 2021) with a symmetric initial height perturbation with respect to the equator and zonal wind profiles derived from ERA5 reanalysis (Hersbach et al, 2020). In addition, we also run TIGAR simulations with symmetric initial height perturbation and idealized zonal jets centered at various latitudes.</p>
EGFs, gravity and crustal models data around the Solonker suture zone in NE China
<p>The observed EGFs, complete Bouguer gravity anomalies data and the 3-D crustal Vs and density models from our joint inversion around the Solonker suture zone in NE China.</p>
Different behavior of density perturbations between dayside and nightside in the Martian thermosphere and the ionosphere associated with atmospheric gravity waves
<div>---------------------</div> <div>GENERAL INFORMATION</div> <div>---------------------</div> <div> </div> <div>1. Title of Dataset: Different behavior of density perturbations between dayside and nightside in the Martian thermosphere and the ionosphere associated with atmospheric gravity waves</div> <div> </div> <div>2. Authors: Nakagawa, England, et al.</div> <div> </div> <div>3. Contact information: hiromu.nakagawa.c1@tohoku.ac.jp</div> <div> </div> <div>4. Date of data collection: April 2024</div> <div> </div> <div> </div> <div>---------------------</div> <div>DATA & FILE OVERVIEW</div> <div>---------------------</div> <div> </div> <div>1. Original MAVEN data access:</div> <div> </div> <div>The MAVEN/NGIMS (level-2, version-8, revision-1) are publicly available in ASCII format on the NASA Planetary Data System (PDS) at https://atmos.nmsu.edu/data_and_services/atmospheres_data/MAVEN/ngims.html.</div> <div> </div> <div>As for references in Figures 2-4, the MAVEN/MAG Calibrated data are publicly available in ASCII format on the NASA Planetary Data System (PDS) at https://pds-ppi.igpp.ucla.edu/search/view/?id=pds://PPI/maven.mag.calibrated.</div> <div> </div> <div> </div> <div>2. File List:</div> <div> </div> <div>[1] correlate_coefficients.txt</div> <div>[2] rmse_170-190_coupling_case.txt</div> <div>[3] rmse_190-210_coupling_case.txt</div> <div>[4] rmse_170-190_ion_specific_case.txt</div> <div>[5] rmse_190_210_ion_specific_case.txt</div> <div> </div> <div>---------------------</div> <div>DATA-SPECIFIC INFORMATION</div> <div>---------------------</div> <div> </div> <div>[1] correlate_coefficients.txt</div> <div>This includes the geometric information and the calculated correlate coefficient between neutrals and ions in all profiles applied in this study. The first header provides the information in the file: Orbit, Year, Month, Day, Hour, Minute, Unix time, SZA(deg), Lon(deg), Lat(deg), LST(hr), CC(CO2-N2), CC(CO2-CO2+). The geometric information corresponds to those at altitude around 190 km. The last two columns represents the correlate coefficients between CO2 and N2 and between CO2 and CO2+. </div> <div> </div> <div>[2] rmse_170-190_coupling_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 170 km and 190 km for the case of ion-neutral coupling case. </div> <div> </div> <div>[3] rmse_190-210_coupling_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 190 km and 210 km for the case of ion-neutral coupling case. </div> <div> </div> <div>[4] rmse_170-190_ion_specific_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 170 km and 190 km for the case of ion-specific case. </div> <div> </div> <div>[5] rmse_190_210_ion_specific_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 190 km and 210 km for the case of ion-specific case. </div> <div> </div> <div>---------------------</div> <div>METHODOLOGICAL INFORMATION</div> <div>---------------------</div> <div> </div> <div>Period to analysis: from March 2015 to August 2020 (orbit number from 713 to 11881)</div> <div> </div> <div>Number of files (CO2): 8,911</div> <div> </div> <div>Number of files (N2): 8,913</div> <div> </div> <div>Number of files (CO2+): 7,053(?)</div> <div> </div> <div>Data selection:</div> <div> </div> <div>1. Neutral species with inbound valid (IV)</div> <div> </div> <div>2. Ion species with SCP (quality flag=0)</div> <div> </div> <div>3. Simultaneous observations of CO2, N2, and CO2+ at altitudes 170-210 km</div> <div> </div> <div>4. All three species CO2, N2, and CO2+ are valid with data sample >10 points in a single orbit.</div> <div> </div> <div>Total number of files after data selection = 3,018</div> <div> </div> <div>Fitting:</div> <div> </div> <div>4th-order polynomial fit to extract the perturbation components of density in the range between 160 and 220 km.</div> <div> </div> <div> </div> <div>Correlation coefficient:</div> <div> </div> <div>The correlation coefficients between perturbations are calculated in the range between 170 and 210 km.</div> <div> </div> <div>Ion-neutral coupling cases whose correlation coefficient between CO2 and CO2+ larger than 0.7: Total number = 839</div> <div> </div> <div>Ion-specific case whose correlation coefficient between CO2 and CO2+ smaller than 0.2: Total number = 823</div> <div> </div> <div>#We also define CME cases based on Lee et al. (2017): Total number = 31 cases</div> <div> </div>
Inhomogeneous magnetization of Tyrrhenian seamounts revealed from gravity and magnetic correlation analysis
<p>The xyz files provide the gravity and magnetic total gradients data of the Magnaghi, Palinuro, Vavilov and Marsili seamounts in the Tyrrhenian Sea (Italy). </p>
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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)
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.