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61 results for “gravity model”
Spherical harmonic models of the gravity field of the Galilean satellites [Galileo]
<p>This archive contains spherical harmonic models of the gravitational potential of the Galilean satellites derived from data collected by the Galileo mission. For all models, the spherical harmonic coefficients are to be used with unnormalized spherical harmonics that exclude the Condon-Shortley phase factor of (-1)^m. The fist line of each file is a header that contains the reference radius (in km), the GM and its uncertainty (in km^3/s^2), and the k2 Love number and its uncertainty (for Io only).</p> <p>The files in this archive with the asociated references are:</p> <ul> <li>Anderson2001_Io_gravity.sh (Anderson et al. 2001)</li> <li>Anderson1998_Europa_gravity.sh (Anderson et al. 1998)</li> <li>Anderson1996_Ganymede_1_gravity.sh (Anderson et al. 1996, encounter 1)</li> <li>Anderson1996_Ganymede_2_gravity.sh (Anderson et al. 1996, encounter 2)</li> <li> <div>Anderson2001_Callisto_gravity.sh (Anderson et al. 2001)</div> </li> </ul>
Spherical harmonic models of the gravity field of Uranus
<p>This archive contains published spherical harmonic models of the gravity field of Uranus. The coefficients are to be used with unnormalized spherical harmonic functions that exclude the Condon-Shortely phase factor of (-1)^m, and the file is formatted in a manner to be read by the <a href="https://shtools.github.io/SHTOOLS/">pyshtools</a> software (using format='shtools'). The header of the file contains the reference radius, GM, GM uncertainty, and maximum degree of the spherical harmonic expansion (all in SI units).</p> <p>* Jacobson2014.sh</p>
Spherical harmonic models of the gravity field of Saturn
<p>This archive contains published spherical harmonic models of the gravity field of Saturn. The coefficients are to be used with unnormalized spherical harmonic functions that exclude the Condon-Shortely phase factor of (-1)^m, and the file is formatted in a manner to be read by the <a href="https://shtools.github.io/SHTOOLS/">pyshtools</a> software (using format='shtools'). The header of the file contains the reference radius, GM, GM uncertainty, and maximum degree of the spherical harmonic expansion (all in SI units).</p> <p>* Jacobson2022.sh</p>
Gravity modeling of the Alpine lithosphere affected by magmatism based on seismic tomography
<p>The Southern Alpine regions have been affected by several magmatic and volcanic events between the Paleozoic and the Tertiary. This activity has undoubtedly had an important effect on the density distribution and structural setting at lithosphere scale. Combining the information from gravity field and a high-resolution seismic tomography has been carried out a new 3D lithosphere density model of the Alpine region.</p>
Non-linear three-mode coupling of gravity modes in rotating slowly pulsating B stars: Stationary solutions and modeling potential
<p>This repository contains the material available online that accompanies <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> (ArXiv link). </p> <p>It contains zipped archives that contain inlists and final data products for the MESA stellar evolution code\(^1\) (version 15140), the GYRE stellar pulsation/oscillation code\(^2\) (version 6.0.1) and the AESolver stellar oscillation mode coupling code\(^3\).</p> <p>In the technical information section below you may find a description of the contents of this repository. The abstract of <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> is also available below.</p> <p> </p> <p><em>Footnotes :</em></p> <p><em>\(^1\): see <a href="https://docs.mesastar.org/en/r15140/" target="_blank" rel="noopener">https://docs.mesastar.org/en/r15140/</a> for additional details about the MESA stellar evolution code.</em></p> <p><em>\(^2\): see <a href="https://gyre.readthedocs.io/en/v6.0.1/">https://gyre.readthedocs.io/en/v6.0.1/</a> for additional details about the GYRE stellar pulsation/oscillation code.</em></p> <p><em>\(^3\): the AESolver code can be downloaded from its Github repository: <a href="https://github.com/JVB11/AESolver" target="_blank" rel="noopener">https://github.com/JVB11/AESolver</a>; its documentation may be consulted at <a href="https://jvb11.github.io/AESolver/" target="_blank" rel="noopener">https://jvb11.github.io/AESolver/</a>.</em></p>
Spherical harmonic models of the gravity field of Jupiter
<p>This archive contains published spherical harmonic models of the gravity field of Jupiter. The coefficients are to be used with unnormalized spherical harmonic functions that exclude the Condon-Shortely phase factor of (-1)^m, and the file is formatted in a manner to be read by the <a href="https://shtools.github.io/SHTOOLS/">pyshtools</a> software (using format='shtools'). The header of the file contains the reference radius, GM, GM uncertainty, and maximum degree of the spherical harmonic expansion (all in SI units).</p> <p>* Kaspi2023.sh (value of GM provided by Y. Kaspi, personal communication)</p>
WINTERC-G: a global upper mantle thermochemical model from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data
<p>WINTERC-G: A global, temperature and compositional model of the lithosphere<br> and upper mantle.<br> Version: v5.4, December 2020, J. Fullea, S. Lebedev, Z. Martinec, N. Celli<br> <br> Contact: Javier Fullea (jfullea@ucm.es)<br> Facultad de Fisica,<br> Universidad Complutense de Madrid (UCM),<br> Spain<br> ////////<br> Geophysics Section,<br> Dublin Institute for Advanced Studies<br> Dublin, Ireland<br> </p> <p>TYPE:<br> This contains files with:<br> i) the model directly on the triangular grid solved for in the surface wave inversion.</p> <p> ii) an interpolated grid at 0.5 deg lateral resolution for the density and density discontinuities used in the gravity field data inversion<br> </p> <p>If you have any questions regarding the methodology or the construction<br> of the model, please contact the authors. If you use the model, we would<br> request that you cite the reference indicated below, and appreciate<br> your feedback regarding the model and its application.</p> <p>Citation:</p> <p>Fullea, J., Lebedev, S., Martinec, Z., & Celli, N. L. (2021). WINTERC-G: mapping the upper mantle thermochemical heterogeneity from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data. Geophysical Journal International, 226(1), 146-191.</p> <p>*******************************<br> Summary: construction of the model.<br> WINTERC-G is a Waveform tomography and Gravity (geoid and gravity anomalies and gradiometric measurements<br> from ESA's GOCE mission) INversion model of the TEmpeRature and Composition of the lithosphere and upper mantle at<br> global scale. WINTERC-G is based on upon the integrated geophysical-petrological<br> approach LitMod (Afonso et al., 2008; Fullea et al. 2009) and, hence, all<br> relevant mantle rock physical properties modelled (seismic velocities and density) are<br> computed within a thermodynamically self-consistent framework allowing for a direct<br> parameterization in terms of the temperature and composition of the lithosphere-upper<br> mantle. The inversion is a two-step procedure. In a first step, we invert surface-wave, Rayleigh and Love<br> fundamental mode dispersion curves from a high resolution global dataset measured using waveform inversion,<br> along with surface heat flow and elevation (isostasy) for temperature and crustal structure<br> using a point-wise, non-linear, gradient-search inversion<br> over a triangular grid with an average 225 km lateral inter-knot spacing. In a second step we<br> use a fully parallelized spherical harmonic formalism to invert satellite gravity field data in<br> order to refine the initial crustal density and mantle composition distributions from the step 1<br> for a fixed temperature field.</p> <p>The parameter space in step 1 includes crust (densities and S-wave velocities for a three-layered crust)<br> and mantle variables (the depth of the thermal Lithosphere-Athenosphere-Boundary,<br> the thickness of the sublithospheric thermal buffer, the sublithospheric temperatures at 3 different<br> equispaced nodes down to 400 km, the lithospheric and sublithospheric mantle compositon, and<br> the the radial anisotropy at the 3 crustal layers and at 56, 80, 110, 150, 200, 260, 330,<br> and 400 km depths.</p> <p>The parameter space in step 2 is defined by the average crustal density, and the<br> mantle composition in the lithosphere and sublithosphere.<br> We use the output crustal density from step 1 as the<br> initial value in step 2 inversion. Mantle densities are derived based on the output temperature<br> field from step 1 (kept fixed) and the bulk mantle composition inversion variables.</p> <p> </p> <p> </p> <p>*******************************</p> <p>This archive contains the following files:<br> README (this file)<br> WINTERC-G_Vp-Vs.lis (triangular grid)<br> WINTERC-G_rad_anis_Vs.lis (triangular grid)<br> WINTERC-G_Temperature.lis (triangular grid)<br> WINTERC-G_Density.lis (triangular grid)<br> WINTERC-G_LAB.lis (triangular grid)<br> WINTERC_T_rho_1D.z (1D average model of temperature and density)<br> rho_*_out.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_continental.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Ice.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Bed.xyz (0.5 deg egular grid for gravity field)<br> Global_Moho_WINTERC-G.xyz (0.5 deg egular grid for gravity field)</p> <p><br> Files in the triangular grid with an average 225 km lateral inter-knot spacing (12232 grid points):</p> <p>* WINTERC-G_Vp-Vs.lis: Vp and Vs (in km/s) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) Vp (km/s) Vs(km/s)<br> 5640 93.72 4.135 -5.0 3.91 2.11</p> <p><br> * WINTERC-G_rad_anis_Vs.lis: radial anisotropy, (Vsh-Vsv)/Vs_iso (in %) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) anisotropy (%)</p> <p>* WINTERC-G_Temperature.lis: temperature (in ºC) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth (km, <0 downwards) T (ºC) dT (%) dT(K) <br> 6437 297.20 -2.524 -259.000 1431.9 -1.91 -27.9<br> The anomalies dT are in % and K with respect to the 1D model in WINTERC_T_rho_1D.z (column 2).</p> <p>* WINTERC-G_Density.lis: density (in kg/m3) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) rho (kg/m3) drho(%) drho(kg/m3)<br> The anomalies drho are in % and kg/m3 with respect to the 1D model in WINTERC_T_rho_1D.z (column 3).</p> <p>* WINTERC_T_rho_1D.z: 1D average model of temperature (column 2 in ºC) and density (column 3 in kg/m3) with a vertical grid step of 2 km <br> 5.00000000 0.0000000000000000 6.0259973839110526<br> 3.00000000 0.0000000000000000 38.960571309690394<br> 1.00000000 0.33634006819423840 174.42296045978722<br> -1.00000000 3.8888495253719624 1692.8437489147236<br> -3.00000000 23.974111923225379 1863.8834351235944<br> -5.00000000 47.727920701943034 2568.2414495590924<br> -7.00000000 89.633398074381162 2819.8386016341910<br> -9.00000000 137.01489361657013 2839.5325893195904<br> -11.0000000 182.35233447017222 2897.6600872935287<br> -13.0000000 224.46247069572485 2945.2036923862997<br> -15.0000000 260.63395547331390 3069.6809340323475<br> -17.0000000 292.28175449521456 3132.4574175461721<br> -19.0000000 322.29571965406632 3145.5747337463940<br> -21.0000000 351.58698283375054 3157.2401512748038<br> -23.0000000 380.30002225705056 3177.0000420059773<br> -25.0000000 408.50259805632055 3183.6651032398490<br> -27.0000000 436.22632217636487 3190.9586785996116<br> -29.0000000 463.48733903170023 3198.9369509456310<br> -31.0000000 490.29841705549831 3209.7229872383764<br> -33.0000000 516.71149258457456 3221.6329506091679<br> ...</p> <p>Files in the interpolated regular grid at 0.5 deg lateral resolution used for gravity field data inversion:</p> <p> * rho_c_out.xyz: average crustal density<br> * rho_submoho_out.xyz: mantle density below the Moho discontinuity<br> * rho_*_out.xyz: mantle density defined at different model depths: 20, 35, 56, 80, 110, 150, 200, 260, 330 and 400 km.</p> <p> Format for the density files:<br> # longitude latitude density (kg/m3)<br> <br> Files containing layer discontinuities:</p> <p> * ETOPO2_km_continental.xyz: surface elevation including ice sheet and 0 in marine areas (km, <0 upwards)</p> <p> * ETOPO2_km_depth_Ice.xyz: surface elevation including ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * ETOPO2_km_depth_Bed.xyz: bedrock surface elevation without ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * Global_Moho_WINTERC-G.xyz: crust-mantle discontinuity depth (km, >0 downwards)</p> <p> Format for the discontinuity files:<br> # longitude latitude depth (km)<br> <br> <br> The gravity field in WINTERC-G is computed using an spherical harmonic formalism and a model discretization<br> in 13 layers with laterally varying density. The first 7 layers are characterized by top and bottom boundaries with laterally varying radius whereas the last 6 layers are defined by top and bottom boundaries with constant radius:</p> <p>1/ Water: from ETOPO2_km_continental.xyz to ETOPO2_km_depth_Ice.xyz with rho=1030 kg/m3 (constant vertically)</p> <p>2/ Ice: from ETOPO2_km_depth_Ice.xyz to ETOPO2_km_depth_Bed.xyz with rho=910 kg/m3 (constant vertically)</p> <p>3/ Crust: from ETOPO2_km_depth_Bed to Global_Moho_WINTERC-G.xyz with rho=rho_c_out.xyz (constant vertically)</p> <p>4/ submoho-20km: from Global_Moho_WINTERC-G.xyz to z_20km (file with 20 km everywhere except where z_moho>20km) with rho=rho_submoho_out.xyz (top) and rho=rho_20km_out.xyz (bottom)</p> <p>5/ 20km-36km: from z_20km (file with 20 km everywhere except where z_moho>20km) to z_36km (file with 36 km everywhere except where z_moho>36km) with rho=rho_20km_out.xyz (top) and rho=rho_36km_out.xyz (bottom)</p> <p>6/ 36km-56km: from z_36km (file with 36 km everywhere except where z_moho>36km) to z_56km (file with 56 km everywhere except where z_moho>56km) with rho=rho_36km_out.xyz (top) and rho=rho_56km_out.xyz (bottom)</p> <p>7/ 56km-80km: from z_56km (file with 56 km everywhere except where z_moho>56km) to 80 km depth with rho=rho_56km_out.xyz (top) and rho=rho_80km_out.xyz (bottom)</p> <p>The next 6 layers are computed using the constant radius option:</p> <p>8/ 80km-110km: from z=80km to z=110 km with rho=rho_80km_out.xyz (top) and rho=rho_110km_out.xyz (bottom)</p> <p>9/ 110km-150km: from z=110km to z=150 km with rho=rho_110km_out.xyz (top) and rho=rho_150km_out.xyz (bottom)</p> <p>10/ 150km-200km: from z=150km to z=200 km with rho=rho_150km_out.xyz (top) and rho=rho_200km_out.xyz (bottom)</p> <p>11/ 200km-260km: from z=200km to z=260 km with rho=rho_200km_out.xyz (top) and rho=rho_260km_out.xyz (bottom)</p> <p>12/ 260km-330km: from z=260km to z=330 km with rho=rho_260km_out.xyz (top) and rho=rho_330km_out.xyz (bottom)</p> <p>13/ 330km-400km: from z=330km to z=400 km with rho=rho_330km_out.xyz (top) and rho=rho_400km_out.xyz (bottom)</p> <p> </p> <p> </p>
Dataset for: "Modeling the Hα Emission Surrounding Spica using the Lyman Continuum from a Gravity-darkened Central Star"
<p><strong>Summary: </strong>This deposit supplements the manuscript, "<em>Modeling the Hα Emission Surrounding Spica using the Lyman Continuum from a Gravity-darkened Central Star</em>", accepted to the Astrophysical Journal. The tar.gz archive file contains an example Cloudy script and associated data. The complete listing of the files included in this archive file are given here: </p> <p> ReadMe Documentation file<br> example_cloudy_input_file.txt Cloudy script<br> spica_i=100_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=110_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=116_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=120_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=130_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=140_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=150_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=160_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=170_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=180_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=90_V5_solar.ascii Spica input stellar spectral energy distribution<br> </p> <p><strong>System requirements:</strong> The input script and SED files correspond to Cloudy code version 17.02:<br> Codebase: <a href="https://trac.nublado.org/">https://trac.nublado.org/</a><br> Primary documentation: Ferland et al. (<a href="https://ui.adsabs.harvard.edu/abs/2017RMxAA..53..385F/abstract">2017RMxAA..53..385F</a>; arXiv:<a href="https://arxiv.org/abs/1705.10877">1705.10877</a>)<br> </p> <p>Additional documentation provided in the ReadMe file.</p>
SDUST2020MGCR: a global marine gravity change rate model determined from multi-satellite altimeter data
<p>SDUST2020MGCR.nc is the global marine gravity change rate model covering 70°S~70°N and 0°~360°E on 5′×5′ grids. The dataset contains geospatial information (latitude, longitude), SDUST2020MGCR and an attachment data (GIA MGCR).</p>
Spherical harmonic models of the gravity field of Titan
<p>This archive contains previously published models of the gravitational field of Saturn's moon Titan.</p> <ul> <li>Durante2019.sh</li> </ul> <p>All models make use of unnormalized spherical harmonic functions that exclude the Condon-Shortley phase factor of (-1)^m.</p>
Spherical harmonic models of the gravity field of Enceladus
<p>This archive contains previously published models of the gravitational field of Saturn's moon Enceladus.</p> <ul> <li>Iess2014.sh (SOL1)</li> <li>Park2024.sh (Case 2)</li> </ul> <p>All models make use of unnormalized spherical harmonic functions that exclude the Condon-Shortley phase factor of (-1)^m.</p>
Spherical harmonic models of the gravity field of Neptune
<p>This archive contains published spherical harmonic models of the gravity field of Neptune. The coefficients are to be used with unnormalized spherical harmonic functions that exclude the Condon-Shortely phase factor of (-1)^m, and the file is formatted in a manner to be read by the <a href="https://shtools.github.io/SHTOOLS/">pyshtools</a> software (using format='shtools'). The header of the file contains the reference radius, GM, GM uncertainty, and maximum degree of the spherical harmonic expansion (all in SI units).</p> <p>* Jacobson2009.sh</p>
Crustal structure of the Volgo-Uralian subcraton revealed by inverse and forward gravity modeling [dataset]
<p>This collection contains data that were used to build a 3D crustal model of the Volgo-Uralian subcraton through inverse and forward gravity modeling.</p> <p>The dataset is subdivided into two folders: (1) Gravity field inversion; (2) Forward gravity modeling. </p>
Gravity waves in Titan's atmosphere: A comparison between linearized wave model calculations and HASI observations
<p>The data for the article "Gravity waves in Titan's atmosphere: A comparison between linearized wave model calculations and HASI observations" (GWTA). </p> <p> </p> <ol> <li>"Titan_CJP_std_chem.dat" is the background atmosphere data of Titan's atmosphere from Strobel's model. It is used in Figure 1 of the article.</li> <li>"HASI_T_p_rho_vsZ_2008.dat" is the data for Cassini-Huygens observations in Titan's atmosphere. It is used in Figure 1 of the article.</li> <li>"Mma-Program-for-GW-on-Titan.txt" is the main Mathematica program to simulate the gravity waves on Titan.</li> <li>"solutions-fun.rar" is the simulation result. This RAR file includes 174 gravity wave samples simulated with different periods and horizontal wavelengths (can be read from the subfile names after uncompressing). These gravity wave solutions are stored as InterpolatingFunction of Mathematica. The solution describes the gravity wave temperature, velocity, and density perturbations profiles from altitude 300km to 2000km. However, they are plain texts and can easily be read by any software. Figures from 2-10 are based on these data.</li> </ol> <p> </p>
A new global marine gravity model NSOAS24 derived from multi-satellite sea surface slopes
<p><span><span><span>NSOAS24 is the global marine gravity anomaly model on a grid of 1′×1′, which is derived based on sea surface slopes from multi-satellite altimetry missions. </span></span></span><span><span><span>Its spatial coverage is 80°S-80°N.</span></span></span></p>
Analysing the intra and interregional components of spatial accessibility gravity model to capture the level of equity in the distribution of hospital services: does they influence patient mobility?
<p>aggregated_data_age55+.csv and distance_matrix_age55+.csv have been included in the second version of the dataset as the reference population is limited to resident with 55 years old or more.</p>
Yerrida Basin 3D geological model and gravity inversion results
<p>This dataset contains an archive for an implicit 3D geological model of the Yerrida Basin, southern Capricorn region, Western Australia.</p> <p><strong><em>Yerrida_Basin_3D.zip </em></strong>is a GeoModeller three dimensional geological model. Also included are 2D and 3D voxets resulting from inversion of gravity data using the geological model as a constraint. Geomodeller software is available from here: <a href="https://www.intrepid-geophysics.com/ig/index.php?page=downloads">https://www.intrepid-geophysics.com/ig/index.php?page=downloads</a></p> <p>This is a companion dataset for the paper submitted to the scientific journal Solid Earth: Mapping undercover: integrated geoscientific interpretation and 3D modelling of a Proterozoic basin.<em> </em>Mark D Lindsay, Sandra Occhipinti, Crystal LaFlamme, Alan Aitken, Lara Ramos.</p> <p> </p>
Insights into the Magmatic Feeding System of the 2021 Eruption at Cumbre Vieja (La Palma, Canary Islands) Inferred from Gravity Data Modeling. Remote Sens. 2023, 15, 1936. https://doi.org/10.3390/rs15071936
<p>Paper: Insights into the magmatic feeding system of the 2021 eruption at Cumbre Vieja (La Palma, Canary Islands) inferred from gravity data modeling <br> F. G. Montesinos1,7, S. Sainz-Maza2,7, D. Gómez-Ortiz3, J. Arnoso4,7, I. Blanco-Montenegro5,7, M. Benavent1,7 E. Vélez4,7, N. Sánchez6 and T. Martín-Crespo3</p> <p>1 Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Plaza de Ciencias 3, 28040 Madrid, Spain.<br> 2 Observatorio Geofísico Central (IGN). C/ Alfonso XII, 3. 28014 Madrid, Spain.<br> 3 Dpt. Biología y Geología, Física y Química Inorgánica, ESCET, Universidad Rey Juan Carlos. C/Tulipán s/n, 28933 Móstoles, Madrid, Spain.<br> 4 Instituto de Geociencias (IGEO), CSIC-UCM. C/ Doctor Severo Ochoa, 7. 28040 Madrid, Spain.<br> 5 Departamento de Física, Escuela Politécnica Superior, Universidad de Burgos. Avda. de Cantabria s/n, 09006 Burgos, Spain.<br> 6 Instituto Geológico y Minero de España (IGME, CSIC), Unidad Territorial de Canarias, Alonso Alvarado, 43, 2A, 35003 Las Palmas de Gran Canaria, Spain.<br> 7 Research Group ‘Geodesia’, Universidad Complutense de Madrid, Spain.</p> <p><br> Corresponding author: Fuensanta G. Montesinos (fuensant@ucm.es)</p> <p>This research is supported by the project PID2019-104726GB-I00/AEI/10.13039/501100011033 funded by the Spanish Research Agency. Further, the University Complutense of Madrid (grants Financiación Grupos 2021, UCM 2022-GRFN14/22) and the Spanish Ministry of Science and Innovation (RD 1078/2021, funding for research activities of the CSIC-PIE project CSIC-LAPALMA-07) supported this research.</p> <p>------------------------------------------------------------------------------------------------</p> <p>Responsible Researchers:<br> - Fuensanta González Montesinos, Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Spain<br> fuensant@ucm.esResponsible Researchers: </p> <p>- José Arnoso Sampedro, Instituto de Geociencias (CSIC-UCM), Spain<br> jose_arnoso@csic.es</p> <p> </p> <p><br> >> The use of this data set is limited to academic or research purposes and it have to be referenced</p> <p><br> Zone:Cumbre Vieja (La Palma Island, Spain)<br> Geodetic Coordinates Datum WGS84<br> Gravity(mGal) and Bouguer Gravity anomaly GRS80 (mGal)(Terrain density 2450 kg/m3)</p> <p>The file GravityCumbreVieja_FGMontesinos_et_al.dat includes the values of gravity and complete Bouguer gravity anomaly (GRS80) calculated for the land gravity stations at the Cumbre Vieja area (La Palma Island, Spain). The gravity values were observed in 142 land gravity stations (Figure 3 in the manuscript) by our group in 2005 and 2021 surveys The positions of the stations were selected to cover most of the Cumbre Vieja area, and the coordinates were obtained by differential GPS (WGS84 Datum). The gravity observations were processed taking into account the usual corrections (instrument height, drift, jumps, etc.). The tidal correction was calculated from gravity tide measurements made in several islands of the Canary Archipelago. All the gravity values referred to absolute gravity stations (Table S1). The procedure to obtain the terrain correction and the Bouguer anomaly map is explained in the manuscript and in the supporting information.</p>
The global marine free air gravity anomaly model SDUST2022GRA
<p>SDUST2022GRA.nc is the global marine free air gravity anomaly model covering 80°S~82°N and 0~360°E on 1′×1′ grids. SDUST2022GRA is recovered from multi-radar and ICESat-2 laser altimeter data to investigate the contribution of ICESat-2.</p>
Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (2/2)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" by T. Kuroda, A.S. Medvedev and E. Yiğit.</p> <p>Each file contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T), zonal wind velocity (u), meridional wind velocity (v) and vertical wind velocity (w). Each tar.xz file contains snapshots of those data in every 1/6 Sol for Ls of 30 degrees. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020).</p> <p>data270rdc-my34.tar.xz: for Ls=270-300 (48 Sols)</p> <p>data300rdc-my34.tar.xz: for Ls=300-330 (51 Sols)</p> <p>data330rdc-my34.tar.xz: for Ls=330-360 (56 Sols)</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.