Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
21
datasets available to search
ShareScore release 0.9.0
Dataset results
21 results for “gravity field”
Simulation of the SLR Space Segment Evolution to Improve the Realization of Terrestrial Reference Frames and Determination of Low-Degree Gravity Field Parameters
<p>These are data obtained from simulation studies of the development of the space segment of the SLR technique. Detailed information can be found in Najder et al. (2025). Najder, J., Sośnica, K., Zajdel, R., & Kur, T. (2025). Simulation of the SLR space segment evolution to improve the realization of terrestrial reference frames and determination of low-degree gravity field parameters. <em>Journal of Geodesy</em>, <em>99</em>(6), 46. https://doi.org/10.1007/s00190-025-01971-5</p>
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>
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>
Detecting axisymmetric magnetic fields using gravity modes in intermediate-mass stars
<p>Typical MESA and GYRE inlists associated with <a href="https://ui.adsabs.harvard.edu/abs/2020arXiv200502411V/abstract">Van Beeck et al. (2020)</a>. MESA version 10398 and GYRE version 5.2.</p> <p>Context: Angular momentum (AM) transport models of stellar interiors require improvements to explain the strong extraction of AM from stellar cores that is observed with asteroseismology. One of the often invoked mediators of AM transport are internal magnetic fields, even though their properties, observational signatures and influence on stellar evolution are largely unknown.</p> <p>Aims: We study how a fossil, axisymmetric internal magnetic field affects period spacing patterns of dipolar gravity mode oscillations in main-sequence stars with masses of 1.3, 2.0 and 3.0 <span class="math-tex">\(\mathrm{M}_{\odot}\)</span> . We assess the influence of fundamental stellar parameters on the magnitude of pulsation mode frequency shifts.</p> <p>Methods: We compute dipolar gravity mode frequency shifts due to a fossil, axisymmetric poloidal-toroidal internal magnetic field for a grid of stellar evolution models, varying stellar fundamental parameters. Rigid rotation is taken into account using the traditional approximation of rotation and the influence of the magnetic field is computed using a perturbative approach.</p> <p>Results: We find magnetic signatures for dipolar gravity mode oscillations in terminal-age main-sequence stars that are measurable for a near-core field strength larger than 10<sup>5</sup> G. The predicted signatures differ appreciably from those due to rotation.</p> <p>Conclusions: Our formalism demonstrates the potential for the future detection and characterization of strong fossil, axisymmetric internal magnetic fields in gravity-mode pulsators near the end of core-hydrogen burning from Kepler photometry, if such fields exist.</p> <blockquote> <p>The publication date is the date of acceptance.</p> </blockquote> <p>J. Van Beeck would like to thank researchers M. Michielsen, C. Johnston, and dr. M. G. Pedersen for their valuable input in the MESA and GYRE computations.</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>
Corresponding Dataset for Gravity Field of Ganymede after the Juno Extended Mission
<p>Corresponding Dataset for Gravity Field of Ganymede after the Juno Extended Mission</p> <p> Luis Gomez Casajus August 2, 2022</p> <p>=============================================================================<br> Introduction<br> =============================================================================</p> <p> This dataset contains the estimated gravity field and its corresponding<br> full covariance matrix. This dataset is provided in order to <br> supplement the submitted article to the "Geophysical Research Letters"<br> journal:</p> <p> Gravity Field of Ganymede after the Juno Extended Mission.<br> L. Gomez Casajus (*), A. I. Ermakov, M. Zannoni, J. T. Keane, <br> D. Stevenson, D. R.Buccino, D. Durante, M. Parisi, R. S. Park, <br> P. Tortora and S. J. Bolton<br> <br> - (*) luis.gomezcasajus@unibo.it</p> <p>=============================================================================<br> File description<br> =============================================================================</p> <p> This archive contains two files within the root directory.<br> <br> ROOT<br> -SOI_gravity_field.txt</p> <p> This file contains the estimated normalized spherical harmonics <br> coefficients of the Ganymede gravity field.<br> The file contains a header row which provides a description of <br> the file.</p> <p> -SOI_cov_matrix.txt</p> <p> This file contains the estimated full covariance matrix (32x32) <br> of the normalized spherical harmonics coefficients of the <br> Ganymede gravity field. The file contains a header row which <br> provides a description of the file. The matrix is represented as<br> a 2 dimensional array whose coefficients follow the following <br> order scheme: C20 C21 S21 C22 S22 C30 C31 ...</p> <p>=============================================================================<br> ACKNOWLEDGMENTS<br> =============================================================================</p> <p> The authors are grateful to William Folkner, to the entire Solar System <br> Dynamics Group and to Robert Haw, former Galileo navigator, for the useful <br> discussions and suggestions regarding the procedures for Galileo data <br> analysis. L.G.C., M.Z., and P.T. are grateful to the Italian Space Agency <br> (ASI) for financial support through Agreement No. 2017-40-H.1-2020, and its <br> extension2017-40-H.02020-13-HH.0, for ESA’s BepiColombo and NASA’s Juno radio<br> science experiments. L.G.C., M.Z., and P.T. acknowledge Caltech and the Jet <br> Propulsion Laboratory for granting the University of Bologna a license to an <br> executable version of MONTE Project Edition S/W. JTK and AIE acknowledge <br> support from the Juno participating scientist program. The work of RP, DB, <br> JTK, and MP was carried out at the Jet Propulsion Lab, California Institute<br> of Technology, under a contract with the National Aeronautics and Space <br> Administration (80NM0018D0004). Government sponsorship acknowledged. </p>
Linking detected gravity modes to axisymmetric internal magnetic fields
<p>The spectropolarimetric techniques used to characterize surface magnetic fields do not probe the stellar interior, and therefore cannot be used to constrain internal magnetic fields. The most promising way to detect/characterize such fields is to consider their effect on stellar oscillations that probe deep stellar layers, the gravity modes. Conventional asteroseismic modelling ignores any effect of a (internal) magnetic field, however, Prat et al. (2019) developed a formalism that takes the perturbative effect into account for axisymmetric, dipolar, fossil magnetic fields confined to the stellar interior, allowing one to perform magneto-asteroseismic modeling for the first time. Most detected gravity modes in intermediate-mass main-sequence pulsating stars are dipolar, and propagate in the direction of rotation (e.g. Li et al. (2020)). In this talk I will discuss the typical signatures expected in period spacing patterns of dipolar gravity modes, according to the Prat et al. (2019) formalism, for a range of stellar models covering fundamental parameters typical for this mass range. We vary the initial metallicity and mass, the magnetic field strength, the rotation rate, and the degree of mixing in the envelope and near-core regions, throughout the evolution of the main sequence. We find detectable signatures that are significantly different from those due to rotation in the period spacing patterns of gravity modes in terminal age main sequence models if the near-core magnetic field strength is higher than 10^5 Gauss. Such signatures can be used in future magneto-asteroseismic modeling based on photometry from space missions such as Kepler (Borucki et al. (2010)), TESS (targets in the continuous viewing zone; Ricker et al. (2015)) and PLATO (Rauer et al. (2014)).</p>
Global Gravity Field Model GAO2012
<p>Global gravity field model as combined solution from satellite missions and surface gravity field dataset</p>
Refined Bathymetric Prediction based on Feature Extraction of Gravity Field Signals: BATHY-FE
<p>BATHY-FE is a refined global seafloor model derived from extraction of learnt bathymetric signatures inherent in gravity field signals. It spans longitudes -180 ~ 180, and latitudes -80 ~ 80. It contains more short-wavelength seafloor features, and is superior to existing bathymetric models in almost all marine regions, especially at regions close to the poles. It is a GMT readable grid file (i.e., a matrix of seafloor model, and vectors of longitudes and latitudes) with a spatial resolution of 15 arc-seconds.</p><p>Included in this repository are a MATLAB livescript and sample data in which a demonstration of the algorithm over a region north of Alaska and Canada is presented.</p>
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>
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>
Gravity-Driven Grainflows on Barchan Slipfaces: Field Data
<p><strong>Introduction</strong></p> <p>Gravity-driven grainflows, or avalanches, occur in many environments on Earth, Mars, and elsewhere. Many aspects of their characteristic behavior have been revealed through laboratory and field studies. Some findings have been, however, inconclusive or contradictory. We present the results from the first comprehensive field study of 1609, gravity-driven grainflows, with measurements of their magnitude, frequency, area, speed, and related wind speeds and sand transport rates, made on the slipface of a 21 m high barchan dune. Key findings support previous research, with relatively small sample sizes from shorter slipfaces, indicating that grainflow frequency increases with sand transport rate and grainflow speed increases with its area. We also found that grainflow magnitude increases with transport rate, which contradicts a commonly made assertion that grainflow magnitude is independent of the sand transport rate. We found significant scaling differences between laboratory and field data concerning the relationship between grainflow area and speed, with laboratory speeds, per unit area, more than two orders of magnitude faster than speeds found in the field. The implications of this work point to the importance of additional field studies and the hazard of using laboratory data alone for modeling grainflow behavior on terrestrial and extraterrestrial dunes.</p> <p>Here we present the data (the four spreadsheets in Natural grainflow dataset.xlsx) and metadata used to support a manuscript reporting our results and conclusions. The study site was on the slipface of a 20+m high barchan dune near Jericoacoara, Ceará, Brazil, and measurements made on November 7 and 9, 2013. The slipface was scanned repetitively with a Leica C10 terrestrial Laser scanner (TLS) on those two days. Details of TLS data processing and limitations are reported in Pelletier et al. (2015). Wind speeds were measured with Gill-type, DC-generating, cup anemometers. Sand transport rates were measured with sets of mesh-type traps described in Sherman et al. (2014). Grainflows that occurred near the apex of the slipface were video-recorded. More detail is provided for each of the individual spreadsheets to describe the measurement or derivation of data types.</p> <p><strong>Spreadsheet 1: Grainflow Area and Speed</strong></p> <p>Columns A and B report the date and time of each observation. Column C reports grainflow area (m<sup>2</sup>) as derived from Difference of Digital Elevation Models using the methods described in Zhang (2021). Column D reports grainflow speeds (ms<sup>-1</sup>) derived from DEM data (grainflow length) and video recordings (grainflow duration).</p> <p><strong>Spreadsheet 2: Grainflow Frequency and Magnitude</strong></p> <p>Columns A and B report the date and time of each observation. Column C reports the duration of a particular scan. Most scans required 7 minutes to complete and then a new scan began. Column D reports number of grainflows detected in an individual scan. Grainflow frequency, Column E, is normalized to one-minute equivalents. Average grainflow magnitude (m<sup>3</sup>) in Column F is derived from measurements of the volumes of the accretionary tails of all grainflows divided by the number of grainflows occurring during a scan.</p> <p><strong>Spreadsheet 3: Wind Speeds</strong></p> <p>Wind speed was measured with a set of three anemometers installed at 20 m spacing at the center of the top of the barchan slipface. Measurements at each anemometer were made at 1 Hz frequency. Columns A and B report the date and time of each observation. Column C reports average wind speeds (ms<sup>-1</sup>) obtained by block-averaging the 1 Hz data to one minute equivalent, and then averaging those data from the three anemometers.</p> <p><strong>Spreadsheet 4: Sand Transport Rates</strong></p> <p>Columns A and B report the date and time of each observation and Column C reports the duration of deployments of pairs of traps. Average transport rates in Column D were obtained by first averaging the trapped sand masses of the trap pair and then converting that value to an equivalent transport rate (gm<sup>-1</sup>s<sup>-1</sup>) for each sample period. The irregularly spaced point data were linearly interpolated to produce a time series with one-minute increments.</p> <p>References:</p> <p>Pelletier, J. D., Sherman, D. J., Ellis, J. T., Farrell, E. J., Jackson, N. L., Li, B., et al. (2015). Dynamics of sediment storage and release on aeolian dune slip faces: A field study in Jericoacoara, Brazil. <em>Journal of Geophysical Research: Earth Surface</em>, <em>120</em>(9), 1911–1934.</p> <p>Sherman, D. J., Swann, C., & Barron, J. D. (2014). A high-efficiency, low-cost aeolian sand trap. <em>Aeolian Research</em>, <em>13</em>, 31–34.</p> <p>Zhang, P. (2021). An algorithm for objective analysis of grainflow morphology, <em>Aeolian Research, 50, </em>100686.</p>
Model and the related code for gravity field determination from the third invariant of the GOCE gravity gradient tensors: I3GG V1.0th
<p>The model and the related code for gravity field determination from the third invariant of the GOCE gravity gradient tensors: I3GG V1.0th</p>
SOUTHTRAC-GW: An airborne field campaign to explore gravity wave 1 dynamics at the world's strongest hotspot
<p>This contains the information on the zonal winds measured by the meteor radar at Tierra del Fuego in Argentina during July - December, 2019. Please contact the author for more information.</p>
Is the lunar magnetic field correlated with gravity or topography?
<p>Supplementary data to the article</p> <p> Gong, S. and Wieczorek, M. (2020) Is the lunar magnetic field correlated<br> with gravity or topography? Journal of Geophysical Research: Planets.</p> <p>This archive contains the Bouguer gravity model used in the analyses of the<br> above cited manuscript, as well as the data files to reproduce Figures 2-3 and<br> Figures S1-S3. For the correlation results, the bandwidth and angular radius of<br> the window were 26 and 10 degrees, respectively, which yields a concentration<br> factor that is better than 99%.</p> <p><br> FILE DESCRIPTIONS</p> <p>34_12_3220_900_80_misfit.sh</p> <p> This file contains the spherical harmonic coefficients of the Bouguer<br> gravity model up to degree and order 900. The two values in the first<br> row correspond to the reference radius of the model in km and the<br> constant GM in km^3/s^-2. To generate this model, all known gravitational<br> contributions from the crust were removed from the free-air gravity,<br> including surface relief, lateral variations in crustal density, and<br> crustal thickness variations. The crustal thickness model is from<br> Wieczorek et al. (2013), which has an average thickness of 34 km, a<br> constant crustal porosity of 12%, and a mantle density of 3200 kg/m^3.</p> <p><br> mc_total_10_26_1_surface.dat</p> <p> Data used to generate the lower panel of Figure 2. This file contains the<br> 95% confidence limits of the average correlation from the Monte Carlo<br> simulations which were performed every 30 degrees in both longitude and<br> latitude. The first two columns correspond to the latitude and longitude,<br> and the third to fifth columns correspond to the 95% confidence limits by<br> using topography, total free-air gravity, and total Bouger gravity,<br> respectively.</p> <p><br> spec_10_26_1_surface.dat</p> <p> Data used to generate Figure 3. Correlation results between total magnetic<br> field and topography, total free-air gravity, and total Bouguer gravity at<br> the surface. The first two columns correspond to the latitude and longitude,<br> and the third to fifth columns correspond to the ratio between the average<br> correlation and its 95% confidence limits by using topography, total<br> free-air gravity, and total Bouger gravity, respectively. If the value is<br> equal to or greater than 1, this indicates that the total magnetic field is<br> positively correlated with the testing field (topography, total free-air<br> gravity, or total Bouguer gravity); If the value is equal to or less than<br> -1, this indicates that the total magnetic field is negatively correlated<br> with the testing field.</p> <p><br> spec_10_26_1_surface_4lwin.dat</p> <p> Data used to generate Figure S1. Correlation results calculated at the<br> surface by removing the first 4*lwin degrees.</p> <p><br> spec_10_26_1_30km.dat</p> <p> Data used to generate Figure S2. Correlation results calculated at 30 km<br> altitude.</p> <p><br> spec_10_26_1_surface_3sigma.dat</p> <p> Data used to generate Figure S3. Correlation results calculated at the<br> surface by using the 99% confidence limits.</p>
Models of the gravity field of Jupiter using Slepian and Spherical Harmonic functions
<p>This dataset contains gravitational models using Slepian and spherical harmonic functions for Jupiter, derived from the high-precision Doppler measurements collected by NASA's Juno mission to Jupiter from periapsis 1 to 17. This dataset is intended to be paired with the following article submitted to the Journal of Geophysical Research-Planets:</p> <p>Marzia Parisi, Eli Galanti, William M. Folkner, Yohai Kaspi and Dustin R. Buccino. "Resolving the latitudinal short-scale gravity field of Jupiter using Slepian functions", Journal of Geophysical Research, 2020 (submitted Feb 2020).<br> <br> This dataset of gravity field models was produced in part at the Jet Propulsion Laboratory by the Juno Gravity Instrument Team and Interiors Working Group.</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.