Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars

Supporting data for peer-reviewed publication entitled: 'Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars', published in A&A. For the purpose of open access, the authors have applied a CC BY licence to the author accepted manuscript version and made it publicly available: <a href="https://arxiv.org/abs/2410.12726">https://arxiv.org/abs/2410.12726</a> Evolutionary models and stability window calculations courtesy of Jermyn et al. 2022 (DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ac4e89">10.3847/1538-4357/ac4e89</a>) are publicly available via: <a href="https://github.com/adamjermyn/conv_trends">https://github.com/adamjermyn/conv_trends</a> TESS full-frame image data are publicly available from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute (STScI): <a href="https://archive.stsci.edu/missions-and-data/tess">https://archive.stsci.edu/missions-and-data/tess</a> TESS light curves (provided in this repository) were extracted using the publicly available tglc (Han & Brandt 2023; DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-3881/acaaa7">10.3847/1538-3881/acaaa7</a>) software package: <a href="https://github.com/TeHanHunter/TESS_Gaia_Light_Curve">https://github.com/TeHanHunter/TESS_Gaia_Light_Curve </a> SLF variability parameters (provided in this repository; cf. Tables 1 and 2 of the paper) were obtained using GP regression with the publicly available celerite2 (Foreman-Mackey et al. 2017; DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-3881/aa9332">10.3847/1538-3881/aa9332</a>) software package: <a href="https://celerite2.readthedocs.io/en/latest/">https://celerite2.readthedocs.io/en/latest/</a>  and confidence intervals were obtained using the publicly available pymc3 (Salvatier et al. 2016; <a href="https://doi.org/10.7717/peerj-cs.55">https://doi.org/10.7717/peerj-cs.55</a>) software package: <a href="https://github.com/pymc-devs/pymc">https://github.com/pymc-devs/pymc</a> This research was supported in part by the National Science Foundation (NSF) under Grant Number NSF PHY-1748958; the Research Foundation Flanders (FWO) with grant agreement numbers 1286521N, 11F7120N, and V411621N; UK Research and Innovation (UKRI) in the form of a Frontier Research grant under the UK government's ERC Horizon Europe funding guarantee (SYMPHONY; grant number: EP/Y031059/1); a Royal Society University Research Fellowship (URF; grant number: URF\R1\231631); and the KU Leuven Research Council (grant number C16/18/005: PARADISE).

opencc-by-4.0Aug 2021View details →

Dataset for: "Modeling the Hα Emission Surrounding Spica using the Lyman Continuum from a Gravity-darkened Central Star"

Summary: This deposit supplements the manuscript, "Modeling the Hα Emission Surrounding Spica using the Lyman Continuum from a Gravity-darkened Central Star", 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:       ReadMe                                          Documentation file     example_cloudy_input_file.txt        Cloudy script     spica_i=100_V5_solar.ascii            Spica input stellar spectral energy distribution     spica_i=110_V5_solar.ascii            Spica input stellar spectral energy distribution     spica_i=116_V5_solar.ascii            Spica input stellar spectral energy distribution     spica_i=120_V5_solar.ascii            Spica input stellar spectral energy distribution     spica_i=130_V5_solar.ascii            Spica input stellar spectral energy distribution     spica_i=140_V5_solar.ascii            Spica input stellar spectral energy distribution     spica_i=150_V5_solar.ascii            Spica input stellar spectral energy distribution     spica_i=160_V5_solar.ascii            Spica input stellar spectral energy distribution     spica_i=170_V5_solar.ascii            Spica input stellar spectral energy distribution     spica_i=180_V5_solar.ascii            Spica input stellar spectral energy distribution     spica_i=90_V5_solar.ascii              Spica input stellar spectral energy distribution   System requirements: The input script and SED files correspond to Cloudy code version 17.02:         Codebase: <a href="https://trac.nublado.org/">https://trac.nublado.org/</a>         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>)   Additional documentation provided in the ReadMe file.

opencc-by-4.0Nov 2022View details →

Data for paper "An adaptive nonlinear iterative method for predicting seafloor topography from altimetry-derived gravity data"

LM is the linear inversion seafloor topography model NLM is the nonlinear inversion seafloor topography model PM is the prior seafloor topography model

opencc-by-4.0Jan 2023View details →

The propagation of gravity waves in Titan's stratosphere

The model code and figure data of our article "The propagation of gravity waves in Titan's stratosphere".  <a href="https://zenodo.org/api/files/a647c3b7-0796-4a3a-96bc-779957496aad/GW-simulation-program.txt">GW-simulation-program.txt</a> is the Mathematica code used to simulate gravity wave propagation. <a href="https://zenodo.org/api/files/a647c3b7-0796-4a3a-96bc-779957496aad/simulations-nowind.rar">simulations-nowind.rar</a> and <a href="https://zenodo.org/api/files/a647c3b7-0796-4a3a-96bc-779957496aad/simulations-wind.rar">simulations-wind.rar</a> are the simulation results for gravity wave with its horizontal propagation direction perpendicular or not perpendicular to the background wind, respectively. <a href="https://zenodo.org/api/files/a647c3b7-0796-4a3a-96bc-779957496aad/FigureData.rar">FigureData.rar</a> contains several data files for figures in our article.

opencc-by-4.0Jul 2023View details →

Forward operator (gravity, Stromboli) for https://doi.org/10.1137/21M1445028

This is the forward operator for the Stromboli test case of the article https://doi.org/10.1137/21M1445028

opencc-by-4.0Jul 2023View details →

Pertubation Profiles Dataset used for "Convection-generated gravity waves in the tropical lower stratosphere from Aeolus wind profiling and ERA5 reanalysis"

These are the perturbation profiles, from 5km to 29.5km, with a 500m grid. In the study, we picked up the data between tropopause-1km to 22km, which was then squared, smoothed, and averaged into one value. We used a 14 points moving average for the smoothing. The data is from 2018-09 to 2022-09, based on the Aeolus L2B Rayleigh clear wind, using only quality flag 1 data. Please email me at mathieu.ratynski@estaca.eu if you're interested in the 100m resolution version, used in the final version of the manuscript.

opencc-by-4.0Dec 2019View details →

The dataset from a submitted journal entitled "Characterization of the Mamasa earthquake source in West Sulawesi based on the earthquake relocation data, gravity data, and coulomb stress change of Palu earthquake series"Dataset for paper

This dataset consists of four files, namely: 1. Coulomb Stress Input file. This data is input data for Coulomb 3.3 software 2. Double Couple Percentage. This table is used for the Spatio-temporal Compensated Linear Vector Dipole (CLVD) analysis 3. Gravity data. This data consists of coordinates, altitude, and Complete Bouguer Anomaly. 4. Residual comparison of before and after the relocation. This table is to ensure that our relocation is successful

opencc-by-4.0May 2020View details →

Detecting axisymmetric magnetic fields using gravity modes in intermediate-mass stars

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. 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. 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 \(\mathrm{M}_{\odot}\) . We assess the influence of fundamental stellar parameters on the magnitude of pulsation mode frequency shifts. 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. 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 105 G. The predicted signatures differ appreciably from those due to rotation. 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. <blockquote> The publication date is the date of acceptance. </blockquote> 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.

opencc-by-4.0Jul 2020View details →

Vertical Wind and Temperature Gravity Wave Perturbations Derived from Na Lidar Observations

The gravity wave perturbations associated with vertical wind and temperature in the mesopause region for heat flux calculations.

opencc-by-4.0Oct 2023View details →

Seamounts Gravity Accompanying Material

Gravity effect of seamount eruptions (mass change), as spherical harmonics coefficients. Accompanying data for: Braitenberg, C., Pastorutti, A. Detectability of Seamount Eruptions Through a Quantum Technology Gravity Mission MOCAST+: Hunga Tonga, Fani Maoré and Other Smaller Eruptions. Surv Geophys (2024). doi:<a href="https://doi.org/10.1007/s10712-024-09839-7">10.1007/s10712-024-09839-7</a> <h4>Data format</h4> The SH expansions are provided in the <a href="http://icgem.gfz-potsdam.de/ICGEM-Format-2011.pdf">ICGEM gfc format</a>. The "model_name" field is populated with three key=value pairs: name, lon, and lat of the modelled seamount.

opencc-by-4.0Nov 2023View details →

Gravity, Free-Air and Bouguer Anomaly Data in the Ivrea-Verbano Zone (Western Alps, Italy)

Gravity dataset collected in the Ivrea-Verbano Zone (IVZ, Western Alps, Italy). The data was collected in the frame of a gravity-based investigation and modelling of the Ivrea Geophysical Body. For citation and further details on the work see Scarponi et al. (2020, GJI): <a href="https://doi.org/10.1093/gji/ggaa263">https://doi.org/10.1093/gji/ggaa263</a>The file contains the gravity data collected in the IVZ region, including free-air anomaly and Bouguer gravity anomaly (in mGal).Longitude, Latitude coordinates are in degrees, elevation in meters.Uncertainty on the final gravity data products and gravity data is 1 mGal.---Data collection, as well as the associated research, were supported by the Swiss National Science Foundation (SNF) (grant numbers PP00P2_157627 and PP00P2_187199).

opencc-by-4.0Feb 2024View details →

ERA5 overviews complementing temperature measurements of ground-based Rayleigh lidars for the investigation of gravity waves generated by moving sources

ERA5 overviews to associate stratospheric gravity waves in temperature measurements from vertically staring (zenith-pointing) ground-based Rayleigh lidars with atmospheric processes. Animations are for a virtual lidar location over the Southern Ocean during research flight RF25 of the DEEPWAVE campaign (July 17 to 19, 2014) and for the location of the COmpact Rayleigh Autonomous Lidar (CORAL) in the lee of the southern Andes. Here, the first overview is for the CORAL measurement from June 22 to 23, 2018. The second one is for the nightly measurements between August 7 and 9, 2020. (a) and (b) emulate the measurement of a vertically staring ground-based lidar and show temperature perturbations after subtracting a temporal running mean of 12h (a) and the mean absolute temperature profile (b). Panels (c) and (d) are vertical sections of stratospheric 𝑇′ along sectors of the latitude circle (c) and meridian (d) of the virtual lidar location. (e) and (f) are corresponding vertical sections of thermal stability 𝑁2 (10−4 s−2, color-coded), potential temperature (K, thin grey lines), and potential vorticity (1, 2, 4 PVU: black, 2 PVU: green). Thin black lines in the vertical sections are zonal (d, f) and meridional (c, e) wind components (solid: positive, dashed: negative). Panel (g) is a horizontal section of the height of the 2 PVU surface (km, color-coded), geopotential height (m, solid lines) and wind barbs at the 850 hPa level. The black vertical line in (a) marks the time for (c)-(g) and dashed lines in (c)-(g) highlight the location of the virtual lidar and profiles in (a) and (b). The provided NETCDF files contain the corresponding CORAL temperature measurements for the two periods with CORAL measurements in 2018 and 2020.

opencc-by-4.0Nov 2023View details →

SDUST2020MGCR: a global marine gravity change rate model determined from multi-satellite altimeter data

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).

Spherical harmonic models of the gravity field of Titan

This archive contains previously published models of the gravitational field of Saturn's moon Titan. <ul> <li>Durante2019.sh</li> </ul> All models make use of unnormalized spherical harmonic functions that exclude the Condon-Shortley phase factor of (-1)^m.

Spherical harmonic models of the gravity field of Enceladus

This archive contains previously published models of the gravitational field of Saturn's moon Enceladus. <ul> <li>Iess2014.sh (SOL1)</li> <li>Park2024.sh (Case 2)</li> </ul> All models make use of unnormalized spherical harmonic functions that exclude the Condon-Shortley phase factor of (-1)^m.

Spherical harmonic models of the gravity field of Neptune

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). * Jacobson2009.sh

Gravity core XRF data from the Porcupine Abyssal Plain

X-ray Fluorescence (XRF) data from three gravity cores obtained during NOC expedition JC231 (2022) for the "Time-series studies at the Porcupine Abyssal Plain Sustained Observatory". These three gravity cores (GC050; GC073; GC076) were collected from different sites at the Porcupine Abyssal Plain (AESA Hill; AESA North Plain; PAP Central respectively).  <table> <tbody> <tr> <td>Ship/Platform</td> <td>Cruise Identifier</td> <td>Sample Identifier</td> <td>Site</td> <td>Number of Sections</td> <td>Section Identifier</td> <td>Date Sample Collected</td> <td>Decimal Latitude</td> <td>Decimal Longitude</td> <td>Water Depth (m)</td> <td>Sampling Device</td> <td>Storage Method</td> <td>Core Length (cm)</td> <td>Core Diameter (cm)</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC050</td> <td>AESA Hill</td> <td>2</td> <td>Section 1</td> <td>09/05/2022</td> <td>48 59.103</td> <td>16 33.17</td> <td>4795</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC050</td> <td>AESA Hill</td> <td>2</td> <td>Section 2</td> <td>09/05/2022</td> <td>48 59.103</td> <td>16 33.17</td> <td>4795</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>50</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC073</td> <td>AESA North Plain</td> <td>3</td> <td>Section 1</td> <td>12/05/2022</td> <td>49 0.657</td> <td>16 33.221</td> <td>4846</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC073</td> <td>AESA North Plain</td> <td>3</td> <td>Section 2</td> <td>12/05/2022</td> <td>49 0.657</td> <td>16 33.221</td> <td>4846</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC073</td> <td>AESA North Plain</td> <td>3</td> <td>Section 3</td> <td>12/05/2022</td> <td>49 0.657</td> <td>16 33.221</td> <td>4846</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC076</td> <td>PAP Central </td> <td>3</td> <td>Section 1</td> <td>12/05/2022</td> <td>48 50.095</td> <td>16 31.331</td> <td>4843</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC076</td> <td>PAP Central </td> <td>3</td> <td>Section 2</td> <td>12/05/2022</td> <td>48 50.095</td> <td>16 31.331</td> <td>4843</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC076</td> <td>PAP Central </td> <td>3</td> <td>Section 3</td> <td>12/05/2022</td> <td>48 50.095</td> <td>16 31.331</td> <td>4843</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>80</td> <td>6.5</td> </tr> </tbody> </table>

opencc-by-4.0Jul 2021View details →

Crustal structure of the Volgo-Uralian subcraton revealed by inverse and forward gravity modeling [dataset]

This collection contains data that were used to build a 3D crustal model of the Volgo-Uralian subcraton through inverse and forward gravity modeling. The dataset is subdivided into two folders: (1) Gravity field inversion; (2) Forward gravity modeling.

opencc-by-4.0Oct 2021View details →

Probabilistic linear inversion of satellite gravity gradient data applied to the northeast Atlantic

% MATLAB scripts to calculate and plot figures as in manuscript by % % Minakov, A., & Gaina, C. (2021). % Probabilistic linear inversion of satellite gravity gradient data applied % to the northeast Atlantic. Journal of Geophysical Research: Solid Earth, % 126, e2021JB021854. https://doi.org/10.1029/2021JB021854 %  % Last modified by alexamin@uio.no, 26/11/2021 % % version v1.1 %  % Contents of arhcive % /data  contains requiried and generated datasets  % /fig   folder for output figures  % /plot  scripts to produce figures  % /tools additional matlab tools and routines % Dataset in ..data/GOCE_NEATLANTIC is structure containing the full model %  %      Cm: [6670×6670 double] posterior model covariance matrix %       m: [29×23×10 double] mean denstity perturbation model %      Cd: [667×667 double] data covariance matrix %       d: [29×23 double] data vector (Trr) %       r: [1×10 double] distance %     lat: [29×1 double] latitute %     lon: [23×1 double] longitude % % Run  /plot/fig_results.m to produce all figures  % % Some scripts require GMT (Wessel et al. 2019) and SHBUNDLE (Sneeuw et al. 2018) software to be installed % and corresponding folders must be added to the matlab search path. % Also ScientificColorMaps7 by F. Crameri (2021) maybe required and have been included in the archive.