How to assess similarities and differences between mantle circulation models and Earth using disparate independent observations: Data and Analysis
<p>Dataset includes simulation output produced by a TERRA simulation for `How to assess similarities and differences between mantle circulation models and Earth using disparate independent observations'. </p> <p> </p> <h3><strong>Description of data file contents</strong></h3> <ul> <li><strong>NC*comp.tar.gz</strong> - compressed archives containing NetCDF files (file-per-process) with TERRA grid data including temperature, velocity, interpolated bulk composition, denisty, and voscosity fields. Can be read using <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>. *dump number</li> <li><strong>NC_seis_037.tar.gz</strong> - compressed archive containing NetCDF files (file-per-process) with predicted seismic properties at the resolution of the TERRA grid generated from the present day state of the simulated mantle, including elastic and anelastic Vs and Vp, bulk sound velocity, and predicted density from mineral phyiscs tables. Can be read using <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>.</li> <li><strong>NC_hpes_037.tar.gz</strong> - compressed archive containing NetCDF files (file-per-process) with interpolated abundances at the resolution of the TERRA grid for isotopes including the heat-producing elements ^40^K, ^232^Th, ^235^U and ^238^U. Can be read using <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>.</li> <li><strong>P_files_037.tar.gz</strong> - compressed archive of TERRA P-files (particle files).</li> <li><strong>C_files_037.tar.gz</strong> - compressed archive of TERRA C-files (grid state files) - together with the P-files describe the full present day state of the simulation. </li> <li><strong>seis_filtered_037.tar.gz</strong> - compressed archive (file-per-layer) with reparameterised and seismically filtered (against S40RTS) present day Vs field.</li> <li><strong>seis_tables.tar.gz</strong> - compressed archive contianing lookup tables of seismic properties for the 3 principal lithologies assumed in the TERRA simulation (harzburgite, lherzolite and basaltic crust).</li> <li><strong>density_037.sph</strong> - Spherical harmonic coefficients for the density field in format to be read by the <a title="propagator" href="https://zenodo.org/records/12696774" target="_blank" rel="noopener">propagator matrix code.</a></li> <li><strong>plumes.pkl, ridges.pkl</strong> - Files containing tracer particle information for particles associated with plumes and ridges. </li> <li><strong>plumes_ridges.py</strong> - Python script containing example code for reading and plotting plumes.pkl and ridges.pkl files. </li> <li><strong>ptcls_rdgs_plms.py, interrogate_particles.py</strong> - Python script and module containing required functions for carrying out post processing routine generating the plumes.pkl and ridges.pkl files. Requires <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>.</li> <li><strong>hst.dat </strong>- Time series of key simulation properties including mantle temperature profile used for calcualting CMB heat flux. </li> <li><strong>terra, interra</strong> - TERRA executable and input parameter file.</li> <li><strong>pyflowng.zip</strong> - Compressed directory containing version of the `pyflowng` code used in this work.</li> <li><strong>mode_splitting_methods.zip</strong> - Compressed directory contianing synthetic splitting function predictions and maps.</li> </ul> <p> </p> <h3><strong>Dump Numbers</strong></h3> <p>Below is a table of dump numbers (final three digits of file names) and the corresponding model times.</p> <table> <tbody> <tr> <td><strong>Dump number </strong></td> <td><strong>Model time (Ma)</strong></td> </tr> <tr> <td>037</td> <td>0 (present day)</td> </tr> <tr> <td>027</td> <td>10</td> </tr> <tr> <td>026</td> <td>20</td> </tr> <tr> <td>025</td> <td>30</td> </tr> <tr> <td>024</td> <td>40</td> </tr> <tr> <td>023</td> <td>50</td> </tr> <tr> <td>022</td> <td>60</td> </tr> <tr> <td>021</td> <td>70 </td> </tr> <tr> <td>020</td> <td>80</td> </tr> <tr> <td>019</td> <td>90</td> </tr> <tr> <td>018</td> <td>100</td> </tr> </tbody> </table>
ShareScore
36/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 0
- Engagement
- 8
Topics
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