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Code and data for molecular dating benchmark based on real and simulated Primates gene trees

<p>Benchmark of molecular clock dating applied to single gene trees separately, whose results are described in&nbsp;&ldquo;Factors influencing the accuracy and precision in dating single gene trees&rdquo; by&nbsp;Guillaume Louvel and Hugues Roest Crollius.</p> <ol> <li>Real Primates gene trees are analyzed to identify&nbsp;what characteristics of a gene tree are related to the precision of dating;&nbsp;</li> <li>alignments are also simulated on the&nbsp;tree of Primates to&nbsp;measure the accuracy of dating under controlled parameters such as the degree of rate variation and the length of the alignment.</li> </ol> <p><strong>Content</strong></p> <p><code>Louvel_Accuracy-dating_results_2024.tar.gz</code>:<br>&nbsp;&nbsp;&nbsp; - <code>notebook/</code>: statistical analyses in Python;<br>&nbsp;&nbsp;&nbsp; - <code>outputs/</code>: html reports with figures/tables resulting from the analysis;<br>&nbsp;&nbsp;&nbsp; - <code>lib/</code>: required libraries.<br>&nbsp;&nbsp;&nbsp; - <code>data/</code>: intermediate data needed for the final analysis (dates, gene tree features);</p> <p><code>Louvel_Accuracy-dating_dating-source_2024.tar.gz</code>:<br>&nbsp;&nbsp;&nbsp; - <code>dating-source/</code>: input data and config files necessary to reproduce <code>data</code>;</p> <p><code>Louvel_Accuracy-dating_raw-data-preparation_2024.tar.gz</code>:<br>&nbsp;&nbsp;&nbsp; - <code>raw-data-preparation/</code>: raw data and steps to produce&nbsp;<code>dating-source</code>.<br><br><strong>Requirements</strong><br><br>This code requires the libraries developed in the lab for this project,<br>available at <a href="https://github.com/DyogenIBENS/">github.com/DyogenIBENS/</a>, but also included here in <code>lib/</code>.<br><br>- <a href="https://github.com/DyogenIBENS/Phylorgs">Phylorgs</a><br>- <a href="https://github.com/DyogenIBENS/LibsDyogen_py3">LibsDyogen_py3</a><br>- <a href="https://github.com/DyogenIBENS/ToolsDyogen_py3">ToolsDyogen_py3</a>.</p>

ShareScore

40/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
12
Harmonization
4
Access
16
Reuse readiness
8
Engagement
0

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