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Emerging Clostridioides difficile ribotypes have divergent metabolic phenotypes

<p>Dataset of multi-well plate reader files related to the analysis of the growth of&nbsp;<em>C. difficile</em> isolates on differnet carbon substrates.&nbsp;</p> <p><strong>Data Collection.&nbsp;</strong>This<strong>&nbsp;</strong>dataset accompanies an <em>mSystems</em>&nbsp;article which is available at <a href="https://doi.org/10.1128/msystems.01075-24">https://doi.org/10.1128/msystems.01075-24</a>. The "Materials and Methods" section in this article fully describes the biological nature of these samples and how the samples were processed and analyzed.&nbsp;&nbsp;</p> <p><strong>Repository Content.&nbsp;</strong>The <em>amiga</em> folders contains both the raw data (see&nbsp;<em>data</em> and <em>mapping </em>sub-folders), intermediate outpus (see&nbsp;<em>dervied</em> sub-folders), and final outputs (see&nbsp;<em>figures</em> and <em>summary</em> sub-folders). Each&nbsp;<em>amiga&nbsp;</em>folder corresponds to a single working directory analyzed by the&nbsp;<a href="https://github.com/firasmidani/amiga"><em>AMiGA</em></a> software. For the <em>amiga-biolog</em> directory, the <em>notes</em> sub-folder also includes text files related to flagging plates and wells for quality issues.</p> <p><strong>Data Organization. </strong>Growth plate data are organized by the following experiments.&nbsp;</p> <ul> <li>biolog</li> <li>validation</li> <li>ribotype-255</li> <li>clade-5</li> <li>yeast-extract-biolog</li> <li>yeast-extract-validation</li> </ul> <p><strong>Data Analysis.&nbsp;</strong>Code used for manipulating and analyzing these samples is also publicly available on GitHub (<a href="https://github.com/firasmidani/cdiff-biolog-growth">https://github.com/firasmidani/cdiff-biolog-growth</a>) under the GNU GPL-3.0 license. The scripts in "analyze-code" can be used to analyze all data and the scripts in the "generate-figures" folder can be used to reproduce all figures included in the manuscript. See the GitHub repository for instructions on how to do so.&nbsp;</p> <p>&nbsp;</p>

ShareScore

36/100

Overall dataset sharing score

Score breakdown

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

Stewardship
4
Harmonization
4
Access
16
Reuse readiness
8
Engagement
4