Replication Data for: Rayleigh invariance allows the estimation of effective CO2 fluxes due to convective dissolution into water-filled fractures

This dataset features both data and code related to the research article titled "Rayleigh Invariance Enables Estimation of Effective CO2 Fluxes Resulting from Convective Dissolution in Water-Filled Fractures." It includes raw data packaged in tarball format, including Python scripts used to derive the results presented in the publication. High-resolution raw data for contour plots is available upon request.<br>
<div class="box">
<div class="number">1</div>
<strong>Download the Dataset:</strong>
<ul>
<li>Download the dataset file using Access Dataset.</li>
<li>Ensure you have sufficient disk space available for storing and processing the dataset.</li>
</ul>
</div>
<div class="box">
<div class="number">2</div>
<strong>Extract the Dataset:</strong>
<ul>
<li>Once the dataset file is downloaded, extract its contents.</li>
<li>The dataset is compressed in a tar.xz format. Use appropriate tools to extract it. For example, in Linux, you can use the following command:
<pre><code>tar -xf Publication_CCS.tar.xz</code></pre>
<pre><code>tar -xf Publication_Karst.tar.xz</code></pre>
<pre><code>tar -xf Validation_Sim.tar.xz</code></pre>
</li>
<li>This will create a directory containing the dataset files.</li>
</ul>
</div>

<div class="box">
<div class="number">3</div>
<strong>Install Required Python Packages:</strong>
<ul>
<li>Before running any code, ensure you have the necessary Python (version 3.10 tested) packages installed. The required packages and their versions are listed in the <code>requirements.txt</code> file.</li>
<li>You can install the required packages using pip:
<pre><code>pip install -r requirements.txt</code></pre>
</li>
</ul>
</div>

<div class="box">
<div class="number">4</div>
<strong>Run the Post Processing Script:</strong>
<ul>
<li>After extracting the dataset and installing the required Python packages, you can run the provided post processing script.</li>
<li>The post processing script (<code>post_process.py</code>) is designed to replicate all the plots from a publication based on the dataset.</li>
<li>Execute the script using Python:
<pre><code>python3 post_process.py</code></pre>
</li>
<li>This script will generate the plots and output them to the specified directory.</li>
</ul>
</div>

<div class="box">
<div class="number">5</div>
<strong>Explore and Analyze:</strong>
<ul>
<li>Once the script has completed running, you can explore the generated plots to gain insights from the dataset.</li>
<li>Feel free to modify the script or use the dataset in your own analysis and experiments.</li>
<li>High-resolution data, such as the vtu's for contour plots is available upon request; please feel free to reach out if needed.</li>
</ul>
</div>

<div class="box">
<div class="number">6</div>
<strong>Small Grid Study:</strong>
<ul>
<li>There is a tarball for the data that was generated to study the grid used in the related publication.</li>
<pre><code>tar -xf Publication_CCS.tar.xz</code></pre>
<li>If you unpack the tarball and have the requirements from above installed, you can use the python script to generate the plots.</li>
</ul>
</div>

<div class="box">
<div class="number">7</div>
<strong>Citation:</strong>
<ul>
<li>If you use this dataset in your research or publication, please cite the original source appropriately to give credit to the authors and contributors.</li>
</ul>
</div>