Supplementary Data for "Sedimentary conditions drive modern pyrite burial flux to exceed oxidation"

<h2>Supplementary Data for "Sedimentary conditions drive modern pyrite burial flux to exceed oxidation"</h2> <p><strong><br>The Supplementary Data</strong> is divided into the following folders:</p> <ol> <li> <p><strong>Model Validation</strong><br>This folder contains a collection of downcore profiles used for model validation. The profiles were compiled from various datasets to assess the accuracy and performance of the model. This Folder also contains a Validation subfolder, where model-data fits for all profiles are shown.&nbsp;</p> </li> <li> <p><strong>Raw Data</strong><br>This folder includes all the original global datasets utilized in the study. These data were compiled from multiple sources and serve as the foundational input for the analysis presented in the publication.</p> </li> <li> <p><strong>Processed Data</strong><br>This folder contains the processed datasets, which include global products derived from the raw data. Additionally, it contains validation data that has been extracted and compiled for use in the study. Subfolders provide gridded global data used as model inputs and the corresponding gridded global outputs generated by the model. All gridded data is provided as comma-delimited <code>.txt</code> files.</p> </li> </ol> <p><strong>Data Structure and Files</strong></p> <p>Within the processed data, you will find the following key files:</p> <ul> <li> <p><strong><code>latitude.txt</code></strong><br>Contains gridded latitude values.</p> </li> <li> <p><strong><code>longitude.txt</code></strong><br>Contains gridded longitude values.</p> </li> <li> <p><strong>Variable Files (e.g., <code>pyrite_burial_rate.txt</code>):</strong><br>Each variable you wish to work with or display is stored in its own file.</p> </li> <li> <p><strong>Output Grids:&nbsp;<br></strong>pyrite burial rate is <strong><code>pyrite_burial_rate.txt</code>&nbsp;</strong>in g cm⁻&sup2; y⁻&sup1;<br>pyrite isotopic composition is <strong><code>delta_pyrite.txt</code>&nbsp;</strong>in &permil; (permil)<br>pyrite formation depth is <strong><code>z_max.txt</code>&nbsp;</strong>in cm <br>total depth-integrated pyrite content is <strong><code>total_mols_pyrite.txt</code></strong> in mol cm⁻&sup2;.</p> </li> </ul> <p>When working with the data, you need to combine the <code>lat</code> and <code>long</code> grids with the variable file of interest to visualize or analyze the dataset.</p> <h2>Working with the Data in MATLAB</h2> <p>If you are using MATLAB, you can display the data using the <code>geoshow</code> function. For example, to display the pyrite burial rate:</p> <ol> <li> <p><strong>Read the Data:</strong></p> <div> <div><code>latitude = read('latitude.txt'); </code></div> <div><code>longitude = read('longitude.txt'); </code></div> <div><code>data = read('pyrite_burial_rate.txt'); </code></div> </div> </li> <li> <p><strong>Display the Data on a World Map:</strong></p> <div> <div>&nbsp;</div> <div><code>worldmap world </code></div> <div><code>geoshow(latitude, longitude, data, 'DisplayType', 'texturemap')</code></div> <div><code>set(gca,'ColorScale','log')</code></div> <div>&nbsp;</div> <div>Alternatively, you can use the provided&nbsp;<code>plot_global_data.m</code> function located in the <code>/plotting_functions</code>&nbsp;<a href="https://zenodo.org/uploads/14808279">Model</a> folder.&nbsp;</div> <div>&nbsp;</div> </div> </li> </ol> <h2>Working with the Data in Python</h2> <p>If you are using Python, you can work with the gridded data as follows:<code>&nbsp;</code></p> <ol> <li> <p><strong>Read the Data:</strong></p> <div> <div><code>import pandas as pd </code></div> <div><code>import numpy as np </code></div> <div><code>import matplotlib.pyplot as plt </code></div> <div><code>import cartopy.crs as ccrs</code></div> <div><code>from matplotlib.colors import LogNorm</code></div> <div>&nbsp;</div> <div><code>latitude = pd.read_csv('GlobalGridsOutput/latitude.txt', delimiter=',', header=None).values </code></div> <div><code>longitude = pd.read_csv('GlobalGridsOutput/longitude.txt', delimiter=',', header=None).values </code></div> <div><code>data = pd.read_csv('GlobalGridsOutput/pyrite_burial_rate.txt', delimiter=',', header=None).values </code></div> </div> <p>&nbsp;</p> </li> <li> <p><strong>Plotting the Data:</strong><br>You can plot the data on a world map using <code>cartopy</code>:</p> <div> <div>&nbsp;</div> <div><code>fig = plt.figure(figsize=(10, 5)) </code></div> <div><code>ax = plt.axes(projection=ccrs.PlateCarree()) </code></div> <div><code>ax.coastlines() </code></div> <div><code>mesh = ax.pcolormesh(longitude, latitude, data, transform=ccrs.PlateCarree(), cmap='viridis') </code></div> <div><code>plt.colorbar(mesh, ax=ax, orientation='vertical', label='Pyrite Burial Rate') </code></div> <div><code>&nbsp;plt.show() </code></div> </div> </li> </ol> <div> <div>&nbsp;</div> </div> <p><br><br></p>