DOI: 10.5281/zenodo.13890800
title: Supplementary Data for "Sedimentary conditions drive modern pyrite burial flux to exceed oxidation"

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

The Supplementary Data is divided into the following folders:





Model ValidationThis 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. 




Raw DataThis 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.




Processed DataThis 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 .txt files.



Data Structure and Files

Within the processed data, you will find the following key files:





latitude.txtContains gridded latitude values.




longitude.txtContains gridded longitude values.




Variable Files (e.g., pyrite_burial_rate.txt):Each variable you wish to work with or display is stored in its own file.




Output Grids: pyrite burial rate is pyrite_burial_rate.txt in g cm⁻² y⁻¹pyrite isotopic composition is delta_pyrite.txt in ‰ (permil)pyrite formation depth is z_max.txt in cm total depth-integrated pyrite content is total_mols_pyrite.txt in mol cm⁻².



When working with the data, you need to combine the lat and long grids with the variable file of interest to visualize or analyze the dataset.

Working with the Data in MATLAB

If you are using MATLAB, you can display the data using the geoshow function. For example, to display the pyrite burial rate:





Read the Data:



latitude = read('latitude.txt'); 

longitude = read('longitude.txt');


data = read('pyrite_burial_rate.txt');






Display the Data on a World Map:



 

worldmap world


geoshow(latitude, longitude, data, 'DisplayType', 'texturemap')

set(gca,'ColorScale','log')

 

Alternatively, you can use the provided plot_global_data.m function located in the /plotting_functions Model folder. 

 




Working with the Data in Python

If you are using Python, you can work with the gridded data as follows: 





Read the Data:



import pandas as pd


import numpy as np


import matplotlib.pyplot as plt


import cartopy.crs as ccrs

from matplotlib.colors import LogNorm

 

latitude = pd.read_csv('GlobalGridsOutput/latitude.txt', delimiter=',', header=None).values


longitude = pd.read_csv('GlobalGridsOutput/longitude.txt', delimiter=',', header=None).values


data = pd.read_csv('GlobalGridsOutput/pyrite_burial_rate.txt', delimiter=',', header=None).values



 




Plotting the Data:You can plot the data on a world map using cartopy:



 

fig = plt.figure(figsize=(10, 5))


ax = plt.axes(projection=ccrs.PlateCarree())


ax.coastlines()


mesh = ax.pcolormesh(longitude, latitude, data, transform=ccrs.PlateCarree(), cmap='viridis')


plt.colorbar(mesh, ax=ax, orientation='vertical', label='Pyrite Burial Rate')


 plt.show()