Curated from repositories that make our lives as geoscientists, hackers and data wranglers easier or just more awesome

An application based on Python and designed as a solution for geology related daily work.

List of resources for mineral exploration and machine learning, generally with useful code and examples.

The Virtual Planet Simulator

A set of tools for getting the most from your geochemical data.

an open-sourced highly automated machine learning Python framework for data-driven geochemistry discovery

GebPy is a Python-based, open source tool for the generation of geological data of minerals, rocks and complete lithological sequences. The data can be generated randomly or with respect to user-defined constraints, for example a specific element concentration within minerals and rocks or the order of units within a complete lithological profile.

A code for calculating the standard state thermodynamic properties at a given temperature and pressure.

Python client for the Earthchem REST and OGC APIs

Easily calling PhreeqcRM from Matlab

A two-phase (water + gas) reactive transport model, written in Julia, that also simulates geochemical reactions between water-gas-mineral phases via a link with PHREEQC.

R package to analyze Groundwater, Geothermal water and Gas Geochemistry information

Read IRMS (Isotope Ratio Mass Spectrometry) data files into R

Thermodynamic calculations and diagrams for geochemistry

'geoquimica' is an open-source package built-in ≥ R 3.6.0 that gathers functions for assist on the exploration data analysis of geochemistry data. This package was built by researchers of the Geological Survey of Brazil.

Run Phreeqc online with Python

Interactive Jupyter notebook exploring the features of GEOROC 2.0. Demonstrates data access via API, data formatting, cleaning, resampling, and visualization for geochemical analysis.

Python library for processing and standardizing carbonate clumped-isotope analyses, from low-level data out of a dual-inlet mass spectrometer to final, “absolute” Δ47, Δ48, and Δ49 values with fully propagated analytical error estimates.

Looking at the problems associated with geoscience datasets for data science

A Python library for geochronology.