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CAOB 2024Workshops

Workshop - Processing of structural data in open source software

Statistical evaluation, recalculation, plotting and interpretation of whole-rock geochemical and mineral data from igneous and metamorphic rocks can be complicated and tedious, a task that can be however simplified by potent, freely available software tools. This hands-on workshop introduces the GCDkit and GCDkit.Mineral packages, written in the popular R statistical language. While the former package became an established standard used by many colleagues worldwide (Janoušek et al. 2006 J. Petrol., Janoušek et al. 2016 Springer monograph –, the latter represents a brand new addition to the GCDkit family of tools (Janoušek et al. in print Amer. Miner. –  

Software requirements

1. Laptop with Windows 10 or 11

2. R language 4.1.3 (please stick to this exact version!), downloadable from

3. GCDkit 6.2, download and detailed instructions:

4. GCDkit.Mineral 1.0, download and detailed instructions:



Janoušek, V., Farrow, C. M. & Erban, V. (2006). Interpretation of whole-rock geochemical data in igneous geochemistry: introducing Geochemical Data Toolkit (GCDkit). Journal of Petrology 47, 1255–1259.

Janoušek, V., Moyen, J. F., Martin, H., Erban, V. & Farrow, C. (2016). Geochemical Modelling of Igneous Processes – Principles and Recipes in R Language. Bringing the Power of R to a Geochemical Community. Berlin: Springer.

Janoušek, V., Farrow, C. M. & Erban, V. (2024). GCDkit.Mineral - a customizable, platform-independent R-language environment for recalculation, plotting and classification of electron-probe micro-analyses of common rock-forming minerals. American Mineralogist, in print (doi: 10.2138/am-2023-9032)

Workshop - Interpretation of WR geochemical data using GCDKit

Python is an easy-to-learn, highly versatile language, which is also free and open source. It is used throughout the geoscience’s community for various applications, from automated data processing and high-level data analysis to creating software that automates tasks and analyses datasets. During the first part of the workshop, we will explore how Python can be used to process and analyze structural data including the basics of quantitative deformation analyses. The high availability of satellite and aerial images is changing the role remote sensing plays in understanding our world, so during the second part of the workshop we will explore how Python can be used to process and analyze remote sensing data with a focus on geological and structural mapping.

Software requirements

Installed software - JupyterLab, Python

If you want to participate actively, you must install Python and JupyterLab on your system. We will use the Miniforge installer, which allows simple and quick installation of all needed software.

  1. Download and execute the latest miniforge installer. There are known issues with using special characters and spaces in the installation location. We recommend users install in a directory without any such characters in the name e.g. C:\Users\john\miniforge3

  2. Download and unzip the workshop material. It is suggested to unzip it into your home directory e.g. C:\Users\john

  3. From the Start menu select the Miniforge Prompt. Change the active working directory, where you download the workshop material, e.g C:\Users\john\caob-workshop

  4. Install all needed Python packages by executing the following command:

    > mamba env create --file environment.yml

  5. Click Enter to proceed, wait a few minutes, and installation is done.

How to access and use JupyterLab?

To open JupyterLab you need to activate the workshop environment and run Jupyter Lab. Open Miniforge Prompt and enter the following commands:

> mamba activate workshop
> jupyter lab

After a successful launch, you should see the JupyterLab running in your browser.

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