Documentation From User Experience

• Marvin Lorber
• Documentation , Software , RDM

This post is a condensed version of a talk at our Data Compentcy College

If you regularly use scientific software written by others, or tried to replicate interesting research that relies on software, you have probably also invested weeks of work to solve a software problem or even given up on a software because of missing documentation. Finding a project that might be the solution to your problem and then failing to run the code is frustrating. Being unable to run a project you have built yourself years ago is even worse. Having experienced all those setbacks myself in the past I want to use this post to channel that frustration to fuel solutions for better documentation for our current and future projects.

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The Carpentries Workshop: Introduction to the Unix Shell, Git, and GitLab

The DKZ.2R presents, as a part of our “Trainings” work package, a Carpentries Workshop on the 18th and 22nd of November on the topic of “Introduction to the Unix Shell, Git, and GitLab”. This is an official Carpentries Workshop and will be hosted on-site at RWTH Aachen University.

Workshop material is available online and will be presented by official Carpentries instructors, who will guide you through the concepts with the help of hands-on exercises and personalized support. The course is designed for beginners and is open to participants from all domains. No prior knowledge is required. If you are interested in taking part in the workshop, you can sign up here.

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Introduction to Machine Learning with Python and Scikit-Learn

As part of our “Trainings” work package, the DKZ.2R creates, curates and presents a variety of free trainings, seminars and courses. Our next offering will be a one and a half day carpentries-style workshop on basic machine learning using python and scikit-learn, to be presented at RWTH Aachen University on 24th and 25th of July, 2025. The workshop will cover the following topics:

• What is Machine Learning / Why bother?
• Supervised Methods (Regression / Classification)
• Ensemble Methods
• Unsupervised Methods (Clustering / Dimensionality Reduction)
• Neural Networks
• Ethics and Implications of Machine Learning

Workshop material is available online and will be presented by instructors who will walk you through the steps and are available for questions throughout the event. If you are interested in taking part in the workshop, you can sign up here.

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