Chapter 1 Introduction

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[Short course session description from]

Reproducibility is unquestionably at the heart of science. Scientists face numerous challenges in this context, not least the lack of concepts, tools, and workflows for reproducible research in Today’s curricula. This short course introduces established and powerful tools that enable reproducibility of computational geoscientific research, statistical analyses, and visualisation of results using R ( in two lessons:

1. Reproducible Research with R Markdown Open Data, Open Source, Open Reviews and Open Science are important aspects of science today. In the first lesson, basic motivations and concepts for reproducible research touching on these topics are briefly introduced. During a hands-on session the course participants write R Markdown ( documents, which include text and code and can be compiled to static documents (e.g. HTML, PDF). R Markdown is equally well suited for day-to-day digital notebooks as it is for scientific publications when using publisher templates.

2. GitLab and Docker In the second lesson, the R Markdown files are published and enriched on an online collaboration platform. Participants learn how to save and version documents using GitLab ( and compile them using Docker containers ( These containers capture the full computational environment and can be transported, executed, examined, shared and archived. Furthermore, GitLab’s collaboration features are explored as an environment for Open Science.

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