Beschreibung

The reproducibility crisis in science stems not only from historically poor data availability, but also from a lack of the context used to glean knowledge from the data. Reproducible science seeks to package the analytical context of data – software environment, data organization, analytical interdependencies, expert comments – into an operational product. This has great benefits in multiplying the impact and usefulness of your scientific work for scientists, journalists – and yourself.

Learning contents

• Why is reproducibility important in science?
• Why should I make my work reproducible?
• What does reproducible analysis mean?
• How can I rethink my workflow to be reproducible?
• Which tools help me to perform reproducible analysis?

Learning outcomes

• Conceptual and operational understanding of reproducibility
• Structuring workflows for individual or collaborative work
• Tools for reproducible workflow management and data collaboration
• Guidance towards structuring projects

Prior knowledge

• Useful for participants who (plan to) use programming in their analytical work:  python, R, julia, etc
• Some basic knowledge of version control (git)

Technical requirements

• Own PC, laptop
• Internet (access to eduroam), web browser (up-to-date)

Further reading

• en.wikipedia.org/wiki/Reproducibility
• Miyakawa "No Raw Data, No Science: Another Possible Source of the Reproducibility Crisis." Molecular Brain 13, no. 1 (2020): 24. molecularbrain.biomedcentral.com/articles/10.1186/s13041-020-0552-2
• Stodden et al. "An Empirical Analysis of Journal Policy Effectiveness for Computational Reproducibility." Proceedings of the National Academy of Sciences 115, no. 11 (2018): 2584-89. www.pnas.org/doi/full/10.1073/pnas.1708290115

Programm/Ablauf