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OT-ST-WS-07 | Reproducibility in science: How and why?
Registration closed
Dr. Arjun Chennu
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.
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?
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)
Requirements
- Own PC, laptop
- Internet (access to eduroam), web browser (up-to-date)
- 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
