OT-ST-WS-07 | Reproducibility in science: How and why?
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.
- 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?
- 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
- Useful for participants who (plan to) use programming in their analytical work: python, R, julia, etc
- Some basic knowledge of version control (git)
Own PC, laptop