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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

 

  • Corti, Louise; van den Eynden, Veerle; Bishop, Libby; Woollard, Matthew (2020): Managing and sharing research data: A guide to good practice. 2nd ed. Los Angeles: SAGE Publications.

When?

25.08.2021, 14:30 - 16:30

26.08.2021, 14:30 - 16:30

27.08.2021, 14:30 - 16:30


Where?

Online via VC


Language?

English

Dr. Arjun Chennu

Group leader, Data Science and Technology, at the Leibniz Centre for Tropical Marine Research (ZMT)

 

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Other status groups or externals:

Free places will be offered to candidates on the waiting list after registration was closed (one week before the workshop takes place).