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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)

 

When? Postponed to:

05.10. - 07.10.2022

10:00 - 12:00

Break

13:00-15:00


Where?

In person, ZMT, Room 1009/1010


Language?

English


Registration deadline: 16.08.2022

Dr. Arjun Chennu

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

 

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