What are we going to learn today?

Objectives

  • Learn what is reproducibility and why does it matter?
  • Gain a number of skills and resources to help us build reproducibility in our everyday workflows
  • Understand the relationship between Reproducible Research and other areas such as Culture and Business Continuity

This workshop aims to take you further down your reproducibility path, by providing concepts and tools you can use in your everyday workflows.

We will also examine how Reproducible Research builds business continuity into your research group, how the culture in your institute ecosystem can affect Reproducibility and how you can identify and address risks to your knowledge.

Intended audience

This course is suitable for any researcher, research student or professional services staff who would like to upskill in reproducible research practices. No prior skills or knowledge is needed.

All disciplines welcome

Due to the number of other RR courses focusing on computational research, this workshop takes a step back to focus more on practices that both non-computational and computational researchers can use. This course intends to be discipline-agnostic, however the authors acknowledge that reproducibility may not fit in some areas of interpretative research.

All skill levels welcome

This course is not aimed taking your from 0% to 100% in Reproducible Research - it instead aims to help you further down your RR path. You should be able to finish this workshop with a few new ideas and skills to start on easy changes. You are always welcome to come back once you feel comfortable and ready to take your next steps.

Terminology

We acknowledge that different disciplines use different terminology. For the purposes of this workshop, when we talk about data, we are referring to the information/knowledge you will gather and study to come to your conclusion.

What to bring?

As an attendee, you won’t need to prepare anything for this workshop. It is suggested you have a notepad or document open for activities.

There won’t be datasets or programs needed for these lessons.