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FAIR in (biological) practice

The course is aimed at researchers in biological and biomedical sciences (PhD students, postdocs, technicians, etc…) who are interested in Open Science and data management. The course explains the FAIR (Findable, Accessible, Interoperable, Reusable) principles for data re-use, and how to practically apply FAIR principles throughout projects’ life cycles.


Open Science is disruptive. It is changing how we do research and how society benefits from it.

We will teach you how through planning and using a powerful set of tools, you can make your outputs ready for public sharing and reuse.

This hands-on 4-session workshop covers the basics of Open Science and FAIR practices, and looks at how to use these core principles in your own projects. The workshop is a mix of lectures and hands-on lessons where you will use the approaches learned and will implement some of the discussed practices.

Target Audience

PhD students, postdocs, technicians, all who actively generate or analyse biological data, mostly experimental biologists. This course can also be of interest to mathematical/biological/computational modelers, data analysts, project managers and advocates of FAIR/Open Data.

Example of a learner profile:

Shania Wang (she/her) - junior postdoc Shania is starting her first postdoctoral stay. She has recently obtained a PhD in Plant Biology and is very skilled in running plant molecular and physiology experiments. She recently published her first publication, but found the process of preparing all the figures and required information for the publisher super tedious and time consuming. As a postdoc, she must find an effective way to organise her own research project and the PhD students she is going to supervise…(read more )

For instructors

Course instructors should check the instructors folder which contains:


You don’t need to have prior knowledge of data management or programming skills. You do need to be willing to learn how to apply FAIR principles in your daily research life.

We expect you to:

  • have a bio/medical background
  • know basics of scientific communication, publications, citations and their importance for careers
  • familiarity with spreadsheets (Excel)
  • familiarity with online bioscience data resources (in general, not any particular resource); for example: searching publications
  • know your ways around files and directories on your own machine

Another course aimed at PIs running their research groups FAIR in (Biological) Practice for PIs is under development in 2022.

Bioinformaticians and those who mostly develop scientific software may also benefit from software specific courses such as Open Science with R or other courses developed by the Carpentries Incubator.

Learning Objectives

After following this lesson, learners will be able to:

  • explain elements of the Open Science movement
  • explain FAIR principles and understand their importance
  • plan their own data management strategy
  • prepare their data for re-use
  • apply approaches and tools into a FAIR-ready research data lifecycle
  • find suitable resources for delivering Open Science and FAIR data



Setup Download files required for the lesson
00:00 1. Welcome Who are we and what are we going to learn?
00:15 2. Introduction to Open Science What is Open Science?
How can I benefit from Open Science?
Why has Open Science become a hot topic?
01:10 3. Being FAIR How to get more value from your own data?
What are the FAIR guidelines?
Why being FAIR matters?
02:10 4. Intellectual Property, Licensing and Openness What is intellectual property?
Why should I consider IP in Open Science?
02:30 5. Introduction to metadata What is metadata?
What do we use metadata for?
02:55 6. Being precise How to make my metadata interoperable?
How to avoid disambiguation?
03:30 7. (Meta)data in Excel How to represent data in tables
04:25 8. Laboratory records How and why to keep good quality records for experiments?
How to streamline and simplify record keeping and its maintenance with electronic lab notebooks and online protocols?
How to keep records FAIR?
05:35 9. Working with files How should I name my files?
How does folder organisation help me
06:35 10. Reusable analysis How keep track of your data analysis procedure?
How to make reproducible plots?
What are Jupyter notebooks
08:05 11. Version control How not to worry about making changes to a project?
What is version control?
What is git and github?
09:19 12. Templates for consistency What are the benefits of having templates?
How do templates make your research FAIR?
10:17 13. Public repositories Where can I deposit datasets?
What are general data repositories?
How to find a repository?
11:22 14. It's all about planning What is the research data life cycle?
How to plan for FAIR sharing?
What is a Data Management Plan (DMP)?
12:40 15. Putting it all together What will your journey to be FAIR productive entail?
12:40 16. Template

12:40 Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.