Topic Definition and Scope
Train-the-Trainer: Why FAIR?
This topic introduces participants to the foundational concepts of the FAIR principles (Findable, Accessible, Interoperable, Reusable). It highlights how stakeholders, researchers, and the broader community benefit from FAIR implementation, while exploring the growing incentives from journals, funding bodies, and institutions that tie FAIR compliance to scientific impact. A fundamental understanding of FAIR and its role across all stages of the research lifecycle is vital for driving successful implementation.
Summary of Tasks and Actions
- 1.0 Pre-Workshop Assignment: Participants read a foundational academic article to familiarize themselves with the FAIR principles before the session begins.
- 2.0 Hands-on Activities: This core segment combines short lectures with individual and group assignments designed to deepen participants’ conceptual understanding. The lesson plan offers a menu of modular activities, allowing trainers to select and adapt exercises based on their specific audience, timeframe, and context.
- 3.0 Post-Session Plenary Discussion: The session concludes with a collaborative discussion centered on the real-world benefits, opportunities, and challenges of applying FAIR principles to active research projects.
Materials and Equipment
- For Participants: A computer and a stable internet connection (if the training is delivered online or uses cloud-based tools).
- For the Trainer: A digital collaboration tool (such as Miro or an equivalent virtual whiteboard). For in-person delivery, this can easily be replaced with physical chart paper, sticky notes, and markers.
References
- Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., … & Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 1-9.
Lesson content
Reading
Have participants read the FAIR Cookbook’s Introducing the FAIR Principles to get an idea of what the FAIR principles entail.”
Lecture:
Recognizing the FAIR principles
Present to participants what each letter in the FAIR acronym means and how they relate to each other
Exercise:
Ways of Making Data FAIR
Divide people into pairs and let them explain to each other how they are already making their data FAIR and what is one thing they can easily do to make their data FAIR
Exercise:
Seeing the Differences in the FAIR acronym
Have participants list what each letter in the FAIR acronym mean, and why these are important for their daily research practices
Lecture:
Presenting FAIR benefits for different roles
Have participants present examples of different stakeholders (e.g., researchers, funders, the public) and discuss how each benefits from FAIR principles
Exercise:
Collecting requirements from external stakeholders
Have participants provide a list of FAIR data requirements from journals and funding bodies and review them together
Exercise:
Exploring FAIR requirements
In pairs, participants look up sample guidelines from a journal or funder and list how they impact data management practices
Exercise:
Exploring issues in implementing FAIR
Have participants list common issues in research that arise from non-FAIR data practices, such as data loss or inaccessibility
Exercise:
Exploring FAIR implementation
Ask participants to analyse case studies of projects that failed due to non-FAIR practices and discuss the repercussions
Lecture:
Introduce participants to key changes needed to adopt FAIR principles within a research team, using simple examples
Exercise:
Pre-design a FAIR implementation plan
Organise a workshop where participants evaluate a real or hypothetical project’s current practices, then develop a detailed action plan to implement the FAIR principles
Exercise:
Discuss the FAIR effect on society
Have participants debate the broader societal impact of adopting FAIR principles, considering different stakeholder perspectives
Exercise:
The post lesson plan reflection
Have participants identify benefits and opportunities to apply FAIR principles in their own project, group and organisation
Additional resources
- FAIR Cookbook - Introduction arrow_outward
- FAIR in (biological) practice - Carpentries course arrow_outward
- Why FAIR - FAIR data principles arrow_outward
- D7.4 How to be FAIR with your data. A teaching and training handbook for higher education institutions | Zenodo arrow_outward
- Ten reasons to share your data arrow_outward
- FAIR Cookbook What are the FAIR Principles? arrow_outward
- Introduction to FAIR principles arrow_outward
- FAIR in action - a flexible framework to guide FAIRification
- The FAIR principles of data management arrow_outward
- Implementing FAIR in data sharing who are the stakeholders and what are their responsibilities? arrow_outward
- Turning FAIR into reality arrow_outward
- FAIR data - ARDC arrow_outward
- Hurdles to implement FAIR principles arrow_outward
- Why is FAIR Data important in 2022? arrow_outward
- Costs of not having FAIR research data arrow_outward
- Stakeholders arrow_outward
The terms4FAIRskills project has created a formalised terminology that describes the competencies, skills and knowledge associated with making and keeping data FAIR.