Making research data FAIR (Findable, Accessible, Interoperable, and Reusable) is of great importance in a data-driven world. Knowledge of the FAIR data principles and their practical application is crucial for maximising the value of data and resources, leading to more efficient research and increased knowledge sharing. By knowing of and adopting the FAIR principles, organisations and researchers can reach new levels of data and resource impacts, leading to numerous benefits for both the researcher community and society at large.
Adopting and embracing the FAIR principles is a vital step towards advancing research and addressing complex challenges in all domains. Journals, institutions, and funding bodies are often requiring that research follows the FAIR principles (e.g., Horizon Europe and NIH).
This lesson plan includes understanding of the importance of the FAIR principles for various stakeholders and the requirements of FAIR data set by journals, institutions, and funding bodies. Additionaly, it explores potential consequences of not applying the FAIR principles and the required changes for FAIR research practices in a project, group or organisation
Lesson content
Have participants read the FAIR Cookbook’s Introducing the FAIR Principles to get an idea of what the FAIR principles entail.”
Present to participants what each letter in the FAIR acronym means and how they relate to each other
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
Have participants list what each letter in the FAIR acronym mean, and why these are important for their daily research practices
Have participants present examples of different stakeholders (e.g., researchers, funders, the public) and discuss how each benefits from FAIR principles
Have participants provide a list of FAIR data requirements from journals and funding bodies and review them together
In pairs, participants look up sample guidelines from a journal or funder and list how they impact data management practices
Have participants list common issues in research that arise from non-FAIR data practices, such as data loss or inaccessibility
Ask participants to analyse case studies of projects that failed due to non-FAIR practices and discuss the repercussions
Introduce participants to key changes needed to adopt FAIR principles within a research team, using simple examples
In groups, participants analyse a project scenario and identify specific changes needed to meet FAIR principles, sharing their findings
Divide participants into groups to identify and share how each stakeholder’s needs align with FAIR principles in a research project
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
Have participants debate the broader societal impact of adopting FAIR principles, considering different stakeholder perspectives
Create a case study analysis where participants evaluate a project’s adherence to a specific institution’s FAIR requirements, suggesting improvements
Facilitate a role-playing scenario where participants present a ‘worst-case scenario’ impact analysis of ignoring FAIR principles for a research project
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.