This unit is about the generics of FAIR (Findable, Accessible, Interoperable, and Reusable). It can be summarised as a concept promoting the use of standardised practices for organising and sharing data and resources, and focuses on making data (F)indable by assigning persistent identifiers and using rich, informative metadata to describe its content. By emphasising (A)ccessibility, it ensures that data can be accessed by both humans and machines, using open formats and providing clear access protocols. (I)nteroperability is achieved by adopting common data standards and formats, enabling data integration and exchange across different systems and platforms. Lastly, (R)eusability is emphasised by providing clear usage rights, as well as licensing and enabling data to be combined with other datasets. The generics of FAIR aim to maximise the potential impact of data and resources by making them more discoverable, accessible, and usable for both researchers and wider communities using programmatic and automatised methods.
Lesson plans
The contents of the lesson plans presented here are vital in providing an overview and building an understanding of fundamental concepts required to put the FAIR principles into practice and place them into a wider context. The FAIR generics lesson plan is divided into several topics organised as a progressive path where each topic can be viewed as a full or partial requirement for the next one:
## 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 substituted 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.
This topic introduces participants to the definitions of the FAIR principles with those of open science. The trainer brings forward how data benefits from openness and also highlights that when data per definition cannot be open. Lastly the trainer should also mentioned how open data relates to being made publicly available in certified and trusted repositories, supported by local as well as national and international data policies. This interactive, 60-minute workshop bridges the theory of Open Science with its practical, real-world application through a series of hands-on, scenario-based activities. Students begin by analyzing complex datasets to dismantle the common misconception that FAIR and Open data are identical, discovering firsthand how data can be highly structured yet safely restricted. They then tackle the ethical and technical friction points of data sharing—such as the legalities of anonymization and protecting vulnerable populations—before engaging with an active matchmaking exercise where they learn to evaluate and choose appropriate repositories (like DataverseNL or domain-specific archives) for sensitive types of data. Finally, the lesson culminates in a gamified Mentimeter showcase that connects researchers directly with their local institutional support networks, including Data Stewards and Privacy Teams, ensuring they leave the session with the concrete tools and resources needed to manage their own research lifecycles responsibly.
The topic aims at increasing the understanding of how the different stages of the data lifecycle relate to the FAIR principles, with the ultimate goal of making data FAIR by design.
This lesson plan equips trainers to teach the value of standardized institutional data policies and the systemic benefits of adopting FAIR principles. Designed for the Health and Life Sciences domain, the module guides trainers through instructing others on how to write and configure a practical FAIR & Open Science Plan. To ground these concepts, the lesson features a real-world case study from the Faculty of Health, Medicine, and Life Sciences at Maastricht University, providing trainers with concrete examples to facilitate discussion and active learning.
## **Topic Definition and Scope**
### **Train-the-Trainer: Foundations of Data Management Plan (DMP) Instruction**
This module provides trainers with the pedagogical framework needed to teach effective DMP creation. The lesson plan covers the core requirements and overarching purpose of a DMP, preparing trainers to confidently explain these concepts to peers and researchers. Special emphasis is placed on identifying common drafting pitfalls and resolving complex legal, ethical, and privacy dilemmas. Additionally, the session prepares trainers to introduce diverse DMP templates, facilitate hands-on drafting exercises, and connect researchers with vital institutional support systems.