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:
This topic introduces the general concept of FAIR, describing why potential and real stakeholders as well as other members of the community benefit from the implementation of the FAIR principles, as well as the increasing incentives from journals, funding bodies and institutions for emphasising FAIR as a factor for scientific impact. A fundamental understanding of FAIR and its role at all stages of research is vital for understanding the FAIR implementation process.
This topic compares the definitions of the FAIR principles with those of open science, how data benefits from openness and when data per definition cannot be open, and 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.
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.
The topic addresses the importance of standardised local institutional policies and describes the overall benefits of FAIR data management and adopting the FAIR principles at an institutional level.
A predefined plan for handling all parts of the data life cycle, from receiving to publishing research data, is vital to any research project, and the road to FAIRification of data requires the various stages of the data life cycle to be explicitly described in a data management plan.
Defining FAIR Objectives for Organisations (expand)
The topic covers the importance of data access policies and describes how individual researchers can mediate the access to e.g. sensitive research data through a clear application and evaluation path independent of the individual researcher. It also emphasises the importance of informed consent forms as a crucial step in the FAIRification of research data.