The planning phase of a project is usually defined by the content concept and challenges that must be overcome to achieve the project’s objectives. However, it is crucial that data producers consider the desired re-use scenarios for humans and machines from the outset of the project. Doing so enhances the reach and significance of the produced research data, thereby also contributing to the scientific reputation of the data producers.
Applying the FAIR principles affects the methods used to compile, model and document data. It affects decisions regarding the formats in which data is stored, how it is stored and passed on, as well as how it is licensed for further use. These factors influence many subsequent steps in project and resource planning.
The FAIR criteria are not solely intended to maximise the value of research results that are published in the customary way. The requirement for FAIRness also applies to the algorithms, tools, and workflows that led to the generation of the research data. All scientific digital research objects – ranging from data to analysis pipelines – should be included in the process of FAIRification, as all components of the research process must be available to ensure transparency, reproducibility, and reusability.
The first step towards sustainability is to analyse your project from the perspective of optimal reusability. When doing so, don't limit your focus too narrowly on immediate or short-term usage scenarios, but also consider medium- and long-term goals. Consider which questions from related disciplines your data could also be relevant to. Prioritise specific types of use, if necessary, to scale your activities when not everything can be implemented with the resources likely to be available. These medium- and long-term expansion stages only need to be outlined roughly at first; the implementation of these goals can happen gradually. However, carefully analyse how your chosen focus will impact the initial decisions and the consequences for other stages of the data lifecycle. This will give you a clearer view of which criteria or tasks build on or depend on one another, allowing you to lay the right foundation for future expansion and new uses.
Especially in memory institutions that are planning the FAIRification of their data management as an internal project, it makes sense to draw up a ‘FAIR roadmap’ that prioritises goals and the associated measures. Particular attention should be paid to the following subject areas, as they often result in a series of further task packages that require specific planning:
In time-limited and clearly defined research projects, some FAIR activities can be relatively easily incorporated into project planning from the outset. But they are much more difficult to integrate into continuous collection cataloguing. This applies, for example, to the very time-consuming retrospective revision and integration of source materials on which the collection documentation is based. These tasks could initially be given a lower priority.