Towards Fairification of learning resources and catalogues—lessons learnt from research communities
Bibliographic Data
Since the introduction of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles in 2016, discussions have evolved beyond the original focus on research data to include learning resources. In 2020, a set of simple rules to FAIRify learning resources was proposed, building on existing expertise within the training community. Disciplinary communities have played an important role in advancing FAIR principles for learning resources, although they have approached FAIRification activities in different ways. These communities range from volunteer-led to funded and independent organisations, however commonly include activities such as organising training and capacity building, and coordinated discussions on disciplinary-focused FAIR best practises and standards. Eight disciplinary community case studies are presented and analysed in this paper to examine the motivations, challenges and opportunities towards FAIRification of learning resources, reflecting on how community structure leads to differing responsibilities. The case studies are based on reflections formulated in 2022, the aim is to pull together the experiences of these different communities, focusing on the processes and challenges they encountered, in order to structure this knowledge across different learning platforms, draw attention to the question of sustainability for learning resources and anticipate improvements in future policies and governance
Data science · Natural language processing · Computer Science · Open Education and E-Learning · Research Data Management Practices · Scientific Computing and Data Management · Artificial Intelligence
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