Abstract

We are working in a time of huge technological advancement and a rapidly changing world, with the tremendous surge in artificial intelligence (AI) use and its application to a whole host of technologies and systems. People working across different industries and disciplines are adapting their ways of working to incorporate, accommodate and accelerate new AI powered technologies. Research managers and administrators are no exception, there is a new focus on the power of technology and a greater interest in technology that can support the industry now and in the future, in the context of rapid advancement.

This greater focus on new technologies and the potential power of AI and machines is paired with the ever increasing emphasis on FAIR Data - but how are the two linked?

FAIR Data, a key pillar of Open Science, has risen on stakeholders’ agendas across the research ecosystem due to increasing funder and government mandates and guidelines. The motivation behind many of the new policies and mandates is essentially to further the pursuit and progress of knowledge and research; and get further, faster.

With the new capabilities we are witnessing from various AI tools, there has never been a more important time to ensure research data is interoperable. While the FAIR principles have long called for research data to be shared in a way that is useful to machines - ensuring it’s truly Findable, Accessible, Interoperable and Reusable - the importance of well described data that can be processed quickly and in large quantities by machines is becoming clearer. We are entering an exciting time for FAIR data and research managers and administrators should keep up with the fast, momentous progress being made thanks to the interactions between FAIR data and machines.

By emphasising the notion that well described research data can contribute to huge advancements of knowledge when it interacts with machines, this presentation aims to reinforce the importance of data stewards in supporting researcher engagement with the FAIR principles, and their role of instilling and promoting these practices, setting researchers up with the skills and tools to proficiently manage their research data in a way that could mean we get further, faster.

We will share examples of academic projects making use of FAIR data and AI to drive systemic change in a research field; highlight how FAIR data is a key player in this next wave of technological advancement and the rise of AI; and how these developments can serve as motivation for managers, administrators and researchers themselves to engage with FAIR data and managing it proficiently at their institutions - showcasing tools that can support RMA now and in the future.