Over the past decade, the FAIR principles which provide guidance in making data Findable, Accessible, Interoperable, and Reusable have transformed the way research is funded and evaluated. In a paradigm shift, funders now routinely require data management plans, which involves researcher training in FAIR practices and data deposition. The FAIR movement has also led to pronounced behavioural changes among researchers, while largely overlooking the essential role of infrastructure: the biodata resources — deposition databases and knowledgebases — that turn scattered data sets into readily-available coherent knowledge.
Without infrastructure, FAIR data policy risks becoming a compliance exercise where data might be shared, but remain fragmented, inconsistently annotated, or practically inaccessible. Achieving FAIR at a global scale and reaping its benefits for discovery, artificial intelligence (AI), and innovation depends on infrastructure designed to capture, curate, and connect research data systematically. In life sciences, such infrastructure is referred to as “biodata resources”.
In this talk, I will argue that investing in biodata resources provides some of the most effective and cost-efficient means of achieving the goal of the FAIR principles. I will call on funders and institutions to provide stable, competitive support for these vital resources such as at a level of at least 1% of research budgets to secure the foundations of FAIR data, accelerate AI-driven discovery, and maximise the impact of public investment in science.