FAIR data enabling new horizons for materials research

TL;DR

This paper emphasizes the importance of implementing FAIR data principles in materials research to unlock the full potential of the vast research data for advancing science and technology.

Contribution

It discusses strategies and the necessity of adopting FAIR data infrastructure to enhance data sharing, interoperability, and AI-driven insights in materials science.

Findings

FAIR data principles are essential for materials research.

Implementing FAIR enables better data sharing and AI applications.

FAIR infrastructure can accelerate scientific discovery.

Abstract

The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.