The enormous amounts of research data produced every day in the fields of Condensed-Matter Physics, Materials Science, and the Chemical Physics of Solids represent a gold mine of the 21st 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, i.e., turn data into knowledge and value? For this, a FAIR (Findable, Accessible, Interoperable, and Re-usable) data infrastructure is a must. Only then, data can be readily shared and explored by data analytics and artificial-intelligence (AI) methods. Making data Findable and AI Ready (a forward-looking interpretation of the acronym) will change the way how science is done today.
The consortium FAIRmat sets out to make this happen.