FAIR Data Infrastructure
For Materials Science,
And Related Research Fields


FAIRmat will install a FAIR (findable, accessible, interoperablereusable) data infrastructure for the wider area of materials science. Materials science represents a broad range of communities that can be characterized by either different classes of materials, by different techniques, or by functionality. As a consequence, the data produced by materials science are enormously heterogeneous and diverse in terms of the 4V of Big Data, that are Volume, Variety, Velocity, and Veracity. To cope with all the diversity, a bottom-up approach that satisfies the needs of the different areas/subcommunities is a must to foster acceptance by the community and participation of a large number of individual researchers and laboratories.

FAIRmat sets out to tackle this challenge by a user-driven approach to develop easy-to-use tools and an infrastructure towards FAIR data processing, storage, curation, sharing, and future use of materials data. To address all of these essential aspects, we have identified several areas (further broken down into tasks) that are shown in the following organigram: