A Proposed Consortium of a German Research-Data Infrastructure (NFDI) on FAIR Data Infrastructure for Materials Science and Related Research Fields
Recommended by the Council for Information Infrastructures (RfII), an urgently needed sustainable infrastructure for provision, interlinkage, maintenance, and options for reuse of research data shall be created in Germany in the coming years. The needs and implementation of this National Research Data Infrastructure (NFDI), should be defined and realized by consortia from a wide variety of scientific disciplines. The funding is provided by the BMBF, and the peer review process is organized by the DFG.
The consortium FAIRmat represents the interests of experimental, theoretical, and computational condensed-matter physics and materials science. This includes, for example, chemical physics of solids, catalysis, electron microscopy, x-ray spectroscopy and more. Our approach and most important principle is to design the infrastructure of this field from bottom up and without bureaucratic hurdles. To this end, we will involve our colleagues in order to implement their needs make research studies more efficient, and to achieve a comprehensive acceptance of the project. The aim of FAIRmat is to improve, expand, and interlink existing infrastructures for the further development of these scientific disciplines and - where necessary - to create new infrastructures. Cooperation between universities and other research institutions as well as industry shall be strengthened.
FAIRmat, representing the materials science pillars of the association FAIR-DI (FAIR Data Infrastructure for Physics, Chemistry, Materials Science, and Astronomy e.V.), has already extensive practical experience and also follows FAIR-DI's mission statement:
Scientific data are a significant raw material of the 21st century. To exploit its value, a proper infrastructure that makes it Findable, Accessible, Interoperable, and Re-purposable – FAIR – is a must. For the fields of computational and experimental materials science, chemistry, and astronomy, FAIRDI sets out to make this happen. This enabling of extensive data sharing and collaborations in data-driven sciences (including artificial intelligence tools) will advance basic science and engineering, reaching out to industry and society.
The precise structure of FAIRmat is still under development. Colleagues are invited to contribute their expertise and actively shape the necessary data infrastructures. This concept will now be further developed jointly. As a first step, an Extended Abstract was submitted to the DFG on March 29, 2019. Download an updated list of supporting institutions.
In essence, FAIRmat covers the following (partially overlapping) areas:
Thank you for your interest and cooperation. Please answer the following questions: