FAIR data enabling new horizons for materials research

Scheffler M, Aeschlimann M, Albrecht M, Bereau T, Bungartz HJ, Felser C, Greiner M, Gross A, Koch CT, Kremer K, Nagel WE, Scheidgen M, Woell C, Draxl C (2022)

Publication Type: Journal article, Review article

Publication year: 2022

Journal

Nature Nature Research

Book Volume: 604

Pages Range: 635-642

Journal Issue: 7907

DOI: 10.1038/s41586-022-04501-x

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.

Involved external institutions

Humboldt-Universität zu Berlin DE Germany (DE) Technische Universität Kaiserslautern DE Germany (DE) Leibniz-Institut für Kristallzüchtung / Leibniz Institute for Crystal Growth DE Germany (DE) Max-Planck-Institut für Polymerforschung (MPI-P) / Max Planck Institute for Polymer Research DE Germany (DE) Technische Universität München (TUM) DE Germany (DE) Max-Planck-Institut für Chemische Energiekonversion (MPI CEC) / Max-Planck-Institute for Chemical Energy Conversion DE Germany (DE) Universität Ulm DE Germany (DE) Technische Universität Dresden DE Germany (DE) Karlsruhe Institute of Technology (KIT) DE Germany (DE) Max-Planck-Institut für Chemische Physik fester Stoffe (MPI CPfS) / Max Planck Institute for Chemical Physics of Solids DE Germany (DE)

How to cite

APA:

Scheffler, M., Aeschlimann, M., Albrecht, M., Bereau, T., Bungartz, H.-J., Felser, C.,... Draxl, C. (2022). FAIR data enabling new horizons for materials research. Nature, 604(7907), 635-642. https://doi.org/10.1038/s41586-022-04501-x

MLA:

Scheffler, Matthias, et al. "FAIR data enabling new horizons for materials research." Nature 604.7907 (2022): 635-642.

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