FAIR data enabling new horizons for materials research
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.
A findable, accessible, interoperable and reusable (FAIR) data infrastructure is discussed to turn the large amount of research data generated by the field of materials science into knowledge and value.
@article{Scheffler_2022, title={FAIR data enabling new horizons for materials research}, volume={604}, ISSN={1476-4687}, url={http://dx.doi.org/10.1038/s41586-022-04501-x}, DOI={10.1038/s41586-022-04501-x}, number={7907}, journal={Nature}, publisher={Springer Science and Business Media LLC}, author={Scheffler, Matthias and Aeschlimann, Martin and Albrecht, Martin and Bereau, Tristan and Bungartz, Hans-Joachim and Felser, Claudia and Greiner, Mark and Groß, Axel and Koch, Christoph T. and Kremer, Kurt and Nagel, Wolfgang E. and Scheidgen, Markus and Wöll, Christof and Draxl, Claudia}, year={2022}, month=apr, pages={635–642} }
