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NOMAD: The FAIR concept for big data-driven materials science

MPG-Autoren
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Draxl,  Claudia
Theory, Fritz Haber Institute, Max Planck Society;
Physics Department, Humboldt-Universität zu Berlin;

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Scheffler,  Matthias
Theory, Fritz Haber Institute, Max Planck Society;
Physics Department, Humboldt-Universität zu Berlin;

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Volltexte (frei zugänglich)

1805.05039.pdf
(Preprint), 405KB

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Zitation

Draxl, C., & Scheffler, M. (2018). NOMAD: The FAIR concept for big data-driven materials science. MRS Bulletin, 43(9), 676-682. doi:10.1557/mrs.2018.208.


Zitierlink: http://hdl.handle.net/21.11116/0000-0001-5756-D
Zusammenfassung
Data are a crucial raw material of this century. The amount of data that have been created in materials science thus far and that continues to be created every day is immense. Without a proper infrastructure that allows for collecting and sharing data, the envisioned success of big data-driven materials science will be hampered. For the fi eld of computational materials science, the NOMAD (Novel Materials Discovery) Center of Excellence (CoE) has changed the scientific culture toward comprehensive and findable, accessible, interoperable, and reusable (FAIR) data, opening new avenues for mining materials science big data. Novel data-analytics concepts and tools turn data into knowledge and help in the prediction of new materials and in the identifi cation of new properties of already known materials.