Data-Driven Discovery in Mineralogy: Recent Advances in Data Resources, Analysis, and Visualization

Robert M. Hazen; Robert T. Downs; Ahmed Eleish; Peter Fox; Olivier C. Gagné; Joshua J. Golden; Edward S. Grew; Daniel R. Hummer; Grethe Hystad; Sergey V. Krivovichev; Congrui Li; Chao Liu; Xiaogang Ma; Shaunna M. Morrison; Feifei Pan; Alexander J. Pires; Anirudh Prabhu; Jolyon Ralph; Simone E. Runyon; Hao Zhong

doi:10.1016/j.eng.2019.03.006

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Engineering ›› 2019, Vol. 5 ›› Issue (3) : 397-405. DOI: 10.1016/j.eng.2019.03.006

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Research Deep Matter & Energy—Review

Data-Driven Discovery in Mineralogy: Recent Advances in Data Resources, Analysis, and Visualization

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Abstract

Large and growing data resources on the diversity, distribution, and properties of minerals are ushering in a new era of data-driven discovery in mineralogy. The most comprehensive international mineral database is the IMA database, which includes information on more than 5400 approved mineral species and their properties, and the mindat.org data source, which contains more than 1 million species/locality data on minerals found at more than 300 000 localities. Analysis and visualization of these data with diverse techniques—including chord diagrams, cluster diagrams, Klee diagrams, skyline diagrams, and varied methods of network analysis—are leading to a greater understanding of the co-evolving geosphere and biosphere. New data-driven approaches include mineral evolution, mineral ecology, and mineral network analysis—methods that collectively consider the distribution and diversity of minerals through space and time. These strategies are fostering a deeper understanding of mineral co-occurrences and, for the first time, facilitating predictions of mineral species that occur on Earth but have yet to be discovered and described.

Keywords

Mineral evolution / Mineral ecology / Skyline diagrams / Network analysis / Cluster analysis / Chord diagrams / Klee diagrams

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Robert M. Hazen, Robert T. Downs, Ahmed Eleish, Peter Fox, Olivier C. Gagné, Joshua J. Golden, Edward S. Grew, Daniel R. Hummer, Grethe Hystad, Sergey V. Krivovichev, Congrui Li, Chao Liu, Xiaogang Ma, Shaunna M. Morrison, Feifei Pan, Alexander J. Pires, Anirudh Prabhu, Jolyon Ralph, Simone E. Runyon, Hao Zhong. Data-Driven Discovery in Mineralogy: Recent Advances in Data Resources, Analysis, and Visualization. Engineering, 2019, 5(3): 397‒405 https://doi.org/10.1016/j.eng.2019.03.006

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Acknowledgements

We are grateful to Ho-Kwang Mao and the organizers of this special issue for the opportunity to share our results. This publication is a contribution to the Deep Carbon Observatory.

Studies of mineral evolution and mineral ecology are supported by grants from the Alfred P. Sloan Foundation (G-2016-7065), the W. M. Keck Foundation (grant entitled “Co-Evolution of the Geosphere and Biosphere”), the John Templeton Foundation (60645), the NASA Astrobiology Institute (1-NAI8_2-0007), a private foundation, and the Carnegie Institution for Science. Sergey V. Krivovichev acknowledges support from the Russian Science Foundation (19-17-00038).

Compliance with ethics guidelines

Robert M. Hazen, Robert T. Downs, Ahmed Eleish, Peter Fox, Olivier C. Gagné, Joshua J. Golden, Edward S. Grew, Daniel R. Hummer, Grethe Hystad, Sergey V. Krivovichev, Congrui Li, Chao Liu, Xiaogang Ma, Shaunna M. Morrison, Feifei Pan, Alexander J. Pires, Anirudh Prabhu, Jolyon Ralph, Simone E. Runyon, and Hao Zhong declare that they have no conflict of interest or financial conflicts to disclose.