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

Research output: Contribution to journalReview article

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)397-405
Number of pages9
JournalEngineering
Volume5
Issue number3
DOIs
StatePublished - Jun 2019

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Environmental Engineering
  • Chemical Engineering(all)
  • Materials Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Engineering(all)

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  • Cite this

    Hazen, R. M., Downs, R. T., Eleish, A., Fox, P., Gagné, O. C., Golden, J. J., Grew, E. S., Hummer, D. R., Hystad, G., Krivovichev, S. V., Li, C., Liu, C., Ma, X., Morrison, S. M., Pan, F., Pires, A. J., Prabhu, A., Ralph, J., Runyon, S. E., & Zhong, H. (2019). Data-Driven Discovery in Mineralogy: Recent Advances in Data Resources, Analysis, and Visualization. Engineering, 5(3), 397-405. https://doi.org/10.1016/j.eng.2019.03.006