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Engineering    2019, Vol. 5 Issue (3) : 397 -405     https://doi.org/10.1016/j.eng.2019.03.006
Research Deep Matter & Energy—Review |
Data-Driven Discovery in Mineralogy: Recent Advances in Data Resources, Analysis, and Visualization
Robert M. Hazena(), Robert T. Downsb, Ahmed Eleishc, Peter Foxc, Olivier C. Gagnéa, Joshua J. Goldenb, Edward S. Grewd, Daniel R. Hummere, Grethe Hystadf, Sergey V. Krivovichevg, Congrui Lic, Chao Liua, Xiaogang Mah, Shaunna M. Morrisona, Feifei Panc, Alexander J. Piresb, Anirudh Prabhuc, Jolyon Ralphi, Simone E. Runyonaj, Hao Zhongc
a Geophysical Laboratory, Carnegie Institution for Science, Washington, DC 20015, USA
b Department of Geosciences, The University of Arizona, Tucson, AZ 85721-0077, USA
c Tetherless World Constellation, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
d School of Earth and Climate Sciences, University of Maine, Orono, ME 04469, USA
e Department of Geology, Southern Illinois University, Carbondale, IL 62901, USA
f Mathematics, Statistics, and Computer Science, Purdue University Northwest, Hammond, IN 46323-2094, USA
g Kola Science Centre of the Russian Academy of Sciences, Apatity, Murmansk Region 184209, Russia
h Department of Computer Science, University of Idaho, Moscow, ID 83844-1010, USA
i Mindat.org, Mitcham CR4 4FD, UK
j Department of Geology and Geophysics, University of Wyoming, Laramie, WY 82071-2000, USA
Abstract
Abstract  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     
Corresponding Authors: Robert M. Hazen   
Issue Date: 11 July 2019
Cite this article:   
Robert M. Hazen,Robert T. Downs,Ahmed Eleish, et al. Data-Driven Discovery in Mineralogy: Recent Advances in Data Resources, Analysis, and Visualization[J]. Engineering, 2019, 5(3): 397 -405 .
URL:  
http://www.engineering.org.cn/EN/10.1016/j.eng.2019.03.006     OR     http://www.engineering.org.cn/EN/Y2019/V5/I3/397
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