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

作     者：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 

作者机构：Geophysical Laboratory Carnegie Institution for Science Washington DC 20015 USA Department of Geology and Geophysics University of Wyoming Laramie WY 82071-2000 USA Department of Geosciences The University of Arizona Tucson AZ 85721-0077 USA Tetherless World Constellation Rensselaer Polytechnic Institute Troy NY 12180 USA School of Earth and Climate Sciences University of Maine Orono ME 04469 USA Department of Geology Southern Illinois University Carbondale IL 62901 USA Mathematics Statistics and Computer Science Purdue University Northwest Hammond IN 46323-2094 USA Kola Science Centre of the Russian Academy of Sciences Apatity Murmansk Region 184209 Russia Department of Computer Science University of Idaho Moscow ID 83844-1010 USA *** Mitcham CR4 4FD UK 

出 版 物：《Engineering》 (工程（英文）)

年 卷 期：2019年第5卷第3期

页      面：397-405页

核心收录：

学科分类：07[理学] 08[工学] 

基　　金：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) 

主　　题：Mineral evolution ecology Skyline diagrams Network analysis Cluster Chord Klee 

摘      要：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 *** 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.