Please wait a minute...
Submit  |   Chinese  | 
Advanced Search
   Home  |  Online Now  |  Current Issue  |  Focus  |  Archive  |  For Authors  |  Journal Information   Open Access  
Submit  |   Chinese  | 
Engineering    2019, Vol. 5 Issue (3) : 397 -405
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, Mitcham CR4 4FD, UK
j Department of Geology and Geophysics, University of Wyoming, Laramie, WY 82071-2000, USA
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 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:     OR
[1]   G. Gastil. The distribution of mineral dates in time and space. Am J Sci. 1960; 258(1): 1-35.
[2]   J.T. Nash, H.C. Granger, S.S. Adams. Geology and concepts of genesis of important types of uranium deposits. Econ Geol. 1981; 63-116.
[3]   A.G. Zhabin. Is there evolution of mineral speciation on Earth?. Dokl Earth Sci Sect. 1981; 247: 142-144.
[4]   Yushkin NP. Evolutionary ideas in modern mineralogy. Zap Vses Mineral Obshch 1982;116(4):432–42. Russian.
[5]   R.M. Hazen, D. Papineau, W. Bleeker, R.T. Downs, J. Ferry, T. McCoy, et al.. Mineral evolution. Am Mineral. 2008; 93(11–12): 1693-1720.
[6]   R.M. Hazen, R.J. Ewing, D.A. Sverjensky. Evolution of uranium and thorium minerals. Am Mineral. 2009; 94(10): 1293-1311.
[7]   R.M. Hazen, A. Bekker, D.L. Bish, W. Bleeker, R.T. Downs, J. Farquhar, et al.. Needs and opportunities in mineral evolution research. Am Mineral. 2011; 96(7): 953-963.
[8]   R.M. Hazen, J.J. Golden, R.T. Downs, G. Hysted, E.S. Grew, D. Azzolini, et al.. Mercury (Hg) mineral evolution: a mineralogical record of supercontinent assembly, changing ocean geochemistry, and the emerging terrestrial biosphere. Am Mineral. 2012; 97(7): 1013-1042.
[9]   R.M. Hazen, D. Papineau. Mineralogical co-evolution of the geosphere and biosphere. In: editor. Fundamentals of geobiology. Oxford: Wiley-Blackwell; 2012. p. 333-350.
[10]   R.M. Hazen, A.P. Jones, L. Kah, D.A. Sverjensky. Carbon mineral evolution. In: editor. Carbon in Earth. Washington, DC: Mineralogical Society of America; 2013. p. 79-107.
[11]   R.M. Hazen, D.A. Sverjensky, D. Azzolini, D.L. Bish, S. Elmore, L. Hinnov, et al.. Clay mineral evolution. Am Mineral. 2013; 98(11–12): 2007-2029.
[12]   R.M. Hazen, X.M. Liu, R.T. Downs, J.J. Golden, A.J. Pires, E.S. Grew, et al.. Mineral evolution: episodic metallogenesis, the supercontinent cycle, and the coevolving geosphere and biosphere. Soc Econ Geolog Special Pub. 2014; 18: 1-15.
[13]   R.M. Hazen, E.S. Grew, M. Origlieri, R.T. Downs. On the mineralogy of the “Anthropocene Epoch”. Am Mineral. 2017; 102(3): 595-611.
[14]   R.M. Hazen. Evolution of minerals. Sci Am. 2010; 302(3): 58-65.
[15]   R.M. Hazen. Paleomineralogy of the Hadean Eon: a preliminary list. Am J Sci. 2013; 313(9): 807-843.
[16]   R.M. Hazen. Mineral evolution, the Great Oxidation Event, and the rise of colorful minerals. Mineralog Record. 2015; 46(805–816): 34.
[17]   Hazen RM. An evolutionary system of mineralogy: proposal for a classification based on natural kind clustering. Am Mineral. In press.
[18]   R.M. Hazen, N. Eldredge. Themes and variations in complex systems. Elements. 2010; 6(1): 43-46.
[19]   R.M. Hazen, J.M. Ferry. Mineral evolution: mineralogy in the fourth dimension. Elements. 2010; 6(1): 9-12.
[20]   J. Golden, M. McMillan, R.T. Downs, G. Hystad, H.J. Stein, A. Zimmerman, et al.. Rhenium variations in molybdenite (MoS2): evidence for progressive subsurface oxidation. Earth Planet Sci Lett. 2013; 366: 1-5.
[21]   E.S. Grew, R.M. Hazen. Evolution of the minerals of beryllium. Stein. 2013; 4-19.
[22]   E.S. Grew, R.M. Hazen. Beryllium mineral evolution. Am Mineral. 2014; 99(5–6): 999-1021.
[23]   S.V. Krivovichev. Structural complexity of minerals: information storage and processing in the mineral world. Mineral Mag. 2013; 77(3): 275-326.
[24]   S.V. Krivovichev. Structural complexity of minerals and mineral parageneses: information and its evolution in the mineral world. In: editor. Highlights in mineralogical crystallography. Berlin/Boston: de Gruyter; 2015. p. 31-74.
[25]   E.S. Grew, R.F. Dymek, J.C.M. De Hoog, S.L. Harley, J.M. Boak, R.M. Hazen, et al.. Boron isotopes in tourmaline from the 3.7–3.8 Ga Isua Belt, Greenland: sources for boron in Eoarchean continental crust and seawater. Geochim Cosmochim Acta. 2015; 163: 156-177.
[26]   S.V. Krivovichev, V.G. Krivovichev, R.M. Hazen. Structural and chemical complexity of minerals: correlations and time evolution. Eur J Mineral. 2018; 30(2): 231-236.
[27]   C. Liu, A.H. Knoll, R.M. Hazen. Geochemical and mineralogical evidence that Rodinian assembly was unique. Nat Commun. 2017; 8(1): 1950.
[28]   G. Hystad, R.T. Downs, R.M. Hazen. Mineral species frequency distribution conforms to a large number of rare events model: prediction of Earth’s missing minerals. Math Geosci. 2015; 47(6): 647-661.
[29]   G. Hystad, R.T. Downs, E.S. Grew, R.M. Hazen. Statistical analysis of mineral diversity and distribution: Earth’s mineralogy is unique. Earth Planet Sci Lett. 2015; 426: 154-157.
[30]   G. Hystad, R.T. Downs, R.M. Hazen, J.J. Golden. Relative abundances for the mineral species on Earth: a statistical measure to characterize Earth-like planets based on Earth’s mineralogy. Math Geosci. 2017; 49(2): 179-194.
[31]   R.M. Hazen, E.S. Grew, R.T. Downs, J. Golden, G. Hystad. Mineral ecology: chance and necessity in the mineral diversity of terrestrial planets. Can Mineral. 2015; 53(2): 295-323.
[32]   R.M. Hazen, G. Hystad, R.T. Downs, J. Golden, A. Pires, E.S. Grew. Earth’s “missing” minerals. Am Mineral. 2015; 100(10): 2344-2347.
[33]   R.M. Hazen, D.R. Hummer, G. Hystad, R.T. Downs, J.J. Golden. Carbon mineral ecology: predicting the undiscovered minerals of carbon. Am Mineral. 2016; 101(4): 889-906.
[34]   R.M. Hazen, G. Hystad, J.J. Golden, D.R. Hummer, C. Liu, R.T. Downs, et al.. Cobalt mineral ecology. Am Mineral. 2017; 102(1): 108-116.
[35]   E.S. Grew, S.V. Krivovichev, R.M. Hazen, G. Hystad. Evolution of structural complexity in boron minerals. Can Mineral. 2016; 54(1): 125-143.
[36]   E.S. Grew, G. Hystad, R.M. Hazen, S.V. Krivovichev, L.A. Gorelova. How many boron minerals occur in Earth’s upper crust?. Am Mineral. 2017; 102(8): 1573-1587.
[37]   R.M. Hazen, J. Ausubel. On the nature and significance of rarity in mineralogy. Am Mineral. 2016; 101(6): 1245-1251.
[38]   C. Liu, G. Hystad, J.J. Golden, D.R. Hummer, R.T. Downs, S.M. Morrison, et al.. Chromium mineral ecology. Am Mineral. 2017; 102(3): 612-619.
[39]   C. Liu, A. Eleish, G. Hystad, J.J. Golden, R.T. Downs, S.M. Morrison, et al.. Analysis and visualization of vanadium mineral diversity and distribution. Am Mineral. 2018; 103(7): 1080-1086.
[40]   S.M. Morrison, C. Liu, A. Eleish, A. Prabhu, C. Li, J. Ralph, et al.. Network analysis of mineralogical systems. Am Mineral. 2017; 102(8): 1588-1596.
[41]   Downs RT. The RRUFF project: an integrated study of the chemistry, crystallography, Raman and infrared spectroscopy of minerals. In: Proceedings of the 19th General Meeting of the International Mineralogical Association; 2006 July 23–28; Kobe, Japan; 2006.
[42]   K.A. Lehnert, D. Walker, B. Sarbas. EarthChem: a geochemistry data network. Geochim Cosmochim Acta. 2007; 71: A559.
[43]   M.D. Wilkinson, M. Dumontier, I.J. Aalbersberg, G. Appleton, M. Axton, A. Baak, et al.. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016; 3: 160018.
[44]   P. Fox, J. Hendler. Changing the equation on scientific data visualization. Science. 2011; 331(6018): 705-708.
[45]   R.M. Hazen. Data-driven abductive discovery in mineralogy. Am Mineral. 2014; 99(11–12): 2165-2170.
[46]   In: editor. Planetary materials. Chantilly: Mineralogical Society of America; 1998.
[47]   S.M. Morrison, R.T. Downs, D.F. Blake, D.T. Vaniman, D.W. Ming, E.B. Rampe, et al.. Crystal chemistry of martian minerals from Bradbury Landing through Naukluft Plateau, Gale crater, Mars. Am Mineral. 2018; 103(6): 857-871.
[48]   X.M. Liu, L.C. Kah, A.H. Knoll, H. Cui, A.J. Kaufman, A. Shahar, et al.. Tracing Earth’s CO2 evolution using Zn/Fe ratios in marine carbonate. Geochem Perspect Lett. 2016; 2: 24-34.
[49]   S.B. Carroll. Chance and necessity: the evolution of morphological complexity and diversity. Nature. 2001; 409(6823): 1102-1109.
[50]   X. Ma, D. Hummer, J.J. Golden, P.A. Fox, R.M. Hazen, S.M. Morrison, et al.. Using visualized exploratory data analysis to facilitate collaboration and hypothesis generation in cross-disciplinary research. ISPRS Int J Geoinf. 2017; 6(11): 368.
[51]   E. Otte, R. Rousseau. Social network analysis: a powerful strategy, also for the information sciences. J Inf Sci. 2002; 28(6): 441-453.
[52]   In: editor. Computational social network analysis: trends, tools and research advances. New York: Springer; 2010.
[53]   C.A.R. Pinheiro. Social network analysis in telecommunications.
[54]   C. Kadushin. Understanding social networks.
[55]   N. Hwang, R. Houghtalen. Fundamentals of hydraulic engineering systems.
[56]   R. Guimerà, S. Mossa, A. Turtschi, L.A.N. Amaral. The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. Proc Natl Acad Sci USA. 2005; 102(22): 7794-7799.
[57]   Dong W, Pentland A. A network analysis of road traffic with vehicle tracking data. In: Proceedings of the American Association of Artificial Intelligence, Spring Symposium, Human Behavior Modeling; 2009 Mar 23–25; Palo Alto, CA, USA; 2009. p. 7–12.
[58]   G.A. Pagani, M. Aiello. The power grid as a complex network: a survey. Phys A. 2013; 392(11): 2688-2700.
[59]   G. Amitai, A. Shemesh, E. Sitbon, M. Shklar, D. Netanely, I. Venger, et al.. Network analysis of protein structures identifies functional residues. J Mol Biol. 2004; 344(4): 1135-1146.
[60]   K. Banda-R, A. Delgado-Salinas, K.G. Dexter, R. Linares-Palomino, A. Oliveira-Filho, D. Prado, et al.. Plant diversity patterns in neotropical dry forests and their conservation implications. Science. 2016; 353(6306): 1383-1387.
[61]   E. Corel, P. Lopez, R. Méheust, E. Bapteste. Network-thinking: graphs to analyze microbial complexity and evolution. Trends Microbiol. 2016; 24(3): 224-237.
[62]   A.D. Muscente, A. Prabhu, H. Zhong, A. Eleish, M.B. Meyer, P. Fox, et al.. Quantifying ecological impacts of mass extinctions with network analysis of fossil communities. Proc Natl Acad Sci USA. 2018; 115(20): 5217-5222.
[63]   E.D. Kolaczyk. Statistical analysis of network data.
[64]   M.E.J. Newman. Networks: an introduction.
[65]   A.S. Asratian, T.M.J. Denley, R. Häggkvist. Bipartite graphs and their applications.
[66]   G. Adomavicius, A. Tuzhilin. Context-aware recommender systems. In: editor. Recommender systems handbook. Boston: Springer; 2011. p. 217-253.
[67]   F. Ricci, L. Rokach, B. Shapira. Introduction to recommender systems handbook. In: editor. Recommender systems handbook. Boston: Springer; 2011. p. 1-35.
[68]   U. Panniello, A. Tuzhilin, M. Gorgoglione. Comparing context-aware recommender systems in terms of accuracy and diversity. User Model User-adapt Interact. 2014; 24(1–2): 35-65.
[69]   O.C. Gagné, F.C. Hawthorne. Bond-length distributions for ions bonded to oxygen: alkali and alkaline-earth metals. Acta Crystallogr B Struct Sci Cryst Eng Mater. 2016; 72(Pt 4): 602-625.
[70]   O.C. Gagné, F.C. Hawthorne. Bond-length distributions for ions bonded to oxygen: results for the non-metals and discussion of lone-pair stereoactivity and the polymerization of PO4. Acta Crystallogr B. 2018; 74: 79-96.
[71]   O.C. Gagné, F.C. Hawthorne. Bond-length distributions for ions bonded to oxygen: metalloids and post-transition metals. Acta Crystallogr B. 2018; 74: 63-78.
[72]   O.C. Gagné, F.C. Hawthorne. Bond-length distributions for ions bonded to oxygen: results for the transition metals and discussion of d0 cations and the Jahn-Teller effect. Acta Cryst B. 2018; 74(Pt 1): 79-96.
[73]   O.C. Gagné. Bond-length distributions for ions bonded to oxygen: results for the lanthanides and actinides and discussion of the f-block contraction. Acta Crystallogr B. 2018; 74: 49-62.
[74]   O.C. Gagné, P.H.J. Mercier, F.C. Hawthorne. A priori bond-valence and bond-length calculations in rock-forming minerals. Acta Crystallogr B. 2018; 74: 470-482.
[75]   O.C. Gagné, F.C. Hawthorne. Comprehensive derivation of bond-valence parameters for ion pairs involving oxygen. Acta Crystallogr B Struct Sci Cryst Eng Mater. 2015; 71(Pt 5): 562-578.
[76]   R. Schutt, C. O’Neil. Doing data science: straight talk from the frontline.
[77]   R. Kitchin. The data revolution: big data, open data, data infrastructures & their consequences.
[1] Stephen M. Malone, Marc J. Weissburg, Bert Bras. Industrial Ecosystems and Food Webs: An Ecological-Based Mass Flow Analysis to Model the Progress of Steel Manufacturing in China[J]. Engineering, 2018, 4(2): 209 -217 .
[2] Zackery B. Morris, Stephen M. Malone, Abigail R. Cohen, Marc J. Weissburg, Bert Bras. Impact of Low-Impact Development Technologies from an Ecological Perspective in Different Residential Zones of the City of Atlanta, Georgia[J]. Engineering, 2018, 4(2): 194 -199 .
Copyright © 2015 Higher Education Press & Engineering Sciences Press, All Rights Reserved.