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Using adjacency matrix to explore remarkable associations in big and small mineral data

Geoscience Frontiers 2024年第5期15卷 61-74页

作者： Xiang Que Jingyi Huang Jolyon Ralph Jiyin Zhang anirudh prabhu Shaunna Morrison Robert Hazen Xiaogang Ma Department of Computer Science University of IdahoMoscowID 83844USA College of Computer and Information Sciences Fujian Agriculture and Forestry UniversityFuzhou 350002China Earth and Planets Laboratory Carnegie Institution for ScienceWashingtonDC 20015USA Hudson Institute of Mineralogy KeswickVA 22947USA

Data exploration,usually the first step in data analysis,is a useful method to tackle challenges caused by big geoscience *** conducts quick analysis of data,investigates the patterns,and generates/refines research questions to guide advanced statistics and machine learning *** background of this work is the open mineral data provided by several sources,and the focus is different types of associations in mineral properties and *** in mineralogy have been applying different techniques for exploring such *** the explored associations can lead to new scientific insights that contribute to crystallography,mineralogy,and geochemistry,the exploration process is often daunting due to the wide range and complexity of factors *** this study,our purpose is implementing a visualization tool based on the adjacency matrix for a variety of datasets and testing its utility for quick exploration of association patterns in mineral ***,software packages,and use cases have been developed to process a variety of mineral *** results demonstrate the efficiency of adjacency matrix in real-world *** the developed works of this study are open source and open access.

关键词： Adjacency matrix Association analysis Data exploration Mineral informatics Open data

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Application of Advanced Analytics and Visualization in Mineral Systems

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Acta Geologica Sinica(English Edition) 2019年第S1期93卷 55-55页

作者： Shaunna M.MORRISON anirudh prabhu Ahmed ELEISH PAN Feifei ZHONG Hao HUANG Fang Peter FOX MA Xiaogang Jolyon RALPH Joshua J.GOLDEN Robert T.DOWNS LIU Chao Simone E.RUNYON Robert M.HAZEN Geophysical Laboratory Carnegie Institution for Science1530 P Street NWWashington DC 20005USA Rensselaer Polytechnic Institute 1008th StTroyNY 12180USA University of Idaho 875 Perimeter DrMoscowID 83844USA *** University of Arizona TucsonAZ 85721USA University of Wyoming 1000 E University AveLaramieWY 82071USA

The key to answering many compelling and complex questions in Earth,planetary,and life science lies in breaking down the barriers between scientific fields and harnessing the integrated,multi-disciplinary power of Earth,planetary,and bioscience data *** have a unique opportunity to integrate large and rapidly expanding"big data"resources,to enlist powerful analytical and visualization methods,and to answer multi-disciplinary questions that cannot be addressed by one field alone.

关键词： minerals network analysis data-driven discovery mineral evolution mineral ecology

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

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Engineering 2019年第3期5卷 397-405页

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 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

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.

关键词： Mineral evolution ecology Skyline diagrams Network analysis Cluster Chord Klee

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