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 language | English (US) |
|---|---|
| Journal | Engineering |
| DOIs | |
| State | Published - Jan 1 2019 |
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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)
Cite this
Data-Driven Discovery in Mineralogy : Recent Advances in Data Resources, Analysis, and Visualization. / Hazen, Robert M.; Downs, Robert T; Eleish, Ahmed; Fox, Peter; Gagné, Olivier C.; Golden, Joshua J.; Grew, Edward S.; Hummer, Daniel R.; Hystad, Grethe; Krivovichev, Sergey V.; Li, Congrui; Liu, Chao; Ma, Xiaogang; Morrison, Shaunna M.; Pan, Feifei; Pires, Alexander J.; Prabhu, Anirudh; Ralph, Jolyon; Runyon, Simone E.; Zhong, Hao.
In: Engineering, 01.01.2019.Research output: Contribution to journal › Review article
}
TY - JOUR
T1 - Data-Driven Discovery in Mineralogy
T2 - Recent Advances in Data Resources, Analysis, and Visualization
AU - Hazen, Robert M.
AU - Downs, Robert T
AU - Eleish, Ahmed
AU - Fox, Peter
AU - Gagné, Olivier C.
AU - Golden, Joshua J.
AU - Grew, Edward S.
AU - Hummer, Daniel R.
AU - Hystad, Grethe
AU - Krivovichev, Sergey V.
AU - Li, Congrui
AU - Liu, Chao
AU - Ma, Xiaogang
AU - Morrison, Shaunna M.
AU - Pan, Feifei
AU - Pires, Alexander J.
AU - Prabhu, Anirudh
AU - Ralph, Jolyon
AU - Runyon, Simone E.
AU - Zhong, Hao
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Chord diagrams
KW - Cluster analysis
KW - Klee diagrams
KW - Mineral ecology
KW - Mineral evolution
KW - Network analysis
KW - Skyline diagrams
UR - http://www.scopus.com/inward/record.url?scp=85066813015&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066813015&partnerID=8YFLogxK
U2 - 10.1016/j.eng.2019.03.006
DO - 10.1016/j.eng.2019.03.006
M3 - Review article
AN - SCOPUS:85066813015
JO - Engineering
JF - Engineering
SN - 2095-8099
ER -