Garnet: A Comprehensive, Standardized, Geochemical Database Incorporating Locations and Paragenesis

Chiama, Kristen; Rutledge, Randolph; Gabor, Morgan; Lupini, Isabella; Hazen, Robert M.; Zhang, Shuang; Boujibar, A.

doi:10.1130/abs/2020se-344505

Cited by 4 publications

(6 citation statements)

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“…An additional component of this work involves analyzing those myriad attributes of mineral specimens via cluster analysis to relate their complex characteristics to their modes of formation, thereby determining the natural kind clustering of these mineral systems. There are many such projects underway, including those examining the formation of pyrite (Gregory et al 2019;Zhang et al 2019), garnet minerals (Chiama et al 2020(Chiama et al , 2022a(Chiama et al , 2022b…”

Section: Successful Use Cases In Mineral Informaticsmentioning

confidence: 99%

What is mineral informatics?

Prabhu

Morrison

Fox

et al. 2023

American Mineralogist

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Minerals are information-rich materials that offer researchers a glimpse into the evolution of planetary bodies. Thus, it is important to extract, analyze, and interpret this abundance of information in order to improve our understanding of the planetary bodies in our solar system and the role our planet's geosphere played in the origin and evolution of life. Over the past several decades, data-driven efforts in mineralogy have seen a gradual increase. The development and application of data science and analytics methods to mineralogy, while extremely promising, has also been somewhat ad-hoc in nature. In order to systematize and synthesize the direction of these efforts, we introduce the concept of "Mineral Informatics," which is the next frontier for researchers working with mineral data. In this paper, we present our vision for Mineral Informatics and the X-Informatics underpinnings that led to its conception, as well as the needs, challenges, opportunities, and future directions of the field. The intention of this paper is not to create a new specific field or a sub-field as a separate silo, but to document the needs of researchers studying minerals in various contexts and fields of study, to demonstrate how the systemization and enhanced access to mineralogical data will increase cross-and interdisciplinary studies, and how data science and informatics methods are a key next step in integrative mineralogical studies.

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Section: Successful Use Cases In Mineral Informaticsmentioning

confidence: 99%

What is mineral informatics?

Prabhu

Morrison

Fox

et al. 2023

American Mineralogist

View full text Add to dashboard Cite

show abstract

“…Althupite (Piret and Deliens 1987), bergenite (Locock and Burns 2003a), metavanmeersscheite (Christ and Clark 1960;Piret and Deliens 1982), mundite (Deliens and Piret 1981), and phurcalite (Plášil et al 2020) have unique structures and are not lumped with other species. We lump arsenovanmeersscheite with isostructural vammeersscheite (Piret and Deliens 1982).…”

Section: Lumping Of Modular Structuresmentioning

confidence: 99%

“…A significant challenge in applying cluster analysis is its dependence on the availability of large databases of mineral properties with multiple attributes. In spite of significant efforts to develop such data resources (Hazen et al 2019;Chiama et al 2020;Golden et al 2020;Prabhu et al 2020), only a few such studies have been attempted. Gregory et al (2019) assembled a database of 3,579 analyses of pyrite trace elements and applied a random forest machine-learning classifier to group the analyses into probable ore-forming environments, including iron oxide copper-gold (IOGG), sedimentary exhalative (SEDEX), and volcanic-hosted massive This is the peer-reviewed, final accepted version for American Mineralogist, published by the Mineralogical Society of America.…”

Section: Cluster Analysismentioning

confidence: 99%

“…The grain morphologies, trace elements, and associations of muscovite from complex pegmatites differ from those in metasediments (Fleet 2003). Similarly, plagioclase from basalt differs in morphology, chemical zoning, compositional range, trace elements, twinning and other microstructures, inclusions, and many other attributes from regional metamorphic plagioclase (Deer et al 2001 Chiama et al 2020).…”

Section: Cluster Analysismentioning

confidence: 99%

“…Pioneering work has been made in petrology databases (e.g., Lehnert et al 2007), while important mineral data resources document properties and localities of all known species (https://rruff.info/ima; https://mindat.org; Downs 2006;Lafuente et al 2015;Golden 2020). A few concerted efforts to tabulate multiple attributes for thousands of specimens for specific mineral species and groups represent encouraging progress (Gregory et al 2019;Chiama et al 2020;Boujibar et al 2021;Cheng et al 2021). However, a more unified and widespread international mineral informatics program is necessary to collect and preserve vast amounts of "dark data" and make those data available in Findable, We suggest that IMA-approved species correspond by lumping criteria to root mineral kinds (a reduction of ~29 %), of which species have only one known paragenetic mode and thus may be directly equated to 1623 natural kinds.…”

Section: Both Lumping and Splittingmentioning

confidence: 99%

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