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 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”. Mineral Informatics is the next frontier for researchers working with mineral data. In this paper, we present our vision for Mineral Informatics, the X-Informatics underpinnings that led to its conception, the needs, challenges, opportunities, and future directions. 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 increased 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.

There has been a significant increase in the amount and accuracy of mineral data (from resources like Mindat, MED or the GEMI) and the improvements in technological resources make it possible to explore and answer large, outstanding scientific questions, such as, understanding the mineral assemblages on Earth and how they compare to assemblages and localities on other planets. In the last couple of years, affinity analysis methods have been used to:1) Predict unreported minerals at an existing locality, 2) Predict localities for a set of known minerals[1]. We’ve chosen to call this application “Mineral Association Analysis”[2]. Affinity analysis is an unsupervised machine learning method that uses mined association rules to find interesting patterns in the data. Most of the metrics used to evaluate market basket analysis methods focus on either the ability of the model to ingest large amounts of data[3], or using a metric based comparison of various algorithms used for association rule mining[4], or on evaluating the rules mined to more efficiently generate association rules[5]. However, when patterns generated in an unsupervised method are used to predict the occurrences of entities such as minerals, there needs to be a way to evaluate the predictions made by the model. It’s in such an area that there has been very little work. In this abstract, we explore the development of a new method to evaluate the results of association rule mining algorithms specifically when used when the association rules generated are utilized in a predictive setting. [1] Prabhu et. al (2019). In AGU Fall Meeting Abstracts (EP23D-2286). [2] Morrison et al. Nat. Geo. (2021) In Prep. [3] Agrawal et al. (1993) SIGMOD’93. [4] Sharma et al. (2012) IJERT 1(06). [5] Üstündağ and Bal (2014) Proc. in Comp.

Transition metal cofactors are crucial for many biological processes. Despite being primarily considered to be toxic, the transition metal cadmium (Cd) was discovered to be a substitute for zinc (Zn) in photosynthetic carbon fixation pathways in marine diatoms. However, it is not known how conditions in the geosphere impacted Cd availability and its incorporation as an alternative metal cofactor for phytoplankton. We employed mineral chemistry network analysis to investigate which geochemical factors may have influenced the availability of Cd and Zn during the putative time period that alternative Cd-based pathway evolved. Our results show that Zn minerals are more chemically diverse than are Cd minerals, but Zn- and Cd-containing minerals have similar mean electronegativities when specifically considering sulfur (S)-containing species. Cadmium and zinc sulfides are the most common Cd- and Zn-containing mineral species over the past 500 million years. In particular, the Cd and Zn sulfides, respectively greenockite and sphalerite, are highly abundant during this time period. Furthermore, S-containing Cd- and Zn minerals are commonly co-located in geologic time, allowing them to be weathered and transported to the ocean in tandem, rather than occurring from separate sources. We suggest that the simultaneous weathering of Cd and Zn sulfides allowed for Cd to be a bioavailable direct substitute for Zn in protein complexes during periods of Zn depletion. The biogeochemical cycles of Zn and Cd exemplify the importance of the coevolution of the geosphere and biosphere in shaping primary production in the modern ocean.

The Astromaterials Data System (AstroMat) is a NASA-funded project, working in close collaboration with the Johnson Space Center (JSC) to provide access to and preserve analytical data from JSC’s astromaterials collections. Meteorite data from close to 1000 peer-reviewed publications, primarily from the JSC Antarctic collections, and data from over 800 lunar publications have been ingested into AstroMat. Data can be explored at the level of reference and sample, or queried interactively through the AstroDB Search (AstroSearch). AstroSearch v 1.0 incorporated lunar and meteorite JSC collections, lunar missions, geofeatures, taxons, analyzed materials, and analysis methods searches. Working closely with domain scientists we have developed AstroSearch v 2.0. This version of the interface enhances the functionality of the original by adding search by chemistry (with comprehensive variable and unit selection), more granular analysis type refinement, and a streamlined customizable data output. The AstroDB Search is paired with the AstroDesk application, where users can login via their ORCiD and save an unlimited number of customized search queries. For researchers who need to submit, archive, and share their data with citable unique identifiers (DOIs) to comply with publisher and funding agency requirements, AstroMat offers a companion service to the AstroDB - the Astromaterials Data Repository (AstroRepo). Through its commitment to long-term data access and preservation, the Astromaterials Data Systems aims to help align cosmochemistry data with the Big-Data Era and reduce time to science for Planetary Sciences researchers by providing FAIR data for next generation scientific applications.