A fundamental goal of mineralogy and petrology is the deep understanding of mineral phase relationships and the consequent spatial and temporal patterns of mineral diversity and distribution in rocks, ore bodies, sediments, meteorites, and other natural polycrystalline materials. Large and growing databases of mineral species, properties, localities, and co-occurrence provide opportunities for data-driven discovery in mineralogy, including the prediction of new mineral species and ore deposits.

Data-driven discovery depends on three key developments: (1) enhanced data resources in mineralogy and petrology; (2) development and implementation of analytical and visualization methods; and (3) creative framing of questions related to mineral diversity, distribution, and co-occurrence in space and time.

We are especially interested in visualization methods that illustrate multiple attributes of complex mineral systems. In particular, network analysis provides a dynamic, quantitative, and predictive visualization framework for employing “big data” to explore complex and otherwise hidden higher-dimensional patterns of diversity and distribution in such mineral systems. Mineral networks (see Figure) facilitate quantitative comparison of lithologies from different planets and moons, analysis of coexistence patterns simultaneously among hundreds of mineral species and their localities, exploration of varied paragenetic modes of mineral groups, and investigation of changing patterns of mineral occurrence through deep time. Mineral network analysis, furthermore, represents an effective visual approach to teaching and learning in mineralogy and petrology.