Deep Carbon through Deep Time

For most of the history of science, data-driven discovery has been difficult and timeconsuming: a lifetime of meticulous data collection and thoughtful synthesis was required to recognize previously hidden, higher-dimensional trends in multivariate data. Recognition of processes such as biological evolution by natural selection (1,2), continental evolution by plate tectonics (3,4), atmospheric and ocean oxygenation by photosynthesis (5,6), and climate change (7,8) required decades of integrated data synthesis preceding the discovery and acceptance of critical Earth phenomena. However, we stand at the precipice of a unique opportunity: to dramatically accelerate scientific discovery by coupling hard-won data resources with advanced analytical and visualization techniques (9,10). Today, Earth and life sciences are generating a multitude of data resources in numerous subdisciplines. Integration and synthesis of these diverse data resources will lead to an abductive, data-driven approach to investigating Earth’s mineralogical and geochemical history, as well as the coevolution of the geosphere and biosphere (11–13). In this chapter, we examine applications of data science in deep carbon research through three “use cases.” The first example focuses on geochemical and mineralogical anomalies from a period in Earth history (~1.3 to 0.9 Ga) when the supercontinent Rodinia was being assembled from previously scattered continental blocks. The second case study examines the diversity and distribution of minerals, notably carbon-bearing minerals, through deep time from the contexts of mineral evolution, mineral ecology, and mineral network analysis. The third and most speculative use case considers ways to analyze and visualize data that relate microbial protein expression to growth environments – complex interconnections that may shed light on Earth’s coevolving microbial ecosystems and near-surface geochemical environments. In each example, discoveries related to Earth’s deep-time evolution have resulted from the analysis and visualization of large data resources fostered by the Deep Carbon Observatory (DCO).