A perspective on data science and geochemistry for tomorrow's resources

Geochemical data underpin the mining value chain from exploration and resource definition to mining and extraction to environmental stewardship. Transforming this complex, multivariate data into actionable insight is critical for accelerating decision-making while minimizing exploration and operational risk. Increasingly, this transformation relies on approaches broadly categorized as data science, which is an interdisciplinary field drawing on statistics, mathematics, and computer science (including machine learning and artificial intelligence) to extract information from large datasets. As analytical technologies continue to expand the dimensionality and volume of geochemical data, these methodologies are becoming essential tools for interpreting these data and addressing practical problems in mineral exploration, mining, and reclamation. This thematic collection, Data Science and Geochemistry for Tomorrow's Resources , highlights recent applications of data science workflows to geochemical data across the mining life cycle. Contributions span scales from regional exploration to deposit-scale characterization and mine closure, and employ diverse methods including multivariate statistics, supervised and unsupervised machine learning, and machine vision, and using integrated geochemical datasets. Collectively, these studies demonstrate how fit-for-purpose workflows built upon established analytical and statistical foundations can translate complex geochemical datasets into actionable insights for mineral discovery, resource evaluation, mine planning, and environmental stewardship. This summary also considers emerging themes, implicit in the collected papers, including the role of generative artificial intelligence as an enabling technology, the growing importance of curated and open geochemical datasets, and the need for updated geoscience education that incorporates data-driven methodologies. Together, these themes suggest that the integration of geochemistry and data science will play an increasingly important role in developing the mineral resources required for future generations.