Interoperable Geochemical Data Infrastructures for Computational Magmatic Studies through Controlled Vocabularies

Computational approaches in geochemistry are increasingly central to advancing our understanding of magmatic and volcanic systems as well as general Earth System processes. These methods rely on the integration of heterogeneous geochemical datasets spanning multiple spatial and temporal scales, analytical techniques, and material types. However, the effective reuse of such data remains limited by inconsistent metadata, ambiguous terminology, and insufficient interoperability between major geochemical data resources.

The Digital Geochemical Data Infrastructure (DIGIS) addresses these challenges as part of the "OneGeochemistry Initiative" by modernizing and integrating two foundational geochemical databases: GEOROC (Geochemistry of Rocks of the Oceans and Continents) and GeoReM (Geological and Environmental Reference Materials). GEOROC and other databases provided to the community through the EarthChem Portal provide open access to millions of geochemical analyses of igneous and metamorphic rocks, minerals, and glasses, while GeoReM curates critically evaluated data on reference materials used for calibration, quality control, and uncertainty assessment in geoanalytical laboratories worldwide. Re-establishing and strengthening interoperability between these complementary resources is essential for computational studies that require traceable, reproducible, and quantitatively robust input data.

This effort requires development and implementation of shared, machine-readable controlled vocabularies covering sample descriptions, lithology, mineralogy, geological setting, analytes, material matrices, methods, and reference materials. These vocabularies harmonize legacy data in GEOROC and GeoReM, while remaining compatible with international data standards developed by the OneGeochemistry Initiative. By linking observational data and rich metadata, the integrated system enables more flexible data filtering, uncertainty-aware model input, and reproducible benchmarking of computational results.

Recent computational studies illustrate the scientific value of such harmonized geochemical data infrastructures. Machine-learning approaches have successfully leveraged large global GEOROC data compilations to quantitatively discriminate tectono-magmatic settings and extract compositional features related to magma generation and evolution. Combining volcanic eruption histories with interoperable GEOROC and PetDB datasets from the EarthChem portal has further enabled data-driven exploration of magma compositional variability across tectonic environments. In parallel, emerging machine-learning-based petrological models, such as thermobarometers trained on large, standardized compositional melt and mineral datasets, demonstrate how consistent geochemical input data are critical for inferring magma storage conditions and differentiation.

This contribution highlights how sustained investment in FAIR-aligned geochemical data infrastructures directly support advances in computational magmatic studies. By improving interoperability of international geochemical databases, such as GEOROC and GeoReM, through controlled vocabularies, we provide a foundation for computational volcanic and magmatic studies, uncertainty-aware analysis, and quantitative modelling.