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Integrating mineralogy with data science is critical to modernizing Earth materials research and its applications to geosciences. Data were compiled on 95 650 garnet sample analyses from a variety of sources, ranging from large repositories (EarthChem, RRUFF, MetPetDB) to individual peer-reviewed literature. An important feature is the inclusion of mineralogical “dark data” from papers published prior to 1990. Garnets are commonly used as indicators of formation environments, which directly correlate with their geochemical properties; thus, they are an ideal subject for the creation of an extensive data resource that incorporates composition, locality information, paragenetic mode, age, temperature, pressure, and geochemistry. For the data extracted from existing databases and literature, we increased the resolution of several key aspects, including petrogenetic and paragenetic attributes, which we extended from generic material type (e.g., igneous, metamorphic) to more specific rock-type names (e.g., diorite, eclogite, skarn) and locality information, increasing specificity by examining the continent, country, area, geological context, longitude, and latitude. Likewise, we utilized end-member and quality index calculations to help assess the garnet sample analysis quality. This comprehensive dataset of garnet information is an open-access resource available in the Evolutionary System of Mineralogy Database (ESMD) for future mineralogical studies, paving the way for characterizing correlations between chemical composition and paragenesis through natural kind clustering (Chiama et al., 2022; https://doi.org/10.48484/camh-xy98). We encourage scientists to contribute their own unpublished and unarchived analyses to the growing data repositories of mineralogical information that are increasingly valuable for advancing scientific discovery.