DOI: 10.5880/digis.e.2025.002
title: Compilation of GEOROC Mineral Compositions filtered by the MIST (Mineral Identification by Stoichiometry) Algorithm

description: The GEOROC database includes helpful compilations of mineral compositions aggregated from measurements reported in decades worth of publications, but it can be challenging to consistently filter mislabeled, inaccurate, or incomplete mineral compositions. MIST (Mineral Identification by Stoichiometry) is a stoichiometry-based computational algorithm that identifies geochemical observations with normalized elemental ratios matching natural minerals. The stoichiometric filters that were manually coded in MIST for over 240 mineral species are based on reported mineral formulas and well-documented examples of mineral chemistry reported in RRUFF and associated databases, typically including a ~5-10% tolerance in stoichiometric ratios based on measurement errors, vacancies, and substitutions. The MIST model can therefore efficiently filter the GEOROC mineral compilation files to recognize compositions whose normalized oxides match the labeled mineral stoichiometry. Furthermore, the MIST output includes results of intermediate data manipulation steps, a detailed stoichiometric formula for each input composition, and consistently calculated mineral endmembers such as Fo, En, Ws, and Fs. MIST is agnostic to the instrument used to collect oxide data. Because MIST uses normalized oxides, it cannot distinguish between some mineral species, where applicable, they are reported as a group (e.g., gypsum/bassanite/anhydrite). MIST can only recognize minerals encoded in the algorithm, so other real but less common minerals will not be recognized. The full list of minerals MIST can recognize, along with more details of the algorithm and results pages, are published in Siebach et al. (https://doi.org/10.1016/j.cageo.2025.106021). 
 
This dataset includes fifteen of the Compiled Mineral files published by GEOROC in 12-2024 including the MIST results (whether or not a species was confirmed by MIST). Prior to running the data through MIST, all files were filtered to only include mineral compositions that included major oxides (e.g., silicate mineral compositions where SiO2 &gt; 0 wt%). Furthermore, all variations of reported Fe were collapsed into a single column representing FeOT. 
 
Metadata is preserved from the original compiled GEOROC files, so users may add additional filters as appropriate for different purposes. Results have not been filtered for reported sum of total oxides, but doing so can help identify particular mineral species (e.g., separate gypsum from bassanite). An additional file preserves the full reference information for each mineral compilation. 
 
We suggest using the compositions that MIST identifies as stoichiometrically consistent with a mineral species as a standardized filter on the GEOROC datasets prior to utilizing the data in machine learning models or similar applications. These may also be helpful any time a user would like standardized formulas or mineral endmember information for these mineral compilations.
description: The DIGIS geochemical data repository is a research data repository in the Earth Sciences domain with a specific focus on geochemical data. It is hosted at GFZ Data Services through a collaboration between the Digital Geochemical Data Infrastructure (DIGIS) for GEOROC 2.0 (https://digis.geo.uni-goettingen.de) and the GFZ Helmholtz Centre for Geosciences. The repository archives, publishes and makes accessible user-contributed, peer-reviewed research data that fall within the scope of the GEOROC database. Compilations of previously published data are also made available on the GEOROC website (https://georoc.eu) as Expert Datasets.