An emerging approach towards creating comprehensive metabolomics knowledge bases is to expand upon traditional spectral libraries by integrating controlled and structured metadata information alongside the mass spectral data. "Reference data-driven metabolomics" uses not only annotated MS/MS spectra, but also all unannotated spectra in combination with metadata-annotated source data (e.g. were the samples derived from foods, personal care products, medications, etc.) as a pseudo spectral library . This strategy was exemplified by linking approximately 100,000 MS/MS spectra to 3,600 foods. A key aspect of this approach is that the foods are organized in a hierarchical ontology to enable granular downstream analyses of the food origins. For instance, an example path in the food hierarchy consists of "fruit → citrus → lemon → pink lemon." This enables performing analyses akin to microbiome science, in which the data may be interpreted at the class, genus, species, or even strain level depending on the research question at hand. Although this approach does not produce exact molecular identities, it provides essential insights into the origin of the data by matching against the reference source data, such as food. Reference data-driven metabolomics using the GNPS platform can increase the number of interpreted MS/MS spectra by up to an order of magnitude, and it has been used to obtain empirical assessments of dietary patterns from untargeted metabolomics data . Metadata-driven analyses can be broadly applied beyond diet readouts to also investigate other exposures (e.g. medications, personal care products, agrichemicals), disease phenotypes, organ system distributions, taxonomic matching, and many other uses. The key aspect that empowers reference data-driven metabolomics is that the spectral data are linked to controlled and curated metadata to be used as a pseudo-reference library. A less flexible but related metadata system is available for GC-MS data using BinBase, which covers a limited set of metadata .