FAIR Metabolomics: Open Datasets, Shared Standards, and the Road to AI-Ready Data

This Research Topic is part of our FAIR series. For other Research Topics in the series, please see below:FAIR Cell Biology Data: From Image Archives to Interoperable Signalling DatasetsFAIR RNA Data: Open Resources for Transcriptomics, Structure, and Regulatory Network ResearchFAIR Genomics: Standards, Repositories, and Reproducibility in Chromatin and Genome ResearchFAIR Mitochondrial Data: Open Resources for Integrative Research in Bioenergetics and DiseaseFAIR Structural Data in the Age of AI: From Deposition to DiscoveryMaking Glycan Data FAIR: Addressing the Interoperability Gap in GlycoscienceMetabolomics sits at the intersection of chemistry, biology, and clinical translation, generating complex, multi-platform datasets that are highly sensitive to experimental conditions. Progress in the field depends on the ability to integrate, compare, and reuse data across studies; yet inconsistent metadata reporting, proprietary data formats, and incomplete deposition remain widespread barriers. This Research Topic calls for submissions that advance FAIR metabolomics, from the development and adoption of community standards such as mzML, ISA-Tab, and the CIMR guidelines, to open repository deposition in MetaboLights and the Metabolomics Workbench. Contributions addressing metabolite identification confidence reporting using the Metabolomics Society's tiered levels, cross-platform harmonisation workflows, and methods that lower the technical barrier to data reuse are particularly welcome. Contributions addressing batch correction, data normalisation workflows, and the documentation of instrument-specific parameters are essential for enabling robust cross-study comparison. We also encourage submissions that demonstrate how open metabolomics datasets have been reanalysed to generate novel insights, and that address multi-omics integration linking metabolite abundances to genomic, proteomic, or clinical data. FAIR² Data Articles presenting fully annotated, repository-deposited metabolomics datasets are a primary submission type for this topic. We also welcome perspectives on the infrastructure gaps that most hinder FAIR metabolomics adoption, and proposals for community-level solutions that could accelerate open data sharing across platforms and research groups. KEY THEMES - Community data standards: mzML, ISA-Tab, CIMR reporting guidelines - Metabolite identification confidence reporting (Metabolomics Society tiers 1-4) - Open repository deposition: MetaboLights, Metabolomics Workbench - Cross-platform metabolomics data harmonisation and batch correction workflows - Multi-omics integration linking metabolomics to genomics, proteomics, and clinical data - Reanalysis of public metabolomics datasets for novel biological discovery AI-ready metabolomics datasets for annotation, pathway, and biomarker modelling - Lowering technical barriers to metabolomics data reuse for non-specialist researchers Topic editors: Michal Ciborowski Source: https://www.frontiersin.org/research-topics/86718/fair-metabolomics-open-datasets-shared-standards-and-the-road-to-ai-ready-data