FAIR² Data in Nutrigenomics and Nutrition Methodology

High-quality nutrition science increasingly depends on datasets that are not only informative, but also well-structured, transparent, and reusable. As nutrition research becomes more data-rich and analytically complex, there is a growing need for resources that connect dietary exposures with genomic, epigenetic, metabolomic, microbiome, phenotypic, and methodological data in ways that support rigorous interpretation and future discovery. This Research Topic will bring together FAIR² Data Articles that advance the fields of Nutrigenomics and Nutrition Methodology by publishing curated, peer-reviewed, citable datasets that strengthen understanding of the relationships between diet, genes, metabolism, and health, while also improving how nutrition research is designed, measured, analyzed, and reported.FAIR² Data Articles are especially valuable in this area because they transform datasets into formal research outputs that are findable, accessible, interoperable, reusable, and AI-ready. In nutrigenomics and methodological nutrition research, the long-term value of a study often lies not only in its immediate findings, but in the quality, richness, and reusability of the underlying data. By publishing datasets alongside structured metadata, clear provenance, data dictionaries, and an interactive portal for exploration and reuse, FAIR² Data Articles support reproducibility, secondary analysis, cross-study comparison, and responsible application of machine learning and artificial intelligence. They also give appropriate credit to researchers who generate high-value nutrition datasets and methodological resources for the wider community.Within Nutrigenomics, this Research Topic seeks FAIR² Data Articles that support the illumination of the interplay between genes, diet, and health, including gene regulation, gene expression, genetic variants, epigenetic mechanisms, metabolomic signatures, and diet–microbiome interactions. Within Nutrition Methodology, the collection welcomes FAIR² Data Articles that enable the improvement of methodological rigor in nutrition science, including those covering human feeding protocols, technical measurement methods, statistical approaches, data generation pipelines, and best practices in data analysis, interpretation, and reporting. We especially encourage submissions where methodological innovation supports robust nutrigenomic inquiry, and where complex nutrition datasets are prepared for broad reuse across human health research.Topics of interest include:o datasets on diet-related epigenetic changes and their links to metabolism, feeding behaviors, physical activity, and nutrition-related diseaseso datasets on genes, gene regulation, gene expression, and genetic variants relevant to nutrition and health outcomeso multi-omics datasets integrating diet, genes, metabolites, and the microbiomeo gut microbiota metagenomic, transcriptomic, or microbial metabolomic datasets relevant to nutrition and host healtho human metabolome datasets identifying biomarkers and molecular signatures of diet and nutrition-related diseaseo datasets derived from human feeding protocols and controlled nutrition interventionso datasets on dietary intake, food composition, origins and processing Topic editors: Annalisa Terranegra, J Bruce German, Angela M Zivkovic Source: https://www.frontiersin.org/research-topics/80267/fair2-data-in-nutrigenomics-and-nutrition-methodology