FAIR² Data Articles in Food Science and Technology: From Lab to Supply Chain, Unlocking AI-Ready Data for Reuse and Impact

Scientific knowledge is fundamentally built on data; yet, for too long, research datasets have remained siloed, poorly documented, and inconsistently formatted. Food science and technology is no exception: high-value data generated across processing trials, characterization studies, packaging performance tests, sensory panels, shelf-life experiments, predictive microbiology, and modelling and simulation often live as supplementary spreadsheets, internal lab files, or proprietary instrument outputs, with little chance of being reused. As the field becomes increasingly data-intensive, with analyses driven by artificial intelligence and machine learning across the agri-food supply chain, merely sharing data is no longer sufficient. To enable responsible and trustworthy reuse, data must be well-structured, machine-actionable, ethically aligned, and rich in contextual metadata. Addressing this challenge, Frontiers has introduced a new article type: the FAIR² Data Article, a citable, peer-reviewed publication format that elevates datasets to become first-class research outputs.A FAIR² Data Article presents a dataset as the primary focus of publication, separate from traditional hypothesis-driven narratives. It allows detailed documentation of how the data was generated, processed, and structured, providing the transparency and provenance needed to evaluate its quality and suitability for reuse. Each article is accompanied by a FAIR² Data Portal that includes interactive visualizations, machine-actionable metadata, an executable Jupyter notebook, and a podcast-style audio summary to enhance accessibility. An embedded AI assistant guides readers through the dataset, helping researchers, food technologists, and industry users engage with the data in a meaningful way.This new standard builds on the FAIR principles — Findable, Accessible, Interoperable, and Reusable — by adding three key dimensions: AI-readiness, ethical alignment, and contextual richness. These additions ensure that datasets are not only usable by machines, but also responsibly managed and fully interpretable by diverse scientific and industrial audiences. As outlined in the FAIR² press release, this approach reflects Frontiers’ continued commitment to open science, reproducibility, and scientific integrity in the age of AI.This Research Topic welcomes FAIR² Data Articles that showcase high-quality, reusable, AI-ready datasets across the breadth of food science and technology, supporting the journal’s mission to advance innovative and sustainable solutions for the agri-food supply chain and the United Nations Sustainable Development Goals (SDG 2, 3, 9, 12, and 13).Topics of interest include, but are not limited to: - food processing datasets from thermal, mild thermal, nonthermal, and cooling technologies, including 3D printing and emerging unit operations - food packaging performance datasets covering migration, barrier properties, active and intelligent systems, and biodegradable materials - shelf-life, quality degradation kinetics, and predictive microbiology datasets - food characterization datasets spanning chemical, nutritional, structural, rheological, and sensory properties - datasets on plant-based, alternative-protein, and underutilized food sources - food fermentation, enzymology, and bioactives datasets, including encapsulation and bioavailability studies - food safety and quality control datasets, including non-destructive detection of chemical and microbial hazards, sensor outputs, and traceability records - food waste valorization and by-product characterization datasets - food modelling and simulation datasets, including computational fluid mechanics, balance-equation models, hybrid and data-driven models - AI- and ML-training datasets for process control, quality monitoring, and digital transformation of the food industry - industry-derived datasets generated under real-world processing conditions, with transparent reporting of methods, limitations, and provenanceSubmissions should ensure that datasets are presented with the documentation, structure, and metadata required for transparent peer review and downstream reuse. By bringing together FAIR² Data Articles from across food biotechnology, characterization, modelling, packaging and preservation, process design and engineering, product design, safety and quality control, and food and soft materials, this Research Topic aims to build a trustworthy, AI-ready data foundation that accelerates discovery, supports reproducibility, and amplifies the real-world impact of food science research. Topic editors: Giovanna Ferrari, Maria G. Corradini Source: https://www.frontiersin.org/research-topics/82367/fair2-data-articles-in-food-science-and-technology-from-lab-to-supply-chain-unlocking-ai-ready-data-for-reuse-and-impact