Showcasing FAIR² Data Articles in Ophthalmology: Unlocking Trustworthy, 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. As science becomes increasingly data-intensive, with analyses driven by artificial intelligence, 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 meta-data. 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 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 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.Across disciplines, researchers face challenges in preparing datasets for reuse; ranging from inconsistent metadata and missing context to unclear licensing and incomplete provenance. These barriers hinder collaboration, slow scientific progress, and limit the societal impact of research. By offering a structured, peer-reviewed venue for publishing high-quality datasets, FAIR² Data Articles aim to unlock the full value of scientific data and make it more accessible, reusable, and trustworthy.This research topic will showcase FAIR² data across the field of ophthalmology to advance discovery in the field.Conflict of interest declaration:Topic Editor Alon Kahana is a consultant for BioTissue Inc., and Alumis Inc. Topic editors: Rohit Saxena, Alon Kahana Source: https://www.frontiersin.org/research-topics/78502/showcasing-fair2-data-articles-in-ophthalmology-unlocking-trustworthy-ai-ready-data-for-reuse-and-impact