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.

To mark the launch of this new article type, this Research Topic welcomes both:

• FAIR² Data Articles that showcase high-quality, reusable, AI-ready datasets, and
• Scholarly perspectives on the development, implementation, and impact of the FAIR² framework across disciplines.

We welcome submissions in scope for the following journals:

• Frontiers in Bioinformatics and Frontiers in Systems Biology will focus on data-rich resources, reuse workflows, and machine-actionable datasets in biology and computational research.
• Frontiers in Research Metrics and Analytics will focus on open science policy, data publishing models, responsible metrics, and the scholarly impact of data reuse.

Topics of interest include, but are not limited to:

• FAIR² Data Articles showcasing reusable datasets
• AI-ready data pipelines and machine-actionable metadata
• Data documentation standards and provenance
• Ethical and responsible data sharing
• Perspectives on FAIR² and its implications for scholarly communication
• Metrics for data reuse and impact
• Infrastructure to support long-term accessibility and trust
• The role of data articles in research assessment and open science policy
• Data sharing and Open Science

We invite researchers, data scientists, policymakers, and open science advocates to contribute to this multidisciplinary collection. Whether you're submitting a FAIR² Data Article or offering a critical perspective on the FAIR² framework and its role in responsible research, your work will help shape the future of data publishing. Join us in shaping the future of responsible data publishing.

Are you interested in contributing a manuscript to this Research Topic?

Showcasing FAIR² Data Articles: Unlocking Trustworthy, AI-Ready Scientific Data for Reuse and Impact