If you’ve applied for funding in the last few years, you’ve probably heard of FAIR data. The principles - Findable, Accessible, Interoperable, and Reusable - have become the global standard for research data management. They are endorsed by major funders, woven into policies like Horizon Europe’s Open Science mandates, and promoted by organisations from the OECD to UNESCO.

But what do they actually mean for researchers and why should you care?

Why FAIR data principles matter

At their core, the FAIR principles are about making science work better.

• Findable: Your dataset should be discoverable by humans and machines alike. A buried dataset is effectively lost.

• Accessible: Others (with appropriate permissions) should be able to view and download your data.

• Interoperable: Data should use community standards and formats so it can work across platforms and disciplines.

• Reusable: Metadata and documentation should be rich enough that others can validate, replicate, or build on your work.

The societal stakes are high. Data that is FAIR is more likely to be found and reused, whether a climate dataset shaping policy, a biomedical dataset accelerating a cure, or an AI-ready dataset training tomorrow’s models. Data that is not FAIR, by contrast, risks being forgotten, duplicated, or wasted.

Why researchers should care

Beyond the broader impact, there are direct benefits:

• Compliance: Most major funders now require FAIR-aligned data management plans. Not meeting these can jeopardize funding.

• Reputation: Making your data FAIR increases citations, visibility, and opportunities for collaboration.

• Efficiency: Reusing existing FAIR data can save huge amounts of time, avoiding “reinventing the wheel.”

Still, for many researchers, putting FAIR into practice feels burdensome. Preparing metadata, navigating repositories, and ensuring interoperability can take weeks, often without meaningful recognition for the effort.

Moving from principles to practice

That’s where the challenge lies: FAIR is widely supported in theory, but patchy in practice. Researchers want to comply, but fragmented tools and high costs make it difficult.

This is where Frontiers FAIR² Data Management comes in. It builds on the global FAIR movement but makes it practical, credit-bearing, and scalable.

With a single submission, researchers can:

• Turn their dataset into a peer-reviewed, citable Data Article and in so doing gain academic credit.

• Receive curation, certification, and lifetime hosting in one integrated workflow.

• Share data via an interactive portal, ensuring it remains discoverable and reusable across disciplines.

• Use Clara, our new AI Data Steward, to reduce weeks of manual prep into minutes without losing control.

• Do all this for a fraction of the traditional costs (CHF 5,500 vs. up to CHF 60,000).

Why this matters now

The FAIR principles were a milestone for open science, but they were only the beginning. To achieve their promise, researchers need tools that make FAIR data not just a policy requirement, but a rewarding part of the research lifecycle.

Frontiers FAIR² takes that step, turning FAIR from principle into practice, ensuring no discovery is wasted, and giving researchers the recognition they deserve.

Field notes takeaway: FAIR principles are shaping the future of science policy. Frontiers FAIR² shows how researchers can move beyond compliance and make their data a lasting, visible contribution to discovery.