The FAIR (Findable, Accessible, Interoperable, and Reusable) principles have become more than a nice-to-have in an era of open science, reproducibility crises, and increasing demands for transparency. For researchers at all career stages, but especially for established scientists leading labs and large grants, ensuring that data and outputs are FAIR is now an expectation built into funding calls, institutional policies, and publishing standards.
Yet, achieving FAIR isn’t just about writing a data management plan and ticking a compliance box. It’s about embedding responsible data stewardship into every stage of the research lifecycle, from data collection and storage to sharing, archiving, and reuse. Tools and platforms play a role, but leadership, planning, and a culture of accountability are what make FAIR sustainable in practice.
Funding agencies worldwide increasingly recognize that open, reusable research data accelerates discovery and maximizes the return on investment for publicly funded research. Major funders like the European Commission’s Horizon Europe program, the National Institutes of Health (NIH), and the UK Research and Innovation (UKRI) now explicitly require FAIR data management plans (DMPs).
For example, the EU’s Horizon Europe program requires researchers to deposit data in trusted repositories that adhere to FAIR principles and to provide clear metadata and persistent identifiers so data can be found and cited. The NIH’s updated Data Management and Sharing Policy makes it clear that scientific data should be managed and shared to the maximum extent possible, while protecting participant privacy, security, and proprietary interests.
This shift reflects a broader accountability trend: funders, journals, and the public expect that data generated with taxpayer or charitable funding will not be confined to institutional firewalls or lost in unstructured file folders on a researcher’s hard drive.
Failure to meet FAIR expectations can have real consequences. Researchers who fail to follow through on DMPs or submit incomplete, inaccessible, or poorly documented data may face funding clawbacks, reputational damage, or difficulties renewing grants.
At the institutional level, non-compliance can erode the credibility of an entire lab or department. For example, not being able to locate datasets during an audit or if data sharing has breached confidentiality can have serious consequences. In some cases, journals may retract papers if the underlying data can’t be verified or reused to reproduce results.
For established scientists managing large teams and multi-institution collaborations, these risks multiply. Poor data stewardship in one project can have a ripple effect on funding pipelines, collaborative networks, and institutional trust.
So, what does “FAIR by design” actually mean? It means building FAIR principles into your workflows and lab culture from the start, not scrambling to retro-fit them at publication time. This is how established researchers can make this happen:
Technology can make or break your FAIR efforts. Researchers sometimes choose tools for convenience without considering whether they align with compliance standards. When evaluating research tools, from electronic lab notebooks (ELNs) to data repositories, look for features like:
In short, tools should support FAIR principles out of the box, so researchers don’t have to reinvent workflows or risk non-compliance.
FAIR must be balanced with responsible data governance. Sensitive or personal data, especially in health and social sciences, requires careful handling. Institutional Review Boards (IRBs) and Ethics Committees still expect researchers to protect participant confidentiality, honor consent agreements, and comply with regulations like GDPR.
When your research involves human subjects, ensure that your FAIR data plan doesn’t inadvertently conflict with ethical approvals. Use clear consent forms that explain how data may be shared and anonymized.
Established scientists shouldn't treat FAIR just as a compliance box but as a leadership responsibility. Senior scientists and principal investigators determine lab culture, mentor early-career researchers, and decide which platforms and processes to adopt.
Ways to lead by example:
Being FAIR by design is about safeguarding the integrity and impact of your research for the long term.
To make FAIR practical and sustainable in your work:
FAIR is the foundation for trustworthy, reproducible, and impactful science. Established scientists who design research workflows and select tools that support FAIR principles from the start will protect their funding and institutional trust while preparing the next generation for success.
We built DeSci Publish to make FAIR simple and sustainable. We help researchers share their data, preprints, and outputs in trusted repositories with robust metadata, persistent identifiers, and tools that support open, reusable science. Join us in making open, FAIR research the default and setting your work up for maximum impact.
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