Transforming data management through Findable, Accessible, Interoperable, and Reusable practices
Imagine a world where every medical breakthrough, every biological discovery, and every clinical insight could be instantly shared, understood, and built upon by researchers across the globe. This vision is precisely what drives the revolutionary FAIR principles that are transforming life sciences today.
In an era where a single research project can generate terabytes of data—equivalent to thousands of hours of high-definition video—our ability to manage this information has become just as crucial as the experiments themselves.
Yet, a startling gap exists: while 90% of life scientists support FAIR principles, only about 22% actually apply them in practice 3 . This implementation gap represents both a monumental challenge and an extraordinary opportunity for the future of scientific discovery.
The European Open Science Cloud for Life Sciences (EOSC-Life) consortium has taken up this challenge. Bringing together 13 European life science research infrastructures, this collaborative effort has laid the foundation for an open, digital space to support biological and medical research 2 6 . Their landmark publication, "Be sustainable": EOSC-Life recommendations for implementation of FAIR principles in life science data handling," represents a watershed moment in the field—a practical guide forged from the real-world experiences of dozens of institutions 8 .
of life scientists support FAIR principles
actually apply FAIR principles in practice
European research infrastructures in EOSC-Life
FAIR represents a fundamental shift in how we approach scientific data. The acronym stands for Findable, Accessible, Interoperable, and Reusable—four simple words that carry profound implications for research practices 5 7 . Unlike traditional data management that primarily focuses on human users, FAIR principles place special emphasis on making data understandable to both humans and machines, recognizing the growing role of computational agents in modern science 7 .
The first step in data reuse is discovery. FAIR requires that data and supplementary materials have rich, machine-readable metadata and are registered or indexed in searchable resources.
Once found, data should be retrievable using standardized, open protocols. Importantly, FAIR doesn't necessarily mean "open access"—data can be accessible yet protected behind appropriate authentication.
This principle enables data integration and analysis across different datasets and platforms. Interoperability requires using formal, shared vocabularies, ontologies, and standardized formats.
The ultimate goal of FAIR is to maximize the future utility of data. Reusability depends on providing rich contextual information about how the data was generated, processed, and analyzed.
| Principle | Key Requirements | Benefits |
|---|---|---|
| Findable | Persistent identifiers, Rich metadata, Searchable resources | Prevents "lost" data, Enables discovery |
| Accessible | Standard retrieval protocols, Authentication when needed | Long-term availability, Secure access control |
| Interoperable | Standardized formats, Shared vocabularies, Ontologies | Enables data integration, Cross-study analyses |
| Reusable | Detailed provenance, Clear licenses, Domain-relevant standards | Supports replication studies, Future discoveries |
EOSC-Life represents one of the most ambitious collaborative efforts in modern life sciences. By uniting 13 research infrastructures across the health and food domains, it creates an unprecedented digital collaborative space where researchers can access data, tools, and workflows through a unified cloud environment 2 6 . This consortium covers virtually all aspects of life science research, creating a critical mass of expertise and resources that no single institution could muster alone.
The project's primary mission is to publish FAIR data resources in the European Open Science Cloud (EOSC) by developing and implementing guidelines and standards that make data from participating research infrastructures findable, accessible, interoperable, and reusable 2 .
EOSC-Life has pioneered a co-creation approach through open hackathons and bring-your-own-data events where the infrastructure is developed hand-in-hand with its user communities 2 .
But EOSC-Life goes far beyond simply setting standards—it actively builds the infrastructure and community needed to support widespread FAIR adoption. This includes implementing cross-disciplinary workflows, addressing the complex policy needs of human research data under GDPR, and providing interoperable provenance information that describes the history of samples and data to ensure reproducibility 2 .
The transition from theoretical principles to daily practice represents the greatest challenge in making data FAIR. EOSC-Life's recommendations are grounded in lessons learned from 27 selected projects that reveal the organizational, technical, financial, and legal/ethical barriers to sustainability in the life sciences 8 . These real-world implementations provide invaluable templates for other institutions embarking on similar journeys.
DANS (Data Archiving and Networked Services) significantly improved the FAIRness of its repository service by transitioning from a generic repository system called EASY to four discipline-specific "Data Stations" 1 .
AnaEE (Analysis and Experimentation on Ecosystems) focuses on semantic interoperability in ecosystem studies 1 .
| Challenge Category | Specific Barriers | EOSC-Life Solutions |
|---|---|---|
| Technical | Fragmented legacy systems, Non-standard metadata | Shared vocabularies, Metadata standards, Integration tools |
| Organizational | Unclear data ownership, Insufficient planning | Data stewardship policies, Governance frameworks |
| Financial | High initial costs, Unclear ROI models | Case studies demonstrating value, Shared infrastructure |
| Legal/Ethical | GDPR compliance, Sensitive data handling | Technical solutions for secure processing, Policy alignment |
Successfully implementing FAIR principles requires both conceptual understanding and practical tools. The good news is that a rich ecosystem of resources has emerged to support researchers on their FAIR journey. From EOSC-Life's perspective, several key components form the essential toolkit for any life scientist committed to making their data Findable, Accessible, Interoperable, and Reusable.
Serves as a cornerstone resource, indexing standards, repositories, and policies to improve findability of resources 3 .
FindabilityOpen source tool that enables semantically annotating and integrating biomedical data, addressing the critical interoperability component 3 .
InteroperabilityProvides templates for data management plans and trains researchers on FAIR data stewardship 3 .
Training| Tool Category | Representative Examples | Primary FAIR Function |
|---|---|---|
| Metadata Standards | DublinCore, DataCite, Schema.org | Interoperability, Reusability |
| Repositories | Zenodo, Figshare, Dataverse | Findability, Accessibility |
| Ontologies/Vocabularies | FAIRsharing, Discipline-specific ontologies | Interoperability |
| Training Resources | FAIRplus, The Carpentries, Research Bazaar | Building capacity for all principles |
The EOSC-Life consortium's extensive experience has yielded actionable recommendations that can guide individual researchers, institutions, and funders toward more sustainable data practices. These insights represent the collective wisdom gained from implementing FAIR principles across diverse life science domains, from large-scale imaging facilities to sensitive human data repositories.
A critical recommendation emphasizes embedding FAIR practices into research workflows from the outset, rather than treating them as an afterthought. This includes determining access conditions early and specifying conditions for restricted access in metadata, conforming to recognized file formats, following field-specific metadata standards, using controlled vocabularies where possible, and providing clear data licenses and comprehensive documentation 4 .
Another key insight recognizes that technical solutions alone are insufficient—sustainable FAIR implementation requires addressing the human and organizational dimensions. This means creating dedicated roles like data stewards, establishing clear governance frameworks that define responsibilities for data quality and access, and embedding FAIR principles into digital lab transformation roadmaps with appropriate resources for long-term maintenance 5 .
Perhaps most importantly, EOSC-Life's work demonstrates that cross-disciplinary collaboration and training are not optional extras but essential components for breaking down the silos that hinder data reuse 8 .
Integrate FAIR principles from project inception
Create dedicated data steward roles
Break down disciplinary silos
The implementation of FAIR principles in life sciences represents more than a technical upgrade to our data management systems—it signifies a profound cultural shift toward more open, collaborative, and cumulative science. The work of EOSC-Life consortium demonstrates that while the path to widespread FAIR adoption presents significant challenges, these are far outweighed by the potential benefits: accelerated discovery, reduced duplication of effort, enhanced reproducibility, and ultimately, better stewardship of our research investments.
As we stand at this crossroads, the "Be sustainable" recommendations offer both a roadmap and a rallying cry for the entire life science community. By embracing these principles, researchers, institutions, and funders collectively contribute to building a rich, interconnected data ecosystem that will serve not only current investigations but generations of scientists to come.
The vision is clear—a future where data flows seamlessly between researchers and across disciplines, where every dataset becomes a building block for future breakthroughs, and where the full value of our scientific efforts is preserved and amplified through thoughtful, sustainable data practices.
The journey toward this future begins with individual choices—to document more thoroughly, to standardize more consistently, to share more openly. Each step forward, no matter how small, brings us closer to realizing the transformative potential of FAIR data in life sciences. The question is no longer whether FAIR principles are worth implementing, but how quickly we can work together to make them the new normal in scientific research.