FAIR data principles are essential across laboratories, yet many teams still struggle to connect the concept to day-to-day operations. As digital tools, automation, and AI reshape how labs generate and use data, the need for structured, reliable, and interoperable information has become more urgent.
This Q&A with Kyle Larsen of USP, explores how FAIR aligns with broader efforts around digital standards and data integrity, and what that means in practice for lab leaders.
The goal is to move beyond theory and show how FAIR can function as a practical framework for managing data as a true operational asset.
How do FAIR principles align with broader efforts around digital standards and data integrity?
FAIR principles align closely with digital standards because both are focused on making data reliable, traceable, and usable in real lab workflows. FAIR describes what good data should look like, while digital standards define how that data is structured, executed, and maintained.
Digitally structured test methods and reference datasets make FAIR practical by building consistency and traceability directly into lab execution. When methods are machine‑readable and tied to trusted standards, data is easier to verify, reproduce, and defend – key requirements for data integrity in regulated environments.
Rather than treating FAIR as a standalone data initiative, we see it as a natural outcome of adopting well-governed digital standards that are built for quality, compliance, and interoperability from the start.
What role do standards play in making data truly findable, accessible, interoperable, and reusable?
FAIR originates from the industry challenge of standardization across platforms. It serves as an enabling foundation for AI and machine learning (ML), as well as for future data structures. While structure and standardization have emerged in key areas, they have not always been easily transferable across systems. The FAIR principles help address this by reducing silos and enabling a more shared, interoperable knowledge set.
Standardized digital methods help the industry in advancing this further by driving alignment on interoperability and reusability, helping translate FAIR from an aspiration into practical, actionable steps at the bench.
Labs often view FAIR as abstract. How can lab leaders translate these principles into concrete actions?
Data is the core work product of any laboratory, and its value extends beyond a single project, informing both immediate decisions and future work. For data to function as a true organizational asset, it must be FAIR.
USP supports this in practice by advancing interoperability and reusability through standardized digital workflows that leverage industry-standard data. These workflows are designed for agnostic integration across diverse digital environments, enabling methods themselves to be consistently interoperable and reusable.
As an active member of organizations like the Allotrope Foundation and the Pistoia Alliance, USP both contributes to and learns from the evolving data standards landscape. Where industry terms already exist, USP adopts them; where unique terms are needed, USP works to introduce and align them within broader, shared frameworks. In this way, USP helps translate FAIR principles into tangible, scalable practices across scientific domains.
What are realistic, incremental steps labs can take to align with FAIR without overhauling everything?
This is an exciting moment for AI in the laboratory space, particularly because agent-based systems can now be layered onto existing data environments. These agents can “communicate” with one another to create greater context and connectivity, surfacing insights across an organization without requiring a full overhaul of current systems.
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A practical first step for labs is to focus on interoperability and reusability when implementing or expanding digital workflows. This means adopting existing data standards, leveraging standardized workflows where possible, and even building these expectations into RFP requirements for new tools and technologies. Frameworks like FAIR provide the structure that makes this possible.
What role will FAIR play as labs adopt more digital and automated workflows?
As automation increases, FAIR becomes essential for execution at scale. Automated systems rely on structured, unambiguous data. FAIR-aligned methods ensure data and processes can move between instruments, systems, and analytics tools without loss of meaning. This supports consistent execution, reliable results, and downstream use of data for review, trending, or regulatory purposes. FAIR is less about data storage and more about making data fit for automated and system-driven workflows. Overall, this is a driver of quality and efficiency.
Anything else to add about implementing FAIR data principles in the lab?
FAIR provides a critical foundation for leveraging AI, enabling stronger predictions and more informed decision-making across projects and throughout a product’s lifecycle.
It brings needed structure to what laboratories have always aimed to document scientific work, while helping ensure that important details are consistently captured. Because practices can vary across teams and environments, FAIR helps preserve the context and specificity required for true understanding and reuse. This is essential in traditional laboratory settings and becomes even more important as labs transition to digital and automated workflows.
