Laboratories generate more data than ever before, yet many organizations still struggle to use that data effectively. The challenge is not just volume, but whether data can be found, understood, and reused across projects, teams, and time.
FAIR data principles—Findable, Accessible, Interoperable, and Reusable—offer a framework to address this gap. The challenge for lab leaders is translating those principles into something that works in everyday lab operations.
Interviews with Nicolas Triballeau, PhD, and Zev Wisotsky, PhD, of Revvity Signals, along with Kyle Larsen of USP, point to a consistent takeaway: the impact of FAIR depends on how data is captured and managed during routine lab work, not just on high-level strategy.
The real breakdown: data without context
For many labs, the problem begins long before data reaches a database or analytics platform. “The breakdown almost always occurs at the point of context—or rather, the absence of it,” say Wisotsky and Triballeau. “Labs are generally good at generating data, but the data is often captured without the metadata that gives it meaning.”
Without metadata—details such as experimental conditions, methods, parameters, and authorship—even well-structured datasets lose their value. A table of results without context cannot support reproducibility, collaboration, or downstream analysis.
Data structure can further limit usability. “When data is bound too early into a fixed structure, it loses the nuance and flexibility needed to answer questions that haven’t been asked yet,” Triballeau and Wisotsky explain. “The result is data that is abundant but effectively inaccessible—siloed, poorly modeled, and stripped of the scientific context that would make it genuinely useful for analysis, collaboration, or AI.” Addressing this issue requires rethinking how data is captured in the first place.
Where FAIR takes shape: the workflow level
That shift happens in the day-to-day work of the lab.
“At the workflow level, FAIR means that when a researcher records an experiment, they are prompted—ideally in a frictionless way—to capture the metadata that contextualizes their results,” say Wisotsky and Triballeau. Capturing context at the point of work improves both accuracy and consistency. It also reduces the need for scientists to reconstruct details after the fact, when information may be incomplete or forgotten.
Systems that shape data quality
Laboratory systems determine how data is captured, structured, and carried forward. Electronic laboratory notebooks (ELNs), laboratory information management systems (LIMS), and instrument software are where scientists interact with data throughout the research process.
“These systems are the primary point of contact between the scientist and the data, which means they are either the greatest enablers or the greatest barriers to FAIR compliance,” say Wisotsky and Triballeau.
When systems are configured to prompt required metadata and support standardized inputs, they help ensure consistency without adding burden. When they are rigid or disconnected, they introduce gaps that are difficult to resolve later. “The most effective systems are those that integrate data capture, schema management, and metadata enrichment into a single, connected workflow,” note Wisotsky and Triballeau.
Even with the right systems in place, however, consistency depends on a shared understanding of how data should be described.
Aligning data through standards and ontologies
As data moves across teams, systems, and disciplines, differences in terminology and structure can quickly undermine usability. “FAIR originates from the industry challenge of standardization across platforms,” Larsen explains.
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Digital standards provide the structure needed for consistency and traceability. They define how data is formatted, executed, and maintained so it can be reliably shared and reused.
Ontologies address a related challenge: differences in language. In many labs, the same concept may be described in multiple ways depending on discipline, geography, or historical practice. “Ontology support allows scientists to compare results that use different vocabulary to describe the same underlying concept,” say Wisotsky and Triballeau. With shared definitions in place, data becomes more comparable and useful across studies.
The next challenge is ensuring that this level of consistency does not come at the cost of added workload.
Improving metadata without adding burden
Concerns about increased administrative work often slow FAIR adoption. In practice, the opposite can be true when systems and workflows are designed effectively. “The key is to make metadata capture a natural part of the scientific workflow rather than an administrative task,” Wisotsky and Triballeau explain.
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Practical approaches include using controlled vocabularies, requiring key fields at the point of entry, and automating metadata capture wherever possible. Registering assays with predefined metadata can further reduce repetitive work.
These strategies shift the responsibility for data quality from individual scientists to the systems and processes that support them. Once that foundation is in place, labs can begin to address broader improvements.
Practical steps for lab leaders
Improving data practices does not require a full system overhaul. Progress often starts with a clearer understanding of current limitations. “Start with an honest audit of your current data quality,” say Wisotsky and Triballeau. They recommend asking questions like:
Can we find our data reliably?
Does it carry enough context to be understood by someone who wasn't in the room when the experiment was run?
Can it be compared meaningfully with data from other studies or other teams?
From there, labs can focus on a few foundational actions: defining a metadata strategy, aligning with established standards, integrating systems, and moving away from rigid data structures that limit flexibility.
Larsen emphasizes the importance of building interoperability and reusability into digital decisions. “Adopting existing data standards…[and] building these expectations into RFP requirements for new tools and technologies” can help labs make steady progress without starting from scratch.
These steps not only improve data quality but also determine how effectively labs can adopt emerging technologies.
Supporting AI, automation, and decision-making
As labs expand their use of AI and automation, the limitations of poorly structured data become more apparent.
“AI is only as powerful as the data it consumes. This is perhaps the most important and most underappreciated truth in the current enthusiasm around AI in life sciences,” say Wisotsky and Triballeau.
Well-structured, contextualized data allows AI models to produce more reliable insights and supports more targeted analysis. Interoperability enables data to be combined across systems, increasing its value.
Automation depends on the same foundation. “Automated systems rely on structured, unambiguous data,” Larsen notes. “FAIR-aligned methods ensure data and processes can move between instruments, systems, and analytics tools without loss of meaning.”
With these capabilities in place, labs can spend less time searching for and interpreting data and more time acting on it.
Sustaining progress over time
The benefits of improved data practices accumulate as more data is captured, structured, and reused over time. Data collected today may need to support analyses that were not anticipated when experiments were run. Preserving context and maintaining flexibility ensures that data remains usable as those needs change.
It also reinforces the importance of adoption at the individual level. When scientists see that better data practices improve their own work—by reducing duplication, improving clarity, and accelerating analysis—those practices become part of the lab’s culture.
Turning principle into practice
Improving how data is captured and managed requires deliberate choices about workflows, systems, and standards. Labs that focus on these areas move beyond storing data to using it effectively.
As Larsen says, “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.”
