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You can find your data (Findable), access it securely (Accessible), and exchange it between systems (Interoperable). However, if no one can understand or reuse it later, its value fades quickly.
The Reusability principle of FAIR is the final, and arguably, the most important step labs can take to turn their isolated datasets into connected, usable resources. It’s about ensuring that data remains useful, not just for the team that generated it, but for future studies, new collaborations, and even automated systems that might mine it for insights in the future.
In research and regulated environments alike, reusability is what transforms data from a temporary record into a long-term scientific and business asset.
According to GO FAIR, reusability is built on a single premise, and further defined by three underlying principles:
In essence, reusable data is well-documented, clearly licensed, and standards-aligned, so that anyone — human or machine — can trust, interpret, and repurpose it.
As is true for the other FAIR data principles, a laboratory information management system (LIMS) can play a major role in enabling reusability. Most modern LIMS systems help by capturing provenance automatically. They do this by tracking every sample, instrument, and workflow step to build a detailed record of how data was generated.
They also maintain version control and data integrity by logging changes to data or metadata, ensuring traceability over time, and enforcing standardized templates and standard operating procedures (SOPs), which secure consistent data capture and reduce ambiguity for downstream users. When integrated with analytical tools, they make it easier to link results back to original samples and conditions.
All of these capabilities create a strong foundation for internal data reuse across the lab.
Nevertheless, reusability in the FAIR sense goes beyond internal efficiency, which can challenge the built-in capabilities of a traditional LIMS. For example, many LIMS systems struggle to fully support explicit licensing and usage terms because they don’t typically manage intellectual property or data-sharing agreements. Their existing metadata fields may not align with recognized ontologies or schema requirements, and if proprietary formats are used, when data is exported or archived, it may lose structure or become inaccessible.
This means that while a traditional LIMS may make your lab’s data traceable, it doesn’t always make it reusable beyond its original context.
To make data fully reusable, labs should pair their traditional LIMS with:
This combination supports lab data reproducibility and transparency — and innovation.
For labs seeking to implement a next-generation informatics platform, Labbit offers a modern alternative to a traditional LIMS. Built on FAIR principles, it can capture lab data provenance, structure, and context in ways that make long-term data reuse beyond the organization a reality, without the need for additional customizations.
Reusability is the ultimate measure of data maturity. Labs that reach this level don’t just manage data, they create assets that fuel future science.
A well-configured traditional LIMS provides the backbone by tracking provenance, enforcing structure, and maintaining traceability. However, true FAIR reusability requires clear licensing, adherence to standards, and long-term stewardship — practices that can be achieved by retrofitting a traditional LIMS or implementing a modern informatics solution.
In the final post in this series, we’ll look at how organizations like the Pistoia Alliance and GO FAIR are helping the scientific community put these principles into practice by building frameworks, standards, and collaborations that make FAIR data achievable at scale.
Is your lab data reusable? Contact us to find out how you can meet this key FAIR principle.
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