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You’ve found your data (Findable) and ensured secure access (Accessible), but can your lab systems actually talk to each other? That’s the challenge of Interoperability, the third of the four FAIR data principles.
Interoperability means that data can be read, interpreted, and reused by different systems, applications, and organizations without manual intervention. In a lab setting, it’s what allows instruments, software platforms, and collaborators to exchange information seamlessly.
Instead of exporting spreadsheets, reformatting files, translating column headers, or re-entering data by hand, labs that implement interoperability can avoid trapping data in technical silos and allow data to flow between systems.
GO FAIR defines interoperability based on three principles focused on machine-to-machine understanding and semantic consistency:
These principles encourage labs to think beyond file formats and APIs. They focus on shared meaning and ensuring that terms such as “sample,” “specimen,” or “assay” mean the same thing in every system that uses them.
A laboratory information management system (LIMS) plays a central role in improving interoperability across lab informatics solutions. Many modern systems offer standardized data models that define common entities such as samples, tests, and instruments. They also support integration capabilities such as RESTful APIs for connecting instruments, electronic lab notebooks (ELNs), and analytical software.
By using controlled vocabularies and ontologies, they can help ensure consistent terminology across teams and workflows. At the same time, if they support structured metadata capture, they can enable downstream systems to understand and reuse data without reformatting.
Together, these features can significantly reduce the friction of sharing data within and beyond the organization.
However, despite these benefits, many traditional LIMS struggle to meet FAIR’s full interoperability vision. They tend to rely on proprietary schemas or database structures that limit the exchange of data with external tools, and they lack support for semantic standards, such as ontology-linked metadata or RDF triples. Furthermore, integrations are often custom-coded direct connections that break when lab systems evolve.
These gaps can result in data capable of moving between systems, but without its meaning attached, which undermines true interoperability.
Achieving FAIR-level interoperability requires a more open, standards-based approach. Labs can strengthen their traditional LIMS by:
Another approach is to future-proof the lab by implementing a modern informatics platform purposefully designed with these practices at the forefront. Labbit, for instance, includes an open API for easy integration with instruments, analysis tools, and external databases; has native support for ontologies and controlled vocabularies to enhance semantic interoperability; and exports data in common, machine-readable formats.
In summary, these practices help enable data to retain both structure and meaning wherever it goes within the lab, across collaborators, or into repositories.
Interoperability is about making data understandable across systems and contexts.
While a traditional LIMS provides the foundation, true FAIR interoperability depends on embracing shared standards and semantic frameworks. When labs adopt interoperable practices or implement an informatics system like Labbit with these practices built in, they not only improve data exchange but also unlock new levels of collaboration, automation, and insight.
In the next post in this series, we’ll explore Reusability, the final FAIR principle, and how it turns data from a one-time result into a long-term asset for science and innovation.
Contact us to discuss how you can implement FAIR interoperable data.
