Making Lab Data Interoperable: The “I” in FAIR Data Principles
You’ve found your data (Findable) and made sure people can access it safely (Accessible). But can your lab systems actually talk to each other? That’s where Interoperability comes in—the third FAIR data principle.
Interoperability basically means your data can be read, understood, and reused by different systems, apps, and organisations without someone having to step in and fix things manually. In a lab, it’s what lets instruments, software, and collaborators share information smoothly.
Instead of exporting spreadsheets, reformatting files, renaming columns, or typing things in again by hand, interoperable systems keep data from getting stuck in technical silos and let it move freely between tools.
How AgiLab LIMS can help
AgiLab LIMS can be a huge help when it comes to interoperability. As a modern system it come with standardised data models for common things like samples, tests, and instruments. We support integrations through our APIs so you can connect instruments, ELNs, and analysis tools easier and faster.
With controlled vocabularies, on our platform we keep terminology consistent across teams. And with our support of structured metadata, other systems can reuse your data without you having to reformat it.
All of this makes sharing data inside and outside the lab much easier.
Where legacy LIMS fall short
That said, many older or traditional LIMS don’t quite get labs all the way to FAIR interoperability. They often rely on proprietary data structures that make exchanging data with external tools difficult. They also tend to lack support for semantic standards like RDF.
Integrations frequently depend on custom, hard-coded connections that break as soon as one system changes.
The result: data might move between systems, but its meaning doesn’t always come with it—undermining true interoperability.
Moving toward FAIR interoperability
Getting to FAIR-level interoperability means adopting more open, standards-based approaches. Labs can strengthen their existing LIMS by:
- Using standard exchange formats like JSON-LD, XML, or ISA-Tab
- Implementing linked-data principles with persistent identifiers and explicit relationships
- Using open-standard APIs like REST for scalable integrations
Another option is to adopt a modern lab informatics platform built with FAIR in mind from the start. AgiLab offers:
- An open API for easy connections to instruments, analysis tools, and external databases
- Built-in support for controlled vocabularies
- Export options in common, machine-readable formats
All of this helps ensure your data keeps both its structure and its meaning—whether it stays in the lab or moves across teams, collaborators, or repositories.
Making data interoperable: the third FAIR principle
Interoperability is all about helping data stay understandable across systems and contexts. A LIMS provides a strong base, but reaching full FAIR interoperability means embracing shared standards and semantic frameworks. Labs that do this—or choose an informatics system like Labbit that already supports it—open the door to smoother data exchange, better collaboration, more automation, and deeper insights.
Next up in this series: Reusability, the final FAIR principle—how it turns data from a one-off output into a long-term asset for science and innovation.