.svg)
In 2016, a group of researchers introduced the FAIR Data Principles for scientific data management and stewardship — guidelines to make scientific data Findable, Accessible, Interoperable, and Reusable. FAIR isn’t a single standard or technology. It’s a framework that helps organizations ensure their data can be located, understood, shared securely, and reused by both humans and machines.
As data volumes grow and collaborations span labs, disciplines, and geographies, adopting FAIR has become essential for accelerating discovery, improving compliance, and getting more value from data. In this series of posts, we’ll describe each of the four FAIR components and their underlying principles — starting with Findable — and how extending a traditional LIMS or implementing a new generation of LIMS can help labs implement FAIR data.
Data that can’t be found is as good as lost. That’s why FAIR data begins with Findability. Making data findable ensures that datasets, metadata, and the context around them are visible, searchable, and identifiable by both humans and machines.
For modern labs, this is more than a convenience. Researchers and operators need to trace samples, link results across experiments or tests, and share information with collaborators or regulatory authorities. Without strong findability, even well-documented data remains siloed.
According to GO FAIR, an initiative working to implement FAIR principles, the Findable component of FAIR relies on four underlying principles:
These guidelines reflect a key shift for labs in how they manage their data. It’s no longer enough to store files neatly. Labs must structure and catalog data so that any authorized person or machine can find it when needed.
A laboratory information management system (LIMS) is often the first tool that comes to mind for improving data findability. Many LIMS platforms support findability, such as by automatically assigning globally unique IDs for samples and records, ensuring that no two samples or batches are confused.
Most modern LIMS also provide powerful search and filtering features, with built-in search tools to query samples, assays, or projects quickly. They also capture metadata at the source, enforcing structured data entry during experiment setup to capture who, what, where, when, and how data was generated. Furthermore, they store metadata alongside data to maintain clarity about context.
These capabilities establish a strong internal foundation for findability, helping lab staff navigate and retrieve information within their own systems.
However, traditional LIMS have limits when it comes to meeting the full FAIR “F” requirements. Most LIMS use local, proprietary identifiers rather than globally persistent identifiers. Further, LIMS databases are often not indexed in searchable repositories.
A workaround is to integrate a lab’s data with data catalogs or registries. However, typically this requires custom configurations or third-party services. As a result, while LIMS make data locally findable, they rarely achieve domain-available, machine-actionable findability on their own.
While a LIMS is a good starting point, if their selection of a LIMS doesn’t natively comply with FAIR, labs need to pair their non-compliant platform with:
This combination transforms internal lab records into domain-relevant discoverable datasets.
A well-implemented traditional LIMS can provide much of the foundation labs need to make data findable by structuring metadata, tracking samples, and enabling internal search. However, for data to be truly FAIR, most LIMS must be extended with the addition of global identifiers, indexing in searchable registries, and domain-specific metadata standards. Modern informatics platforms offer much more comprehensive FAIR coverage.
A consultant with domain expertise can help your lab integrate these components with your existing LIMS. Alternatively, your lab could choose to implement a new generation of LIMS, such as Labbit, which is designed to make data FAIR from the start and help your lab unlock innovation.
In the next post in this series, we’ll explore the “A” in FAIR — Accessibility — and discuss how a LIMS can balance open discovery with secure, standards-based data access.
In the meantime, learn about six challenges your lab might face when implementing FAIR data principles and the benefits of a consolidated view of your data.
