Clinical diagnostic laboratories can gain a significant advantage by ensuring their data conforms to FAIR principles — that is, data is easily findable, accessible, interoperable, and reusable. But many labs don’t recognize the importance of FAIR data or they find that implementing these principles is challenging.
At Semaphore, we believe FAIR data sets forward-looking labs apart from their peers, giving them advantages when it comes to collaborating with other organizations and integrating new tools into their existing lab systems.
In this post, we discuss what FAIR data principles are, the benefits for labs that apply them, and six of the common challenges you might experience if your lab chooses to implement FAIR data.
What are FAIR principles?
Mark D. Wilkinson, along with other academic and industry representatives, originally published the FAIR Data Principles in an article titled FAIR Guiding Principles for Scientific Data Management and Stewardship in Scientific Data. Their goal in defining the principles was to enhance “the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.”
- Findable: (Meta)data are assigned a globally unique and persistent identifier; data are described with rich metadata; metadata clearly and explicitly include the identifier of the data they describe; and (meta)data are registered or indexed in a searchable resource.
- Accessible: (Meta)data are retrievable by their identifier using a standardized communications protocol; and metadata are accessible, even when the data are no longer available.
- Interoperable: (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation; (meta)data use vocabularies that follow FAIR principles; and (meta)data include qualified references to other (meta)data.
- Reusable: (Meta)data are richly described with a plurality of accurate and relevant attributes.
For the full list of principles, visit Go Fair. On the site, you’ll also find a framework guiding FAIRification and implementation networks for an ecosystem of organizations working together on FAIR initiatives.
Why FAIR data is important for labs
Applying FAIR principles to data can help labs minimize the time and work associated with transferring data between systems. For example, if your lab has any equipment that is not yet integrated with your laboratory information management system (LIMS), FAIR data can make it significantly easier to share data between systems within your lab, resulting in fewer manual processing errors.
If your lab is considering re-platforming or upgrading to a new LIMS, FAIR data can simplify this process considerably, because you are less likely to need to transform the data so it’s readable by the new system. FAIR data also makes it easier to integrate data from other sources or federate data in a virtual view.
Adherence to FAIR principles can also help your lab align with broader initiatives that promote open science, data sharing, and transparency in research. For instance, FAIR data can enable collaboration with other organizations, accelerate research progress and enable reproducible scientific findings. All this supports innovations that can improve patients’ daily experiences or even save lives.
According to Ian Harrow and Thomas Liener of The Pistoia Alliance, in their article for European Pharmaceutical Review, implementing FAIR data principles is now a critical endeavor. They reveal that the European Union has reported that the lack of FAIR data is costing the European economy €10.2 billion (US$11.35 billion) annually and has the potential to result in a further €16 billion (US$17.8 billion) in losses each year. Extrapolating these numbers worldwide, it’s easy to see that FAIR data principles could have a profound effect, not just for individual labs, but for the global economy and all of humankind.
Six common challenges to adopting FAIR principles
It’s important to note that adopting FAIR principles is not always a straightforward process. Further, labs may not have the resources available to take on the complex task or the expertise to do so. Here are some of the difficulties we have seen labs face:
1. Data fragmentation
In labs, data is often scattered across various platforms, databases, and file formats, making it challenging to locate and access. Non-standardized metadata and inconsistent data organization hinder effective data discovery and reuse.
2. Limited accessibility
Data access may be restricted due to proprietary or privacy concerns, leading to limited availability, and hampering collaboration and reproducibility. Legal and ethical considerations and concerns about data security, confidentiality, and intellectual property can pose barriers to implementing FAIR data and open data sharing.
3. Interoperability issues
Incompatibility between different software systems, tools, and formats makes data integration and interoperability challenging. A lack of standardized data models, ontologies, and controlled vocabularies can impede the exchange and integration of FAIR data across disciplines and research domains.
4. Data quality and documentation
Inadequate documentation, incomplete metadata, and inconsistent data formats can affect data quality and reliability. Poor documentation prevents fully understanding the data context, making it challenging to validate and interpret results.
5. Infrastructure and resources
Labs may lack the necessary infrastructure, resources, and technical expertise to implement effective data management practices. This includes issues related to storage, backup, data curation, and long-term preservation.
6. Cultural and incentive barriers
The scientific community traditionally emphasizes publishing research outcomes rather than sharing raw data. This mindset, coupled with limited recognition and incentives for data sharing, can discourage researchers from implementing FAIR data and making their data openly available.
Is your lab up for the challenge?
While these issues can slow a lab’s journey to FAIR data, they can all be overcome. With the right strategy and skills, or with the help of a consultant familiar with navigating the application of FAIR principles in a clinical laboratory setting, your lab could benefit from time savings, fewer errors, better collaboration, and even enhanced reputation.
Semaphore has many years of experience applying software engineering best practices to the challenges facing modern laboratories. We have domain knowledge, an understanding of the regulatory environment that many labs work within, and expertise in working with LIMS and other systems commonly used by labs in their workflows. Our goal is to solve your software problems so you can focus on the science.
Another option is to upgrade to a next-generation LIMS that is built on FAIR data. Labbit, for example, is a truly interoperable FAIR-compliant system that offers all the benefits of FAIR data — including effortless data integration, improved data consistency, flexible data querying, an adaptive data model, provenance, and open data and data sharing.
Labs that adopt FAIR principles now will join the ranks of leaders, participating in innovative scientific initiatives and reinforcing their position in the market. At the same time, they will future-proof their business and be able to take advantage of opportunities we can’t yet imagine.
If you’d like to discuss your lab’s options for implementing FAIR data principles, contact us today for a free consultation.