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Launch of the new FAIR Metrolijn website
To build an integrated health data infrastructure, it is essential that research and healthcare data are Findable, Accessible, Interoperable and Reusable (FAIR). But how do you ensure your data meets these principles?
That’s where the FAIR Metroline comes in, now live at fairmetroline.org. The FAIR Metroline outlines clear, actionable steps to support your journey toward making data FAIR. Developed collaboratively by the Metroline Writing Group, a dedicated team of experts from Health-RI and regional partners, each page is carefully crafted to ensure clarity and usability. Each page undergoes an external expert review by the Reviewing group to ensure accuracy and relevance.
While the development of new Metroline pages will continue on the internal Health-RI Confluence for now, we will gradually migrate content to the website, so we can even better serve our users with their needs to make data FAIR.
Furthermore, the following pages of the FAIR Metroline have recently been published:
- Analyse data semantics – In this step, the aim is to gain more insight into the existing data, or the data that you aim to collect. Clearly defining the meaning (semantics) of the data is an important step for creating the semantic model, as well as for data collection via, for example, electronic case report forms (eCRFs).
- Design solution plan – This step is about turning the findings from the pre-FAIR assessment into a clear, actionable plan. It means choosing the right tools, deciding who does what, and making sure the process is simple and effective so data can actually become FAIR.
- Obtain informed consent – To ensure your data and materials can be reused in the future, your subject information sheet (SIS) and informed consent form (ICF) must address reuse. This Metroline step provides consideration and resources for preparing your SIS and ICF.
We thank our writers and reviewers for their continued commitment and contributions.
Additionally, we are pleased to announce that the following pages are ready for external review on Confluence:
- Register structural metadata – This step focuses on how to share your resource’s Structural Metadata - an explanation of what each piece of your data means and how it’s organised. Publishing it helps others understand, find and reuse your data more easily. This step also shows how to make the metadata readable by computers, which can support specific FAIR objectives you may already have.
- Apply (meta)data model – Think of your data like a book in a library. A metadata model is like the card in the catalogue that tells people what the book is about and who wrote it. A data model is like the book’s table of contents – it helps everyone understand what’s inside and how to read it. Using both makes it easier for people and machines to find, understand, and reuse your data.
- Apply common data elements - Common data elements (CDEs) are standardised data elements, such as variables and measurements, paired with defined rules for how values should be recorded. They are developed to promote consistency and reuse in data collection across different settings, enabling seamless integration and comparison. This step encourages you to search for relevant CDEs and offers guidance on what to do if no suitable CDE is available.
- Assess FAIRness - Now that you’ve FAIRified your data, it’s time to check the resulting FAIRness and decide if you’ve reached your goals. Use tools and other methods to assess if your data is truly Findable, Accessible, Interoperable, and Reusable. If needed, adjust or improve things so your data stays FAIR in the long run.