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The FAIR principles aim to make data: Findable, Accessible, Interoperable and Reusable to maximize the reuse of (research) data. See this page for more information on FAIR.
Where do you start to make your dataset or other digital resource FAIR? Which steps are involved in making data FAIR? What do these steps mean and, importantly, how do you practically do these steps? The FAIR metroline provides guidance to help you reach your FAIR goals.

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Given the large number of steps, we will prioritise adding detailed how-to descriptions and real-life examples of process steps based on the development of the Dutch National Health Data Catalogue. Using this approach we ensure focus is on steps that are relevant right now. For the current status of the steps see this page.

Furthermore, we are also developing FAIR steps together with funders, to incorporate FAIR into projects from the start. The exact approach is still under development.

Overview Metroline FAIRification steps:

References & Further reading: 

• The Metroline - Steps for your FAIRification Workflow

• Critical Steps towards Large-Scale Implementation of the FAIR Data Principles

• FAIRopoly – FAIRification Guidance for ERN Patient Registries  

• FAIR in Action – a Flexible Framework to guide FAIRification

• A Generic Workflow for the Data FAIRification Process   

• GO FAIR Foundation

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