Scenario Overview
Research data can be difficult to combine and reuse when the same concepts are defined or collected differently across studies. Even seemingly straightforward data elements may differ in their meaning, values, units or formats. When designing a new dataset, you can address these differences from the start by clearly defining what you want to collect and aligning your data elements with existing standards where possible. This makes it easier to understand, compare and combine your data with data from other studies, whether for your own analyses or for reuse by others.
Examples
My genomics study protocol includes several data elements that need to be collected, including sex and BMI. I want to align these data elements with existing standards and initiatives so that the data are interoperable from the start.
Complexity
Medium
Key Experts
Data steward
Outcome
Interoperable dataset
Prerequisites
Before starting this scenario, you should have the following prerequisites in place.
Define FAIRification objectives
Have a FAIR data steward on board
Identify and select training
Your Journey
Create a basic codebook for your data
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Improve the codebook
