Data interoperability refers to the ways in which data is formatted that allow diverse datasets to be merged or aggregated in meaningful ways. It is a key aspect of the FAIR Data Principles, constituting the “I” in FAIR.

For data to be interoperable, they need to measure phenomena in the same way, meaning that each variable needs to ask the same question and format answers in the same way. So, for example, if one dataset asks “what is the date of your birth” and answers are formatted as dates, a second dataset that asks the age of the subject may not be interoperable with the first without significant data cleaning. 

Data interoperability relies on metadata and data documentation, as without proper documentation researchers would not know which datasets and variables are comparable. Data interoperability is frequently accomplished through the use of data standards, which are community-agreed-upon approaches to the collection and organization of data. Data interoperability may also be accomplished through the use of Common Data Elements, which are precisely defined questions with a set of allowable responses.