What Is Interoperability?

The seminal article on FAIR defines interoperability as “the ability of data or tools from non-cooperating resources to integrate or work together with minimal effort.”

Is the “non-cooperating” part important here? It is. You can think of this as adversarial interoperability, as distinct from “indifferent interoperability” (I don’t care if you reuse this…for now) or “cooperative interoperability” (please reuse this…for now). The term “adversarial” doesn’t mean “evil” – it means that a software agent acting on someone else’s behalf isn’t “on your team”, and is allowed to pursue goals that may surprise you.

Let’s play with etymology a bit. To coordinate is to “co-ordinate”, where “ordinate” shares a root with “ordinal”, i.e. categorical ordering, taxonomic classification. To coordinate is to “ordinate together.” In particular, we might “co-elaborate”/“co-labor” (collaborate) on data schema and on sanctioned operations with data, i.e. we “co-operate”. Inter-operation, on the other hand, is distinct from co-operation (i.e., “intra-operation”, within a group).

Why is such non-cooperative interoperability important? We have more data in the published literature than we know what to do with, unless we use machines. We’re still seeing data published in a way that 19th-century scientists would understand, rather than in a way appropriate to this century, with connectivity to electronic media and computing power. Researchers need machine agents to “read” on their behalf, without prior coordination.

Does this make sense to you? Disagree vehemently? Let me know.

This post was adapted from a note sent to my email list on Scientific Data Unification.
I'd love for you to subscribe.