How does a Research Software Engineer (RSE) — often responsible for developing infrastructure to manage and share digital research objects (data, models, code, notebooks, workflows, etc.) — get from “Yes, FAIR sounds great, but how?” to “I better understand what the FAIR principles really mean and how I can put them into practice.”? I hope the diagram below can help.

I’ve been trying to grok architecture patterns as presented by Percival and Gregory ¹ to support domain-driven design and event-driven microservices with Python. I hope you find the diagram below useful. A microservices approach seems apt for FAIR-enabling services that need to be composed, flexibly, for any given research artifact’s digital lifecycle.

The “one platform ¹ to rule them all” is unlikely to be realized for scientific research in any domain. Rather, instead of small and numerous on-premises silos for data + code + compute, we are on track to achieve large and somewhat less numerous cloud-based silos. ² What’s the alternative?

I have sought to identify and enumerate core FAIR-enabling services. I attempted a five-week experiment to expand on my tentative list, but I did not complete it. The list wasn’t compelling for me. I have been brewing an updated list of core FAIR-enabling services, which I hope to be less bombastic about.

I have sought to identify and enumerate core FAIR-enabling services. I attempted a five-week experiment to expand on my tentative list, but I did not complete it. The list wasn’t compelling for me. I have been brewing an updated list of core FAIR-enabling services, which I hope to be less bombastic about.

Don’t. Identifiers should be opaque. If you’re given an owl:sameAs assertion from a party you trust, use that. If you need to mint surrogates because what you’re given aren’t Globally Unique, Persistent and Resolvable Identifiers (GUPRIs) ¹ , either house your inheritance as local parts/suffixes in your global namespace, assert datatype properties to record the historical correspondence, or both.

Is a metadata record “almost” expressed in the same language you used for your filter criteria?

Why would one consider indexing validators? Reuse. The value of reuse seems obvious for structural and semantic specification , i.e. schemas and controlled vocabularies – there is opportunity to perceive two datasets as aligned.

Indexing identifiers is key to disambiguating entities. Wikipedia has disambiguation pages. For example, there are various concepts in mathematics and computing, various computing products, and various companies that identify with the term “Precision”. I made disambiguation pages for same-chemical-formula inorganic crystal structures for the Materials Project. Indexing identifiers is also key to unifying entities.

How do you validate a reified trace of digital-object provenance? Is it even possible? This is syntactic validation. Values that should be strings are strings, dates are dates, lists are lists, you know the drill… Is it plausible? This is semantic validation.