Day 1 of my five-week experiment to elaborate on FAIR-enabling services, and I already find myself fallen flat on my face.

Yesterday, I proposed that a strategy for implementing the FAIR principles for research data management can focus on ensuring five FAIR-enabling services , which in turn will prompt tactical choices of FAIR-enabling resources that may satisfactorily address each question and thereby produce a comprehensive implementation profile.

(The following is a transcript of my recent podcast episode on this topic.) There is a FAIR Implementation Profile ontology, and it talks about FAIR-enabling resources. So these are corresponding to questions.

Here are some identifier services listed as such by FIP Wizard, a free-to-signup online tool to guide a user in creating and publishing a machine-actionable FAIR Implementation Profile (FIP): Old IGSN International Generic Sample Number before integration with DataCite SDN CDI PID | SeaDataNet CDI PID SeaDataNet Common Data Persistent Identifier U.S. Department of Energy Office of Scientific and Technical Information (OSTI) Data ID Service

Repurposing data is hard sometimes. Given a current application’s data-worldview – i.e., its schema – one cannot in general pull in historical data collected for different applications because those applications had different worldviews – i.e., they used different data schemas.

Inference based on semantic retrieval is more robust than inference based on syntactic parsing. In order to be authoritative, identifiers should be assigned as early as practicable in the creation process, but minting is not binding. Identifier resolution delays binding; identifier structures induce binding. Moral: Structure identifiers late (or never) in the minting process.

It’s almost forgotten now, but “Generation X” - a term now used to describe people born between 1965 and 1980 - was originally the title of a novel. Vancouver author Douglas Coupland wrote the novel and coined the phrase to describe what it felt like to be alive at a certain time in a certain point in your life.

An ontologist can bridge ¹ domain expertise and software development via production of a semi-informal so-called intermediate representation ² that can be understood by domain experts, and a formal ontology / knowledge graph that represents the domain in a machine-actionable way.

I have been ruminating on core values in service of stewardship of evolving scientific knowledge. Specifically, what principles can I lean upon to guide me in the design of robustly interoperable digital objects?

Developers often resort to shotgun parsing : scattering data checks and fallback values in various places throughout the system’s main logic. ¹ The habit of scattering parser-like behaviour throughout an application’s code and the resulting inconsistencies in data handling can often lead not just to annoying complications and bugs, but also security vulnerabilities. ² This is about reading data.