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.

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.

This week on Machine-Centric Science, I interviewed Shreyas Cholia, currently at the Lawrence Berkeley National Laboratory in Berkeley, California. Topics we spoke about included: data lifecycles, edge computing for data firehoses, provenance, standards, broad versus detailed domain vocabularies, scope for common APIs, and identifier leveling.

Arrangements of bits have structure just like arrangements of atoms have structure. Interoperability is about aligning structure. Processing, properties, performance – if their characterization can be repeated, they have information structure.

This week on Machine-Centric Science, I interviewed Patrick Huck, currently staff on the Materials Project at the Lawrence Berkeley National Laboratory in Berkeley, California. We talk about choices and considerations in implementing FAIR. LISTEN NOW » There are show notes at the link above.

“My data model is JSON”. JSON is not a data model. JSON has no semantics in the context of information systems; JSON defines neither how data “behaves” nor how machines can compute with it. “My data is just JSON”. Your data is never just JSON; you always impose external semantics. “JSON is easy to understand”. What does the field "harrastukset" mean?

Whether deliberate ¹ or inevitable ² , the hourglass architecture of the Internet supports a great diversity of applications implemented using a great diversity of supporting services: Could there be a minimal “spanning layer” protocol for FAIR-principled ³ applications and services? The FAIR Digital Object (FDO) has emerged as a conceptual nexus for consideration of such a protocol.