Don't archive you assets — frontier them
Don’t archive you assets1 — frontier them. Research is a living process.
Don’t archive you assets1 — frontier them. Research is a living process.
model-memo The things of concern1 are materials, processes, measurements, and ingredients.
For each layer in the structured-content stack,1 from least to most volatile (i.
The key technical foundations for FAIRifying data are (1) ubiquitous persistent identifiers; (2) rich controlled metadata; and (3) granular programmatic access.
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.
I’ve been trying to grok architecture patterns as presented by Percival and Gregory1 to support domain-driven design and event-driven microservices with Python.
The “one platform1 to rule them all” is unlikely to be realized for scientific research in any domain.
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.
Don’t. Identifiers should be opaque. If you’re given an owl:sameAs assertion from a party you trust, use that.
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.
Indexing identifiers is key to disambiguating entities. Wikipedia has disambiguation pages.
How do you validate a reified trace of digital-object provenance?
Rory Macneil nails it:1 This seems to me to be a really important problem.
Given a representation of (meta)data that dcterms:conformsTo some data profile, you may wish to translate it to another data profile.