Conceptual Provenance

We often think of provenance as a physical thing, tracking the history of a sample and of what we measured. But the provenance of a result started when someone had the idea or the request to measure it. The metadata for a result is not just the parameters on the instrument, or how much sample, or which sample – it’s all those other steps upstream.

Conceptual metadata are like tags, meaningful handles. In a research environment, they allow you to find things based on a concept as opposed to e.g. having to know that it was Jim that ran this on Thursday and Jim always puts his results in this folder. In the absence of conceptual metadata, the best search algorithm you have is often your personal network – having to know who typically runs this kind of thing and then asking them where it’s stored. In a research environment where (a) there’s a fair bit of turnover (students graduating, postdocs moving on), and (b) we have a lot of stuff done outside our team, e.g. through transient collaborations, that personal-network algorithm gets harder and harder to rely on. So it results in the repetition of experiments.

When conceptual context – describing planned experiments, methodologies, sample prep, execution of measurement, calculated/reduced results – lives in separate systems, you have orphaned context and meaning separate from the ultimate results. So, in order to bring information together in a report/presentation, or to fully describe and record the findings for something like a publication or filing, somebody has to read from one system and type or copy/paste stuff into another. There are compounding opportunities for human error, dropping units, putting things in the wrong blank, typing strings that are non-standard, etc. This makes for a very fragile and tenuous handle on the conceptual provenance of a measurement/result.

At any given point in a research workflow, we know a lot about our stuff. Somebody in our electronic universe knows what we need to know – perhaps we just haven’t enabled an electronic flow of that context to capture with the data. This is useful not just for integration for indexes and search downstream, but it also ensures that context, no matter who comes and goes from the team, is more or less permanently embedded with the data.

(I borrowed heavily from this fantastic podcast episode – check it out.)

This post was adapted from a note sent to my email list on Scientific Data Unification.
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