The Loon collects research-data management models. It’s a hobby, of sorts. She can’t get tired of wondering how information scientists will restate and revisualize data management next.
(If you get the sense that the Loon thinks not a few of these models redundant and unnecessary, gold star!)
At least one well-known data-management concern proudly pastes its particular model prominently on its website. The Loon scratched her head with one webbed foot for quite some time about this, wondering why the practice bothered her so.
She thinks she’s worked it out. Most data-management models (the one she is thinking of no exception) map extremely poorly to the research-project cycles and timelines that researchers are accustomed to. The milestones researchers think about—grant applications, awards, data capture, data analysis, interim-report writing, article authoring, renewal applications, and so forth—barely appear in data-management models.
This makes data-management models all by themselves horribly poor tools for communicating with researchers. They’re just too foreign, not least because they inappropriately place data at the center of the research universe—which in most researchers’ eyes is just ludicrous.
Fortunately, this is an eminently solvable problem, though solving it will require adroit visualization skill (in which the Loon must admit she herself is terribly deficient) and either considerable empathy or a good deal of ethnographic-style research. Let’s have at it, data-curation community. The Loon will send a feather for the cap of the author(s) of the first model that situates data concerns properly in a holistic research-cycle context.
- Additional hurdles to novel library services