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The “Good Enough” Metadata Specialist

· 4 min read
Emily McKibbon
LINCS Metadata Co-op


My first job in the museums field was in 2008, right at the height of the Great Recession. The digitization team I joined had just lost roughly a quarter of their staff in a series of buyouts and layoffs, and the mood was grim. We were tasked with getting a large collection of historic photographs online, and the sooner the better—the only rub was that the collection wasn’t fully catalogued, and doing so properly would take time that we did not have. The pressure was on to justify our jobs, and so the discussions we had about metadata leaned towards the provisional. If the database is unpopulated, does just the accession number suffice? Okay, what about the accession number and artist? The solutions we came up with reflected the stressors of that moment: we aimed for something good enough in lieu of something exemplary, carefully balancing data requirements with the drive to generate content.

For the next decade and a half or so, Winnicott’s “good enough” parent has been a guiding principle in my role as a data custodian... I have been a “good enough” cataloguer at a number of different institutions, only ever providing my database the simplest of things: where it is, who it’s by, who gave it to us, and whether it will fit in our freight elevator. I have been a “good enough” digital curator when it comes to sharing collections material online, giving only the metadata required to make our digital assets usable for local contexts.

Working at LINCS has been a major paradigm shift for me professionally, not least because “good enough” is a different benchmark in this new context. I’m currently a Metadata Specialist, assisting LINCS Ontology System Analyst Erin Canning in generating documentation for some of our partners in this project, focusing on the Canadian Centre for Ethnomusicology,, the Yellow Nineties Personography, and the University of Saskatchewan Art Gallery. For each of these, I’ve been responsible for generating TTL code snippets and diagrams that describe how data will be mapped into our triplestore using CIDOC CRM and FRBRoo, which extends the CRM to capture the underlying semantics of bibliographic information.

These diagrams and snippets form part of our application profiles—one umbrella profile that captures the LINCS project as a whole, and one for each of the datasets that LINCS comprises. These bring together ontology patterns, our metadata elements drawn from multiple namespaces, policies and guidelines related to their use, decisions we’ve made that deviate from community standards in order to accurately capture the nuances of our dataset, and other decisions relating to the data target we’re working towards. The application profile is a living document that enables communications between researchers, ontology specialists, data interface developers, and others, but it also provides a trail of breadcrumbs for other organizations that might be interested in replicating how we’ve mapped, ingested, and federated so many divergent cultural datasets into ResearchSpace. Documenting everything so completely requires a great deal of care and attention to detail, but that’s the point: while in some projects it might be good enough for someone to infer some unspecified detail from scanty documentation, or to phone a person and ask them what they intended, in this case, good enough documentation allows a project like ours to have an impact long after the grant funds have been spent and the final reports are filed. To be not only sustainable, but replicable, we need to take the time to document things now. In this sense, we’re attentive to both data and content: neither takes precedence over the other.

There is a kind of satisfaction that comes from doing something very well. While I’m happy to take part in any activity that eases communication over a large, geographically dispersed team, I also like the idea of casting breadcrumbs behind us as we go. Linked Open Data has yet to reach its full utility in the cultural realm, and I’m looking forward to the day when I can query a museum database to determine not only whether an artwork fits in an elevator, but more complex questions that reflect the kind of inferencing possible with semantic data. It’s exciting to know that what we’re doing might inspire others to follow—and that the documents there are good enough to guide their path.