It is always good to occasionally get out of the local bubble and talk to people in other domains who do similar, or not similar, work. Today I had the privilege of speaking at the New England Technical Services Librarians (NETSL) meeting with Patrick Yott, a colleague from Northeastern. We were invited to speak about “Digital Repository Services with Fedora.”
As I entered the conference this morning, I realized that, contrary to most conferences I attend, here was a group of a few hundred people, and I knew virtually none of them besides the contingent from UConn who were also attending. These folks spend much of their time thinking about how to describe and make available materials of all types and in all formats and I briefly wondered how they would take to an archivist talking to them about organizing and managing a digital repository. I found to my pleasure, but not my surprise, that this group was talking and thinking about many of the same things as I was, and were keenly interested in what we had to say about modeling content objects in a digital repository, and even better, had interesting ideas on how to do that.
Patrick and I are separately building Fedora repositories at our respective institutions and building the teams we need to make these repositories realities. We both know and understand the value of metadata and metadata librarians who think broadly about resource management and description, and how metadata extends well beyond descriptive cataloging.
The takeaway that I’m getting from the conference so far-which continues as I write this during the lunch break- is that the important metadata work for us in the digital repository domain is to focus on developing and modeling the relationships among our digital repository objects so that they are flexible enough to exist in multiple domains and in multiple discovery systems
So, while we are working at UConn to develop a technical system to leverage a common repository of heterogeneous content, we also know that any technical system is only as good as the data (and the data models) that reside in it.