Data Modeling the Digital Humanities


Today in class we talked about data modeling; specifically as it relates to the Urban Research Tool, but also to the digital humanities more generally. The readings for this week hopefully did a good job of addressing exactly what data modeling is, and hopefully our class discussion added to that.
As we mentioned in class, while searching for additional background reading on the topic of data modeling as it specifically applies to the digital humanities, Shannon discovered “Knowledge Organization and Data Modeling in the Humanities”: “a three-day workshop … focusing on questions of data modeling for the humanities,” with a summary to be published at some point following the event. I excerpted some quotes from that website for the class presentation, but wanted to reproduce those here on our class blog. As far as we can tell, the summary publication has not come out yet, but there are some valuable nuggets to be gleaned even from some of the abstracts.
We can read this quote from Elena Pierazzo’s abstract as addressing the question of why it is valuable to spend so much time thinking about data modeling in the context of, for example, our Urban Media Archaeology class: “Modeling can be seen as the intellectual activity which lies at the base of any computational effort, namely the methods and the languages we invent to communicate our understanding of a particular cultural object (such as a text, a statue, a piece of music) to the computer and, via the computer, to the users.”
And from the workshop summary: “We want to use this event to consider how digital models of knowledge representation in the humanities have developed and how the various models now available to us … shape and inflect the research objects we create and the research we undertake with them.”
In other words, in order to work within any kind of digital- or software-based system, the researcher must “model” their concepts in such a way that the system can operate on them — and this process, this translation of a concept into a computable object, has so many deep implications for the way knowledge is produced and shaped that researchers must be empowered to take this step themselves, accounting for all the subtlety and nuance that their particular subject may require.
Reiterating this, Trevor Muñoz writes: “Data modeling is an immensely-important but largely under-discussed topic in digital humanities (it’s certainly under-represented in the published literature — though this conference is part of an effort to change that). Data modeling is part of the crucial ‘DH-specific’ intellectual work of translating between the (often implicit) understandings that scholars have of the objects they study and the affordances of a particular digital technology (which might be a relational database or TEI XML or any number of things).”
Moving specifically to the topic of modeling time, and building on our Drucker & Nowviskie reading for this week, Stefan Gradmann asks: “How do we model time and process context in such environments? … We seem to have firmer ground for contextualisation on the spatial side: GeoNames, GeoCoordinates and the like seem to be much more stabilized conceptual areas. Maybe because the denotative aspect is stronger in space than in time?!” In many ways, the URT is a direct manifestation of these problems! “The map” has been much easier for us to conceptualize and implement than “the timeline,” which we are still working on.
And finally, describing the workshop goals: “These models lie at the heart of our work as digital humanists, and yet the theory and practice of information modeling is still treated in the literature primarily as a technical topic rather than as constitutive of humanities research and practice.”
So perhaps a better question then would be to consider why this practice is still treated as a “technical topic,” separate from other aspects of the research process. Perhaps we could even imagine some alternative teleology of software, in which, within a different historical context, these types of systems could have been developed specifically with this kind of work in mind, instead of something that must be “translated to.” Regardless, experimenting with bridging this gap is something we will be doing throughout the semester.

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