In the academic year 2017-18, I had the pleasure of being visiting fellow at the Pufendorf Institute at the University of Lund, in the DATA theme. Interdisciplinarity was a recurring topic in the exchanges with members of the theme group and the other visiting scholars. In particular, I had the opportunity of speaking on this topic at the closing workshop of the theme group, when I reflected on the intersection of interdisciplinarity with Big Data (or as I prefer to formulate it, as a practice and not as a object/thing, the intensification of data in knowledge production).
Interdisciplinarity, as a ‘framing issue’ and in relation to digital data, is coming up again in a budding collaboration. This gave rise to a very nice first brainstorm with Noortje Marres and Sybille Lammes in the cracks of the conference Science and Technology Indicators 2018 in Leiden.
Two related questions that I had started addressing last year resurfaced for me in that stimulating conversation: Why would we want to shape/nurture/defend/score with interdisciplinary practices in the first place? And in particular, how do we entwine interdisciplinarity and different types of data-intensive practices?
I hope to think with this emerging group further about this, and am consolidating some of my answers-in-the-making here:
Think hooks, like on a well functioning piece of velcro… knowledge that has been produced in contact, interaction or exchange with other disciplines or other practices is knowledge that is not hermetic, that has hooks that can allow it to connect to other surfaces. It is knowledge that can stick, it is knowledge that can bind.
Interdisciplinary interaction creates these hooks through friction –roughening up the surface through contact. And these interactions clarify what is needed for the hooks and eyes to meet.
Like any of us in a self-conscious moment, knowledge that has been produced in an interdisciplinary context is knowledge that knows how to blush, that is visibly, outwardly modest. It knows its limitations, it’s aware of its assumptions, it’s sensitive to boundaries.
This third aspect is the one where I see specific features of knowledge most clearly being shaped by the intersection of data intensification and interdisciplinarity. Processes of data intensification involve increased formalisation. Across projects (at the Virtual Knowledge Studio and later in Energysense) I experienced again and again how formalisation and infrastructural work that are part of data intensification are crucial ways of becoming aware of assumptions, limitations and opportunities. Building digital resources together involves intense interaction about the epistemic practices of research. And these yield important experiences and insights, especially resonant when they are formed in the course of providing each other with accounts–in the sense of stories, not in the sense of communication of financial information about economic entities.
What might that look like, really concretely? Once you’ve had to explain your work to someone from another discipline or to the data scientists or designers in the team, and when you’ve repeatedly experienced how a question about how to formalize a type of data or result leads to a question about methods, then to concepts, and traditions in the field and then back to data collection, once you’ve really explained these connected practices forwards and backwards so that the others get it, once you’ve really explored what formalisation can and cannot do and felt through playing around with the prototype how infrastructuring changes you research practices… you’ll find it hard to think about your data and disciplinary baggage as something self-evident and transparent and to separate it from these accounts.
Perhaps I am getting quite normative in middle-age. In any case, I’m curious and excited to see how these questions and answers will develop in the course of further collaboration with colleagues at CWTS, Leiden University and at the Center for Interdisciplinary Methodologies at the University of Warwick.