The place of models and modelling in Digital Humanities: Intersections with a Research Software Engineering perspective

This paper aims to bridge Digital Humanities (DH) and Research Software Engineering (RSE) communities. It argues that the production of models is the core contribution of RSE to the epistemology of DH. We adopt an inclusive definition of models and modelling (Ciula et al. 2018) which spans the whole range from ‘deformative’ to empirical modelling (Smithies 2017: 168), including formal or predictive modelling (Joslyn and Turchin 1993), and the technical solutions produced in the process as well as the know-how, languages and documentation which accompany this production. From this wide perspective, models are also artefacts which can be studied across the history of science and of the humanities tradition (Bod 2018) and in comparison with other modelling practices in science. RSE practice is grounded on a strong conscience of the experimental apparatus and the iterative critique of models built (and often deflated) for a purpose. The challenge is to recognise the idiosyncrasy and situatedness of modelling practices and artefacts while devising methods to expose the scalability of the underlying workflows and modelling processes. We reflect on the epistemology of DH from the practical perspective of our RSE lab - King’s Digital Lab (KDL) - and the research processes embedded in our Software Development Lifecycle. The human element is at the core of the technical ecosystem we research and operate in. We acknowledge that KDL models and modelling are co-constitutive of human expertise, technical systems and operational methods, all aspiring to an environment conducive of open knowledge. We use project-specific examples, including data modelling and knowledge representation practices, to demonstrate how some of our research into model-making processes challenge the perception of the technical work of RSE within DH as a stale, mechanistic and uncritical procedural activity.