Digital Humanities at the intersection of three approaches to data visualisation: statistical graphics, data humanism, and humanistic interpretation

Digital humanities has a critical role in the progress of the data visualisation field. First, as humanities scholars, they engage with the concept of knowledge as interpretation. Second, they also leverage computational tools and statistical methods for the analysis and visualisation of data and metadata. And finally, digital humanities projects are a space for experimentation where different epistemic cultures (which often go beyond the humanities and computer science) negotiate new forms of knowledge (Fickers and Tatarinov 2022). Digital humanities is therefore in a privileged position to benefit from the full potential of data visualisation, using and improving existing methods and tools, and participating in the development of those that are much needed but do not yet exist. This article presents a classification of current approaches to data visualisation into (I) statistical graphics, (II) data humanism and (III) humanistic interpretation. These three approaches are based on four main aspects: a) the intellectual habits around the definition of data, b) the characteristics of the data visualisation and its objectives, c) the relation between the data and the data visualisation, and d) the expected user interaction. The classification is not intended to narrowly categorise any data visualisation, but rather to help navigate the visualisation continuum between quantitative and interpretative practices, and to identify gaps and opportunities for future development. Based on the study of more than 500 data visualisations, a list of recurring elements, which are not easily accessible in existing tools, has been extracted and proposed as a starting point for further research