Data visualization has been suggested to be a form of language. To that end, several connections between knowledge of data visualization and natural languages have been noted by the visualization research community, to the point that it is now commonplace to discuss how people might “read” a visualization. Notable examples of such connections include knowledge of literacy, which has strongly inspired the notion of data visualization literacy; language acquisition in early childhood, which plays an important role in the concept of constructive visualization; active reading, which was studied in the context of working with data visualizations; rhetoric, which has inspired work on visualization rhetoric; and critical text analysis, which has given way to critical “readings” of data visualizations that build on the rich traditions of humanistic scholarship.
However, these many ways to use the rich knowledge that we have about our natural languages to inform visualization research and design currently exists as isolated islands of knowledge. To date, the work to collect these different approaches, to connect them, and to uncover what we might have missed in terms of knowledge translation between these two knowledge domains remains.
This PhD aims to contribute insights from considering the domains of data visualization and natural languages together. It asks, what are the parallels between the domains, what are the unexplored areas, what are the strong examples of inspiration, and how might we build from those?
The project will survey existing research that intersect the two domains to establish a framework that help to understand and think about the two domains. The framework, readings within the domains, and consultations with language experts will help uncover unexplored areas and incongruencies between the domains and provide opportunities for grand breakthroughs. For example, in visualization, “authoring” means to manually create a well-known visualization design, while in the context of natural languages, “authoring” has a different meaning. Might there be other ways to consider visualization authoring that can be fruitfully applied in visualization research? Based on the opportunities identified in the framework, the project will explore promising directions in combining the two knowledge domains.
We envision this work to lead to new insights about visualization authoring, visualization production, and visualization reading. While we imagine most of this work to contribute to the emerging body of work in data visualization, it will consider contributions to the rich existing body of work on languages that are inspired from data visualization knowledge.
The candidate for this project should have a strong background in:
- The field of digital design or computer science. The candidate should have a good understanding of data visualization, and might possess the skills needed for development of research prototypes, or
- The field of social sciences and humanities (such as, but not limited to, language, linguistics, or cognition). The candidate should be interested in pursuing more technical research and a particular interest in data and data visualization.
Ideally, the candidate presents a relevant and interesting profile that intersects the two mentioned fields, or a strong background in one of the two and a genuine interest in and understanding of the other.
Start date: 1 September 2022
Proposed supervisors: Lone Malmborg, Søren Knudsen
Contact: Søren Knudsen, email@example.com
Research Group: Human-Centered Data Science
If successful, the position is fully financed by ITU.