Experimenting with Dataphys, a Physicalization Toolkit for Representing Spatio-Temporal Environmental Data

Data physicalization offers an exciting opportunity for engaging people with physical representations of data that can be examined and manipulated to gain a mental model of a set of variables and their relationships. This is of paramount importance not only for researchers and professionals but also for improving citizens’ awareness in different domains, impacting civil society, and pushing behavior change. Accessibility of data is another important issue that can benefit from the adoption of data physicalization. Despite this potential, several pitfalls prevent the wide adoption of this paradigm, including the lack of a methodology for guiding the design of the representations and toolkits permitting to engage citizens in their creation without requiring them to start from scratch. This paper intends to contribute to the discussion by presenting and evaluating Dataphys, a toolkit for creating physical representations of data. The toolkit is modular, conceived for the application to different scenarios and domains, printed with cheap technologies, and open to modifications to ease its adoption. The evaluation was carried out in the context of a workshop with the participation of 17 master students in computer science, and focused on the gathering of environmental data collected along a path and the construction of a physical representation. The study’s results confirm the interest in the approach and offer insights for improving it.