DAPTEC – Data Physicalisation Technology
Our vision is to produce a framework that will comprise a set of best practices for designing, developing and delivering data physicalisation. The framework (and resulting physical and multi-sensory presentation of data) will be used to inform decision making for both public and private sectors. It will benefit public bodies enabling them to reach their citizen stakeholders in new and effective ways, data agencies in opening new physical data markets and creating new commercial products, and academic institutions by creating new impactful knowledge around the processing and communication of truly ‘insightful’ data.
What do we mean by data physicalisation?
It can seem like quite a daunting term, but the concept of data physicalisation is really quite simple. When thinking of the traditional way of presenting data, the standard is to gravitate towards a chart or a graph of some kind. However, this predominantly virtual/ screen based way of presenting data can pose its own challenges in terms of accessibility and inclusivity. For example, some graphs representing many different data sets often overlaid on top of each other can be difficult for the everyday person to interpret and understand. Data physicalisation, on the other hand, is an emerging concept that takes data visualisation to a new level by encoding data in an engaging physical form (Jansen & Dragicevic, 2013). Depending on its form, physicalized data can be touched, heard, seen, tasted or smelled in its final state. It is tangible in that it has a presence in our physical world. It is a physical artefact whose material properties encode data in new accessible and inclusive ways to derive insight and meaning (see Data Physicalisation examples here).
Data Physicalisation can also involve the process behind the transformation of specific data values into its physical state. Of particular interest to this project, it is the idea of encoding data in the behaviour, functionality, performance, or even affordance of an object. In his paper Hogan (2018) highlights that data insight can be generated from the overall experience of the sensification.