DataChopin: A collaborative interface for data visualisation and composition on large interactive screens

DataChopin is a research prototype that explores the democratisation of data analytics and sensemaking capabilities. The collaborative interface is a major outcome of sustained research efforts at the Urban Informatics Research Lab, part of the QUT Design Lab, in citizen science and public engagement with urban data. It was developed over the course of multiple iterations spanning across three use cases. Its distinctive characteristics are the use of large, interactive displays as a shared desktop, as well as flexible composition mechanisms for incremental construction of visualisations. By studying interfaces for composing data and visual forms, this research contributes to the understanding of the fundamental components and structure of visualisations, resulting in a general and flexible compositional model. This lays the groundwork for data exploration tools that allow non-expert users to mix, match, and manipulate datasets to obtain visual representations requiring little to no programming knowledge

DataChopin - Designing interactions for visualisation composition in a co-located, cooperative environment

This article presents our interaction design for DataChopin, based on an extensive survey and classification of visualisation software for exploratory data analysis. Its distinctive characteristics are the use of a large-scale display wall as a shared desktop, as well as flexible composition mechanisms for incremental and piece-wise construction of visualisations. We chose composability as a guiding principle in our design, since it is essential to open-ended exploration, as well as collaborative analysis. For one, it enables truly exploratory inquiry by letting users freely examine different combinations of data, rather than offering a predetermined set of choices. Perhaps more importantly, it provides a foundation for data analysis through collaborative interaction with visualisations. If data and visualisations are composable, they can split into independent parts and recombined during the analytical process, allowing analysts to seamlessly transition between closely- and loosely-coupled work