Artivity: a tool to collect contextual research data for art practice

In recent years, academic research through art practice has been recognised as a critical part of the development of art education. Artists/researchers embark on research and come to conclusions and new understandings. Their outputs are often artworks which have evolved through processes. Documenting these processes is not always easy and artists often consider documentation as a burden. Documentation is limited to the final output with little additional information demonstrating how the artwork has been developed. Such information is important for future art historians and artists.
In this paper we present Artivity, a self-archiving software tool which runs as a daemon in the background of the artist's desktop and allows the systematic collection of contextual research data from supported applications. This includes data about the techniques and patterns of usage that the artist engages with when working with creative software. Every action undertaken on the digital canvas (such as changing properties or transforming objects) is mapped to the W3C PROV ontology (https://www.w3.org/TR/prov-o/) and recorded in a local RDF triple store. Actions also include web browsing and file downloading. The user can configure which applications can feed data to the deamon. Captured data can be extended to include other contextual information such as the geographical location of the artist or the hardware specifications.
The recording daemon and the data model for Artivity are stable. The development of Artivity's GUI is ongoing. The adoption of the tool is still at experimental research level and we have only undertaken preliminary analysis of two datasets with promising results, which we present. We conclude our paper with showcasing the integration of the software with the popular repository software EPrints, which allows the seamless submission of outputs with the associated contextual data to an EPrints instance for reviewing by repository managers.