Arguments and interpretations made while researching the arts are based on historical evidence. The technical analysis of artworks and artists' archives supply such evidence with the objective being to provide answers on how and why an artwork was made, i.e. how can artistic practice be described? Identifying such evidence in digital art is difficult due to the lack of the artwork's physical manifestation. Available resources about the artwork often concern long-term digital preservation, e.g., Serexhe (2012), and not the practice. Information on the process and context of the artwork is typically lost. In this paper we present a method for capturing part of this contextual data. We also present an implementation in the form of a software tool called Artivity. We highlight a case study with data by the artist Gino Ballantyne. We discuss the limitations of our approach and the expected use of the tool.

In this paper we introduce our NEPOMUK-based Semantic Desktop for the Windows platform. It uniquely features an integrative user interface concept which allows a user to focus on personal information management while relying on the Property Projection agent for semi-automated file management.

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.