In mid 2020, as Australia began to feel the full force of the Covid-19 crisis, a group of researchers from across the University were brought together by a common interest: to uncover how our education and creative industries sectors are reacting and adapting to an environment mediated by physical isolation, social disruption, and health and economic crises. This group formed a collective under the title #DataCreatives to explore this research theme. At the heart of the #DataCreatives approach is a desire for research to be data led – that is, #DataCreatives first seeks out data sources, then extracts data, then explores the narrative that can be constructed from that data, and uses this to guide the formulation of research questions. Several months into the collaboration, the #DataCreatives team realised that one of the strengths of the collective was the framework within which the team was operating. That is, the mechanisms by which we established an online ecosystem to allow collaboration and communication to co-design a data-led, creative, research program.
This manifesto documents the #DataCreatives ecosystem. The impetus for this is manifold, principally:
As a record of how we interact, communicate, and create in the online space. We utilize a range of tools and mechanisms to facilitate collaborative research, and it is important to document these as a record of this collaboration’s work.
As a blueprint for future collaborations. A key aspect of #DataCreatives is the trialling of new technologies and tools. Some are effective, and some are not. Through outlining these technologies and tools, including the context in which they were implemented, this manifesto acts as a blueprint for future collaborations.
As an act of research in itself. Like many creative industries and education sectors, especially in Victoria, #DataCreatives operates in an exclusively online environment. Additionally, the team consists of researchers, educators, and practitioners who, in addition to being part of the #DataCreatives collective, continue to produce work in these creative industries sectors. Therefore, as the entire #DataCreatives collective are ingroup members of creative industries collaborations (by virtue of being part of the current team), and as various members are ingroup members of specific creative industries sectors (as educators, practitioners, and consumers), we contend that the #DataCreatives ecosystem reflects ecosystems of other creative industries sectors which are the target of our study.
Framework for understanding online collaboration and data discovery
As discussed above, the #DataCreatives collaboration exists in the same online ecosystem as the education and creative industries sectors we seek to study. Therefore, reflecting on our own online activities may provide insight into the online activities of other sectors of interest. Our online activities are conducted via a range of platforms (e.g. Zoom, Twitter, email). These platforms capture data about our actions such as audio, video, text, and metrics. The data captured represents our data trace – the digital footprint of our actions and interactions on a particular platform. Since most platforms are activity specific, the location of a data trace is predicated on the activities being performed. For example, YouTube is well equipped (affords) for video streaming which suits online performances (activity), therefore YouTube is a good place to find data on trends in live streaming concerts (data trace).
The relationship between activity, platform, and data trace is captured withing the system of (inter)actions – a framework for understanding the association between desired goals and the mediums through which these are realised (Spreadborough et al., forthcoming). By applying the principles of the system of (inter)action to the present discussion of tools and techniques in this document, we can better understand how we came to choose different platforms. Additionally, we may develop a stronger understanding of what motivates others in education and creative industries to choose certain platforms, which may lead us to uncover data traces that inform our examination of how education and creative industries sectors are showing resilience in the face of Covid-19.
The following is a brief overview of the system of (inter)action (shown in Figure 1). All (inter)actions within the online world begin with a Goal. Goals may be directional or non-directional. A directional goal is one in which a presenter delivers content to an audience, as in a concert or a lecture for example. A non-directional goal has two functions. First, it provokes discussion and thus establishes dialogue as in, for example, a student discussion group or a reading group. Second, it creates co-constructed knowledge as in, for example, a collaborative composition or a group assessment. The primary difference between discussion and co-construction is the construction of knowledge. The slanted brackets indicate that these categories of goals exist on a spectrum – for example, discussion may be a necessary precursor to co-construction as it provokes dialogue which leads to the creation of new knowledge.
One’s goals will influence the Flow of an (inter)action. Here, Tempo and Mode a closely interlinked. Tempo refers to the speed with which (inter)actions are performed. Tempo sits on a spectrum between immediate, as in live chat, and delayed, as in forum posts or responsive artwork. Mode refers to how the (inter)action is performed (e.g. writing a blog post or creating an image). Some modes allow for engagement through Manipulation (e.g. games and virtual reality) while others allow for engagement through Exchange (e.g. writing, images and “reactions” to posts). Modes are often combined in fluid ways as indicated by the slanted brackets (e.g. as in the simultaneous use of chat and text while playing a game). The desired Tempo of an (inter)action may influence the choice of mode (e.g. a Zoom call – audio visual – to achieve immediate (inter)action). Conversely, a desired Mode may influence the Tempo (e.g. it may only be possible to use text chat, which will influence rate of (inter)action). Mode and Tempo are inextricably linked and contribute to the Flow of the (inter)action.
The Flow of an (inter)action shapes the Medium (what we have above called the platform) in which that (inter)action is performed. In creative industries associated with organisations, especially education and research, mediums may be Institutionally mediated. For example, LMS and internal communication systems. These mediums are typically designed to be fit for a wide variety of purposes, of which creative industries is only one. Public mediums may be commercial, such as YouTube or Slack, or open source, such as GitHub Pages or blogs. Mediums can often sit between commercial and open source, such as Flicker which allows for scaled back free accounts, or Slack which does not incur a fee but data has a limited retention period. Ultimately, it is the Goal and Flow of the (inter)action which influences the Medium used and thus the location of the data trace. For example, if you are looking for data traces of collaborative composition, you are more likely to find these in Mediums which allow for non-directional (inter)action and a Flow with a close to immediate tempo that utilises Modes which mirror the goal of the activity (e.g. preference for audio and visual over graphic).
Figure 1. (Inter)action system (Spreadborough et al., forthcoming).