Understanding Distributed Cooperative Work through Data Traces

Abstract

Understanding cooperative work in distributed and hybrid settings is complex due to the interplay of multiple artifacts, technologies, and collaboration styles. This paper uses an auto-ethnographic approach to explore the collaborative practices of a geographically distributed scientific research team over one year, introducing Data Traces as a novel method for capturing interactions with artifacts and team members. We examine how Data Traces can support reflection and development of long-term group collaboration by providing detailed insights—such as patterns of teamwork and coordination—into the collaborative practices of the team. Our analysis produced three main findings: (1) Data Traces help visualize temporal rhythms in cooperative work, (2) Data Traces are shaped by the proximity across collocated, hybrid, or distributed participants, and (3) Data Traces cannot capture invisible work. These results contribute to the broader discussion on data tracking in CSCW by demonstrating the potential of Data Traces as a feature of cooperative technologies supporting group reflection connecting the use and relations of artifacts, devices, and applications in the larger ecology of artifacts.