UC Berkeley

Data are essential products of scientific work that move among and through research infrastructures over time. Data constantly changes due to evolving practices and knowledge, requiring improvisational work by scientists to determine the effects on analyses. Today for end users of datasets much of the information about changes, and the processes leading to them, is invisible—embedded elsewhere in the work of a collaboration. Simultaneously scientists use increasing quantities of data, making ad hoc approaches to identifying change difficult to scale effectively. Our research investigates data change by examining how scientists make sense of change in datasets being created and sustained by the collaborative infrastructures they engage with. We examine two forms of change, before examining how trust and project rhythms influence a scientist’s notion that the newest available data are the best. We explore the opportunity to design tools and practices to support user examinations of data change and surface key provenance information embedded in research infrastructures.