Data-technology communities: collaboration and diversity in data- and technology-intensive multidisciplinary research

Abstract Multidisciplinary research addresses complex problems that exceed the scope of single disciplines, but often lacks methodological, theoretical, or goal-based unification. In this paper, I investigate what brings members of data- and technology-intensive multidisciplinary projects together, and what enables them to collaborate effectively and produce valuable scientific outcomes. Drawing on the Haly.Id case study, an agricultural project that aimed at gathering extensive data and developing cutting-edge technologies to manage invasive pests, I propose the concept of ‘data-technology communities’. These are heterogeneous groups of individuals who are not tied by a problem agenda, an adaptive problem space, or a set of practices; rather, they come together by virtue of a common interest in and use of shared data and data collection technologies. Through this common focus, members interact, learn from one another, and collaborate within the delimitations imposed by specific constraints. I further argue that the data-technology community framework provides a useful lens for understanding a growing trend in biological data-intensive research, where the production of large datasets and advanced technologies is often prioritized over critical reflection on their biological purpose and application.