We introduce the reader to crowd science by discussing prominent projects including Foldit, Galaxy Zoo, and Polymath.•
We identify key characteristics of crowd science projects and distinguish them from other modes of knowledge production.•
We consider heterogeneity among crowd science projects and discuss which types of scientific problems may benefit most (and least) from crowd science.•
We discuss organizational challenges crowd science projects face and conjecture how these challenges may be overcome.•
We conclude with an agenda for future research as well as implications for funding agencies and policy makers.
A growing amount of scientific research is done in an open collaborative fashion, in projects sometimes referred to as “crowd science”, “citizen science”, or “networked science”. This paper seeks to gain a more systematic understanding of crowd science and to provide scholars with a conceptual framework and an agenda for future research. First, we briefly present three case examples that span different fields of science and illustrate the heterogeneity concerning what crowd science projects do and how they are organized. Second, we identify two fundamental elements that characterize crowd science projects – open participation and open sharing of intermediate inputs – and distinguish crowd science from other knowledge production regimes such as innovation contests or traditional “Mertonian” science. Third, we explore potential knowledge-related and motivational benefits that crowd science offers over alternative organizational modes, and potential challenges it is likely to face. Drawing on prior research on the organization of problem solving, we also consider for what kinds of tasks particular benefits or challenges are likely to be most pronounced. We conclude by outlining an agenda for future research and by discussing implications for funding agencies and policy makers.