  Online citizen science projects involve recruitment of volunteers to assist
researchers with the creation, curation, and analysis of large datasets.
Enhancing the quality of these data products is a fundamental concern for teams
running citizen science projects. Decisions about a project's design and
operations have a critical effect both on whether the project recruits and
retains enough volunteers, and on the quality of volunteers' work. The
processes by which the team running a project learn about their volunteers play
a critical role in these decisions. Improving these processes will enhance
decision-making, resulting in better quality datasets, and more successful
outcomes for citizen science projects. This paper presents a qualitative case
study, involving interviews and long-term observation, of how the team running
Galaxy Zoo, a major citizen science project in astronomy, came to know their
volunteers and how this knowledge shaped their decision-making processes. This
paper presents three instances that played significant roles in shaping Galaxy
Zoo team members' understandings of volunteers. Team members integrated
heterogeneous sources of information to derive new insights into the
volunteers. Project metrics and formal studies of volunteers combined with
tacit understandings gained through on- and offline interactions with
volunteers. This paper presents a number of recommendations for practice. These
recommendations include strategies for improving how citizen science project
team members learn about volunteers, and how teams can more effectively
circulate among themselves what they learn.
