Attract & Engage Visitors for Tabular Data Maintenance: a Longitudinal Naturalistic Field Study

Maintaining an evolving tabular dataset over time requires attracting and engaging enough accurate, unpaid contributors to visit and maintain it. We developed Drafty, a large publicly accessible tabular dataset of Computer Science faculty profiles. We observed Drafty’s visitors’ edits to the data, the accuracy of those edits, and engagement with the system. In March 2022, we released new add-ons to create four “sources” to attract more visitors: organic, static, asking, and dynamic. Our dynamic sources create automatic insights from our evolving tabular dataset by automatically creating aggregate statistics and sharing them on CS Open Rankings and Twitter. Visits from automatic insights made 3 times more edits than Drafty’s everyday organic visitors. Also, visits from our low-effort automatic dynamic sources and high-effort asking sources (i.e., friendsourcing) had similar accuracy. Our longitudinal study demonstrates that new features requiring minimal human effort (i.e., automatic insights) can help attract and sustain an anonymous community of visitors to maintain our large, evolving tabular dataset.