In this paper we propose a novel method for quality assessment of crowdsourced data.
It computes user reputation scores without requiring ground truth; instead, it is based on the consistency among users.
In this pilot study, we perform some explorative data analysis on two real crowdsourcing projects by the New York Public Library:
extracting building footprints as polygons from historical insurance atlases, and geolocating historical photographs.
We show that the computed reputation scores are plausible and furthermore provide insight into user behavior.