  The ever-increasing quantity and complexity of scientific production have
made it difficult for researchers to keep track of advances in their own
fields. This, together with growing popularity of online scientific
communities, calls for the development of effective information filtering
tools. We propose here a method to simultaneously compute reputation of users
and quality of scientific artifacts in an online scientific community.
Evaluation on artificially-generated data and real data from the Econophysics
Forum is used to determine the method's best-performing variants. We show that
when the method is extended by considering author credit, its performance
improves on multiple levels. In particular, top papers have higher citation
count and top authors have higher $h$-index than top papers and top authors
chosen by other algorithms.
