  A citation is a well-established mechanism for connecting scientific
artifacts. Citation networks are used by citation analysis for a variety of
reasons, prominently to give credit to scientists' work. However, because of
current citation practices, scientists tend to cite only publications, leaving
out other types of artifacts such as datasets. Datasets then do not get
appropriate credit even though they are increasingly reused and experimented
with. We develop a network flow measure, called DataRank, aimed at solving this
gap. DataRank assigns a relative value to each node in the network based on how
citations flow through the graph, differentiating publication and dataset flow
rates. We evaluate the quality of DataRank by estimating its accuracy at
predicting the usage of real datasets: web visits to GenBank and downloads of
Figshare datasets. We show that DataRank is better at predicting this usage
compared to alternatives while offering additional interpretable outcomes. We
discuss improvements to citation behavior and algorithms to properly track and
assign credit to datasets.

Information Retrieval