2020/01/16 by Tong Zeng, Longfeng Wu, Zeng, Tong +6
Computer Science · Decision Sciences · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Research Data Management Practices #Scientific Computing and Data Management #cs.IR #scientometrics and bibliometrics research
paper · pdf · doi:10.48550/arxiv.2001.05917
PII: S1751-1577(19)30184-1
arxiv created 2020/01/16 · openalex publication_date 2020/01/16 · arxiv updated 2020/01/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.