Do citations and readership identify seminal publications?

Abstract

In this paper, we show that citation counts work better than a random baseline (by a margin of 10%) in distinguishing excellent research, while Mendeley reader counts don't work better than the baseline. Specifically, we study the potential of these metrics for distinguishing publications that caused a change in a research field from those that have not. The experiment has been conducted on a new dataset for bibliometric research called TrueImpactDataset. TrueImpactDataset is a collection of research publications of two types -- research papers which are considered seminal works in their area and papers which provide a literature review of a research area. We provide overview statistics of the dataset and propose to use it for validating research evaluation metrics. Using the dataset, we conduct a set of experiments to study how citation and reader counts perform in distinguishing these publication types, following the intuition that causing a change in a field signifies research contribution. We show that citation counts help in distinguishing research that strongly influenced later developments from works that predominantly discuss the current state of the art with a degree of accuracy (63%, i.e. 10% over the random baseline). In all setups, Mendeley reader counts perform worse than a random baseline.