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This content will become publicly available on February 10, 2019

Title: Do citations and readership identify seminal publications?

Here, this work presents a new approach for analysing the ability of existing research metrics to identify research which has strongly influenced future developments. More specifically, we focus on the ability of citation counts and Mendeley reader counts to distinguish between publications regarded as seminal and publications regarded as literature reviews by field experts. The main motivation behind our research is to gain a better understanding of whether and how well the existing research metrics relate to research quality. For this experiment we have created a new dataset which we call TrueImpactDataset and which contains two types of publications, seminal papers and literature reviews. 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 quality. Finally, our research shows that citation counts work better than a random baseline (by a margin of 10%) in distinguishing important seminal research papers from literature reviews while Mendeley reader counts do not work better than the baseline.
ORCiD logo [1] ; ORCiD logo [2] ; ORCiD logo [3] ; ORCiD logo [2]
  1. Open Univ., Milton Keynes (United Kingdom); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Open Univ., Milton Keynes (United Kingdom)
Publication Date:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Additional Journal Information:
Journal Volume: 115; Journal Issue: 1; Journal ID: ISSN 0138-9130
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
Country of Publication:
United States
99 GENERAL AND MISCELLANEOUS; Information retrieval; Scholarly communication; Publication datasets; Data mining; Research evaluation; Bibliometrics; Altmetrics
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