To read the full version of this content please select one of the options below:

Identifying landmark publications in the long run using field-normalized citation data

Lutz Bornmann (Max Planck Society, Munich, Germany)
Adam Ye (Center for Bioinformatics, Peking University, Beijing, China)
Fred Ye (School of Information Management, Nanjing University, Nanjing, China)

Journal of Documentation

ISSN: 0022-0418

Article publication date: 24 January 2018

Issue publication date: 7 February 2018

456

Abstract

Purpose

The purpose of this paper is to propose an approach for identifying landmark papers in the long run. These publications reach a very high level of citation impact and are able to remain on this level across many citing years. In recent years, several studies have been published which deal with the citation history of publications and try to identify landmark publications.

Design/methodology/approach

In contrast to other studies published hitherto, this study is based on a broad data set with papers published between 1980 and 1990 for identifying the landmark papers. The authors analyzed the citation histories of about five million papers across 25 years.

Findings

The results of this study reveal that 1,013 papers (less than 0.02 percent) are “outstandingly cited” in the long run. The cluster analyses of the papers show that they received the high impact level very soon after publication and remained on this level over decades. Only a slight impact decline is visible over the years.

Originality/value

For practical reasons, approaches for identifying landmark papers should be as simple as possible. The approach proposed in this study is based on standard methods in bibliometrics.

Keywords

Citation

Bornmann, L., Ye, A. and Ye, F. (2018), "Identifying landmark publications in the long run using field-normalized citation data", Journal of Documentation, Vol. 74 No. 2, pp. 278-288. https://doi.org/10.1108/JD-07-2017-0108

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

Related articles