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Search for evergreens in science: A functional data analysis

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  • Zhang, Ruizhi
  • Wang, Jian
  • Mei, Yajun

Abstract

Evergreens in science are papers that display a continual rise in annual citations without decline, at least within a sufficiently long time period. Aiming to better understand evergreens in particular and patterns of citation trajectory in general, this paper develops a functional data analysis method to cluster citation trajectories of a sample of 1699 research papers published in 1980 in the American Physical Society (APS) journals. We propose a functional Poisson regression model for individual papers’ citation trajectories, and fit the model to the observed 30-year citations of individual papers by functional principal component analysis and maximum likelihood estimation. Based on the estimated paper-specific coefficients, we apply the K-means clustering algorithm to cluster papers into different groups, for uncovering general types of citation trajectories. The result demonstrates the existence of an evergreen cluster of papers that do not exhibit any decline in annual citations over 30 years.

Suggested Citation

  • Zhang, Ruizhi & Wang, Jian & Mei, Yajun, 2017. "Search for evergreens in science: A functional data analysis," Journal of Informetrics, Elsevier, vol. 11(3), pages 629-644.
    • Handle: RePEc:eee:infome:v:11:y:2017:i:3:p:629-644
    DOI: 10.1016/j.joi.2017.05.007
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    1. J. O. Ramsay, 1998. "Estimating smooth monotone functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 365-375.
    2. Rodrigo Costas & Thed N. van Leeuwen & Anthony F.J. van Raan, 2010. "Is scientific literature subject to a ‘Sell-By-Date’? A general methodology to analyze the ‘durability’ of scientific documents," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(2), pages 329-339, February.
    3. Nicoleta Serban & Ana-Maria Staicu & Raymond J. Carroll, 2013. "Multilevel Cross-Dependent Binary Longitudinal Data," Biometrics, The International Biometric Society, vol. 69(4), pages 903-913, December.
    4. Angelika Linde, 2009. "A Bayesian latent variable approach to functional principal components analysis with binary and count data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 93(3), pages 307-333, September.
    5. Jian Wang & Bart Thijs & Wolfgang Glänzel, 2015. "Interdisciplinarity and Impact: Distinct Effects of Variety, Balance, and Disparity," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
    6. Philippe Besse & J. Ramsay, 1986. "Principal components analysis of sampled functions," Psychometrika, Springer;The Psychometric Society, vol. 51(2), pages 285-311, June.
    7. Anthony F. J. van Raan, 2004. "Sleeping Beauties in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 59(3), pages 467-472, March.
    8. Jian Wang, 2013. "Citation time window choice for research impact evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 851-872, March.
    9. Aurel Avramescu, 1979. "Actuality and Obsolescence of Scientific Literature," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 30(5), pages 296-303, September.
    10. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
    11. Colavizza, Giovanni & Franceschet, Massimo, 2016. "Clustering citation histories in the Physical Review," Journal of Informetrics, Elsevier, vol. 10(4), pages 1037-1051.
    12. Susanne E. Baumgartner & Loet Leydesdorff, 2014. "Group-based trajectory modeling (GBTM) of citations in scholarly literature: Dynamic qualities of “transient” and “sticky knowledge claims”," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 797-811, April.
    13. Juan D Rogers, 2010. "Citation analysis of nanotechnology at the field level: implications of R&D evaluation," Research Evaluation, Oxford University Press, vol. 19(4), pages 281-290, October.
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