References
- Abramo, G., D’Angelo, C.A., & Zhang, L. (2018). A comparison of two approaches for measuring interdisciplinary research output: The disciplinary diversity of authors vs the disciplinary diversity of the reference list. Journal of Informetrics, 12(4), 1182–1193.
- Adams, J. (2018). Information and misinformation in bibliometric time-trend analysis. Journal of Informetrics, 12(4), 1063–1071.
- Ahlgren P., Chen, YW., Colliander, C., & van Eck, N.J. (2019). Community detection using citation relations and textual similarities in a large set of PubMed publications. In: Catalano, G., Daraio, C., Gregori, M., Moed, H.F., & Ruocco, G., (Eds.). In Proceedings of the 17th International Conference on Scientometrics & Informetrics (ISSI2019). (pp. 1380–1391). Rome: Efesto.
- Avramescu, A. (1979). Actuality and obsolescence of scientific literature. Journal of the American Society for Information Science, 30(5), 296–303.
- Bellégo, C., & Pape, L.D. (2019). Dealing with the logs of zeros in regression models. Série des Documents de Travail, 2019–13. Available at SSRN:
https://ssrn.com/abstract=3444996 orhttp://dx.doi.org/10.2139/ssrn.3444996 - Egghe, L., & Rao, I.K.R. (1992a). Citation age data and the obsolescence function: Fits and explanations. Information Processing & Management, 28(2), 201–217.
- Egghe, L., & Rao, I.K.R. (1992b). Classification of growth models based on growth rates and its applications. Scientometrics, 25(1), 5–46.
- Egghe, L., Rao, I.K.R., & Rousseau, R. (1995). On the influence of production on utilization functions: Obsolescence or increased use? Scientometrics, 34(2), 285–315.
- Hu, X.J., & Li, X. (2019). Do “rejuvenated” articles exist? In Proceedings of the 15th International Conference on Webometrics, Informetrics, & Scientometrics (WIS) & the 20th COLLNET Meeting, (pp. 23–29). Dalian: Dalian University of Technology.
- Leydesdorff, L. (1988). Problems with the ‘measurement’ of national scientific performance. Science and Public Policy, 15(3), 149–152.
- Leydesdorff, L., & Bornmann, L. (2016). The operationalization of “Fields” as WoS Subject Categories (WCs) in evaluative bibliometrics: The cases of “Library and Information Science” and “Science & Technology Studies”. Journal of the Association for Information Science and Technology, 67(3), 707–714.
- Liu, Y.X., & Rousseau, R. (2008). Definitions of time series in citation analysis with special attention to the h-index. Journal of Informetrics, 2(3), 202–210.
- Liu, Y.X., & Rousseau, R. (2010). Knowledge diffusion through publications and citations: A case study using ESI-fields as unit of diffusion. Journal of the American Society for Information Science and Technology, 61(2), 340–351.
- Matricciani, E. (1991). The probability distribution of the age of references in engineering papers. IEEE Transactions on Professional Communication, 34(1), 7–12.
- Milanez, D.H., Noyons, E., & de Faria, L.I.L. (2016). A delineating procedure to retrieve relevant publication data in research areas: the case of nanocellulose. Scientometrics, 107(2), 627–643.
- Nakamoto, H. (1988). Synchronous and diachronous citation distributions. In: L. Egghe & R. Rousseau. (Eds.), Informetrics 87/88 (pp. 157–163). Amsterdam: Elsevier Science Publishers.
- Rousseau, R., Egghe, L., & Guns, R. (2018). Becoming Metric-Wise. A bibliometric guide for researchers. Kidlington (UK): Chandos-Elsevier.
- Rousseau, R., Jin, B.H., Yang, N., & Liu, X. (2001). Observations concerning the two- and three-year synchronous impact factor, based on the Chinese Science Citation Index. Journal of Documentation, 57(3), 349–357.
- Sjögårde, P., & Ahlgren, P. (2018). Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics. Journal of Informetrics, 12(1), 133–152.
- Stringer, M.J., Sales-Pardo, M., & Amaral, L.A.N. (2008). Effectiveness of journal ranking schemes as a tool for locating information. PLoS One, 3(2): e1683.
- Stringer, M.J., Sales-Pardo, M., & Amaral, L.A.N. (2010). Statistical validation of a global model for the distribution of the ultimate number of citations accrued by papers published in a scientific journal. Journal of the American Society for Information Science and Technology, 61(7), 1377–1385.
- Sun, J.J., Min, C., & Li, J. (2016). A vector for measuring obsolescence of scientific articles. Scientometrics, 107(2), 745–757.
- Thelwall, M. (2016). Citation count distributions for large monodisciplinary journals. Journal of Informetrics, 10(3), 863–874.
- Waltman, L., & van Eck, N.J. (2012). A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology, 63(12), 2378–2392.
- Wang, D., Song, C., & Barabási, A.L. (2013). Quantifying long-term scientific impact. Science, 342(6154), 127–132.
- Yan, E.J. (2015). Research dynamics, impact, and dissemination: A topic-level analysis. Journal of the Association for Information Science and Technology, 66(11), 2357–2372.
- Yin, Y., & Wang, D.S. (2017). The time dimension of science: Connecting the past to the future. Journal of Informetrics, 11(2), 608–621.
- Zitt, M., Lelu, A., Cadot, M., & Cabanac, G. (2019). Bibliometric delineation of scientific fields. In: W. Glänzel, H.F. Moed, U. Schmoch, M. Thelwall (Eds.), Springer Handbook of Science and Technology Indicators (pp. 25–68). Cham: Springer.