A discussion of measuring the top-1% most-highly cited publications: quality and impact of Chinese papers

Author:

Wagner Caroline S.ORCID,Zhang LinORCID,Leydesdorff LoetORCID

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Computer Science Applications,General Social Sciences

Reference43 articles.

1. Ahlgren, P., Persson, O., & Rousseau, R. (2014). An approach for efficient online identification of the top-k percent most cited documents in large sets of Web of Science documents. ISSI Newsletter, 10(4), 81–89.

2. Archambault, É., Beauchesne, O. H., & Caruso, J. (2011). Towards a multilingual, comprehensive and open scientific journal ontology. In Proceedings of the 13th international conference of the International Society for Scientometrics and Informetrics (pp. 66–77). Durban.

3. Bensman, S. J. (2007). Garfield and the impact factor. Annual Review of Information Science and Technology, 41(1), 93–155.

4. Bornmann, L, De Moya Anegón, F., & Leydesdorff, L. (2010). Do scientific advancements lean on the shoulders of giants? A bibliometric investigation of the Ortega hypothesis. PLoS ONE, 5(10). https://doi.org/10.1371/journal.pone.0013327.

5. Bornmann, L., & Mutz, R. (2011). Further steps towards an ideal method of measuring citation performance: The avoidance of citation (ratio) averages in field-normalization. Journal of Informetrics, 5(1), 228–230.

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