Studying the characteristics of scientific communities using individual-level bibliometrics: the case of Big Data research-Reference-Cited by-同舟云学术

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Studying the characteristics of scientific communities using individual-level bibliometrics: the case of Big Data research

Published:2021-06-05 Issue:8 Volume:126 Page:6965-6987
ISSN:0138-9130
Container-title:Scientometrics
language:en
Short-container-title:Scientometrics

Author:

Lyu Xiaozan^ORCID,Costas Rodrigo

Funder

China Scholarship Council

the National Science Foundation of China

the DST-NRF Centre of Excellence in Scientometrics and Science, Technology and Innovation Policy (SciSTIP)

Publisher

Springer Science and Business Media LLC

Subject

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

Link

https://link.springer.com/content/pdf/10.1007/s11192-021-04034-6.pdf

Reference48 articles.

1. Abramo, G., & D’Angelo, C. A. (2011). National-scale research performance assessment at the individual level. Scientometrics, 86(2), 347–364. https://doi.org/10.1007/s11192-010-0297-2

2. Abramo, G., D’Angelo, C. A., & Di Costa, F. (2018). The effects of gender, age and academic rank on research diversification. Scientometrics, 114(2), 373–387. https://doi.org/10.1007/s11192-017-2529-1

3. Abramo, G., D’Angelo, C. A., & Murgia, G. (2016). The combined effects of age and seniority on research performance of full professors. Science and Public Policy, 43(3), 301–319. https://doi.org/10.1093/scipol/scv037

4. Akoka, J., Comyn-Wattiau, I., & Laoufi, N. (2017). Research on Big Data—A systematic mapping study. Computer Standards and Interfaces, 54, 105–115. https://doi.org/10.1016/j.csi.2017.01.004

5. Allison, P. D., & Stewart, J. A. (1974). Productivity differences among scientists : Evidence for accumulative advantage. American Sociological Review, 39(4), 596–606

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