The scientometrics of successful women in science

Charisse Madlock-Brown, David Eichmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper examines the effects of gender differences in collaboration on research outcomes. We analyzed network characteristics of seventeen medical research institutions that are Clinical and Translational Science Awardees (CTSA) to determine if network connectivity characteristics have the potential to help mitigate the performance gap between the sexes. We determined betweenness centrality to identify well-connected researchers. Then we used clustering coefficient to determine how tightly connected their collaborators were with each other. We correlate these scores with productivity (number of total publications for each author), and h-index (the number of papers h for which an author has h citations). We also provide data on how network characteristics vary by role for each gender studied. Our results indicate that being well connected is more highly correlated with success for women than men for most of the institutions we studied. We believe these results can be leveraged to improve success rates for women in the future.

Original languageEnglish (US)
Title of host publicationProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
EditorsRavi Kumar, James Caverlee, Hanghang Tong
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages654-660
Number of pages7
ISBN (Electronic)9781509028467
DOIs
StatePublished - Nov 21 2016
Event2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States
Duration: Aug 18 2016Aug 21 2016

Other

Other2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
CountryUnited States
CitySan Francisco
Period8/18/168/21/16

Fingerprint

science
medical research
Productivity
gender-specific factors
productivity
gender
performance

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Sociology and Political Science
  • Communication

Cite this

Madlock-Brown, C., & Eichmann, D. (2016). The scientometrics of successful women in science. In R. Kumar, J. Caverlee, & H. Tong (Eds.), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 (pp. 654-660). [7752307] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2016.7752307

The scientometrics of successful women in science. / Madlock-Brown, Charisse; Eichmann, David.

Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. ed. / Ravi Kumar; James Caverlee; Hanghang Tong. Institute of Electrical and Electronics Engineers Inc., 2016. p. 654-660 7752307.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Madlock-Brown, C & Eichmann, D 2016, The scientometrics of successful women in science. in R Kumar, J Caverlee & H Tong (eds), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016., 7752307, Institute of Electrical and Electronics Engineers Inc., pp. 654-660, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, United States, 8/18/16. https://doi.org/10.1109/ASONAM.2016.7752307
Madlock-Brown C, Eichmann D. The scientometrics of successful women in science. In Kumar R, Caverlee J, Tong H, editors, Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 654-660. 7752307 https://doi.org/10.1109/ASONAM.2016.7752307
Madlock-Brown, Charisse ; Eichmann, David. / The scientometrics of successful women in science. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. editor / Ravi Kumar ; James Caverlee ; Hanghang Tong. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 654-660
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