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  • Item type:Publication,
    The scientometrics of successful women in science
    (2016)
    Madlock-Brown, C.
    ;
    Eichmann, D.
    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. © 2016 IEEE.
  • Item type:Publication,
    Gender-specific patterns in the artificial intelligence scientific ecosystem Open PDF 
    (2022)
    Hajibabaei, A.
    ;
    Schiffauerova, A.
    ;
    Ebadi, A.
    Gender disparity in science is one of the most focused debating points among authorities and the scientific community. Over the last few decades, numerous initiatives have endeavored to accelerate gender equity in academia and research society. However, despite the ongoing efforts, gaps persist across the world, and more measures need to be taken. Using social network analysis, natural language processing, and machine learning, in this study, we comprehensively analyzed gender-specific patterns in the highly interdisciplinary and evolving field of artificial intelligence for the period of 2000–2019. Our findings suggest an overall increasing rate of mixed-gender collaborations. From the observed gender-specific collaborative patterns, the existence of disciplinary homophily at both dyadic and team levels is confirmed. However, a higher preference was observed for female researchers to form homophilous collaborative links. Our core-periphery analysis indicated a significant positive association between having diverse collaboration and scientific performance and experience. We found evidence in support of expecting the rise of new female superstar researchers in the artificial intelligence field. © 2022