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  • Item type:Publication,
    Using Bibliometric Analysis to Measure and Understand the Gender Gap in Published Computing Books: Gender Gap in Computer Science Open PDF 
    (2021)
    Kadriu, A.
    ;
    Sahatqija, K.
    ;
    Abazi-Bexheti, L.
    The purpose of the research presented in this paper is the investigation of the gender gap in published computing books. The book titles from the DBLP computer science bibliography were the basis for this investigation. The conducted research involves co-authorship network exploration using social network analysis methods, as well as content learning by keyword extraction and ranking from book titles. The findings show that female authors tend to publish fewer books in computing than their male colleagues, and there is a huge gap of women regarding the collaboration. There are just two women names within the 50 author names with the highest social network top metrics, indicating collaboration. Regarding the extracted keywords, though there are differences, results do not show some huge divergences when it comes to the used language for computing titles. Copyright © 2021, IGI Global.
  • Item type:Publication,
    Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications Open PDF 
    (2022)
    Sarvghad, A.
    ;
    Franqui-Nadal, R.
    ;
    Reznik-Zellen, R.
    ;
    Chawla, R.
    ;
    Mahyar, N.
    We present the results of a scientometric analysis of 30 years of IEEE VIS publications between 1990-2020, in which we conducted a multifaceted analysis of interdisciplinary collaboration and gender composition among authors. To this end, we curated BiblioVIS, a bibliometric dataset that contains rich metadata about IEEE VIS publications, including 3032 papers and 6113 authors. One of the main factors differentiating BiblioVIS from similar datasets is the authors' gender and discipline data, which we inferred through iterative rounds of computational and manual processes. Our analysis shows that, by and large, inter-institutional and interdisciplinary collaboration has been steadily growing over the past 30 years. However, interdisciplinary research was mainly between a few fields, including Computer Science, Engineering and Technology, and Medicine and Health disciplines. Our analysis of gender shows steady growth in women's authorship. Despite this growth, the gender distribution is still highly skewed, with men dominating (~75%) of this space. Our predictive analysis of gender balance shows that if the current trends continue, gender parity in the visualization field will not be reached before the third quarter of the century (~2070). Our primary goal in this work is to call the visualization community's attention to the critical topics of collaboration, diversity, and gender. Our research offers critical insights through the lens of diversity and gender to help accelerate progress towards a more diverse and representative research community. IEEE