AI Article Synopsis

  • The study analyzes 30 years of IEEE VIS publications (1990-2020) focusing on interdisciplinary collaboration and gender composition among authors, utilizing the BiblioVIS dataset which includes detailed metadata on 3,032 articles and 6,113 authors.
  • Interdisciplinary collaboration has increased over the years, primarily among a few fields such as Computer Science, Engineering, and Medicine, but there is still a noticeable gender imbalance with men making up about 75% of authorship despite a gradual increase in women's participation.
  • Predictive analysis suggests that gender parity in the visualization field is unlikely to be achieved before around 2070, highlighting the need for greater attention to diversity and collaboration in research.

Article Abstract

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 articles 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.

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Source
http://dx.doi.org/10.1109/TVCG.2022.3158236DOI Listing

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