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Visualising Bibliographic Metadata Using CAQDAS in the Research on the Gender Gap in STEM Studies in Higher Education
(2023)
Verdugo-Castro, S.
;
Sánchez-Gómez, M.ªC.
;
García-Holgado, A.
;
García-Peñalvo, F.J.
;
Costa, A.P.
Besides providing the consulted publication’s findings, the literature review can offer information through metadata. Computer-Assisted Qualitative Data Analysis Software (CAQDAS) can support visualising bibliometric metadata through RIS files. The usefulness is to provide an interactive image of the reality and actuality of scientific production. This article presents a case study to exemplify how metadata can be analysed and visualised using CAQDAS. The topic for the case study is the gender gap in STEM studies in higher education. The study aims to identify the value and usefulness of data visualisation in representing bibliometric data to support literature review processes. The phenomenon of the gender gap in the STEM education sector is used as a case study. The research questions addressed by the study are: (1) What does CAQDAS contribute to the results obtained?; (2) What are the possible causes of the gender gap? The analysis concludes that the cultural, social, educational, family, and peer group environment generates positive and negative force fields when deciding which studies to pursue; some people follow the patterns expected of them according to their gender. Finally, data visualisation helps understand the scientific evolution of a phenomenon and supports the research on a particular topic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications Open PDF
(2022)
Sarvghad, A.
;
Franqui-Nadal, R.
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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

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