VLDB 2026 Research / reviewers in the wild / expert
Rolando Franqui-Nadal
dblp:348/2536
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0001-5789-279XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics
scientometrics |
0.7 | 1 | 2023 | Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications · IEEE Trans. Vis. Comput. Graph. 2023 |
Computational social science and digital humanities › scientometrics
bibliometric analysis |
0.2 | 1 | 2023 | Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications · IEEE Trans. Vis. Comput. Graph. 2023 |
Methods — techniques the papers use, named apart from their topics
predictive trend analysis · 1.3gender inference · 1.3bibliometric dataset curation · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS PublicationsabstractWe 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. Ali Sarvghad, Rolando Franqui-Nadal, Rebecca Reznik-Zellen, Ria Chawla, Narges Mahyar |
IEEE Trans. Vis. Comput. Graph. | 2 |