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Rebecca Reznik-Zellen

dblp:26/10026 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0001-9321-8284ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

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

TopicWeightPapersLastEvidence papers
Visualization and visual analytics
scientometrics
0.712023
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
0.212023
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
YearPublicationVenuePosition
2023 Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications
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.
Ali Sarvghad, Rolando Franqui-Nadal, Rebecca Reznik-Zellen, Ria Chawla, Narges Mahyar
IEEE Trans. Vis. Comput. Graph.3
2008 InterNano: e-Science for the Nanomanufacturing Community
abstract
As network-enabled scholarship produces huge quantities of formal and informal research outputs in a variety of formats and varying levels of access, it is "enhanced" science that will facilitate the discovery, selection, and analysis of information that are a necessary part of the scientific research cycle particularly among interdisciplinary research communities. The national nanomanufacturing network (NNN) has developed a Web service, internano, to support the information needs of the nanomanufacturing community within this network-enabled research environment. Based on the clearinghouse concept, InterNano brings together heterogeneous resources and research outputs with networking and computational tools to enable discovery, facilitate selection of information, and encourage collaboration. With seventeen content features and services, internano's goal is not just to make information work within this community more efficient, but also to enable researchers to act on their discoveries within the same virtual framework.
Rebecca Reznik-Zellen, Bob Stevens, Michael Thorn, Jeff Morse, Mark D. Smucker, James Allan 0001, David M. Mimno, Andrew McCallum, Mark Tuominen
eScience1