Broadening Intellectual Diversity in Visualization Research Papers

Lee, Bongshin; Isaacs, Katherine E.; Szafir, Danielle Albers; Marai, G. Elisabeta; Turkay, Çağatay; Tory, Melanie; Carpendale, Sheelagh; Endert, Alex

doi:10.1109/mcg.2019.2914844

Cited by 32 publications

(17 citation statements)

References 7 publications

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“…Provenance-driven visualization and, with it, the visual disclosure of different stages of transformation and interpretation of historical and cultural collections can also be an opportunity for encouraging interdisciplinary research not only at the intersection of visualization and humanities fields, but also involving public audiences. Many researchers from the humanities and visualization community have pushed for such an approach [10,12] and also for incorporating more diverse perspectives in visualization research [17]. Through provenance-driven visualization we aim to trigger new questions about historical and cultural collections, both regarding insights they can promote as well as opportunities for design.…”

Section: Discussionmentioning

confidence: 99%

Externalizing Transformations of Historical Documents: Opportunities for Provenance-Driven Visualization

Vancisin

Orr

Hinrichs

2020

Preprint

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Transcription, annotation, digitization and/or visualization are common transformations that historical documents such as national records, birth/death registers, university records, letters or books undergo. Reasons for those transformations span from the (physical) protection of the original materials to disclosure of "hidden" information or patterns within the documents. Even though such transformations bring new insights and perspectives on the documents, they also modify the documents' content, structure, and/or artifactual form and thus, occlude prior knowledge and interpretation. When it comes to visualization as a means to transform historical documents from written to abstract visual form, there is typically little acknowledgment or even understanding of the previous transformation steps these documents have gone through. The "tremendous rhetorical force" [3] of visualization, we argue, should not be at the expense of the multiple pasts, contexts, and curators that are inherent in historical record collections. Rather, the urgent question for the fields of visualization and the (digital) humanities is how to better support awareness of these multiple layers of interpretation and the people behind them when representing historical documents. We begin to address this question based on a collection of historical university records by (a) investigating common transformation processes of historical documents, and (b) discussing opportunities and challenges for making such transformations transparent through what we call "provenance-driven visualization"; the idea for a visualization that makes visible the layers of transformation (including interpretation, re-structuring, and curation) inherent in historical documents.

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Section: Discussionmentioning

confidence: 99%

Externalizing Transformations of Historical Documents: Opportunities for Provenance-Driven Visualization

Vancisin

Orr

Hinrichs

2020

Preprint

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“…We find our corpus a mixture of technique and system papers that confound the discussion. A technique paper typically solves a single task, while a system paper could consist of multiple components each for different tasks [17]. As such, we aim to decompose system papers into abstract tasks that allow for a collectivelyexhaustive taxonomy of tasks.…”

Section: Coding and Classificationmentioning

confidence: 99%

“…The task abstraction is critical since it allows for domain-agnostic and consistent descriptions of research questions across different disciplines. Besides, it facilitates an organized discussion reconciling distinct visualization paper types [17], i.e., a technique paper focuses on a single task, while a system paper might accomplish multiple tasks. In total, we note seven common tasks and discuss the AI approaches for each task separately.…”

Section: Introductionmentioning

confidence: 99%

AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Wang

Shu

et al. 2021

Preprint

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Visualizations themselves have become a data format. Akin to other data formats such as text and images, visualizations are increasingly created, stored, shared, and (re-)used with artificial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data. We define visualization data as the digital representations of visualizations in computers and focus on visualizations in information visualization and visual analytic. We build our survey upon a corpus spanning ten different fields in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of surveyed papers and related material available online at ai4vis.github.io.

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“…Cockburn et al [9] argued that the adoption of Registered Reports to major venues could be daunting, but that reviewing workflows could easily be adapted to allow it. Visualization papers come in many different types [18,20], and not all of them report a user study. Those who do not, would not be able to benefit from the addition of Registered Reports as a publication type since no experimental design is included in them.…”

Section: Registered Reports For Visualization Research: Why and Howmentioning

confidence: 99%

Publishing Visualization Studies as Registered Reports: Expected Benefits and Researchers’ Attitudes

Besançon¹,

Bezerianos²,

Dragicevic³

et al. 2021

Preprint

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Background: Registered Reports are publications in which study proposals are peer reviewed and pre-accepted before the study is ran. Their adoption in other disciplines has been found to promote research quality and save time and resources. Objectives: We offer a brief introduction to Registered Reports and their expected benefits for visualization research. We then report a survey of the visualization community on their attitudes towards Registered Reports. This survey takes the form of a quasi Registered Report, the first one presented at a visualization venue as far as we know.Method: We will run an online survey that will be open from the end of August until the day of the alt.VIS workshop.Results: Not available yet.Conclusion: Not available yet.Reproducibility: All the material is available on osf.io/4nrma/.

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Broadening Intellectual Diversity in Visualization Research Papers

Abstract: Promoting a wider range of contribution types can facilitate healthy growth of the visualization community, while increasing the intellectual diversity of visualization research papers. In this article, we discuss the importance of contribution types and

Cited by 32 publications

References 7 publications

Externalizing Transformations of Historical Documents: Opportunities for Provenance-Driven Visualization

Externalizing Transformations of Historical Documents: Opportunities for Provenance-Driven Visualization

AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Publishing Visualization Studies as Registered Reports: Expected Benefits and Researchers’ Attitudes

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