Data visualization in virtual reality (VR) shows great promises as a tool to represent complex and multidimensional datasets and provide immersive visual experiences. As there exist issues of visual blocks caused by scaling data objects and limited interaction settings in VR, in this research, we aim to design an autonomous and playful data visualization playground in VR by embedding interactive elements into the world. We transformed the commonly-used 2D visualizations, e.g., bar chart, line chart, and map, into 3D VR data visualization formats. Moreover, we also implemented interactions that allows users to walk, jump, slide, teleport, and switching viewpoints. We expect to provide a more clear and enjoyable data visualization representations for multivariate datasets through distinguishing colors, textures, and a customized interaction approach for each visualization. We believe our work can provide better insights to future design on how to construct an immersive VR world for data visualization. Next, we will conduct user studies to test the effects of VR interaction methods on people's potential perceptions and cognition.

Explore the Data Visualization Playground in Virtual Reality

Understanding and evaluating uncertainty play a key role in decision-making. When a viewer studies a visualization that demands inference, it is necessary that uncertainty is portrayed in it. This paper showcases the importance of representing uncertainty in visualizations. It provides an overview of uncertainty visualization and the challenges authors and viewers face when working with such charts. I divide the visualization pipeline into four parts, namely data collection, preprocessing, visualization, and inference, to evaluate how uncertainty impacts them. Next, I investigate the authors' methodologies to process and design uncertainty. Finally, I contribute by exploring future paths for uncertainty visualization.

Maybe, Maybe Not: A Survey on Uncertainty in Visualization

The data visualization community often focuses on identifying and putting best practices into action when thinking about chart and dashboard authoring. These practices often incorporate recommendations informed by human perception and are often prescriptive around precision at the expense of semantics. Drawing from the design of meaningful icons, symbols, and imagery, we argue that data visualization needs to be made more accessible to the general population. By focusing more on intention rather than just precision, can we symbolize visualizations and change the lanes for both the researcher and practitioner community?

Changing Lanes: Towards Symbolizing Visualizations

One sunny June afternoon in a remote castle deep in Saarland, a group of visualization researchers of many stripes — from different countries, disciplines and generations — came together to discuss teaching and learning in visualization. Our first order of business in Saarland was to develop a shared vision: a manifesto of sorts that would guide us towards strategies to broaden data visualization skills, make them more common and accessible, and enable this empowerment. That exercise failed. Instead of creating one shared manifesto (who are we to do that, anyway?), we took a different, more personal approach. We found inspiration in others' manifestos, we crafted individual value and commitment statements ("me-ifestos"), we shared these with each other and reflected on them. The process of writing these commitment statements was illuminating and motivating. It was a positive experience with such visceral effects that we want to share the feeling with the visualization community. And so we share a collection of "me-ifesto" excerpts from the authors and a call to action: we request that readers craft and share their own. 

*With contributions from multiple participants at the Dagstuhl Seminar 22261. Please see PDF for full author list.

Me-ifestos for Visualization Empowerment in Teaching (and Learning?)

We present a survey of the design space of player-created strategic visualizations for video games, to better understand how players make sense of complex game mechanics and incorporate feedback from fellow players. We present several examples of these visualizations, and contrast them to developer-created visualizations, both in information presentation and purpose. We find that there is a rich community-building aspect to visualization development within game 'fandoms', facilitated by cross-platform exchange and iterative development, including: social media, informational 'wikis', and in-game modifications ('modding'). Finally, we consider player-created visualizations in the context of a broader imagining about the future of visualization development for non-game but analogous strategic applications. We maintain a collection of tagged and categorized examples of player-created video game visualizations at https://dev.universalities.com/playerviz.html

Ready Player Viz: Player-Created Strategic Visualizations for Video Games

AI image generation from language prompts has advanced rapidly in little over a year. It is about to change the way we design data visualization. This picture essay considers some of the possibilities and the trade-off between the new expressiveness it provides us with and the potential loss of effectiveness that comes with learning new and more complex graphical languages. It questions whether traditional notions of locatable objects and retinal variables are still applicable in this more sophisticated graphical environment.

Beyond the Walled Garden: A Visual Essay in Five Chapters

Graph layout algorithms strive to improve the utility of node-link visualizations or graph drawings by optimizing for readability criteria. One such criteria that has been widely used is to count edge crossings. Prior work has focused solely on \emphasize{minimizing} the number of edge crossings, including provably-optimal layout algorithms for layered graphs. The research community has completely ignored the other side of the coin - can we optimally maximize edge crossings? This paper answers this question in the affirmative. Our WORSTisfimal layout algorithm produces the most unreadable layered graph drawing. It does so by using linear programming to produce a provably-optimally-awful solution. We hope that this groundbreaking result opens up an entirely new field of inquiry for graph drawing researchers - optimally-worst layout algorithms.

The worst graph layout algorithm ever

Traditionally, data visualisation has mostly focused on finding visual encodings most suitable for two-dimensional displays
and static visual examination tasks. With the emergence of Immersive Analytics, researchers explored the use of physical navigation in
3D spaces and more embodied techniques to interact with data. Yet the sense-making process to explore them has remained more
or less the same to the one used on 2D screens. In this paper, we propose to take advantages of immersive technology to propose
a more subjective experience of data. We present Ride Your Data (RYD), an immersive experience in which people ride a virtual
roller coasters whose shape is dictated by data, allowing them to literally ride on their data, with climbs and drops corresponding to
increases and decreases in data values. After presenting the design of RYD, we present and discuss three examples of virtual roller
coasters made from real datasets. We then discuss research challenges behind the design of such experiences, and more generally,
data-driven immersive experiences.

Ride Your Data: Raise your Arms, Scream, and Experience your Data from a Roller Coaster Cart

As a blockchain-based application, Non-Fungible Token (NFT) has received worldwide attention over the past few years. Digital artwork is the main form of NFT that can be stored on different blockchains. Although the NFT market is rapidly developing, potential ethical and racial fairness issues are arising in NFT artworks due to a  lack of ethical guidelines or censorship. Therefore, in this research, we investigated the potential ethical issues of CryptoPunk, one of the most long-lasting NFT collections, and visualized its visual appearance’s genetic distribution, associations between selling prices, gender/skin tone factors, and transaction network. We further explored the ethical issues related to CryptoPunks from three aspects: design, trading transactions, and related topics on Twitter. We provided five visualization sections to highlight our findings about CryptoPunks’ ethics issues. From our data analysis and visualization interpretations, we found that (1) around 1.6 times more male punks were created in the initial design process than the female ones; (2) lighter-skinned punks tend to sell for higher prices; (3) the topic related to identity equality in NFTs is rarely mentioned on Twitter. These findings and visualizations from CryptoPunks example provide a preliminary understanding of the racial and gender inequity ethical problems, and also a potential approach for exploring future ethical-related research in the NFT domain.


Are racial and gender inequalities emerging in the NFT artwork? A visual exploration of CryptoPunks

In this manifesto, we put forward the idea of data alchemy as a narrative device to discuss storytelling and transdisciplinarity in visualization. We use text-to-image generative art mainly based on Disco Diffusion (DD) to develop the concept, and structure our argument in ten propositions, as if they were ten issues of a comic novel on data alchemy: Ad Disco Diffusionem. To follow the argument, the reader must immerse themselves in our miro board, and navigate a multimedia semiotic topology that includes comics, videos, code demos, and ergotic literature in a true alchemic sense. Our colorful, sometimes poetic exposition should not distract the reader from the seriousness of the ideas discussed, but ultimately it is about the journey.

I Learn to Diffuse, or Data Alchemy 101: a Mnemonic Manifesto

The scientific community produces many papers with dull and uninteresting titles. We wanted to explore how researchers break these standards and create memorable names. In this work, we present a coherent view of the current naming conventions of IEEE VIS papers and propose an online tool for their automatic generation. We took advantage of the predictable behavior of the naming habits of the vis community, and we compared them with cultural references and paper titles from other scientific fields. Based on our findings, we formed two iterations of vis naming grammar and used the second one in a publicly available tool. Lastly, inspired by similar works, we created a quiz comprising real and fake vis titles.

* (This Name Can Be Automatically Generated)

In this manuscript, we take a look at the dystopian future that could lie ahead of us should we fail to consider the impact of Plan S. It is the year 2030 and researchers from Europe have not been submitted their research to IEEE VIS for a number of years. We quickly retrace what has happened to lead to this unfortunate outcome in the hope to avoid it and start an important conversation that would raise awareness in the VIS community about the importance and immediacy of Plan S.

DyStopia: Into a potential future of IEEE VIS under Plan S

Other models of visualising aim at a (re)formulation of contemporary expectations and narratives concerning data and their visualisations as a very specific model of thinking data visualisation. It is precisely how and with what intention we work on and discuss visualisations that defines the conceptual space we open to this cultural technique. The concept of "other models" first points to the consequences and limitations of these ways of thinking. My positioning of the "other" consists first of the description of what it wants to distinguish itself from. I understand the "other visualising" as a chance to make the normative mode of data visualisation visible and discussable. In the discourse of visualisation, there is not yet an established language for critiquing the expectations of data images. The "other visualising" therefore establishes a negative way of reading the cultural and image phenomenon. As a first concretisation of these models, I formulate in the following a differently directed definition: data visualisation as intended violence.

IEEE 2022 Workshop altVIS Submissions