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[This corrects the article DOI: 10.1371/journal.pcbi.1008259.].
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ParaView is one of the most prominent software tools for scientific visualization used by scientists around the world. Color is a primary conduit to visually map data to its representation and, thus, enable investigation and interpretation of the data. Colormap selection has a significant impact on the data revealed; its design and selection is a critical aspect of scientific data visualization. A common choice for a user is the program's default colormap, so careful consideration of this default is consequential. Although the current default colormap in ParaView, a succession of hues from cool blue to warm red, has served the community well, research shows that more nuanced colormap configurations increase discriminability while maintaining other critical metrics. These findings inspire us to revisit and update the default colors in ParaView. Here we present a new ParaView default colormap, the criteria and methods of development, and example visualizations and analytic metrics.
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Traditional approaches to data visualization have often focused on comparing different subsets of data, and this is reflected in the many techniques developed and evaluated over the years for visual comparison. Similarly, common workflows for exploratory visualization are built upon the idea of users interactively applying various filter and grouping mechanisms in search of new insights. This paradigm has proven effective at helping users identify correlations between variables that can inform thinking and decision-making. However, recent studies show that consumers of visualizations often draw causal conclusions even when not supported by the data. Motivated by these observations, this article highlights recent advances from a growing community of researchers exploring methods that aim to directly support visual causal inference. However, many of these approaches have their own limitations, which limit their use in many real-world scenarios. This article, therefore, also outlines a set of key open challenges and corresponding priorities for new research to advance the state of the art in visual causal inference.
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As mobile and wearable devices are becoming increasingly powerful, access to personal data is within reach anytime and anywhere. Currently, methods of data exploration while on-the-go and in-situ are, however, often limited to glanceable and micro visualizations, which provide narrow insight. In this article, we introduce the notion of databiting, the act of interacting with personal data to obtain richer insight through lightweight and transient exploration. We focus our discussion on conceptualizing databiting and arguing its potential values. We then discuss five research considerations that we deem important for enabling databiting: contextual factors, interaction modalities, the relationship between databiting and other forms of exploration, personalization, and evaluation challenges. We envision this line of work in databiting could enable people to easily gain meaningful personal insight from their data anytime and anywhere.
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Native game engines have long been the 3-D development platform of choice for research in mixed and augmented reality. For this reason, they have also been adopted in many immersive visualization and immersive analytics systems and toolkits. However, with the rapid improvements of WebXR and related open technologies, this choice may not always be optimal for future visualization research. In this article, we investigate common assumptions about native game engines versus WebXR and find that while native engines still have an advantage in many areas, WebXR is rapidly catching up and is superior for many immersive analytics applications.
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Unsurprisingly, we have observed tremendous interests and efforts in the application of machine learning (ML) to many data visualization problems, which are having success and leading to new capabilities. However, there is a space in visualization research that is either completely or partly agnostic to ML that should not be lost in this current VIS+ML movement. The research that this space can offer is imperative to the growth of our field and it is important that we remind ourselves to invest in this research as well as show what it could bear. This Viewpoints article provides my personal take on a few research challenges and opportunities that lie ahead that may not be directly addressable by ML.
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Some 15 years ago, Visualization Viewpoints published an influential article titled Rainbow Color Map (Still) Considered Harmful (Borland and Taylor, 2007). The paper argued that the "rainbow colormap's characteristics of confusing the viewer, obscuring the data and actively misleading interpretation make it a poor choice for visualization." Subsequent articles often repeat and extend these arguments, so much so that avoiding rainbow colormaps, along with their derivatives, has become dogma in the visualization community. Despite this loud and persistent recommendation, scientists continue to use rainbow colormaps. Have we failed to communicate our message, or do rainbow colormaps offer advantages that have not been fully appreciated? We argue that rainbow colormaps have properties that are underappreciated by existing design conventions. We explore key critiques of the rainbow in the context of recent research to understand where and how rainbows might be misunderstood. Choosing a colormap is a complex task, and rainbow colormaps can be useful for selected applications.
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Visualization researchers and visualization professionals seek appropriate abstractions of visualization requirements that permit considering visualization solutions independently from specific problems. Abstractions can help us design, analyze, organize, and evaluate the things we create. The literature has many task structures (taxonomies, typologies, etc.), design spaces, and related "frameworks" that provide abstractions of the problems a visualization is meant to address. In this Visualization Viewpoints article, we introduce a different one, a problem space that complements existing frameworks by focusing on the needs that a visualization is meant to solve. We believe it provides a valuable conceptual tool for designing and discussing visualizations.
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In the past two decades, research in visual analytics (VA) applications has made tremendous progress, not just in terms of scientific contributions, but also in real-world impact across wide-ranging domains including bioinformatics, urban analytics, and explainable AI. Despite these success stories, questions on the rigor and value of VA application research have emerged as a grand challenge. This article outlines a research and development agenda for making VA application research more rigorous and impactful. We first analyze the characteristics of VA application research and explain how they cause the rigor and value problem. Next, we propose a research ecosystem for improving scientific value, and rigor and outline an agenda with 12 open challenges spanning four areas, including foundation, methodology, application, and community. We encourage discussions, debates, and innovative efforts toward more rigorous and impactful VA research.
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We present a conceptual framework for the development of visual interactive techniques to formalize and externalize trust in machine learning (ML) workflows. Currently, trust in ML applications is an implicit process that takes place in the user's mind. As such, there is no method of feedback or communication of trust that can be acted upon. Our framework will be instrumental in developing interactive visualization approaches that will help users to efficiently and effectively build and communicate trust in ways that fit each of the ML process stages. We formulate several research questions and directions that include: 1) a typology/taxonomy of trust objects, trust issues, and possible reasons for (mis)trust; 2) formalisms to represent trust in machine-readable form; 3) means by which users can express their state of trust by interacting with a computer system (e.g., text, drawing, marking); 4) ways in which a system can facilitate users' expression and communication of the state of trust; and 5) creation of visual interactive techniques for representation and exploration of trust over all stages of an ML pipeline.
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The focus of this Visualization Viewpoints article is to provide some background on quantum computing (QC), to explore ideas related to how visualization helps in understanding QC, and examine how QC might be useful for visualization with the growth and maturation of both technologies in the future. In a quickly evolving technology landscape, QC is emerging as a promising pathway to overcome the growth limits in classical computing. In some cases, QC platforms offer the potential to vastly outperform the familiar classical computer by solving problems more quickly or that may be intractable on any known classical platform. As further performance gains for classical computing platforms are limited by diminishing Moore's Law scaling, QC platforms might be viewed as a potential successor to the current field of exascale-class platforms. While present-day QC hardware platforms are still limited in scale, the field of quantum computing is robust and rapidly advancing in terms of hardware capabilities, software environments for developing quantum algorithms, and educational programs for training the next generation of scientists and engineers. After a brief introduction to QC concepts, the focus of this article is to explore the interplay between the fields of visualization and QC. First, visualization has played a role in QC by providing the means to show representations of the quantum state of single-qubits in superposition states and multiple-qubits in entangled states. Second, there are a number of ways in which the field of visual data exploration and analysis may potentially benefit from this disruptive new technology though there are challenges going forward.
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Encoding data visually is at the heart of visualization. We usually assume that encodings are read as specified (i.e., if a bar chart is drawn by the length of the bars based on the data, that is also how we read them). In this paper, we question this assumption and demonstrate that observed encodings often differ from the ones used to specify the visualization. The value of a chart also often comes from higher level derived encodings, and which encodings end up getting used also depends on the user's task.
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Scientific visualization is a key approach to understanding the growing massive streams of data from scientific simulations and experiments. In this article, I review technology trends including the positive effects of Moore's law on science, the significant gap between processing and data storage speeds, the emergence of hardware accelerators for ray-tracing, and the availability of robust machine learning techniques. These trends represent changes to the status quo and present the scientific visualization community with a new set of challenges. A major challenge involves extending our approaches to visualize the modern scientific process, which includes scientific verification and validation. Another key challenge to the community is the growing number, size, and complexity of scientific datasets. A final challenge is to take advantage of emerging technology trends in custom hardware and machine learning to significantly improve the large-scale data visualization process.
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Armazenamento e Recuperação da Informação , Aprendizado de Máquina , TecnologiaRESUMO
In this article, we present a digital platform for unmanned traffic management, UTM City, for research on visualization, simulation, and management of autonomous urban vehicle traffic. Such vehicles orient themselves automatically and provide services ranging from transport to remote presence and surveillance, and new regulations and standards for authorization and monitoring are currently being developed to accommodate for such services. Our system has been developed in close collaboration with domain experts that have contributed with scenarios and participated in numerous workshops to explore the use of visualization in airborne drone traffic monitoring, management, and development of the air space. We share here our experiences with this system and explore the need for visualization in future scenarios to ensure safe, free, and efficient air spaces.
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Visualization is inherently diverse and is employed in countless domains to enable meaningful interactions with data. There is tremendous opportunity in embracing disciplinary diversity to widen the pool of contributions to visualization design, research, and practice. We describe a few examples of diverse approaches: scientific method, design studies, tool building, participatory research, and co-design with communities, data storytelling, and autographic design. We discuss opening the aperture, pushing back on what we, as a community, deem acceptable and rigorous, and what can be gained through greater inclusivity of approaches.
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In this Viewpoint article, we describe the persistent tensions between various camps on the "right" way to conduct evaluations in visualization. Visualization as a field is the amalgamation of cognitive and perceptual sciences and computer graphics, among others. As a result, the relatively disjointed lineages in visualization understandably approach the topic of evaluation very differently. It is both a blessing and a curse to our field. It is a blessing, because the collaboration of diverse perspectives is the breeding ground of innovation. Yet it is a curse, because as a community, we have yet to resolve an appreciation for differing perspectives on the topic of evaluation. We explicate these differing expectations and conventions to appreciate the spectrum of evaluation design decisions. We describe some guiding questions that researchers may consider when designing evaluations to navigate differing readers' evaluation expectations.
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Gráficos por Computador , Projetos de PesquisaRESUMO
We introduce a new research area in visual analytics (VA) aiming to bridge existing gaps between methods of interactive machine learning (ML) and eXplainable Artificial Intelligence (XAI), on one side, and human minds, on the other side. The gaps are, first, a conceptual mismatch between ML/XAI outputs and human mental models and ways of reasoning, and second, a mismatch between the information quantity and level of detail and human capabilities to perceive and understand. A grand challenge is to adapt ML and XAI to human goals, concepts, values, and ways of thinking. Complementing the current efforts in XAI towards solving this challenge, VA can contribute by exploiting the potential of visualization as an effective way of communicating information to humans and a strong trigger of human abstractive perception and thinking. We propose a cross-disciplinary research framework and formulate research directions for VA.
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Inteligência Artificial , Aprendizado de Máquina , HumanosRESUMO
Public awareness and concern about climate change often do not match the magnitude of its threat to humans and our environment. One reason for this disagreement is that it is difficult to mentally simulate the effects of a process as complex as climate change and to have a concrete representation of the impact that our individual actions will have on our own future, especially if the consequences are long term and abstract. To overcome these challenges, we propose to use cutting-edge artificial intelligence (AI) approaches to develop an interactive personalized visualization tool, the AI climate impact visualizer. It will allow a user to enter an address-be it their house, their school, or their workplace--and it will provide them with an AI-imagined possible visualization of the future of this location in 2050 following the detrimental effects of climate change such as floods, storms, and wildfires. This image will be accompanied by accessible information regarding the science behind climate change, i.e., why extreme weather events are becoming more frequent and what kinds of changes are happening on a local and global scale.
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In recent years, research on immersive environments has experienced a new wave of interest, and immersive analytics has been established as a new research field. Every year, a vast amount of different techniques, applications, and user studies are published that focus on employing immersive environments for visualizing and analyzing data. Nevertheless, immersive analytics is still a relatively unexplored field that needs more basic research in many aspects and is still viewed with skepticism. Rightly so, because in our opinion, many researchers do not fully exploit the possibilities offered by immersive environments and, on the contrary, sometimes even overestimate the power of immersive visualizations. Although a growing body of papers has demonstrated individual advantages of immersive analytics for specific tasks and problems, the general benefit of using immersive environments for effective analytic tasks remains controversial. In this article, we reflect on when and how immersion may be appropriate for the analysis and present four guiding scenarios. We report on our experiences, discuss the landscape of assessment strategies, and point out the directions where we believe immersive visualizations have the greatest potential.