2025Activity​​​‌ reportProject-TeamAVIZ

2.1 Objectives

Aviz (Analysis‌​‌ and VIsualiZation) is a​​ multidisciplinary project that seeks​​​‌ to improve data exploration‌ methods, techniques, and tools‌​‌ based on Interactive Visualization.​​ Visualization, in general, refers​​​‌ to the graphical representation‌ of data or concepts‌​‌ to aid access, distribution​​ or explanations of data.​​​‌ Card et al. give‌ a general definition for‌​‌ visualization as

“the use​​ of computer-supported, interactive, visual​​​‌ representations of data to‌ amplify cognition.” 51

Taking‌​‌ this definition, visualization is​​ a means of creating​​​‌ visual aids that lead‌ to insight in the‌​‌ underlying data sets. It​​ is not about producing​​​‌ nice pictures but about‌ making data understandable and‌​‌ explorable so that visualizations​​ help viewers gain knowledge​​​‌ about the data. It‌ is about aiding the‌​‌ process of forming a​​ mental model for the​​​‌ acquired data and so‌ helping the viewer to‌​‌ understand underlying concepts, patterns,​​ and connections within the​​​‌ data 71. In‌ partiular, visualization has the‌​‌ goal to improve humans'​​ sensemaking of complex data​​​‌ by taking advantage of‌ the capabilities of their‌​‌ vision system: visual information​​ can be processed in​​​‌ parallel and with a‌ high bandwidth into the‌​‌ human cognitive centers 79​​. Ware defines five​​​‌ advantages of visualization 79‌:

1. Comprehension: Supports the‌​‌ comprehension of large amounts​​ of data.
2. Pattern Perception:​​​‌ Previously unnoticed properties of‌ data may emerge.
3. Problem‌​‌ Analysis: Problems within the​​ data may become immediately​​​‌ apparent.
4. Adaptability: facilitates understanding‌ of large- and small-scale‌​‌ features of data.
5. Interpretation:​​ Hypothesis formulation is facilitated.​​​‌

Visualization encompasses the display‌ of data, either real‌​‌ or simulated, from large​​ information spaces or information​​​‌ systems that can be‌ structured or unstructured, and‌​‌ augmented with automatic techniques​​ such as machine learning.​​​‌ Basic visualization techniques include‌ surface rendering, volume rendering,‌​‌ animation, satellite photographs, fluid​​ flows, as well as​​​‌ network data, multi-dimensional tables‌ of abstract measurements, unstructured‌​‌ data such as text,​​ and even models closely​​​‌ connected to data. The‌ Aviz team has expertise‌​‌ in all these areas​​ of visualization.

The conceptual​​​‌ Data Analysis Pipeline related‌ to four of the‌​‌ themes of AVIZ.

As​​ shown in Figure 1​​​‌, visualization deals with‌ the data analysis pipeline‌​‌ and research in visualization​​ has been addressing all​​​‌ the stages with less‌ emphasis on the two‌​‌ initial ones and the​​ last one. In its​​​‌ initial incarnation, Aviz has‌ been focusing on interaction‌​‌ in combination with visualization,​​ physical presentation, and perception.​​​‌ We now want to‌ expand our research to‌​‌ wider questions, both in​​ the human-side and in​​​‌ the system side. For‌ the human side, we‌​‌ want to better understand​​​‌ human perception and cognition​ to improve the visualization​‌ techniques, so as to​​ better convey information to​​​‌ the human brain. We​ also want to better​‌ understand the human biases​​ to overcome them when​​​‌ possible, or provide methods​ to avoid them otherwise.​‌

On the system side,​​ we want to expand​​​‌ the scope of visualization​ that is currently limited​‌ to relatively small datasets​​ and relatively simple analytical​​​‌ methods. To achieve scalability​ in visualization, we will​‌ focus on a paradigm​​ shift: progressive data analysis​​​‌. Long-running computations currently​ hamper the exploration and​‌ visualization process because human's​​ attention is limited by​​​‌ latency constraints. We want​ to design exploratory systems​‌ that provide continuous feedback​​ and allow interactions at​​​‌ any time during computation.​ The new progressive data​‌ analysis paradigm offers these​​ capabilities, but to be​​​‌ usable, it requires the​ whole analytical pipeline to​‌ be re-implemented, and visualization​​ and interaction techniques to​​​‌ be adapted.

2.2 Research​ Themes

Aviz's research on​‌ Visualization and Visual Analytics​​ is organized around five​​​‌ research themes, described in​ more detail in the​‌ next section. Instead of​​ addressing point problems, each​​​‌ research theme will address​ several stages of the​‌ visualization pipeline in a​​ holistic manner, as summarized​​​‌ in fig:diagram.

1. Progressive​ Data Analysis and Scalability​‌ will address visualization scalability​​ problems. Existing data analysis​​​‌ systems (such as Tableau​  72, R  73​‌, or Python with​​ its data analysis ecosystem​​​‌ 59) are not​ scalable for exploratory analysis​‌ because their latency is​​ not controllable. This theme​​​‌ will lay out the​ foundations of progressive data​‌ analysis systems, which generate​​ estimates of the results​​​‌ and updates the analyst​ continuously at a bounded​‌ pace. It will focus​​ on all the stages​​​‌ of the data analysis​ pipeline: data management mechanisms,​‌ data analysis modules, as​​ well as visualizations, perception,​​​‌ understanding, and decision making​  53

2. Physicality in​‌ Input and Output will​​ seek to better understand​​​‌ the benefits of physicality​ for information. Although beyond-desktop​‌ environments for visualization are​​ generating more and more​​​‌ interest, theories and empirical​ data are lacking. This​‌ theme will consolidate the​​ nascent areas of data​​​‌ physicalization, situated visualization, and​ immersive visualization.

3. Perception,​‌ Cognition, and Decision Making​​ will study how we​​​‌ perceive and understand visualizations​ in order to develop​‌ generalized guidelines for optimizing​​ effectiveness. It will generalize​​​‌ results obtained with simple​ charts to more complex​‌ visualizations of large datasets,​​ establish theories on the​​​‌ use of abstraction in​ visualization, and contribute new​‌ empirical knowledge on decision​​ making with visualizations.

4.​​​‌ Methodologies for Visualization Research​ will develop new methods​‌ to ground the study​​ of the above scientific​​​‌ questions, and to benefit​ visualization more generally. This​‌ theme will develop evidence-based​​ strategies for communicating quantitative​​​‌ empirical findings, and will​ promote methodological discussions and​‌ open research practices within​​ the field.

5. Visualization​​​‌ Techniques. This research direction​ is more general than​‌ the ones above. We​​ study several dedicated visualization​​​‌ approaches that intersect with​ our major goals above​‌ but adress more generic​​ issues related to creative​​ visualizations and interactions in​​​‌ general.

3.1 Research Axis 1:‌​‌ Progressive Data Analysis and​​ Scalability

Permanent involved: Jean-Daniel​​​‌ Fekete

Scalability is a‌ major issue in visualization,‌​‌ although the problem has​​ never been well defined​​​‌ until recently  67 and‌ has received less attention‌​‌ in visualization than in​​ other data-science related domains.​​​‌

While data analysis has‌ made tremendous progress in‌​‌ scalability in the last​​ decade, this progress has​​​‌ only benefited “confirmatory” analysis‌ or model-based computation; progress‌​‌ in data exploration has​​ lagged behind. Existing data​​​‌ analysis systems do not‌ support data exploration at‌​‌ scale because, for large​​ amounts of data or​​​‌ for expensive computations, their‌ latency is not controllable:‌​‌ computations can take minutes,​​ hours, even days and​​​‌ months. Cognitive psychologists have‌ shown that humans' cognitive‌​‌ capabilities degrade when latency​​ increases 65, 70​​​‌. Miller 65 points‌ out that the feedback‌​‌ of a system should​​ remain below 10 seconds​​​‌ to maintain the user's‌ attention. Therefore, to try‌​‌ to limit the latency,​​ analysts currently resort to​​​‌ complex, inefficient, and unsatisfactory‌ strategies, such as sampling‌​‌ with its issues related​​ to representativity and information​​​‌ loss.

To address the‌ scalability problem under controlled‌​‌ latency, instead of performing​​ each computation in one​​​‌ long step that forces‌ the analyst to wait‌​‌ for an unbounded amount​​ of time, a progressive​​​‌ system generates estimates of‌ the results and updates‌​‌ the analyst continuously at​​ a bounded pace. The​​​‌ process continues until the‌ computation is complete, or‌​‌ it stops early if​​ the analyst considers that​​​‌ the quality of the‌ estimates is sufficient to‌​‌ make a decision. During​​ the process, a progressive​​​‌ system allows users to‌ monitor the computation with‌​‌ visualizations and steer it​​ with interactions.

While the​​​‌ topic of progressive data‌ analysis has started to‌​‌ emerge in the late​​ 90's, it has remained​​​‌ marginal practically because it‌ touches three fields of‌​‌ computer science that are​​ traditionally separate: data management,​​​‌ data analysis, and visualization.‌ Research on progressive data‌​‌ analysis remains fragmented; the​​ solutions proposed are partial​​​‌ and the different solutions‌ cannot always be combined.‌​‌ We have organized a​​ Dagstuhl seminar on Progressive​​​‌ Data Analysis and Visualization‌ 52, 74 that‌​‌ acknowledged the harm of​​ this topical separation and​​​‌ devised a research agenda.‌ Aviz will participate in‌​‌ this agenda with specific​​ assets and published a​​​‌ book about the topic‌  3 to explain the‌​‌ problems and create a​​ research agenda.

Aviz is​​​‌ actively working on designing‌ and implementing the ProgressiVis‌​‌ language that is natively​​ progressive 54. The​​​‌ language relies on a‌ Python interpreter but its‌​‌ execution semantics is different​​ in the sense that​​​‌ all the operations that‌ would take time to‌​‌ execute are performed progressively.​​ The ProgressiVis system touches​​​‌ all the stages of‌ the conceptual data analysis‌​‌ pipeline of fig:diagram; it​​ integrates data management mechanisms,​​​‌ data analysis modules, as‌ well as visualizations, perception,‌​‌ understanding, and decision making.​​ Aviz will strengthen its​​​‌ work on the implementation‌ of a natively progressive‌​‌ data science system. Such​​​‌ a system will lead​ to the following research​‌ topics:

1. Progressive language kernel​​ and data management mechanisms​​​‌
2. Progressive algorithms and computation​ strategies
3. Progressive visualizations
4. Management​‌ of uncertainties, computed from​​ the algorithms and conveyed​​​‌ to the analysts.

A​ tutorial on the ProgressiVis​‌ toolkit was presented during​​ the IEEE VIS 2025​​​‌ Conference and was well​ attended. We are working​‌ toward enriching the toolkit​​ so it can be​​​‌ applied for general analyses​ and visualizations in the​‌ forthcoming year. We also​​ apply progressive visualization with​​​‌ the ParcoursVis tool, for​ the exploration of large​‌ scale medical patient patways,​​ with millions of patients​​​‌ over a decade. ParcoursVis​ reaches an unmatched scalability​‌ due to its progressive​​ rendering, allowing it to​​​‌ be applied to large​ scale medical datasets, at​‌ the regional and country​​ level.

3.2 Research Axis​​​‌ 2: Physicality in Input​ and Output

Permanents involved​‌: Petra Isenberg, Tobias​​ Isenberg, Jean-Daniel Fekete

 

 

 

This​​​‌ is a compilation of​ images showing work the​‌ Aviz team did in​​ the area of "physicality​​​‌ in input and output".​ The first image shows​‌ the Zooids project about​​ miniature robots that can​​​‌ produce data physicalizations. The​ second shows a visualization​‌ for a smartwatch and​​ fitness band about sleep​​​‌ data. The third shows​ situated visualizations in a​‌ video game. The third​​ shows a visualization in​​​‌ augmented reality and a​ selection technique for choosing​‌ certain points.

During​​ the last five years,​​​‌ we expanded our work​ on beyond-desktop environments for​‌ visualization. Our team has​​ made contributions in the​​​‌ areas of data physicalization,​ visualization for wearable devices,​‌ situated and embedded visualization,​​ and visualization in augmented​​​‌ reality.

Data Physicalization:​ Data physicalization is a​‌ rich and vast research​​ area that studies the​​​‌ use of physical artifacts​ to convey data. It​‌ overlaps with a number​​ of research areas including​​​‌ information/scientific visualization, visual analytics,​ tangible user interfaces, shape-changing​‌ interfaces, fabrication, as well​​ as graphic design, architecture,​​​‌ and art. Physical data​ visualizations tap into our​‌ lifelong experience of perceiving​​ and manipulating the physical​​​‌ world, either alone or​ with other people. Among​‌ the earliest man-made artifacts​​ are physical representations of​​​‌ semantic concepts that provide​ physical metaphors that allow​‌ us to reason, remember,​​ and communicate. With the​​​‌ advent of computers, we​ have substituted physical representations​‌ with pixels on a​​ computer screen. The resurgence​​​‌ of physicalization as a​ research area, following our​‌ early definitions 58,​​ asks what we have​​​‌ lost in this transformation.​ Certainly, a computer-based visualization​‌ allows us to zoom​​ an image, transform variables​​​‌ in real time, and​ to zoom through virtual​‌ computer-based world. However, these​​ representations can sever the​​​‌ relationship to the natural​ world, depriving us of​‌ the touch, feel, and​​ emotion that comes from​​​‌ interacting with real objects.​ We studied several aspects​‌ of data physicalization including​​ technical challenges of constructing​​​‌ physicalizations, potential benefits, and​ how historical examples could​‌ transfer to a modern​​ world.

Visualization for Wearable​​ and Mobile Devices: In​​​‌ the area of wearable‌ and mobile-devices we engaged‌​‌ in device-driven research where​​ we considered how small​​​‌ device form factors may‌ influence how we need‌​‌ to design visualizations and​​ how we can use​​​‌ them. Portable and wearable‌ personal devices such as‌​‌ fitness tracking armbands, hand-held​​ GPS trackers, smart watches,​​​‌ or mobile phones are‌ very small displays that‌​‌ are capable of producing​​ data themselves (through sensors),​​​‌ downloading it from other‌ sources (through Wifi or‌​‌ Bluetooth), and displaying it​​ immediately 61. Often​​​‌ the data is shown‌ in the form of‌​‌ visualizations that have to​​ be adapted to the​​​‌ small display size. We‌ consider very small visualizations‌​‌ that are often used​​ on such devices under​​​‌ the term “micro visualization”‌ and have been working‌​‌ towards a better understanding​​ of the complexities involved​​​‌ in designing and using‌ micro visualizations but also‌​‌ studied the influence of​​ the unique context of​​​‌ use of mobile devices‌ on visualization use and‌​‌ design.

Situated and Embedded​​ Data Representations: We​​​‌ study how embedding data‌ visualizations in the context‌​‌ of the data sources​​ can empower people to​​​‌ make effective use of‌ their data in a‌​‌ variety of application contexts.​​ Our goal in this​​​‌ work is to go‌ beyond the traditional platforms‌​‌ of data analytics by​​ using situated data visualizations​​​‌ on various types of‌ non-traditional displays. In a‌​‌ situated data visualization, the​​ data is directly visualized​​​‌ near the physical space,‌ object, or person it‌​‌ originates from 80.​​ For example, a person​​​‌ may attach small e-ink‌ displays embedded with sensors‌​‌ at various locations of​​ their house or their​​​‌ workplace, to better understand‌ their use of space,‌​‌ of equipment, or of​​ energy resources. Or a​​​‌ person who wishes to‌ exercise more may use‌​‌ an augmented reality device​​ to visualize their past​​​‌ running performance in-place. New‌ situated data visualizations like‌​‌ these can surface information​​ in the environment—allowing viewers​​​‌ to interpret data in-context‌ and take action in‌​‌ response to it 81​​.

Visualization using Augmented​​​‌ Reality Devices: Many‌ datasets are 3D-spatial in‌​‌ nature and researchers and​​ practitioners could benefit from​​​‌ seeing them in true‌ 3D space. This is‌​‌ where immersive technologies shine,​​ and the recent advances​​​‌ in VR and AR‌ headset technologies have made‌​‌ such displays accessible to​​ the general public—the lack​​​‌ of large dedicated VR‌ installations such as a‌​‌ CAVE is not preventing​​ the use of immersive​​​‌ rendering anymore. Nonetheless, the‌ investigation of 3D datasets‌​‌ also frequently requires researchers​​ to use tools such​​​‌ as scripted analysis and‌ statistical evaluation, and such‌​‌ direction of investigation will​​ continue to be a​​​‌ cornerstone of scientific work.‌ In our investigations we‌​‌ are thus interested in​​ looking at, in particular,​​​‌ hybrid setups that allow‌ researchers to use the‌​‌ best of both worlds:​​ traditional workstations combined with​​​‌ an AR overlay for‌ stereoscopic rendering of 3D‌​‌ data 78.

3.3​​ Research Axis 3: Perception,​​​‌ Cognition and Decision-Making

Permanents‌ involved: Petra Isenberg, Tobias‌​‌ Isenberg, Jean-Daniel Fekete, Florent​​​‌ Cabric

As we collect​ increasingly large amounts of​‌ data in fields such​​ as climate science, finance,​​​‌ and medicine, the need​ to understand and communicate​‌ that data becomes more​​ important. Data visualizations are​​​‌ often used to give​ an overview of information,​‌ however it can be​​ challenging to predict whether​​​‌ these visualizations will be​ effective before spending resources​‌ to develop them. Consequently,​​ researchers make use of​​​‌ experimental methods from visual​ perception and cognition to​‌ study how we perceive​​ and understand visualizations in​​​‌ order to develop generalized​ guidelines for optimizing effectiveness.​‌ On this research axis​​ we have three focus​​​‌ areas:

Perception of Visualizations​ in Novel Contexts. Novel​‌ technology and usage contexts​​ have several characteristics for​​​‌ data visualizations that warrant​ a (re-)evaluation of how​‌ well people can perceive​​ visualizations. Characteristics we were​​​‌ particularly interested in include:​

• Physical factors: Many​‌ now common displays have​​ characteristics that warrant (re-)evaluation​​​‌ of what we know​ about visualizations to be​‌ displayed on them. Small​​ form factors of displays​​​‌ such as smartwatches and​ fitness bands are an​‌ obvious characteristic. Some screens​​ deviate from the standard​​​‌ rectangular form we are​ familiar with and use​‌ a circular geometry, which​​ is another interesting design​​​‌ constraint for visualization.
• Data​ display mobility: Data​‌ display mobility captures the​​ movement of the display(s)​​​‌ containing visual representations of​ data. Fixed, movable, carryable,​‌ wearable, and independently moving​​ displays can be differentiated​​​‌ along this dimension. We​ conducted some of our​‌ research on carryable devices,​​ including mobile devices such​​​‌ as smartphones and tablets​ but also wearable displays​‌ such as smartwatches and​​ head-mounted displays.
• Context-of-use:​​​‌ Many novel displays are​ used in contexts that​‌ are much unlike traditional​​ office settings. Visualizations here​​​‌ may be subjected to​ different lighting conditions and​‌ viewers may only afford​​ very quick glances at​​​‌ the displays themselves. For​ example, when in a​‌ car the driver can​​ only afford very quick​​​‌ glances at the GPS​ before returning to the​‌ primary task of arriving​​ safely.

Within this focus​​​‌ area we worked on​ some of these aspects;​‌ such as the need​​ for quick glances, the​​​‌ display size of visualizations,​ the movement of viewers​‌ or the data, and​​ the understanding of 3D​​​‌ augmented reality spaces. We​ relied mostly on mixed-methods​‌ user studies where a​​ quantitative analysis methodology was​​​‌ coupled with interviews or​ questionnaires.

Illustrative Visualization. This​‌ focus area takes inspiration​​ from illustrators' decades to​​​‌ centuries of experience on​ perception and cognition to​‌ better portray scientific subject​​ matter. Another input arises​​​‌ from the field of​ non-photorealistic rendering which has​‌ developed numerous techniques of​​ stylizing images and other​​​‌ input data. Traditionally, illustrative​ visualization has thus been​‌ applied primarily to data​​ with a concrete spatial​​​‌ mapping in 2D and,​ more frequently, in 3D​‌ space.

Another main direction​​ of research in this​​​‌ context is what the​ role of abstraction is​‌ in illustrative visualization 77​​, 76 as well​​​‌ as visualization in general,​ and specifically how we​‌ can provide dedicated means​​ to control the abstraction​​ being applied to visual​​​‌ representations of data. This‌ means that we need‌​‌ to go beyond seeing​​ abstraction only as a​​​‌ side-product of stylization as‌ it has traditionally been‌​‌ viewed in many approaches​​ in non-photorealistic rendering as​​​‌ well as illustrative visualization‌ to date, and investigate‌​‌ how we can interactively​​ adjust it to provide​​​‌ practitioners with a means‌ to find the best‌​‌ visual representations for a​​ given task. For example,​​​‌ we have investigated this‌ question in the context‌​‌ of structural biology 83​​ or DNA nanostructures 62​​​‌, 63, 64‌ as well as the‌​‌ use of patterns as​​ a visual variable for​​​‌ the representation of abstract‌ data 55. We‌​‌ also want to expand​​ this work to other​​​‌ application domains in the‌ future.

Decision-Making with Visualizations.‌​‌ Human decision-making and cognitive​​ biases are important research​​​‌ topics in the fields‌ of psychology, economics and‌​‌ marketing. Visualization systems are​​ increasingly used to support​​​‌ decision-making: large companies switch‌ to visualization solutions to‌​‌ improve their decisions in​​ a range of areas,​​​‌ where large sums of‌ money or human lives‌​‌ are at stake. More​​ and more, the ultimate​​​‌ goal of visualization is‌ not to understand patterns‌​‌ in the data and​​ get insights as was​​​‌ traditionally assumed, but to‌ make good decisions. In‌​‌ order to fully understand​​ how information visualizations can​​​‌ support decision-making, it is‌ important to go beyond‌​‌ traditional evaluations based on​​ data understanding, and study​​​‌ how visualizations interact with‌ human judgment, human heuristics,‌​‌ and cognitive biases.

We​​ pursue this important stream​​​‌ of research by investigating‌ decision-making in the presence‌​‌ of uncertainty and incomplete​​ information, in connection with​​​‌ the topics discussed in‌ sec:progressive and the use‌​‌ of visualizations to support​​ social choice and group​​​‌ decisions in the presence‌ of conflicts of interest.‌​‌ How cognitive biases interact​​ with visual perception is​​​‌ also an important and‌ difficult question that has‌​‌ remained largely unexplored. More​​ recently, we started a​​​‌ line of research on‌ the visualization of values‌​‌ spanning multiple orders of​​ magnitude that we call​​​‌ Order of Magnitude Values‌ (OMVs) 1, 11‌​‌, 32, 33​​. We designed novel​​​‌ visualizations to represent them,‌ and study the cognitive‌​‌ support required to make​​ sense of such numbers​​​‌ in the context of‌ accounting.

3.4 Research Axis‌​‌ 4: Methodology for Visualization​​ Research

Permanents involved: Petra​​​‌ Isenberg, Tobias Isenberg, Jean-Daniel‌ Fekete

An important aspect‌​‌ of any scientific research​​ is to establish and​​​‌ follow rigorous and effective‌ methodologies for acquiring new‌​‌ knowledge. In the field​​ of Visualization in particular,​​​‌ scientific discourse on the‌ validity, use, and establishment‌​‌ of methodologies is important​​ as the field is​​​‌ highly interdisciplinary, with diverse‌ influences and opinions. It‌​‌ is important to establish,​​ for example, what level​​​‌ of rigor the field‌ should require of its‌​‌ methods, how to choose​​ among established methods and​​​‌ methodologies, and how to‌ best communicate the results‌​‌ of our empirical research.​​ We focus our efforts​​​‌ on three main topics‌ related to visualization research‌​‌ methodologies.

Promoting and Following​​​‌ Open Research Practices

For​ issues with open research​‌ practices to be addressed,​​ educational materials and guidelines​​​‌ need to be written,​ so researchers have clarity​‌ about how to make​​ their research more credible.​​​‌ Aviz members are working​ with the organizing bodies​‌ of the visualization research​​ community to establish incentives​​​‌ for making research artifacts​ and potentially establish minimal​‌ requirements for openness in​​ published articles. Meanwhile, it​​​‌ is important to continue​ measuring and cataloging openness​‌ in the field to​​ monitor progress. The goal​​​‌ is to improve the​ credibility and applicability of​‌ the field’s research.

Shaping​​ the Scientific Visualization Community​​​‌ Aviz researchers are heavily​ involved in the organization​‌ structure of IEEE visualization​​ conferences, the most prestigious​​​‌ conference in our field,​ by proposing workshops, tutorials,​‌ serving on various organizing​​ committees, steering committees, editorial​​​‌ boards. We, in particular,​ aid the process by​‌ providing data collection and​​ analysis services through the​​​‌ vispubdata.org dataset that we​ are collecting, cleaning, and​‌ making available to the​​ community. The dataset has​​​‌ already been used in​ research (e. g., 60​‌) but also to​​ shape the scientific community​​​‌ by proposing program committee​ members, new processes, and​‌ was used by the​​ Visualization Restructuring Committee (ReVISe).​​​‌ We are also involved​ in the EuroVis community​‌ and participate at multiple​​ levels to its organization​​​‌ and management.

3.5 Research​ Axis 5: Visualization Techniques​‌

Permanents involved: Petra Isenberg,​​ Tobias Isenberg, Jean-Daniel Fekete,​​​‌ Florent Cabric, Frederic Vernier​

This image represents a​‌ new type of visualization​​ to visualize dynamic hypergraphs.​​​‌

We​ study several dedicated visualization​‌ approaches that intersect with​​ our major goals above.​​​‌ One such set of​ approaches, particularly needed at​‌ the intersection of AR​​ and VR, are those​​​‌ that fundamentally require both​ 3D and 2D representations:​‌ how to combine 3D​​ representations with 2D representations,​​​‌ how both are linked​ with each other (initial​‌ work: 68), and​​ how we need to​​​‌ design the interaction to​ most effectively and efficiently​‌ solve practical problems. The​​ visualization field will benefit​​​‌ from a more fundamental​ and generalizable understanding of​‌ such hybrid 3D/2D environments.​​ The individual research into​​​‌ the specific directions will​ provide a part of​‌ this understanding, but we​​ also plan to arrive​​​‌ at a more theoretical​ understanding of this space.​‌ For this purpose we​​ plan to formulate the​​​‌ properties of the respective​ components and their respective​‌ limitations and boundaries, how​​ the individual components affect​​​‌ each other, and how​ the existing hybrid settings​‌ overcome the limitations and​​ boundaries at the moment.​​​‌ Next, we aim to​ derive an interaction language​‌ to describe the current​​ situation, to then be​​​‌ able to generalize the​ concepts to be able​‌ to inform the design​​ of future interactive visualization​​​‌ systems.

We have also​ started to explore techniques​‌ to visualize dynamic hypergraphs​​ 75 (see fig:ExamplesOfVisualizationTechniques) and​​​‌ their clustering 66.​ These dynamic hypergraphs are​‌ very effective at representing,​​ e.g., people mentioned in​​ documents across time, such​​​‌ as publications, contracts, personal‌ records, etc. This new‌​‌ line of research is​​ stirring interest in the​​​‌ visualization community but also‌ in very different communities,‌​‌ such as social history,​​ cybersecurity, health monitoring for​​​‌ bridges, and linguistics. We‌ are now continuing to‌​‌ improve the visualization technique​​ to better scale, and​​​‌ to support aggregation using‌ interactive and automated techniques.‌​‌ Our technique for clustering​​ social networks (social hypergraphs)​​​‌ based on ensemble clustering‌ 66 is also applicable‌​‌ to other structures (simple​​ graphs and tabular data).​​​‌ We want to generalize‌ it and scale it‌​‌ to larger ensembles.

4.1 Natural​​​‌ Sciences

We often deal‌ with data that is‌​‌ inherently three-dimensional in nature,​​ for example, from fields​​​‌ such as biology, astronomy,‌ or medicine. We work‌​‌ with experts from the​​ respective natural sciences to​​​‌ create, for example, illustrative‌ visualizations of scientific data,‌​‌ such as a continuous​​ zooming technique from the​​​‌ nucleus of a cell‌ all the way down‌​‌ to the atom configuration​​ of the DNA, for​​​‌ example, for the application‌ in education. Also our‌​‌ work with immersive technology​​ is often applied to​​​‌ application cases from the‌ natural sciences.

4.2 Social‌​‌ Sciences

We collaborate with​​ social science researchers from​​​‌ EHESS Paris on the‌ visualization of dynamic networks;‌​‌ they use our systems​​ (GeneaQuilts 49, Vistorian​​​‌ 69, PAOHVis 75‌, PK-Clustering 66)‌​‌ and teach them to​​ students and researchers. Our​​​‌ tools are used daily‌ by ethnographers and historians‌​‌ to study the evolution​​ of social relations over​​​‌ time. In the social‌ sciences, many datasets are‌​‌ gathered by individual researchers​​ to answer a specific​​​‌ question, and automated analytical‌ methods cannot be applied‌​‌ to these small datasets.​​ Furthermore, the studies are​​​‌ often focused on specific‌ persons or organizations and‌​‌ not always on the​​ modeling or prediction of​​​‌ the behavior of large‌ populations. The tools we‌​‌ design to visualize complex​​ multivariate dynamic networks are​​​‌ unique and suited to‌ typical research questions shared‌​‌ by a large number​​ of researchers. This line​​​‌ of research was supported‌ by the DataIA “HistorIA”‌​‌ project, and by the​​ “IVAN” European project. We​​​‌ currently collaborate with PayAnalytics,‌ an Icelandic company to‌​‌ visualize data to help​​ companies close their gender​​​‌ pay gaps.

4.3 Medicine‌ and Sports

We collaborate‌​‌ with the Health-Data-Hub on​​ the analysis and visualization​​​‌ of French Social Security‌ (CNAM) Data, patient pathways‌​‌ for various medical treatments,​​ to help referent doctors​​​‌ and epidemiologists make sense‌ of French health data.‌​‌ In particular, we are​​ working on a subset​​​‌ of the CNAM Data‌ focused on urinary problems,‌​‌ and we have received​​ very positive feedback from​​​‌ doctors who can see‌ what happens to the‌​‌ patients treated in France​​ vs. what they thought​​​‌ happened through the literature.‌ This project is getting‌​‌ a lot of traction​​ from our partners in​​​‌ medicine, epidemiology, and the‌ economy of health.

We‌​‌ are also collaborating with​​ the “Assistance Publique -​​​‌ Hôpitaux de Paris” AP-HP‌ with a funded project‌​‌ called URGE, aimed at​​​‌ improving the emergency services​ for the Parisian hospitals.​‌ See the press announcement​​.

We collaborate with​​​‌ multiple researchers on projects​ that apply visualizations to​‌ the sports and personal​​ health context. Specifically, we​​​‌ are interested in providing​ effective visualizations for fitness​‌ trackers that are worn​​ during sports activities. We​​​‌ have done extensive research​ on micro visualizations for​‌ smartwatches where we focused​​ on how quickly people​​​‌ can retrieve information from​ their trackers. In addition,​‌ we have been working​​ on embedding situated visualizations​​​‌ in sports videos to​ aid audiences but also​‌ athletes and coaches in​​ associating data with the​​​‌ sports activity itself. The​ current ANR grant SportsViz​‌ will focus on this​​ topic where we will​​​‌ also collaborate with French​ sports associations through our​‌ collaborators at Ecole Central​​ de Lyon.

5.1​ Impact of research results​‌

Aviz' work on illustrative​​ visualization (Section 3.3)​​​‌ and immersive data exploration​ (Section 3.2) has​‌ the potential to be​​ integrated into future teaching​​​‌ materials for students in​ schools, visitors in museums,​‌ or similar.

Aviz' work​​ on visualization of large​​​‌ documents corpora with Cartolabe​ is used to present​‌ the results of the​​ French “Grand débat”, as​​​‌ well as other citizen​ expressions.

Aviz' work on​‌ the gender pay gap​​ aims at improving decision​​​‌ making for closing the​ adjusted pay gap.

Aviz'​‌ work on situated visualization​​ for sports videos aims​​​‌ to improve spectator and​ fan experiences.

Aviz' contributions​‌ to the interactive fitting​​ of molecule structures to​​​‌ Cryo-EM maps has lead​ to the implementation of​‌ the DiffFit module for​​ use in the popular​​​‌ tool ChimeraX, has already​ been downloaded approx. 3000​‌ times.

6.1​​​‌ Awards

7.1 Latest​​ software developments

7.2 New platforms

7.3 Open data

8.1 Visualizing‌​‌ Orders of Magnitude Values​​

8.1.1 Lost in Magnitudes:​​​‌ Exploring Visualization Designs for‌ Large Value Ranges

Participants:‌​‌ Katerina Batziakoudi [Berger-Levrault],​​ Florent Cabric, Stéphanie​​​‌ Rey [Berger-Levrault], Jean-Daniel‌ Fekete [correspondent].

The‌​‌ image shows our design​​ space and the defined​​​‌ dimensions. Our design space‌ encompasses three dimensions: MARKS‌​‌ (green), DATA (blue), and​​ VISUAL CHANNELS (red). The​​​‌ image illustrates an example‌ of using our design‌​‌ space as an interactive​​ table, where a mark​​​‌ is selected, and visual‌ channels are assigned to‌​‌ data attributes. Grey cells​​ are invalid, according to​​​‌ the visualization literature. After‌ checking for integrity constraints,‌​‌ a visualization is generated​​ to perform the tasks.​​​‌

We explore the​ design of visualizations for​‌ values spanning multiple orders​​ of magnitude 1;​​​‌ we call them Orders​ of Magnitude Values (OMVs).​‌ Visualization researchers have shown​​ that separating OMVs into​​​‌ two components, the mantissa​ and the exponent, and​‌ encoding them separately, overcomes​​ limitations of linear and​​​‌ logarithmic scales. However, only​ a small number of​‌ such visualizations have been​​ tested, and the design​​​‌ guidelines for visualizing the​ mantissa and exponent separately​‌ remain under-explored. To initiate​​ this exploration, better understand​​​‌ the factors influencing the​ effectiveness of these visualizations,​‌ and create guidelines, we​​ adopt a multi-stage workflow.​​​‌ We introduce a design​ space for visualizing mantissa​‌ and exponent, systematically generating​​ and qualitatively evaluating all​​​‌ possible visualizations within it.​ From this evaluation, we​‌ derive guidelines. We select​​ two visualizations that align​​​‌ with our guidelines and​ test them using a​‌ crowdsourcing experiment, showing they​​ facilitate quantitative comparisons and​​​‌ increase confidence in interpretation​ compared to the state-of-the-art.​‌ Material and illustrations are​​ available under CC-BY 4.0​​​‌ at osf.io/uke76.

8.1.2​ Beyond Log Scales: Toward​‌ Cognitively Informed Bar Charts​​ for Orders of Magnitude​​​‌ Values

Participants: Katerina Batziakoudi​ [Berger-Levrault], Stéphanie Rey​‌ [Berger-Levrault], Jean-Daniel Fekete​​ [correspondent].

Comparison of​​​‌ five bar charts showing​ order-of-magnitude differences in seven​‌ budget categories labeled A​​ to G. Each chart​​​‌ maps categories on the​ x-axis and budget values​‌ in euros on the​​ y-axis. The first chart​​​‌ uses a linear scale;​ smaller values are barely​‌ visible due to compression.​​ The second uses a​​​‌ logarithmic scale; values are​ more readable but require​‌ multiple grid lines, and​​ overlapping occurs in higher​​​‌ ranges. The third chart​ uses the EplusM scale,​‌ a piecewise linear scale​​ where mantissas are interpolated​​​‌ linearly between logarithmically spaced​ exponents. The fourth chart,​‌ called Bricks, adds discrete​​ stacked blocks to visually​​​‌ encode mantissa values. The​ fifth chart, Multi-Magnitude, introduces​‌ scale words like “millions”​​ and “billions” by splitting​​​‌ the y-axis into separate​ faceted rows.

In this work, we​​ challenge the dominant use​​​‌ of logarithmic scales to‌ communicate values spanning multiple‌​‌ orders of magnitude to​​ the general public. Focusing​​​‌ on bar charts, we‌ incorporate cognitive insights into‌​‌ visualization design to better​​ align with how humans​​​‌ perceive OMVs. Studies in‌ cognitive psychology suggest that,‌​‌ for large numerical ranges​​ such as millions and​​​‌ billions, people do not‌ think logarithmically. Instead, they‌​‌ perceive numbers in a​​ piecewise linear manner, grouping​​​‌ values into scale words‌ (e.g., millions) and applying‌​‌ linear reasoning within each​​ group. We build upon​​​‌ a recently introduced piecewise‌ linear scale, EplusM, and‌​‌ validate its use in​​ bar charts, which we​​​‌ refer to as EplusM‌ bar charts. We also‌​‌ introduce two novel variants​​ of the EplusM bar​​​‌ chart informed by findings‌ in numerical perception: Bricks,‌​‌ which builds on the​​ concepts of round numbers​​​‌ and subitizing, and Multi-Magnitude,‌ which leverages categorical perception‌​‌ of large numbers. In​​ a crowdsourced experiment, we​​​‌ evaluate four bar chart‌ designs: 1) Log, 2)‌​‌ EplusM, 3) Bricks, and​​ 4) Multi-Magnitude, across value​​​‌ retrieval and quantitative comparison‌ tasks. Our results show‌​‌ that EplusM bar charts​​ are significantly preferred over​​​‌ logarithmic designs, increase user‌ confidence, and reduce perceived‌​‌ mental demand, while maintaining​​ task performance. These findings​​​‌ suggest that EplusM bar‌ charts can serve as‌​‌ effective alternatives to logarithmic​​ ones when visualizing OMVs​​​‌ for general audiences. Material‌ and illustrations are available‌​‌ under CC-BY 4.0 at​​ osf.io/hybvp.

8.2 Reframing​​​‌ Pattern: A Comprehensive Approach‌ to a Composite Visual‌​‌ Variable

Participants: Tingying He​​ [The University of Utah,​​​‌ Scientific Computing and Imaging‌ Institute], Jason Dykes‌​‌ [City University, London],​​ Petra Isenberg, Tobias​​​‌ Isenberg [correspondent].

Diagram‌ of the process to‌​‌ generate a pattern. A​​ pattern is described by​​​‌ three sets of pattern‌ attributes: (1) the spatial‌​‌ arrangement of primitives, (2)​​ the appearance relationships among​​​‌ primitives, and (3) the‌ retinal visual variables applied‌​‌ to each individual primitive​​ that define its appearance.​​​‌ We illustrate the attributes‌ with pattern samples constructed‌​‌ both with a lattice​​ and without one.

We present a new​​ comprehensive theory for explaining,​​​‌ exploring, and using pattern‌ as a visual variable‌​‌ in visualization. Although patterns​​ have long been used​​​‌ for data encoding and‌ continue to be valuable‌​‌ today, their conceptual foundations​​ are precarious: the concepts​​​‌ and terminology used across‌ the research literature and‌​‌ in practice is inconsistent,​​ making it challenging to​​​‌ use patterns effectively and‌ to conduct research to‌​‌ inform this widespread practice.​​ To address this problem,​​​‌ we conduct a comprehensive‌ cross-disciplinary literature review that‌​‌ clarifies ambiguities around the​​​‌ use of “pattern” and​ “texture”. As a result​‌ we offer a new​​ consistent treatment of pattern​​​‌ as a composite visual​ variable composed of structured​‌ groups of graphic primitives​​ that can serve as​​​‌ marks for encoding data​ individually and collectively. This​‌ new and widely applicable​​ formulation opens a sizable​​​‌ design space for the​ visual variable pattern, which​‌ we formalize as a​​ new pattern system characterized​​​‌ by three sets of​ variables: the spatial arrangement​‌ of primitives, the appearance​​ relationships among primitives, and​​​‌ the retinal visual variables​ used on individual primitives.​‌ We show how our​​ pattern system relates to​​​‌ existing visualization theory and​ highlight opportunities for visualization​‌ design. We further explore​​ patterns based on complex​​​‌ spatial arrangements, demonstrating explanatory​ power and connecting our​‌ conceptualization to broader theory​​ on maps and cartography.​​​‌

8.3 Visualization in Motion​

Participants: Lijie Yao [Xi’an​‌ Jiaotong-Liverpool University, correspondent],​​ Federica Bucchieri [Université Paris-Saclay,​​​‌ CNRS, Inria, LISN],​ Victoria McArthur [Carleton University]​‌, Petra Isenberg,​​ Anastasia Bezerianos [Université Paris-Saclay,​​​‌ CNRS, Inria, LISN].​

We present a systematic​‌ review, an empirical study,​​ and a first set​​​‌ of considerations for designing​ visualizations in motion, derived​‌ from a concrete scenario​​ in which these visualizations​​​‌ were used to support​ a primary task. In​‌ practice, when viewers are​​ confronted with embedded visualizations,​​​‌ they often have to​ focus on a primary​‌ task and can only​​ quickly glance at a​​​‌ visualization showing rich, often​ dynamically updated, information. As​‌ such, the visualizations must​​ be designed so as​​​‌ not to distract from​ the primary task, while​‌ at the same time​​ being readable and useful​​​‌ for aiding the primary​ task. For example, in​‌ games, players who are​​ engaged in a battle​​​‌ have to look at​ their enemies but also​‌ read the remaining health​​ of their own game​​​‌ character from the health​ bar over their character’s​‌ head. Many trade-offs are​​ possible in the design​​​‌ of embedded visualizations in​ such dynamic scenarios, which​‌ we explore in-depth in​​ this paper with a​​​‌ focus on user experience.​ We use video games​‌ as an example of​​ an application context with​​​‌ a rich existing set​ of visualizations in motion.​‌ We begin our work​​ with a systematic review​​​‌ of in-game visualizations in​ motion. Next, we conduct​‌ an empirical user study​​ to investigate how different​​​‌ embedded visualizations in motion​ designs impact user experience.​‌ We conclude with a​​ set of considerations and​​​‌ trade-offs for designing visualizations​ in motion more broadly​‌ as derived from what​​ we learned about video​​​‌ games. All supplemental materials​ of this paper are​‌ available at osf.

9.1 Bilateral​ contracts with industry

Participants:​‌ Jean-Daniel Fekete, Katerina​​ Batziakoudi.

CIFRE PhD​​​‌ fellowship of Katerina Batziakoudi​ with the Company Berger-Levrault​‌ (2023–2026).

10.1 International research​​​‌ visitors

10.2 National initiatives​​​‌

• Program:
  ANR PRC (ANR-19-CE33-0012)‌
• Project acronym:
  EMBER
• Project‌​‌ title:
  Situated Visualizations for​​ Personal Analytics
• Duration:
  2020​​​‌ – 2025. Total funding:‌ 712 k€
• Coordinator:
  Pierre‌​‌ Dragicevic
• Other partners:
  Inria​​ Bordeaux, Sorbonne Université
• Participants​​​‌ in AVIZ:

  Participants: Petra‌ Isenberg.

• Abstract:
  The‌​‌ Ember project studies how​​ situated data visualization systems​​​‌ can help people use‌ their personal data (e.g.,‌​‌ fitness and physiological data,​​ energy consumption, banking transactions,​​​‌ online social activity,…) for‌ their own benefit. Although‌​‌ personal data is generated​​ in many areas of​​​‌ daily life, it remains‌ underused by individuals. Rarely‌​‌ is personal data subjected​​ to an in-depth analysis​​​‌ and used to inform‌ daily decisions. This research‌​‌ aims to empower individuals​​ to improve their lives​​​‌ by helping them become‌ advanced consumers of their‌​‌ own data. This research​​ builds on the area​​​‌ of personal visual analytics,‌ which focuses on giving‌​‌ the general public effective​​ and accessible tools to​​​‌ get insights from their‌ own data. Personal visual‌​‌ analytics is a nascent​​ area of research, but​​​‌ has so far focused‌ on scenarios where the‌​‌ data visualization is far​​ removed from the source​​​‌ of the data it‌ refers to. The goal‌​‌ of this project is​​ to address the limitations​​​‌ of traditional platforms of‌ personal data analytics by‌​‌ exploring the potential of​​ situated data visualizations. In​​​‌ a situated data visualization,‌ the data is directly‌​‌ visualized near the physical​​ space, object, or person​​​‌ it refers to. Situated‌ data visualizations have many‌​‌ potential benefits: they can​​ surface information in the​​​‌ physical environment and allow‌ viewers to interpret data‌​‌ in-context; they can be​​ tailored to highlight spatial​​​‌ connections between data and‌ the physical environment, making‌​‌ it easier to make​​ decisions and act on​​​‌ the physical world in‌ response to the insights‌​‌ gained; and they can​​ embed data into physical​​​‌ environments so that it‌ remains visible over time,‌​‌ making it easier to​​ monitor changes, observe patterns​​​‌ over time and collaborate‌ with other people. Website:‌​‌ ember.inria.fr/.

11.1 Promoting scientific activities​​​‌

11.2 Teaching -‌ Supervision - Juries -‌​‌ Educational and pedagogical outreach​​

• Training: Jean-Daniel Fekete IEEE​​​‌ VIS Tutorial "Building Progressive‌ Visual Analytics Systems with‌​‌ ProgressiVis", Vienna, Austria
• Training:​​ Jean-Daniel Fekete , Ambre​​​‌ Assor , “Modeling and‌ Interactive Visualization of Healthcare‌​‌ Pathways“, 5h eq. TD,​​ École des hautes études​​​‌ en santé publique, Rennes,‌ France.
• Master: Petra Isenberg‌​‌ , Natkamon Tovanich ,​​ “Visual Analytics”, 48h, M2,​​​‌ CentraleSupelec, France.
• Master: Petra‌ Isenberg , Anastasia Bezerianos‌​‌ , Katerina Batziakoudi “Interactive​​ Data Visualization”, 21h, M1/2,​​​‌ Université Paris-Saclay, France.
• Licence:‌ Tobias Isenberg , “Introduction‌​‌ to Computer Graphics”, 18h​​ en équivalent TD, L3,​​​‌ Polytech Paris-Saclay, France.
• Master:‌ Tobias Isenberg , Tingying‌​‌ He , “Data Visualization”,​​ 36h en équivalent TD,​​​‌ M2, CentraleSupélec, France.
• Licence:‌ Ambre Assor , “Introduction‌​‌ to Computer Graphics -​​ Lab Class”, 24h en​​​‌ équivalent TD, L3, Polytech‌ Paris-Saclay, France.
• Master: Anne-Flore‌​‌ Cabouat , “Winter School“,​​ 11h eq. TD, M1​​​‌ (HCI Master), Université Paris-Saclay‌
• Master: Florent Cabric ,‌​‌ “Analysis“, “Human-Computer Interaction, “Introduction​​ to database“, and “Supervised​​​‌ Project“,  160h eq. TD,‌ BUT Informatique, IUT d'Orsay,‌​‌ Université Paris-Saclay
• Master: Frédéric​​ Vernier , class and​​​‌ lab class. “Information visualization”,‌ 24h en équivalent TD,‌​‌ M2 ISC, Faculté des​​ sciences d'Orsay. Université Paris-Saclay,​​​‌ France.
• Master: Frédéric Vernier‌ , class and lab‌​‌ class. “Web development with​​ node.js”, 24h en équivalent​​​‌ TD, M2 HCI, Faculté‌ des sciences d'Orsay. Université‌​‌ Paris-Saclay, France.
• Licence: Frédéric​​ Vernier , class and​​​‌ lab class. “Introduction to‌ Computer Graphics”, 42h en‌​‌ équivalent TD, L1-MI, Faculté​​ des sciences d'Orsay. Université​​​‌ Paris-Saclay, France.
• Licence: Frédéric‌ Vernier , class and‌​‌ lab class. “Advanced Computer​​ Graphics”, 36h en équivalent​​​‌ TD, L2-MI, Faculté des‌ sciences d'Orsay. Université Paris-Saclay,‌​‌ France.
• Licence: Frédéric Vernier​​ , class and lab​​​‌ class. “Web development”, 42h‌ en équivalent TD, L3‌​‌ computer sciences, Faculté des​​ sciences d'Orsay. Univ. Paris-Saclay,​​​‌ France.

11.3 Popularization

12.1​ Major publications

• 1 inproceedings​‌Best paperK.Katerina​​ Batziakoudi, F.Florent​​​‌ Cabric, S.Stéphanie​ Rey and J.-D.Jean-Daniel​‌ Fekete. Lost in​​ Magnitudes: Exploring Visualization Designs​​​‌ for Large Value Ranges​.CHI 2025 -​‌ CHI Conference on Human​​ Factors in Computing Systems​​​‌Yokohama, JapanApril 2025​, 1-18 Article No.:​‌ 1170HALDOIback​​ to textback to​​​‌ textback to text​
• 2 articleA.-F.Anne-Flore​‌ Cabouat, T.Tingying​​ He, P.Petra​​​‌ Isenberg and T.Tobias​ Isenberg. PREVis: Perceived​‌ Readability Evaluation for Visualizations​​.IEEE Transactions on​​​‌ Visualization and Computer Graphics​311January 2025​‌, 1083–1093HALDOI​​back to textback​​​‌ to text
• 3 book​J.-D.Jean-Daniel Fekete,​‌ D.Danyel Fisher and​​ M.Michael Sedlmair,​​​‌ eds. Progressive Data Analysis:​ Roadmap and Research Agenda​‌.EurographicsNovember 2024​​, 231HALDOI​​​‌back to text
• 4​ articleA.Alaul Islam​‌, T.Tingying He​​, A.Anastasia Bezerianos​​​‌, T.Tanja Blascheck​, B.Bongshin Lee​‌ and P.Petra Isenberg​​. Visualizing information on​​ smartwatch faces: A review​​​‌ and design space.‌Information VisualizationOctober 2025‌​‌HALDOI
• 5 article​​G. M.Gabriela Molina​​​‌ León, A.Anastasia‌ Bezerianos, O.Olivier‌​‌ Gladin and P.Petra​​ Isenberg. Talk to​​​‌ the Wall: The Role‌ of Speech Interaction in‌​‌ Collaborative Visual Analytics.​​IEEE Transactions on Visualization​​​‌ and Computer Graphics31‌1January 2025,‌​‌ 941--951HALDOIback​​ to text
• 6 inproceedings​​​‌Best paperT.Tobias‌ Rau, T.Tobias‌​‌ Isenberg, A.Andreas​​ Koehn, M.Michael​​​‌ Sedlmair and B.Benjamin‌ Lee. Traversing Dual‌​‌ Realities: Investigating Techniques for​​ Transitioning 3D Objects between​​​‌ Desktop and Augmented Reality‌ Environments.Proceedings of‌​‌ the SIGCHI Conference on​​ Human Factors in Computing​​​‌ Systems (CHI 2025)CHI‌ 2025 - CHI Conference‌​‌ on Human Factors in​​ Computing SystemsYokohama, Japan​​​‌ACM2025, article‌ no. 1236, 16 pages‌​‌HALDOIback to​​ textback to text​​​‌
• 7 articleL.Lijie‌ Yao, F.Federica‌​‌ Bucchieri, V.Victoria​​ Mcarthur, A.Anastasia​​​‌ Bezerianos and P.Petra‌ Isenberg. User Experience‌​‌ of Visualizations in Motion:​​ A Case Study and​​​‌ Design Considerations.IEEE‌ Transactions on Visualization and‌​‌ Computer Graphics311​​January 2025, 174--184​​​‌HALDOIback to‌ text
• 8 articleL.‌​‌Lixiang Zhao, T.​​Tobias Isenberg, F.​​​‌Fuqi Xie, H.-N.‌Hai-Ning Liang and L.‌​‌Lingyun Yu. SpatialTouch:​​ Exploring Spatial Data Visualizations​​​‌ in Cross-reality.IEEE‌ Transactions on Visualization and‌​‌ Computer Graphics311​​January 2025, 897–907​​​‌HALDOIback to‌ textback to text‌​‌
• 9 inproceedingsY.Yue​​ Zhao, Y.Yunhai​​​‌ Wang, X.Xu‌ Luo, Y.Yanyan‌​‌ Wang and J.-D.Jean-Daniel​​ Fekete. Libra: An​​​‌ Interaction Model for Data‌ Visualization.CHI 2025‌​‌ - CHI Conference on​​ Human Factors in Computing​​​‌ SystemsYokohama, JapanApril‌ 2025, 1-17, Article‌​‌ No.: 1169HALDOI​​

12.2 Publications of the​​​‌ year

12.3 Cited‌ publications

• 49 articleA.‌​‌Anastasia Bezerianos, P.​​Pierre Dragicevic, J.-D.​​​‌Jean-Daniel Fekete, J.‌Juhee Bae and B.‌​‌Ben Watson. GeneaQuilts:​​ A System for Exploring​​​‌ Large Genealogies.IEEE‌ Transactions on Visualization and‌​‌ Computer Graphics166​​October 2010, 1073-1081​​​‌HALDOIback to‌ text
• 50 articleP.‌​‌Philippe Caillou, J.​​Jonas Renault, J.-D.​​​‌Jean-Daniel Fekete, A.-C.‌Anne-Catherine Letournel and M.‌​‌Michèle Sebag. Cartolabe:​​ A Web-Based Scalable Visualization​​​‌ of Large Document Collections‌.IEEE Computer Graphics‌​‌ and Applications412​​April 2021, 76--88​​​‌HALDOIback to‌ textback to text‌​‌
• 51 bookS. K.​​Stuart K. Card,​​​‌ J. D.Jock D.‌ Mackinlay and B.Ben‌​‌ Shneiderman, eds. Readings​​​‌ in information visualization: using​ vision to think.​‌San Francisco, CA, USA​​Morgan Kaufmann Publishers Inc.​​​‌1999back to text​
• 52 articleJ.-D.Jean-Daniel​‌ Fekete, D.Danyel​​ Fisher, A.Arnab​​​‌ Nandi and M.Michael​ Sedlmair. Progressive Data​‌ Analysis and Visualization (Dagstuhl​​ Seminar 18411).Dagstuhl​​​‌ Reports8102018​, 1--32URL: https://doi.org/10.4231/DagRep.8.1.1​‌DOIback to text​​
• 53 bookJ.-D.Jean-Daniel​​​‌ Fekete, D.Danyel​ Fisher and M.Michael​‌ Sedlmair. Progressive Data​​ Analysis.EurographicsNovember​​​‌ 2024, 231HAL​DOIback to text​‌back to text
• 54​​ unpublishedJ.-D.Jean-Daniel Fekete​​​‌ and R.Romain Primet​. Progressive Analytics: A​‌ Computation Paradigm for Exploratory​​ Data Analysis.July​​​‌ 2016, https://arxiv.org/abs/1607.05162 -​ working paper or preprint​‌HALback to text​​
• 55 articleT.Tingying​​​‌ He, Y.Yuanyang​ Zhong, P.Petra​‌ Isenberg and T.Tobias​​ Isenberg. Design Characterization​​​‌ for Black-and-White Textures in​ Visualization.IEEE Transactions​‌ on Visualization and Computer​​ Graphics301January​​​‌ 2024, 1019--1029HAL​DOIback to text​‌
• 56 articleP.Petra​​ Isenberg, F.Florian​​​‌ Heimerl, S.Steffen​ Koch, T.Tobias​‌ Isenberg, P.Panpan​​ Xu, C. D.​​​‌Charles D Stolper,​ M.Michael Sedlmair,​‌ J.Jian Chen,​​ T.Torsten Möller and​​​‌ J.John Stasko.​ vispubdata.org: A Metadata Collection​‌ about IEEE Visualization (VIS)​​ Publications.IEEE Transactions​​​‌ on Visualization and Computer​ Graphics239September​‌ 2017, 2199--2206HAL​​DOIback to text​​​‌
• 57 inproceedingsT.Tobias​ Isenberg. The State​‌ of Reproducibility Stamps for​​ Visualization Research Papers.​​​‌Proceedings of BELIVIEEE​ Computer SocietySt. Pete​‌ Beach, United StatesOctober​​ 2024, 97--105HAL​​​‌DOIback to text​
• 58 phdthesisY.Yvonne​‌ Jansen. Physical and​​ Tangible Information Visualization.​​​‌Université Paris Sud-Paris XI​2014back to text​‌
• 59 miscE.Eric​​ Jones, T.Travis​​​‌ Oliphant, P.Pearu​ Peterson and others.​‌ SciPy: Open source scientific​​ tools for Python.​​​‌[Online; accessed <today>]2001​, URL: http://www.scipy.org/back​‌ to text
• 60 article​​S.S. Latif and​​​‌ F.F. Beck.​ VIS Author Profiles: Interactive​‌ Descriptions of Publication Records​​ Combining Text and Visualization​​​‌.IEEE Transactions on​ Visualization and Computer Graphics​‌251January 2019​​, 152-161DOIback​​​‌ to text
• 61 book​B.Bongshin Lee,​‌ R.Raimund Dachselt,​​ P.Petra Isenberg and​​​‌ E. K.Eun Kyoung​ Choe. Mobile Data​‌ Visualization.0Chapman​​ and Hall/CRCDecember 2021​​​‌, 346HALDOI​back to text
• 62​‌ articleH.Haichao Miao​​, E.Elisa De​​​‌ Llano, T.Tobias​ Isenberg, M. E.​‌M. Eduard Gröller,​​ I.Ivan Barišic and​​​‌ I.Ivan Viola.​ DimSUM: Dimension and Scale​‌ Unifying Maps for Visual​​ Abstraction of DNA Origami​​​‌ Structures.Computer Graphics​ Forum373June​‌ 2018, 403-413HAL​​DOIback to text​​​‌
• 63 articleM.M​ Miao, E.Elisa​‌ De Llano, J.​​Johannes Sorger, Y.​​Yasaman Ahmadi, T.​​​‌Tadija Kekic, T.‌Tobias Isenberg, M.‌​‌ E.M. Eduard Gröller​​, I.Ivan Barišic​​​‌ and I.Ivan Viola‌. Multiscale Visualization and‌​‌ Scale-Adaptive Modification of DNA​​ Nanostructures.IEEE Transactions​​​‌ on Visualization and Computer‌ Graphics241January‌​‌ 2018, 1014--1024HAL​​DOIback to text​​​‌
• 64 articleH.Haichao‌ Miao, T.Tobias‌​‌ Klein, D.David​​ Kouřil, P.Peter​​​‌ Mindek, K.Karsten‌ Schatz, M. E.‌​‌M. Eduard Gröller,​​ B.Barbora Kozl\'iková,​​​‌ T.Tobias Isenberg and‌ I.Ivan Viola.‌​‌ Multiscale Molecular Visualization.​​Journal of Molecular Biology​​​‌4316March 2019‌, 1049--1070HALDOI‌​‌back to text
• 65​​ inproceedingsR. B.Robert​​​‌ B. Miller. Response‌ Time in Man-computer Conversational‌​‌ Transactions.Proc. of​​ the Fall Joint Computer​​​‌ Conference, Part ISan‌ Francisco, CaliforniaACM1968‌​‌, 267--277URL: http://doi.acm.org/10.1145/1476589.1476628​​DOIback to text​​​‌back to text
• 66‌ articleA.Alexis Pister‌​‌, P.Paolo Buono​​, J.-D.Jean-Daniel Fekete​​​‌, C.Catherine Plaisant‌ and P.Paola Valdivia‌​‌. Integrating Prior Knowledge​​ in Mixed Initiative Social​​​‌ Network Clustering.IEEE‌ Transactions on Visualization and‌​‌ Computer Graphics272​​February 2021, 1775​​​‌ - 1785HALDOI‌back to textback‌​‌ to textback to​​ text
• 67 articleG.​​​‌Gaëlle Richer, A.‌Alexis Pister, M.‌​‌Moataz Abdelaal, J.-D.​​Jean-Daniel Fekete, M.​​​‌Michael Sedlmair and D.‌Daniel Weiskopf. Scalability‌​‌ in Visualization.IEEE​​ Transactions on Visualization and​​​‌ Computer Graphics307‌January 2024, 3314‌​‌ - 3330HALDOI​​back to text
• 68​​​‌ miscE. A.Ebrar‌ A. D. Santos,‌​‌ J.Jiayi Hong and​​ T.Tobias Isenberg.​​​‌ A Design Space for‌ Linked 2D and 3D‌​‌ Visual Representations.Poster​​October 2022HALback​​​‌ to text
• 69 inproceedings‌V.Vanessa Serrano Molinero‌​‌, B.Benjamin Bach​​, C.Catherine Plaisant​​​‌, N.Nicole Dufournaud‌ and J.-D.Jean-Daniel Fekete‌​‌. Understanding the Use​​ of The Vistorian: Complementing​​​‌ Logs with Context Mini-Questionnaires‌.Visualization for the‌​‌ Digital HumanitiesPhoenix, United​​ StatesOctober 2017HAL​​​‌back to text
• 70‌ articleB.Ben Shneiderman‌​‌. Response Time and​​ Display Rate in Human​​​‌ Performance with Computers.‌ACM Comput. Surv.16‌​‌3September 1984,​​ 265--285URL: http://doi.acm.org/10.1145/2514.2517DOI​​​‌back to text
• 71‌ bookR.Robert Spence‌​‌. Information Visualization.​​Boston, MA, USAAddison-Wesley​​​‌ Publishing Company2000back‌ to text
• 72 misc‌​‌Tableau Software.Last​​ accessed Feb 20182018​​​‌, URL: http://www.tableau.comback‌ to text
• 73 manual‌​‌R. C.R Core​​ Team. R: A​​​‌ Language and Environment for‌ Statistical Computing.R‌​‌ Foundation for Statistical Computing​​Vienna, Austria2019,​​​‌ URL: https://www.R-project.orgback to‌ text
• 74 articleC.‌​‌Cagatay Turkay, N.​​Nicola Pezzotti, C.​​​‌Carsten Binnig, H.‌Hendrik Strobelt, B.‌​‌Barbara Hammer, D.​​ A.Daniel A. Keim​​​‌, J.-D.Jean-Daniel Fekete‌, T.Themis Palpanas‌​‌, Y.Yunhai Wang​​​‌ and F.Florin Rusu​. Progressive Data Science:​‌ Potential and Challenges.​​CoRRabs/1812.080322018,​​​‌ URL: http://arxiv.org/abs/1812.08032back to​ text
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