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.” 74

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 127. 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 136. Ware defines five advantages of visualization 136:

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.

Three main areas of visualization have evolved in the computer science community: Scientific Visualization, Information Visualization, and Visual Analytics. Scientific visualization is primarily concerned with displaying real or simulated scientific data. Basic visualization techniques for this area include surface rendering, volume rendering, and animation. Typical examples include processing of satellite photographs, fluid flows, or medical data. Datasets in information visualization typically come from large information spaces or information systems and are both structured or unstructured. Examples include network data, multi-dimensional tables of abstract measurements, or unstructured data such as text. Visual analytics, finally, is concerned with augmenting human-led data exploration with automatic techniques such as machine learning. The Aviz team has expertise in all three areas of visualization.

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 four 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 will address visualization scalability problems. Existing data analysis systems (such as Tableau  130, R  131, or Python with its data analysis ecosystem  105) 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.

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.

3.1 Progressive Data Analysis

Permanent involved: Jean-Daniel Fekete

While data analysis has made tremendous progresses 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  118, 126. Miller  118 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.

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  82, 132 that acknowledged the harm of this topical separation and devised a research agenda. Aviz will participate in this agenda with specific assets.

Aviz has recently started to design and implement the ProgressiVis language that is natively progressive  83. 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.

3.2 Physicality in Input and Output

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

In the past few years, researchers at Aviz have begun to investigate beyond-desktop environments for visualization 92, 93, 97, 113. In particular, our team has made important contributions in the areas of large-display visualizations 90, 70, 89, tangible interaction for scientific visualization  64, 63, 66, and data physicalization  98, 102, 100, 112. We will pursue and consolidate this stream of research by focusing on how to leverage physicality in visualization, both as an input channel and as an output channel. We will more specifically i) explore physically-situated and physically-embedded visualizations for personal analytics; and ii) study augmented-reality-based scientific visualization.

3.3 Perception, Cognition and Decision Making

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

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 (e.g., graphs, maps, infographics, etc.) 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. While many past studies provide a research groundwork using simple and small visualizations (such as a bar graph with four bars), there has been little research on perception and cognition for more complex visualizations used for large datasets with thousands or millions of elements. Novel techniques researched by Aviz to alleviate complexity can be more effectively used if we understand when and why these techniques become beneficial.

3.4 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. Aviz researchers have in the past been very active in contributing critical reflection on ways to assess different forms of value offered by visualization and visual analytics research and design (e. g., 79, 101, 107, 108, 110, 124, 135). This included work on novel research methods but also existing methods and tools such as statistics. As part of the new team we will focus our efforts on three main topics related to visualization research methodologies.

4.1 Natural Sciences

As part of a CORDI PhD project, we collaborated with researchers at CERN on interactive data visualization using augmented reality, with the goal to better understand this new visualization environment and to support the physicists in analysing their 3D particle collision data. As part of another CORDI PhD project, we collaborated with researchers at the German Center for Climate Computation (DKRZ) and the Helmholtz-Zentrum Hereon (formerly Helmholtz-Zentrum Geesthacht, HZG), to better understand collaborative data exploration and interaction in immersive analytics contexts. Finally, as part of the Inria IPL “Naviscope,” we collaborate with researchers at INRAE (as well as other Inria teams) on interactive visualization tools for the exploration of plant embryo development. We also work with experts from the natural sciences in general to create 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.

4.2 Social Sciences

We collaborate with social science researchers from EHESS Paris on the visualization of dynamic networks; they use our systems (GeneaQuilts 69, Vistorian 125, PAOHVis 133, PK-Clustering 121) 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 is 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

We collaborate with the Health-Data-Hub on the analysis and visualization of CNAM Data “parcours de santé” 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 starting but is already getting a lot of traction from our partners in medicine, epidemiology, and economy of health.

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

5.1 Impact of research results

Aviz' work on illustrative visualization 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.

Open science: Aviz regularly shares full research material on the repository of the Center for Open Science to facilitate scrutiny, reuse, and replication:

• Visualization in Motion: A Research Agenda and Two Evaluations 39 — osf.io/km3s2/, osf.io/9c4bz/, osf.io/t748d/, osf.io/t748d/,gitlab.inria.fr/lyao/visinmotion, GRSI Stamp
• Reflections on Visualization in Motion for Fitness Trackers 42 — hal.inria.fr/hal-03775633
• Situated Visualization in Motion for Swimming 48 — hal.inria.fr/hal-03700406
• Situated Visualization in Motion for Video Game 53 — hal.inria.fr/hal-03694019
• Situated Visualization in Motion in Video Game for Different Types of Data 46 — hal.inria.fr/hal-03700418
• BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations 3 — osf.io/fxs76, osf.io/djrn3, github.com/tingying-he/beauvis, GRSI Stamp
• LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning 20 — osf.io/rhyg4, gitlab.inria.fr/jhong/lineaged, GRSI Stamp
• Do You Believe Your (Social Media) Data? A Personal Story on Location Data Biases, Errors, and Plausibility as well as their Visualization 22 — osf.io/u8ejr, github.com/tobiasisenberg/Motion-Plausibility-Profiles, GRSI Stamp
• Point Specification in Collaborative Visualization for 3D Scalar Fields Using Augmented Reality 30 — osf.io/43j9g, osf.io/7a6yr, github.com/MickaelSERENO/SciVis_Server/tree/CHI2020
• Hybrid Touch/Tangible Spatial Selection in Augmented Reality 31 — osf.io/qu634, osf.io/pwauq, osf.io/rvpuc, github.com/MickaelSERENO/SciVis_Server/tree/CHI2020, github.com/MickaelSERENO/TangibleBrushAR_GRSI, GRSI Stamp
• Understanding Differences between Combinations of 2D and 3D Input and Output Devices for 3D Data Visualization 37 – osf.io/7gsk8, github.com/xiyaowang/2D3DInOutputFor3DVis
• Preferences and Effectiveness of Sleep Data Visualizations for Smartwatches and Fitness Bands 41 – osf.io/yz8ar/
• Context Specific Visualizations on Smartwatches 55 – osf.io/vhn43/
• Studying Early Decision Making with Progressive Bar Charts 27 — osf.io/tn2ph/
• Scalability in Visualization 29 — osf.io/xrvu7/
• Six methods for transforming layered hypergraphs to apply layered graph layout algorithms 16 — osf.io/grvwu/
• Pyramid-based Scatterplots Sampling for Progressive and Streaming Data Visualization 15 — ProgressivePyramidBasedSampling and www.yunhaiwang.net/Vis2021/progressive-sampling/

6.1 Concours

• Petra Isenberg was nominated as DR2 at Inria

6.2 Awards

• Our IEEE VIS best paper on “Perception! Immersion! Empowerment! Superpowers as Inspiration for Visualization” was published in IEEE TVCG 38.
• Mickaël Sereno, Petra Isenberg received special recognitions for outstanding reviews for the CHI 2023 conference.

7.1 New software

7.2 New platforms

7.2.2 VisImageNavigator

Participants: Petra Isenberg [Inria Paris-Saclay, correspondent], Tobias Isenberg [Inria Paris-Saclay].

An image of the VisImageNavigator interface showing images collected from IEEE VIS papers in a grid format with several sliders and checkboxes to filter by time and conference.

AVIZ members are contributing to making available for research a dataset of IEEE VIS images at ieee-dataport.org/open-access/ieee-vis-figures-and-tables-image-dataset49. The set of images can be browsed online at vispubfigures.github.io/VisPubFigures/. The work is primarily led by Jian Chen at Ohio State University.

8.1 Hybrid Touch/Tangible Spatial Selection in Augmented Reality

Participants: Mickaël Sereno [Inria Paris-Saclay, correspondent], Stéphane Gosset [Inria Paris-Saclay], Lonni Besançon [Linköping Univ.], Tobias Isenberg [Inria Paris-Saclay].

Our three AR mappings. In Natural Mapping (a), the user extrudes the drawn 2D shape in a direct approach. The position of the virtual tablet overlaps the position of the physical tablet. In Relative-Aligned (b), the orientations of the virtual and physical tablets with respect to the physical space are the same during an extrusion. However, the user controls the virtual plane with relative motions. In this example, the user stepped back before starting remotely an extrusion. Finally, the Relative-Full mode (c) creates two coordinate systems. Movements from the one defined by the physical tablet are mapped to the coordinate system defined by the virtual table. In this example, the user, after having placed the tablet in front of the dataset, stepped back and rotated around the dataset which the user now is facing sideways. As the user moves along the normal of the physical tablet, its virtual counterpart also moves along its own normal axis (the blue arrows)

We study tangible touch tablets combined with Augmented Reality Head-Mounted Displays (AR-HMDs) to perform spatial 3D selections 9. We are primarily interested in the exploration of 3D unstructured datasets such as cloud points or volumetric datasets. AR-HMDs immerse users by showing datasets stereoscopically, and tablets provide a set of 2D exploration tools. Because AR-HMDs merge the visualization, interaction, and the users’ physical spaces, users can also use the tablets as tangible objects in their 3D space. Nonetheless, the tablets’ touch displays provide their own visualization and interaction spaces, separated from those of the AR-HMD. This raises several research questions compared to traditional setups. In this paper, we theorize, discuss, and study different available mappings for manual spatial selections using a tangible tablet within an AR-HMD space. We then study the use of this tablet within a 3D AR environment, compared to its use with a 2D external screen.

8.2 Visualization in Motion: A Research Agenda and Two Evaluations

Participants: Lijie Yao [Inria Paris-Saclay, correspondent], Anastasia Bezerianos [Univ. Paris-Saclay], Romain Vuillemot [Ecole Centrale Lyon], Petra Isenberg [Inria Paris-Saclay].

Visualization scenarios that involve different types of relative movement between viewers and visualization: (a): 0 A.D. game characters with attached health meters, (b): an augmented basketball match from the tool Clipper CourtVision. (c): a walkable visualization of the general organization of scholars at ENAC in France , . (d): an on-street bar chart that can be driven or walked by created by the Respect New Haven activist group. (e): a runner looking at her fitness data. (f): a person checking financial charts on her phone while walking to a meeting.

We contribute a research agenda for visualization in motion and two experiments to understand how well viewers can read data from moving visualizations 13. We define visualizations in motion as visual data representations that are used in contexts that exhibit relative motion between a viewer and an entire visualization. Sports analytics, video games, wearable devices, or data physicalizations are example contexts that involve different types of relative motion between a viewer and a visualization. To analyze the opportunities and challenges for designing visualization in motion, we show example scenarios and outline a first research agenda. Motivated primarily by the prevalence of and opportunities for visualizations in sports and video games we started to investigate a small aspect of our research agenda: the impact of two important characteristics of motion—speed and trajectory on a stationary viewer's ability to read data from moving donut and bar charts. We found that increasing speed and trajectory complexity did negatively affect the accuracy of reading values from the charts and that bar charts were more negatively impacted. In practice, however, this impact was small: both charts were still read fairly accurately.

8.3 BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations

Participants: Tingying He [Univ. Paris-Saclay, correspondent], Petra Isenberg [Inria Paris-Saclay], Raimund Dachselt [Technische Universität Dresden], Tobias Isenberg [Inria Paris-Saclay].

Our BeauVis scale in its recommended version showing 5 Likert items with the prompts: enjoyable, likable, pleasing, nice, appealing. The question on to top reads To what extent do you agree that this visualization is...? The end-points of the Likert items range from strongly disagree to strongly agree.

We developed and validated a rating scale to assess the aesthetic pleasure (or beauty) of a visual data representation: the BeauVis scale. With our work we offer researchers and practitioners a simple instrument to compare the visual appearance of different visualizations, unrelated to data or context of use. Our rating scale can, for example, be used to accompany results from controlled experiments or be used as informative data points during in-depth qualitative studies. Given the lack of an aesthetic pleasure scale dedicated to visualizations, researchers have mostly chosen their own terms to study or compare the aesthetic pleasure of visualizations. Yet, many terms are possible and currently no clear guidance on their effectiveness regarding the judgment of aesthetic pleasure exists. To solve this problem, we engaged in a multi-step research process to develop the first validated rating scale specifically for judging the aesthetic pleasure of a visualization (osf.io/fxs76). Our final BeauVis scale consists of five items, “enjoyable,” “likable,” “pleasing,” “nice,” and “appealing.” Beyond this scale itself, we contribute (a) a systematic review of the terms used in past research to capture aesthetics, (b) an investigation with visualization experts who suggested terms to use for judging the aesthetic pleasure of a visualization, and (c) a confirmatory survey in which we used our terms to study the aesthetic pleasure of a set of 3 visualizations.

8.4 LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning

Participants: Jiayi Hong [Inria Paris-Saclay, correspondent], Alain Trubuil [InraE Paris-Saclay], Tobias Isenberg [Inria Paris-Saclay].

Screenshot of LineageD, for the example of assigning a 256-cell embryo (in progress). The core elements are the Main 3D View of the embryo, here exploded and with the target cell highlighted, and the Hierarchy Tree of the lineage, which is interactively established by the biologists. The Target and Sister View shows the relative position of the target cell within the whole embryo and its proposed sister. The Thumbnail View of the tree provides the context of the full hierarchy. Finally, the Operation Panel supports further control of the tool.

We developed LineageD—a hybrid web-based system to predict, visualize, and interactively adjust plant embryo cell lineages. Currently, plant biologists explore the development of an embryo and its hierarchical cell lineage manually, based on a 3D dataset that represents the embryo status at one point in time. This human decision-making process, however, is time-consuming, tedious, and error-prone due to the lack of integrated graphical support for specifying the cell lineage. To fill this gap, we developed a new system to support the biologists in their tasks using an interactive combination of 3D visualization, abstract data visualization, and correctable machine learning to modify the proposed cell lineage. We use existing manually established cell lineages to obtain a neural network model. We then allow biologists to use this model to repeatedly predict assignments of a single cell division stage. After each hierarchy level prediction, we allow them to interactively adjust the machine learning based assignment, which we then integrate into the pool of verified assignments for further predictions. In addition to building the hierarchy this way in a bottom-up fashion, we also offer users to divide the whole embryo and create the hierarchy tree in a top-down fashion for a few steps, improving the ML-based assignments by reducing the potential for wrong predictions. We visualize the continuously updated embryo and its hierarchical development using both 3D spatial and abstract tree representations, together with information about the model's confidence and spatial properties. We conducted case study validations with five expert biologists to explore the utility of our approach and to assess the potential for LineageD to be used in their daily workflow. We found that the visualizations of both 3D representations and abstract representations help with decision making and the hierarchy tree top-down building approach can reduce assignments errors in real practice.

9.1 International initiatives

9.2 National initiatives

• Program:
  ANR PRC (ANR-19-CE33-0012)
• Project acronym:
  EMBER
• Project title:
  Situated Visualizations for Personal Analytics
• Duration:
  2020 – 2023. Total funding: 712 k€
• Coordinator:
  Pierre Dragicevic
• Other partners:
  Inria Bordeaux, Sorbonne Université
• Abstract:
  The Ember project will study 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/.

• Program:
  Inria IPL
• Project acronym:
  NAVISCOPE
• Project title:
  Image-guided navigation and visualization of large data sets in live cell imaging and microscopy
• Duration:
  2018 – 2022.
• Coordinator:
  Charles Kevrann (Inria Rennes)
• Participants in AVIZ:
  Tobias Isenberg, Jiayi Hong
• Other partners:
  Inria Rennes and other Inria centers
• Abstract:

  Nowadays, the detection and visualization of important localized events and process in multidimensional and multi-valued images, especially in cell and tissue imaging, is tedious and inefficient. Specialized scientists can miss key events due to complexity of the data and the lack of computer guidance. In this proposal, we plan to develop original and cutting-edge visualization and navigation methods to assist scientists, enabling semi-automatic analysis, manipulation, and investigation of temporal series of multi-valued volumetric images, with a strong focus on live cell imaging and microscopy application domains. We will build Naviscope upon the strength of scientific visualization and machine learning methods in order to provide systems capable to assist the scientist to obtain a better understanding of massive amounts of information. Such systems will be able to recognize and highlight the most informative regions of the dataset by reducing the amount of information displayed and guiding the observer attention. In the Naviscope project, we address the three following challenges and issues:

  • Novel machine learning methods able to detect the main regions of interest, and automatic quantification of sparse sets of molecular interactions and cell processes during navigation to save memory and computational resources.
  • Novel visualization methods able to encode 3D motion/deformation vectors and dynamics features with color/texture-based and non-sub-resolved representations, abstractions, and discretization, as used to show 2D motion and deformation vectors and patterns.
  • Effective machine learning-driven navigation and interaction techniques for complex functional 3D+Time data enabling the analysis of sparse sets of localized intra-cellular events and cell processes (migration, division, etc.).

  Finally, we will have also to overcome the technological challenge of gathering up the software developed in each team to provide a unique original tool for users in biological imaging, and potentially in medical imaging. Website: project.inria.fr/naviscope/.

Other projects:

• DATAIA project HistorIA “Computational social sciences and information visualization join forces to explore and analyze large historical databases”. Duration: 36 months. Total funding: 240k€. Partners: Inria Saclay, Telecom Paris. Coordinators: Jean-Daniel Fekete, Christophe Prieur.
• Health Data Hub “ParcoursVis” post-doctoral funding. Duration: 24 months. Total funding: 220k€. Partners: Inria Saclay, Health Data Hub. Coordinator: Jean-Daniel Fekete. See www.aviz.fr/Research/ParcoursVis.
• AP-HP / Inria joint project URGE. Duration 48 months. Total funding: 520k€. Partners: Inria, Hôpital Saint Antoine, Sorbonne Université. Coordinators: Xavier Leroy (Inria), Youri Yordanov (AP-HP, Inserm, Sorbonne Université).
• Action Exploratoire: EquityAnalytics: Visual Analytics for Pay Equity, Action Exploratoire. Post-Doctoral Funding. Duration 24 months. Total funding: 106k€. Coordinator: Petra Isenberg. Partners: PayAnalytics.

10.1 Promoting scientific activities

10.2 Teaching - Supervision - Juries

10.3 Popularization

11.1 Major publications

• 1 articleX.Xin Chen, J.Jian Zhang, C.-W.Chi-Wing Fu, J.-D.Jean-Daniel Fekete and Y.Yunhai Wang. Pyramid-based Scatterplots Sampling for Progressive and Streaming Data Visualization.IEEE Transactions on Visualization and Computer Graphics281January 2022, 593-603
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• 2 articleS.Sara Di Bartolomeo, A.Alexis Pister, P.Paolo Buono, C.Catherine Plaisant, C.Cody Dunne and J.-D.Jean-Daniel Fekete. Six Methods for Transforming Layered Hypergraphs to Apply Layered Graph Layout Algorithms.Computer Graphics Forum413June 2022, 1467-8659
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• 3 articleT.Tingying He, P.Petra Isenberg, R.Raimund Dachselt and T.Tobias Isenberg. BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations.IEEE Transactions on Visualization and Computer Graphics291January 2023, 363–373
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• 4 articleJ.Jiayi Hong, A.Alain Trubuil and T.Tobias Isenberg. LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning.Computer Graphics Forum413June 2022, 195--207
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• 5 articleT.Tobias Isenberg, Z.Zujany Salazar, R.Rafael Blanco and C.Catherine Plaisant. Do you believe your (social media) data? A personal story on location data biases, errors, and plausibility as well as their visualization.IEEE Transactions on Visualization and Computer Graphics289September 2022, 3277–3291
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• 6 inproceedingsA.Alaul Islam, R.Ranjini Aravind, T.Tanja Blascheck, A.Anastasia Bezerianos and P.Petra Isenberg. Preferences and Effectiveness of Sleep Data Visualizations for Smartwatches and Fitness Bands.CHI 2022 - Conference on Human Factors in Computing SystemsNew Orleans, LA, United StatesApril 2022
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• 7 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 GraphicsDecember 2022
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• 8 articleM.Mickael Sereno, L.Lonni Besançon and T.Tobias Isenberg. Point Specification in Collaborative Visualization for 3D Scalar Fields Using Augmented Reality.Virtual Reality2642022, 1317–1334
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• 9 articleBest paperM.Mickael Sereno, S.Stéphane Gosset, L.Lonni Besançon and T.Tobias Isenberg. Hybrid Touch/Tangible Spatial Selection in Augmented Reality.Computer Graphics Forum413June 2022, 403--415
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• 10 articleN.Natkamon Tovanich, P.Pierre Dragicevic and P.Petra Isenberg. Gender in 30 Years of IEEE Visualization.IEEE Transactions on Visualization and Computer Graphics281January 2022, 497-507
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• 11 articleN.Natkamon Tovanich, N.Nicolas Soulié, N.Nicolas Heulot and P.Petra Isenberg. MiningVis: Visual Analytics of the Bitcoin Mining Economy.IEEE Transactions on Visualization and Computer Graphics281January 2022, 868-878
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• 12 articleW.Wesley Willett, B. A.Bon Adriel Aseniero, S.Sheelagh Carpendale, P.Pierre Dragicevic, Y.Yvonne Jansen, L.Lora Oehlberg and P.Petra Isenberg. Perception! Immersion! Empowerment! Superpowers as Inspiration for Visualization.IEEE Transactions on Visualization and Computer Graphics281January 2022, 22-32
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• 13 articleL.Lijie Yao, A.Anastasia Bezerianos, R.Romain Vuillemot and P.Petra Isenberg. Visualization in Motion: A Research Agenda and Two Evaluations.IEEE Transactions on Visualization and Computer Graphics2810October 2022, 3546-3562
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11.2 Publications of the year

11.3 Cited publications

• 58 inproceedingsS.Sameer Agarwal, H.Henry Milner, A.Ariel Kleiner, A.Ameet Talwalkar, M.Michael Jordan, S.Samuel Madden, B.Barzan Mozafari and I.Ion Stoica. Knowing when You'Re Wrong: Building Fast and Reliable Approximate Query Processing Systems.Proceedings of the 2014 ACM SIGMOD International Conference on Management of DataSIGMOD '14New York, NY, USASnowbird, Utah, USAACM2014, 481--492URL: http://doi.acm.org/10.1145/2588555.2593667
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• 59 articleD.Dan Ariely. Seeing Sets: Representation by Statistical Properties.Psychological Science1222001, 157--162URL: http://www.jstor.org/stable/40063604
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• 60 inproceedingsB.Benjamin Bach, P.Pierre Dragicevic, S.Samuel Huron, P.Petra Isenberg, Y.Yvonne Jansen, C.Charles Perin, A.Andre Spritzer, R.Romain Vuillemot, W.Wesley Willett and T.Tobias Isenberg. Illustrative Data Graphics in 18th–19th Century Style: A Case Study.Posters at the IEEE Conference on Visualization (IEEE VIS, October 13--18, Atlanta, GA, USA)2013
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• 61 articleS. K.Sriram Karthik Badam, N.Niklas Elmqvist and J.-D.Jean-Daniel Fekete. Steering the Craft: UI Elements and Visualizations for Supporting Progressive Visual Analytics.Computer Graphics Forum363June 2017, 491--502
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• 62 inproceedingsL.Leilani Battle, M.Marco Angelini, C.Carsten Binnig, T.Tiziana Catarci, P.Philipp Eichmann, J.-D.Jean-Daniel Fekete, G.Giuseppe Santucci, M.Michael Sedlmair and W.Wesley Willett. Evaluating Visual Data Analysis Systems: A Discussion Report.HILDA'18: Workshop on Human-In-the-Loop Data AnalyticsHouston, TX, United StatesJune 2018
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• 63 inproceedingsL.Lonni Besançon, M.Mehdi Ammi and T.Tobias Isenberg. Pressure-Based Gain Factor Control for Mobile 3D Interaction using Locally-Coupled Devices.CHI 2017 - ACM CHI Conference on Human Factors in Computing SystemsNew YorkACM2017, 1831--1842
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• 64 articleL.Lonni Besançon, P.Paul Issartel, M.Mehdi Ammi and T.Tobias Isenberg. Hybrid Tactile/Tangible Interaction for 3D Data Exploration.IEEE Transactions on Visualization and Computer Graphics231January 2017, 881--890
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• 65 inproceedingsL.Lonni Besançon, P.Paul Issartel, M.Mehdi Ammi and T.Tobias Isenberg. Interactive 3D Data Exploration Using Hybrid Tactile/Tangible Input.Journées Visu 2017Rueil-Malmaison, France2017
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• 66 inproceedingsL.Lonni Besançon, P.Paul Issartel, M.Mehdi Ammi and T.Tobias Isenberg. Mouse, Tactile, and Tangible Input for 3D Manipulation.Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI)New YorkACM2017, 4727--4740
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• 67 techreportL.Lonni Besançon, P.Paul Issartel, M.Mehdi Ammi and T.Tobias Isenberg. Usability Comparison of Mouse, Touch and Tangible Inputs for 3D Data Manipulation.https://arxiv.org/abs/1603.08735v2InriaApril 2016
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• 68 inproceedingsL.Lonni Besançon, A.Amir Semmo, D. J.David J. Biau, B.Bruno Frachet, V.Virginie Pineau, E. H.El Hadi Sariali, R.Rabah Taouachi, T.Tobias Isenberg and P.Pierre Dragicevic. Reducing Affective Responses to Surgical Images through Color Manipulation and Stylization.Proceedings of the Joint Symposium on Computational Aesthetics, Sketch-Based Interfaces and Modeling, and Non-Photorealistic Animation and RenderingNew YorkACM2018, 4:1--4:13
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• 69 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 Graphics166October 2010, 1073-1081
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• 70 articleA.Anastasia Bezerianos and P.Petra Isenberg. Perception of Visual Variables on Tiled Wall-Sized Displays for Information Visualization Applications.IEEE Transactions on Visualization and Computer Graphics18122012, 2516-2525
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• 71 miscR.Renaud Blanch. Visualiser un milliard (et au-delà) d'évènements en Python~: de l'idée au prototype interactif.March 2018, URL: https://gricad.univ-grenoble-alpes.fr/video/visualisez-milliard-et-au-dela-d-evenements-en-python-l-idee-au-prototype-interactif
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• 72 inproceedingsJ.Jeremy Boy, A. V.Anshul Vikram Pandey, J.John Emerson, M.Margaret Satterthwaite, O.Oded Nov and E.Enrico Bertini. Showing people behind data: Does anthropomorphizing visualizations elicit more empathy for human rights data?Proceedings of the 2017 CHI Conference on Human Factors in Computing SystemsACM2017, 5462--5474
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• 73 incollectionW.Wolfgang Büschel, J.Jian Chen, R.Raimund Dachselt, S.Steven Drucker, T.Tim Dwyer, C.Carsten Görg, T.Tobias Isenberg, A.Andreas Kerren, C.Chris North and W.Wolfgang Stuerzlinger. Interaction for Immersive Analytics.Immersive AnalyticsBerlin /HeidelbergSpringer2018, 495--138
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• 74 bookS. K.Stuart K. CardJ. D.Jock D. MackinlayB.Ben ShneidermanReadings in information visualization: using vision to think.San Francisco, CA, USAMorgan Kaufmann Publishers Inc.1999
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• 75 articleE.Evanthia Dimara, A.Anastasia Bezerianos and P.Pierre Dragicevic. Conceptual and Methodological Issues in Evaluating Multidimensional Visualizations for Decision Support.IEEE Transactions on Visualization and Computer Graphics242018
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• 76 articleE.Evanthia Dimara, A.Anastasia Bezerianos and P.Pierre Dragicevic. The Attraction Effect in Information Visualization.IEEE Transactions on Visualization and Computer Graphics2312017
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• 77 phdthesisE.Evanthia Dimara. Information Visualization for Decision Making: Identifying Biases and Moving Beyond the Visual Analysis Paradigm.Paris Saclay2017
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• 78 inproceedingsP.Pierre Dragicevic, B.Benjamin Bach, N.Nicole Dufournaud, S.Samuel Huron, P.Petra Isenberg, Y.Yvonne Jansen, C.Charles Perin, A.Andre Spritzer, R.Romain Vuillemot, W.Wesley Willett and T.Tobias Isenberg. Visual Showcase: An Illustrative Data Graphic in an 18th–19th Century Style.Visual Showcase at the Joint ACM/EG Symposium on Computational Aesthetics, Sketch-Based Interfaces and Modeling, and Non-Photorealistic Animation and Rendering (Expressive, July 19--20, Anaheim, CA, USA)2013, URL: https://tobias.isenberg.cc/VideosAndDemos/Bach2013IDG
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• 79 techreportP.Pierre Dragicevic. HCI Statistics without p-values.RR-8738InriaJune 2015, 32
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• 80 inproceedingsP.Pierre Dragicevic, W.Wesley Willett and T.Tobias Isenberg. Illustrative Data Graphic Style Elements.Posters at the Joint ACM/EG Symposium on Computational Aesthetics, Sketch-Based Interfaces and Modeling, and Non-Photorealistic Animation and Rendering (Expressive, July 19--20, Anaheim, CA, USA)2013, URL: https://tobias.isenberg.cc/VideosAndDemos/Bach2013IDG
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• 81 incollectionT.Tim Dwyer, K.Kim Marriott, T.Tobias Isenberg, K.Karsten Klein, N.Nathalie Riche, F.Falk Schreiber, W.Wolfgang Stuerzlinger and B.Bruce Thomas. Immersive Analytics: An Introduction.Immersive AnalyticsBerlin /HeidelbergSpringer2018, 11--23
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• 82 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
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• 83 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
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• 84 bookM.Minos Garofalakis, J.Johannes Gehrke and R.Rajeev Rastogi. Data stream management: processing high-speed data streams.Springer2016
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• 85 bookB. K.Bhaskar Kumar Ghosh and P. K.Pranab Kumar Sen. Handbook of sequential analysis.CRC Press1991
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• 86 articleO.Oliver Glauser, W.-C.Wan-Chun Ma, D.Daniele Panozzo, A.Alec Jacobson, O.Otmar Hilliges and O.Olga Sorkine-Hornung. Rig animation with a tangible and modular input device.ACM Transactions on Graphics (TOG)3542016, 144
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• 87 articleS.Steve Haroz and D.David Whitney. How capacity limits of attention influence information visualization effectiveness.IEEE Transactions on Visualization and Computer Graphics18122012, 2402--2410
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• 88 articleD.Dandan Huang, M.Melanie Tory, B. A.B. Adriel Aseniero, L.Lyn Bartram, S.Scott Bateman, S.Sheelagh Carpendale, A.Anthony Tang and R.Robert Woodbury. Personal Visualization and Personal Visual Analytics.IEEE TVCG2132015, 420--433
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• 89 articleP.Petra Isenberg, P.Pierre Dragicevic, W.Wesley Willett, A.Anastasia Bezerianos and J.-D.Jean-Daniel Fekete. Hybrid-image visualization for large viewing environments.IEEE TVCG19122013, 2346--2355
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• 90 articleP.Petra Isenberg, D.Danyel Fisher, S. A.Sharoda A. Paul, M.Meredith Ringel Morris, K.Kori Inkpen and M.Mary Czerwinski. Collaborative Visual Analytics Around a Tabletop Display.IEEE TVCG185May 2012, 689--702
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• 91 incollectionT.Tobias Isenberg. Interactive Exploration of Three-Dimensional Scientific Visualizations on Large Display Surfaces.Collaboration Meets Interactive SpacesBerlin, HeidelbergSpringer2016
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• 92 articleP.Petra Isenberg, T.Tobias Isenberg, T.Tobias Hesselmann, B.Bongshin Lee, U.Ulrich von Zadow and A.Anthony Tang. Data Visualization on Interactive Surfaces: A Research Agenda.IEEE CG & A332mar # / 2013
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• 93 articleP.Petra Isenberg and T.Tobias Isenberg. Visualization on Interactive Surfaces: A Research Overview.i-com123November 2013, 10--17
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• 94 inproceedingsP.Paul Issartel, L.Lonni Besançon, F.Florimond Guéniat, T.Tobias Isenberg and M.Mehdi Ammi. Preference Between Allocentric and Egocentric 3D Manipulation in a Locally Coupled Configuration.ACM Symposium on Spatial User InteractionNew YorkACM2016, 79--88
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• 95 inproceedingsP.Paul Issartel, L.Lonni Besançon, T.Tobias Isenberg and M.Mehdi Ammi. A Tangible Volume for Portable 3D Interaction.2016 IEEE International Symposium on Mixed and Augmented RealityLos AlamitosIEEE Computer Society2016, 215--220
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• 96 articleP.Paul Issartel, F.Florimond Guéniat, T.Tobias Isenberg and M.Mehdi Ammi. Analysis of Locally Coupled 3D Manipulation Mappings Based on Mobile Device Motion.MIT Presence261October 2017, 66-95
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• 97 articleY.Yvonne Jansen and P.Pierre Dragicevic. An Interaction Model for Visualizations Beyond The Desktop.IEEE Transactions on Visualization and Computer Graphics1912November 2013, 2396 - 2405
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• 98 inproceedingsY.Yvonne Jansen, P.Pierre Dragicevic and J.-D.Jean-Daniel Fekete. Evaluating the efficiency of physical visualizations.Proc. CHIACM2013, 2593--2602
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• 99 inproceedingsY.Yvonne Jansen, P.Pierre Dragicevic and J.-D.Jean-Daniel Fekete. Tangible Remote Controllers for Wall-Size Displays.ACM annual conference on Human Factors in Computing Systems. CHI '12ACMAustin, TX, United StatesMay 2012, 2865-2874
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• 100 inproceedingsY.Yvonne Jansen, P.Pierre Dragicevic, P.Petra Isenberg, J.Jason Alexander, A.Abhijit Karnik, J.Johan Kildal, S.Sriram Subramanian and K.Kasper Hornb\ae}k. Opportunities and Challenges for Data Physicalization.Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI)ACMNew York, NY, United StatesApril 2015
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• 101 miscY.Yvonne Jansen, K.Kasper Hornbaek and P.Pierre Dragicevic. What Did Authors Value in the CHI'16 Reviews They Received?May 2016, 596 - 608
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• 102 phdthesisY.Yvonne Jansen. Physical and Tangible Information Visualization.Université Paris Sud-Paris XI2014
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• 103 articleJ.Jaemin Jo, J.Jinwook Seo and J.-D.Jean-Daniel Fekete. PANENE: A Progressive Algorithm for Indexing and Querying Approximate k-Nearest Neighbors.IEEE Transactions on Visualization and Computer Graphics262February 2020, 1347-1360
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• 104 articleJ.Jaemin Jo, F.Frédéric Vernier, P.Pierre Dragicevic and J.-D.Jean-Daniel Fekete. A Declarative Rendering Model for Multiclass Density Maps.IEEE Transactions on Visualization and Computer Graphics251January 2019, 470-480
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• 105 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/
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• 106 bookD.Daniel Kahneman. Thinking, Fast and Slow.Farrar, Straus and Giroux2011
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• 107 miscM.Matthew Kay, S.Steve Haroz, S.Shion Guha and P.Pierre Dragicevic. Special Interest Group on Transparent Statistics in HCI.May 2016, 1081--1084
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• 108 miscM.Matthew Kay, S.Steve Haroz, S.Shion Guha, P.Pierre Dragicevic and C.Chat Wacharamanotham. Moving Transparent Statistics Forward at CHI.WorkshopMay 2017, 534-541
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• 109 inproceedingsS.Stefanie Klum, P.Petra Isenberg, R.Ricardo Langner, J.-D.Jean-Daniel Fekete and R.Raimund Dachselt. Stackables: combining tangibles for faceted browsing.Proceedings of the International Working Conference on Advanced Visual InterfacesACM2012, 241--248
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• 110 articleH.Heidi Lam, P.Petra Isenberg, T.Tobias Isenberg and M.Michael Sedlmair. BELIV 2014 Special Issue.Information Visualization154October 2016, 286-287
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• 111 articleS.S. Latif and F.F. Beck. VIS Author Profiles: Interactive Descriptions of Publication Records Combining Text and Visualization.IEEE Transactions on Visualization and Computer Graphics251January 2019, 152-161
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• 112 phdthesisM.Mathieu Le Goc. Supporting Versatility in Tangible User Interfaces Using Collections of Small Actuated Objects.Université Paris-SaclayDecember 2016
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• 113 articleB.Bongshin Lee, P.Petra Isenberg, N.Nathalie Henry Riche and S.Sheelagh Carpendale. Beyond Mouse and Keyboard: Expanding Design Considerations for Information Visualization Interactions.IEEE TVCG1812December 2012, 2689--2698
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• 114 articleD.David López, L.Lora Oehlberg, C.Candemir Doger and T.Tobias Isenberg. Towards an Understanding of Mobile Touch Navigation in a Stereoscopic Viewing Environment for 3D Data Exploration.IEEE Transactions on Visualization and Computer Graphics225May 2016, 1616-1629
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• 115 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, 403-413
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• 116 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--1024
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• 117 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 BiologyTo appear2019
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• 118 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
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• 119 articleM.Michael Morgan, C.Charles Chubb and J. A.Joshua A. Solomon. A `dipper' function for texture discrimination based on orientation variance.Journal of Vision81108 2008, 9-9URL: https://dx.doi.org/10.1167/8.11.9
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• 120 articleN.Nicola Pezzotti, J.-D.Jean-Daniel Fekete, T.Thomas Höllt, B.Boudewijn Lelieveldt, E.Elmar Eisemann and A.Anna Vilanova. Multiscale Visualization and Exploration of Large Bipartite Graphs.Computer Graphics Forum373June 2018, 12
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• 121 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 Graphics272February 2021, 1775 - 1785
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• 122 bookD.Dario Rodighiero. Mapping Affinities: Democratizing Data Visualization.Métis Presses07 2021
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• 123 inproceedingsD.Dario Rodighiero. Printing Walkable Visualizations.Proc. of the Transdisciplinary Image Conference04 2018
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• 124 proceedingsM.Michael SedlmairP.Petra IsenbergT.Tobias IsenbergN.Narges MahyarH.Heidi LamProceedings of the Sixth Workshop on ``Beyond Time and Errors: Novel Evaluation Methods for Visualization'' (BELIV 2016, October 24, Baltimore, Maryland, USA).New YorkACM2016
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• 125 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 2017
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• 126 articleB.Ben Shneiderman. Response Time and Display Rate in Human Performance with Computers.ACM Comput. Surv.163September 1984, 265--285URL: http://doi.acm.org/10.1145/2514.2517
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• 127 bookR.Robert Spence. Information Visualization.Boston, MA, USAAddison-Wesley Publishing Company2000
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• 128 articleT. D.Timothy D Sweeny, S.Steve Haroz and D.David Whitney. Perceiving group behavior: Sensitive ensemble coding mechanisms for biological motion of human crowds..Journal of Experimental Psychology: Human Perception and Performance3922013, 329
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• 129 articleD. A.Danielle Albers Szafir, S.Steve Haroz, M.Michael Gleicher and S.Steven Franconeri. Four types of ensemble coding in data visualizations.Journal of Vision16503 2016, 11-11URL: https://dx.doi.org/10.1167/16.5.11
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• 130 miscTableau Software.Last accessed Feb 20182018, URL: http://www.tableau.com
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• 131 manualR. C.R Core Team. R: A Language and Environment for Statistical Computing.R Foundation for Statistical ComputingVienna, Austria2019, URL: https://www.R-project.org
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• 132 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/1812.08032
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• 133 articleP. R.Paola R Valdivia, P.Paolo Buono, C.Catherine Plaisant, N.Nicole Dufournaud and J.-D.Jean-Daniel Fekete. Analyzing Dynamic Hypergraphs with Parallel Aggregated Ordered Hypergraph Visualization.IEEE Transactions on Visualization and Computer Graphics271January 2021, 1-13
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• 134 articleI.Ivan Viola and T.Tobias Isenberg. Pondering the Concept of Abstraction in (Illustrative) Visualization.IEEE Transactions on Visualization and Computer Graphics249September 2018, 2573--2588
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• 135 inproceedingsC.Chat Wacharamanotham, S.Shion Guha, M.Matthew Kay, P.Pierre Dragicevic and S.Steve Haroz. Special Interest Group on Transparent Statistics Guidelines.CHI 2018 - Conference on Human Factors in Computing SystemsMontreal QC, FranceACM PressApril 2018, 1-4
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• 136 bookC.Colin Ware. Information Visualization -- Perception for Design.Morgan Kaufmann Series in Interactive TechnologiesSan Francisco, CA, USAMorgan Kaufmann Publishers2000
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• 137 articleW.Wesley Willett, Y.Yvonne Jansen and P.Pierre Dragicevic. Embedded Data Representations.IEEE Transactions on Visualization and Computer Graphics2017, 461 - 470
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• 138 articleM.Matthew van der Zwan, W.Wouter Lueks, H.Henk Bekker and T.Tobias Isenberg. Illustrative Molecular Visualization with Continuous Abstraction.Computer Graphics Forum303May 2011, 683--690URL: https://tobias.isenberg.cc/VideosAndDemos/Zwan2011IMV
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