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.” 73

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 133. 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 143. Ware defines five advantages of visualization 143:

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 fig:diagram, 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  137, R  138, or Python with its data analysis ecosystem  109) 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  125, 132. Miller  125 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  85, 139 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  86. 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: Pierre Dragicevic, Petra Isenberg, Tobias Isenberg, Jean-Daniel Fekete

In the past few years, researchers at Aviz have begun to investigate beyond-desktop environments for visualization 96, 97, 101, 120. In particular, our team has made important contributions in the areas of large-display visualizations 94, 69, 93, tangible interaction for scientific visualization  61, 59, 63, and data physicalization  102, 106, 104, 119. 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) lay out the scientific foundations for data physicalization; ii) explore physically-situated and physically-embedded visualizations for personal analytics; and iii) study augmented-reality-based scientific visualization.

3.3 Perception, Cognition and Decision Making

Permanents involved: Steve Haroz, Pierre Dragicevic, 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, Steve Haroz, Pierre Dragicevic, Tobias Isenberg

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., 80, 105, 111, 112, 115, 130, 142). 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 collaborate 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 collaborate 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.

4.2 Social Sciences

We collaborate with social science researchers from EHESS Paris on the visualization of dynamic networks; they use our systems (GeneaQuilts 68, Vistorian 131, PAOHVis 28, PK-Clustering 4) 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 also collaborate on the BART initiative, a joint project with IRT-SystemX on the analysis and visualization of blockchain data, in collaboration with economists from Université Paris-Saclay.

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 a 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.

5.1 Impact of research results

Aviz' work on illustrative visualization (e. g., see Sections 3.3.3 and 8.3) 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 anthropographics (Section 8.6) is aiming at improving assistance to populations in need.

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

• Antropographic studies  34 – osf.io/xqae2/
• Anthropographics Design Space  126 – osf.io/wfgmp/
• Open Science Tutorial – osf.io/vfnwp/
• Point specification in AR  23 – osf.io/43j9g/ and osf.io/7a6yr/
• Readability of micro visualizations on mobile devices – osf.io/yz8ar/
• Reducing Affective Responses to Surgical Images through Color Manipulation and Stylization  65 – osf.io/4pfes/ and osf.io/34vzj/
• Selection techniques for tightly packed 3D objects  32 – osf.io/cewhn/ and osf.io/yze5n/
• Smart watch survey – osf.io/nwy2r/
• Spatial 3D Visualization interfaces  8 – osf.io/jtx75/
• Study on the discovery and selection of cells in 3D plant embryo datasets – osf.io/yze5n/
• Threats of a Replication Crisis in Empirical Computer Science  74 – osf.io/hkqyt/
• Transparency of CHI Research Artifacts – osf.io/csy8q/
• User Study of Annotation 3D Placements in Collaborative Augmented Reality Environments – osf.io/7a6yr/
• VIS open practice resources – osf.io/dc6mt/
• Visualization errors and bias  15 – osf.io/u8ejr/

6.1 Awards

Best paper award for

• IEEE VIS 2021: Wesley Willett, Bon Adriel Aseniero, Sheelagh Carpendale, Pierre Dragicevic, Yvonne Jansen, Lora Oehlberg, and Petra Isenberg. "Perception! Immersion! Empowerment!: Superpowers as Inspiration for Visualization." IEEE Transactions on Visualization and Computer Graphics (2021).

Best paper runner-up/honorable mention award:

• IEEE ICBC 2021: Natkamon Tovanich, Nicolas Soulié, Nicolas Heulot, Petra Isenberg. "An empirical analysis of pool hopping behavior in the Bitcoin blockchain." IEEE International Conference on Blockchain and Cryptocurrency.

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-dataset10. 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 Superpower-Inspired Framework for Data Visualization

Participants: Wesley Willett [Univ. Calgary], Bon Adriel Aseniero [Autodesk], Sheelagh Carpendale [Simon Fraser Univ.], Pierre Dragicevic [Inria Bordeaux], Yvonne Jansen [CNRS, LaBRI], Lora Oehlberg [Univ. Calgary], Petra Isenberg [Inria Paris-Saclay, correspondent].

We explored how the lens of fictional superpowers can help characterize how visualizations empower people and provide inspiration for new visualization systems 29. Researchers and practitioners often tout visualizations' ability to “make the invisible visible” and to “enhance cognitive abilities.” Meanwhile superhero comics and other modern fiction often depict characters with similarly fantastic abilities that allow them to see and interpret the world in ways that transcend traditional human perception. We investigated the intersection of these domains, and showed how the language of superpowers can be used to characterize existing visualization systems and suggest opportunities for new and empowering ones. We introduced two frameworks: The first characterizes seven underlying mechanisms that form the basis for a variety of visual superpowers portrayed in fiction. The second identifies seven ways in which visualization tools and interfaces can instill a sense of empowerment in the people who use them. Building on these observations, we illustrated a diverse set of “visualization superpowers” and highlighted opportunities for the visualization community to create new systems and interactions that empower new experiences with data. Material and illustrations are available under CC-BY 4.0 at osf.io/8yhfz.

8.2 Visualization and Clustering of Hypergraphs

Participants: Jean-Daniel Fekete [correspondant], Paola Valdivia [Inria&Telecom Paris], Alexis Pister [Inria&Telecom Paris], Catherine Plaisant [Inria&Univ. of Maryland], Paolo Buono [Univ. of Bari].

We propose a new paradigm, called PK-clustering 22, to help social scientists create meaningful clusters in social networks. Many clustering algorithms exist but most social scientists find them difficult to understand, and tools do not provide any guidance to choose algorithms, or to evaluate results taking into account the prior knowledge of the scientists. Our work introduces a new clustering paradigm and a visual analytics user interface that address this issue. It is based on a process that 1) captures the prior knowledge of the scientists as a set of incomplete clusters, 2) runs multiple clustering algorithms (similarly to clustering ensemble methods),3) visualizes the results of all the algorithms ranked and summarized by how well each algorithm matches the prior knowledge, 5)evaluates the consensus between user-selected algorithms and 6) allows users to review details and iteratively update the acquired knowledge. We believe our clustering paradigm offers a novel constructive method to iteratively build knowledge while avoiding being overly influenced by the results of often-randomly selected black-box clustering algorithms. PK-clustering relies the PAOHVis representation 28 for dynamic hypergraphs.

This work is conducted in the context of the HistorIA project funded by DataIA, and of the IVAN project.

More on the project web page aviz.fr/pkclustering.

8.3 Spatially controlled illustrative visualization of the genome

Participants: Sarkis Halladjian [Inria Paris-Saclay], Haichao Miao [TU Wien], David Kouřil [TU Wien], M. Eduard Gröller [TU Wien], Ivan Viola [KAUST], Tobias Isenberg [Inria Paris-Saclay, correspondant].

We present Multiscale Unfolding 1, an interactive technique for illustratively visualizing multiple hierarchical scales of DNA in a single view, showing the genome at different scales and demonstrating how one scale spatially folds into the next. The DNA's extremely long sequential structure—arranged differently on several distinct scale levels—is often lost in traditional 3D depictions, mainly due to its multiple levels of dense spatial packing and the resulting occlusion. Furthermore, interactive exploration of this complex structure is cumbersome, requiring visibility management like cut-aways. In contrast to existing temporally controlled multiscale data exploration, we allow viewers to always see and interact with any of the involved scales. For this purpose we separate the depiction into constant-scale and scale transition zones. Constant-scale zones maintain a single-scale representation, while still linearly unfolding the DNA. Inspired by illustration, scale transition zones connect adjacent constant-scale zones via level unfolding, scaling, and transparency. We thus represent the spatial structure of the whole DNA macro-molecule, maintain its local organizational characteristics, linearize its higher-level organization, and use spatially controlled, understandable interpolation between neighboring scales. We also contribute interaction techniques that provide viewers with a coarse-to-fine control for navigating within our all-scales-in-one-view representations and visual aids to illustrate the size differences. Overall, Multiscale Unfolding allows viewers to grasp the DNA's structural composition from chromosomes to the atoms, with increasing levels of “unfoldedness,” and can be applied in data-driven illustration and communication.

8.4 Survey of collaborative work in augmented reality

Participants: Mickaël Sereno [Inria Paris-Saclay], Xiyao Wang [Inria Paris-Saclay], Lonni Besançon [Linköpping University], Michael J. McGuffin [École de technologie supérieure], Tobias Isenberg [Inria Paris-Saclay, correspondant].

In Augmented Reality (AR), users perceive virtual content anchored in the real world. It is used in medicine, education, games, navigation, maintenance, product design, and visualization, in both single-user and multi-user scenarios. Multi-user AR has received limited attention from researchers, even though AR has been in development for more than two decades. We present the state of existing work at the intersection of AR and Computer-Supported Collaborative Work (AR-CSCW) 5, by combining a systematic survey approach with an exploratory, opportunistic literature search. We categorize 65 papers along the dimensions of space, time, role symmetry (whether the roles of users are symmetric), technology symmetry (whether the hardware platforms of users are symmetric), and output and input modalities. We derive design considerations for collaborative AR environments, and identify under-explored research topics. These include the use of heterogeneous hardware considerations and 3D data exploration research areas. This survey is useful for newcomers to the field, readers interested in an overview of CSCW in AR applications, and domain experts seeking up-to-date information.

8.5 Visualization for monitoring and exploration of the Bitcoin blockchain

Participants: Natkamon Tovanich [SystemX, correspondant], Nicolas Heulot [SystemX], Jean-Daniel Fekete [Inria], Nicolas Soulié [Université Paris-Saclay, Univ Evry, Institut Mines-Télécom Business School, LITEM].

In the last year we actively worked on visual analytics tools to support the analysis of the Bitcoin blockchain. We focused on Bitcoin mining. Mining blocks is a critical component of the Bitcoin ecosystem, helping to keep the system secure, valid, and stable. At the same time, mining is a resource-intensive activity that continues to get more and more difficult. Mining pools have emerged to address this issue and to ensure a more stable and predictable income by sharing computing power. Yet, increased centralization of the mining power is also not without dangers (e.g., the 51% attack), and, thus, it is important to better understand and analyze mining pool activities in Bitcoin. We made several contributions: an extensive data collection on Bitcoin mining pools, the development of two custom visualizations and a comprehensive tool called MiningVis 27, a first exploratory data analysis leading to hypotheses and documented activities about pools’ main features such as market share, reward rules, or location 37. One extended analysis paper received a best paper runner up award at IEEE ICBC 35. Finally, our system BitConduite 17 was published in IEEE CG&A. It is a visual analytics approach for explorative analysis of financial activity within the Bitcoin network, offering a view on transactions aggregated by entities, i. e. by individuals, companies or other groups actively using Bitcoin.

8.6 Visualizations for promoting empathy and compassion

Participants: Luiz Morais [Inria], Yvonne Jansen [CNRS], Nazareno Andrade [Universidade Federal de Campina Grande], Pierre Dragicevic [correspondant].

When showing data about people, visualization designers and data journalists often use design strategies that presumably help the audience relate to those people. The term “anthropographics” has been recently coined to refer to this practice and the resulting visualizations. Anthropographics is a rich and growing area, but the work so far has remained scattered. Despite preliminary empirical work and a few web essays written by practitioners, there is a lack of clear language for thinking about and communicating about anthropographics. In an article recently published at IEEE TVCG 126, we addressed this gap by introducing a conceptual framework and a design space for anthropographics. Our design space consists of seven elementary design dimensions that can be reasonably hypothesized to have some effect on prosocial feelings or behavior (Figure 8). It extends a previous design space and is informed by an analysis of 105 visualizations collected from newspapers, websites, and research papers. We used our conceptual framework and design space to discuss trade-offs, common design strategies, as well as future opportunities for design and research in the area of anthropographics. Our corpus of anthropographic visualizations can be explored at luizaugustomm.github.io/anthropographics/.

Empirical studies have recently examined whether anthropographics promote empathy, compassion, or the likelihood of prosocial behavior, but findings have been inconclusive so far. In an article recently published at ACM CHI 34, we contributed a detailed overview of past experiments, and two new experiments that use large samples and a combination of design strategies to maximize the possibility of finding an effect. We tested an information-rich anthropographic against a simple bar chart (Figure 9), asking participants to allocate hypothetical money in a crowdsourcing study. We found that the anthropographic had, at best, a small effect on money allocation. Such a small effect may be relevant for large-scale donation campaigns, but the large sample sizes required to observe an effect and the noise involved in measuring it make it very difficult to study in more depth. We discussed implications for our findings, such as the importance of testing alternative anthropographic design strategies as well as using alternative methodologies, such as real money donation tasks or observations in the field, for example, by testing alternative designs on the website of a real charity. Data and code are available at osf.io/xqae2.

8.7 Mobile Data Visualization

Participants: Petra Isenberg [Inria, correspondant], Alaul Islam [Inria, Paris-Saclay], Tanja Blascheck [University of Stuttgart], Anastasia Bezerianos [Paris-Saclay], Bongshin Lee [Microsoft Research], Eun Kyoung Choe [University of Maryland].

With an increasing use of mobile devices in everyday life, it is important to understand how to design data visualizations for small displays. A nuanced understanding is especially important since existing guidelines for visualization and data interaction design do not transfer well to mobile devices. Throughout the year several projects of the team contributed to an increased understanding of mobile data visualization design.

We co-edited a book on Mobile Data Visualization 39 and contributed to four chapters in the book. Specifically an introduction to the topic 46, a chapter on glanceable interactions for mobile visualization 41, an ideation methodology for novel representations for mobile devices 44, and a chapter on 3D mobile interaction 40.

We also published a replication study on our previous work in which we tested differences in visualization size 31.

MobileVisFixer 30 was a collaboration with the University of HongKong, Microsoft Research, and Monash University on how to automatically suggest problems and solutions with mobile friendly visualizations.

9.1 International research visitors

9.2 International Initiatives

• Program:
  ANR PRCI (ANR-18-CE92-0059-01)
• Project acronym:
  MicroVis
• Project title:
  Micro visualizations for pervasive and mobile data exploration
• Duration:
  Until 2/28/2023
• Coordinator:
  Petra Isenberg
• Other partners:
  University of Stuttgart, Germany
• Abstract:
  The goal of this joint Franco-German project is to study very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. We will study human perception of and interaction with micro visualizations given small as well as complex data. The increasing demand for data visualizations on small mobile devices such as fitness tracking armbands, smart watches, or mobile phones drives our research. Specifically, we define micro visualizations as small-scale visualizations that lack or have a limited set of reference structures such as labels, data axes, or grid lines and have a small physical footprint of a few square centimeters. Micro visualizations can be as simple as small unit-based visualizations such as a battery indicator but also include multi-dimensional visualizations such as star glyphs, small geographic visualizations or even small network visualizations. Although micro visualizations are essential to mobile visualization contexts, we know surprisingly little about their general visual and interaction design space or people’s ability in interpreting micro visualizations. We will address this gap by proposing a common framework, conducting empirical studies to understand people’s abilities to interpret these visualizations while in motion, and by developing a software toolkit to aid practitioners in developing micro visualizations for emerging mobile and wearable displays.

9.3 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/.

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.
• Naviscope Inria Project Lab on Image-guided NAvigation and VIsualization of large data sets in live cell imaging and microSCOPy; collaboration with several Inria project teams and external collaborators; this grant supports a PhD position and funds travel and equipment.

10.1 Promoting scientific activities

10.2 Teaching - Supervision - Juries

10.3 Popularization

11.1 Major publications

• 1 articleS.Sarkis Halladjian, D.David Kouřil, H.Haichao Miao, E.Eduard Gröller, I.Ivan Viola and T.Tobias Isenberg. Multiscale Unfolding: Illustratively Visualizing the Whole Genome at a Glance.IEEE Transactions on Visualization and Computer Graphics2022
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• 2 bookB.Bongshin Lee, R.Raimund Dachselt, P.Petra Isenberg and E. K.Eun Kyoung Choe. Mobile Data Visualization.0Chapman and Hall/CRCDecember 2021, 346
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• 3 articleY.Yucheng Lu, L.Luyu Cheng, T.Tobias Isenberg, C.-W.Chi-Wing Fu, G.Guoning Chen, H.Hui Liu, O.Oliver Deussen and Y.Yunhai Wang. Curve Complexity Heuristic KD-trees for Neighborhood-based Exploration of 3D Curves.Computer Graphics Forum402May 2021, 461–474
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• 4 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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• 5 articleM.Mickael Sereno, X.Xiyao Wang, L.Lonni Besançon, M. J.Michael J Mcguffin and T.Tobias Isenberg. Collaborative Work in Augmented Reality: A Survey.IEEE Transactions on Visualization and Computer Graphics2022
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• 6 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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• 7 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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11.2 Publications of the year

11.3 Cited publications

• 54 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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• 55 articleD.Dan Ariely. Seeing Sets: Representation by Statistical Properties.Psychological Science1222001, 157--162URL: http://www.jstor.org/stable/40063604
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• 56 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 18–19Century Style: A Case Study.Posters at the IEEE Conference on Visualization (IEEE VIS, October 13--18, Atlanta, GA, USA)2013
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• 57 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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• 58 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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• 59 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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• 60 inproceedingsL.Lonni Besançon and P.Pierre Dragicevic. The Continued Prevalence of Dichotomous Inferences at CHI.CHI '19 Extended Abstracts on Human Factors in Computing SystemsGlasgow, United KingdomMay 2019
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• 61 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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• 62 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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• 63 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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• 64 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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• 65 articleL.Lonni Besançon, A.Amir Semmo, D. J.David J. Biau, B.Bruno Frachet, V.Virginie Pineau, E. H.El Hadi Sariali, M.Marc Soubeyrand, R.Rabah Taouachi, T.Tobias Isenberg and P.Pierre Dragicevic. Reducing Affective Responses to Surgical Images and Videos Through Stylization.Computer Graphics Forum391January 2020, 462--483
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• 66 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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• 67 articleR.Ruth Beyth-Marom, F.Fiona Fidler and G.Geoff Cumming. STATISTICAL COGNITION: TOWARDS EVIDENCE-BASE PRACTICE IN STATISTICS AND STATISTICS EDUCATION..Statistics Education Research Journal722008
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• 68 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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• 69 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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• 70 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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• 71 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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• 72 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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• 73 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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• 74 articleA.Andy Cockburn, P.Pierre Dragicevic, L.Lonni Besançon and C.Carl Gutwin. Threats of a replication crisis in empirical computer science.Communications of the ACM638August 2020, 70--79
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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 18–19Century 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 incollectionP.Pierre Dragicevic. Fair statistical communication in HCI.Modern Statistical Methods for HCISpringer2016, 291--330
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• 80 techreportP.Pierre Dragicevic. HCI Statistics without p-values.RR-8738InriaJune 2015, 32
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• 81 articleP.Pierre Dragicevic and Y.Yvonne Jansen. Blinded with Science or Informed by Charts? A Replication Study.IEEE Transactions on Visualization and Computer Graphics241January 2018, 781-790
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• 82 inproceedingsP.Pierre Dragicevic, Y.Yvonne Jansen, A.Abhraneel Sarma, M.Matthew Kay and F.Fanny Chevalier. Increasing the Transparency of Research Papers with Explorable Multiverse Analyses.CHI 2019 - The ACM CHI Conference on Human Factors in Computing SystemsGlasgow, United KingdomMay 2019
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• 83 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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• 84 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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• 85 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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• 86 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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• 87 bookM.Minos Garofalakis, J.Johannes Gehrke and R.Rajeev Rastogi. Data stream management: processing high-speed data streams.Springer2016
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• 88 bookB. K.Bhaskar Kumar Ghosh and P. K.Pranab Kumar Sen. Handbook of sequential analysis.CRC Press1991
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• 89 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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• 90 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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• 91 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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• 92 articleJ.James Intriligator and P.Patrick Cavanagh. The Spatial Resolution of Visual Attention.Cognitive Psychology4332001, 171---216URL: http://www.sciencedirect.com/science/article/pii/S0010028501907558
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• 93 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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• 94 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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• 95 incollectionT.Tobias Isenberg. Interactive Exploration of Three-Dimensional Scientific Visualizations on Large Display Surfaces.Collaboration Meets Interactive SpacesBerlin, HeidelbergSpringer2016
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• 96 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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• 97 articleP.Petra Isenberg and T.Tobias Isenberg. Visualization on Interactive Surfaces: A Research Overview.i-com123November 2013, 10--17
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• 98 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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• 99 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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• 100 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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• 101 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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• 102 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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• 103 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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• 104 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\aek. 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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• 105 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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• 106 phdthesisY.Yvonne Jansen. Physical and Tangible Information Visualization.Université Paris Sud-Paris XI2014
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• 107 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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• 108 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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• 109 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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• 110 bookD.Daniel Kahneman. Thinking, Fast and Slow.Farrar, Straus and Giroux2011
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• 111 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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• 112 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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• 113 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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• 114 inproceedingsR.Robert Kosara and S.Steve Haroz. Skipping the Replication Crisis in Visualization: Threats to Study Validity and How to Address Them.Evaluation and Beyond - Methodological Approaches for VisualizationBerlin, GermanyOctober 2018
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• 115 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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• 116 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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• 117 inproceedingsM.Mathieu Le Goc, L. H.Lawrence H Kim, A.Ali Parsaei, J.-D.Jean-Daniel Fekete, P.Pierre Dragicevic and S.Sean Follmer. Zooids: Building Blocks for Swarm User Interfaces.Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST)Tokyo, JapanOctober 2016
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• 118 articleM.Mathieu Le Goc, C.Charles Perin, S.Sean Follmer, J.-D.Jean-Daniel Fekete and P.Pierre Dragicevic. Dynamic Composite Data Physicalization Using Wheeled Micro-Robots.IEEE Transactions on Visualization and Computer Graphics2018
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• 119 phdthesisM.Mathieu Le Goc. Supporting Versatility in Tangible User Interfaces Using Collections of Small Actuated Objects.Université Paris-SaclayDecember 2016
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• 120 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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• 121 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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• 122 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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• 123 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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• 124 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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• 125 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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• 126 articleL.Luiz Morais, Y.Yvonne Jansen, N.Nazareno Andrade and P.Pierre Dragicevic. Showing Data about People: A Design Space of Anthropographics.IEEE Transactions on Visualization and Computer Graphics2020
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• 127 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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• 128 articleB. A.Brian A. Nosek and D.Daniël Lakens. Registered Reports: A method to increase the credibility of published results.Social Psychology453may 2014, 137--141URL: https://doi.org/10.1027/1864-9335/a000192
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• 129 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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• 130 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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• 131 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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• 132 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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• 133 bookR.Robert Spence. Information Visualization.Boston, MA, USAAddison-Wesley Publishing Company2000
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• 134 inproceedingsS.Saiganesh Swaminathan, C.Conglei Shi, Y.Yvonne Jansen, P.Pierre Dragicevic, L. A.Lora A. Oehlberg and J.-D.Jean-Daniel Fekete. Supporting the Design and Fabrication of Physical Visualizations.Proc.  CHIToronto, Ontario, CanadaACM2014, 3845--3854
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• 135 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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• 136 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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• 137 miscTableau Software.Last accessed Feb 20182018, URL: http://www.tableau.com
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• 138 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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• 139 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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• 140 articleJ. C.Jeffrey C Valentine, A. M.Ariel M Aloe and T. S.Timothy S Lau. Life after NHST: How to describe your data without ``p-ing'' everywhere.Basic and Applied Social Psychology3752015, 260--273
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• 141 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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• 142 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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• 143 bookC.Colin Ware. Information Visualization -- Perception for Design.Morgan Kaufmann Series in Interactive TechnologiesSan Francisco, CA, USAMorgan Kaufmann Publishers2000
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• 144 articleJ. M.Jelte M. Wicherts, C. L.Coosje L. S. Veldkamp, H. E.Hilde E. M. Augusteijn, M.Marjan Bakker, R. C.Robbie C. M. van Aert and M. A.Marcel A. L. M. van Assen. Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking.Frontiers in Psychology72016, 1832URL: https://www.frontiersin.org/article/10.3389/fpsyg.2016.01832
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• 145 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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• 146 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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