Applied Visualization
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Tuesday's Evening Fast Forward Session
Fast Forward Session on Tuesday Oct. 27 EVENING (for Wednesday's events and presentations) be in the Red Lacquer Ballroom by 17:55 sharp Title Authors Seat # InfoVis Session: Applications [08:30–10:10, Grand] Visual Mementos: Reflecting Memories with Personal Alice Thudt, Dominikus Baur, Samuel Huron, Sheelagh - Data Carpendale 1 Roeland Scheepens, Christophe Hurter, Huub van de - Visualization, Selection, and Analysis of Traffic Flows Wetering, Jarke van Wijk 2 - Visually Comparing Weather Features in Forecasts P. Samuel Quinan, Miriah Meyer 3 Hendrik Strobelt, Bilal Alsallakh, Joseph Botros, Brant - Vials: Visualizing Alternative Splicing of Genes Peterson, Mark Borowsky, Hanspeter Pfister, Alexander 4 Lex TimeSpan: Using Visualization to Explore Temporal Mona Hosseinkhani Loorak, Charles Perin, Noreen - Multidimensional Data of Stroke Patients Kamal, Michael Hill, Sheelagh Carpendale 5 SciVis Session: Tasks and Applications [08:30–10:10, State] A Classification of User Tasks in Visual Analysis of Bireswar Laha, Doug Bowman, David Laidlaw, John - Volume Data Socha 6 Using Maximum Topology Matching to Explore Jorge Poco, Harish Doraiswamy, Marian Talbert, Jeffrey - Differences in Species Distribution Models Morisette, Claudio Silva 7 Visual Verification of Space Weather Ensemble Alexander Bock, Asher Pembroke, M. Leila Mays, Lutz - Simulations Rastaetter, Anders Ynnerman, Timo Ropinski 8 A Visual Voting Framework for Weather Forecast Hongsen Liao, Yingcai Wu, Li Chen, Thomas M. Hamill, - Calibration Yunhai Wang, Kan Dai, Hui Zhang, Wei -
Scientific Visualization
Report from Dagstuhl Seminar 11231 Scientific Visualization Edited by Min Chen1, Hans Hagen2, Charles D. Hansen3, and Arie Kaufman4 1 University of Oxford, GB, [email protected] 2 TU Kaiserslautern, DE, [email protected] 3 University of Utah, US, [email protected] 4 SUNY – Stony Brook, US, [email protected] Abstract This report documents the program and the outcomes of Dagstuhl Seminar 11231 “Scientific Visualization”. Seminar 05.–10. June, 2011 – www.dagstuhl.de/11231 1998 ACM Subject Classification I.3 Computer Graphics, I.4 Image Processing and Computer Vision, J.2 Physical Sciences and Engineering, J.3 Life and Medical Sciences Keywords and phrases Scientific Visualization, Biomedical Visualization, Integrated Multifield Visualization, Uncertainty Visualization, Scalable Visualization Digital Object Identifier 10.4230/DagRep.1.6.1 1 Executive Summary Min Chen Hans Hagen Charles D. Hansen Arie Kaufman License Creative Commons BY-NC-ND 3.0 Unported license © Min Chen, Hans Hagen, Charles D. Hansen, and Arie Kaufman Scientific Visualization (SV) is the transformation of abstract data, derived from observation or simulation, into readily comprehensible images, and has proven to play an indispensable part of the scientific discovery process in many fields of contemporary science. This seminar focused on the general field where applications influence basic research questions on one hand while basic research drives applications on the other. Reflecting the heterogeneous structure of Scientific Visualization and the currently unsolved problems in the field, this seminar dealt with key research problems and their solutions in the following subfields of scientific visualization: Biomedical Visualization: Biomedical visualization and imaging refers to the mechan- isms and techniques utilized to create and display images of the human body, organs or their components for clinical or research purposes. -
The Fourth Paradigm
ABOUT THE FOURTH PARADIGM This book presents the first broad look at the rapidly emerging field of data- THE FOUR intensive science, with the goal of influencing the worldwide scientific and com- puting research communities and inspiring the next generation of scientists. Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud- computing technologies. This collection of essays expands on the vision of pio- T neering computer scientist Jim Gray for a new, fourth paradigm of discovery based H PARADIGM on data-intensive science and offers insights into how it can be fully realized. “The impact of Jim Gray’s thinking is continuing to get people to think in a new way about how data and software are redefining what it means to do science.” —Bill GaTES “I often tell people working in eScience that they aren’t in this field because they are visionaries or super-intelligent—it’s because they care about science The and they are alive now. It is about technology changing the world, and science taking advantage of it, to do more and do better.” —RhyS FRANCIS, AUSTRALIAN eRESEARCH INFRASTRUCTURE COUNCIL F OURTH “One of the greatest challenges for 21st-century science is how we respond to this new era of data-intensive -
The Diversity of Data and Tasks in Event Analytics
The Diversity of Data and Tasks in Event Analytics Catherine Plaisant, Ben Shneiderman Abstract— The growing interest in event analytics has resulted in an array of tools and applications using visual analytics techniques. As we start to compare and contrast approaches, tools and applications it will be essential to develop a common language to describe the data characteristics and diverse tasks. We propose a characterisation of event data along 3 dimensions (temporal characteristics, attributes and scale) and propose 8 high-level user tasks. We look forward to refining the lists based on the feedback of workshop attendees. KEYWORDS having the same time stamp, medical data are often Temporal analysis, pattern analysis, task analysis, taxonomy, big recorded in batches after the fact. data, temporal visualization o The relevant time scale may vary (from milliseconds to years), and may be homogeneous or not. INTRODUCTION o Data may represent changes over time of a status The growing interest in event analytics (e.g. Aigner et al, 2011; indicator, e.g. changes of cancer stages, student status or Shneiderman and Plaisant, 2016) has resulted in an array of novel physical presence in various hospital services, or may tools and applications using visual analytics techniques. As represent a set of events or actions that are not exclusive researchers compare and contrast approaches it will be helpful to from one another, e.g. actions in a computer log or series develop a common language to describe the diverse tasks and data of symptoms and medical tests. characteristics analysts encounter. o Patterns may be very cyclical or not, and this may vary The methodology of design studies - which primarily focus on over time. -
Data Visualization by Nils Gehlenborg
Data Visualization Nils Gehlenborg ([email protected]) Center for Biomedical Informatics / Harvard Medical School Cancer Program / Broad Institute of MIT and Harvard ISMB/ECCB 2011 http://www.biovis.net Flyers at ISCB booth! Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg A good sketch is better than a long speech. Napoleon Bonaparte Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg Minard 1869 Napoleon’s March on Moscow Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 4 I believe when I see it. Unknown Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg Anscombe 1973, The American Statistician Anscombe’s Quartet mean(X) = 9, var(X) = 11, mean(Y) = 7.5, var(Y) = 4.12, cor(X,Y) = 0.816, linear regression line Y = 3 + 0.5*X Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 6 Anscombe 1973, The American Statistician Anscombe’s Quartet Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 7 Exploration: Hypothesis Generation trends gaps outliers clusters - A large data set is given and the goal is to learn something about it. - Visualization is employed to perform pattern detection using the human visual system. - The goal is to generate hypotheses that can be tested with statistical methods or follow-up experiments. Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 8 Visualization Use Cases Presentation Confirmation Exploration Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 9 Definition The use of computer-supported, interactive, visual representations of data to amplify cognition. Stu Card, Jock Mackinlay & Ben Shneiderman Computer-based visualization systems provide visual representations of datasets intended to help people carry out some task more effectively.effectively. -
Pop-Out, Illusions, Interaction
Pop-Out, Illusions, Interaction DS 4200 FALL 2020 Prof. Cody Dunne NORTHEASTERN UNIVERSITY Slides and inspiration from Michelle Borkin, Krzysztof Gajos, Hanspeter Pfister, Miriah Meyer, Jonathan Schwabish, and David Sprague 1 CHECK-IN 2 PREVIOUSLY, ON DS 4200… 4 Color Vocabulary and Perceptual Ordering Darkness (Lightness) Saturation Hue ? ? Based on Slides by Miriah Meyer, Tamara Munzner 5 Color Deficiencies (Color Blindness) Based on Slides by Hanspeter Pfister, Maureen Stone 6 Check your images/colormaps for issues! http://www.vischeck.com/vischeck/vischeckImage.php https://www.color-blindness.com/coblis-color-blindness-simulator/ 7 Color Brewer http://colorbrewer2.org 8 Color Advice Summary Use a limited hue palette • Control color “pop out” with low-saturation colors • Avoid clutter from too many competing colors Use neutral backgrounds • Control impact of color • Minimize simultaneous contrast Use Color Brewer etc. for picking scales Don’t forget aesthetics! Based on Slides by Hanspeter Pfister, Maureen Stone 9 NOW, ON DS 4200… 10 POP-OUT EFFECTS 15 POP-OUT EFFECTS COLOR Healey, 2012 16 POP-OUT EFFECTS SHAPE Healey, 2012 17 POP-OUT EFFECTS “CONJUNCTION” (HARDER TO FIND RED CIRCLE!) Healey, 2012 18 POP-OUT EFFECTS MOTION Healey, 2012 19 POP-OUT EFFECTS Healey, 2012 20 POP-OUT EFFECTS Healey, 2012 21 Use these “popout” effects to help design effective visualizations! (E.g., draw viewer’s attention to main points, effective redundant encodings, etc.) Ware, VTFD 22 Discriminability and Separability The question of discriminability is: if you encode data using a particular visual channel, are the differences between items perceptible to the human as intended? Munzner, VAD 23 Textures easy hard Ware, VTFD 25 Textures: Interference Ware, VTFD 26 ILLUSIONS AND TRICKS 28 Visual Attention & Change Blindness 30 Visual Attention & Change Blindness Task: Identify the lumps/nodules in the patient’s lungs to look for cancer or abnormal growth. -
Eurovis 2019 Eurographics / IEEE VGTC Conference on Visualization 2019
EuroVis 2019 Eurographics / IEEE VGTC Conference on Visualization 2019 Porto, Portugal June 3 – 7, 2019 Organized by EUROGRAPHICS THE EUROPEAN ASSOCIATION FOR COMPUTER GRAPHICS IEEE Visualization and Graphics Technical Committee General Chairs Alfredo Ferreira – INESC-ID, Instituto Superior Técnico, Universidade de Lisboa Joaquim A. Jorge – INESC-ID, Instituto Superior Técnico, Universidade de Lisboa Full Papers Chairs Michael Gleicher – University of Wisconsin Ivan Viola – KAUST Heike Leitte – TU Kaiserslautern STARs Chairs Robert S. Laramee – Swansea University Steffen Oeltze – Dept. of Neurology, University of Magdeburg Michael Sedlmair – Jacobs University Short Papers Chairs Jimmy Johansson – Linköping University Filip Sadlo – Heidelberg University G. Elisabeta Marai – University of Illinois at Chicago Posters Chairs João Madeiras Pereira – Universidade de Lisboa Renata Raidou – TU Wien DOI: 10.1111/cgf.13725 https://www.eg.org https://diglib.eg.org Eurographics Conference on Visualization (EuroVis) 2019 Volume 38 (2019), Number 3 M. Gleicher, H. Leitte, and I. Viola (Guest Editors) Organizers and Sponsors Eurographics Conference on Visualization (EuroVis) 2019 Volume 38 (2019), Number 3 M. Gleicher, H. Leitte, and I. Viola (Guest Editors) Preface EuroVis 2019, the 21th Eurographics / IEEE VGTC Conference on Visualization, was held in Porto, Portugal, on June 3-7, 2019. The proceedings are published as a special issue of the Eurographics Computer Graphics Forum journal. The conference, which started in 1990 as the Eurographics Workshop on Visualization in Scientific Com- puting and was called VisSym after 1999, has been known as EuroVis since 2005. EuroVis attracts contributions that broadly cover the field of visualization. Topics include visualization techniques for spatial data, such as volumet- ric, tensor, and vector field datasets, and for non-spatial data, such as graphs, text, and high-dimensional datasets. -
Quantitative Literacy to New Quantitative Literacies
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/319160466 Quantitative Literacy to New Quantitative Literacies Chapter · May 2017 CITATIONS READS 0 39 3 authors, including: Rohit Mehta James P. Howard California State University, Fresno Johns Hopkins University 32 PUBLICATIONS 42 CITATIONS 13 PUBLICATIONS 0 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: MSU-Wipro STEM & Leadership View project I Wonder: Research To Practice View project All content following this page was uploaded by Rohit Mehta on 18 October 2017. The user has requested enhancement of the downloaded file. 5 Quantitative Literacy to New Quantitative Literacies Jeffrey Craig, Rohit Mehta; James P. Howard II Michigan State University; University of Maryland University College 5.1 Introduction Steen and colleagues [42] made their Case for Quantitative Literacy based on the premise that the 21st-century, pri- marily due to technology changes, is a significantly more quantitative environment than any previous time in history. The design team who wrote the case made rhetorical observations about the increasing prevalence of numbers in society in the United States; phrases like [a] world awash in numbers [42] precede nearly every piece of literature regarding quantitative literacy (or numeracy). The authors used this rhetoric to position people relative to the demands of social systems in the world. Specifically, numeracy has emerged as a partner to literacy because the social world is being integrated with numbers due to recent technological advances. Steen argued that as the printing press gave the power of letters to the masses, so the computer gives the power of number to ordinary citizens [40, p. -
Surfacing Visualization Mirages
Surfacing Visualization Mirages Andrew McNutt Gordon Kindlmann Michael Correll University of Chicago University of Chicago Tableau Research Chicago, IL Chicago, IL Seattle, WA [email protected] [email protected] [email protected] ABSTRACT In this paper, we present a conceptual model of these visual- Dirty data and deceptive design practices can undermine, in- ization mirages and show how users’ choices can cause errors vert, or invalidate the purported messages of charts and graphs. in all stages of the visual analytics (VA) process that can lead These failures can arise silently: a conclusion derived from to untrue or unwarranted conclusions from data. Using our a particular visualization may look plausible unless the an- model we observe a gap in automatic techniques for validating alyst looks closer and discovers an issue with the backing visualizations, specifically in the relationship between data data, visual specification, or their own assumptions. We term and chart specification. We address this gap by developing a such silent but significant failures visualization mirages. We theory of metamorphic testing for visualization which synthe- describe a conceptual model of mirages and show how they sizes prior work on metamorphic testing [92] and algebraic can be generated at every stage of the visual analytics process. visualization errors [54]. Through this combination we seek to We adapt a methodology from software testing, metamorphic alert viewers to situations where minor changes to the visual- testing, as a way of automatically surfacing potential mirages ization design or backing data have large (but illusory) effects at the visual encoding stage of analysis through modifications on the resulting visualization, or where potentially important to the underlying data and chart specification. -
Ways of Visualizing Data on Curves
Edinburgh Research Explorer Ways of Visualizing Curves Citation for published version: Bach, B, Ren, Q, Perin, C & Dragicevic, P 2018, Ways of Visualizing Curves. in Proceedings of the 5th Biennial Transdisciplinary Imaging Conference 2018. Edinburgh, UK, The Latent Image, Edinburgh, United Kingdom, 18/04/18. https://doi.org/10.6084/m9.figshare.6104705 Digital Object Identifier (DOI): 10.6084/m9.figshare.6104705 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: Proceedings of the 5th Biennial Transdisciplinary Imaging Conference 2018 General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 24. Sep. 2021 Transimage 2018 Proceedings of the 5th Biennial Transdisciplinary Imaging Conference 2018 Ways of Visualizing Data on Curves Benjamin Bach [email protected] Charles Perin [email protected] University of Edinburgh, Edinburgh, UK City University, London, UK Qiuyuan Ren [email protected] Pierre Dragicevic [email protected] University of Edinburgh, Edinburgh, UK Inria, Saclay, France Visual variables used to encode data on the curve in the enrichment stage. -
Particle-Based Sampling and Meshing of Surfaces in Multimaterial Volumes
Particle-Based Sampling and Meshing of Surfaces in Multimaterial Volumes The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Meyer, Miriah, Ross Whitaker, Robert M. Kirby, Christian Ledergerber, and Hanspeter Pfister. 2008. Particle-based sampling and meshing of surfaces in multimaterial volumes. IEEE Transactions on Visualization and Computer Graphics 14(6): 1539-1546. Published Version doi:10.1109/TVCG.2008.154 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:4100251 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#OAP Particle-based Sampling and Meshing of Surfaces in Multimaterial Volumes Miriah Meyer, Ross Whitaker, Member, IEEE, Robert M. Kirby, Member, IEEE, Christian Ledergerber, and Hanspeter Pfister, Senior Member, IEEE Abstract— Methods that faithfully and robustly capture the geometry of complex material interfaces in labeled volume data are im- portant for generating realistic and accurate visualizations and simulations of real-world objects. The generation of such multimaterial models from measured data poses two unique challenges: first, the surfaces must be well-sampled with regular, efficient tessella- tions that are consistent across material boundaries; and second, the resulting meshes must respect the nonmanifold geometry of the multimaterial interfaces. This paper proposes a strategy for sampling and meshing multimaterial volumes using dynamic particle systems, including a novel, differentiable representation of the material junctions that allows the particle system to explicitly sam- ple corners, edges, and surfaces of material intersections. -
Juan Morales Data Scientist
Juan Morales Data Scientist Researcher in Visual Analytics and Human-Computer Interaction. Programming lover. Avid learner. Personal Information Name: Juan Morales del Olmo Birth Date: July 18th, 1985 Homepage: http://bit.ly/juanmorales Education 2009–2013 PhD on Advanced Computing for Science and Engineering, Universidad Politécnica de Madrid (UPM), Madrid, Graduated with honor, Cum laude. 2008–2009 Artificial Intelligence MSc, Universidad Politécnica de Madrid (UPM), Madrid, pending Final Project. 2003–2008 Computer Science, Universidad Politécnica de Madrid (UPM), Madrid, Grade. 90th percentile by marks Research Experience 2013–Present Postdoc Researcher, UPM, Madrid. Developing interactive tools that help neuroscientist make sense of their data. Designing the architecture of the Visualization Framework to be used in the Human Brain Project, in collaboration with 3 international teams. May–Nov 12 Visitor Researcher at HCIL, UMD, Maryland. Under the advise of Ben Shneiderman and Catherine Plaisant. Involved in EventFlow project, a tool for the analysis of millions of event sequences. Publications 9 Published Most of them in JCR Q1 Journals like Neuroinformatics, Journal of Neuroscience or papers Frontiers in Neuroanatomy. H-Index 3. Google Scholar Conferences 6 Internat. Most of them are top congresses like IEEE VIS, ACM CHI or ECML PKDD. Lecturing conferences in three of them. http://bit.ly/juanmorales T SkypeID: juanmoralesdelolmo • H +34 608 029 224 B [email protected] 1/2 Computer skills Python Pandas, Numpy, Scipy, Matplotlib, JavaScript D3, Lodash, React, Angular, Back- VTK, ITK, Qt, Django, Flask, ZMQ, bone, jQuery, jQuery UI Gevent, Celery R Shiny, ggplot2, Statspat C++ Qt, VTK, ITK, STD, ZMQ Sys Admin Linux, Docker, Nginx, Apache Development Emacs, Eclipse, Qt Creator, R Studio, tools Git, SVN, CMake Database SQL, MongoDB Reporting LATEX, Sweave Languages Spanish First Language English Full professional Academic Experience 2014, 2015 Data Visualization, University Master in Graphical Computation and Simulation, U-tad, Madrid.