anchor.diff:  TVCG,TVCG,Efficient Local Statistical Analysis via Point-Wise Histograms in Tetrahedral Meshes and Curvilinear Grids ,T),"Bo Zhou, Yi-Jen Chiang, Cong Wang",,,,,,,vimeo 289789310,,,2018,SciVis,Estrel Hall A+B,Thursday,Scalable Techniques,25-Oct,11:40 AM,12:00 PM,
   TVCG,TVCG,Shadow Accrual Maps: Efficient Accumulation of City-Scale Shadows over Time ,T),"Fabio Miranda, Harish Doraiswamy, Marcos Lage, Luc Wilson, Mondrian Hsieh, Claudio T. Silva",,,,,,,vimeo 289789412,,,2018,SciVis,Estrel Hall A+B,Thursday,Scalable Techniques,25-Oct,12:00 PM,12:20 PM,
   TVCG,TVCG,A Scalable Hybrid Scheme for Ray-Casting of Unstructured Volume Data ,T),"Roba Binyahib, Tom Peterka, Matthew Larsen, Kwan-Liu Ma, Hank Childs",,,,,,,vimeo 289789339,,,2018,SciVis,Estrel Hall A+B,Thursday,Scalable Techniques,25-Oct,12:20 PM,12:40 PM,
  -InfoVis,TVCG,Charticulator: Interactive Construction of Bespoke Chart Layouts ,J),"Donghao Ren, Bongshin Lee, Matthew Brehmer",https://donghaoren.org/publications/infovis18-charticulator.pdf,"We present Charticulator, an interactive authoring tool that enables the creation of bespoke and reusable chart layouts. Charticulator is our response to most existing chart construction interfaces that require authors to choose from predefined chart layouts, thereby precluding the construction of novel charts. In contrast, Charticulator transforms a chart specification into mathematical layout constraints and automatically computes a set of layout attributes using a constraint-solving algorithm to realize the chart. It allows for the articulation of compound marks or glyphs as well as links between these glyphs, all without requiring any coding or knowledge of constraint satisfaction. Furthermore, thanks to the constraint-based layout approach, Charticulator can export chart designs into reusable templates that can be imported into other visualization tools. In addition to describing Charticulators conceptual framework and design, we present three forms of evaluation: a gallery to illustrate its expressiveness, a user study to verify its usability, and a click-count comparison between Charticulator and three existing tools. Finally, we discuss the limitations and potentials of Charticulator as well as directions for future research. Charticulator is available with its source code at https://charticulator.com.",https://charticulator.com/app/index.html,https://github.com/Microsoft/charticulator,,,video https://charticulator.azureedge.net/videos/charticulator-supplemental.mp4,,2018,2018,InfoVis,Estrel Hall C,Thursday,Interaction,25-Oct,11:00 AM,11:20 AM,
  +InfoVis,TVCG,Charticulator: Interactive Construction of Bespoke Chart Layouts ,J),"Donghao Ren, Bongshin Lee, Matthew Brehmer",https://donghaoren.org/publications/infovis18-charticulator.pdf,"We present Charticulator, an interactive authoring tool that enables the creation of bespoke and reusable chart layouts. Charticulator is our response to most existing chart construction interfaces that require authors to choose from predefined chart layouts, thereby precluding the construction of novel charts. In contrast, Charticulator transforms a chart specification into mathematical layout constraints and automatically computes a set of layout attributes using a constraint-solving algorithm to realize the chart. It allows for the articulation of compound marks or glyphs as well as links between these glyphs, all without requiring any coding or knowledge of constraint satisfaction. Furthermore, thanks to the constraint-based layout approach, Charticulator can export chart designs into reusable templates that can be imported into other visualization tools. In addition to describing Charticulator's conceptual framework and design, we present three forms of evaluation: a gallery to illustrate its expressiveness, a user study to verify its usability, and a click-count comparison between Charticulator and three existing tools. Finally, we discuss the limitations and potentials of Charticulator as well as directions for future research. Charticulator is available with its source code at https://charticulator.com.",https://charticulator.com/app/index.html,https://github.com/Microsoft/charticulator,,,video https://charticulator.azureedge.net/videos/charticulator-supplemental.mp4,,2018,2018,InfoVis,Estrel Hall C,Thursday,Interaction,25-Oct,11:00 AM,11:20 AM,
   InfoVis,TVCG,Embedded Merge & Split: Visual Adjustment of Data Grouping ,J),"Ali Sarvghad, Bahador Saket, Alex Endert, Nadir Weibel",http://bahadorsaket.com/publication/EMS.pdf,"Data grouping is among the most frequently used operations in data visualization. It is the process through which relevant information is gathered, simplified, and expressed in summary form. Many popular visualization tools support automatic grouping of data (e.g., dividing up a numerical variable into bins). Although grouping plays a pivotal role in supporting data exploration, further adjustment and customization of auto-generated grouping criteria is non-trivial. Such adjustments are currently performed either programmatically or through menus and dialogues which require specific parameter adjustments over several steps. In response, we introduce Embedded Merge & Split (EMS), a new interaction technique for direct adjustment of data grouping criteria. We demonstrate how the EMS technique can be designed to directly manipulate width and position in bar charts and histograms, as a means for adjustment of data grouping criteria. We also offer a set of design guidelines for supporting EMS. Finally, we present the results of two user studies, providing initial evidence that EMS can significantly reduce interaction time compared to WIMP-based technique and was subjectively preferred by participants.",,,,,youtube Z2rL6WF6TLY,,2018,2018,InfoVis,Estrel Hall C,Thursday,Interaction,25-Oct,11:20 AM,11:40 AM,
   TVCG,TVCG,Smart Brushing for Parallel Coordinates ,T),"Richard Roberts, Robert S Laramee, Gary A Smith, Paul Brookes, Tony D'Cruze",http://cs.swan.ac.uk/~csbob/research/callCenter/brushing/roberts18smart.pdf,"The Parallel Coordinates plot is a popular tool for the visualization of high-dimensional data. One of the main challenges when using parallel coordinates is occlusion and overplotting resulting from large data sets. Brushing is a popular approach to address these challenges. Since its conception, limited improvements have been made to brushing both in the form of visual design and functional interaction. We present a set of novel, smart brushing techniques that enhance the standard interactive brushing of a parallel coordinates plot. We introduce two new interaction concepts: Higher-order, sketch-based brushing, and smart, data-driven brushing. Higher-order brushes support interactive, flexible, n-dimensional pattern searches involving an arbitrary number of dimensions. Smart, data-driven brushing provides interactive, real-time guidance to the user during the brushing process based on derived meta-data. In addition, we implement a selection of novel enhancements and user options that complement the two techniques as well as enhance the exploration and analytical ability of the user. We demonstrate the utility and evaluate the results using a case study with a large, high-dimensional, real-world telecommunication data set and we report domain expert feedback from the data suppliers.",,,,,vimeo 289789480,,,2018,InfoVis,Estrel Hall C,Thursday,Interaction,25-Oct,11:40 AM,12:00 PM,
   TVCG,TVCG,"Multidimensional Projection for Visual Analytics: Linking Techniques with Distortions, Tasks, and Layout Enrichment ",T),Luis Gustavo Nonato and Michael Aupetit,http://www.lcad.icmc.usp.br/~nonato/pubs/mdp-survey.pdf,"Visual analysis of multidimensional data requires expressive and effective ways to reduce data dimensionality to encode them visually. Multidimensional projections (MDP) figure among the most important visualization techniques in this context, transforming multidimensional data into scatter plots whose visual patterns reflect some notion of similarity in the original data. However, MDP come with distortions that make these visual patterns not trustworthy, hindering users to infer actual data characteristics. Moreover, the patterns present in the scatter plots might not be enough to allow a clear understanding of multidimensional data, motivating the development of layout enrichment methodologies to operate together with MDP. This survey attempts to cover the main aspects of MDP as a visualization and visual analytic tool. It provides detailed analysis and taxonomies as to the organization of MDP techniques according to their main properties and traits, discussing the impact of such properties for visual perception and other human factors. The survey also approaches the different types of distortions that can result from MDP mappings and it overviews existing mechanisms to quantitatively evaluate such distortions. A qualitative analysis of the impact of distortions on the different analytic tasks performed by users when exploring multidimensional data through MDP is also presented. Guidelines for choosing the best MDP for an intended task are also provided as a result of this analysis. Finally, layout enrichment schemes to debunk MDP distortions and/or reveal relevant information not directly inferable from the scatter plot are reviewed and discussed in the light of new taxonomies. We conclude the survey providing future research axes to fill discovered gaps in this domain.",,,,,vimeo 289789153,,,2018,InfoVis,Estrel Hall C,Thursday,Interaction,25-Oct,12:00 PM,12:20 PM,
  -TVCG,TVCG,Exploration Strategies for Discovery of Interactivity in Visualizations ,T),"Tanja Blascheck, Lindsay MacDonald Vermeulen, Jo Vermeulen, Charles Perin, Wesley Willett, Thomas Ertl, Sheelagh Carpendale",https://hal.archives-ouvertes.fr/hal-01705792/document,"We investigate how people discover the functionality of an interactive visualization that was designed for the general public. While interactive visualizations are increasingly available for public use, we still know little about how the general public discovers what they can do with these visualizations and what interactions are available. Developing a better understanding of this discovery process can help inform the design of visualizations for the general public, which in turn can help make data more accessible. To unpack this problem, we conducted a lab study in which participants were free to use their own methods to discover the functionality of a connected set of interactive visualizations of public energy data. We collected eye movement data and interaction logs as well as video and audio recordings. By analyzing this combined data, we extract exploration strategies that the participants employed to discover the functionality in these interactive visualizations. These exploration strategies illuminate possible design directions for improving the discoverability of a visualizations functionality.",,,http://innovis.cpsc.ucalgary.ca/supplemental/Exploration-Strategies/#data,,vimeo 289789025,,,2018,InfoVis,Estrel Hall C,Thursday,Interaction,25-Oct,12:20 PM,12:40 PM,
  +TVCG,TVCG,Exploration Strategies for Discovery of Interactivity in Visualizations ,T),"Tanja Blascheck, Lindsay MacDonald Vermeulen, Jo Vermeulen, Charles Perin, Wesley Willett, Thomas Ertl, Sheelagh Carpendale",https://hal.archives-ouvertes.fr/hal-01705792/document,"We investigate how people discover the functionality of an interactive visualization that was designed for the general public. While interactive visualizations are increasingly available for public use, we still know little about how the general public discovers what they can do with these visualizations and what interactions are available. Developing a better understanding of this discovery process can help inform the design of visualizations for the general public, which in turn can help make data more accessible. To unpack this problem, we conducted a lab study in which participants were free to use their own methods to discover the functionality of a connected set of interactive visualizations of public energy data. We collected eye movement data and interaction logs as well as video and audio recordings. By analyzing this combined data, we extract exploration strategies that the participants employed to discover the functionality in these interactive visualizations. These exploration strategies illuminate possible design directions for improving the discoverability of a visualization's functionality.",,,http://innovis.cpsc.ucalgary.ca/supplemental/Exploration-Strategies/#data,,vimeo 289789025,,,2018,InfoVis,Estrel Hall C,Thursday,Interaction,25-Oct,12:20 PM,12:40 PM,
   CG&A,CGA,OpenSpace: Changing the Narrative of Public Dissemination in Astronomical Visualization from What to How,,"Alexander Bock, Emil Axelsson, Carter Emmart, Masha Kuznetsova, Charles Hansen, Anders Ynnerman",,,,,,,vimeo 289785793,,2018,2018,Other,Room III,Thursday,NA,25-Oct,11:00 AM,11:20 AM,
   CG&A,CGA,Belle2VR: A Virtual-Reality Visualization of Subatomic Particle Physics in the Belle II Experiment,,"Zach Duer, Leo Piilonen, and George Glasson",,,,,,,vimeo 289785809,,2018,2018,Other,Room III,Thursday,NA,25-Oct,11:20 AM,11:40 AM,
   CG&A,CGA,Application-Driven Design: Help Students Understand Employment and See the “Big Picture”,,"Li Liu, Deborah Silver, Karen Bemis",,,,,,,vimeo 289785826,,2018,2018,Other,Room III,Thursday,NA,25-Oct,11:40 AM,12:00 PM,
  
positive.code: SciVis,TVCG,A Declarative Grammar of Flexible Volume Visualization Pipelines ,J),"Min Shih, Charles Rozhon, Kwan-Liu Ma",,,,,,,vimeo 290327611,,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,11:40 AM,12:00 PM,
  TVCG,TVCG,Multi-Material Volume Rendering with a Physically-Based Surface Reflection Model ,T),"Oleg Igouchkine, Yubo Zhang, Kwan-Liu Ma",,,,,,,vimeo 289789120,,,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,12:00 PM,12:20 PM,
  TVCG,TVCG,A Generative Model for Volume Rendering ,T),"Matthew Berger, Jixian Li, Joshua A. Levine",,,,,,,vimeo 289789468,,,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,12:20 PM,12:40 PM,
  InfoVis,TVCG,Vistrates: A Component Model for Ubiquitous Analytics ,J),"Sriram Karthik Badam, Andreas Mathisen, Roman Rädle, Clemens Nylandsted Klokmose, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/vistrates/vistrates.pdf,"Visualization tools are often specialized for specific tasks, which turns the users analytical workflow into a fragmented process performed across many tools. In this paper, we present a component model design for data visualization to promote modular designs of visualization tools that enhance their analytical scope. Rather than fragmenting tasks across tools, the component model supports unification, where componentsthe building blocks of this modelcan be assembled to support a wide range of tasks. Furthermore, the model also provides additional key properties, such as support for collaboration, sharing across multiple devices, and adaptive usage depending on expertise, from creating visualizations using dropdown menus, through instantiating components, to actually modifying components or creating entirely new ones from scratch using JavaScript or Python source code. To realize our model, we introduce VISTRATES, a literate computing platform for developing, assembling, and sharing visualization components. From a visualization perspective, Vistrates features cross-cutting components for visual representations, interaction, collaboration, and device responsiveness maintained in a component repository. From a development perspective, Vistrates offers a collaborative programming environment where novices and experts alike can compose component pipelines for specific analytical activities. Finally, we present several Vistrates use cases that span the full range of the classic anytime and anywhere motto for ubiquitous analysis: from mobile and on-the-go usage, through office settings, to collaborative smart environments covering a variety of tasks and devices.",,https://github.com/karthikbadam/Vistrates,,,vimeo 289784994,,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:00 AM,11:20 AM,
  InfoVis,TVCG,SmartCues: A Multitouch Query Approach for Details-on-Demand through Dynamically Computed Overlays ,J),"Hariharan Subramonyam, Eytan Adar",,,,,,,vimeo 289784720,,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:20 AM,11:40 AM,
  InfoVis,TVCG,"Multiple Coordinated Views at Large Displays for Multiple Users: Empirical Findings on User Behavior, Movements, and Distances ",J),"Ricardo Langner, Ulrike Kister, Raimund Dachselt",,,,,,,vimeo 289784543,,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:40 AM,12:00 PM,
  InfoVis,TVCG,Visualizing Ranges over Time on Mobile Phones: A Task-Based Crowdsourced Evaluation ,J),"Matthew Brehmer, Bongshin Lee, Petra Isenberg, Eun Kyoung Choe",https://hal.inria.fr/hal-01857469/document,"In the first crowdsourced visualization experiment conducted exclusively on mobile phones, we compare approaches to visualizing ranges over time on small displays. People routinely consume such data via a mobile phone, from temperatures in weather forecasting apps to sleep and blood pressure readings in personal health apps. However, we lack guidance on how to effectively visualize ranges on small displays in the context of different value retrieval and comparison tasks, or with respect to different data characteristics such as periodicity, seasonality, or the cardinality of ranges. Central to our experiment is a comparison between two ways to lay out ranges: a more conventional linear layout strikes a balance between quantitative and chronological scale resolution, while a less conventional radial layout emphasizes the cyclicality of time and may prioritize discrimination between values at its periphery. With results from 87 crowd workers, we found that while participants completed tasks more quickly with linear layouts than with radial ones, there were few differences in terms of error rate between layout conditions. We also found that participants performed similarly with both layouts in tasks that involved comparing superimposed observed and average ranges.",,https://github.com/Microsoft/RangesOnMobile/blob/master/StudyApp,https://github.com/Microsoft/RangesOnMobile/blob/master/StudyDataAnalysis,,vimeo 289784625,,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,12:00 PM,12:20 PM,
  
negative.code: VAST,VAST,SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach ,C),"Michael Blumenschein, Michael Behrisch, Stefanie Schmid, Simon Butscher, Deborah R. Wahl, Karoline Villinger, Britta Renner, Harald Reiterer, Daniel Keim",,,,,,,vimeo 289787735,,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:00 PM,3:20 PM,
  TVCG,TVCG,ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color ,T),"Shenghui Cheng, Wei Xu, Klaus Mueller",,,,,,,vimeo 289788955,,,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:20 PM,3:40 PM,
  VAST,VAST,EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection ,C),"Quan Li, Kristanto Sean Njotoprawiro, Hammad Haleem, Qiaoan Chen, Chris YI, Xiaojuan Ma",,,,,,,vimeo 289788000,,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:40 PM,4:00 PM,
  InfoVis,TVCG,Evaluating 'Graphical Perception' with CNNs,,"Daniel Haehn, James Tompkin, Hanspeter Pfister",https://github.com/Rhoana/perception/tree/master/PAPER,"Convolutional neural networks can successfully perform many computer vision tasks on images. For visualization, how do CNNs perform when applied to graphical perception tasks? We investigate this question by reproducing Cleveland and McGill's seminal 1984 experiments, which measured human perception efficiency of different visual encodings and defined elementary perceptual tasks for visualization. We measure the graphical perceptual capabilities of four network architectures on five different visualization tasks and compare to existing and new human performance baselines. While under limited circumstances CNNs are able to meet or outperform human task performance, we find that CNNs are not currently a good model for human graphical perception. We present the results of these experiments to foster the understanding of how CNNs succeed and fail when applied to data visualizations.",,https://github.com/rhoana/perception,,,vimeo 280506639,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,2:20 PM,2:40 PM,
  InfoVis,TVCG,NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models ,J),"Shusen Liu, Zhimin Li, Tao Li, Vivek Srikumar, Valerio Pascucci, Peer-Timo Bremer",http://www.sci.utah.edu/~shusenl/publications/paper_entailVis.pdf,"With the recent advances in deep learning, neural network models have obtained state-of-the-art performances for many linguistic tasks in natural language processing. However, this rapid progress also brings enormous challenges. The opaque nature of a neural network model leads to hard-to-debug-systems and difficult-to-interpret mechanisms. Here, we introduce a visualization system that, through a tight yet flexible integration between visualization elements and the underlying model, allows a user to interrogate the model by perturbing the input, internal state, and prediction while observing changes in other parts of the pipeline. We use the natural language inference problem as an example to illustrate how a perturbation-driven paradigm can help domain experts assess the potential limitation of a model, probe its inner states, and interpret and form hypotheses about fundamental model mechanisms such as attention.",,,,,vimeo 289784737,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,2:40 PM,3:00 PM,
  InfoVis,TVCG,Elastic Documents: Coupling Text and Tables through Contextual Visualizations for Enhanced Document Reading ,J),"Sriram Karthik Badam, Zhicheng Liu, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/elastic-documents/elastic-documents.pdf,"Today's data-rich documents are often complex datasets in themselves, consisting of information in different formats such as text, gures, and data tables. These additional media augment the textual narrative in the document. However, the static layout of a traditional for-print document often impedes deep understanding of its content because of the need to navigate to access content scattered throughout the text. In this paper, we seek to facilitate enhanced comprehension of such documents through a contextual visualization technique that couples text content with data tables contained in the document. We parse the text content and data tables, cross-link the components using a keyword-based matching algorithm, and generate on-demand visualizations based on the reader's current focus within a document. We evaluate this technique in a user study comparing our approach to a traditional reading experience. Results from our study show that (1) participants comprehend the content better with tighter coupling of text and data, (2) the contextual visualizations enable participants to develop better summaries that capture the main data-rich insights within the document, and (3) overall, our method enables participants to develop a more detailed understanding of the document content.",,,,,vimeo 289784966,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:00 PM,3:20 PM,
  InfoVis,TVCG,Augmenting Visualizations with Interactive Data Facts to Facilitate Interpretation and Communication ,J),"Arjun Srinivasan, Steven Drucker, Alex Endert, John Stasko",https://arjun010.github.io/static/papers/voder-infovis18.pdf,"Recently, an increasing number of visualization systems have begun to incorporate natural language generation (NLG) capabilities into their interfaces. NLG-based visualization systems typically leverage a suite of statistical functions to automatically extract key facts about the underlying data and surface them as natural language sentences alongside visualizations. With current systems, users are typically required to read the system-generated sentences and mentally map them back to the accompanying visualization. However, depending on the features of the visualization (e.g., visualization type, data density) and the complexity of the data fact, mentally mapping facts to visualizations can be a challenging task. Furthermore, more than one visualization could be used to illustrate a single data fact. Unfortunately, current tools provide little or no support for users to explore such alternatives. In this paper, we explore how system-generated data facts can be treated as interactive widgets to help users interpret visualizations and communicate their findings. We present Voder, a system that lets users interact with automatically-generated data facts to explore both alternative visualizations to convey a data fact as well as a set of embellishments to highlight a fact within a visualization. Leveraging data facts as interactive widgets, Voder also facilitates data fact-based visualization search. To assess Voder's design and features, we conducted a preliminary user study with 12 participants having varying levels of experience with visualization tools. Participant feedback suggested that interactive data facts aided them in interpreting visualizations. Participants also stated that the suggestions surfaced through the facts helped them explore alternative visualizations and embellishments to communicate individual data facts.",,,,,vimeo 289784943,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:20 PM,3:40 PM,
  InfoVis,TVCG,What Do We Talk About When We Talk About Dashboards? ,J),"Alper Sarikaya, Michael Correll, Lyn Bartram, Melanie Tory, Danyel A Fisher",https://alper.datav.is/assets/publications/dashboards/dashboards-preprint.pdf,"Dashboards are one of the most common use cases for data visualization, and their design and contexts of use are considerably different from exploratory visualization tools. In this paper, we look at the broad scope of how dashboards are used in practice through an analysis of dashboard examples and documentation about their use. We systematically review the literature surrounding dashboard use, construct a design space for dashboards, and identify major dashboard types. We characterize dashboards by their design goals, levels of interaction, and the practices around them. Our framework and literature review suggest a number of fruitful research directions to better support dashboard design, implementation, and use.",,,,,vimeo 289785315,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:40 PM,4:00 PM,
  SciVis,TVCG,Visualization of Bubble Formation in Porous Media ,J),"Hui Zhang, Steffen Frey, Holger Steeb, David Uribe, Thomas Ertl, Wenping Wang",,,,,,,vimeo 290325359,,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,2:20 PM,2:40 PM,
  SciVis,TVCG,Gaia Sky: Navigating the Gaia Catalog ,J),"Toni Sagristà Sellés, Stefan Jordan, Thomas Mueller, Filip Sadlo",,,,,,,vimeo 290325474,,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,2:40 PM,3:00 PM,
  