Multiple Views: Visualization Research Explained

@vis_research

Explaining visualization research to non-researchers. One blog post at a time. Editors: .

Vrijeme pridruživanja: studeni 2018.

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  1. New post! "What eye tracking can tell you about visualizations (and other images)" by Zoya Bylinskii discusses advances in understanding and predicting what we look at in visualizations to help guide eye-catching designs.

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  2. New! “Material Traces as Autographic Visualizations” by : “Autographic visualization is a set of techniques for revealing visible traces. Instead of mapping data to visual variables, the designer sets the conditions that allow a trace to emerge.“

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  3. 23. pro 2019.

    New on Multiple Views: I reflect on the lack of uncertainty in many (most?) visualizations (aka All I Want For Christmas is Some Error with Those Estimates!) And, how we might get past it by thinking more deeply about inferences from graphs Happy holidays!

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  4. New on : considers a classic comparison between animation and static views, introducing new results for mobile contexts: Visualizing Trends on Mobile Phones: Animation or Small Multiples?

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  5. New on : "Same Data, Multiple Perspectives: Curse of Expertise in Visual Data Communication" by .

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  6. New Post: "How to engage different audiences with the same graph" | A study of how our beliefs shape how we look at climate data from and Yu Luo.

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  7. New post: "A picture may be worth a thousand words, but words frame a picture" | Titles and text play a fundamental role in visualization. Here is research demonstrating how they affect what people get out of a chart: .

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  8. New post: "Data is Personal. What We Learned from 42 Interviews in Rural America." | Based on award-winning paper by , Sofia Ayuso, and

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  9. New post on a scientific look to "chart choosers" by ! -> "Multiple views on how to choose a visualization"

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  10. New post! -> "Word Clouds: We Can’t Make Them Go Away, So Let’s Improve Them"

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  12. New post: "Let’s Talk about Natural Language Interfaces for Data Visualization" - Arjun Srinivasan showcases recent advancements in the use of natural language to create data visualizations.

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  13. How can data visualization help people make sense of neural networks? In this post , , Robert Pienta and describe their survey summarizing what visualization researchers are doing to make deep learning less of a black-box: .

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  14. New post: How do you visualize machine learning? @henddkn and @S_Gehrmann describe current and future research - "Interactive Visualization as Mediator Between Human and Machine Intelligence"

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  15. New post: "It didn’t matter what clever new visualization techniques we came up with, it wasn’t going to help our collaborators with their decision-making." - Nina McCurdy talks about visualizing data that contain "implicit errors":

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  16. New post: how do you visualize temporal data on mobile phones? Radial or linear layout? describes his study on visualizing ranges over time in mobile phones: .

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  17. Thinking of a PhD in visualization? Niklas Elmqvist () from UMD wrote a splending guide for us: | And checkout the funny graphics he created for the article!

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