Voyagers and voyeurs: supporting asynchronous collaborative information visualization

By Jeffrey Heer, Fernanda B. Viégas, Martin Wattenberg. International Conference on Human Factors in Computing Systems, 2007

327 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/4630fd699591c03e31ece839aef2d496b7ea301d

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Citations in the corpus

Research directions in data wrangling: Visualizations and transformations for usable and credible data (2011, unknown): “Indeed, such discoveries appear to be a common occurrence in social data analysis environments.(67,68) What interaction techniques might allow such users to annotate, and potentially correct, data quality issues discovered during subsequent analysis? How can these discoveries be fruitfully propagate”
Twitinfo: aggregating and visualizing microblogs for event exploration (2011, background): “Info builds up a folksonomy of labeled events over time [11, 18].”
Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations (2009, methodology): “us [10] used animated stacked graphs to explore demographic data.”
Collaborative visualization: Definition, challenges, and research agenda (2011, background): “73 The focus in collaborative visualization, however, is on a speciﬁc type of audience with either speciﬁc analysis questions and interests or even speciﬁc data-related background knowledge (“expert users”).”
Empirical Studies in Information Visualization: Seven Scenarios (2012, unknown): “Analysis of logs and user traces were the main sources of information analyzed in studies of distributed collaborative web-based information visualization tools [33], [90].”
Scented Widgets: Improving Navigation Cues with Embedded Visualizations (2007, background): “Interactive visualization applications such as sense.us [12] capture a number of social activity metrics that are typically invisible to users, but which could serve as valuable navigation cues.”
Human-AI Collaboration in Data Science (2019, methodology): “[17, 24, 26, 27, 48, 53]).”
Evaluating Information Visualizations (2008, background): “As information visualizations start to be designed for collaborative purposes, both distributed [31, 78] and co-located [35], these heuristics will also be important.”
How do Data Science Workers Collaborate? Roles, Workflows, and Tools (2020, background): “lmost all of the proposed tools and features in data science focus only on the technical users’ scenarios (e.g., data scientists and data engineers), such as how to better understand and wrangle data [18, 41], or how to better write and share code [49, 81, 92, 93]. In this work, we want to present a”
Interactive dynamics for visual analysis (2012, methodology): “10,22 To support the analysis life cycle, visual analytics tools should support social interaction.”
Graphical Histories for Visualization: Supporting Analysis, Communication, and Evaluation (2008, methodology): “In contrast , state logging ( as URLs ) is common for web browsing histories .”
The Effects of Interactive Latency on Exploratory Visual Analysis (2014, methodology): “These categories align well to codes used in prior work for content analysis of comments posted in collaborative visual analysis systems [21, 41].”
Visualization of omics data for systems biology (2010, background): “Web-based tools show great promise for facilitating collaboration between scientists at different location”
ManyEyes: a Site for Visualization at Internet Scale (2007, background): “Sense.us[12] is probably the research project that comes closest in spirit to Many Eyes.”