ManyEyes: a Site for Visualization at Internet Scale
By Fernanda B. Viégas, Martin Wattenberg, Frank van Ham, Jesse Kriss, Matt McKeon. IEEE Transactions on Visualization and Computer Graphics, 2007
884 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/7cb01d32e4d4201093c41135b301296e433a3d25
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Jeffrey Heer
- Michael Bostock
- Niklas Elmqvist
- Sheelagh Carpendale
- Eric Gilbert
- Tanushree Mitra
- Pierre Dragicevic
- Jean-Daniel Fekete
In Sociology and Political Science
- Jeffrey Heer
- Sheelagh Carpendale
- Eric Gilbert
- Tanushree Mitra
- Michelle X. Zhou
- Michael Bostock
- Jean-Daniel Fekete
- Martin Wattenberg
Citations in the corpus
- Graphical inference for infovis (2010, neutral): “Manyeyes [11] has an explicit comparison tool.”
- Visualization of omics data for systems biology (2010, background): “Web-based tools show great promise for facilitating collaboration between scientists at different location”
- 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.”
- Ground: A Data Context Service (2017, background): “We are not the first to suggest that the socio-technical network behavior of a data-centric organization has value (see, e.g., collaborative visual analysis [36, 37]).”
- Word Cloud Explorer: Text Analytics Based on Word Clouds (2014, background): “To appear in: Proceedings of the 47th Hawaii International Conference on System Science (HICSS 2014), January 6-9, 2014 IEEE Computer Society”
- Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation (2008, background): “Other examples include the animated rotations in cone trees [25] that are used to show selection, the use of motion as a display dimension of its own in visualization [5], and the animated transitions and state changes in the Many Eyes [39] system.”
- Crowdsourcing graphical perception: using mechanical turk to assess visualization design (2010, background): “Moreover, as visualizations become increasingly prominent online [33, 35], web-based experimentation may improve ecological validity by reaching a diverse population of subjects and display configurations.”
- Science Mapping: A Systematic Review of the Literature (2017, background): “…networks (Shannon et al., 2003), the ground breaking work of visual analytics (Thomas & Cook, 2005), Many Eyes – the popular Web-based visualization platform (Viégas et al., 2007), and a framework of seven types of interaction techniques in information visualization (Yi et al., 2007) (Figure 23).”
- Empirical Studies in Information Visualization: Seven Scenarios (2012, neutral): “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].”
- ECharts: A declarative framework for rapid construction of web-based visualization (2018, methodology): “Grammars of Visualization Charting tools such as Excel[9] and ManyEyes[10] support rapid generation of charts by selecting appropriate forms from predefined visual templates.”
- The language that gets people to give: phrases that predict success on kickstarter (2014, methodology): “We used the online site Many Eyes [54] to build these visualizations.”
- Big Data computing and clouds: Trends and future directions (2013, methodology): “ManyEyes [129] from IBM allows users to upload their data, select a visualisation method – varying from basic to advanced – and publish their results.”
- Exploratory Search: Beyond the Query-Response Paradigm (2009, background): “Wolfe’s model of visual search (1994) is similar, and it postulates that one surveys the available information initially, then targets points of interest within the broader visual field for more complex interpretation and understanding.”
- Picbreeder: A Case Study in Collaborative Evolutionary Exploration of Design Space (2011, background): “Similarly, in IBM’s Many Eyes (Vi´egas et al., 2007), users can create and share graphic visualizations of data, from which the Internet community can draw and share their interpretations.”
- The Word Tree, an Interactive Visual Concordance (2008, methodology): “As previously noted [13], visualizations of”