Our aim is to provide a collection of high quality learning materials that are relevant to those interested in the field of Visual Analytics, and provide mechanisms that encourage their access and use.
In the past we handled such issues on an ad hoc basis. We have contacted authors who have submitted materials to ask their permission to distribute their works under the Creative Commons licensing scheme. All current and future submissions will be covered under the 'Attribution Non-commerical Share Alike' version.
When you submit materials, we assume that you hold (or have the appropriate permissions to distribute) the copyright on any materials you donate. If you find material in the repository to which you hold the copyright and which you would like removed, please send us an email. Likewise, just notify us if you'd like to have previously donated material removed for some reason.
Email us with any questions, comments, criticism or suggestions. We'd love to hear from you!
The Visual Analytics Education Digital Library receives support from a grant by the National Visualization and Analytics Center (NVAC) at the Pacific Northwest National Laboratory (PNNL) to the Southeast Regional Visualization and Analytics Center (SERVAC), which is a joint effort at the University of North Carolina - Charlotte and Georgia Tech. It is also supported in part by the Stephen J. Fleming Chair in Telecommunications at the Georgia Tech College of Computing.
The repository uses infrastructure originally developmed for the Human-Centered Computing Education Digtial Library (HCC EDL). Edward Clarkson, Andy Cox and Andy Wu, all students of Jim Foley at Georgia Tech, have all contributed to its development.
You must be referring to search results like this. The graphic at right on that page is a treemap, a visualization technique for hierarchical data. The image displays each document as a small box; documents that are in the same category get up in larger boxes together. For documents appearing in the search results, the boxes are colored according to their document type. Clicking on the small squares expands the search result listing to show its full details, including a thumbnail treemap that highlights only that particular document.
We hope that this method of presenting search results will give users a better idea of how their results are distributed across the library contents as a whole. We also think that this display will make it easier for users to identify outlier search results, which tend to be very interesting or very uninteresting. What do you think? Tell us!