The CoRAL team (formerly known as fab4) has been making great progress on our task.

The task is to develop a visualization service for the fab4 browser project, whose objective is to support communities of readers by allowing them to share their comments on the documents they read. The original fab4 project simply provides a canvas on which to place and view annotations of different types on a document.

In our work, we assume that the fab4 user community consists of researchers either reviewing papers for the purpose of writing surveys and related-research sections of new publications or for the purpose of accepting/rejecting papers submitted to a conference. For that reason, we are extending the fab4 concept of comments and annotations with polarity (likes/dislikes notes) and we support comments as responses to comments, since literature reviewing is a fundamentally a discourse task. Correspondingly, the CoRAL extension, which is the task of the UCOSP team, is designed to visualize the activity of the community so that they can perceive intuitively who is reading what papers, what the readers think of the papers they read, what papers are being read recently and/or frequently, what papers are generally agreed upon to be “good” and which ones cause more disagreement among their readers.

At this point, we have developed (a) a general framework around a container of visualizations supported by a set of data-retrieval and layout utilities, and (b) a set of specific interactive visualizations to illustrate the social network implicit in the fab4 community and the communication threads around papers. We are working on a temporal visualization of reviewing activity around a paper and on a person-centric visualization reporting on one’s papers of interest.

Snapshots of the interface will be coming soon, but in the mean time, please let us know ( what else you think CoRAL should be supporting, or, more generally, what we should be thinking about. You can see what we are up to at

Eleni Stroulia on behalf of Samaneh Bayat, Ilia Pak, Chong Su, Jia Sun and Keen Sung.