Action Science Explorer: Interactive Data Visualization for Rapid Understanding of Scientific Literature

Action Science Explorer: Interactive Data Visualization
for Rapid Understanding of Scientific Literature
Cody Dunne, Robert Gove, Ben Shneiderman, Judith Klavans, and Bonnie Dorr
Overview: Action Science Explorer (ASE) supports users in
Reference Management & Reviewing
Imported articles are shown in table view with
metadata. A preview below the table shows the
reference, abstract, and any user-assigned
review for selected articles.
Export References
Easily export references
to Word, OpenOffice,
BibTeX, HTML, etc.
Full Text PDF
and Plain Text.
rapidly generating surveys of academic literature. ASE uses:
• bibliometric lexical link mining to create a citation network and
citation context,
• clustering and multi-document summarization to extract key points,
• and potent network analysis and visualization tools.
Data Import
Users load the results of
a database query.
Text w/Citation Links
Article Groups
When clicked, each cited
article is selected in all
other views.
Group articles manually
or by metadata into
hierarchical, overlapping
Citation Context
Search for Articles
Each article has citations
and the surrounding
context for each from the
citing paper, containing:
• summaries,
• critical reception, and
• citations to follow-up
Clicking on a citation
context shows the citing
paper’s full text.
Use regular expressions
to search metadata.
Rank by Feature
Select metadata or
dynamically computed
citation network statistics
to rank articles. The
ranking is shown in a list
and colored by value.
The citation context of all
selected documents is
summarized. The short
summary shows the key
points from the total
citation context.
Filter Articles
Use the double-ended
slider to filter out articles
in all views.
Find Trends, Outliers, & Errors
Citation Network Visualization
Community-Finding Algorithm
Use scatterplots to find interesting data.
Nodes show articles with edges
between them showing citations.
Nodes are arranged using a forcedirected layout algorithm and colored
by their ranking in the rank table.
Topologically related papers are grouped using
Newman’s fast heuristic and shown using convex hulls.
Get more information at
This work has been partially supported by the National Science Foundation
grant IIS 0705832: "iOPENER: A Flexible Framework to Support Rapid
Learning in Unfamiliar Research Domains"
University of Maryland, Human-Computer Interaction Lab