  We describe a practical approach for visual exploration of research papers.
Specifically, we use the titles of papers from the DBLP database to create what
we call maps of computer science (MoCS). Words and phrases from the paper
titles are the cities in the map, and countries are created based on word and
phrase similarity, calculated using co-occurrence. With the help of heatmaps,
we can visualize the profile of a particular conference or journal over the
base map. Similarly, heatmap profiles can be made of individual researchers or
groups such as a department. The visualization system also makes it possible to
change the data used to generate the base map. For example, a specific journal
or conference can be used to generate the base map and then the heatmap
overlays can be used to show the evolution of research topics in the field over
the years. As before, individual researchers or research groups profiles can be
visualized using heatmap overlays but this time over the journal or conference
base map. Finally, research papers or abstracts easily generate visual
abstracts giving a visual representation of the distribution of topics in the
paper. We outline a modular and extensible system for term extraction using
natural language processing techniques, and show the applicability of methods
of information retrieval to calculation of term similarity and creation of a
topic map. The system is available at mocs.cs.arizona.edu.
