into algorithm plug-ins, it is algorithmically possible to modularize visualization and interaction design. Future work will focus on developing “ visualization layers” supporting selection and combination of reference systems, projections/distortions, graphic designs, clustering/grouping, and interactivity.

Streaming data. The number of data sets that are generated and must be understood in real time is increasing; examples are patient-surveillance data streams and models of epidemics that predict the numbers of susceptible, infected, and recovered individuals in a population over time. EpiC tool development funded by the National Institutes of Health contributes algorithms that read and/or output streams of data tuples, enabling algorithms to emit their results as they run, not only on completion. Data-graph visualizations plot these tuple streams in real time, resizing (shrinking) the temporal axis over time.

Web services. The OSGi/CIShell-based tools discussed here are standalone desktop applications supporting offline work on possibly sensitive data, using a GUI familiar to target users. However, some application domains also benefit from online deployment of macroscopes. While the OSGi specification provides basic support for Web services, CIShell must still be extended to make it easy for domain scientists to design their own macroscope Web services.

Incentive design. Many domain experts have trouble trying to use an evolving set of thousands of possibly relevant data sets compiled for specific studies of inconsistent quality and coverage, saved in diverse formats, and tagged using terminology specific to the original research domains. In addition, thousands of algorithms that support different functionality and diverse input and output formats are written in different languages by students and experts in a range of scientific domains and packaged as algorithms or tools using diverse licenses. More-effective means are needed to help domain experts find the data sets and algorithms most relevant for their work, bundle them into efficient workflows, and relate the results to existing work. Scholarly markets resembling a Web 2.0

version of Craigslist.org can help ease the sharing, navigation, and utilization of scholarly data sets and algorithms, reinforcing reputation mechanisms by, say, providing ways to cite and acknowledge users who share, highlight most downloaded and highest-rated contributions, and offer other means for making data sets, algorithms, workflows, and tutorials part of a valued scholarly record.

acknowledgments

I would like to thank Micah Linnemeier and Russell J. Duhon for stimulating discussions and extensive comments. Bruce W. Herr II, George Kampis, Gregory J. E. Rawlins, Geoffrey Fox, Shawn Hampton, Carol Goble, Mike Smoot, Yanbo Han, and anonymous reviewers provided valuable input and comments to an earlier draft. I also thank the members of the Cyberinfrastructure for Network Science Center ( http://cns.iu.edu), the Network Workbench team (http://nwb. cns.iu.edu), and Science of Science project team ( http://sci2.cns.iu.edu) for their contributions toward this work. Software development benefits greatly from the open-source community. Full software credits are distributed with the source, but I especially acknowledge Jython, JUNG, Prefuse, GUESS, GnuPlot, and OSGi, as well as Apache Derby, used in the Sci2 tool. This research is based on work supported by National Science Foundation grants SBE-0738111, IIS-0513650, and IIS-0534909 and National Institutes of Health grants R21DA024259 and 5R01MH079068. Any opinions, findings, and conclusions or recommendations expressed here are those of the author and do not necessarily reflect the views of the National Science Foundation.

 

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Katy Börner ( [e-mail]) is the Victor h. yngve Professor of Information science at the school of library and Information science, adjunct Professor at the school of Informatics and Computing, and Founding Director of the Cyberinfrastructure for network science Center ( http://cns.iu.edu) at Indiana university, bloomington, In.

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