2007 MAC/MLA NIH does not endorse or recommend any commercial products, processes, or services. The view and opinions of the authors do not necessarily state or reflect those of the US Government, and they may not be used for advertising or product endorsement purposes. Treemaps in the library: making data appetizing Survey Questions: Rank your overall knowledge of the EndNote software. (Advanced, Intermediate, Beginning, None) Rank your knowledge of EndNote's 'Cite While You Write' feature. (Advanced, Intermediate, Beginning, None) Do you know how to create or modify output styles in EndNote? (Yes, No, I'm Learning) Pictures of data are common - we find graphs and charts in presentations, reports, newspapers, and magazines. Nearly all of these come in a few conventional forms (pies, bars, lines, etc.). These are often inadequate, however, for showing datasets which are large, complex, or non- numerical. Fast computers now allow for more sophisticated and interactive charts. These aren’t just pretty pictures – a good overview of data can help someone understand the structure of a problem, identify trends or exceptions, or highlight their options much more rapidly than a column of text or numbers. You may already be familiar with some examples of interactive, visualized search results: Suggested Resources Card, S. K., MacKinlay, J. D., & Shneiderman, B. (1999). Readings in Information Visualization: Using Vision to Think. San Francisco: Morgan Kaufmann. Human Computer Interaction Lab, University of Maryland. (2006). Visualization. Retrieved September 28, 2007, from Tufte, E. (1983). The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press. In all four quadrants, dark blue represents advanced knowledge of a particular aspect of the software. Comparing top and bottom indicates that staff are more familiar with EndNote vs. RefMan. Comparing left and right indicates that their knowledge increased between 2006 and Checked out more often Less often In this poster we discuss a particular form of visualization called a treemap and how it can be used to review library data. The treemap was developed by Ben Shneiderman and colleagues of the Human Computer Interaction Laboratory at the University of Maryland. The software we used can be downloaded from their website ( free for academic or trial use. This site also has documentation and links to licensing information. (Other products are available.) StarTree ® (Inxight Software) used to display LexisNexis files Metasearch results graphically presented by Kartoo.com Library catalog results via AquaBrowser® (Medialab Solutions) Treemaps Methods 1. Enter data in spreadsheet, then open spreadsheet in treemap software. 2. Set options in treemap software. 3. Explore options until treemap is useful (and aesthetically pleasing). Use treemap chart to gain understanding of your data. Example 2: Survey data Boxes: Answers to survey questions Size: # of responses Color: Arbitrary but consistent across questions Color legend Choose variables for size and color Data: NLM budgets, FY 1997, 2004, Sources: Annual fiscal reports, Big vs. Small: NCBI gets more funding than Toxicology Information Bright green: Library Operations budget had a relatively big increase Dark green: Smaller increase Black: Almost no change Red: Decrease Example 1: Collection analysis In 2006 and 2007, our librarians were asked to report their level of expertise with EndNote and Reference Manager. We used publicly available software to explore new ways of looking at typical library data: circulation, surveys, and a budget. We offer the following comments, most of which also apply to other types of Information Visualization (IV). Treemaps … + Are eye-catching. People seem naturally curious about them. + Can be faster to process than spreadsheets or other charts, by using color and size (and sometimes position), We found this was especially helpful for collection data, where circulation data needed to be interpreted in the context of collection size. People can make sense of colors and sizes pretty quickly, freeing mental energy for decision making. This is a major theme of IV in general. + Are interactive and customizable. Although not shown here, this is another major theme of IV. Users should be able to redraw the data to suit their needs. + Can help you see the big picture, or draw your attention to details to be explored further. Another major theme of IV. If you set up a hierarchy, you should be able to move between different levels readily. + Show proportions well. The relative sizes of the parts of the whole are obvious. - Are initially cryptic. A clear legend, and perhaps an explanation of treemaps in general, must be provided. - Require acquiring and organizing data. As with any chart, it doesn’t draw itself. The spreadsheet must have a certain structure for the treemap software to use it. - Require tinkering. Colors can be garish or hard to distinguish, especially if viewers may be color blind. Data analysis is often a chore. We encourage librarians to consider novel presentation styles that help staff, patrons, or administrators understand their data. Whether by treemaps or other means, properly prepared information can help them understand complex data and make decisions more easily. Boxes: Subject areas Size: # of volumes in each Color: Total checkouts This treemap shows the relative size and circulation of the 72 subject areas in our collection. This can help identify areas to be expanded or weeded. Example 3: Collection analysis, revisited Boxes: Individual volumes in Biochemistry Size: (Not used) Color: Brighter for more checkouts Related to Example 1, but prepared in a different way, this treemap breaks our Biochemistry collection down into LC classes. This can help us narrow down the specific areas that are so popular. QP 601: Enzymes QP 531: Inorganic Substances QP 606: Transferases Introduction We would like to thank Prof. Ben Shneiderman and HCIL for granting us permission to use their software (and encouraging us) to explore library applications, and the NIH Library Collections Management Team for providing the data in Examples 1 and 3. Conclusions Smaller boxes = fewer items Larger boxes = more items EndNote Ref Man Rex R. Robison, PhD, MLS & Kathryn E. Dudley, MLS National Institutes of Health Library, Bethesda, MD