Learn more about visualizing bibliometric data.

1. Information Visualization
Karen E. Gutzman
March 2014

2. Road map
What’s a librarian to do?
2
Words to live by
Resources

3. Words to live by
• Edward Tufte’s seminar: Presenting
Data and Information
• Katy Börner’s course: Information
Visualization
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4. Words to live by
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…the task of the designer is to give visual access to the subtle
and the difficult …
-Edward Tufte (2001, p. 191)

5. Words to live by
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Graphics reveal data
 greatest number of ideas
 shortest time
 least ink
 smallest space
Example 1. Brenner (2002)

6. Words to live by
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Graphics reveal data
 greatest number of ideas
 shortest time
 least ink
 smallest space

7. Words to live by
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Graphics reveal data
 greatest number of ideas
 shortest time
 least ink
 smallest space
Example 2. Tufte (n.d.)

8. Resources
• Data sources
• Visualization tools
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9. Resources: data sources
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10. Resources: visualization tools
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Supports visualization of datasets
 Temporal
 Geospatial
 Topical
 Network analysis

11. What’s a
librarian to do?
• Dream up ideas
• Use the data
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12. Temporal data
Types of visualizations
 Time-Series Plot
 Histogram
 Stacked graph
 Burst Detection (see visualization)
What to visualize
 The increase in the number of
citations for a specific publication
over time
 Number of new collaborators
participating in a specific program
 The amount of funding provided to
an organization or researcher (see
visualization)
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13. Geospatial Data
Types of visualizations
 Proportional Symbol Map
 Choropleth Maps (see visualization)
What to visualize
 The location (state/country) of
researchers, experts, or collaborators
 The amount of funding on specific
topic by geographical area
 The location of authors who have
cited, viewed, or downloaded a
particular publication or work (see
visualization)
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14. Topical Data
Types of visualizations
 Cross Maps
 Wordle
 Topic Bursts (see visualization)
What to visualize
 Word co-occurrence on a single
document, book or other text-
based research output combined
with burst detection (see
visualization)
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15. Topical Data
Types of visualizations
 Cross Maps
 Wordle (see visualization)
 Topic Bursts
What to visualize
 Word co-occurrence on a single document,
book or other text-based research output
combined with burst detection (see
visualization)
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Word bursts of top 50 terms in the abstracts of the top 10
most highly cited publications for Dr. Name Redacted.
(from wordle.net)

16. Network Data
Types of visualizations
 Radial Tree Graph
 Tree Map
 Social Networks (see visualization)
What to visualize
 The hierarchy of a network drive or organization
 Identify highly connected authors or papers
through collaborations or citations (see
visualization)
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Dr. Name Redacted
Dr. Co-Author 2
Dr. Co-Author 1
Author Co-occurrence
(co-author) Network
Sci2 Team. (2009). Science of Science (Sci2) Tool. Indiana University and SciTech Strategies, https://sci2.cns.iu.edu.

17. Sources
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Börner, K., Polley, D.E. (2014). Visual Insights. Cambridge, MA. Massachusetts Institute of
Technology
Brenner, H. (2002). Long-term survival rates of cancer patients achieved by the end of the
20th century: a period analysis. The Lancet, 360, pp. 1131-1135
Tufte, E. (2001). The Visual Display of Quantitative Information. 2nd Ed. Cheshire, CT.
Graphics Press.
Tufte, E. (n.d.) Cancer survival rates: tables, slopegraphs, barcharts. [online forum] Retrieved
from: http://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0000Jr
Sci2 Team. (2009). Science of Science (Sci2) Tool. Indiana University and SciTech Strategies,
https://sci2.cns.iu.edu
Information Visualization Portfolio: http://visualizinginformationportfolio.blogspot.com/

18. Thank you
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This research was supported in part by an appointment to the NLM Associate Fellowship Program sponsored by
the National Library of Medicine and administered by the Oak Ridge Institute for Science and Education.