A presentation by bioinformationist Marci Brandenburg for the Medical Library Association Webinar: Applying Data Management Strategies: A Showcase of Data Management Projects on November 11, 2016.

1. What is Visualization?
“The action or fact of visualizing; the power or
process of forming a mental picture or vision of
something not actually present to the sight; a
picture thus formed.”
-Oxford English Dictionary
"visualization, n." OED Online. Oxford University Press, June 2016. Web. 27 June 2016.

2. Outline
Overview of Visualization
Library Services
Instruction/Consultation
Types of Visualization
Design Principles
Referrals
Finding and Manipulating Data
Challenges
Acknowledgments

3. OVERVIEW OF VISUALIZATION

4. Anscombe’s Quartet
https://en.wikipedia.org/wiki/Anscombe%27s_quartet

5. https://en.wikipedia.org/wiki/Anscombe%27s_quartet
Anscombe’s Quartet

6. Danger, Will Robinson
Vigen T. Spurious Correlations. Cambridge, Massachusetts. 2015. Web. 10 March
2014. <http://www.tylervigen.com/view_correlation?id=1703>

7. http://www.albany.edu/museum/w
wwmuseum/work/
lombardi/images/lombardi1.jpg
“Mark Lombardi (1951-
2000) draws on the major
political and financial
scandals of the day to
create large-scale linear
diagrams that at first
glance look like celestial
maps….”
-University Art Museum.
University of Albany,
State University of New
York.

8. Viégas F., Wattenberg, M. Hint.fm. Cambridge, Massachusetts. 2014. Web. 10 March 2014.
<http://hint.fm/wind>

9. Data Visualization
Data visualization deals with communicating information
about an existing data set through a visual medium.
Goals
• Take advantage of the brain’s ability to efficiently
process visuals. A high volume of information can
often be easily understood through an image.
• Want the viewer to learn something about the data
that could not be easily understood by reading the
raw data.
• Visual is presented in a simple manner. Don’t
overload the viewer with many unnecessary details.

10. LIBRARY SERVICES

11. Instruction
• Specific visualization
software tools (ex.
Cytoscape, R)
• Basic principles (when
appropriate, good vs
bad, etc)
• Curriculum
• Data management
practices
Consultation
• Specific visualization
software tools with
researcher data (ex.
Cytoscape)
• Range of visualization
tools to meet a specific
purpose or type of data
• Charts/graphs for
manuscript (pie chart vs
bar chart vs network
graph)
• Opportunity to stress
good data management

12. TYPES OF VISUALIZATION
Instruction/Consultation

13. Graphs
Goal is to communicate the relationships
among the data efficiently.
Common Types of
Graphs:
• Simple Graphs
• Bar graphs, scatter
plot, pie chart
• Network Graphs
• Shows relationships
between points
Cytoscape Consortium. 2016. Web. 30 June 2016.
<http://cytoscape.org/what_is_cytoscape.html>

14. http://www.albany.edu/museum/w
wwmuseum/work/
lombardi/images/lombardi1.jpg
“Mark Lombardi (1951-
2000) draws on the major
political and financial
scandals of the day to
create large-scale linear
diagrams that at first
glance look like celestial
maps….”
-University Art Museum.
University of Albany,
State University of New
York.

15. The American Southwest. 2016. Web. 31 October 2016.
<http://www.americansouthwest.net/california/yosemite/glen-aulin-trail-map.html>
Map-Based

16. Snow, J. Snow on Cholera being a Reprint of Two Papers. London: Oxford
University Press, 1936. Print.
Map based
example -
Cholera
Outbreak
Created
by
John Snow

17. 3D can untangle a graph: entire new dimension to place nodes
Need to think about if our visualization would really benefit from 3D.
• Does it overcomplicate the visualization?
• Does the 3D graph provide more information to the viewer, or does it just look
different?
2D vs. 3D Visualization

18. Just because we can…
Andy Kirk. Data visualization: a successful design process. Birmingham:
United Kingdom, 2012. eBook.

19. DESIGN PRINCIPLES
Instruction/Consultation

20. Visual Weight
Guiding the Eye
Eric Maslowski. University of Michigan 3D Lab. http://um3d.dc.umich.edu. 2008.

21. Size and Shape
Eric Maslowski. University of Michigan 3D Lab. http://um3d.dc.umich.edu. 2008.

22. Value and Hue
Color
Eric Maslowski. University of Michigan 3D Lab. http://um3d.dc.umich.edu. 2008.

23. Contrast
Eric Maslowski. University of Michigan 3D Lab. http://um3d.dc.umich.edu. 2008.

24. Visual Assumptions
Eric Maslowski. University of Michigan 3D Lab. http://um3d.dc.umich.edu. 2008.

25. Visual Assumptions
Eric Maslowski. University of Michigan 3D Lab. http://um3d.dc.umich.edu. 2008.

26. Visual Assumptions
Eric Maslowski. University of Michigan 3D Lab. http://um3d.dc.umich.edu. 2008.

27. Referrals
• Visualization landscape at
institution/organization
• Locations/units that offer visualization services
• Know people and technology
• U-M Library Data Visualization webpage
(http://www.lib.umich.edu/data-visualization)
• Group email address
• Educate all librarians to be able to make
better referrals

28. Finding and Manipulating Data
• Subscriptions to various data sets
• Help researchers find publicly available
data
• Manipulate and clean data to work in
desired visualization software
• Deep Blue Data
• ICPSR

29. CHALLENGES

30. Challenges
• Lot of visualization tools and resources
• Constantly changing
• Some are very complex
• Wide range of visualization types
• Focus on principles; rely on others
• Making the library the go-to place for
visualization services
• Resources

31. Acknowledgments
• Eric Maslowski - Director, Creative Applications. Co-
Director, Digital Media Commons. University of
Michigan
• Justin Joque - Visualization Librarian, University of
Michigan
• Jean Song – Assistant Director Academic and
Clinical Engagement, Taubman Health Sciences
Library, University of Michigan

32. Marci Brandenburg
mbradenb@umich.edu