The document provides an overview of visualization, focusing on its definition, principles, and applications in library services. It discusses the importance of data visualization for effective communication of information and details various types of visualization tools and techniques. Furthermore, it addresses challenges in the field and acknowledges key contributors to the presented content.

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