The document provides an overview of structured data presentation tools for digital humanities scholars. It discusses the difference between data presentation and analysis, and highlights some early pioneers of data visualization like William Playfair and Charles Minard. The document then examines challenges in using visualization for the humanities. It also profiles several structured data presentation tools, including TimeFlow, Google Fusion Tables, Many Eyes, and Omeka. Hands-on examples are provided using the Exhibit framework to create interactive visualizations like faceted browsing, searching, tables, timelines, and maps.

1. An Introduction to Structured
Data Presentation
New Perspectives on Old Data
Shawn Day
Digital Humanities Observatory
14 November 2012
http://www.slideshare.net/shawnday/structured-data-presentation

2. Objective
To appreciate the variety of structured data
presentation tools available to digital humanities
scholars and to be able to judge between them.

3. Agenda
Data Presentation versus Data Analysis?
The Readings
Exhibit Thesis
The Data Vis Challenge to the Humanities
Products to be have an awareness of
Hands On Install and Config
Exhibit
OMEKA?

4. The Two Faces of Data Visualisation
One of the keys to good visualization is understanding
what your immediate (and longer term) goals are.
Are you visualizing data to understand what’s in it, or
are you trying to communicate meaning to others?
You - Visualisation for Data Analysis
Others - Visualisation for Presentation

5. Information Visualisation:
Challenge for the Humanities
To use the vast stores of digitised data we are
collecting we need to develop a digital fluency
Access
Exploration
Visualisation
Analysis
Collabouration

6. The Challenges
Developing new genres for complex info
presentation
creating a literacy that has same rigour and
richness as current scholarship
expanding text-based pedagogy to include
simulation, animation and spatial and geographic
representation

7. The Opportunity
Balance complexity with conciseness
Balance accuracy with essence
Speak authoritatively, yet inspire exploration and
personal insight

8. A Short History
Originated in Computer Science
Disseminated into broader scientific realm
A late comer to the humanities
Tufte: concise - clear - accurate

9. William Playfair (1758 - 1823)
bar chart
pie chart
time series
This is a file from the Wikimedia Commons.

10. John Snow (1813 - 1858)
Dot Plot
Spatial Analysis
This is a file from the Wikimedia Commons

11. Charles Minard (1781 - 1870)
Flow Diagram
Multi-Vector Information Visualisation
This is a file from the Wikimedia Commons

12. Tools for collection are far more successful to date
than those for exploration

13. New Influences
Simulation - 3D What if?
Monitor - Real time data
Collabouration - Many Eyes

14. The Challenges to the Use of
Visualisation
Too Easy to confuse, miscommunicate or
downright lie
Break or lack basic visual design principles
Fail to understand the data, the audience or the
problem being solved
Fail to appreciate the visceral or emotional power
of graphics
Lack of technical skills in this domain

15. Structured Data Presentation Tools
(a tiny subset)
Webservices Frameworks
TimeFlow Gephi
Google Fusion Tables Exhibit (Exercise)
Many Eyes GraphViz
Prefuse
Hosted D3
Omeka (Omeka) Processing

16. TimeFlow

17. Google FusionTables

18. Many Eyes

19. Hands-On Exercise: Simile Exhibit

20. Looking at Exhibit

21. Setup and Preparation
Do Not Use Safari - Firefox or Chrome should be
X
fine
You can find instructions at: http://myeye.ie/ftp1/
exhibit/recipe.txt
Need to copy datafiles:
http://myeye.ie/ftp1/exhibit/nobelists.js?action=raw
http://myeye.ie/ftp1/exhibit/index1.html

22. Background on Exhibit
Exhibit was developed at MIT to provide a
lightweight framework for the presentation,
searching and faceted browsing of digital collections.
Exhibit lets you easily create web pages with
advanced text search and filtering functionalities,
with interactive maps, timelines, and other
visualizations

23. So What?...
Little programming (JavaScript Template);
No database (JSON text);
a series of useful ‘instantly interactive’
visualisations.

24. Background
http://www.simile-widgets.org/exhibit/
A couple examples…
Canadian Network for Economic History
Comox Valley Crime Stoppers
Research at the DHO
DHO: Discovery

25. Exhibit in a Nutshell

26. The Simplest Exhibit
<html>
! <head>
! ! <title>MIT Nobel Prize Winners</title>
! ! <link href="nobelists.js" type="application/json" rel="exhibit/data" />
! ! <script src=http://static.simile.mit.edu/exhibit/api-2.0/exhibit-api.js type="text/javascript"></
script>
! <style></style>
! </head>
! <body>
! ! <h1>MIT Nobel Prize Winners</h1>
! ! <table width="100%”>
! ! <tr valign="top”>
! ! <td ex:role="viewPanel”><div ex:role="view"></div></td><td width="25%”>browsing controls here… </
td></tr>
</table>
</body>
</html>

27. The Data
{
"items" : [
{ type : "Nobelist",
label : "Burton Richter",
! ! ! latlng: "42.359089,-71.093412",
discipline : "Physics",
shared : "yes",
"last-name" : "Richter",
"nobel-year" : "1976",
relationship : "alumni",
"co-winner" : "Samuel C.C. Ting",
"relationship-detail" : "MIT S.B. 1952, Ph.D. 1956",
imageURL : "http://nobelprize.org/nobel_prizes/
physics/laureates/1976/richter_thumb.jpg"
},
………
]
}

28. The Simplest View

29. Add Faceted Browsing
Explore data in
context
Filter data by
attributes

30. Faceted Browsing Code
<div ex:role="facet"
ex:expression=".discipline"
ex:facetLabel="Discipline"></div>
<div ex:role="facet"
ex:expression=".relationship"
ex:facetLabel="Relationship"></div>
<div ex:role="facet" ex:expression=".shared"
ex:facetLabel="Shared?"></div>
<div ex:role="facet" ex:expression=".deceased"
ex:facetLabel="Deceased?"></div>

31. Add Search and Sort

32. Search Code
<div ex:role="facet"
ex:facetClass="TextSearch"></div>

33. Add a Table View

34. Table Code
<div
ex:role="exhibit-view”
ex:viewClass="Exhibit.TabularView”
ex:columns=".label, .imageURL, .discipline, .nobel-
year, .relationship-detail”
ex:columnLabels="name, photo, discipline, year,
relationship with MIT”
ex:columnFormats="list, image, list, list, list”
ex:sortColumn="3”
ex:sortAscending="false">
</div>

35. Add a Timeline

36. Timeline Code
<script src="http://static.simile.mit.edu/exhibit/
extensions-2.0/time/time-extension.js"
type="text/javascript"></script>
+
<div ex:role="view"
ex:viewClass="Timeline"
ex:start=".nobel-year"
ex:colorKey=".discipline">
</div>

37. Add a Map View

38. Wrapup: Exhibit
Pros Cons
Simple Limited Scalability
Lightweight Some cross-browser
No server required issues
A host of Restrictions on Look
visualisations and Feel
Embeddable in other Extensive
systems - customisation means
ExhibitPress getting into code
Here comes Exhibit 3

39. Moving Beyond with Exhibit
Ensemble Project Advanced Tutorial:
http://ensemble.ljmu.ac.uk/q/calbooklet

40. OMEKA for Curated Collections
http://omeka.net

41. Omeka Basics
OAI/PMH
Exhibit
Metadata
Page
CSV
Section
Page
Section
Page etc...
Page
Collection(s)
Metadata Tag(s) Type Metadata Tag(s) Type Metadata Tag(s) Type
Item Item Item
Representations Representations Representations

42. OMEKA
http://iridium.omeka.net/exhibits/show/
carlingford/day1
http://www.omeka.net/dashboard
Omeka.org versus Omeka.net
Sign-Up at: http://www.omeka.net

43. Where to go next
http://datajournalism.stanford.edu/
Bamboo - DIRT (Digital Research Toolkit)
Timeline Tools
Visualisation in Education
Visual Complexity

44. Academic Visualisation?
There’s lots of published papers out there
...what can you do with them?
http://www.autodeskresearch.com/projects/citeology

45. The Life on An Idea through Citations

47. Data Visualisation Lessons from Tufte
1. Show the Data
2. Provoke Thought about the Subject at Hand
3. Avoid Distorting the Data
4. Present Many Numbers in a Small Space
5. Make Large Datasets Coherent
6. Encourage Eyes to Compare Data
7. Reveal Data at Several Levels of Detail
8. Serve a Reasonably Clear Purpose
9. Be Closely Integrated with Statistical and Verbal Descriptions of
the Dataset

48. What Visual Techniques Exist?
Connecting your data with the right visualisation
What is your message?
How do we know what we might use?
Start with your Exploratory/Research/Analytical
Environment (last seminar)
How do visuals fit into your narrative?

49. What Visual Techniques Exist?
Connecting your data with the right visualisation

50. Thanks for your attention