Workshop two of a two-workshop series for graduate-level English students. Find part one here: https://www.slideshare.net/gesinaphillips/creating-metadata-for-data-visualization-100296871

1. Data visualization through
network graphing
CREATING, EVALUATING, AND CONTEXTUALIZING VISUALIZED DATA

2. Remember this?

3. Now it looks
like this

4. But how?

5. The semi-technical details
Tools: Palladio (Stanford Humanities + Design Research Lab); Excel/Google Sheets
Data sources: Nodes spreadsheet, Edges spreadsheet
Steps:
1. Enter data in a uniform manner (you did this!)
2. Clean data to eliminate deviations from uniform input (e.g. dates formatted 2017-12-06 vs.
12/06/2017)
3. Clean up data to eliminate mistakes (e.g. Isadora, Duncan; 1983-03-83)
4. Splice together first and last name rows (in the correct order!) into full names
5. Associate the correct full name with the ID and Main Person ID columns in the Edges
spreadsheet; put these names in their own new columns, ConnectName and MainName (e.g.
make sure that every row with ID 1000 lists “Mina Loy” under MainName)
6. Plug into Palladio!

6. Network graphing review
Nodes: people, objects, or concepts existing
in some sort of relationship
◦ Your bio subjects are our nodes. The people
with whom your bio subject had relationships
are nodes.
Edges: connections between nodes
◦ We made connections between our nodes by
defining them in relation to one another.
Node: Ford Maddox Ford Node: Janet FlannerEdge
Janet
Flanner
Ford Maddox
Ford

7. Network graphing review
Every person has a network of connections with other people. And those people
have their own network. And the people in that network have their own
network…
https://gfycat.com/gifs/detail/showyremoteerne

8. Network graphing review
Think of the ways in which a network graph—especially a graph based on
historical/archival research—may be incomplete.
◦ What judgement calls have you made as a researcher regarding the data you
recorded?
◦ Where do you as the researcher choose to stop?
◦ What relationship information is recorded? What has been lost?
◦ What social or cultural factors may lead to information being recorded and retained
(or not)?
These should sound like familiar questions for you as humanities researcher.
Think about these questions within the context of this project; we’ll discuss
them soon!

9. Very quick
Palladio demo
JUST TO GIVE YOU AN IDEA OF HOW IT WORKS, AND TO EMBOLDEN
YOU TO PLAY AROUND WITH IT!

10. Okay, so what?
WHY NOT JUST WRITE ABOUT IT?

11. Network graphing as investigation
Think about how you arrange notes (physically or digitally) in outlines, concept
maps, etc. to see how ideas fit together. This network graph allows us to visually
investigate how people fit together.
Although you could do this narratively, a graph can highlight:
◦ Gaps
◦ Surprising connections
◦ Centrality (the degree of connectedness/influence of an individual in a network)

12. Network graphing as investigation
Think about the role of dark matter in physics. Physicists infer the role or
presence of dark matter based on unexplained (yet consistent) effects on other,
observable entities. Thus, certain phenomena are explained in terms of this
effect:
“The researchers observed seven dwarf galaxies that are thought to be full of dark matter
because the motion of the stars within them cannot be fully explained by their mass alone.” [x]
Gaps or other anomalies in a network graph may indicate the existence of a
previously un- or under-investigated individual who influenced that network.

13. What do you notice? What or who surprises you?
Keep in mind: just because the graph looks
a certain way or asserts a certain thing
doesn’t mean that it’s right and you’re
wrong! Critical thinking with regard to data
visualization is the same as critical thinking
with regard to academic writing—it just
requires a little more context.

14. Network graphing as argument
Like any argument, a network graph can be questioned, critiqued, or even
invalidated.
Let’s go back to our questions from earlier: What assumptions were made
in the creation of this graph?
◦ What judgement calls did you make as a researcher regarding the data you
recorded?
◦ Where did you as the researcher choose to stop?
◦ What relationship information is recorded? What has been lost?
◦ What social or cultural factors may lead to information being recorded and
retained (or not)?
Do we see these assumptions in the visualization?

15. The most important caveat
A visualization does nothing “on its own.”
All visualizations are created from gathered data.
◦ Even automatically harvested data carries assumptions: What data was collected?
What data was retained? What data was used for visualization and what was
omitted? What sample was used? Who was included in/omitted from that sample?
Programs (created by people) and/or researchers (also people) make choices
about how visualizations are displayed.
Visualizations require narrative context and interpretation, like any other data
source. Humanities methods are inseparable from digital methods within the
“digital humanities,” even though it often looks like a lot of button-pushing.

16. Open discussion
What worked?:
◦ What do you think this method of visualization is this good for?
◦ What can we learn from a visualization like this?
What didn’t work?:
◦ Who or what is missing from this visualization?
◦ What doesn’t it capture adequately?
◦ What is incomplete?
What can be changed, explained, or reinforced?:
◦ What would you change about the approach or data collected?
◦ In what ways does this visualization need to be supplemented by narrative analysis?
◦ What new questions might you ask?

17. Assessment
PLEASE GIVE US YOUR THOUGHTS BY COMPLETING OUR SURVEY