Closing plenary from the 2019 Network Analysis and Digital Art History Getty Advanced Workshop at the University of Pittsburgh.

1. How to Fail Interdisciplinarily
Network Analysis and Digital Art History
August 2nd, 2019
David Newbury, J. Paul Getty Trust
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2. "Science is what we understand
well enough to explain to a
computer.
Art is everything else we do."
— Donald Knuth, A=B
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3. This is not a value
judgment.
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4. This is not a value
judgment.
It is a disciplinary lens.
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5. Pareidolia
We make meaning out of
incomplete, insufficient data.
We use disciplinary techniques
and training to do so.
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6. Art Historians are trained to
develop insight into lived
experience through
critical looking.
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7. 1. Who created the project?
2. What is their research question?
3. What is their data? How did they obtain it?
4. What tools did they use?
5. how did they present it?
6. Who is the audience?
7. Do they make an argument or interpret their results?
8. What could other scholars use their project to do? Is the data
available? Should it be?
-- Pamela Fletcher
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8. Technologists are trained to
solve problems by describing
them using code formal logic
and using that description to
analyze data and generate
results.
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9. 1. Establish patterns at the abstract level
2. Work out consequences of those patterns
3. Compare to what’s actually observed
4. Hope that consequences aren’t trivial and are right
-- Cosma Shalizi
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10. Researchers are trained to
observe, hypothesize,
analyze, and synthesize
new knowledge.
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11. 1. A question is identified as being potentially answerable
through computation.
2. The required information from the specified field(s) is
identified and gathered.
3. This information is transformed and regularized into
structured digital information, or data.
4. This data is analyzed through a computational process,
producing a set of results.
5. These results are synthesized into new domain knowledge.
--Alison Langmead
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12. These are the same ways to
approach the task of developing
new insights described through
different disciplinary lenses.
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13. Your projects are interdisciplinary in amazing ways...
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14. Your projects are interdisciplinary in amazing ways...
...and it is going to get hard.
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15. — 10% of the work is data entry.
— 30% of the work is fighting with tools.
— 40% of the work is data cleaning.
— 15% of the work is writing down what you've done.
— 3% of the work is staring at bubbles and dragging
them around on a screen.
— 2% of the work is actual "analysis".
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16. How to Fail:
#1: Believe the
lies.
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17. Computer as God.
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18. Just-So Stories.
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19. 19

20. Oracles answer questions.
Their answers are correct.
Their answers are not true.
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21. 21

22. You bring your own truth
to the oracle.
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23. How to Fail:
#2: Trust the
model.
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24. "When you design and build a
computer system, you first
formulate a model of the problem
you want it to solve, and then
construct the computer program
in its terms."
- Brian Cantwell Smith, The Limits of Correctness (1985)
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25. On October 5, 1960, the
American Ballistic Missile Early-
Warning System indicated Soviet
missiles headed towards the
United States.
The moon had risen, and was
reflecting radar signals back to
earth. Needless to say, this lunar
reflection hadn't been predicted
by the system's designers.
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26. Whose fault was it?
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27. "...every act of conceptualization,
analysis, categorization, does a
certain amount of violence to its
subject matter, in order to get at the
underlying regularities that group
things together."
- Brian Cantwell Smith, The Limits of Correctness (1985)
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28. On Exactitude in Science
A 1:1 scale map
is not a useful
abstraction.
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29. There will never be
a correct data model.
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30. There will only be
useful data models.
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31. How to fail:
#3: Love the
Software.
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32. The Halting Problem.
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33. The Computer
Goes Ding.
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34. The Computer
Goes BZZZZRT.
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35. Your software
is made for you.
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36. How to Fail:
#4: Trust the
Bubbles.
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37. What was a painstaking process of
calculation and correlation—for
example, in the construction of a table
of variables—becomes a flash of
intuition. And all-at-once intuition is
traditionally the way that angels know,
in contrast to the plodding
demonstrations of humans.
— Lorraine Daston, On Scientific Observation
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38. Emergence
The whole is identified
before the parts.
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39. Reification
Our mind fills in the gaps.
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40. Multi-Stability
The mind seeks to avoid
uncertainty.
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41. Invariance
We’re good at recognizing
similarities and differences.
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42. Proximity
Association of near things.
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43. Similarity
Linking of similar things.
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44. Enclosure (or Common Fate)
Items put together are together.
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45. Closure
We fill in the gaps.
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46. Continuity
We assume that hidden things
exist
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47. Connection
We follow lines.
Together, these describe
Preattentive Attributes.
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48. You are trained
to recognize patterns.
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49. Your training
will protect you.
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50. Your training
will mislead you.
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51. Use your training.
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52. [V]isualization and spatial history
are not about producing illustrations
or maps to communicate things that
you have discovered by other means.
It is a means of doing research; it
generates questions that might
otherwise go unasked
— Richard White, What is Spatial History?
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53. Exploratory Data Visualization
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54. Graphs have a tendency of
making a data set look
sophisticated and important,
without having solved the
problem of enlightening the
viewer.
— Ben Fry,Visualizing Data
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55. Explanatory data visualization
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56. How to Fail
#5: Work in
Isolation.
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57. Data Systems:
Collection → Computation → Representation.
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58. "The problem with the way that
most of us operate within a data
system is that we’ve designed our
roles such that we can almost
never see the whole thing from
where we are."
— Jer Thorp, You Say Data, I Say System
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59. "Data lets you be wrong."
— Matt Lavin
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60. "The Computer"
is inanimate.
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61. The oracle does not
control your fate.
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62. There is meaning
in the noise.
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63. Trust each other.
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64. Science advances whenever an
Art becomes a Science. And the
state of the Art advances too,
because people always leap into
new territory once they have
understood more about the old.
— Donald Knuth, A=B
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65. Thank you.
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