Slides from the winter workshop on digital scholarship and programming at the Sherman Centre for Digital Scholarship at McMaster University.

1. Winter 2015 Session #1:
Exploring Programming in Digital
Scholarship
February 12, 2015
Paige Morgan
Sherman Centre for Digital Scholarship

2. Programming is complex
enough that just figuring
out what you want to do
and what sort of language
you need is work.

3. Thinking that you ought to be able
to do everything almost
immediately is a recipe for feeling
terrible.

4. Photo by MK Fautoyére, via Flickr

5. There will always be new
programs and platforms
that you will want to
experiment with.

6. Working with technology
means periodically starting
from scratch -- a bit like
working with a new time
period or culture; or figuring
out how to teach a new
class.

8. Being able to effectively
communicate about your
project as it relates to
programming is a skill in
itself.

9. What can programming
languages do?

10. Programming languages
can...• search for things
• match things
• read things
• write things
• receive information, and give it
back, changed or unchanged
• count things
• do math
• arrange things in quantitative or
random order
• respond: if x, do y OR do x until
y happens
• compare things for similarity
• go to a file at a location, and
retrieve readable text
• display things according to
instructions that you provide
• draw points, lines, and shapes

11. They can also do many or
all of these things in
combination.

12. Example #1
• find all the statements in quotes ("") from a
novel.
• count how many words are in each statement
• put the statements in order from smallest
amount of words to largest
• write all the statements from the novel in a
text file

13. Example #2
• allow a user to type in some information, i.e.,
"Benedict Cumberbatch"
• compare “Benedict Cumberbatch” to a much
larger file
• retrieve any data that matches the
information
• print the retrieved information on screen

14. Example #3
• "read" two texts -- say, two plays by Seneca
• search for any words that the two plays have in
common
• print the words that they have in common on
screen
• calculate what percentage of the words in each
play are shared
• print that percentage onscreen

15. Example #4
• if the user is located in geographic
location Z, i.e., 45th and University, go
to an online address and retrieve some
text
• print that text on the user’s tablet
screen
• receive input from the user and respond

16. However...
• In Example #1, the computer is focusing on
things that characters say. But what if you want
to isolate speeches from just one character?
• In Example 2, how does the computer know
how much text to print? Will it just print
"Benedict Cumberbatch" 379 times, because
that's how often it appears in the larger file?

17. These are the areas of
programming where
critical thinking and
specialized disciplinary
knowledge become vital.

18. The Difference
• Humans are good at differentiating
between material in complex and
sophisticated ways.
• Computers are good at not
differentiating between material unless
they’ve been specifically instructed to
do so.

19. Computers work with
data.
You work with data, too --
but you may have to do
extra work to make your
data readable by
computer.

20. Ways to make your data
machine-readable• Annotate it with markup language
• Organize it in patterns that the
computer can understand
• Add metadata that is not explicitly
readable in the current format (i.e.,
hardbound/softbound binding;
language:English; date of record
creation)

21. Depending on the data
you have, and the way
you annotate or structure
it, different things become
possible.

22. Your goal is to make the
data As Simple As
Possible -- but not so
simple that it stops being
useful.

23. Depending on the data
you work with, the work of
structuring or annotating
becomes more
challenging, but also
more useful.

24. The work of creating data
is social.

25. Many programming languages
have governing bodies that
establish standards for their
use:
• the World Wide Web (W3C)
Consortium
(http://www.w3.org/standards/)
• the TEI Technical Council

26. Data Examples
• Annotated (Markup Languages: HTML,
TEI)
• Structured (MySQL)
• Combination (Linked Open Data)
• Object-Oriented Programming (Java,
Python, Ruby on Rails)

27. Markup: HTML
<i> This text is
italic.</i>
=
This text is italic.

28. Markup: HTML
<a href=“http://www.dmdh.org”>
This text</a> will take you to a webpage.
=
This text will take you to a webpage.

29. Markup: HTML
Anything can be data -- and markup
languages provide instructions for how
computers should treat that data.

30. Markup: HTML
HTML is used to format text on webpages.
<p> separates text into paragraphs.
<em> makes text bold (emphasized).
These are just a few of the HTML formatting instructions
that you can use.

31. HTML Syntax Rules
• Open and closed tags: <> and </>
• Attributes (2nd-level information)
defined using =“”

32. Markup languages are
popular in digital
humanities because lots
of humanists work with
texts.

33. Without markup
languages, the things that
a computer can search for
are limited.

34. Ctrl + F: any text in iambic
pentameter.

35. With markup, the
things you can
search for are only
limited by your
interpretation.
Markup: TEI

36. TEI
(Text Encoding Initiative)
Markup: TEI

37. Poetry w/ TEI
<text xmlns="http://www.tei-c.org/ns/1.0" xml:id="d1">
<body xml:id="d2">
<div1 type="book" xml:id="d3">
<head>Songs of Innocence</head>
<pb n="4"/>
<div2 type="poem" xml:id="d4">
<head>Introduction</head>
<lg type="stanza">
<l>Piping down the valleys wild, </l>
<l>Piping songs of pleasant glee, </l>
<l>On a cloud I saw a child, </l>
<l>And he laughing said to me: </l>
</lg>

38. Grammar w/ TEI
<entry>
<form>
<orth>pamplemousse</orth>
</form>
<gramGrp>
<gram type="pos">noun</gram>
<gram
type="gen">masculine</gram>
</gramGrp>
</entry>

39. TEI’s syntax rules are
identical to HTML’s --
though your normal
browser can’t work with
TEI the way it works with
HTML.

40. TEI is meant to be a
highly social language
that anyone can use and
adapt for new purposes.

41. In order for TEI to
successfully encode texts,
it has to be adaptable to
individual projects.

42. Anything that you can isolate
(and put in brackets) can
(theoretically) be pulled out and
displayed for a reader.

43. TEI can be used to encode more than just text:
<div type="shot">
<view>BBC World symbol</view>
<sp>
<speaker>Voice Over</speaker>
<p>Monty Python's Flying Circus tonight comes to you live
from the Grillomat Snack Bar, Paignton.</p>
</sp>
</div>
<div type="shot">
<view>Interior of a nasty snack bar. Customers around, preferably
real people. Linkman sitting at one of the plastic tables.</view>
<sp>
<speaker>Linkman</speaker>
<p>Hello to you live from the Grillomat Snack Bar.</p>
</sp>
</div>

44. Or, you could encode all
Stephenie Meyer’s
Twilight according to its
emotional register.

45. Whether you include or
exclude some aspect of
the text in your markup
can be very important
from an academic
perspective.

46. The challenge of creating
good data is one reason
that collaboration is so
important to digital
scholarship.

47. Wise Data Collaboration
• Avoid reinventing the wheel (has
someone else already created an
effective method for working with this
data?)
• Consider the labor involved vs. the
outcome (and future use of the data you
create.)

48. Structured Data

49. Study Scenario #1
• You study urban espresso stands: their
hours, brands of coffee, whether or not
they sell pastries, and how far the
espresso stands are from major
roadways.

50. Study Scenario #2
• You study female characters in novels
written between 1700 and 1850.
Encoding a whole novel just to study
female characters isn’t practical for you.

51. Both scenarios involve
aggregating information,
rather than encoding it.

52. Structured Data: Example
#1
(MySQL)ID Name Location Hours Coffee Brand Pastries (Y/N) Distance from
Street
008 Java the Hut 56
Farringdon
Road,
London, UK
7:00 a.m.-
2:00 p.m.
Square Mile
Roasters
N 25 meters
009 Prufrock
Coffee
18
Shoreditch
High Street
7:00 a.m. –
10:00 p.m.
Monmouth Y 10 meters

54. Structured Data:
Example #2 (RDF)

55. Object-Oriented
Programming
• Java, Python, C++, Perl, PHP, Ruby, etc.
• Widely used, highly flexible, very powerful

56. What’s an “object”?
• An object is a structure that contains data in
one or more forms.
• Common forms include strings, integers, and
arrays (groups of data).
• Example (handout)

57. Object-oriented programming, cont’d
• Learning a bit about an OOP language can
help you become accustomed to working
with programming
• Reading OOP code can also be useful
• Many free tutorials are available
• Goal: to be able to converse more effectively
with professional programmers, rather than
become an expert yourself.

58. How your data is
structured will influence
the technology that you
(can) use to work with it.

59. Digital scholars see
creating machine-
readable data as valuable
scholarship.

60. Examples
• Homer Multi-Text Project
• Modernist Versions Project
• Scalar (platform)
• Century Ireland

61. Exercise:
You Create the Data!

62. Your data determines your
project.

63. Every project has data.
Text objects, images, tags, geographical
coordinates, categories, records, creator
metadata, etc.

64. Even if you’re not planning to
learn any programming skills,
you are still working with data.

65. Next time:
Programming on the Whiteboard
February 19th, 3:00-5:00 p.m., Sherman
Centre
• Cleaning data before you work with it!
• Identifying specific programming tasks
• How access affects your project idea
• Flash project development
• Homework: bring some data to work
with.