demystifying digital humanities: winter 2014 session #1

DMDH Winter 2014 Session #1:Exploring Programming in the Digital Humanities

Programming is complex enough that just figuring out what you want to do and

what sort of language you need is work.

Thinking that you ought to be able to do everything almost immediately is a recipe for

feeling terrible.

Being aware that it is genuine work, and not just work for newbies,

matters.

Photo by MK Fautoyére, via Flickr

There will always be new programs and

platforms that you will want to experiment

with.

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.

What can programming languages do?

Programming languages can...

They can also do all these things in combination.

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

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

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

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

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?

These are the areas of programming where critical thinking and

humanities skills become vital.

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.

Computers work with data.

You work with data, too -- but in

most cases, you'll have to make

your data readable by computer.

How to make your data machine-readable

•Annotate it with markup language

•Organize it in patterns that the computer can understand

•Add data that is not explicitly readable in the current format (i.e., hardbound/softbound binding; language:English; date of record creation)

Depending on the data you have, and the way

you annotate or structure it, different

things become possible.

Your goal is to make the data As Simple As Possible -- but not so simple that it

stops being useful.

Depending on the data you work with, the

work of structuring or annotating becomes

more challenging, but also more useful.

The work of creating data is social.

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

BREAK!

Data Examples

•Annotated (Markup Languages: HTML, TEI)

•Structured (MySQL)

•Combination (Semantic Web)

Markup: HTML

<i> This text is italic.</i> =

This text is italic.

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.

Markup: HTML

Anything can be data -- and markup languages provide instructions for how

computers should treat that data.

Markup: HTMLHTML 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.

HTML Syntax Rules

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

Markup languages are popular in digital

humanities because lots of humanists work

with texts.

Without markup languages, the things that a computer can

search for are limited.

Ctrl + F: any text in iambic pentameter.

With markup, the things you can

search for are only limited by your interpretation.

Markup: TEI

TEI(Text Encoding

Initiative)

Markup: TEI

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>

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

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

TEI is meant to be a highly social language

-- meaning that the committee who

maintains its standards want it to be something that anyone

can use.

In order for TEI to successfully encode texts, it has to be

adaptable to individual projects.

Anything that you can isolate (and put in brackets) can (theoretically) be pulled

out and displayed for a reader.

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>

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

Whether you include or exclude some

aspect of the text in your markup can be very important from

an academic perspective.

The challenge of creating good data is

one reason that collaboration is so important to digital

scholarship.

Data Collaboration

•Avoid reinventing the wheel (has the markup for this text already been done?)

•Consider the labor involved vs. the outcome (and future use of the data you create.)

Structured Data

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.

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.

Both scenarios involve aggregating

information, rather than encoding it.

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

Structured Data: Example #2 (RDF)

How your data is (or can be) structured will

influence the technology that you

(can) use to work with it.

Digital humanists see creating machine-readable data as

valuable scholarship.

Examples

•Homer Multi-Text Project

•Modernist Versions Project

•Scalar (platform)

•Century Ireland

Exercise: You Create the Data!

Your data determines

your project.

Every project has data.

Text objects, images, tags, geographical coordinates, categories, records, creator

metadata, etc.

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

with data.

Next time:Programming on the Whiteboard

February 1st, 9:30, CMU 202•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.

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