How are history and computing related? How does computing support or change the goals of historians, in terms of theory, methodology, pedagogy and publication? How does history inform computing practices? This class is designed as a graduate-level introduction to some of the debates, historiographic challenges, and practical undertakings that arise when these two worlds combine.

This course was a dual-enrolled course with H301 Digital History as a test of whether an introductory digital history course would function well for both graduates and undergraduates.

Pages

1. H585 Digital History
2. Week 1: Intro and Text Mining (2015-01-14)
3. Week 3: Messy Evidence vs Clean Data (2017-01-25)
4. Week 4: Topic Modeling & Corpus Linguistics (2017-01-30)
5. Week 5: Maps & spatial history (2017-02-06)
6. Week 6: Networks and Data Viz (2017-02-13)
7. Week 7: Sustainability and Access (2017-02-20)
8. Week 8: Project Management and Collaboration (2017-02-27)
9. Week 9: Publics and publications (2017-03-06)
10. Week 11: Images & Augmented Reality (2016-03-03)
11. Week 12: DigHist and Analog Pedagogy (2016-04-07)
12. Week 15: Final thoughts, or a controversy (2016-04-28)
13. Week 16: Poster Session (2016-05-05)
14. Week X: Named Entity Recognition & Natural Language Processing (2017-02-06)

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H585 Digital History

 

See SP17: DIGITAL HISTORY: 33554 for the full undergraduate syllabus (in Course Calendar)

Weeks 1-2: Data Gathering and Familiarization

Get to know your data.

• Are your sources images? Physical objects? PDFs of typed text? Actual copy-and-pasteable files?
  • Will you need to transcribe things to analyze your sources digitally?
• • Can you access the information in your sources as text or geographic latitude-longitude or as a network? If not, what information do you need to extract from your sources?
• How many sources are there? Written by whom? Where?
• Think about your questions and the sources you have available
  • How will you segment/chunk your corpus of sources into smaller chunks to make your questions answerable?
  • Change over time? Your matrix should include periodization
  • Comparison between people? Your matrix should include authors
  • Comparison between places? Your matrix should include geographic divisions
  • What else will you need?

Weeks 3-6, Jan 25-Feb 13: Basic Methdology

• Week 3: Messy Evidence vs Clean Data (2017-01-25)
• Week 4: Topic Modeling & Corpus Linguistics (2017-01-30)
• Week 5: Maps & spatial history (2017-02-06)
• Week 6: Networks and Data Viz (2017-02-13)

Weeks 7-9, Feb 20-March 6: Practical Concerns and Presentation

• Week 7: Sustainability and Access (2017-02-20)
  • Rough sketch of project proposal due, including draft proof-of-concept digital-exploration plan.
• Week 8: Project Management and Collaboration (2017-02-27)
  • Peer review of classmates' project proposal due.
• Week 9: Publics and publications (2017-03-06)
  • Revision plan for project proposal due.

March 13: Spring Break

Weeks 10-15, March 20-Apr 24: Experimental Methodologies and Theoretical Concerns

WE WILL MAKE CHANGES TO THIS SECTION BASED ON YOUR NEEDS

• Week X: Named Entity Recognition & Natural Language Processing (2017-02-06)
• Week 11: Images & Augmented Reality (2016-03-03)
• Week 12: DigHist and Analog Pedagogy (2016-04-07)
• Week 15: Final thoughts, or a controversy (2016-04-28)

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Week 1: Intro and Text Mining (2015-01-14)

A practical application of text mining designed to jumpstart a conversation about:

• the affordances of human vs digital distributed cognition
• what it means to do digital history and/or digital humanities

Reading

• Please visit http://www.kalanicraig.com/2015/10/history-and-the-digital-world-h585/ for a sense of what the syllabus might look like.

In class

• A demonstration of distributed cognition: Sample DH and DigHist Syllabi and a TitanPad collaborative document containing the topic models we generated manually
• A survey: Interest and Tech Survey

Resources/Practicum After Class

• Twitter: Sign up, find a few historians to follow (using the hashtag #twitterstorians, and use our class hashtag #DHIntro16 in your class-relevant Tweets this term.

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Week 3: Messy Evidence vs Clean Data (2017-01-25)

Is evidence different than data? How do we know? How do we get data? How do we navigate the difference between aggregate and anecdotal? How do we navigate different data sets ranging from small-scale personal archival collection to large paywalled data to public data scraping? What form should these data take? How are these definitions and approaches affected by distributed-cognition approaches (human or computer) that help us collect, transcribe and clean data?

Reading

• Shawn Graham, Ian Milligan, Scott Weingart, “The Third Wave of Computational History” in The Historian’s Macroscope – working title (Under contract with Imperial College Press. Open Draft Version, Autumn 2013, http://themacroscope.org; accessed May 22, 2015)
• Trevor Owens, “Defining Data for Humanists: Text, Artifact, Information or Evidence?“, Journal of Digital Humanities, Vol. 1, No. 1 (Winter 2011, accessed May 24, 2015).
• Christof Schöch, “Big? Smart? Clean? Messy? Data in the Humanities” in Journal of Digital Humanities, Vol. 2, No. 3 (Winter 2013, accessed May 24, 2015).
• Trevor Muñoz, “Data Curation as Publishing for the Digital Humanities“, Journal of Digital Humanities, Vol. 2, No. 3 (Summer 2013, accessed May 24, 2015).
• Geoffrey Rockwell, "Crowdsourcing the Humanities", section on Distributed Knowing (pp 149-153), in Collaborative Research in the Digital Humanities (2012): https://books.google.com/books?id=uPqC6o2pqLgC&pg=PA150&lpg=PA150&dq=distributed+cognition+digital+humanities&source=bl&ots=NN60RgkNwl&sig=kqjIiwev9ORaxBLSHwIwH3Xen1E&hl=en&sa=X&ved=0ahUKEwjw_s2vn7bKAhWhloMKHcgaBZIQ6AEIHTAA#v=onepage&q=distributed%20knowing&f=false (pp 151/2 are unavailable on Google Books but a quick skim is really the point)

Tasks Before Our Meeting

• Download a text editor (http://www.barebones.com/products/TextWrangler/ for Mac or http://www.sublimetext.com/2 for either Mac or Windows)
• Find/explore a collection of digitized sources in your subfield (can be your own). How would you reduce these sources to a set of rows and columns in a spreadsheet? To text documents? What kinds of questions lend themselves to the answers this spreadsheet might provide? What choices do you have to make? Is there an implicit historical argument underlying those choices?
• Find one example of crowdsourcing and document its strengths and weaknesses.
• Bring your own documents. I can provide a sample data set for this week and next, but it's best that you start working with your own documents as early in the semester as possible. If these documents aren't digitized, please email me.

Resources/Practicum After Class

• Scraping data with wget (http://programminghistorian.org/lessons/automated-downloading-with-wget) or OutwitHub (https://www.outwit.com/products/hub/)
• Practice moving text between Word, a text editor and Excel and see what line returns, spaces, tabs and quotes do as you move between the programs.
• Regular expression tutorial for flexible search/replace: RegexOne (also note the simple reference chart on the right side of the page)
• Regular expression cheat sheet for SublimeText: https://github.com/dmikalova/sublime-cheat-sheets/blob/master/cheat-sheets/Regular%20Expressions.cheatsheet
• Cleaning/scrubbing/chunking text files online: Lexos
• Cleaning Data with OpenRefine: Download Open Refine and then use the Programming Historian tutorial

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Week 4: Topic Modeling & Corpus Linguistics (2017-01-30)

What can we do with large scale data (once it's clean) from a textual perspective? How does it change how we talk about data (statistics vs anecdote), and/or use interdisciplinary approaches like computational linguistics? How does data mining help us with data cleanup?

Reading

• Shawn Graham, Ian Milligan and Scott Weingart, “Basic Text Mining: Word Clouds, their Limitations, and Moving Beyond Them” in The Historian’s Macroscope – working title (Under contract with Imperial College Press. Open Draft Version, Autumn 2013, http://themacroscope.org; accessed May 22, 2015)
• Johanna Drucker, “5B. Data Mining and Text Analysis” in Intro to Digital Humanities: Concepts, Methods, and Tutorials for Students and Instructors (UCLA Center for Digital Humanities, Sept 2013; accessed May 23, 2015).
• Ben Schmidt, “When you have a MALLET, everything looks like a nail” in Sapping Attention (Nov 2, 2012; accessed May 28, 2015)
• Michelle Moravec, “Corpus Linguistics for Historians” in History in the City (Dec 2013; accessed May 28, 2015)
• OPTIONAL: Under the hood of Topic Modeling's statistical model. http://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/

Tasks Before Class

• Install the MALLET GUI from https://iu.box.com/s/t4jh9oo7oo4rigfiqeustiaw8rgc7erc
• Install AntConc from http://www.laurenceanthony.net/software/antconc/
• Play with both using our text documents from Week 2.
• Note that this requires you to do a little cleaning, probably moving back and forth between Sublime and Lexos.
• Don't forget to save a new version of your text when you successfully complete a major step.

Resources/Practicum After Class

• The command-line tutorial for MALLET from Programming Historian. You know the analytical vocabulary now; use that to help make the technical vocabulary more approachable.

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Week 5: Maps & spatial history (2017-02-06)

How does space fit into historical analysis? What do maps and cartograms have in common? Are we GIS practitioners or neogeographers (and what is neogeography anyway)?

Reading

• GIS vocabulary
  • SKIM Maps vs cartograms: http://www-personal.umich.edu/~mejn/election/2012/
  • SKIM neogeography: http://news.nationalgeographic.com/news/2006/04/0425_060425_map_blogs.html
  • Points lines and polygons (up to "Manipulating Spatial Data")
  • SKIM georectification
• Mixing visualization and mapping: LIDAR (example at https://www.gov.uk/government/news/lasers-reveal-lost-roman-roads)
• Samples of mapping in argumentation (read these closely)
• an academic writing in a less formal space
• an academic writing a traditional journal article along with its accompanying online appendix

Tasks Before Class

• Play with mymaps.google.com, adding points lines and polygons
• Play with the sample data at Palladio (focus on mapping only)

Resources/Practicum After Class

• Lincoln Mullen's Spatial History Workshop (shameless editorialization: it is awesome)
• Spatial Data in R: https://pakillo.github.io/R-GIS-tutorial/

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Week 6: Networks and Data Viz (2017-02-13)

Does seeing data instead of reading about evidence change how we ask questions? Make arguments? What works and what doesn’t in data visualization?

Readings

• John Theibault, “Visualizations and Historical Arguments” in Jack Dougherty and Kristen Nawrotzki, eds., Writing History in the Digital Age, final 2013 publisher version (accessed May 22, 2015),
• Scott B. Weingart, “Demystifying Networks, Parts I & II” in Journal of Digital Humanities, Vol. 1, No. 1 (Winter 2011, accessed May 20, 2015).
• An example: Micki Kaufman, “Quantifying Kissinger Intro Video” in “Everything on Paper Will Be Used Against Me:” Quantifying Kissinger (July 2014; accessed May 28, 2015)
• On data visualization
• The basics: Noah Iliinsky, Questions to Ask About Data Viz (October 2012; accessed May 22, 2015)
• On networks specifically: https://dhs.stanford.edu/visualization/more-networks/
• On maps: http://www.citylab.com/design/2015/06/when-maps-lie/396761/?utm_source=SFFB
• SKIM: On experimental use of data visualization: http://bookworm.benschmidt.org/posts/2016-01-19-txtLAB450.html

Tasks before class

• Miriam Posner, Getting Started with Palladio (again, but this includes network diagrams and faceting in addition to mapping only)
• An introduction to network diagrams in Google Fusion Tables
• Download Gephi and get it running on your machine.

Resources/practicum after class

• Martin Grandjean, An introduction to network analysis using Gephi
• Clement Levallois, Dynamic Networks in Gephi (inc. date stamps)

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Week 7: Sustainability and Access (2017-02-20)

How does a digital history project, with all of its moving parts and collaborators, get off the ground? How do we back up our stuff? How do we future-proof our stuff? How do we make sure our stuff is accessible to, and informed by, a variety of potential audiences?

• Posts on “Digital Historiography and the Archives” in AHA Today (Jan 2014, from a session at the AHA Annual Meeting; accessed May 20, 2015), including in particular
• Michael J. Kramer, “Going Meta on Metadata (Links to an external site.)“, in Journal of Digital Humanities, Vol. 3, No. 2 (Summer 2014, accessed May 20, 2015).
• "2: Metadata and Text Markup", Stanford's Tooling Up for the Digital Humanities
• John Willinsky, “History” (Chapter 13 in The Access Principle: The Case for Open Access to Research and Scholarship, Massachusetts Institute of Technology, 2006)
  • An example: http://www.kalanicraig.com/workflow/workflow-for-paywalled-data/
• George Williams, “Disability, Universal Design, and the Digital Humanities” in Matthew K. Gold, ed., Debates in the Digital Humanities (University of Minnesota, 2012 Print Edition; accessed May 31, 2015)
• Tara McPherson, “Why Are the Digital Humanities So White? or Thinking the Histories of Race and Computation“, in Matthew K. Gold, ed., Debates in the Digital Humanities (University of Minnesota, 2012 Print Edition; accessed May 31, 2015)
• Optional but good (and a late addition): a 7 minute interview with Ian Milligan, "Preserving digital history is imperative to save cultural history"

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Week 8: Project Management and Collaboration (2017-02-27)

How does a digital history project, with all of its moving parts and collaborators, get off the ground? How do we pass our stuff to someone else? At what point in a DH project do we start to think about project management issues?

Reading

• Sharon Leon, Project Management for Humanists
• Simon Appleford and Jennifer Guiliano, Building Your First Work Plan
• Bethany Nowviskie, Ten Questions to Ask Before You Take On a Project (my title for Ten rules for humanities scholars new to project management)
• Lisa Fagin Davis, A DH Road Trip Detour (or what happens when we don't think about project management)

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Week 9: Publics and publications (2017-03-06)

How does digital history and its emphasis on distributed knowledge and collaboration change the history profession? How do we approach the scholarly world as digital historians?

Reading

• “History as a Book Discipline” in Perspectives on History (April 2015, accessed May 24, 2015).
• “Guidelines for the Professional Evaluation of Digital Scholarship in History”, American Historical Association, Ad Hoc Committee on Professional Evaluation of Digital Scholarship by Historians, April 2015.
• On non-linear/non-traditional publications:
• An File round-4-NEH-Mellon-Digital-Publications.docx could not be included in the ePub document. Please see separate zip file for access. for NEH-Mellon Fellowships for Digital Publication (with guidelines from the NEH from last year, File Feb2015_DHIGMemo.to.Panelists.pdf could not be included in the ePub document. Please see separate zip file for access.)
• My "Workflows for Paywalled Text" (for the "How did they make that" section at DHCommons Journal)
• Overview
• Simple Excel-only version (with some of the example files linked)
• Complex SQL/scripting version (still in draft)
• On thinking about publications for the public
• Sharon Leon, http://www.6floors.org/bracket/2015/04/01/sharing-lessons-learned/ and http://blog.historians.org/2015/03/histories-national-mall-place-based-public-history/

Tasks before class

• How would our long-form work change if it weren't presented in linear textual form?
• Start with your current course project.
• Think about what would need to be included if it were part of a long-form 90,000 word project. What would you include? What would work as linear text and what wouldn't?
• What technical support would you need? A web site? Scalar? Something else?
• How would you think about peer review?

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Week 11: Images & Augmented Reality (2016-03-03)

How does image processing work? What does it contribute to a study of history? Can augmented reality provide additional substance to the question of historical research? 

Reading

• Melissa Terras, Chapter 4: Image processing in the digital humanities in Claire Warwick, Melissa Terras, and Julianne Nyhan, eds., Digital Humanities in Practice
• Virtual Paul’s Cross Quick Guide and documentation of research principles
• Rome Reborn: New Directions for Cultural Virtual Reality (a paper) and the Gallery
• The History Curriculum in 2023
• A case study of sorts: The History Department with a Fab Lab

Tasks before class

• If you could bring a(n?) historical object to class that students struggle to engage with--because they don't have experience of its usage, size, proportion, feel, emotional effect, whatever--what would it be? Please find several photos and, if possible, line illustrations of it.
• What object do you use every day that drives you nuts, because you have fabulous ideas to improve it if you just had the expertise to make it yourself?
• Think about the not-so-distant past, in which most people were makers. How can you use the concepts behind the maker movement to help students understand material culture in your time period.
• How do you think future historians will fit the maker movement into a history of material culture? Of social history? Of cultural history?

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Week 12: DigHist and Analog Pedagogy (2016-04-07)

How do we use digital history to shape teaching and learning? How do the theoretical debates around digital history shape our pedagogical practices? How do technical limitations in university classrooms shape our teaching, digital or analog?

Reading

• ADDENDUM: GIS, Text mining and network analysis in analog classrooms, published version: http://digitalcommons.georgiasouthern.edu/ij-sotl/vol11/iss1/7/
• Digital humanities theory in the student-teacher relationship
• On designing lesson plans vs not: http://www.digitalpedagogylab.com/hybridped/mmid-conversations-design-trust-discovery/
• On reading vs not: http://www.digitalpedagogylab.com/hybridped/pedagogies-reading-reading/
• A delightfully brief look at educational theory: the Venn diagram at the end of http://www.digitalpedagogylab.com/hybridped/constructionism-reborn/
• Wikipedia in the classroom: http://quod.lib.umich.edu/d/dh/12230987.0001.001/1:6/--writing-history-in-the-digital-age?g=dculture;rgn=div1;view=fulltext;xc=1#6.3

This week's class:

My goal is to give you an abbreviated sense of what it feels like to participate in a variety of online pedagogical exercises:

• Discussion forums, the most common method of asynchronous interaction in online courses.
• On Thursday before class, 10 minutes of asynchronous discussion: What are the limitations/problems with Wikipedia in the classroom? The advantages?
• Make one post. Reply to one post.
• Synchronous chat, one of two common simultaneous interaction options
• 30 minutes.
• Text-based chat about tools that support digital history methodology in both analog and digital classrooms, starting promptly at 6:30 on Thursday.
• The other simultaneous interaction is video conference. Zoom is my favorite of these both for its dependability and functionality, but I'll be sitting in the airport on my way to a conference and I don't want to subject you to all-too-frequent boarding announcements. We can test it for 5 minutes just to play with its screen sharing and notation functions.
• Wikipedia, a common way of staging non-paper-based research projects
• 60-75 minutes
• Choose an educational theory from the diagram at http://www.digitalpedagogylab.com/hybridped/constructionism-reborn/ that addresses one of your classroom concerns/needs (authorship, student ownership, project focus, or collaboration, or a blend). I've built several into our in-class experience already:
• Problem Based Learning (the giant poster paper we wrote on during our work on the primary source list)
• Constructionism (the maker space experience)
• Sociocultural theory (Vygotsky by way of Engestrom in the Activity Theory triangle)
• Find the Wikipedia article on your chosen educational theory. By 8:30 or 9 Thursday night:
• Copy and paste it to a Canvas page (Link has been removed because content is not present or cannot be resolved.) .
• Add a short section (200 words ish) on how the theory would inform your redesign of
• an undergraduate lesson in your field using one of the digital history methodologies we've learned this semester.
• one of the online lessons we undertook this week (including this Wikipedia lesson).
• Mark your edits clearly (by changing the text color to blue)
• Back to discussion forums, the most common method of asynchronous interaction in online courses.
• On Friday: 20 minutes of asynchronous discussion, not including reading. Read the educational theory Wikis your classmates chose.
• Post to the discussion forum: How do the educational theories you and your classmates edited in the Wiki assignment help you understand the limitations and advantages of the tools we've used in the last few days? Of digital history methodology in the history classroom in general?
• Make two posts, reply to two posts.

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Week 15: Final thoughts, or a controversy (2016-04-28)

What's next? How do historians not in this class feel about what's next? Do we agree?

Reading

These are long, and it's the end of the semester. If you're crunched for time, skim the introduction and Lara Putnam's article and focus on the exchange between Cohen/Mandler and Armitage/Guldi.

• “Introduction to exchange on The History Manifesto” in The American Historical Review (Volume 120 Issue 2 April 2015) (File The American Historical Review-2015-Exchange_ On -527-9.pdf could not be included in the ePub document. Please see separate zip file for access.)
• Deborah Cohen and Peter Mandler, “The History Manifesto: A Critique”, in The American Historical Review (Volume 120 Issue 2 April 2015) (File The American Historical Review-2015-Cohen-530-42.pdf could not be included in the ePub document. Please see separate zip file for access.)
• David Armitage and Jo Guldi, “Manifesto: A Reply to Deborah Cohen and Peter Mandler“ in The American Historical Review (Volume 120 Issue 2 April 2015) (File The American Historical Review-2015-Armitage-543-54.pdf could not be included in the ePub document. Please see separate zip file for access.)
• Lara Putnam, On the "Digitized Turn": File The American Historical Review-2016-Putnam-377-402.pdf could not be included in the ePub document. Please see separate zip file for access.

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Week 16: Poster Session (2016-05-05)

You are cordially invited to partake of cheese, dessert, other refreshments, and end-of-term presentations at House Awesomesauce* from 6:30-8:30 pm on Thursday, May 5.

3630 E Tamarron Dr. There's plenty of room for everyone to park in the driveway so turn down the cul de sac rather than parking at the top on the street.

* When one of Joshua's students asked if he would be taking my name, he responded that we were both changing our last name. His student suggested we become "The Drs. Awesomesauce." It stuck.

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Week X: Named Entity Recognition & Natural Language Processing (2017-02-06)

How can we take basic text mining one step further? What kinds of new data do these processes generate? How do we balance the flexibility of command-line work with the time savings we get from GUI interfaces for this kind of process?

Reading

• The nitty-gritty of Topic Modeling and Bag-Of-Words natural language processing at http://www.themacroscope.org/?page_id=553 (to tie this week's extension of basic text mining to more advanced text mining processes)
• The NLTK (Natural Language Toolkit) tutorial at http://desilinguist.org/pdf/crossroads.pdf
• Do this week's tasks.
• Then come back and read the NLTK tutorial at desilinguist again.

Tasks Before Class

• Play with the GUI demo (http://nlp.stanford.edu:8080/ner/) of Stanford's Named Entity Recognizer (http://nlp.stanford.edu/software/CRF-NER.shtml)
• Play with the NLTK online demos at http://text-processing.com/demo/

Resources/Practicum After Class

• The command-line tutorial for MALLET from Programming Historian
• TreeTagger, for Part Of Speech tagging in any language (http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/; this can help with named entity recognition or evaluation of linguistic change over time in the absence of a trained model for your language for topic modeling or corpus linguistics)
• TnT, statistical part-of-speech tagging that is slightly more sophisticated than TreeTagger and gives optional possibilities for each word with percentage values for each variance (http://www.coli.uni-saarland.de/~thorsten/tnt/)
• Python and NLTK sentiment analysis tutorial: http://www.laurentluce.com/posts/twitter-sentiment-analysis-using-python-and-nltk/
• Stanford sentiment analysis tree banking: http://nlp.stanford.edu/sentiment/treebank.html

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