Schedule

This schedule is your go-to resource for the most up-to-date syllabus, reading materials, and assignments. While we will strive to stick to this schedule, please understand that it is not set in stone – updates may occur. Should there be any important changes to the schedule, assignments, or reading materials, you’ll receive an email notification!

Here’s a quick overview to the different types of class meetings we’ll have:

  • Discussion: Sessions that combine lecturing with discussions, emphasizing critical engagement with the assigned materials.
  • Lab: Hands-on sessions applying concepts in a practical setting.
  • Present: Sessions where students present their projects or research.
  • Field Trip: Visit to Princeton University Library Special Collections.

Week 1 - Intro to and Foundations of Digital Humanities

Week 1 - Intro to and Foundations of Digital Humanities | Introduction to Digital Humanities

In this module, we will take our time to become acquinted with the contours that delineate the field known as Digital Humanities. We discuss what are the essential or minimal ingredients (if there are any), and critically examine various definitions. We traverse the landscape of Digital Humanities and explore how early definitions of the field differ from more recent attempts at defining it.

↪ Jan 30 Discussion Acquaintances
↪ Feb 1 Discussion Foundations

Week 2 - Data Culture(s)

Week 2 - Data Culture(s) | Introduction to Digital Humanities

This week we survey the expansive terrain of Digital Humanities. First, we’ll engage in a discussion on ‘data’ as a core concept, and the methodologies that propel the field forward. Later on this week, we focus on practical applications of metadata and data description.

↪ Feb 6 Discussion Getting our data together
↪ Feb 8 Discussion Metadata and data description

Week 3 - A Project of Our Own

Week 3 - A Project of Our Own | Introduction to Digital Humanities

In this module, we will start on our class-crowdsourced project featuring a dataset that captures the essence of Princeton University: the Princeton University Historical Postcard Collection. Part of this collection has been digitized and is available through the Princeton University Digital Library. We will outline the various stages of this project, from conception to completion, and identify the tools and techniques that will assist us throughout. We will also pay a visit to the Mudd Library where we will view the collection up close. In the second meeting of this week, we investigate the critical procedures involved in data cleaning, and how OpenRefine can be used in this process.

↪ Feb 13 Field Trip Exploring Princeton's Postcards CANCELED DUE TO SNOW STORM ☃️
↪ Feb 15 Field Trip Exploring Princeton's Postcards

Week 4 - Molding and Modeling Data (and cleaning it too after the snow storm)

Week 4 - Molding and Modeling Data (and cleaning it too after the snow storm) | Introduction to Digital Humanities

In this module, we investigate the critical procedures involved in annotating data, thereby preparing it for analysis. These processes invariably entail the making of mindful decisions. As we gear up for the hands-on tutorials, we also explore an integral component of data analysis: data modeling. We will demystify this process, its bearing on our data handling techniques, and what constitutes an effective data model. In the second meeting of this week, students will present their Data Biographies.

↪ Feb 20 Lab Data Cleaning
↪ Feb 22 Present Data Biographies
  • Presentation overview:
    • On this day, students will present their Data Biographies. This is a chance to share the intriguing stories behind the datasets you’ve explored!
    • Each presentation should last approximately – but also no more than – 4 minutes, followed by a brief (~1 minute) Q&A session.
    • Focus on the narrative of your Data Biography, presenting on aspects you find interesting, such as origin, collector(s), collection method(s), intended use, and any limitations or ethical considerations.
    • Use visuals or excerpts from your dataset to illustrate your points and engage the audience.
    • For more details, refer to the assignment description.
  • Presentation guidelines:
    • Maximum of five (5) slides per presentation.
    • Please add your slides to this shared slide deck before the start of the class.
    • Strict time limit of four (4) minutes for each presentation.
    • Order of presentations:
    Order - Presenter(s)Order - Presenter(s)
    1 - Alison8 - Helen
    2 - Layla9 - Colin + Melissa
    3 - Talia10 - James
    4 - Clay11 - Raphaela + Emanuelle
    5 - Andrew12 - Pippa
    6 - Anya13 - Yaashree
    7 - Ethan14 - Pia

Week 5 - Text Digitization and Markup Languages

Week 5 - Text Digitization and Markup Languages | Introduction to Digital Humanities

During our first session this week, we will look at into the technology behind making text digitally readable. Our historic postcard collection provides the perfect case study for exploring both Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) technologies. In our subsequent session, we will explore the world of markup languages, such as HTML and XML. We will discuss their significance in structuring and presenting digital content, and explore how these languages serve as the backbone of content representation on the web.

↪ Feb 27 Lab Text Recognition Technologies
↪ Feb 29 Lab Markup Languages

Week 6 - Distant Reading

Week 6 - Distant Reading | Introduction to Digital Humanities

This week begins with a lecture on Distant Reading, where we will explore the theoretical framework and methodologies for analyzing large bodies of text. We’ll discuss how this approach can uncover patterns and trends that are not immediately apparent, setting the stage for the hands-on lab session where we will be leveraging the web-based Voyant Tools, known for their user-friendly, powerful capabilities for visual text analysis. This module is designed to be both instructive and engaging, providing you with practical experience using digital tools to analyze large textual data sets. Drawing inspiration from the works of El Khatib and Ross (2022), we will conduct an ecological reading of Emily Brontë’s novel Jane Eyre, and an exploratory thematic analysis of Mary Shelley’s Frankenstein.

↪ Mar 5 Discussion Distant Reading
↪ Mar 7 Lab Applying Voyant Tools

🌴 Spring Break 🌴

Enjoy a well-deserved break!

Week 7 - Topic Modeling

Week 7 - Topic Modeling | Introduction to Digital Humanities

Topic modeling is a useful tool for exploring large collections of text. It can be used to identify themes in a corpus, to identify outliers, and to explore the relationships between different texts. It is a form of unsupervised machine learning, which means that it does not require a human to provide a training set of labeled data. During the first session of this week, we will look at how the sieving out of topics works, and how to interpret the results. In the second session, Laure Thompson will visit our class and we’ll use jsLDA to build a topic model of our own.

↪ Mar 19 Discussion Topic Modeling
↪ Mar 21 Lab Topic Modeling with jsLDA

Week 8 - Stylometry

Week 8 - Stylometry | Introduction to Digital Humanities

In this module, we will discover the world of stylometry, a field that probes the unique stylistic fingerprint of authors. Utilizing the user-friendly package Stylo, we will undertake a critical review and replication of Patrick Juola’s study (2013), which aimed to reveal the author hiding behind the pseudonym Robert Galbraith. In doing so, we will not only explore the power of stylometric techniques, but also discuss the broader implications of this type of research, emphasizing the ethical and scholarly consequences.

↪ Mar 26 Discussion Stylometry
↪ Mar 28 Lab Authorship Attribution with Stylo

Week 9 - Data Visualization and GIS

Week 9 - Data Visualization and GIS | Introduction to Digital Humanities

This module focuses on the visual dimension of Digital Humanities research. Often, the dissemination of results takes the form of graphs or they are displayed via graphical interfaces. We will explore a whole spectrum of data visualization practices, citing examples of the good, the bad, and even the downright ugly. In this sense, the session is aimed to prepare us for the coming labs on mapping and network analysis.

↪ Apr 2 Discussion Data Visualization
  • Slides
  • Pre-Class Reflection:
    • McCandless, David. The Beauty of Data Visualization. TEDGlobal 2010. Video.
  • Pre-Class Exercise:
    • Every year since 2012, the Digital Humanities community has recognized outstanding contributions through the Digital Humanities Awards 🏆. On the award website, you’ll find examples of data visualization that are not only stunning but also effective in conveying complex information.
      1. Explore the nominees from this year, or past years (2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022).
      2. Choose a visualization that resonates with you.
      3. Capture a screenshot of this visualization and add it to our session’s slide deck (limit yourself to one slide; for interactive visualizations, include a link to allow us to explore the visualization in detail during class).
      4. While a formal presentation isn’t required, prepare to discuss the following in class: What is being depicted? In what ways is it presented? Why do you believe this visualization is impactful? Think about its strengths and any potential weaknesses.
↪ Apr 4 Lab GIS

Week 10 - Network Analysis and Intellectual Property Rights

Week 10 - Network Analysis and Intellectual Property Rights | Introduction to Digital Humanities

This week, we continue our exploration of visualization in Digital Humanities with a focus on network analysis. Building on our discussion on data visualization, we’ll look at web-based platforms like Flourish and software like Gephi and learn how network analysis provides valuable insights into relational data. In the latter part of the week, we shift our attention to critical issues such as copyright, fair use, and the principles of open access.

↪ Apr 9 Lab Network Analysis CANCELED
↪ Apr 11 Lab Network Analysis

Week 11 - Python for the Curious

Week 11 - Python for the Curious | Introduction to Digital Humanities

This week, we will look at the role of coding in the humanities with a gentle introduction to Python, a programming language as approachable as it is powerful. Our two sessions will provide a panoramic view of Python’s syntax and its practical applications in Humanities research. Through hands-on exercises, we’ll learn to appreciate the role of code in analyzing texts, visualizing data, and perhaps most importantly, in sharpening our critical thinking about coding literacy. Ultimately, the goal of this week is to get a taste of programming in Python, with fear being an exception that is not allowed to interrupt the process.

Or, stated in a Pythonic way:

try:
    learn_python()
except Fear as obstacle:
    handle(obstacle)  # apply strategies to overcome fear
else:
    print('Congratulations on embracing coding literacy!')  # celebrate
↪ Apr 16 Discussion Intellectual Property Rights
↪ Apr 18 Lab Coding Literacy

Week 12 - Roles, Responsibilities, and the Future

Week 12 - Roles, Responsibilities, and the Future | Introduction to Digital Humanities

In this concluding week, we begin by exploring the ethical dimensions of Digital Humanities, with a focus on critical issues such as algorithmic bias and transparency in the age of AI. We confront these developments by analyzing their impacts, potential, and the risks they create. In the final session, students will showcase the progress of their own research proposals, which involves elements of data collection, preparation, and analysis methods. Colleagues from the Center for Digital Humanities at Princeton University are invited, providing an opportunity for everyone to gain feedback on their proposals.

↪ Apr 23 Discussion Ethical Considerations
↪ Apr 25 Present Work-in-Progress Presentations