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.

↪ Sept 3 Discussion Acquaintances
↪ Sep 5 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.

↪ Sept 10 Discussion Getting our data together

  • Slides

  • Pre-Class Exercise:
    • Play around with Google’s Ngram Viewer to investigate two terms of your choice (e.g. ‘car’ vs. ‘automobile’). Analyze and interpret the trends you observe. Consider the following: what intriguing patterns – if any – emerge? Can specific historical events explain shifts in the usage of these terms? You are welcome to do a quick online search to support your interpretations, but keep it concise – no more than three sentences for each term.
    • Post your analysis along with a screenshot of your NGram search in the #ngrams channel on Slack before the day of our class.
  • Pre-Class Perusall Annotation (no Slack reflection, just annotations on Perusall!):
↪ Sept 12 Guest Lecture Metadata and data description

Week 3 - From Artifacts to Data

Week 3 - From Artifacts to Data | Introduction to Digital Humanities

In this module, we investigate the critical procedures involved in data creation and cleaning, and how OpenRefine can be used in this process. In our second session, we will pay a visit to the Princeton University’s Special Collections where will take a tour of the Digitization Studio.

↪ Sept 17 Lab Data Cleaning CANCELED
↪ Sept 19 Field Trip Special Collections Studio Tour
  • For this class, we’ll meet at the Firestone Library, C-level (“the bunker”) at 11:00AM.
  • Pre-Class Activities (no Perusall annotations, nor reflection required!):
    • Familiarize yourself with the Princeton’s Digital Repository to get a sense of the extent of Princeton University’s digitized materials. Specifically, explore The Infant’s Library from the Cotsen Children’s Library. Reflect on how the physical characteristics of this item - a miniature bookcase with books! - are represented digitally. Consider how well the digitization captures the physicality of the item.
    • Listen to this podcast episode, where Dot Porter (Curator of Digital Research Services at UPenn’s Schoenberg Institute for Manuscript Studies) is interviewed by Stewart Varner (Managing Director of the Price Lab at UPenn).

Week 4 - Molding and Modeling Data

Week 4 - Molding and Modeling Data | Introduction to Digital Humanities

In this module, we investigate the critical procedures involved in data creation and cleaning, and how OpenRefine can be used in this process. These processes invariably entail the making of mindful decisions. During our second session this week, we will look at the technology behind making text digitally readable.

↪ Sept 24 Lab Data Cleaning
↪ Sept 26 Discussion Text Recognition Technologies, pt. I

Week 5 - Text Digitization (pt. II) and Distant Reading (pt. I)

Week 5 - Text Digitization (pt. II) and Distant Reading (pt. I) | Introduction to Digital Humanities

During our first session this week, we will continue with our exploring of Handwritten Text Recognition (HTR) and Optical Character Recognition (OCR) technologies. In the second session, we will shift our focus to Distant Reading, a method of analyzing large bodies of text to uncover patterns and trends that are not immediately apparent.

↪ Oct 1 Lab Text Recognition Technologies, pt. II
↪ Oct 3 Lab Distant Reading

Week 6 - Distant Reading (pt. II) and Data Biographies

Week 6 - Distant Reading (pt. II) and Data Biographies | Introduction to Digital Humanities

This week begins with a 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.

↪ Oct 8 Lab Applying Voyant Tools
↪ Oct 10 Present Data Biographies
  • Slides
    • 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/feedback 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, and a very strict time limit of four - 4! - minutes. You will be cut off if you exceed this limit!
      • Please add your slides to the shared slide deck (link coming soon) before the start of the class.
    • Order of presentations (TBA)

🍂 Fall Recess 🍂

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!

↪ Oct 22 Discussion Topic Modeling
↪ Oct 24 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.

↪ Oct 29 Discussion Stylometry
↪ Oct 31 Lab Authorship Attribution with Stylo

Week 9 - Network Analysis

Week 9 - Network Analysis | 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.

↪ Nov 5 Discussion Network Analysis I
↪ Nov 10 Lab Network Analysis II
  • Slides
    • Please install the free, open-source program Gephi on your computer before class. You can download the software here.

Week 10 - 'Making' in DH and GIS

Week 10 - ‘Making’ in DH and GIS | Introduction to Digital Humanities

This module introduces the hands-on dimension of Digital Humanities research, focusing on how “making” is becoming an essential part of its practice. We’ll explore the role of embodiment in research through activities like 3D printing, electronics, and other forms of physical creation. This will set the stage for our upcoming lab on GIS, by examining how physical and spatial perspectives can enhance DH projects.

↪ Nov 12 Field Trip Makerspace
  • We will meet at the Makerspace in the Lewis Science Library, A-level at 11:00AM. No need to bring your laptop to this session!
↪ Nov 14 Lab GIS
  • Slides
  • Pre-Class Reflection and Perusall Annotations (Perusall annotations only required for the first—Presner et al. (2014)—reading!):

Week 11 - Guest Lecture & Python for the Curious

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

In the first session this week, we welcome a guest discussion led by Paavo Van der Eecken (University of Antwerp), a visiting Fulbright scholar working in Digital Humanities. Paavo’s research focuses on the annotation of age, race, class, and gender in the illustrations of historical children’s literature. His talk will provide insights into how data-driven approaches intersect with the study of visual and cultural representation.

The second session will explore the role of coding in the humanities with a gentle introduction to Python, a programming language as approachable as it is powerful. Our session will provide a panoramic view of Python’s syntax and its practical applications in Humanities research. 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
↪ Nov 19 Discussion Annotating Diversity in Children's Literature
↪ Nov 21 Lab Coding Literacy

🦃 Thanksgiving Recess 🌽

Week 12 - Roles, Responsibilities, and the Future

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

In our concluding week, we will revisit the topics explored throughout the semester, reflecting on the ethical dimensions of Digital Humanities and considering critical issues such as algorithmic bias and transparency in the age of AI. This will help frame the ongoing and future work of (digital) humanities scholars. The first session (Dec. 3) will be held remotely and will feature a discussion-based format, providing an opportunity to wrap up the class, ask final questions, and look ahead. For the second session (Dec. 5), there will be no live class. Instead, you will submit and asynchronously review recorded work-in-progress presentations.

↪ Dec 3 Discussion Roles & Responsibilities
↪ Dec 5 Present Work-in-Progress Presentations
  • No live class will be held for this session.
  • Instead, submit a 4~5 minute work-in-progress video presentation detailing the current state of your final assignment. This is an opportunity to share ideas, express doubts, and gather feedback from your peers and instructor.
  • Consider addressing the following points in your presentation
    1. Narrative/research question/hypothesis/reasons why your project is important
    2. Dataset identification and collection/potential issues with the data (with or without solutions)
    3. DH methods/tools/frameworks that you are considering implementing
    4. Challenges you are facing
  • Submission guidelines
    • Videos must be uploaded to the designated folder (referenced on Slack, see here) by 11:59 PM on Dec 4.
    • Name your file as: Lastname_Firstname_PresentationTitle.mov|mp4|....
    • Keep your presentation within the 5-minute limit.
    • You may use any recording software (e.g., Zoom, QuickTime).
    • While optional, using slides or screen sharing to support your points is highly recommended.
  • Feedback expectations
    • Videos will be shared on Perusall for asynchronous peer feedback and discussion. Link will be shared here once all videos are collected!
    • You are required to provide feedback on at least two peer presentations using a rubric that will be shared in advance.
    • Peer feedback is due by 11:59 PM on Dec 6.
  • Once again…
    • The video presentation shows your work-in-progress, so it is 100% okay if you are still figuring out some of the specifics of your project! Use the opportunity to put forward questions/doubt and explore your ideas.