Digital History Seminar

Students will complete a substantial digital history project that demonstrates mastery of computational methods in historical research. The final project may take one of two primary forms:

• Option 1: Digital History Article - An article-length piece of writing that employs digital methods to make a historiographical interpretation. This format integrates computational analysis directly into traditional scholarly writing. Examples of such work can be found in journals like Current Research in Digital History, Journal of Digital History, or the Journal of Cultural Analytics.
• Option 2: Digital History Project and Website - A digital history project and website based on original historical research with a substantial written component. This format creates a public-facing digital resource while maintaining rigorous scholarly standards. Students can look at examples like America’s Public Bible, Religious Ecologies, Mapping Early American Elections, or Mapping the Gay Guides.

Regardless of format chosen, all projects will include three core components:

• Dataset - Every student will create a documented dataset from primary source research. The dataset must include a data biography explaining your methodology and should be hosted in its own GitHub repository (can be private if necessary for ethical or copyright reasons). The specific structure and format of the dataset will vary depending on your chosen methodology and will be discussed in consultation with the professor.
• Code - Projects will include computational analysis code appropriate to your chosen method (data manipulation, text analysis, mapping, network analysis, etc.). All code should be well-commented, reproducible, and hosted in a GitHub repository with transparent documentation of methods used. The complexity and type of code will vary based on your project’s methodological requirements and will be determined in consultation with the professor.
• Written Component - A substantial written analysis (7,500-10,000 words) that integrates your digital findings with existing scholarship. The format will be determined in consultation with the professor based on your chosen project type.

• A (94-100%))
• A- (93-90%)
• B+ (87-89%)
• B (84-86%)
• B- (80-83%)
• C+ (77-79%)
• C (74%-76%)
• C- (73-70%)

• Cathy Birkenstein and Gerald Graff, They Say/I Say (2018).
• Zachary Schrag, The Princeton Guide to Historical Research (2021).

Due to the significant scope and technical complexity of digital history projects, it is essential that students maintain the deadlines and milestones outlined in the course schedule. Digital history work follows a sequential process where datasets must be completed before analysis can begin, and analysis must be substantially underway before students can formulate their final arguments and begin serious writing. Students who fall behind on early milestones will find themselves unable to complete quality work by the semester’s end - you do not want to reach November without a finished dataset.

All assignments are due on the dates specified in the syllabus. Late submissions will result in a 10% grade deduction for each day the assignment is overdue. Assignments more than 7 days late will not be accepted. Given the scaffolded nature of this course, where each component builds upon the previous one, students must start early and work efficiently throughout the semester. Extensions may be granted only in exceptional circumstances and must be requested well in advance of the due date.

Regular attendance and active participation are essential components of this seminar. Students are expected to attend all class sessions and engage meaningfully in discussions, peer review activities, and collaborative work. Given the workshop-style nature of many class meetings and the importance of peer feedback in the digital history process, your presence contributes not only to your own learning but to the success of your classmates’ projects as well.

If you must miss a class due to illness or other unavoidable circumstances, please notify the professor as soon as possible and make arrangements to catch up on any missed content or assignments. Frequent absences will negatively impact your participation grade and may hinder your ability to complete the course successfully.

This course teaches advanced digital history methods involving R, Python, and other programming languages. You are encouraged to use AI tools (ChatGPT, Claude, Copilot, etc.) as coding assistants and learning coaches to help you debug code, troubleshoot errors, learn syntax and programming concepts, understand functions and methods, get assistance with code structure and optimization, and interpret error messages. However, you must maintain rigorous standards for their use and remember that you are the historian with domain expertise.

When using AI for coding assistance, you must understand every line of AI-generated code before submitting it, test and verify all AI-suggested solutions, and remain responsible for checking AI work for accuracy and appropriateness to your specific historical research context. You must stay the “historian in the loop,” making informed decisions about your research and code rather than blindly accepting AI suggestions. Use AI as a tool to enhance your learning and productivity, not as a crutch that prevents you from developing essential programming skills.

AI use is strictly prohibited for things like data analysis, interpretation of results, writing papers, reflections, or any written assignments, generating historical arguments or conclusions, or completing assignments without genuinely understanding the underlying processes and concepts. Any use of AI for analysis or written work will result in immediate failure of that assignment and potentially the class. Remember that per the Graduate Handbook’s minimum grade policy and the Graduate School’s probation policies, failing this course (defined as less than a C) may result in academic probation. It may also lead to the termination of your assistantship or dismissal from the program. You are the historian with specialized knowledge and critical thinking skills that cannot and should not be outsourced to AI systems.