Course description

Introduction to Data in Social Context explores how the idea of data, and its associated methodologies, have been constructed and challenged in modern society. Rather than seeing data as a new phenomena that arrived with the advent of Amazon Web Storage, this course looks at how data and other ways of measuring and collecting "information" emerged in the last two centuries.

Some questions this course asks is:

• Who gets to define and deploy data? What’s the relationship(s) between data and power?
• What are the longer historical antecedents of current methods in data-driven research? How did the emergence of new technologies shape the creation of social categories, and vice versa?
• How has data empowered and erased certain communities? What might the future of data and society look like?

This course contextualizes data in a longer historical perspective to help us understand contemporary debates over data ethics and rights; privacy and surveillance; data as a commodity and data governance.

Learning Objectives:

• Introduction to historical thinking. This course will explore both theories of data, as well as how new technologies and methodologies have enabled the rise of data. Through selected historical case studies, this course will consider the relationships between current and historic data practices. Students will learn to thinking critically about data and its histories.
• Introduction to historical methods. This course will introduce students to how historians contextualize and interpret historical sources. Through weekly reading responses and assignments, students will have the opportunity to create data histories.

<aside> 💡 Notes: This course could be taught either with or without digital history methods depending on the students. If I were to include digital history methods, I would shift the syllabus to focus a bit more on the history of computing.

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Sample Assignments

1. Weekly Reading Responses

   <aside> 💡 Notes: Depending on if this course is designed to introduce students to historical writing or primarily historicizing data, I would use weekly reading responses to either generate questions for discussion or have the students connect our readings to recent coverage of data issues.

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2. Dataset Biography Project

   Working either individually or in small groups, students will select a commonly used dataset and produce a contextual biography of the dataset. This biography should go beyond the "official" narrative of the dataset to consider the following questions:

   • Why was this data collected, instead of alternatives? How was this data collected? How has the data been used by other researchers?
   • Is this data representative? What sorts of features are in the dataset? What might be missing from the dataset? What are alternative ways you could have collected this data?
   • How does this dataset compare to earlier examples we've considered? How would you connect this dataset to some of our historical case studies?

   Example datasets will include the Enron emails dataset and ImageNet, and projects will be posted on the class website.

3. Data of the Future: Un-essay Final Project

Following the example of Daniel Paul O’Donnell and Ryan Cordell https://s18tot.ryancordell.org/assignments/unessay/, rather than assigning students a final essay, I want to encourage them to work creatively while thinking about how data impacts their lives and society. Over the course of the semester, students will develop a dataset, and then decide on how best to present both the information and their interpretation. Building upon the Dataset Biography Project students will submit an initial proposal, outlining their vision, timeline, plan of action, and how they should be evaluated. The prompt for the dataset will be the question of what data would you want to share with future historians so that they would understand the world as it exists in 2020.

Potential inspiration for un-essay formats include:

1. Giorgia Lupi and Stefanie Posavec Dear Data Postcards http://www.dear-data.com/theproject where students would collect data on themselves and using analog data visualizations to communicate what they've captured.
2. Mini Onuoha The Library of Missing Datasets http://mimionuoha.com/the-library-of-missing-datasets where students consider why certain datasets are "missing" and how that relates to social norms and historical legacies.
3. Tyler Vigen Spurious Correlations https://www.tylervigen.com/spurious-correlations where students could combine multiple datasets to consider how we capture current phenomena quantitatively and the normative power of numbers.