Aisling Quigley, PhD, aquigley@macalester.edu, aislingquigley.com
Office Hours:
Mondays, 3:30-4:30 PM in my office Library L03B
By appointment (please send me an email or use my Calendly)
This website!
Links to some resources in Google Drive
What does it mean to represent "culture" by "data"? - Lev Manovich
What do data look like in the humanities? How can data and databases inform our understanding of culture, or, alternately, be manipulated to distort the truth? Increasingly, computational methods are being used to ask questions and look for patterns in cultural data (from museums, libraries, archives, and elsewhere). This class provides an introduction to some of the digital methods and tools used to investigate humanities data and databases, while encouraging critical engagement with the many ethical and design questions that arise in the collection, analysis, and presentation of data. In this course we will read articles from a range of disciplines, engage in activities reflecting on our own collecting practices and daily routines, and explore and articulate ways that digital technologies may be used more effectively and ethically.
You'll emerge from this class with the ability to:
Collect, curate, analyze, and present data.
Critically engage with data collections, considering their content, context, and structure.
Examine the realities and cultural impacts of big data and machine learning on human beings.
Confidently experiment with digital tools and simple data sets.
Balance the discoverability component of playing with digital tools with the very real possibility/probability of failure ("productive" failure).
Explore and articulate ways that digital technologies may be used effectively and ethically.
Employ digital literacy skills.
Week 1-2: September 6-15th: Introduction- Context and Framing
September 11: Sign up for your preferred date for contributing discussion questions/prompts.
Week 3-4: September 18-22nd: Collection
September 20: Group Project Interest Survey
September 22: Project Sleuthing Exercise
Week 5: September 25-October 6th: Organization
September 29: Collection Deep Dive Assignment
Week 6: October 9-13th: Cleaning Data
Week 7: October 16-20th: Data Analysis- Visualization
October 20: Data Cleaning Lab Reflection
Week 8: October 23-27th: Mapping Data
October 25: Group Project Charter and 3 Research Questions
Fall Break 🍁 NO CLASS ON OCTOBER 27 🍁
Week 9: October 30-November 3rd: Text Analysis
November 1: Group Project Data Critique
November 3: Personal Geographies
Week 10: November 6-10th: Network Analysis
November 10: Personal Data Postcard
Week 11-13: November 13-December 1st: Data Privacy, Digital Preservation, and More
November 29: Group Project 3 Draft Visualizations
Thanksgiving Break 🍁NO CLASS NOVEMBER 22 OR 24🍁
Week 14: December 4-8th: Data Ethics and the Quantified Self
December 6: Group Project Presentation Tool
December 8: Data Autoethnography
Weeks 15: December 11-13th: Group Work Day and Project Presentations
December 13: Project Presentations
This iteration of the syllabus was inspired by the work of many others, including Rahul Bhargava, Mackenzie Brooks, Paul Fyfe, Chelsea Gunn, Anna Lauren Hoffmann, Catherine D'Ignazio, Lauren Klein, Alison Langmead, Lev Manovich, Kristen Mapes, Shannon Mattern, Jim McGrath, Ashley Nepp, Miriam Posner, Kiri Sailiata, Annette Vee, Roger Whitson