Queens of Antiquity

Project Summary

Understanding how to generate, analyze, and work with datasets in the humanities is often a difficult task without learning how to code or program. In humanities centered courses, we often privilege close reading or qualitative analysis over other methods of knowing, but by learning some new quantitative techniques we better prepare the students to tackle new forms of reading. This class will work with the data from the HathiTrust to develop ideas for thinking about how large groups and different discourse communities thought of queens of antiquity like Cleopatra and Dido.

Please refer to https://sites.duke.edu/queensofantiquity/ for more information.

Themes and Categories
Interdisciplinary Inquiry
Data Expeditions, Digital Humanities
Year
2018

Graduate Student: Grant Glass

Faculty: Dr. Charlotte Sussman

Course: “Queens of Antiquity” (English 390S-7; Spring 2018)

Grant Glass taught this Data Expedition activity to students in ENGL 290, a spring 2019 course aimed at undergraduates. This experience exemplified that by introducing simple “distant reading” or qualitative concepts in a humanities undergraduate classroom, students would be able to use these tools to drive new types of research questions and think about how reading can include qualitative analysis.

The goals were to give students an introduction to “distant reading,” show how data and collections are created, what algorithms we can apply to those collections, and what types of analysis we can do from the results.

Over the course of two, 1.5-hour class sessions, 10 undergraduates were given the opportunity to create their own datasets and explore the results. For the end product, students created posts to discuss how the visualizations created from their collections helped them better understand.

Guiding Questions

  • What visualization is the most useful? Why?
  • What does the visualization help you understand about the corpus? What does it obscure?
  • What research questions can you generate from the visualization?

The Dataset

Dido

Elizabeth 1

Anne

Cleopatra

In-Class Exercises

Creating Collections with Hathitrust

Understanding the Visualizations

Related People

Related Projects

Ecological Data Analysis

Over the course of two, one and a half hour sessions we led students in the Duke Marine Lab Marine Ecology class (Biology 273LA) on a data expedition using the statistical programming environment R. We gave an introduction to big data, the role of big data in ecology, important things to consider when working with data (quality control, metadata, etc.), dealing with big data in R, what the Tidyverse is, and how to organize tidy data (see class PowerPoint). We then led a hands-on coding workshop where we explored an open-access citizen science dataset of aquatic plants along U.S. east coast (see dataset details below).

Social Network Analysis Basics: Case Studies from Game of Thrones and the National Hockey League

The goal of this Data Expedition was to introduce students to the exploration of social networks data using R. Students learned to load and plot a social network in R and then perform some basic analyses on two different networks: Hockey Fights in the National Hockey League in 2018-2019 and characters in Game of Thrones Season 3. Students used social network analysis to better understand who is connected to whom, how frequently they interact, and how they are interacting.

Text Analysis of Political Speech

The data that students see in their statistics courses are often constrained to numeric and tabular data. However, there is an exciting field of data science and statistics known as text analysis. This expedition introduces students to the concept of treating text as data frames of words, and demonstrates how to perform basic analyses on bodies of text using R. Tweets of four Democratic candidates for the 2020 Primary are used as data, and demonstrated text analysis techniques in the expedition include comparisons of word frequencies, log-odds ratios for word usage, and pairwise word correlations.

See all Research