Invisible Adaptations: From Hamlet to the Avengers

Project Summary

Have you ever read or watched a movie and realized that you have seen the same story before?  How do you know if you are watching an adaptation? A team of students led by UNC-Chapel Hill graduate student Grant Glass, will develop means to track the movement of adaptations within contemporary culture through machine learning techniques. Drawing upon a variety of textual information drawn from historical and digital sources, the project team will have the opportunity to work with many different types of data. Students will identify features of different master narratives, which will be used to demonstrate how certain stories are modified and retold over and over again. By creating this training dataset, the team will use algorithms to identify adaptations in previously unidentified works. This will allow scholars to better understand at scale how certain narratives are adapted into new stories and forms.

Faculty Lead: Grant Glass

Project Manager: TBD

Themes and Categories
Interdisciplinary Inquiry
Data+, Digital Humanities
Year
2019
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu
APPLY NOW!

Related Projects

Getting Granular on Social Determinants of Health

Social and environmental contexts are increasingly recognized as factors that impact health outcomes of patients. This team will have the opportunity to collaborate directly with clinicians and medical data in a real-world setting. They will examine the association between social determinants with risk prediction for hospital admissions, and to assess whether social determinants bias that risk in a systematic way. Applied methods will include machine learning, risk prediction, and assessment of bias. This Data+ project is sponsored by the Forge, Duke's center for actionable data science.

Project Leads: Shelly Rusincovitch, Ricardo Henao, Azalea Kim

Project Manager: TBD

Investigating oil and gas production in the United Kingdom

Producing oil and gas in the North Sea, off the coast of the United Kingdom, requires a lease to extract resources from beneath the ocean floor and companies bid for those rights. This team will consult with professionals at ExxonMobil to understand why these leases are acquired and who benefits. This requires historical data on bid history to investigate what leads to an increase in the number of (a) leases acquired and (b) companies participating in auctions. The goal of this team is to create a well-structured dataset based on company bid history from the U.K. Oil and Gas Authority; data which will come from many different file structures and formats (tabular, pdf, etc.). The team will curate these data to create a single, tabular database of U.K. bid history and work programs.

On the Shelf: exploring oil and gas production in the Gulf of Mexico

Producing oil and gas in the Gulf of Mexico requires rights to extract these resources from beneath the ocean floor and companies bid into the market for those rights. The top bids are sometimes significantly larger than the next highest bids, but it’s not always clear why this differential exists and some companies seemingly overbid by large margins. This team will consult with professionals at ExxonMobil to curate and analyze historical bid data from the Bureau of Ocean Energy Management that contains information on company bid history, infrastructure, wells, and seismic survey data as well as data from the companies themselves and geopolitical events. The stretch goal of the team will be to see if they can uncover the rationale behind historic bidding patterns. What do the highest bidders know that other bidders to not (if anything)? What characteristics might incentivize overbidding to minimize the risk of losing the right to produce (i.e. ambiguity aversion)?