Dig@IT: Virtual Reality in Archaeology

Project Summary

A virtual reality system to recreate the archaeological experience using data and 3D models from the neolithic site of Çatalhöyük, in Anatolia, Turkey. 

Themes and Categories
Year

Project Team

  • Emmanuel Shiferaw,ECE/CS, Duke University
  • Cheng Ma , ME/CS, Duke University
  • Regis Kopper, DiVE, Duke University
  • Maurizio Forte, AAHVS, Duke University
  • Nicola Lercari,  World Heritage, UC Merced 

Project Objectives

  • Develop archaeological VRapp containing models of real site. 
  • Allow manipulation of artifacts/”digging” within system. 

Description

  • Can view information from existing archaeological database contextually, in 3D space, for objects documented by field archaeologists.
  • Allows for measurement, analysis of artifacts and land on-site.
  • Built for Oculus and DiVE.
  • For DiVE, companion apps built for Google Glass and iPad, which dynamically display information from Catalhoyuk site database relating to feature being examined. 

Workflow

  • Digital Archaeologists capture 3D models of dig site and landscape through image-based modeling (photogrammetry), laser scanning, LIDAR, etc.
  • 3D models of site, artifacts, are imported into Unity3D game engine, where: 
    • Interactions and display are built to allow analysis and discovery within the application.
    • Application is built with Oculus Rift as head mounted-display, and Razer Hydra tracked wands as input devices. 

Download the project poster (PDF).

 

Related Projects

The visibility of hate groups such as the Alt-Right became mainstream into contemporary political culture during the Unite the Right Rally in Charlottesville, VA in 2017. This project aims to explore methods to quantify the presence of Latinxs within the Alt-Right, particularly in how they racialize themselves in a space that often spews hate towards Mexicans and other marginalized groups from Latin America. Using data from multiple sources (such as Twitter, Stormfront, and Breitbart), we developed a corpus of tweets, subthreads, and articles, and analyzed this data using basic natural language processing (NLP) techniques.

Project Lead: Cecilia Márquez

Project Manager: Susan Jacobs

 

Click here to view the team's project summary slides

 

Watch the team's final presentation (on Zoom) here:

We apply word embedding models to corpora from the start of the Early Modern period, when the market economy began to dramatically expand in England. Word embedding models use neural networks to map vectors to words so that semantic relationships are preserved within the vectors’ geometry. Such models have been successful in understanding cultural trends and stereotypes in large corpora of texts, but these techniques are infrequently used on texts dating much farther back than the 19th century. Using newly developed methods for analyzing word embeddings, we track the development of the meanings of words related to consumerism, including their relationships with gender over time.

 

Project Leads: Astrid Giugni, Jessica Hines

Project Manager: Chris Huebner

 

Click here to view the team's final poster

 

Watch the team's final presentation (on Zoom) here:

Led by Dr. Eva Wheeler, this project considers how racial language in African American literature and film is rendered for international audiences and traces the spread of these translations. To address the study’s primary questions, the team analyzed a preliminary dataset and explored the relationship between translation strategy and different categories of racial language. The team also conducted a macro-level analysis of the linguistic, temporal, and geographic spread of African American stories using the IMDB and WorldCat databases. We have found a large amount of variation in how African American stories are rendered, which can in part be explained through a social scientific lens.

 

Project Lead: Eva Wheeler

 

Project Manager: Bernard Coles

 

Click here to view the team's project poster

 

Watch the team's final presentation (in Zoom) here: