Art Markets: Paris Paintings

Project Summary

What drove the prices for paintings in 18th Century Paris?

Themes and Categories
Year
Contact
Paul Bendich
bendich@math.duke.edu

Graduate students: Hilary Cronheim and Sandra van Ginhoven, Duke Art, Law and Markets Initiative-DALMI

Faculty instructor: Mine Çetinkaya-Rundel

Course: STA112FS Better Living Through Data Science: Exploring/Modeling/Predicting/Understanding

What drove the prices for paintings in 18th Century Paris?

  • Auction price data
  • Visual characteristics of paintings
  • Sales information
  • Art dealers’ strategies

Methods used:

  • Data exploration
  • Variable interactions
  • Model fit and selection
  • Interaction variables
  • Prediction

Related Projects

A team of students led by Humanities Unbounded Fellow Eva Michelle Wheeler will explore how culturally-bound language in African-American literature and film is rendered for international audiences and will map where and into which languages these translations are occurring. Students will use a reference dataset to build and annotate a translation corpus, explore the lexical choices and translation strategies employed by translators, and conduct a macro-level analysis of the geographic and linguistic spread of these types of translations. The results of this project will bring a quantitative dimension to what has largely been a qualitative analysis and will contribute to ongoing academic conversations about language, race, and globalization.  

Project Lead: Eva Wheeler

This project allowed students in BIOL 268D (Mechanisms of Animal Behavior) to explore the relationship between estrogen, female sexual swellings, and male mating success in wild baboons using data from the Amboseli Baboon Research Project. Students learned how to use the popular R packages dplyr and ggplot2 to calculate descriptive statistics about the dataset and perform data visualization to understand and explore patterns in animal mating behavior and sexual signals.

Ecological data comes in various shapes and sizes. When conducting an ecological study, it is common to have population data (such as snail counts) and continuous sensor data (such as stream temperature with 35,000 data points collected each year!). Ecologists must reconcile data collected at different spatial and temporal scales in order to make inferences about their study systems. Luckily, there are standard practices and toolsets that ecologists use. In this data expedition, we ingest, arrange and query data collected in the field through various methods into formats that can be analyzed. We then use different plot types, data transformations and statistical tests, such that our analyses are appropriate for the type of data. We examine both field data collected by students and also large open-source datasets that can be scraped from the web and analyzed locally.

 

Each year, the Field ecology students measure physical, chemical, and biological characteristics of the Eno River. The Eno River also has been continuously monitored for numerous environmental parameters as part of the StreamPulse project (Duke and other collaborators worldwide). StreamPulse collects data from instream sensors, such as temperature and dissolved oxygen to estimate ecosystem processes such as metabolism. So, we are able to compare data collected in the field course to long term monitoring efforts.