Data Expeditions

A Data Expedition is an element of an undergraduate course that introduces students to exploratory data analysis.

Pairs of graduate students, often from different disciplines, work with the course instructor to formulate a question that will engage the students, and a pathway through a dataset that will provide insight.

Graduate student participants will receive a travel grant. Browse our current projects to find opportunities.

Projects

This Data Expedition introduces students to network tools and approaches and invites students to consider the relationship(s) between social networks and social imaginaries. Using foundation-funding data that was collected from the The Foundation Directory Online, the Data Expedition enables students to visualize and explore the relationship between networks, social imaginaries, and funding for higher education. The Data Expedition is based on two sets of data. The first set list the grants received by Duke University in 2016 from five foundations: The Bill and Melinda Gates Foundation, Fidelity Charitable Gift Fund, Silicon Valley Community Foundation, The Community Foundation of Western North Carolina, and The Robert Wood Johnson Foundation. The second set lists the names of board members from Duke University and each of these five foundations along with the degree granting institution for their undergraduate education. For the sake of this exercise, the degree granting institutions data was fabricated from a randomized list of the top twenty-five undergraduate institutions.

This Data Expedition seeks to introduce students to statistical analysis in the field of international development. Students construct a index of wealth/poverty based on asset holdings using four datasets collected under the umbrella of the Living Standards Measurement Survey project at the World Bank. We selected countries to represent different continents with comparable and recent survey data: Bulgaria (2007), Tajikistan (2009), Tanzania (2010-2011), and Panama (2008).

First, we construct an index of wealth based on household assets in the different countries using Principle Components Analysis. Once a poverty index is constructed, students seek to understand what the main drivers of wealth/poverty are in different countries. We include variables for health, education, age, relationship to the household head, and sex. Students then use regression analysis to identify the main drivers of poverty in different countries.

This data expedition explores the local (ego) patent citation networks of three hybrid vehicle-related patents. The concept of patent citations and technological development is a core theme in innovation and entrepreneurship, and the purpose of these network explorations is to both quantitatively and visually assess how innovations are connected and what these connections mean for the focal innovations and the technologies that draw on those patents in the future. The expedition was incorporated as part of the Sociology of Entrepreneurship class, where students are thinking about the emergence and diffusion of innovations.

Large publicly available environmental databases are a tremendous resource for both scientists and the general public interested in climate trends and properties. However, without the programming skills to parse and interpret these massive datasets, significant trends may remain hidden from both scientists and the public. In this data exploration, students, over the course of three hours, accessed two large, publicly available datasets, each with greater than 4 million observations. They learned how to use R and RStudio to effectively organize, visualize and statistically explore trends in deep sea physical oceanography.  

Our aim was to introduce students to the wealth of possibilities that human genotyping and sequencing hold by illustrating firsthand the power of these datasets to identify genetic relatives, using the story of the Golden State Killer’s capture with public genetic databases.

This Data Expedition introduced hypothesis-driven data analysis in R and the concept of circular data, while providing some tools for importing it and analyzing it in R.

The aim of this data expedition was to give students an introduction to stable isotopes and how the data can be used to understand trophic dynamics. 

Marine mammals exhibit extreme physiological and behavioral adaptions that allow them to dive hundreds to thousands of meters underwater despite their need to breathe air at the surface. Through the development of new remote monitoring technologies, we are just beginning to understand the mechanisms by which they are able to execute these extreme behaviors. Long- term animal-borne tags can now record location, dive depth, and dive duration and then transmit these data to satellite receivers, enabling remote access to behavior occurring both many kilometers out to sea and several kilometers below the ocean surface. 

The aim of this Data Expedition was for students to learn hands-on data visualization techniques using a variety of data types. Students first discussed how data visualization is useful, and tips to make graphs both visually appealing and easy to understand. 

Understanding of how to manipulate, analyze, and display large datasets is an essential skill in the life sciences. Introducing students to the concepts of coding languages and showing them the diversity of tasks that can be accomplished using a flexible coding scheme like R is an important step in the training of any life sciences professional. For students taking lab-based courses, who are often required to analyze the datasets they produce in class, learning these techniques can be helpful both in the short-term (i.e., during the semester) and for their future careers.

Matt and Ken led two labs for the engineering section of STA 111/130, an introductory course in statistics and probability. The lab assignments were written by Matt and Ken in order to bridge the gap between introductory linear regression, which is often explained in terms of a static, complete dataset, and time series analysis, which is not a common topic in introductory courses. 

Graduate Students: Kendra Kaiser and John Mallard

Faculty: Michael O’Driscoll

Course: Landscape Hydrology, EOS 323/723

Graduate Student: Jacob Coleman, 3rd year Ph.D. student in Statistical Science

Faculty Instructor: Colin Rundel

Class: STA 112, Data Science

Graduate student: Hamza Ghadyali          

Faculty instructor: Dr. Paul Bendich

Course: MATH 412 – Topology with Applications

In this Data Expedition, Duke undergraduates were introduced to a real world traffic citation data set. Provided by Dr. Frank R. Baumgartner, a political scientist at UNC, the data consist of 15 years of traffic stops, with over 18 million observations of 53 variables.

Dr. Guillermo Sapiro, professor in Pratt School of Engineering at Duke University, conducts ongoing autism research. Using image processing, he attempts to program a computer to detect whether babies (around eight to 14 months of age) display a sign of autism. This very early detection enables doctors to train these babies (when their brain plasticity is high) to behave in ways to counter the behavioral limitations autism imposes, thus allowing these babies to act more normally as they grow up. 

Graduate students: Aaron Berdanier and Matt Kwit, University Program in Ecology & Nicholas School of the Environment

Faculty instructors: Rebecca Vidra

Course: ENVIRON 102, Fall 2014

Using social network analysis to predict survival in large-brained mammals.

Students learned to visualize high-dimensional gene expression data; understand genetic differences in the context of gene networks; connect genetic differences to physiological outcomes; and perform simple analyses using the R programming language.

This data expedition introduced students to “sliding windows and persistence” on time series data, which is an algorithm to turn one dimensional time series into a geometric curve in high dimensions, and to quantitatively analyze hybrid geometric/topological properties of the resulting curve such as “loopiness” and “wiggliness.”

Introduce NBA and MLB datasets to undergraduates to help them gain expertise in exploratory data analysis, data visualization, statistical inference, and predictive modeling.

Questions asked: Do males and females scent mark equally? Do lemurs scent mark equally in breeding and non-breeding seasons?

STEM education often presents a very sanitized version of the scientific enterprise. To some extent, this is necessary, but overemphasizing neat-and-tidy results and scripted protocol assignments poses the risk of failing to adequately prepare students for the real-world mess of transforming experimental data into meaningful results. The fundamental aim of this project was to guide students in processing large real-world datasets far beyond their academic comfort zone so as to give them a more realistic understanding of how science works.

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