Data Expeditions Projects
This data expeditions module used three full course sessions to introduce undergraduate hydrology students with minimal programming background to: Public water data (water quantity and chemistry) Spatial analysis of water data 2 core, spatial datasets produced by the USGS that enable spatial analysis The programming language R R based tools for water data Spatial analysis and maps […]
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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- […]
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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. Graduate Students: Jenn Coughlan, Ryan Campbell Course: Biology 490s – Methods in Comp Bio & Genomics Over two 70-minute class […]
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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 […]
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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 […]
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This data expedition focused on the mechanisms animals use to orient using environmental stimuli, the methods that scientists use to test hypotheses about orientation, and the statistical methods used with circular orientation data. Students collected their own data set during the class period, performed hypothesis testing on their data using circular statistics in R, and […]
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In this two-day, virtual data expedition project, students were introduced to the APIM in the context of stress proliferation, linked lives, the spousal relationship, and mental and physical health outcomes. Stress proliferation is a concept within the stress process paradigm that explains how one person’s stressors can influence others (Thoits 2010). Combining this with the […]
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Graduate Students: Kendra Kaiser and John Mallard Faculty: Michael O’Driscoll Course: Landscape Hydrology, EOS 323/723 The goals of this exercise were twofold: introduce students to scientific programming languages and reinforce hydrological concepts through an assignment that utilized a publically available high-frequency dataset. Although analysis of environmental data is almost always performed using MATLAB, R, or […]
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Graduate Student: Jacob Coleman, 3rd year Ph.D. student in Statistical Science Faculty Instructor: Colin Rundel Class: STA 112, Data Science Data management, summarization, and exploration with R package dplyr Data visualization through R package ggplot2 Worked with state-of-the-art data pulled from online source Summary In this Data Exploration, students were introduced to baseball dataset Statcast, […]
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Graduate student: Hamza Ghadyali Faculty instructor: Dr. Paul Bendich Course: MATH 412 – Topology with Applications Highlights of Data Expedition Students explored daily observations of local climate data spanning the past 35 years. Topological Data Analysis, or TDA for short, provides cutting-edge tools for studying the geometry of data in arbitrarily high dimensions. Using TDA tools, students […]
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Fluid mechanics is the study of how fluids (e.g., air, water) move and the forces on them. Scientists and engineers have developed mathematical equations to model the motions of fluid and inertial particles. However, these equations are often computationally expensive, meaning they take a long time for the computer to solve. To reduce the computation […]
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Female baboons occasionally exhibit large swellings on their behinds. Although these “sexual swellings” may evoke disgust from human on-lookers, they provide important information to group members about a female’s reproductive state. To figure out what these sexual swellings mean and whether male baboons notice, we need to look at the data. This data expedition explores […]
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A large and growing trove of patient, clinical, and organizational data is collected as a part of the “Help Desk” program at Durham’s Lincoln Community Health Center. Help Desk is a group of student volunteers who connect with patients over the phone and help them navigate to community resources (like food assistance programs, legal aid, […]
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We led a 75-minute class session for the Marine Mammals course at the Duke University Marine Lab that introduced students to strengths and challenges of using aerial imagery to survey wildlife populations, and the growing use of machine learning to address these “big data” tasks. Graduate students: Gregory Larsen and Patrick Gray, Marine Science & Conservation […]
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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 […]
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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. Graduate student: Derek Owens-Oas Faculty instructor: David Banks Course: STA 111 – Probability […]
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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. Graduate students: Liana Burghardt and Colin Maxwell, PhD candidates, Biology Department Faculty instructor: Danielle Armaleo Course: Collaboration with Dr. Armaleo in Bio 214 (Cellular and […]
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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.” Graduate student: Chris Tralie Faculty instructors: Paul Bendich […]
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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. Graduate students: Joseph Feldblum, Evolutionary Anthropology, and Vivienne Foroughirad, Marine Science & Conservation Faculty instructor: Julie Teichroeb
Showing 1-20 of 48 results