From Data to Science: An Introduction to the National Health and Nutrition Examination Survey (NHANES)

Project Summary

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.

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

Graduate students: Jameson Clarke and Rick Gawne

Faculty instructor: Fred Nijout

Focus: Questions that can be asked and answered using large public health databases

Skills gained: (I) Accessing and using NHANES, (II) Statistical analyses using JMP

Results: Presentations describing findings and pitfalls of large data exploration

Related Projects

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.