A Shallow Dive into Deep Sea Data

Project Summary

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.  

Themes and Categories

Graduate Students: Sarah Solie (Biology) and Arielle Fogel (University Program in Genetics and Genomics, Evolutionary Anthropology)

Faculty Member: Dr. Kate Thomas

Course: Biology 190: Life in the Deep Sea

Students gained experience exploring patterns in multivariate oceanographic data, relevant to their coursework, to answer the following four questions:

  1. How does average temperature and salinity at the surface of the ocean compare to the temperature and salinity at 3000 meters below the surface?
  2. Do the trends observed in question 1 differ across tropical, temperate, and polar climates?
  3. What is the relationship between ocean temperature and salinity across depths ranging continuously from the surface to 5500 meters below sea level?
  4. Do the trends observed in question 2 differ across tropical, temperate, and polar climates?

As students pursued these questions, they were introduced to R, a free software program that provides powerful tools for statistical computing and graphics, and RStudio, an integrated development environment frequently used for easier programming in R. They learned valuable skills for future data analysis, including:

  1. Accessing and downloading two physical oceanography databases (salinity and temperature) from the National Oceanic and Atmospheric Administration (NOAA) and National Oceanographic Data Center (NODC) World Ocean Atlas 2013 - https://www.nodc.noaa.gov/OC5/woa13/woa13data.html
  2. Importing and inspecting a dataset in .csv format in RStudio
  3. Installing and using R packages
  4. Tidying data such that it was interpretable for R analysis
  5. Manipulating data included subsetting, filtering, transforming, and summarizing data
  6. Creating a new categorical variable and assigning values to it based on existing data
  7. Using graphical visualization (see Graphics Created) including:
    1. Boxplots (Figures 1-2)
    2. Scatterplots (Figures 3-6)
  8. Performing statistical tests including:
    1. A two sample t-test
  9. Best practices for data wrangling and analysis (e.g. inspecting data after manipulation, annotating code)

At the end of the exercise, students were provided with additional online resources to continue exploring data with R and RStudio.

The Datasets

Students accessed and explored two massive datasets from the National Oceanic and Atmospheric Administration (NOAA) and National Oceanographic Data Center (NODC) World Ocean Atlas 2013. Specifically, they used the annual temperature statistical mean and the annual salinity statistical mean datasets which contained temperature or salinity observations, respectively, across depth (up to 5500 meters), location (at 1o spatial resolution), and time (1955-2012).

Graphics Created

Temperature by Depth and Climate
Figure 1. Temperature at the surface versus 3000 meters below sea level and its relation to climate
Salinity by Depth and Climate
Figure 2. Salinity at the surface versus 3000 meters below sea level and its relation to climate.
Temperature by depth and climate
Figure 3. Temperature by depth and climate.
Salinity by depth and climate
Figure 4. Salinity by depth and climate.
Average temperature by depth and climate
Figure 5. Average temperature by depth and climate.
Average salinity by depth and climate
Figure 5. Average temperature by depth and climate.

Course Materials

Please see the R Markdown file titled “deep_sea_data.Rmd” as well as the PDF version, which includes figures, titled “deep_sea_data.pdf”.

Student Feedback

“I learned that programming is probably 10% writing out the code and 90% figuring out what went wrong. It is a ton of troubleshooting, and through that troubleshooting is a lot of frustration. However, it was also a lot of fun doing it. Problem solving has always been enjoyable for me, so I had a good time figuring out what I did wrong.”

“It was ... cool learning all of the different manners in which you can analyze data using the program and also compile all of the information—over 4 million data points—into very easy to read graphs that made interpreting the data very simple.”

“I think it was an amazing experience to make 4 million data [points] into [a] few intuitive graphs.”

“Using the skills I learned in these lessons, I can convey a huge group of data that seems chaotic into a series of tables that [are] both easy to see and easy to analyze.”

“I can understand why and how to use the codes with the instruction of the teachers.”

“Coding [in R] made it easier to graph complicated scientific results with many variables that programs like Excel would struggle with.”

Data Sources

  1. Locarnini, R. A., A. V. Mishonov, J. I. Antonov, T. P. Boyer, H. E. Garcia, O. K. Baranova, M. M. Zweng, C. R. Paver, J. R. Reagan, D. R. Johnson, M. Hamilton, and D. Seidov, 2013. World Ocean Atlas 2013, Volume 1: Temperature. S. Levitus, Ed., A. Mishonov Technical Ed.; NOAA Atlas NESDIS 73, 40 pp.
  2. Zweng, M.M, J.R. Reagan, J.I. Antonov, R.A. Locarnini, A.V. Mishonov, T.P. Boyer, H.E. Garcia, O.K. Baranova, D.R. Johnson, D.Seidov, M.M. Biddle, 2013. World Ocean Atlas 2013, Volume 2: Salinity. S. Levitus, Ed., A. Mishonov Technical Ed.; NOAA Atlas NESDIS 74, 39 pp.

Related 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.