Lab 2 – The Display of Oceanographic Data
This chapter was created as a data skills precursor to the rest of the data lab chapters. It is meant to assist students in understanding data visualizations that they will encounter in the later activities. The chapter is scaffolded with increasing difficulty so that students are introduced to the different data visualization types in the introduction and then practice or apply their knowledge in each of the lab activities within the chapter.
Approximate Time Involved
- Each of the 5 activities may take 30-50 minutes for students to explore, discuss and complete the assigned questions.
- Instructors should plan to implement this activity prior to other Ocean Data Labs as background on different types of data visualizations and their interpretation.
- Alternately, instructors could use different Lab 2 activities prior to other lab activities that use similar data visualizations. Consult instructor guides for suggestions on Lab 2 sections that are aligned with each later lab.
After completing this lab students will be able to:
- Distinguish between commonly used graph types in oceanography: Time series, profile, vertical section contour plot, bathymetry contour map, bubble charts
- Identify key components of a scientific graph including axes scales, legends, multiple y-axes
- Discover that real data can be quite variable, and that variability can be reduced if data are averaged
- Describe data patterns such as maximum and minimum values, and trends over time or distance
- Identify gaps in data and infer likely reasons for the gaps
- Describe the use of the oceanographic convention of plotting depth increasing down for station profiles and vertical sections
These learning outcomes are introduced and practiced in the Lab 2 activities as follows:
|Learning outcome||Activity 2.1||Activity 2.2||Activity 2.3||Activity 2.4||Activity 2.5|
|4||Introduced||Guided practice||Guided practice||Guided practice|
None for classes with student access to a computer or tablet. Instructors might optionally supply printouts of the graphs and questions if in a classroom without computers.
What students should know before this activity
This lab activity introduces students to basic data literacy by stepping them through not only different types of data visualizations but the basics of how to read each data visualization.
Background that would assist students while completing the lab exercises include
- Scientific method and data collection – why we make data visualizations from raw data
- Basic features of a graph, including x-axis, y-axis, axes labels
- Bathymetry and ocean basin features helpful but not mandatory (activity 2.3)
- Latitude and longitude (activity 2.4 and 2.5). Lab 1.3 introduces this concept.
What instructors should know before this activity
This chapter is a good chapter to cover early on in an introductory oceanography course or use as a refresher for upper level courses. It is designed to cover in order. However, you may only want to use some of the activities as a pre-lab that relate to some of the later data lab chapters that you are using, therefore each lab chapter was developed in a modular approach.
Students may have seen similar types of data visualizations that are in this lab activity; however, they may not understand how to read those visualizations or graphs. Introductory students may not be familiar with graphs that show high variability. In addition, some students may have weak skills in how to read the graph at all. Please don’t assume that your students in introductory courses fully understand graphing or how to read graphs.
Real scientific data is often very messy, yet we tend to distill the information down into overly simplified data visualizations in textbooks. It’s important for students to realize the variability within data collected and to be able to identify major trends within the data. As scientists we have the ability to look beyond the variability to see increasing or decreasing trends, seasonality or diurnal trends that are often missed by introductory level students (see Kastens et al., 2016 in Resources). This activity is scaffolded not only with different types of data visualizations that the students will encounter in oceanography and other science classes, but also with increasing complexity in data skills.
Instructors should familiarize themselves with the different types of data visualizations that the students will encounter within this activity.
It you are in a classroom or lecture hall without computers, you can either ask students to bring in their own laptops to work on the activities or project the graphics and have them answer the questions working in groups or as a discussion. Alternately you can go over the introduction to this activity explaining the types of data visualizations and assign the activities as homework.
LAB 2 student Answer Sheet form can be used to collect student answers.
A good introductory video, if your students did not watch it for Lab 1, is the Tools of Science: Data as a Tool that explains where the OOI data comes from. Instructors should have already covered the scientific method and data collection in an effort for students to understand why there’s a need to turn raw data into data visualizations before this lab.
Instructors also may want to refresh the student’s memory on what an average is, how it is calculated and its uses or misuses.
A good activity prior to the data skills lab may be to have students make general observations. Being able to describe what they are looking at is very important. In addition, trying to put what they are observing into context with their existing knowledge can help in student understanding and tap into higher order thinking skills. Using a gallery walk can be a good observation activity for introductory students by showing partial figures or graphs to simply get students talking about what their observations are in groups. Having students describe a variable that is increasing or decreasing, cyclic, etc…, without the axes/labels and without knowing what the variable is, can be very important first step to making simple observations.
Students often misinterpret the lack of data as a decrease to zero. It’s important for students to understand that a gap in data is the lack of data. They need to realize that scientific equipment sometimes fails or gets fouled and may not record data during a certain time period. We have purposely included a data gap within an activity.
In comparing different graphs students may forget the importance of scale or units on the x and/or y-axis. There have been many expensive and/or dangerous errors when mistakes are made. In the introduction to Lab 2, we provide a sample station profile with different scales for comparison. Have your students make comparisons prior to progressing to the equivalent scale temperature profile graphs and point out the potential to misinterpret the comparison when scales are unequal.
- Instructors should review the importance of scale prior to the students completing Activity 2.2.
- The following article details some famous instances of mistakes, some funny and some tragic that are good examples for why scale or units are important. Great miscalculations: The French railway error and 10 others
One of the goals of Activity 2.2 is getting students to understand the oceanographic data may be plotted slightly differently than what they see in a typical math class. Common misunderstandings occur when students don’t understand why zero is at the top of a y-axis. Explain that zero is the surface of the ocean and increasing numbers on the y-axis represent depth. We typically use positive numbers increasing downward because it’s understood that in oceanography we deal primarily with depth below sea level instead of height above sea level. Students will encounter this axes orientation on station profile and vertical section graphs.
Activity 2.2 Station profiles asks about thermoclines. Instructors may want to explain what a thermocline is and what it looks like on a graph, as students may be confused with identifying the thermocline in this activity.
Students don’t always realize that there’s variability within the water column in the oceans. As instructor you may want to prompt their previous knowledge about characteristics of a water column. For instance, ask if they ever jumped into a deep pool or water body in the summertime when the water may be warm on the surface but they hit a small thermocline and it’s cooler underneath. This can give them a visualization of a difference within the water column that they have experienced within their lifetime.
Bathymetric charts (Activity 2.3) will help students understand that there can be different visualizations for the bathymetry, some more exact than others providing depths whereas others may provide a better visualization using color. It’s important that students understand latitude and longitude prior to this activity.
In the vertical sections (Activity 2.4 ) we explain the relationship between station profiles and vertical sections. Students sometimes have a hard time visualizing the station profile from the vertical section. Students may also confuse the vertical section contour lines for depth contour lines instead of the property such as temperature or salinity.
Upper-level classes may want to add on to this lab by making their own graphs from the raw data. Instructors can provide raw data from one of the data explorations (below each exploration you may “download data” to provide it to students) and have the students plot the data in Excel or another software tool.
Upper-level research classes may want to explore a research question on their own and download data direct from the Ocean Observatories Initiative via Python. [Link to instructions and sample Python notebook]
[Will add links to PPT slides with background information, handouts/worksheets of the graphs and questions]
Other videos that might be useful in teaching the scientific method and sampling in oceanography can be found at Tools of Science YouTube channel.
A recommended reading for instructors that examines the differences between expert (you) and novice (your student) interpretations of data visualizations:
- Kastens, K. A., Shipley, T. F., Boone, A. P., & Straccia, F. (2016). What Geoscience Experts And Novices Look At, And What They See, When Viewing Data Visualizations. Journal of Astronomy & Earth Sciences Education (JAESE), 3(1), 27-58. https://doi.org/10.19030/jaese.v3i1.9689