Using Ocean Data View to Explore and Visualize Data

One of the challenges of working with large, complex systems and datasets is making sense of sometimes overwhelmingly large amounts of information. The human brain is simply not prepared to analyze thousands or millions of numbers and identify patterns or draw conclusions. However, the human brain is extremely good at visual pattern recognition. Computers can bridge this gap by processing large datasets and presenting them in a visual format, ready for human analysis. Consider the difference between a spreadsheet full of numbers and a colorful weather forecast map - one provides a lot more understandable information! If you haven't yet, you can read some background on the revolutionary importance of computer power.

Ocean Data View (ODV) is a free program that oceanographers use to visualize and map water masses, chemical distributions, and biogeochemical transformations. In this activity, you will learn how to use ODV to create a visual representation of changes in ocean chemistry over time. Specifically, you will be able to see how pH (a measurement of acidity/basicity) is changing in the upper 50 m of the water column with time. This type of data is of interest to climate scientists investigating the process of ocean acidification, one of the effects of increasing atmospheric CO2 concentrations due to human emissions.

The data you will be working with is "bottle data" from the Hawaiian Ocean Time (HOT) Series. This data was collected using a device called a Niskin bottle that allows water to be sampled at different depths and then analyzed for its various properties (salinity, oxygen, nutrients, temperature, pH, etc.). These samples were taken at various time points in the same location 100 miles north of Oahu, Hawaii. Follow the link above to learn more about the dataset.

First we will need to download ODV, then we will need to download usable data.

A. Download ODV:

  1. Register for the non-commercial version

  2. Click on the ODV_Application folder

  3. Click on the Latest_Version folder

  4. Select the folder corresponding to your operating system

  5. Click on the .dmg (Mac) or .exe (Windows) file to download.

B. Download Bottle Data from the Hawaiian Ocean Time (HOT) Series

DOWNLOAD - click to prompt the download of a pre-packaged (zipped) ODV file for 20 years of HOT bottle data

TIP: Create a folder called "ODV Data" to save this zipped file in before extracting.

C. Install the dataset

Unzip the datafile. During unzipping make sure you preserve the directory structure as stored in the zip-file.

Open the folders ("HOT" then "bottle") and double-click on the file "HOT_bottle_data_1988-2008.odv". This should open automatically with ODV; if not, tell your computer to use ODV for this type of file.

You should see a screen like the one below.

D. Explore the Data

The opening screen already contains a lot of interesting information! Take a moment to explore the window, including the graphs, data panel to the right, and menus at the top. Consider the following graphs.

  • What information is shown by the graphs? What do they all have in common?

  • What do you think the measurement of "Pressure [dbar]" correlates with in the ocean? Do you notice anything unusual about the y-axis labels? Why do you think this has been done for these graphs?

  • What happens if you click at different points on the graphs?

  • Are there any terms you don't understand on this screen? Take a moment to research any unfamiliar ones.

As you likely noticed, each graph shows a different measurement from ocean water (temperature, salinity (salt content), dissolved oxygen, etc.) plotted against varying pressure. Pressure correlates with water depth - pressure is low at the surface, and grows as you go deeper. Pressure is much easier to measure than depth so it provides a proxy for depth. Conveniently, one decibar (dbar, a unit of pressure) is roughly equal to one meter in depth, so 1000 dbar = 1000 meters deep.

The y-axis has been flipped upside-down (low pressure at the top) so that it matches our mental model of the ocean, with the "top" of the ocean at the top of the graph. If you click on particular points on the graphs, you can jump between different specific measurements and see specific information about them in the bottom right corner.

A lot can be learned even from simple two-variable graphs. Examine the graph for dissolved oxygen. What do you notice about its shape? Where is there the most dissolved oxygen in the water? It appears that there is the most oxygen (points farthest to the right) right at the surface of the ocean, which makes sense, since oxygen dissolves in from the atmosphere. Oxygen concentration drops sharply in the upper layer of the ocean, reaching a low at about 800 dbar (meters) down. Interestingly, dissolved oxygen levels then rise steadily as ocean depth increases. Do you have any hypotheses about why this might be true? To learn more, read about the oxygen minimum zone (OMZ).

E. Visualizing Behavior Over Time

While the graphs we just examined are very informative about the state of the ocean, they are missing a critical variable: time. If we want to learn about how climate change is affecting the oceans, it is essential to look at changes over time. Let's investigate this question: how is pH changing with time in the top 50 meters of the ocean?

a. Add “time” as a derived variable. From the top menu in ODV, select “View → Derived variables”, under “Time” select Time (station date&time), and click “Add”. Click “OK” to accept the default date range.

b. Change the layout template to 1 scatter window. From the top menu in ODV, select “View → Layout templates → 1 SCATTER window."

c. Change the axes. Right click on the scatterplot, click on X-variable and change to the newly derived “Time (station date&time)” variable (bottom of the list). Right click again on the scatter plot, click on Y-variable and change to “pH”

d. Constrain the data to the top layer of the ocean, down to 50 dbar (meters). Right click on the scatterplot, click on “Sample Filter → Customize Settings." Select the “Range” tab at the top, make sure that “Pressure” is the variable selected, and type in 0 - 50 as the acceptable range. Press "OK".

e. The full range function will automatically zoom in on the new range of data. On a mac, you can use the shortcut (command+F) or you can right click on the scatterplot and select Full Range.

Examine the data at this stage and consider what it represents. You have a two dimensional graph with TIME as the independent variable and pH as the dependent variable. Do you notice any trends?

It can be hard to see trends by looking at a lot of noisy data. In order to make trends clearer, it is helpful to calculate and graph regression lines. These lines summarize all of the data into one single line. There are two useful types that ODV can plot - a least-squares line, which produces a straight line always, or a moving average line, which averages the data at a series of points and then connects them into a bumpy line. Both can be valuable to help see patterns that are not obvious.

f. Add regressions to your plots. Right click on the scatterplot, click on "Extras → Statistics". Here you can see the mean and standard deviation for the plotted variables. Select “Moving average” from the drop down menu of the Fitted Curves section of the window. Click “Construct curve” and then “Show curve”, and close.

Repeat the process, selecting "Least-squares line" from the drop down menu instead. Observe the differences and similarities between the two lines.

g. Adding color to help visualize changes. Your graph right now is made up of black dots. You can tell high and low pH values from where they are on the graph, but it would be helpful to have a color coded key to enhance the visual pattern. To achieve this, right click on the graph and choose "Z variable." Select "pH" from the list (yes, the same variable as the y-axis!). The dots will become color coded with a scale shown to the right of the graph.

h. Gridded field view. For even more visual help, let's change the display mode so we see a smooth trend instead of individual dots. Right-click on the graph and choose "Properties". Select "Display Style" at the top of the window. Switch the style to "Gridded Field" by clicking the button to the right. Leave all the settings as they are and click "OK". You should now see a lovely rainbow graph that enhances the pattern you have already observed.

i. Reflect. What can we learn from this graph? Consider our original question: how is pH changing with time in the top 50 meters of the ocean? Do you have any hypotheses to explain what you are seeing?

Notice that even though ocean depth/pressure is not a variable on our graph, we filtered the dataset to only display those values, so we are addressing our question. You could easily adjust the filter to look at more or less data points. Try it out!

F. Looking at the ocean column using Pressure as a variable

What if we were interested in what was happening at different depths in the ocean? Recall from the first graphs we looked at that a variable that can be used to model ocean depth is pressure (dbar), where 1 dbar is approximately equal to 1 meter of depth.

a. Remove existing regression lines. There are two ways to do this. Right click directly on a regression line and select "Manage Graphics Objects". Choose any objects labeled "Polyline" and click "Delete". OR: right click on the graph, go to "Extras → Manage Graphics Objects," and follow the same steps.

b. Remove the sample selection criteria restraints. (Reverse step d. above). Click on “Sample Filter → Customize Settings." Select the “Range” tab at the top, make sure that “Pressure” is the variable selected, and click "Relax this range filter". This will allow data points from all depths.

c. Change the Y-axis to pressure. Ensure the range is reversed, so 0 is at the top (modeling the top later of the ocean). If it is not, right click the graph, choose "Y variable", and check the box "Reverse range" at the bottom of the window. This can also be done from the Data tab in the Properties window.

d. Ensure the Z-variable is still pH and the Display Style is still Gridded Field. IMPORTANT: You may notice that the color gradient seems odd now. Go back to Z-variable and double click on pH again. This will reset the color gradient to suit the current data display, instead of just the top 50 meters.

e. Look for patterns. Examine the pattern of pH levels relative to ocean pressure. How would you describe the pattern? Where is pH lowest? Highest? Where does it change the most? The least?

You may notice that the data has some significant gaps. Does this mean the water at those depths has no pH? Of course not. It just means the data does not exist in this dataset. Data is always a product of human activity, and subject to schedules, funding, instrumentation, and human error. In this case, it's likely that it was not practical to collect pH values at all depths or on all data gathering missions. You can explore what data is contained in each point on your graph by clicking in different places and observing the values in the right section of the display.

G. Explore the Data

Now that you have a sense for how to produce data visualizations using ODV, it's your turn to produce new plots to address questions. Here are a few ideas to get you started, but the sky's the limit! You could research the role of different ocean components and then see if you can observe changes with time or depth that support (or contradict!) reported findings.

      • What variables can you find that have more complete datasets than pH?

      • How do patterns of dissolved oxygen and temperature correlate? Are they changing with time?

      • Can you find any other variables that seem to have the same pattern over time as pH? (Hint: Consider what chemical change is causing pH changes in the water)

Students, please take this 1-minute survey, now that you've completed this activity. We are interested in learning about your experience so we can improve these resources. All responses to this survey are anonymous, all questions are optional, and your feedback is much appreciated.

Curriculum Contributors and Supporters

Monica Orellana, Principal Scientist, ISB and University of Washington
Meredith Jennings, Postdoctoral Fellow, University of Miami, 2018
Sarah Bercovici: Postdoctoral Fellow, University of Miami, 2018
Barb Steffens: Teacher Scholar, ISB
Claudia Ludwig: Director of Systems Education Experiences, ISB
Mari Knutson-Herbert Teacher Scholar, 2018, ISB
Anne Thompson: Research Assistant Professor, Portland State University
Anne Gillies: Teacher Scholar, 2019, ISB

Funding to support the development of this lesson was provided by the National Science Foundation Award OCE 1634009 to Dr. Monica Orellana. The content of these pages was created by students for students with the help of teachers and scientists. The views expressed herein are those of the authors and do not necessarily reflect the views of NSF or ISB.