Discrete vs. Continuous Data
There are two key benefits of ocean serving systems: they collect data over long time periods, and in high-resolution.
Many oceanographic experiments rely on a single cruise or mooring deployment. But when a location is designated as part of an observing system, it becomes semi-permeant with multiple moorings succeeding each other. This allows scientist to build up a large dataset, which can be used to study seasonal and long-term processes.
Observatories also allow scientists to collect data on a regular basis. Many research projects consist of a single cruise, or sometimes a few periodic visits to a site. This only provides scientists with a small snapshot of what is going on when they happen to be visiting, and that is rarely during storms which is when all the fun happens!
A mooring or underwater node on the other hand, can collect data every hour, minute or second, providing a high-resolution temporal dataset from which processes at many time scales can be studied. Whether it’s a synoptic pattern of storm fronts moving through, a diurnal or semi-diurnal tidal cycle, or a micro-scale process occurring on the time span of seconds to minutes (think turbulence and mixing), being able to fill in the gaps is an invaluable benefit.
Let’s turn this into an activity…
To introduce this concept to students or public audiences, one of my favorite activities is the Cool Classroom’s “Discrete vs. Continuous Data.” In this activity, students are presented with a sequence of 6-12 pictures (shown above). These are essentially snapshots of data, or alternatively you can call them pieces of evidence taken from the real world. (Remember, photos can be data too!)
I typically start off slow by asking the audience to make observations from the first image. As people explain what they see, I emphasize that there is an important difference between facts that they can observe (e.g. the woman is wearing a rain suit, on a boardwalk in a marsh area) and inferences they might be making. (Is it raining? Is she walking forward? Is this in NJ? – You can’t really tell from the first photo.) In this activity, the facilitator’s questions are essential to guiding students’ understanding of the data and how they can properly interpret it.
As the audience looks at additional still shots, a larger story begins to unfold. However, it’s remains important to distinguish what one can definitively say has happened based on the photos (she moved forward, she’s looking over the boardwalk), and what one can only hypothesize or guess happened between the shots. (Did she knock the bucket off, or did it disappear some other way? Did she fall or jump off the boardwalk? We don’t really know, exactly, though we can make some inferences.)
Finally, the audience gets to watch a video of the same sequence. Thanks to the benefit of high-resolution data, they get to observe what really happened on the boardwalk.
Of course, what makes this activity fun, is that there are effectively two stories that can be told with widely different circumstances, even though the outcome is the same. Students can make a number of guesses from the photos, but they don’t really know what happened until seeing the video.
And that’s exactly the benefit of observing systems. Continuous, high-resolution data, provides scientists with the observations they need to really understand what is happening in-between the few snapshots they collect on a ship. And because of this, observatories provide us with a new window to observe processes as they are happening in the ocean and, more importantly, in greater detail than we have been able to see before.
If you’re interested in using this activity with your class, you can download the Discrete vs. Continuous Data powerpoint file.
Personally, I also think this is a great idea for a science-based creative writing assignment: Create two versions of the same story using both discrete and continuous perspectives. I’d love to see what students could come up with.
Special thanks to Lisa A. and JCNERR for creating the video.