Oceanography | Vol. 38, No. 2
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instructors to adapt both the teaching approach and the struc
ture of the activities to suit their specific student audiences. The
activity was performed during a single 3-hour laboratory class,
but based on our experience, dividing this activity into two sec
tions and separate 1.5-hour classes, with at home preparation
taking no more than 1 hour (before each 1.5-hour classes) may
be better suited to student’s learning pace. Below, we present a
suggested structure for the activity, informed by our experience
teaching this lab and by student feedback.
BACKGROUND LECTURE (15 minutes)
Ideally, dedicate lecture time prior to the lab activities to intro
duce the necessary concepts. Use the background sections pro
vided as a guide for this session. To help provide context for stu
dents and instructors without direct experience in oceanography,
we have included figures in the online supplementary materials
showing R/V Endeavor, the fluorometers installed on the under
way system, and fieldwork from this project. This foundational
lecture is essential for preparing students for the lab.
SECTION 1 (1 hour of homework, 1.5 hours in class)
The data provided stem from six separate R/V Endeavor cruises.
A few days before the lab session, assign each student a unique
cruise dataset, ensuring one of each of the six cruises is covered
by at least one student. Provide each student with the lab instruc
tions document, the corresponding .csv file, and the activity
template (all available in the online supplementary materials).
Groups of two to three students can also be considered.
As part of their homework (at most 1 hour), students should
review the instructions for both parts of the activity and work
through the first steps of Part 1 of Section 1. During the lab, the
instructor will guide students through the activity step-by-step,
ensuring everyone is able to complete the assigned tasks.
The instructor has access to all the final templates (in the
online supplementary materials) and formatted example figures
(created in MATLAB) to demonstrate expected results. Students
are encouraged to format their own figures, allowing for student
independence and creativity.
SECTION 2 (1 hour of homework, 1.5 hours in class)
Similar to Section 1, students should complete the steps of Part 1
independently before the lab session. The lab will begin by
reviewing their progress, focusing on the required linear regres
sion. The instructor will compile students’ results and compare
them to the expected outcomes provided in the supplemen
tary materials.
The second part of this section involves collaborative group
work, where students combine their results to address the pro
posed questions. The activity concludes with a group discussion
on the significance of fluorescence data post-calibration, as well
as an exploration of data quality processes, FAIR principles, and
open-access data.
CLASSROOM DISCUSSIONS
Throughout both activities, we recommend incorporating
“council moments” where the instructor pauses the session to
address proposed questions and facilitate discussions. We pro
vide sample questions and discussion topics.
BACKGROUND
We are in an era of big data, where high-resolution sensors mea
sure and transmit information at unprecedented rates, partic
ularly in the field of oceanography. Oceanographic data come
from a wide variety of sources, including sensors on ships,
observing platforms, and satellites. For these data to be use
ful and accessible to diverse users, data need to be processed in
ways that adhere to strict scientific standards and made avail
able as open access through data portals. Formalizing data han
dling approaches has led to the development of FAIR principles
that make data findable, accessible, interoperable, and reusable
(Wilkinson et al., 2016). We aim to demonstrate that critical
aspects of data validation and management require human-in-
the-loop intervention to ensure that data remain FAIR to the
scientific community.
Oceanic physical parameters (e.g., temperature, salinity,
depth) and biogeochemical parameters (e.g., dissolved oxygen,
bio-optical properties, nitrate, and carbonate system chemis
try components) are routinely measured using sensors. These
sensors can be deployed on different platforms (research ves
sels, mooring buoys, profiling floats, gliders) and can generate a
substantial volume of data. Interpreting environmental param
eters recorded by autonomous sensors can be challenging, and
post-processing is required, even if they have been calibrated by
the manufacturer before deployment. Several factors can make
the manufacturer’s calibration insufficient for ensuring accu
rate measurements, including sensor drift, mechanical issues,
and biofouling. While manufacturer-calibrated “raw” data offer
valuable insights into the relative changes in a given parame
ter during deployment, our goal is to demonstrate the critical
importance of post-calibration to obtain accurate absolute val
ues. These values are essential for making meaningful compar
isons and supporting oceanographic research. Biogeochemical
parameters, such as dissolved oxygen or chlorophyll a (Chl-a)
fluorescence, provide good examples of the challenges asso
ciated with post-calibration for research purposes, as they
require human-in-the-loop (HITL) calibration and valida
tion (Palevsky et al., 2024). Using data obtained directly from
the sensor, with raw voltages/signals converted into parameter
concentrations using manufacturer-provided coefficients and
equations, can lead to erroneous absolute values and interpre
tations. Therefore, developing and applying robust procedures
for both automated and HITL post-deployment data process
ing is essential to produce science-ready data from bio-optical
and chemical sensors.