June 2025

Oceanography | Vol. 38, No. 2

74

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.