Research and management of aquatic ecosystems toward resiliency goals requires long-term monitoring. Monitoring systems, such as the Integrated Ocean Observing System, Long Term Ecological Research Networks, and the Ocean Observatories Initiative, stream large volumes of data. These datasets are complex in not only their size, but also in the variety of types of data and multiple overlapping signals in real-world systems. The next generation of aquatic scientists need strong data literacy skills to be able to synthesize these datasets and interpret drivers of ecosystem change. Therefore, it is critical that higher education experiences build students’ skills in working with authentic data at all levels. Presentations focused on new opportunities to engage undergraduate students in authentic data experiences using real-world datasets to teach aquatic science processes are welcomed. Discussions may include classroom curriculum, other training of data skill development at the undergraduate or graduate level, data management, use of software for accessing and interacting with data, data visualization, pattern interpretation, and related skills needed to make meaning of complex, real-world data.
Lead Organizer: Denise Bristol, Hillsborough Community College - SouthShore (dbristol@hccfl.edu)
Co-organizers:
Anna Pfeiffer-Herbert, Stockton University (Anna.Pfeiffer-Herbert@stockton.edu)
Janice McDonnell, Rutgers University (mcdonnel@marine.rutgers.edu)
Presentations
02:30 PM
WEAVING OCEAN OBSERVING DATA INTO UNDERGRADUATE MARINE SCIENCE EDUCATION (9015)
Primary Presenter: Anna Pfeiffer-Herbert, Stockton University (Anna.Pfeiffer-Herbert@stockton.edu)
Ocean observing systems such as the Ocean Observatories Initiative (OOI) enable the long-term monitoring necessary to tackle big environmental challenges. Learners entering the observational oceanography field must move from the novice level of recognizing trends and patterns in small and simple data sets toward the expert level of making meaning from messy real-world data at some point in their educational career. This challenging development requires scaffolding and multiple chances to practice with increasingly complex data examples. I will discuss course sequences in an undergraduate marine science program that provides opportunities for students to ocean oceanographic data fluency. The sequence begins with observatory data curated to demonstrate ocean phenomena, provided by the OOI Data Labs project, and builds up to hypothesis-driven independent research. This progression will be illustrated with case studies of instructor professional development and student pathways through the curriculum and beyond.
02:45 PM
Using courses as vehicles to develop long-term time series data for student research (9223)
Primary Presenter: Sasha Seroy, University of Washington (sseroy@uw.edu)
Undergraduate courses that emphasize field research skills and data literacy practices help prepare students for a variety of entry-level careers in the geosciences. Courses that are offered annually, or more frequently, are excellent platforms to develop local long-term time series of student generated data through repeated sampling. A course-based time series enables students to gain experience collecting their own data in the field, understand their data in the context of historical patterns, and gain access to larger data sets to conduct more sophisticated analyses. We present examples from an annually offered upper-level undergraduate marine biology field course where students contribute to and use long-term time series to build skills in multiple research training modules. To investigate intertidal community structure over time, students conduct established transects to contribute to a 40-year data set. To evaluate tidal effects on oceanographic patterns, students deploy a CTD over a tidal cycle to contribute to a multi-year observing effort in San Juan Channel, WA. In these example modules, students conduct a short research project where they use the course time series to test a hypothesis of their own development. This structure facilitates regular environmental monitoring through accessible course-based research, enables students to take ownership over a local data set and provides opportunities for students to build and develop critical data literacy skills. This model can also be adapted for courses at various levels and disciplines.
03:00 PM
Expanding research impact through better data and code practices (9417)
Primary Presenter: Lindsay Platt, Consortium of Universities for the Advancement of Hydrologic Science, Inc (CUAHSI) (lplatt@cuahsi.org)
Data cleaning and management are often viewed as the less glamorous part of a scientific endeavor, despite consuming a significant amount of time. As water data collection methods have expanded and scientists need to integrate more and more data into their analyses, some traditional scientific approaches have failed to scale. For many scientists, wrangling data and conducting scientific analyses now requires new skills and techniques, including learning to code in languages such as R and Python. Mastery of these skills can significantly improve efficiency, veracity, and flexibility of scientific research, enabling reproducible results and reusable methods. In this session, we will cover some of the essential concepts and approaches that researchers of any technical ability can adopt to improve their data analysis workflows, including best practices for code organization and development, cultural shifts necessary to support and encourage development of these skills, and a brief introduction to programmatic solutions for automating workflows. Researchers will leave this session feeling empowered to improve their data-handling techniques in order to build more robust, trustworthy, and innovative scientific workflows that can support the growing demands for scientific vigor in a “big data” world.
03:15 PM
Enhancing Data Literacy in Undergraduate Oceanography: A Scaffolded Python Exercise for Exploring Primary Production Variability Using OOI Data (9438)
Primary Presenter: Mikelle Nuwer, University of Washington Seattle (mrasmuss@uw.edu)
The development of data literacy skills to analyze and interpret large, complex datasets is essential for addressing how and at what pace the oceans are changing. Authentic data from sources like the Ocean Observatories Initiative (OOI) offer a valuable resource for cultivating these skills in undergraduate education. We have developed a new set of Python exercises complementing the existing OOI Data Lab on primary production (Lab 7). These activities aim to deepen students' understanding of oceanographic concepts while simultaneously developing data literacy skills. Students explore how the timing and magnitude of primary production vary with latitude, forming and testing hypotheses using real-world OOI data. Multiple Python activities are structured in a way to guide students through progressively complex data analysis tasks beginning with exposure to simple Python code. Students begin by recreating and customizing basic time series plots, adding a series from a second location, and building up to exposure to more advanced data visualization techniques. This approach introduces Python programming skills while reinforcing and deepening spatial variability in oceanographic processes, and is meant to be instructor-friendly, even for those without extensive Python expertise. This data lab activity gives students the opportunity to independently explore variables and create plots to investigate relationships between parameters, building students’ confidence in applying the scientific method to test hypotheses and draw evidence-based conclusions. These activities may serve as a template adaptable for other teaching approaches in undergraduate education, offering a framework for integrating data literacy across various oceanographic topics and course levels. This framework allows for flexibility in coding abilities for both students and instructors and ensures consistency across existing and future activities. By combining conceptual learning with hands-on data analysis methods, these exercises help to prepare students to synthesize complex datasets and interpret broad ecosystem processes. Specific implementation strategies and potential impacts on undergraduate oceanography education will be discussed.
03:30 PM
An open source textbook for facilitating undergraduate education in marine computer vision (9498)
Primary Presenter: Atticus Carter, University of Washington (attcart@uw.edu)
As careers and internships in marine imaging continue to grow, there is a notable lack of customized educational resources for onboarding and skill-building in this specialized field, particularly in marine image data literacy. To address this gap, I present a pilot course titled "Computer Vision across the Marine Sciences," which will be taught as a special topics course during the winter quarter of 2025 and made available online indefinitely for free. The course is accompanied by an open-source interactive textbook and lab manual, available at oceancv.org, which provides hands-on learning materials. This course aims to equip students with best practices and current research in marine imaging techniques, alongside intermediate Python programming skills. Our instructional approach is grounded in constructivist learning theory, emphasizing active, self-guided exploration. Key components of the course include the use of Google CoLabs to facilitate interactive, cloud-based learning environments where students can engage with Python syntax and functions relevant to marine imaging. Through a combination of flipped classroom structures, synchronous activities infused with active learning, and individualized final projects, students will be encouraged to apply their learning in practical, research-driven contexts. The course design prioritizes accessibility, ensuring that learners with varying levels of prior experience can achieve similar success through higher engagement with course resources. By analyzing data from surveys, student work, assessments, and focus group feedback, I aim to continuously refine the course to better support self-guided scientific inquiry and skill acquisition. I believe that this approach to teaching computer vision and Python within the context of marine imaging will be broadly applicable and beneficial across the earth sciences and other scientific domains.
03:45 PM
HANDS-ON POST-CALIBRATION OF IN VIVO FLUORESCENCE USING OPEN ACCESS DATA – A GUIDED JOURNEY FROM FLUORESCENCE TO PHYTOPLANKTON BIOMASS (9440)
Primary Presenter: Amanda Herbst, University of Rhode Island Graduate School of Oceanography (amandaeherbst@gmail.com)
Oceanographic data, hailing from ship-based sensors, observing platforms, and satellites, are being collected at increasingly high resolutions and require processing to make them accessible to a broad range of users adhering to rigorous scientific standards. Processing raw sensor-based data is essential for a multitude of reasons, including noise reduction, unit conversion, parameter derivation, and calibration. Consequently, data management skills are crucial for handling these increasingly large volumes of data. Therefore, to promote these skills in the next generation of aquatic scientists and introduce them to the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) data, we have developed an activity that guides undergraduate students through the post-calibration of underway Chl-a fluorescence data from six NorthEast US Shelf Long-Term Ecological Research cruises. Building on a successful summer internship, this activity develops students' skills in data acquisition, processing, visualization, and management. Working with real-world datasets equips future aquatic scientists with the tools to synthesize complex datasets, interpret ecosystem changes, and apply aquatic science in practical contexts.
EP02B - Building Data Literacy Skills in the Next Generation of Aquatic Scientists
Description
Time: 2:30 PM
Date: 29/3/2025
Room: W206B