Using Authentic Data from the Ocean Observatories Initiative in Undergraduate Teaching

The Ocean Observatories Initiative (OOI), funded by the National Science Foundation, provides a unique source of continuous, long-term oceanographic data from arrays of co-located instruments measuring physical, chemical, geological, and biological properties at seven locations in the coastal and open ocean of the North and South Atlantic and Pacific. The wealth of freely-accessible data provided by these platforms, many of which can be viewed in real or near-real time, provides an opportunity to bring these authentic data into undergraduate classrooms. However, the large volume of raw data and the inherent complexities of working with real-world data from such dynamic settings can pose a challenge to educators seeking a curated dataset focused on specific phenomena or learning objectives.

In this presentation, we will highlight existing educational resources derived from OOI data that are ready for other educators to incorporate into their own classrooms, as well as opportunities for new resources to be developed by the community. We will present examples of OOI data-based lessons we have designed and incorporated into our own introductory undergraduate oceanography courses in a range of settings at different types of institutions and with varied class sizes. Many of these lessons are based around existing interactive online data widgets (available at https://datalab.marine.rutgers.edu/explorations/) with curated OOI data on primary productivity, salinity, and tectonics and seamounts that we used to illustrate key oceanographic processes aligned with our course learning objectives as well as introduce students to the skills of authentic data analysis. We also present examples of more open-ended OOI-based research projects undertaken by advanced undergraduate students, and opportunities to flexibly adapt and develop new OOI data-focused activities suitable for a range of course learning objectives (i.e oceanography concepts or data science skills), course levels, and learning environments. We invite other educators to develop and implement new educational resources based on OOI data, and will synthesize existing tools and resources as a practical how-to guide to support new resource development.