The Argo Online School: An e-learning tool to get started with Argo

The Argo Online School (AoS) is a collection of videos, animations and hands-on Python-driven Jupyter notebooks designed to make the data accessible from more than 4,000 profiling floats that constitute the Argo program. The Argo program samples, in near real-time, the upper 2,000 meters of the ocean using a fleet of floats that drift with the ocean currents. The AoS consists of 28 chapters about the Argo program and its data, organized into a total of 3 lessons and 2 self-assessment sections: • Lesson 1 is intended to introduce the basics of Argo, its objectives and key elements, such as the structure of the Argo floats and their operation in the open ocean. • Lesson 2 focuses on the data coming from the Argo network, from its organization to its accessibility. Data quality control is also addressed through its two main modes: Real-Time and Delayed-Mode. • Lesson 3 is the AoS hands-on component, and it requires basic knowledge of Python. It provides a set of instructions for preparing a Python environment in case the user wants to run the Python Jupyter Notebooks on a local machine. This environment already includes recommended packages. The walk-through of Lesson 3 shows how to work with the netCDF format, how to access and process Argo data, and create visualizations to enhance understanding of the information derived from Argo data. The target audience of the AoS is high school, undergraduate or early graduate students. The programming content in Lesson 3 offers an ideal opportunity to support students pursuing a technical or science curriculum. Lessons 1 and 2 form a closed module and can be used independently by learners who wish to focus solely on Argo. It is recommended not to skip any lesson in the AoS, as the content is carefully structured from simpler to more complex concepts, providing a progressive learning experience. Users have the opportunity to self-assess their learning progress through the proposed interactive self-assessments in the AoS. The AoS has been designed to be expanded in the future to follow the implementation of new features in the Argo program.

The AoS has been possible thanks to the collaboration of the Euro-Argo members, the Argo Steering Team (https://argo.ucsd.edu) and has been funded by the European Union’s Horizon 2020 research and innovation program under grant agreement Euro-Argo RISE 824131 (https://www.euro-argo.eu/EU-Projects/Euro-Argo-RISE-2019-2022). The audiovisual work has been recorded and edited by Rafael Méndez Pérez (http: //rafaelmendezp.com/) while proofreading and english coaching support was provided by Agustín Prunell-Friend. The self-assessment sections are based on John M. Shea (Smith et al., 2021) software.

Peer reviewed