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 # Summary
 
-The **Argo Online School (AoS)** is a set of videos, animations and hands-on python driven jupyter notebooks designed to make the data from the more than 4000 profiling floats that constitute the Argo program, accessible. The Argo program samples, in near real time, the upper 2000 meters of the ocean using a fleet of floats that drift with the ocean currents. The AoS is integrated by 28 chapters about the Argo program and its data, organized into a total of 3 lessons and 2 self-assessment sections:
+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 4000 profiling floats that constitute the Argo program. The Argo program samples, in near real-time, the upper 2000 meters of the ocean using a fleet of floats that drift with the ocean currents. The AoS is integrated by 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 offers a set of instructions to prepare a python environment in case the user wants to run the python Jupyter Notebooks in a local machine. This environment already includes recommended packages. The walkthrough of Lesson 3 shows how to work with the netCDF format, how to access and process Argo data, and how to produce fancy plots for understanding information from Argo data.
+* Lesson 3 is the AoS hands-on component, and it requires basic knowledge of python. It provides a set of instructions forpreparing a python environment in case the user wants to run the python Jupyter Notebooks in a local machine. This environment already includes recommended packages. The walkthrough 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 are high school, undergraduate or early graduate students, and the programming content included in Lesson 3 provides an ideal opportunity to support students with technical or science curriculum. Lessons 1 and 2 are themselves a closed module and could be used by a learner without using lesson 3, assuming the goal is simply to learn about Argo.  It is recommended not to skip any lesson in the AoS as the content is carefully connected from less to greater complexity, guaranteeing a progressive learning. The user has the opportunity to self-assess the learning process carried out during their course in the AoS through the proposed quizzes.
+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, ensuring progressive learning. Users have the opportunity to self-assess their learning progress through the proposed quizzes in the AoS.
 
-The AoS has been designed to have the possibility to be expanded to follow the implementation of new features in the Argo program.
+The AoS has been designed to be scalable to follow the implementation of new features in the Argo program.
 
 # Statement of need
 
-Argo [@roemmich-2019; @riser-2016] is an international program that collects information from inside the ocean using a fleet of floats that drift with the ocean currents. The floats move up and down between the surface and a mid-water level measuring ocean variables and spend almost all their life below the surface. Argo has been collecting more than 100,000 oceanographic profiles per year since 2012 and is composed of approximately 4,000 floats, constituting the major component of the [Global Ocean Observing System](https://www.goosocean.org/).
+Argo [@roemmich-2019; @riser-2016] is an international program that collects information from inside the ocean using a fleet of floats that drift with the ocean currents. The floats move up and down between the surface and a mid-water level, measuring ocean variables, and spend almost their entire lifespan below the surface. Since 2012, Argo has collected over 100,000 oceanographic profiles per year and consists of approximately 4,000 floats, making it the major component of the [Global Ocean Observing System](https://www.goosocean.org/).
 
-Although data from Argo [@Argo2021] is publicly and freely available, it is a huge dataset, with thousands of files and the free documentation is rather complex since it has to cope with the many different models of floats and sensors, and the traceability of the quality control of all the measurements. The Argo community has always been aware of the difficulties of early users to manage the complex and large datasets from the network. In that sense, the Argo Online School addresses an existing need for the Argo community to communicate on the Argo programme and how its invaluable dataset can be used for various applications.
+Although data from Argo [@Argo2021] is publicly and freely available, it represents a vast dataset with thousands of files. The accompanying documentation can be quite complex, as it must address the various models of floats and sensors, as well as the traceability of quality control for all measurements. The Argo community has always recognized the challenges faced by early users in managing the complex and extensive datasets from the network. In this context, the Argo Online School meets the need for effective communication about the Argo program and how its invaluable dataset can be utilized for various applications.
 
-The potential of e-learning is used in the Argo Online School to offer all kinds of resources to users and thus promote and improve the access and use of the Argo data. In this way, the AoS is defined as an e-learning tool based on an interactive environment similar to other already established educational platforms. It is a tool with great educational potential that not only seeks to show the basic steps to use Argo data, but also to empower users so that they face future _programming_ scenarios in their academic and professional stage.
+The Argo Online School leverages the potential of e-learning tools to provide a variety of resources to users, thereby promoting and enhancing access to and use of Argo data. In this way, the AoS is defined as an e-learning tool that offers an interactive environment similar to other established educational platforms. It is a tool with significant educational potential that aims not only to demonstrate the basic steps for using Argo data but also to empower users to tackle future _programming_ scenarios in their academic and professional careers.
 
 The AoS was presented at the [2nd Ocean Observers Workshop](https://bit.ly/3pUChmJ) on November 20th, 2021  and at the [22nd Argo Data Management Team meeting](https://bit.ly/3e39rLL) on December 10, 2021.
 
 # Notes on instructional design
 
-The AoS aims to teach the basic foundations to understand and use the Argo data. The AoS does not attempt to show everything about the Argo program, since the Argo documentation is available from the Argo Data Managment Team ([http://www.argodatamgt.org/Documentation](http://www.argodatamgt.org/Documentation)) [@ArgoManual2022, @ArgoQCManual2022] for deeper learning. The AoS does not pretend to be a library or Application Programming Interface to ease Argo data access, since there are other initiatives with that purpose; among others, *Argopy*, a python library for Argo data beginners and experts [@Maze2020] or  *ArgoVis*, a web application for fast delivery, visualization, and analysis of argo Data [@Argovis].
+The AoS aims to teach the fundamental concepts needed to understand and use Argo data. It does not seek to cover every aspect of the Argo program, as comprehensive documentation is available from the Argo Data Management Team ([http://www.argodatamgt.org/Documentation](http://www.argodatamgt.org/Documentation)) [@ArgoManual2022, @ArgoQCManual2022] for deeper learning. The AoS does not aim to serve as a library or Application Programming Interface to ease Argo data access, as other initiatives already fulfill that purpose, among others, *Argopy*, a python library for Argo data beginners and experts [@Maze2020] or  *ArgoVis*, a web application for fast delivery, visualization, and analysis of argo Data [@Argovis].
 
-The AoS is a set of videos and hands-on python driven jupyter notebooks, designed to for high school or graduate students in any discipline, and it offers:
+The AoS is a set of videos and hands-on python driven jupyter notebooks, designed for high school or graduate students in any discipline, and it offers:
 
 * An overview of the Argo program and an assessment of the need for Argo.
 * A description of how the Argo data is organized.
@@ -57,17 +57,17 @@ The AoS is a set of videos and hands-on python driven jupyter notebooks, designe
 * A review of the main characteristics of the quality controls used: Real-Time and Delayed-Mode.
 * Step-by-step instructions on data access, processing, and product generation, through the execution of commands based on the programming language python.
 
-All content is divided into three lessons: 1. The Argo Program, that describes the basic concepts of this ocean observing network; 2. The Argo Data, that describes how the data is organized; and 3. Using the Argo data, that uses the knowledge of the previous sections and python driven jupyter notebooks to teach how to use the data. Finally, a quiz section is included for self-appraisal.
+All content is divided into three lessons: 1. The Argo Program, that describes the basic concepts of this ocean observing network; 2. The Argo Data, that describes how the data is organized; and 3. Using the Argo data, that takes the knowledge of the previous sections and python driven jupyter notebooks to teach how to use the data. Finally, a quiz section is included for self-appraisal.
 
-Lessons 1 and 2 have been designed for students with knowledge similar to those acquired to get into high school, this is, basic knowledge about the relationships between the Earth’s processes, weather and climate; and basic skills in interpreting maps, charts, and tables to organize and analyze data. These two lessons are aimed at users with minimal or no knowledge of the Argo network, and itself are a closed module and could be used by a learner without using lesson 3, assuming the goal is simply to learn about Argo. Lessons 1 and 2 contain 32 short videos and 14 chapters and require about 5 hours to be completed. Lesson 3 is intended for advanced users, as it requires basic programming skills in python. However, lesson 3 is duly explained step by step, to facilitate the transition of users coming from lessons 1 and 2. The basic recommendations and instructions for configuring the hands-on section of the AoS are also provided, whether the users want to work online or if they want to work on their local computer. Specific python libraries and packages are recommended to guarantee the correct functioning of the AoS. Lesson 3 contains 8 short videos and 14 chapters and requires about 10 hours to be completed, assuming basic knowledge of python.
+Lessons 1 and 2 are designed for students with knowledge comparable to that acquired upon entering high school. This includes basic understanding of the relationships between Earth's processes, weather, and climate, as well as foundational skills in interpreting maps, charts, and tables for organizing and analyzing data. These lessons are aimed at users with minimal or no prior knowledge of the Argo network. They formed a closed module and can be used by a learner who wish to focus solely on Argo. Lessons 1 and 2 include 32 short videos and 14 chapters, requiring approximately 5 hours to complete. Lesson 3 is designed for advanced users, as it requires basic programming skills in Python. However, lesson 3 is duly explained step by step, to facilitate the transition of users coming from Lessons 1 and 2. The AoS provides basic recommendations and instructions for configuring the hands-on section, whether users want to work online or on their local computer. Specific Python libraries and packages are recommended to ensure the correct functioning of the AoS. Lesson 3 includes 8 short videos and 14 chapters, requiring approximately 10 hours to complete, assuming a basic knowledge of Python.
 
-The AoS has been developed using markdown, python driven Jupyter notebooks [@Kluyver2016jupyter] and jupyter-book [@JupyterBook2020], open source projects that allow editing control in a clear and easy way, and also permits web-based interactive development environments that contain code, visualizations, and texts. It is widely used for data science, and inspired by the course _[An Introduction to Earth and Environmental Data Science](https://earth-env-data-science.github.io/intro.html) [@Abernathey2021]. 
+The AoS has been developed using markdown, python driven Jupyter notebooks [@Kluyver2016jupyter] and jupyter-book [@JupyterBook2020], open source projects that allow editing control in a clear and easy way, and also permits web-based interactive development environments that contain code, visualizations and texts. It is widely used for data science, and inspired by the course _[An Introduction to Earth and Environmental Data Science](https://earth-env-data-science.github.io/intro.html) [@Abernathey2021]. 
 
-The AoS is accessible through the web page of Euro-Argo [https://www.euro-argo.eu/argo-online-school](https://www.euro-argo.eu/argo-online-school), but all the content that builds it is hosted in a public github repository: [https://github.com/euroargodev/argoonlineschool](https://github.com/euroargodev/argoonlineschool). As long as the Argo program continues to grow, the AoS will, too. This first version contains the basic content of Argo, subsequent versions will show the newest aspects of the Argo network, such as biochemical measurements, deep observations, etc. Anybody interested in helping to further develop the AoS may open and issue on the public github repository to begin to organize the update.
+The AoS is accessible through the Euro-Argo webpage https://www.euro-argo.eu/argo-online-school, but all the content that makes it up is hosted in a public GitHub repository.: [https://github.com/euroargodev/argoonlineschool](https://github.com/euroargodev/argoonlineschool). As long as the Argo program continues to grow, the AoS will too.This first version contains the basic content of Argo; subsequent versions will showcase the newest aspects of the Argo network, such as biochemical measurements, deep observations and more. Anyone interested in helping to further develop the AoS may open an issue on the public GitHub repository to begin organizing the update.
 
-Given the structure of the AoS, it could be used for educational purposes. For high school students, lessons 1 and 2 could be used as a project with the aim of finding science questions that ocean observations, such as Argo,  may help to understand, and how.  For students with a curriculum that includes programming in python, other projects could be to find the seasonal change in temperature at the surface at a given ocean, and compare it with the changes at 1000 m or 2000 m, or explain - sometimes is very hard- the trajectory of a given float.  Teachers are welcome to open an issue in the github repository to get help in how to develop more projects.
+Given the structure of the AoS, it could be used for educational purposes. For high school students, lessons 1 and 2 could be a project to identify scientific questions that ocean observations, like those from Argo, may help address while developing essential know-how.  For students with a curriculum that includes programming in Python, other projects could involve finding the seasonal change in surface temperature at a specific ocean and comparing it with changes at 1000 m or 2000 m, or explaining the trajectory of a given float, which can be quite challenging.  Teachers are welcome to open an issue in the github repository to get assistance in how to developdeveloping more projects.
 
-As part of the Argo community, the AoS follows the same philosophy in terms of data access, and to guarantee barrier-free learning, the information and data provided in the AoS is available in open access to the public free of charge and therefore, no subscription is required.
+As part of the Argo community, the AoS follows the same philosophy regarding data access. To ensure barrier-free learning, the information and data provided in the AoS are available to the public free of charge, with no subscription required.
 
 # Acknowledgements