ECCO-Hackweek / EH24-Drifters

Lagrangian analysis of ECCO ocean transport estimates in Julia
BSD 3-Clause "New" or "Revised" License
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Sample Project

This is an example of how teams can structure their project repositories and format their project README.md file.

When creating a project repository from this template choose "Public" so other participants can follow progress. Add a "topic" to your repository details (click on the gear icon next to the "About" section on the repository page) to help others find your work (e.g. ecco-hackweek-2024).

Files and folders in your project repository

This template provides the following suggested organizaiton structure for the project repository, but each project team is free to organize their repository as they see fit.

Recommended content for your README.md file:

(you can remove the content here and above from your final project README.md file so that it begins with the Project or Team Name title below)

Drifters

Introduction

Provide a brief introduction describing the proposed work. Be sure to also decribe what skills team members will get to learn and practice as part of this project.

Collaborators

List all participants on the project. Here is a good space to share your personal goals for the hackweek and things you can help with.

Name Personal goals Can help with Role
Ciara Pimm I want to learn specific python libraries for working with these data I can help with understanding our dataset, Team Member
Yuanyuan Song Team Member
Kylie Kinne Team Member
Gael Forget Team Member

The problem

Provide a few sentences describing the problem are you going to explore. If this is a technical exploration of software or data science methods, explain why this work is important in a broader context and specific applications of this work.

Data and Methods

Data

Briefly describe and provide citations for the data that will be used (size, format, how to access).

Existing methods

How would you or others traditionally try to address this problem? Provide any relevant citations to prior work.

Proposed methods/tools

What new approaches would you like to implement for addressing your specific question(s) or application(s)?

Will your project use machine learning methods? If so, we invite you to create a model card!

Additional resources or background reading

Optional: links to manuscripts or technical documents providing background information, context, or other relevant information.

Project goals and tasks

Project goals

List the specific project goals or research questions you want to answer. Think about what outcomes or deliverables you'd like to create (e.g. a series of tutorial notebooks demonstrating how to work with a dataset, results of an anaysis to answer a science question, an example of applying a new analysis method, or a new python package).

Tasks

What are the individual tasks or steps that need to be taken to achieve each of the project goals identified above? What are the skills that participants will need or will learn and practice to complete each of these tasks? Think about which tasks are dependent on prior tasks, or which tasks can be performed in parallel.

Project Results

Use this section to briefly summarize your project results. This could take the form of describing the progress your team made to answering a research question, developing a tool or tutorial, interesting things found in exploring a new dataset, lessons learned for applying a new method, personal accomplishments of each team member, or anything else the team wants to share.

You could include figures or images here, links to notebooks or code elsewhere in the repository (such as in the notebooks folder), and information on how others can run your notebooks or code.