Each summer in the Gulf of Mexico, a hypoxic zone develops with economical and ecological impacts. Since 2001, government agencies have been tasked with decreasing the total area of the deadzone by 60% with little to no progress. The biogeochemical dynamics of the Gulf of Mexico result from complex, non-linear interactions between changes in land use, specifically nitrogen, Mississippi River runoff, and ocean temperature and density gradients. To mitigate the size of the dead zone, it is imperative to gain a mechanistic understanding of the drivers of interannual variability. ECCO, specifically ECCO-Darwin, provides a wonderful opportunity to study these mechanisms.
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.
contributors/
Each team member can create their own folder under contributors, within which they can work on their own scripts, notebooks, and other files. Having a dedicated folder for each person helps to prevent conflicts when merging with the main branch. This is a good place for team members to start off exploring data and methods for the project.notebooks/
Notebooks that are considered delivered results for the project should go in here.scripts/
Code that is shared by the team should go in here (e.g. functions or subroutines). These will be files other than Jupyter Notebooks such as Python scripts (.py)..gitignore
This file sets the files that will be globally ignored bygitfor the project. (e.g. you may want git to ignore temporary files or large data files, read more about ignoring files here)environment.yml
condaenvironment description needed to run this project.README.md
Description of the project (see suggested headings below)model-card.md
Description (following a metadata standard) of any machine learning models used in the project
| Name | Personal goals | Can help with | Role |
|---|---|---|---|
| J. Zaiss | I want to learn how to access ECCO data and perform analyses | ??? | Project Lead |
Briefly describe and provide citations for the data that will be used (size, format, how to access).
How would you or others traditionally try to address this problem? Provide any relevant citations to prior work.
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!
Optional: links to manuscripts or technical documents providing background information, context, or other relevant information.
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).
- Goal 1
- Goal 2
- ...
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.
- Task 1 (all team members will learn to use GitHub)
- Task 2 (team members will use the scikit-learn python library)
- Task 2a (assigned to team member A)
- Task 2b (assigned to team member B)
- Task 3
- ...
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.
Final Poster: https://docs.google.com/presentation/d/1JlS2XUhSA8QBUzPQLVESupjZ94fFt3SyZiuQD1I2psw/edit?usp=sharing
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.