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AI for Natural Methane

Welcome to the "AI for Natural Methane" repository, part of the Environmental Data Science Innovation and Inclusion Lab (ESIIL). This repository serves as the central hub for our working group, hosting our project description, proposals, member bios, codebase, and more.

Our Project

Harmonizing Natural Methane Datasets using Knowledge Guided Machine Learning

Atmospheric methane (CH4) is the second most powerful greenhouse gas after carbon dioxide and grew at the fastest rate ever recorded in 2020-2022. Slowing or reversing the accelerating growth in atmospheric CH4 will require an improved understanding of the global CH4 budget, which is currently underconstrained. Natural CH4 budgets are responsible for ~40% of the total global CH4 budgets but remain the most uncertain factor. This AI for natural CH4 working group aims to build a novel framework that integrates scientific knowledge and machine learning to harmonize simulated and observed datasets from global wetlands and soil sinks to quantify the spatial and temporal changes of global natural CH4 fluxes. Specifically, we will harmonize every possible form of the global natural CH4 datasets, including field-based CH4 fluxes from chamber and eddy-covariance measurements and simulated CH4 fluxes from bottom-up process-based models and top-down atmospheric assimilation models. As an output of this working group, we will generate and publicly share harmonized measurement datasets and global natural CH4 flux products from 1980 to present.

Project Proposal

[Link to the detailed project proposal document or include the proposal directly in the repository. This should outline the goals, methodologies, anticipated challenges, and projected timelines.] CHANGE TO A PDF LINK LATER

Group Members

[List the names and a brief description of each group member, possibly linking to their personal or professional web pages.]

  • Member 1: Youmi Oh, University of Colorado Boulder, PI
  • Member 2: Sparkle Malone, Yale University, Co-PI, Gavin McNicol (University of Illinois Chicago), Licheng Liu (University of Minnesota)
  • Member 3: Gavin McNicol, University of Illinois Chicago, Co-PI
  • Member 4: Licheng Liu, University of Minnesota, Co-PI, tech lead

Code Repository

This section of the repository will include all the code developed for the project. You can structure it as follows:

  • Analysis Code: Scripts for data analysis, statistical modeling, etc.
  • Data Processing: Scripts for cleaning, merging, and managing datasets.
  • Visualization: Code for creating figures, charts, and interactive visualizations.

Meeting Notes and Agendas

Meeting notes and agendas will be regularly updated here to keep all group members informed and engaged with the progress and direction of the project.

Contributing to This Repository

We welcome contributions from all group members. To maintain the quality and integrity of the repository, please adhere to the following guidelines:

  • Make sure all commits have a clear and concise message.
  • Document any major changes or decisions in the meeting notes.
  • Review and merge changes through pull requests to ensure oversight.

Getting Help

If you encounter any issues or have questions about how to contribute, please refer to the ESIIL Support Page or contact the repository maintainers directly.

Customize Your Repository

As a new working group, you'll want to make this repository your own. Here's how to get started:

  1. Edit This Readme: Replace the placeholder content with information about your specific project. Ensure that the introduction, project overview, and objectives clearly reflect your group's research focus.

  2. Update Group Member Bios: Add details about each group member's expertise, role in the project, and professional background. Include links to personal or professional web pages to foster community engagement and collaboration.

  3. Organize Your Code: Structure your codebase in a way that is logical and accessible. Use directories and clear naming conventions to make it easy for all members to find and contribute to different parts of the project.

  4. Document Your Data: Include a data directory with README files explaining the datasets, sources, and any preprocessing steps. This will help new members understand and work with the project's data effectively.

  5. Outline Your Methods: Create a detailed METHODS.md file where you describe the methodologies, software, and tools you will be using in your research. This transparency will support reproducibility and collaborative development.

  6. Set Up Project Management: Utilize the 'Issues' and 'Projects' features on GitHub to track tasks, discuss ideas, and manage your workflow. This can help in maintaining a clear view of progress and priorities.

  7. Add a License: Choose and include an appropriate open-source license for your project, ensuring that the broader community understands how they can use and contribute to your work.

  8. Create Contribution Guidelines: Establish a CONTRIBUTING.md file with instructions for members on how to propose changes, submit issues, and contribute code.

  9. Review and Merge Workflow: Decide on a workflow for reviewing and merging changes. Will you use branch protection? Who will have merge privileges? Document this process to avoid confusion.

  10. Establish Communication Channels: Beyond GitHub, set up additional communication channels like Slack, Discord, or email lists for quick and informal discussions.

Remember, the goal is to make your repository clear, accessible, and useful for all current and future members of your working group. Happy researching!

Last update: 2024-05-09