Welcome to the EnviroLLM Guidelines repository. This repository is the central hub for our working group, encompassing our project overview, proposals, team member information, codebase, and more.
Environmental challenges are increasingly complex and pressing, requiring rigorous and rapid synthesis of broad bodies of research for evidence-based actions. To address this need, scientists are increasingly relying on artificial intelligence to analyze vast amounts of research and policy documents. However, they lack clear guidelines for how to use these powerful tools effectively and ethically to address pressing environmental concerns. Our working group brings together experts from research institutions, policy think-tanks, conservation organizations, and a primarily undergraduate institution (PUI) to develop best practices for using AI-powered text analysis in environmental evidence synthesis and policy analysis. By combining high-performance computing resources with undergraduate research experiences, we aim to create a model for inclusive environmental data science that bridges the gap between large research universities and PUIs. Working with undergraduate students through course-based research experiences, we will develop and test user-friendly tools for analyzing conservation literature and environmental policies. This approach not only advances environmental science but also creates new pathways for undergraduates to participate in cutting-edge research using NSF’s advanced computing infrastructure and helps train the scientific workforce of the 21st century. The resulting guidelines and tools will help researchers worldwide more easily and thoughtfully use AI for environmental evidence and policy syntheses, while our educational model will show how to involve undergraduate researchers in advanced computational text analysis projects. This work represents a crucial step toward more inclusive, ethical, and effective use of AI in environmental science, while developing materials to train diverse undergraduate students in environmental data science research.
- Access detailed documentation on our GitHub Pages site.
- Find comprehensive guides, tutorials, and additional resources.
Information forthcoming.
- Charlotte Chang
- Brian Robinson
- J.T. Erbaugh
- Kemen Austin
- Max Callaghan
- Samantha Cheng
- Karletta Chief
- Amrita Gupta
- Lian Pin Koh
- Sara Kuebbing
- Biljana Macura
- Sparkle Malone
- Lucas Meyer
- Michal Nachmany
- Rhita Simorangkir
- Caitlin Augustin
- Stephanie Hampton
- Yuta Masuda
- John Poulsen
- William Sutherland
- Niraj Swami
- 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.
- Regular updates to keep all group members informed and engaged with the project's progress and direction.
- Contributions from all group members are welcome.
- Please adhere to these guidelines:
- Ensure commits have clear and concise messages.
- Document major changes in the meeting notes.
- Review and merge changes through pull requests for oversight.
- If you encounter any issues or have questions, please refer to the ESIIL Support Page or contact the repository maintainers directly.
- Edit This Readme: Update with information specific to your project.
- Update Group Member Bios: Add detailed information about each group member's expertise and role.
- Organize Your Code: Use logical structure and clear naming conventions.
- Document Your Data: Include a data directory with README files for datasets.
- Outline Your Methods: Create a METHODS.md file for methodologies and tools.
- Set Up Project Management: Use 'Issues' and 'Projects' for task tracking.
- Add a License: Include an appropriate open-source license.
- Create Contribution Guidelines: Establish a CONTRIBUTING.md file.
- Review and Merge Workflow: Document your process for reviewing and merging changes.
- Establish Communication Channels: Set up channels like Slack or Discord for 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!