The Great Lakes AI Lab harnesses machine learning and artificial intelligence techniques to address environmental challenges and hazards facing the Great Lakes and the communities they support.
- Fork & Clone: Fork our repositories and clone them to your computing platform.
- Branching: Create a new branch for your contributions.
- Commits: Make clear and descriptive commits following our commit message conventions.
- Pull Requests: Submit pull requests against the main branch, detailing your changes comprehensively, please.
- Code Review: Await code review and respond to feedback. Keep it collaborative!
- Testing: Ensure your code modifications pass tests associated with the repository. (We don't have these yet)
- We welcome collaborations from researchers, scientists, and enthusiasts interested in our field.
- Connect with us through specific channels to discuss potential collaborations or research projects.
- Contribute by sharing your research findings, papers, or insights related to our area of study.
- Submit your work following the guidelines outlined in [link to submission guidelines].
- Join discussions and share ideas in our research-focused community forums or seminars (coming soon).
- Participate in workshops or conferences organized by our institute (coming soon).
- Share datasets, tools, or resources that could benefit the research community.
- Ensure proper documentation and permissions for shared resources.
- Engage in peer review processes for research papers or studies within our community.
- Provide constructive feedback and insights for improving research work.
- Acknowledge and cite CIGLR's work or resources used in your research.
- Respect and adhere to the licensing terms of shared resources.
Below are some useful resources for data science, machine learning, and artificial intelligence for Great Lakes science
- NOAA Center for Artificial Intelligence (NCAI) Resources
- NOAA Presentation on AI-Ready Open Data
- scikit-learn Python package
- [Any other important resources for the community]