--- title: "Contributing to Awesome AI for Science" task: "" lineage_type: import upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/cf292eeb/CONTRIBUTING.md upstream_sha: cf292eeb imported_at: 2026-06-26 prompt_class: catalogue upstream_changes: accepted author: upstream validated: false --- # Contributing to Awesome AI for Science Thank you for your interest in contributing to Awesome AI for Science! 🎉 This project aims to be the most comprehensive and up-to-date collection of AI resources for scientific research. Your contributions help researchers worldwide discover tools and knowledge that can accelerate scientific discovery. ## 📋 Table of Contents - [How to Contribute](#how-to-contribute) - [Types of Contributions](#types-of-contributions) - [Contribution Guidelines](#contribution-guidelines) - [Formatting Guidelines](#formatting-guidelines) - [Review Process](#review-process) - [Code of Conduct](#code-of-conduct) ## 🤝 How to Contribute ### Quick Contribution (for small additions) 1. **Fork** this repository 2. **Edit** the README.md file directly in GitHub 3. **Add** your resource in the appropriate section 4. **Submit** a pull request ### Detailed Contribution (for larger changes) 1. **Fork** this repository to your GitHub account 2. **Clone** your fork locally: ```bash git clone https://github.com/your-username/awesome-ai-for-science.git cd awesome-ai-for-science ``` 3. **Create** a new branch for your contribution: ```bash git checkout -b add-new-resource ``` 4. **Make** your changes to the README.md file 5. **Commit** your changes: ```bash git add README.md git commit -m "Add [resource name] to [section]" ``` 6. **Push** to your fork: ```bash git push origin add-new-resource ``` 7. **Submit** a pull request from your fork to this repository ## 🔧 Types of Contributions We welcome these types of contributions: ### ✅ Adding New Resources - **Tools & Software**: AI tools that help with research workflows - **Papers & Publications**: Influential papers in AI for Science - **Datasets**: High-quality scientific datasets - **Models**: Pre-trained models for scientific applications - **Educational Content**: Courses, tutorials, books - **Communities**: Research groups, conferences, forums ### ✅ Improving Existing Content - **Better Descriptions**: More accurate or detailed descriptions - **Updated Links**: Fixing broken or outdated links - **Reorganization**: Improving the structure and categorization - **Additional Information**: Adding missing details or context ### ✅ General Improvements - **Typo Fixes**: Grammar, spelling, and formatting corrections - **New Categories**: Suggesting new sections or reorganization - **Documentation**: Improving this contributing guide or README ## 📝 Contribution Guidelines ### Resource Quality Standards Before adding a resource, ensure it meets these criteria: - **✅ Relevance**: Directly related to AI applications in scientific research - **✅ Quality**: Well-documented, actively maintained, or highly cited - **✅ Accessibility**: Publicly available (open source, free, or with free tier) - **✅ Uniqueness**: Not already listed in the repository - **✅ Functionality**: Actually works and provides value to researchers ### What NOT to Include - **❌ Commercial Products**: Purely commercial tools without free access - **❌ Broken Links**: Resources that are no longer available - **❌ Personal Projects**: Small, unmaintained personal repositories - **❌ Duplicates**: Resources already listed elsewhere in the repo - **❌ Off-topic**: Resources not related to AI or scientific research ## 📐 Formatting Guidelines ### General Format ```markdown - [Resource Name](URL) - Brief description of what it does and why it's useful ``` ### Examples of Good Entries ```markdown - [AlphaFold](https://github.com/deepmind/alphafold) - Revolutionary protein structure prediction using deep learning - [Elicit](https://elicit.org/) - AI research assistant that helps with literature review and evidence synthesis - [Materials Project](https://materialsproject.org/) - Computational materials database with ML-predicted properties ``` ### Description Guidelines - **Length**: 5-15 words ideally, max 20 words - **Style**: Clear, informative, avoid marketing language - **Focus**: What it does and scientific domain - **Tone**: Professional and objective ### Link Guidelines - **Use HTTPS**: Always use secure links when available - **Direct Links**: Link to the main project page, not sub-pages - **GitHub**: For open source projects, link to the GitHub repository - **Papers**: Link to the official publication (DOI preferred) ### Section Organization - **Alphabetical Order**: Within each subsection, maintain alphabetical order - **Appropriate Section**: Place resources in the most specific relevant section - **New Sections**: Propose new sections if existing ones don't fit ## 🔍 Review Process ### What We Look For 1. **Accuracy**: Correct information and working links 2. **Formatting**: Follows the style guide 3. **Placement**: Resource is in the appropriate section 4. **Quality**: Meets our quality standards 5. **Uniqueness**: Not a duplicate ### Timeline - **Initial Review**: Within 7 days - **Feedback**: We'll provide constructive feedback if changes are needed - **Final Decision**: Merge or close within 14 days ### Review Criteria ✅ **Approve** if: - Meets all quality standards - Follows formatting guidelines - Adds clear value to the collection 🔄 **Request Changes** if: - Minor formatting or description issues - Needs better categorization - Requires additional context ❌ **Reject** if: - Doesn't meet quality standards - Off-topic or commercial - Duplicate of existing entry ## 🎯 Code of Conduct ### Our Standards - **Be Respectful**: Treat all contributors with respect - **Be Constructive**: Provide helpful, actionable feedback - **Be Collaborative**: Work together to improve the resource - **Be Patient**: Understand that reviews take time ### Unacceptable Behavior - Harassment or discriminatory language - Spam or self-promotion without value - Disruptive or unconstructive criticism - Violations of intellectual property ## 🆘 Getting Help ### Questions? - **Issues**: Open an issue for questions about contributions - **Discussions**: Use GitHub Discussions for general questions - **Email**: Contact the maintainers for sensitive issues ### Common Questions **Q: Can I add my own research project?** A: Yes, if it's high-quality, well-documented, and provides clear value to the scientific community. **Q: What if a resource becomes outdated?** A: Please open an issue or submit a PR to remove or update it. **Q: Can I reorganize entire sections?** A: Major reorganizations should be discussed in an issue first to gather feedback. **Q: What about resources behind paywalls?** A: We prefer freely accessible resources, but important papers or tools with free tiers are acceptable. --- ## 🙏 Thank You! Your contributions make this resource valuable for researchers worldwide. Every addition, fix, and improvement helps accelerate scientific discovery through AI. **Happy Contributing!** 🚀 --- *For questions about this guide, please open an issue or start a discussion.*