life-science-ai-prompts/upstream/ai-boost-awesome-ai-for-science/catalogue/CONTRIBUTING.md

title, task, lineage_type, upstream_source, upstream_sha, imported_at, prompt_class, upstream_changes, author, validated

title	task	lineage_type	upstream_source	upstream_sha	imported_at	prompt_class	upstream_changes	author	validated
Contributing to Awesome AI for Science		import	https://github.com/ai-boost/awesome-ai-for-science/blob/cf292eeb/CONTRIBUTING.md	cf292eeb	2026-06-26	catalogue	accepted	upstream	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
• Types of Contributions
• Contribution Guidelines
• Formatting Guidelines
• Review Process
• 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:

   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:

   git checkout -b add-new-resource
   

4. Make your changes to the README.md file
5. Commit your changes:

   git add README.md
   git commit -m "Add [resource name] to [section]"
   

6. Push to your fork:

   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• Updated Links: Fixing broken or outdated links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

- [Resource Name](URL) - Brief description of what it does and why it's useful

Examples of Good Entries

- [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.

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🙏 Thank You!

Your contributions make this resource valuable for researchers worldwide. Every addition, fix, and improvement helps accelerate scientific discovery through AI.

Happy Contributing! 🚀

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For questions about this guide, please open an issue or start a discussion.