Hugging Science

Hugging Science is a curated, LLM-friendly index of scientific datasets, models, blog posts, and interactive demos for ML researchers. Use it to find candidate resources, then verify their author documentation and scientific suitability; curation does not establish quality, openness, or executable compatibility.

There are two related surfaces, and you should use both:

• The catalog at huggingscience.co — a static, parseable index of resources across 17 scientific domains. It exposes llms.txt (compact), llms-full.txt (full content), and topics/<slug>.md (per-domain). These are markdown files designed to be fetched and read.
• The hugging-science Hugging Face organization — huggingface.co/hugging-science — community-submitted datasets, models, and a changing collection of Gradio, Docker and static Spaces. Not every listing exposes an inference API.

The catalog points to resources hosted on the broader Hugging Face Hub. For example, ESM2 supports Transformers, while Evo2 requires its native runtime and OpenGenome2 needs explicit file-format handling. The catalog provides discovery; use each resource through its verified loader or service.

When to use this skill

Engage this skill when the user's task involves AI/ML applied to science. Common signals: