Hugging Science

Hugging Science is a curated, LLM-friendly index of scientific datasets, models, blog posts, and interactive demos for ML researchers. Use it when a scientific ML question lands in front of you — it's much higher signal than generic search and the entries are pre-filtered for quality and openness.

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, a few models, and ~27 interactive Spaces (notably BoltzGen for protein/binder design, Dataset Quest for submissions, and Science Release Heatmap for ecosystem visualization).

The catalog points to resources hosted on the broader Hugging Face Hub. So an entry like arcinstitute/opengenome2 is a regular HF dataset that you load with the datasets library; an entry like facebook/esm2_t33_650M_UR50D is a regular HF model you load with transformers. The catalog's job is curation and discovery; usage goes through standard Hugging Face APIs.

When to use this skill

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

• Names a scientific domain (protein, genome, molecule, crystal, weather, climate, galaxy, EEG, microbiome, pathology, plasma, …)
• Asks "is there a dataset/model for X" where X is scientific
• Wants to fine-tune on scientific data, evaluate on scientific benchmarks, or reproduce a scientific ML paper
• Asks about specific known scientific models (Evo-2, ESM2, BoltzGen, Nucleotide Transformer, AlphaFold-derived, etc.)