hugging-science

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 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:

If the task is generic ML (recommendation systems, chatbot RAG, vision on cats and dogs), this skill is not the right tool — defer to general HF Hub knowledge instead.

Core workflow

Most invocations follow this five-step loop. Don't skip discovery — the value of Hugging Science is that it has already filtered hundreds of resources down to high-signal picks per domain.

1. Identify the domain(s)

Map the user's task to one or more of the 17 topic slugs:

astronomy · benchmark · biology · biotechnology · chemistry · climate · conservation · earth-science · ecology · energy · engineering · genomics · materials-science · mathematics · medicine · physics · scientific-reasoning

Some tasks span multiple topics (e.g., drug discovery → chemistry + biology + medicine). Fetch each relevant topic.

2. Fetch the relevant catalog content

Use the bundled script for clean, structured access:

python scripts/fetch_catalog.py topic biology
python scripts/fetch_catalog.py topic materials-science --filter models
python scripts/fetch_catalog.py search "protein language model"
python scripts/fetch_catalog.py all     # full llms-full.txt

You can also fetch the raw markdown directly:

Each entry is a markdown block with Type, Tags, HuggingFace URL (or Link for blogs), and a one-line description. See references/topics-and-slugs.md for the entry schema and slug list.

3. Pick the right resource(s)

Read the descriptions and tags. Match to the user's task with judgment, not keyword overlap. Things to weigh:

If you're not sure which resource to pick, briefly present the top 2–3 candidates to the user with their tradeoffs, then proceed once they choose. Don't pick silently when the choice materially changes the work.

For domain-specific go-to picks (the "if in doubt, start here" entries), see references/flagship-resources.md.

4. Use the resource

The mechanics depend on resource type. Read the matching reference file before writing code:

The reference files are short and focused. If you're already fluent in the relevant API, skim; if not, read fully before writing code. The patterns are different from generic HF usage in a few important places (e.g., trust_remote_code requirements, scientific-data dtype gotchas).

5. Cite the methodology

When the catalog has a blog post matching the task (Type: blog or in the Blog Posts section of a topic file), include its URL when you explain your approach to the user. Methodology blogs are written by the dataset/model authors and answer "why this design" questions that model cards usually skip. Treat them like citations — a one-line "see for the methodology behind X" is plenty.

Authentication: HF_TOKEN

Many catalog resources are gated (clinical data, large foundation models, private Spaces). Authenticate via the HF_TOKEN environment variable.

Load HF_TOKEN from a .env file when available — that's where the user keeps secrets. Use python-dotenv at the top of any script that hits the HF API:

from dotenv import load_dotenv
load_dotenv()    # picks up HF_TOKEN from .env in cwd or any parent dir

If .env doesn't exist or doesn't define HF_TOKEN, fall back gracefully — many resources are public and work without it. Don't hard-code tokens, don't echo them, and don't suggest huggingface-cli login as the primary path; the user prefers .env.

The .env file should contain a line like:

HF_TOKEN=hf_...

If you're creating a new project, also add .env to .gitignore if it isn't already there.

A few important things to remember

The catalog is curated, not exhaustive. If a user needs a specific resource and Hugging Science doesn't list it, that doesn't mean it doesn't exist on HF Hub. Search HF Hub directly as a fallback. But always start with the catalog when the domain matches — the curation is the value.

The entries are pointers. Don't try to "use Hugging Science" as if it were an API. There is no Hugging Science inference endpoint. Every actionable resource lives on HF Hub or as a HF Space, and you use it via the standard HF tooling.

Many scientific models require trust_remote_code=True. Custom architectures (Evo-2, many genomics/materials models) ship custom modeling code. This is normal in this ecosystem. Pass the flag and inform the user.

Scientific datasets are often large and weirdly-shaped. Genomics corpora can be billions of tokens; cosmology images can be hundreds of GB; materials datasets contain non-standard objects (crystal structures, graphs). Use streaming (streaming=True on load_dataset) by default for anything claimed to be over a few GB, and inspect schema before assuming columns.

Spaces are great for one-off scientific generations. If the user wants to design a binder for a target protein or run inference on a hosted model demo, calling the Space via gradio_client is faster and cheaper than spinning up the model locally. Check references/using-spaces.md first — huggingface.co/hugging-science has ~27 of these.

The catalog itself may evolve. Entries get added regularly; occasionally entries change slugs. If a URL 404s, refetch the topic file or llms.txt to get the current state — don't paper over the failure.

Bundled resources