Datasets:

EMBO
/

pubmed-research-classifier

pretty_name: PubMed Research Classifier Labels
license: other
license_name: embo-internal
license_link: LICENSE.md
task_categories:
  - text-classification
language:
  - en
tags:
  - pubmed
  - openalex
  - research-classification
  - bibliometrics
  - embo
size_categories:
  - 10M<n<100M
configs:
  - config_name: v1.0.0
    data_files: data/v1.0.0/labels.csv
    default: true
dataset_info:
  config_name: v1.0.0
  features:
    - name: PMID
      dtype: string
    - name: class
      dtype: string
    - name: probability
      dtype: float64
  splits:
    - name: train
      num_examples: 30426295

PubMed Research Classifier Labels

Private lookup table of research / non-research labels for PubMed IDs.


PyPI package	`pubmed-research-classifier` (≥ 0.3.0 for Hub lookup)
This dataset	Hub release v1.0.0 (labels scored with package 0.2.0 weights)
Docs	Package README on PyPI

Coverage (v1.0.0): labels for all OpenAlex works that have a PubMed ID (PMID), drawn from the OpenAlex corpus snapshot used in this project, with PubMed-linked works up to April 2026. Every distinct PMID in that slice is included (30,426,295 unique IDs). Works without a PMID are out of scope for this table.

This Hub dataset is versioned independently (v1.0.0, v1.1.0, …) so future classifier / corpus refreshes can ship updated label tables without breaking consumers pinned to an older revision.

Field	Version
Dataset release	v1.0.0 (this revision)
Corpus coverage	All OpenAlex works with PMID, up to April 2026
Label model (weights)	`pubmed-research-classifier==0.2.0`
Recommended client	`pubmed-research-classifier>=0.3.0` (`[cache]` extra)
Decision rule	`research` if P(non-research) < 0.75, else `non-research` (τ = 0.75)
Unique PMIDs	30,426,295

Dataset structure

Column	Type	Description
`PMID`	string	Numeric PubMed ID (no `pmid:` / URL prefix)
`class`	string	`research` or `non-research`
`probability`	float	Model estimate of P(non-research)

Class counts in v1.0.0:

`class`	Count
`research`	24,113,963
`non-research`	6,312,332

Files:

data/v1.0.0/labels.csv

Later releases add data/v1.1.0/, data/v2.0.0/, etc., each exposed as a dataset config with the same three columns.

How to use with `pubmed-research-classifier` (≥ 0.3.0) — recommended

Install the optional cache extra and set a Hub token with read access to this private dataset:

pip install "pubmed-research-classifier[cache]>=0.3.0"
export HF_TOKEN=hf_xxxxxxxx   # or: huggingface-cli login

from pubmed_research_classifier import (
    load_label_cache,
    lookup_pmid,
    classify_pmid,
)

# First call downloads v1.0.0 (~1.2 GB) and builds a local DuckDB index
# under ~/.cache/pubmed_research_classifier/ (override with PUBMED_RC_CACHE_DIR).
cache = load_label_cache(revision="v1.0.0")
print(len(cache))  # 30426295

lookup_pmid("10006576")
# {"PMID": "10006576", "class": "research",
#  "probability": 0.009..., "source": "cache"}

# Cache hit → Hub row; miss → run the bundled MLP (needs classify() fields)
classify_pmid("10006576")
classify_pmid(
    "99999999",
    record={
        "title": "...",
        "abstract": "...",
        "pub_types": ["Journal Article"],
        "n_authors": 3,
        "n_refs": 12,
    },
)

Batch lookup (order-preserving):

rows = cache.lookup_many(["10006576", "10047518", "99999999"])
# [dict, dict, None]

classify(...) without the cache still works for raw text / embeddings (see the PyPI README).

Monthly Hub refresh (maintainers)

Needs a token with write access. Merge a CSV of new PMIDs (PMID,class,probability) into the previous table and upload a new revision:

pubmed-rc-publish-labels \
  --new-csv new_pmids.csv \
  --base-revision v1.0.0 \
  --new-revision v1.1.0 \
  --upload

Or from Python: publish_label_revision(..., upload=True).

How to load with 🤗 Datasets (without the package)

Requires a Hugging Face token with access to this private dataset.

from datasets import load_dataset

# Default config = latest published table (currently v1.0.0)
ds = load_dataset(
    "EMBO/pubmed-research-classifier",
    token=True,          # or pass a token string / use HF_TOKEN
)

# Pin a release
ds = load_dataset(
    "EMBO/pubmed-research-classifier",
    name="v1.0.0",
    token=True,
)

print(ds["train"][0])
# {'PMID': '10006576', 'class': 'research', 'probability': 0.009...}

For large joins, prefer DuckDB / Polars on the CSV (or the package’s local DuckDB file) rather than building a giant Python dict.

Provenance

v1.0.0 covers all OpenAlex works sourced from PubMed (i.e. with a PMID) up to April 2026 — one row per distinct PMID in that OpenAlex/PubMed-linked slice.

Labels were computed from OpenAlex-derived work tables (people works, life-science author works, and a large ModernBERT-embedded OpenAlex shard) by:

Building per-PMID features (ModernBERT title/abstract embeddings + PubMed metadata).
Running pubmed-research-classifier==0.2.0 in embedding mode on GPU.
Exporting the unique-PMID result table uploaded here.

Local mirror of this file (EMBO DGX):

/raid/shared/apply-classify-article/data/pubmed-research-classifier_cached-results_0.2.0.csv

Versioning policy

Hub dataset tag / config	Meaning
`v1.0.0`	First EMBO release of the PMID label table (scored with classifier 0.2.0)
`v1.x.0`	Additive / corrective corpus updates (same schema); use package ≥ 0.3.0 to load
`v2.0.0`	Breaking schema or new primary classifier generation

Always pin revision= / name= (Hub) or load_label_cache(revision=...) (package) in production code.

License / access

Private EMBO dataset. Do not redistribute outside authorised Hugging Face accounts/organisations. Contact EMBO data owners for access requests.