Europe PMC & PubMed Research Paper Search

Search Europe PMC and PubMed literature by topic, author, journal, year and open-access status. Export identifiers, abstracts, citations and full-text links. This Actor turns the official Europe PMC REST API into bounded, structured datasets for analysis, enrichment, monitoring, and AI-agent workflows.

What data can I extract?

79 flat columns per paper, every one of them straight from the official Europe PMC REST API:

• Identifiers - PMID, PMCID, DOI, NIH manuscript id, journal ISSN, e-ISSN and NLM ID
• Citation - journal, abbreviation, volume, issue, pages, print and electronic publication dates
• Authorship - full author string, first and last author, author count, ORCID iDs and every institutional affiliation
• Subject indexing - MeSH terms, MeSH major topics, author keywords and indexed chemical substances
• Funding - funding agencies, grant IDs and grant count
• Impact and access - citation count, open-access flag, licence, best full-text availability, PDF link and every full-text URL
• Integrity - retraction flag, retraction and erratum citations, and all comment/correction links
• Preprints and patents - preprint server and version, plus patent country, type, application number, date and IPC/EPO classifications
• Mined entities (optional) - genes and proteins, diseases, organisms, chemicals, Gene Ontology terms, experimental methods and accession numbers

Every run writes flat records to the default dataset. Download results as JSON, CSV, Excel, XML, or access them through the Apify API.

All 79 columns

Identity — author_orcids, doi, entity_accession_numbers, first_author_full_name, first_author_orcid, grant_ids, id, journal_nlm_id, manuscript_id, patent_application_number, pmcid, pmid, title, version_number

Status — comment_correction_types, has_data, has_pdf, has_references, has_supplementary_material, has_text_mined_terms, is_author_manuscript, is_open_access, is_retracted, patent_classifications, patent_type, publication_status, publication_types

Dates — date_of_revision, electronic_publication_date, embargo_date, first_index_date, issue, patent_application_date, print_publication_date, pub_year, publication_date, publisher

Location — abstract, patent_country, retraction_notice

People — author_count, authors, first_author, last_author

Contact — data_link_tags, full_text_url, full_text_urls, pdf_url, url

Counts and measures — cited_by_count, entity_count, grant_count

Other detail — affiliation, affiliations, chemicals, database_cross_references, eissn, entity_chemicals, entity_diseases, entity_experimental_methods, entity_genes, entity_go_terms, entity_organisms, erratum_notice, full_text_availability, funders, in_epmc, issn, journal, journal_abbrev, keywords, language, license, mesh_major_topics, mesh_terms, pages, pub_model, source, volume

Input example

{
  "query": "large language models in medicine",
  "year_from": 2022,
  "open_access_only": true,
  "max_items": 100
}

Set include_entities to true to add the mined-entity columns. They come from the Europe PMC annotations API, batched eight articles per request, so the extra cost is roughly one request per eight results rather than one per record.

max_items is a hard output and billing ceiling. The default input is intentionally limited to 10 records so Store tests and first runs stay inexpensive.

Output example

{
  "id": "42658322",
  "source": "MED",
  "pmid": "42658322",
  "pmcid": "PMC13522008",
  "doi": "10.1007/s10544-026-00843-9",
  "title": "Intranasal CRISPR lipid nanoparticles targeting MAPK9 attenuate neuroinflammation after traumatic brain injury.",
  "authors": "Kara G, Holcomb M, Hijazi AA, Ali Y, López-Espinosa J, Cruz-Pineda L, Park P, Flinn H, Taylor N, Galbraith T, McMahon L, Rostomily R, Leonard F, Villapol S.",
  "first_author": "Kara G",
  "journal": "Biomedical microdevices",
  "journal_abbrev": "Biomed Microdevices",
  "issn": "1387-2176",
  "volume": "28",
  "issue": "3",
  "pages": "58",
  "pub_year": "2026",
  "publication_date": "2026-08-27",
  "publication_types": "research-article, Journal Article",
  "keywords": "Macrophages, Traumatic brain injury, Microglia, neuroinflammation, Intranasal Delivery, Lipid Nanoparticles, Crispr-cas12a, Mapk9",
  "abstract": "Traumatic brain injury (TBI) induces a sustained neuroinflammatory response involving activated microglia and infiltrating myeloid cells, contributing to secondary brain damage and long-term neurological dysfunction. Modulating these inflammatory responses toward a more reparative phenotype represen",
  "affiliation": "Department of Neurosurgery and Center for Neuroregeneration, Houston Methodist Research Institute, Houston, TX, USA.",
  "cited_by_count": 3,
  "is_open_access": true,
  "license": "cc by-nc-nd",
  "has_pdf": true,
  "full_text_url": "https://doi.org/10.1007/s10544-026-00843-9",
  "url": "https://europepmc.org/article/MED/42658322",
  "eissn": "1572-8781",
  "journal_nlm_id": "100887374"
}

51 further columns are omitted here for length — the full list is above, and every column appears in the export whether or not the source populated it.

Use with AI agents and MCP

Apify's hosted MCP server can discover and call this Actor. A suitable agent request is:

Find 100 open-access biomedical papers about large language models in medicine published since 2022.

Use this exact Actor input:

{
  "query": "large language models in medicine",
  "year_from": 2022,
  "open_access_only": true,
  "max_items": 100
}

The Actor succeeds with a nonempty default dataset and exposes its default dataset through the top-level Output schema.

Pricing and cost control

Output is billed per result at $0.003 per result (about $3.00 per 1,000 results), plus a $0.0005 Actor-start charge billed once per gigabyte of memory at run start. Use max_items to cap both output volume and charges. The price shown on the Apify Store listing is authoritative.

Use max_items to cap returned and billable records. Invalid input is rejected before unnecessary work wherever possible.

Common use cases

• Source-specific research and market intelligence
• Structured exports for spreadsheets, warehouses and BI systems
• Entity enrichment and monitoring pipelines
• Retrieval and data collection by AI agents

Reliability

The Actor uses bounded pagination, retries transient upstream failures, deduplicates records where the source exposes stable identifiers, and fails explicitly when the source cannot provide usable output. Production default-input canaries verify a nonempty structured dataset in under five minutes.

Limitations and responsible use

• Coverage, field availability and update timing are controlled by the upstream public source.
• Optional fields can be null or absent when the source does not publish them.
• This Actor does not bypass authentication, access controls, CAPTCHAs, or source rate limits.
• Customers remain responsible for lawful use, applicable source terms, and restrictions on downstream decisions.

FAQ

What is the best way to run this Europe PMC paper search Actor?

Start with the 10-record default, inspect the dataset, then increase max_items and narrow the available filters for your use case.

Do I need my own API key?

No external API key is required unless the Input tab explicitly says otherwise. Apify credentials are used normally when invoking the Actor through Apify APIs or MCP.

Can an AI agent call it?

Yes. The strict input schema acts as the tool signature, and the dataset plus Output schemas describe the returned records.

Can I export the results?

Yes. Apify datasets support JSON, CSV, Excel, XML and API retrieval.

Support and changes

Open an issue on the Actor page with a redacted input and run ID. See ./CHANGELOG.md for contract and maintenance updates.