Crossref Scraper

Scrape scholarly metadata across all publishers from Crossref by keyword — title, authors, DOI, journal, citations, subjects, publication dates, and abstracts. Built on the official Crossref REST API: no API key or setup needed.

Search a topic like graph neural network, get a clean, dated list of matching works with DOIs — ready to export to CSV, Excel, or JSON, or to power a citation tracker or literature review.

Features

• Official, reliable source — reads the public Crossref REST API.
• Every publisher — Crossref indexes 150M+ works across journals, conferences, books, preprints and datasets, all linked by DOI.
• Rich metadata — authors, journal, citation counts, subjects, reference counts and plain-text abstracts.
• Deep pagination — cursor-based paging fetches thousands of works per query.

Use cases

• Literature reviews — build a dated, deduplicated corpus of works on a topic.
• Citation & impact tracking — sort by citation count to find the most influential papers.
• DOI enrichment — resolve keywords to DOIs and clean metadata for downstream tools.
• Research monitoring — schedule daily on your keywords and catch new publications.

Input

Field	Type	Description
searchQueries	string[]	Search queries (required).
fromDate	string	YYYY-MM-DD; only works published on/after this date.
type	enum	Work type filter (journal article, proceedings, book chapter, preprint, dataset).
sortBy	enum	relevance, published (newest), or citations (most cited).
maxItems	integer	Max works per query. Default 100.

Example

{
  "searchQueries": ["graph neural network"],
  "type": "journal-article",
  "sortBy": "citations",
  "fromDate": "2023-01-01",
  "maxItems": 100
}

Output

{
  "doi": "10.1109/tnnls.2020.2978386",
  "title": "A Comprehensive Survey on Graph Neural Networks",
  "authors": ["Zonghan Wu", "Shirui Pan", "Fengwen Chen"],
  "publisher": "Institute of Electrical and Electronics Engineers (IEEE)",
  "journal": "IEEE Transactions on Neural Networks and Learning Systems",
  "type": "journal-article",
  "publishedDate": "2021-01",
  "citedByCount": 4821,
  "referenceCount": 187,
  "subjects": ["Artificial Intelligence", "Computer Networks and Communications"],
  "abstract": "Deep learning has revolutionized many machine learning tasks...",
  "url": "https://doi.org/10.1109/tnnls.2020.2978386",
  "query": "graph neural network",
  "scrapedAt": "2026-08-11T19:00:00.000Z"
}

Download the dataset as JSON, CSV, Excel, or XML, or pull it via the Apify API.

Pricing

Pay-per-event: $0.01 per run start (per GB of run memory) plus $0.50 per 1,000 works. You only pay for works actually returned. See the Pricing tab for current rates.

Notes

• Data comes from the public Crossref REST API.
• Abstracts are only available for a subset of works (publishers that deposit them) and are cleaned from JATS XML to plain text.

Disclaimer

This is an unofficial Actor. It is not affiliated with or endorsed by Crossref. It returns publicly available scholarly metadata. You are responsible for how you use the data.