OpenAlex Scraper 🎓

Scrape millions of scholarly works, author profiles and citation data from OpenAlex — by search query, with no API key. Clean, structured JSON built for literature reviews, research intelligence, citation analysis and open-science datasets.

OpenAlex is a fully open index of the global research graph (250M+ works, 90M+ authors, journals, institutions and concepts). This actor turns any keyword search into a tidy dataset you can export to CSV, JSON, Excel or push straight into your data warehouse.

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💡 What you get

Pick a mode and hand it a list of search queries.

Mode: works (papers)

Every record is one scholarly work:

Field	Description
openalex_id	OpenAlex work ID (e.g. W2163605009)
doi	Clean DOI (e.g. 10.1145/3065386)
title	Paper title
year / publication_date	Year and full publication date
cited_by_count	Total citations
authors	List of author names
institutions	Unique list of affiliated institutions
journal	Source / journal / venue name
is_oa / oa_url / pdf_url	Open-access status and full-text links
concepts	List of topic / concept labels
type	Work type (article, review, dataset, …)

Mode: authors (profiles)

Every record is one author:

Field	Description
openalex_id	OpenAlex author ID
name	Author display name
orcid	ORCID iD (when known)
works_count	Number of works
cited_by_count	Total citations
institution	Last known institution

Every record also carries source: "openalex" and a scraped_at timestamp. All fields are nullable — missing data becomes null rather than breaking your pipeline.

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🚀 Input

{
  "mode": "works",
  "queries": ["neural networks", "crispr"],
  "maxItems": 100
}

Input	Type	Description
mode	select	works (papers) or authors (profiles). Default works.
queries	array	One or more free-text search queries. Results are merged and de-duplicated.
maxItems	integer	Total records across all queries (1–1000). The budget is split evenly per query.

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📤 Example output (works)

{
  "openalex_id": "W2163605009",
  "doi": "10.1145/3065386",
  "title": "ImageNet classification with deep convolutional neural networks",
  "year": 2017,
  "publication_date": "2017-05-24",
  "cited_by_count": 75715,
  "authors": ["Alex Krizhevsky", "Ilya Sutskever", "Geoffrey E. Hinton"],
  "institutions": ["Google (United States)", "University of Toronto"],
  "journal": "Communications of the ACM",
  "is_oa": true,
  "oa_url": "http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf",
  "pdf_url": "http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf",
  "concepts": ["Softmax function", "Convolutional neural network"],
  "type": "article",
  "source": "openalex",
  "scraped_at": "2026-07-20T21:07:00Z"
}

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💰 Pricing

Cheap and predictable — roughly $0.5 per 1,000 records. The actor uses OpenAlex's polite pool for fast, reliable responses and runs on just 512 MB, so most jobs cost a few cents.

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🎯 Use cases

• Literature review — pull every paper on a topic with citations, authors and open-access PDFs.
• Research intelligence — track who is publishing what, where, and how often it is cited.
• Citation analysis — rank works and authors by impact across a field.
• Open science datasets — build reproducible corpora with DOIs and OA links for downstream NLP/ML.

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🔗 Related actors

• Semantic Scholar Scraper
• Crossref Scraper
• ORCID Scraper
• arXiv Scraper

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🔍 Keywords

openalex scraper, scholarly works api, academic paper scraper, citation data, research metadata, open access papers, author profiles, doi lookup, bibliometrics, literature review tool, scholarly search, science of science, research graph, no api key.

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This actor uses only the public OpenAlex API and adds a polite contact email for reliability. Data © OpenAlex, CC0.