🎓 OpenAlex Scholarly Works Scraper

🚀 Export academic works, authors, institutions, and more from OpenAlex in seconds. Filter by search query, entity type, or custom filters. No coding, no API keys required.

🕒 Last updated: 2026-04-16 · 📊 30+ fields · 🔄 Runs on Apify cloud or locally · 📁 Export: JSON, CSV, Excel

The OpenAlex Scholarly Works Scraper connects to OpenAlex, the free and open catalog of 250M+ scholarly records that succeeded Microsoft Academic Graph. It supports 7 entity types: works, authors, institutions, sources, concepts, publishers, and funders. Each record includes 30+ structured fields with titles, DOIs, citation counts, open access status, author details, institutional affiliations, and more. Whether you need 10 papers for a quick lookup or millions of records for a large-scale bibliometric study, this tool handles it efficiently.

Built for researchers conducting literature reviews, bibliometricians analyzing citation networks, university administrators tracking institutional output, and data teams building scholarly knowledge graphs. The scraper uses the OpenAlex API with support for free-text search and the full OpenAlex filter syntax. Providing a contact email puts your requests in the "polite pool" for faster processing.

Target Audience	Use Cases
Academic Researchers	Literature reviews, citation analysis
Bibliometricians	Citation network mapping, impact studies
University Administrators	Institutional output tracking
Data Scientists	Knowledge graph construction, NLP corpus building
Funding Agencies	Research output assessment, grant evaluation
Library Scientists	Collection development, trend analysis

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📋 What the OpenAlex Scholarly Works Scraper does

• 📝 Extracts scholarly work metadata including titles, abstracts, DOIs, publication dates, and citation counts for bibliometric analysis
• 👥 Collects author profiles with names, ORCID IDs, institutional affiliations, and publication histories
• 🏫 Gathers institution data including names, types, locations, and research output statistics
• 📰 Pulls source information for journals, conferences, and repositories with ISSN, publisher, and open access details
• 🔗 Captures concept and topic data for subject classification and research trend analysis
• 📊 Tracks open access status with OA type, OA URL, and license information for each work

The scraper queries the OpenAlex API with your search terms and optional filters, handles cursor-based pagination, and processes results efficiently. The OpenAlex filter syntax supports field-level filtering like publication_year:2024,is_oa:true,authorships.institutions.country_code:US for precise targeting.

💡 Why it matters: OpenAlex is the largest free scholarly database, covering 250M+ works, 90M+ authors, and 100K+ institutions. This scraper gives you structured access to this data without writing API integration code.

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🎬 Full Demo

🚧 Coming soon...

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⚙️ Input

Field	Type	Required	Description
maxItems	integer	No	Maximum records to collect. Free users: limited to 10. Paid users: up to 1,000,000.
entity	string	No	Entity type: works, authors, institutions, sources, concepts, publishers, or funders.
search	string	No	Free text search across titles, abstracts, and display names.
filter	string	No	OpenAlex filter string (e.g., "publication_year:2024,is_oa:true").
email	string	No	Contact email for OpenAlex "polite pool" (faster processing). Optional.

Example 1: Search for machine learning papers

{
  "entity": "works",
  "search": "machine learning",
  "maxItems": 100
}

Example 2: Open access papers from US institutions in 2024

{
  "entity": "works",
  "search": "climate change",
  "filter": "publication_year:2024,is_oa:true,authorships.institutions.country_code:US",
  "maxItems": 500,
  "email": "researcher@university.edu"
}

⚠️ Good to Know: Providing your email address puts your requests in OpenAlex's "polite pool" for faster rate limits. The filter syntax supports dozens of fields. Free users are automatically limited to 10 items per run.

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📊 Output

🧾 Schema

Emoji	Field	Type	Description
📝	title	string	Work title or entity display name
🆔	id	string	OpenAlex ID
🔗	doi	string	Digital Object Identifier (works)
🌐	url	string	OpenAlex URL
📅	publicationDate	string	Publication date (works)
📅	publicationYear	number	Publication year
👥	authors	array	Author names and affiliations
📊	citationCount	number	Total citations received
📊	citedByCount	number	Number of citing works
📖	abstract	string	Article abstract (when available)
📰	source	string	Journal or venue name
🔓	isOpenAccess	boolean	Whether the work is open access
🔓	oaType	string	OA type (gold, green, bronze, hybrid)
🔗	oaUrl	string	URL to free version
⚖️	license	string	License type
🏷️	concepts	array	Associated concepts/topics
🏫	institutions	array	Author institutions
🌍	countries	array	Author country codes
📊	referencedWorksCount	number	Number of references
📊	relatedWorksCount	number	Number of related works
🔢	volume	string	Journal volume
🔢	issue	string	Journal issue
📄	pages	string	Page range
🏷️	type	string	Work type (article, book, etc.)
🔢	orcid	string	Author ORCID ID (authors entity)
🏫	affiliation	string	Current affiliation (authors)
📊	worksCount	number	Total works (authors/institutions)
📊	hIndex	number	H-index (authors)
📅	scrapedAt	string	Data collection timestamp
❌	error	string	Error message if extraction failed

📦 Sample records

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✨ Why choose this Actor

Feature	Details
📊 250M+ records	Access the largest free scholarly database
🔍 7 entity types	Works, authors, institutions, sources, concepts, publishers, funders
🔓 Open access tracking	OA status, type, URL, and license for every work
📊 Citation metrics	Citation counts, h-index, and referenced works
🔧 Advanced filters	Full OpenAlex filter syntax for precise queries
📁 Multiple export formats	JSON, CSV, Excel for any workflow
⚡ Polite pool support	Provide email for faster processing

📈 Typical performance: Collects 500+ records per minute in polite pool mode. A dataset of 10,000 works takes roughly 20 minutes.

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📈 How it compares to alternatives

Feature	This Actor	Direct API Integration	Generic Scrapers
30+ structured fields per record	✅	✅ (requires coding)	Partial
7 entity types in one tool	✅	✅ (requires coding)	❌
No coding required	✅	❌	❌
Export to CSV/JSON/Excel	✅	❌ (raw JSON)	Partial
Automatic pagination	✅	Manual	Partial
Scheduled runs	✅	Custom setup	Partial
Filter syntax support	✅	✅	❌

All the features of the OpenAlex API, without writing a single line of code.

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🚀 How to use

1. Create a free Apify account - Sign up here (includes free credits)
2. Open the OpenAlex Scholarly Works Scraper - Navigate to the Actor page and click "Start"
3. Choose your entity type - Select works, authors, institutions, or another entity type
4. Set your search and filters - Enter a search query and optional OpenAlex filters
5. Run and download - Click "Start", wait for completion, then export as JSON, CSV, or Excel

⏱️ First results appear in under 10 seconds. A typical run of 100 records completes in about 30 seconds.

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💼 Business use cases

Academic Research Build citation network datasets Track research trends by topic over time Find collaborators at specific institutions Monitor open access adoption in your field	University Administration Track institutional research output Benchmark against peer institutions Generate faculty publication reports Monitor author h-indexes and citation impact
Data Science & AI Build scholarly knowledge graphs Create NLP training corpora from abstracts Analyze collaboration patterns Train topic classification models	Funding & Policy Assess research output for grant evaluation Track funded research productivity Analyze open access compliance rates Map research activity by country and institution

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🔌 Automating OpenAlex Scholarly Works Scraper

Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });
const run = await client.actor("parseforge/openalex-scraper").call({
  entity: "works",
  search: "machine learning",
  filter: "publication_year:2024,is_oa:true",
  maxItems: 200
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

from apify_client import ApifyClient
client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("parseforge/openalex-scraper").call(run_input={
    "entity": "works",
    "search": "machine learning",
    "filter": "publication_year:2024,is_oa:true",
    "maxItems": 200
})
items = list(client.dataset(run["defaultDatasetId"]).iterate_items())
print(items)

• 📖 Apify API Reference
• 🐍 Python API Client docs
• 🟢 Node.js API Client docs

Schedules: Set up weekly or monthly runs with Apify Schedules to track new publications, monitor citation growth, or maintain up-to-date researcher profiles.

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❓ Frequently Asked Questions

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🔌 Integrate with any app

• 🔗 Make (Integromat) - Connect OpenAlex data to 1,000+ apps with visual workflows
• 🔗 Zapier - Trigger actions when new scholarly records match your criteria
• 🔗 Slack - Get notifications when new papers are published in your field
• 🔗 Airbyte - Sync scholarly data to your data warehouse
• 🔗 GitHub - Automate research data pipelines with GitHub Actions
• 🔗 Google Drive - Export scholarly data directly to Google Sheets

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🔗 Recommended Actors

Actor	Description
📚 PubMed Citation Scraper	Extract citation data and metadata from PubMed biomedical literature
📖 PLOS Journals Scraper	Collect article data from PLOS ONE and other PLOS journals
🧬 Crossref Scraper	Collect DOI metadata and citation information from Crossref
📰 medRxiv Scraper	Extract health sciences preprint data from medRxiv
📄 Semantic Scholar Scraper	Query the Semantic Scholar API for academic paper data

💡 Pro Tip: Use OpenAlex to find papers by topic, then cross-reference with the Crossref Scraper for detailed citation metadata and reference lists.

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🆘 Need Help? Open our contact form and we will get back to you within 24 hours. For bug reports, feature requests, or integration help, we are here to assist.

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Disclaimer: This Actor is provided as-is, without warranty. It is not affiliated with or endorsed by OpenAlex or OurResearch. Use it responsibly and in compliance with applicable terms of service. The authors are not responsible for how the collected data is used. Always verify data accuracy for critical applications.