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📖 Crossref DOI Metadata Scraper

🚀 Extract citation metadata for 155M+ DOIs from Crossref in seconds. Search by query, filter by title, author, or DOI. No coding, no API keys required.

🕒 Last updated: 2026-04-23 · 📊 30+ fields · 📚 155M+ DOIs indexed · 🔍 Title, author, and free-text search

Copy to your AI assistant

Copy this block into ChatGPT, Claude, Cursor, or any LLM to start using this actor.

parseforge/crossref-scraper on Apify. Call:ApifyClient("TOKEN").actor("parseforge/crossref-scraper").call(run_input={...}), then client.dataset(run["defaultDatasetId"]).list_items().items for results. Key inputs:maxItems(integer,default10),query(string,default"attention is all you need"),queryTitle(string),queryAuthor(string),filter(string),doi(string). Full actor spec: fetch build via GEThttps://api.apify.com/v2/acts/parseforge~crossref-scraper(Bearer TOKEN). Get token: https://console.apify.com/account/integrations

Crossref is the largest DOI registration agency, indexing over 155 million research papers, book chapters, conference proceedings, datasets, and preprints. This scraper connects to the Crossref Works API and returns structured citation metadata including titles, authors, publication dates, journals, DOIs, citation counts, abstracts, license information, and funding details. Whether you need metadata for a single DOI or want to search across the entire Crossref database, the scraper handles pagination and rate limiting automatically.

Researchers, librarians, and data analysts use this actor to build citation databases, verify publication records, analyze research trends, and enrich existing datasets with DOI metadata. Instead of querying the Crossref API manually and parsing JSON responses, you get clean, structured data exported as JSON, CSV, or Excel. Every record includes the full title, all authors with ORCID IDs when available, journal name, volume, issue, pages, publication date, license, funder information, and reference lists.

📋 What the Crossref Scraper does

• 🔍 Free-text search across titles, authors, and container titles in the 155M+ DOI database
• 📝 Title-specific search to find publications matching exact title keywords
• 👤 Author search to find all works by a specific researcher
• 🎯 Single DOI lookup to fetch full metadata for a specific publication
• 🔧 Filter strings to narrow results by type, date, ORCID, publisher, and more
• 📧 Polite pool access by providing an email for faster Crossref response times

The scraper queries the Crossref Works API, retrieves matching records, and extracts full citation metadata for each item. Results include the publication title, all authors (with ORCID IDs), journal or container title, volume, issue, pages, publication dates, DOI, license info, funder details, reference count, citation count, and direct links. Each record is timestamped and includes the content type (journal-article, book-chapter, etc.).

💡 Why it matters: Crossref's API returns complex nested JSON that requires parsing. This scraper flattens and normalizes the data, delivering clean records ready for spreadsheets, databases, or analysis tools. Add your email to get routed to Crossref's faster "polite pool."

🎬 Full Demo

🚧 Coming soon...

⚙️ Input

Example 1: Free-text search

{
"query":"attention is all you need",
"maxItems":10
}

Example 2: Filtered author search with date range

{
"queryAuthor":"Hinton, Geoffrey",
"filter":"type:journal-article,from-pub-date:2020",
"email":"your@email.com",
"maxItems":100
}

⚠️ Good to Know: Providing an email address routes your requests to Crossref's "polite pool," which has faster response times and higher rate limits. The filter field accepts Crossref filter syntax. See Crossref API docs for all available filter options.

📊 Output

🧾 Schema

📦 Sample records

✨ Why choose this Actor

📊 Search across 155M+ DOIs and collect up to 1,000,000 records per run with full citation metadata.

📈 How it compares to alternatives

Get structured citation metadata at scale without writing API code or managing pagination.

🚀 How to use

1. Create an Apify account - Sign up free with $5 credit
2. Open the Crossref DOI Metadata Scraper - Navigate to the actor page on Apify
3. Enter your search query - Type keywords, an author name, or a specific DOI
4. Add optional filters - Set date range, publication type, or provide your email for faster responses
5. Click Start - The actor collects matching records and delivers structured citation data

⏱️ A typical run with 10 records completes in under 30 seconds.

💼 Business use cases

🌟 Beyond business use cases

Data like this powers more than commercial workflows. The same structured records support research, education, civic projects, and personal initiatives.

🤖 Ask an AI assistant about this scraper

Open a ready-to-send prompt about this ParseForge actor in the AI of your choice:

• 💬 ChatGPT
• 🧠 Claude
• 🔍 Perplexity
• 🅒 Copilot

❓ Frequently Asked Questions

🔌 Automating Crossref Scraper

Integrate the Crossref Scraper into your workflow using the Apify API or client libraries.

Node.js:

import{ ApifyClient }from'apify-client';
const client =newApifyClient({token:'YOUR_API_TOKEN'});
const run =await client.actor("parseforge/crossref-scraper").call({
query:"attention is all you need",
maxItems:50,
email:"your@email.com"
});
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/crossref-scraper").call(run_input={
"query":"attention is all you need",
"maxItems":50,
"email":"your@email.com"
})
items =list(client.dataset(run["defaultDatasetId"]).iterate_items())
print(items)

• 📖 Apify API reference
• 🐍 Python client docs
• 📦 Node.js client docs

Schedules: Set up recurring runs to track new publications matching your query, monitor citation count changes, or build growing bibliometric datasets. Configure daily, weekly, or monthly schedules from the Apify Console.

🔌 Integrate with any app

• 🔗 Make (Integromat) - Connect citation data to Google Sheets, Notion, or any of 1,500+ apps
• 🔗 Zapier - Trigger workflows when new citation records are collected
• 🔗 Slack - Get notified when a Crossref data run completes
• 🔗 Airbyte - Stream citation metadata into your data warehouse
• 🔗 GitHub - Store citation datasets in repositories for version control
• 🔗 Google Drive - Automatically save CSV exports to shared folders

🔗 Recommended Actors

💡 Pro Tip: Combine the Crossref Scraper with the PubMed Scraper to get both citation metadata and full biomedical abstracts for the same publications.

🆘 Need Help? Open our contact form and we will get back to you within 24 hours. We are happy to help with custom setups, integrations, or feature requests.

Disclaimer: This actor is not affiliated with, endorsed by, or connected to Crossref. It accesses publicly available data through the Crossref Works API. Use responsibly and in accordance with Crossref's Metadata Terms of Use.