CrossRef Academic Papers & DOI Citations Radar

What can CrossRef Academic Papers & DOI Citations Radar do?

CrossRef Academic Papers & DOI Citations Radar extracts public data from the CrossRef scholarly metadata registry in an automated, lightweight scraper. Retrieve structured records, identifiers, status metrics, and metadata in seconds without managing proxies or scraping infrastructure.

📦 What you can extract	⚙️ Features & integrations
📚 Publication Details: Article title, publication date, DOI identifier 🏛️ Publisher & Journal: Container title, publisher name, ISSN, volume/issue 👥 Authors & Affiliations: Author names, ORCID identifiers, affiliations 📊 Citations & References: Cited-by count, reference count, license type ⏱️ Timestamps: Indexed date, published print date, extraction time	⚡ Zero proxies required: Direct, fast cloud execution 📅 Scheduling & automation: Run hourly, daily, or on-demand ⬇️ Multi-format export: Download JSON, CSV, Excel, or XML 🦾 API & SDK ready: Native Python and Node.js SDK integrations 🤖 AI & LLM friendly: Clean structured data for Claude, GPT, and agents

How much will scraping cost?

This Actor uses a transparent Pay-Per-Event (PPE) pricing model:

• Only $1.25 per 1,000 results ($0.0013 per record).
• Free Monthly Tier: Apify provides $5 in free usage credits every month, allowing you to scrape up to 4,000 records completely free each month.
• No recurring monthly subscriptions, no proxy fees, and no charge for runs that return 0 results.

How do I use CrossRef Academic Papers & DOI Citations Radar?

1. Create an account: Sign up for free at console.apify.com.
2. Open the Actor: Navigate to CrossRef Academic Papers & DOI Citations Radar in the Apify Store or Console.
3. Configure parameters: Enter your target search filters or leave the default settings to test.
4. Click Start: Hit Save & Start and watch live logs as records are extracted.
5. Download your data: Export in JSON, CSV, or Excel, or consume via the Apify API.

⬇️ Input Parameters

Configure extraction using the following input fields:

Parameter	Type	Default	Description
query	string	machine learning	Search topic, keyword, or paper title (e.g., machine learning, quantum computing, genomics).
rows	integer	20	Number of publication records to retrieve (5 - 50).

⬆️ Output & Sample Dataset

Extracted items are stored in your default dataset. Here is an example of the clean JSON record structure:

[
  {
    "doi": "10.1038/s41586-020-2649-2",
    "title": "Language Models are Few-Shot Learners",
    "containerTitle": "Nature Machine Intelligence",
    "publisher": "Springer Nature",
    "authors": [
      "Tom B. Brown",
      "Benjamin Mann"
    ],
    "isReferencedByCount": 8400,
    "resourceLink": "https://doi.org/10.1038/s41586-020-2649-2",
    "extractedAt": "2026-11-04T08:00:00.000Z"
  }
]

💼 What can you do with this data?

• Scientific Literature Review: Automatically gather recent peer-reviewed publications by topic.
• Citation Analytics: Track paper influence and cross-journal impact metrics over time.
• R&D Scouting: Discover emerging patents, academic breakthroughs, and top researchers.
• Knowledge Graph Enrichment: Feed verified scientific DOIs into semantic search graphs.

❓ Frequently Asked Questions

Do I need proxies to run this Actor?

No. This Actor connects directly to public data endpoints and does not require paid residential or datacenter proxies.

Can I schedule this Actor to run automatically?

Yes. Use the Schedules tab in Apify Console to run this Actor automatically every hour, daily, or on any custom cron schedule.

Can I integrate this scraper with Python or Node.js?

Yes. You can invoke this Actor and stream results directly using the official Apify Python SDK or Apify JavaScript/TypeScript SDK.

How can I export my data?

You can download your dataset at any time in JSON, CSV, Excel, XML, or HTML table directly from the Apify Console or via API.