Google Scholar Scraper — Extract Academic Papers, Citations & Research Data

A powerful Google Scholar scraper that extracts academic paper metadata including titles, authors, abstracts, citation counts, publication details, and PDF links. Search by keyword, topic, or author name. Ideal for literature reviews, bibliometric analysis, and research data collection.

Features

• Lightning fast results — 50 papers scraped in ~10 seconds
• Rich academic metadata — titles, authors, abstracts, citation counts, publication venues, PDF links, publication years
• Citation tracking — extract citation counts for impact analysis and research benchmarking
• Year filtering — narrow results by publication year range to focus on recent or historical research
• Multi-query support — search multiple keywords or topics in a single run
• Author search — find papers by specific researchers (e.g., "Geoffrey Hinton", "Yann LeCun")
• Automatic pagination — fetches up to 500 results per query with intelligent page handling
• Structured JSON output — clean, well-formatted data ready for analysis, databases, or AI pipelines

Use Cases

• Literature reviews — collect papers systematically for academic research and systematic reviews
• Bibliometric analysis — measure research impact, track citation trends, analyze collaboration networks
• Competitor intelligence — monitor competitor research output and publication patterns
• Grant writing — find related work, citation context, and research gaps for proposals
• AI & machine learning — feed structured academic data into LLMs for summarization, classification, or knowledge graphs
• Content creation — generate research-backed articles, newsletters, and educational materials

Input

Field	Type	Default	Description
searchQueries	string[]	["machine learning"]	Keywords or topics to search on Google Scholar
authorUrls	string[]	[]	Author names to search (e.g., "Geoffrey Hinton")
maxResults	integer	50	Max papers per query (1–500)
yearLow	integer	2000	Minimum publication year for filtering
yearHigh	integer	2026	Maximum publication year for filtering
sortBy	string	"relevance"	Sort by "relevance" or "date" (newest first)
articlesOnly	boolean	true	Exclude patents and non-article results

Output

Each paper record includes 13 fields of structured metadata:

Field	Type	Description
title	string	Full academic paper title
authors	string[]	Author names parsed from publication metadata
publicationInfo	string	Journal, venue, year, and publisher details
abstract	string	Paper abstract or snippet from Google Scholar
citationCount	integer	Number of citations (from Google Scholar)
paperUrl	string	Direct link to the paper or landing page
pdfUrl	string|null	Direct PDF download link when available
sourceType	string	Source type: HTML, PDF, or BOOK
year	integer	Publication year extracted from metadata
citationsUrl	string|null	Link to papers citing this paper
relatedUrl	string|null	Link to related articles on Google Scholar
scrapedAt	string	ISO 8601 timestamp of when data was scraped
searchQuery	string	The original search query that produced this result

Example Usage

Search by Topic

{
    "searchQueries": ["deep learning cancer detection", "transformer architecture"],
    "maxResults": 100,
    "yearLow": 2020,
    "yearHigh": 2025,
    "sortBy": "relevance"
}

Search by Author Name

{
    "authorUrls": ["Geoffrey Hinton", "Yann LeCun"],
    "maxResults": 50,
    "yearLow": 2015
}

Quick Test Run

{
    "searchQueries": ["reinforcement learning"],
    "maxResults": 10
}

Pricing

This actor uses a pay-per-result pricing model at $0.005 per paper scraped.

Results	Cost
10 papers	$0.05
50 papers	$0.25
100 papers	$0.50
500 papers	$2.50

Fast, reliable, and cost-effective academic data extraction. No additional infrastructure or API keys required.

Example Output

{
    "title": "Deep learning",
    "authors": ["Y LeCun", "Y Bengio", "G Hinton"],
    "publicationInfo": "Nature, 2015 - nature.com",
    "abstract": "Deep learning allows computational models that are composed of multiple processing layers...",
    "citationCount": 86734,
    "paperUrl": "https://www.nature.com/articles/nature14539",
    "pdfUrl": null,
    "sourceType": "HTML",
    "year": 2015,
    "citationsUrl": null,
    "relatedUrl": null,
    "scrapedAt": "2026-05-03T08:53:26.141Z",
    "searchQuery": "deep learning"
}

Why Use This Google Scholar Scraper?

• No setup required — works out of the box with zero configuration
• No browser or proxy needed — pure API-based extraction is faster and more reliable
• Consistent structured data — every record follows the same schema for easy processing
• Built for scale — handle hundreds of queries with automatic rate limiting and retries
• AI-ready output — clean JSON format perfect for feeding into LLMs, RAG pipelines, or data warehouses