Google Scholar Scraper

Scrapes Google Scholar for academic papers. Extracts the full canonical scholar-vertical schema: title, authors (with profile links), publication venue, publisher, year, citation counts, DOI, PDF/HTML/bibtex/abstract URLs, versions count, cluster ID, subjects/keywords, free PDF flag, and more.

What this Actor collects

The Actor converts Google Scholar results into one clean JSON record per scholarly work, including authors and profile links, venue and publisher, year, citation and version counts, DOI, document links, subjects, keywords, access status, and search provenance when available.

• Uses the input limits and filters below to control the crawl.
• Stores source-backed fields defined by the 48-field dataset schema.
• Omits optional fields when the source does not expose a value instead of writing nulls or fabricated placeholders.

Use cases

• Literature and catalog research
• Entity and citation enrichment
• Specialized-source monitoring

Input

Provide input in JSON. Fields marked required must be supplied. The Default / example column shows a schema default when one exists; otherwise it shows a documented prefill or fixture value.

Field	Type	Required	Default / example	Description
query	string	Yes	"machine learning"	The academic search query to look up on Google Scholar
maxItems	integer	No	50	Maximum number of scholar results to retrieve
yearFrom	integer	No	—	Filter results published from this year onwards (optional)
yearTo	integer	No	—	Filter results published up to this year (optional)
sortBy	string	No	"relevance"	Sort results by relevance or date
proxyConfiguration	object	No	{"useApifyProxy":true,"apifyProxyGroups":["RESIDENTIAL"]}	Proxy settings for the scraper. Use Apify residential proxies to avoid blocks.

Example input

{
  "query": "transformer neural network",
  "maxItems": 50,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "GOOGLE_SERP"
    ]
  },
  "sortBy": "relevance"
}

Output

The default dataset contains one item per scholarly publication result. The following are the most useful fields; DOI, citation links, versions, document URLs, subjects, and access fields depend on the Scholar result.

Field	Type	Description
position	integer	Position
title	string	Title
authors	array	Authors
publication	string	Publication
year	integer	Year
citedBy	integer	Cited By
searchQuery	string	Search Query
scrapedAt	string	Scraped At
type	string	Type
description	string	Description
snippet	string	Snippet
url	string	URL
link	string	Link
query	string	Query
resultType	string	Result Type
page	integer	Page

Example dataset item

This compact example is taken from local Actor storage. Long text and nested collections are shortened for documentation only.

{
  "position": 1,
  "title": "Gradient-based learning applied to document recognition",
  "authors": [
    "Yann LeCun",
    "Léon Bottou"
  ],
  "publication": "Proceedings of the IEEE",
  "year": 1998,
  "citedBy": 58655,
  "searchQuery": "transformer neural network",
  "scrapedAt": "2026-07-23T11:51:14.711Z",
  "type": "scholar",
  "description": "Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network …",
  "snippet": "Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network …",
  "url": "https://doi.org/10.1109/5.726791"
}

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