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Google Scholar Scraper 🎓🔍

Effortlessly scrape academic data from Google Scholar with this powerful and flexible Actor! 🚀

Features ✨

• 📚 Scrape scholarly articles, papers, and citations
• 🔢 Retrieve up to 5000 results per run
• 🌐 Support for multiple countries and languages
• ⏳ Time-based filtering options
• 💾 Results saved directly to your Apify dataset

Use Cases 💡

• 🧪 Academic research and literature reviews
• 📊 Bibliometric analysis and citation tracking
• 🏫 University and research institution benchmarking
• 🔬 Trend analysis in scientific publications
• 👨‍🔬 Author and researcher profiling

How It Works 🛠️

1. Set your search parameters in the input
2. The Actor fetch Google Scholar data
3. Results are processed and saved to your Apify dataset
4. Repeat until the desired number of results is reached or all available data is collected

Input Parameters 📝

• maxItems: Maximum number of results to collect (100-5000)
• query: Your search term
• gl: Google country code (e.g., "us" for United States)
• hl: Interface language (e.g., "en" for English)
• lr: Language of search results (e.g., "lang_en" for English)
• cr: Country restriction for search results
• nfpr: No auto-correction of spelling (0 or 1)
• filter: Whether to filter similar results (0 or 1)
• scholar_include_citations: Include citations in results (true/false)
• scholar_year_min: Minimum year for results
• scholar_year_max: Maximum year for results
• scholar_patents_courts: Filter for patents and court cases
• scisbd: Scholar sort parameter (1 or 2)

Output 📊

The Actor provides detailed information about each scholarly result, including:

• Title
• Authors
• Publication info
• Number of citations
• Link to the article
• Brief summary or abstract
• And more!

Get started with Google Scholar Scraper today and unlock valuable academic insights for your research projects! 🚀🎓

Input Example

A full explanation of an input example in JSON.

{
"maxItems":100,
"query":"rocket"
}

Output sample

The results will be wrapped into a dataset which you can always find in the Storage tab. Here's an excerpt from the data you'd get if you apply the input parameters above:

And here is the same data but in JSON. You can choose in which format to download your data: JSON, JSONL, Excel spreadsheet, HTML table, CSV, or XML.

[
{
"position":1,
"title":"Liquid-propellant rocket engine throttling: A comprehensive review",
"link":"https://arc.aiaa.org/doi/abs/10.2514/1.49791",
"domain":"arc.aiaa.org",
"displayed_link":"MJ Casiano, JR Hulka, V Yang - Journal of propulsion and power, 2010 - arc.aiaa.org",
"pdf_link":"https://ntrs.nasa.gov/api/citations/20090037061/downloads/20090037061.pdf",
"snippet":"… and dynamics of an liquid-propellant rocket engine as well as difficulties and … rocket engine \nthrottling centered around engines from the United States. Several liquid-propellant rocket …",
"cited_by_count":253,
"cited_by_link":"https://www.google.com/scholar?cites=7502999951619349366&as_sdt=2005&sciodt=0,5&hl=fr",
"versions_count":11,
"versions_link":"https://www.google.com/scholar?cluster=7502999951619349366&hl=fr&as_sdt=0,5"
},
{
"position":2,
"title":"Knowledge management–not rocket science",
"link":"https://www.emerald.com/insight/content/doi/10.1108/13673270510590191/full/html",
"domain":"www.emerald.com",
"displayed_link":"D Call - Journal of Knowledge management, 2005 - emerald.com",
"pdf_link":"http://j.pelet.free.fr/publications/km/Knowledge_management_not_rocket_science.pdf",
"snippet":"Purpose – To provide a broad understanding of knowledge management and how to \nsuccessfully implement knowledge management. Design/methodology/approach – Using a wide …",
"cited_by_count":198,
"cited_by_link":"https://www.google.com/scholar?cites=1591768766360518117&as_sdt=2005&sciodt=0,5&hl=fr",
"versions_count":7,
"versions_link":"https://www.google.com/scholar?cluster=1591768766360518117&hl=fr&as_sdt=0,5"
},
...
]