# Google Scholar Research Scraper (`b3264/google-scholar-scraper`) Actor

Extract academic papers, citations, and author data from Google Scholar. No API key needed.

- **URL**: https://apify.com/b3264/google-scholar-scraper.md
- **Developed by:** [Brandon Hamm](https://apify.com/b3264) (community)
- **Categories:** AI, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.50 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Google Scholar Research Scraper

Extract academic papers, citations, authors, and publication data from Google Scholar. Search by keywords and get structured research data.

### Features

- **Keyword search**: Search for papers by any query
- **Year filtering**: Filter results by publication year range
- **Citation counts**: Get citation counts for each paper
- **Author data**: Extract author names, publication venues, and profile IDs
- **PDF links**: Get direct PDF links when available
- **Snippet extraction**: Get abstract snippets for each paper
- **No API key required**: Uses Google Scholar's public interface

### Input Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `searchQueries` | string\[] | `["machine learning healthcare"]` | List of search terms |
| `maxResults` | integer | 20 | Max papers per query (10 per page) |
| `yearFrom` | integer | 0 | Papers from this year onwards (0 = no filter) |
| `yearTo` | integer | 0 | Papers up to this year (0 = no filter) |
| `includePatents` | boolean | false | Include patents in results |
| `includeCitations` | boolean | true | Include citation counts |
| `language` | string | `en` | Search language |

### Output Fields

- `title` — Paper title
- `authors` — Author names
- `publication` — Publication venue (journal, conference, etc.)
- `year` — Publication year
- `snippet` — Abstract snippet
- `url` — Paper URL
- `citationCount` — Number of citations
- `pdfUrl` — Direct PDF link (when available)
- `types` — Paper types (e.g., \[PDF], \[BOOK], \[CITATION])
- `authorProfileId` — Google Scholar author profile ID
- `searchQuery` — The query that found this paper

### Use Cases

- Literature reviews for academic research
- Citation analysis and bibliometrics
- Author impact tracking
- Research trend identification
- Competitive intelligence for academic publishing
- AI training data from academic papers

### Related Actors

- [FinViz Stock Screener Scraper](https://apify.com/b3264/finviz-stock-screener) — Financial data extraction
- [Google Maps Lead Intelligence](https://apify.com/b3264/google-maps-lead-intelligence) — B2B lead generation
- [Review Aggregator](https://apify.com/b3264/review-aggregator) — Multi-platform review scraping

### Notes

- Google Scholar may show CAPTCHAs under heavy use. The actor uses Apify proxies to minimize this.
- Results are limited to what Google Scholar displays (typically 1000 per query).
- Citation counts are as reported by Google Scholar at the time of scraping.

# Actor input Schema

## `searchQueries` (type: `array`):

List of search terms (keywords, paper titles, or author names)

## `maxResults` (type: `integer`):

Maximum number of papers to extract per search query (10 per page)

## `yearFrom` (type: `integer`):

Only include papers published from this year onwards (optional)

## `yearTo` (type: `integer`):

Only include papers published up to this year (optional)

## `includePatents` (type: `boolean`):

Include patents in search results

## `includeCitations` (type: `boolean`):

Include citation counts in output

## `language` (type: `string`):

Search language (2-letter code)

## `customProxyUrl` (type: `string`):

Optional HTTP proxy URL (e.g., http://user:pass@ip:port). Leave empty for direct connection. curl\_cffi Chrome impersonation bypasses Google bot detection without proxy.

## Actor input object example

```json
{
  "searchQueries": [
    "machine learning healthcare"
  ],
  "maxResults": 20,
  "yearFrom": 0,
  "yearTo": 0,
  "includePatents": false,
  "includeCitations": true,
  "language": "en",
  "customProxyUrl": ""
}
```

# Actor output Schema

## `papers` (type: `string`):

Dataset containing extracted academic papers with titles, authors, citations, and metadata.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "searchQueries": [
        "machine learning healthcare"
    ],
    "maxResults": 20,
    "yearFrom": 0,
    "yearTo": 0,
    "includePatents": false,
    "includeCitations": true,
    "language": "en",
    "customProxyUrl": ""
};

// Run the Actor and wait for it to finish
const run = await client.actor("b3264/google-scholar-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "searchQueries": ["machine learning healthcare"],
    "maxResults": 20,
    "yearFrom": 0,
    "yearTo": 0,
    "includePatents": False,
    "includeCitations": True,
    "language": "en",
    "customProxyUrl": "",
}

# Run the Actor and wait for it to finish
run = client.actor("b3264/google-scholar-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "searchQueries": [
    "machine learning healthcare"
  ],
  "maxResults": 20,
  "yearFrom": 0,
  "yearTo": 0,
  "includePatents": false,
  "includeCitations": true,
  "language": "en",
  "customProxyUrl": ""
}' |
apify call b3264/google-scholar-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,b3264/google-scholar-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/TvC9bRtX5MbgERd6G/builds/cWqR1YpIkQKG79Ys7/openapi.json