# Google Scholar Scraper — Papers, Authors, Cites (`scrape.badger/google-scholar-scraper`) Actor
Scrape Google Scholar at scale: paper search with year range + language filters, author profile lookup (h-index, i10-index, interests, co-authors, full article list), citation formats (MLA, APA, Chicago, Harvard, Vancouver) with BibTeX / RIS / EndNote / RefWorks exports.
- **URL**: https://apify.com/scrape.badger/google-scholar-scraper.md
- **Developed by:** [Scrape Badger](https://apify.com/scrape.badger) (community)
- **Categories:** Automation, Developer tools, Other
- **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $7.00 / 1,000 scholar pages
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 a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
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.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use 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
### What does Google Scholar Scraper do?
Scrape [Google Scholar](https://scholar.google.com) at scale — papers, author profiles, author citation charts, and citation format exports (MLA / APA / Chicago / BibTeX / RIS).
### Why use Google Scholar Scraper?
- **Five modes.** Search Papers, Search Author Profiles, Get Author Profile (with articles), Get Author Citation Chart, Get Paper Citation Formats.
- **Year range filter.** `as_ylo` / `as_yhi` for date-bound literature reviews.
- **Citation export.** Direct MLA / APA / Chicago / Harvard / Vancouver + BibTeX / RIS.
- **Author citation chart.** Per-year citation counts for academic impact tracking.
- **Cheapest scholar actor on Apify.** $0.70 / 1k papers.
### What data can Google Scholar Scraper extract?
| Field | Type | Description |
|---|---|---|
| record_type | string | `paper` / `author` / `article` / `citation_chart_point` / `citation_format` |
| title | string | Paper / author name |
| authors | string | Author list |
| publication | string | Journal / venue |
| cited_by | number | Citation count |
| year | number | Publication year |
| link | string | Paper URL |
| snippet | string | Abstract preview |
| cluster_id | string | Google Scholar's paper ID |
| author_id | string | Google Scholar's author ID |
### How to scrape Google Scholar
1. Click **Try for free**.
2. Pick `mode`.
3. For Search Papers: enter `q`, optional `as_ylo` / `as_yhi` year range.
4. For Author Profile: enter `author_id`.
5. For Citation Formats: enter `q` (cluster_id).
6. Click **Start** — papers / authors stream into the dataset.
### How much will it cost?
**$0.007 per page (list modes) · $0.003 per single-shot call.** ≈ $0.70 per 1,000 papers. Author chart / cite format = single-shot $0.003 each.
#### Competitor benchmark
| Actor | Author | Price | Notes |
|---|---|---|---|
| easyapi/google-scholar-scraper | easyapi | ~$10 / 1k papers | Search-only |
| scrapestorm/google-scholar-scraper | ScrapeStorm | ~$8.99 / 1k | Search-only |
| marco_gullo/google-scholar | Marco Gullo | Variable | Per-run |
| **scrape-badger/google-scholar-scraper** | **ScrapeBadger** | **$0.70 / 1k papers** | **5 modes in one actor** |
### Input
Configure the run in the **Input** tab above, or pass a JSON object matching the fields below when calling the Actor via the Apify API.
| Field | Required | Description |
|---|---|---|
| mode | ✅ | One of 5 modes. |
| q | Search modes | Query (keyword / cluster_id / author name). |
| author_id | Author modes | Google Scholar author ID. |
| as_ylo / as_yhi | — | Year range filter. |
| as_sdt | — | Article type filter. |
| num / page | — | Pagination. |
| mauthors | Search Author Profiles | Author name query. |
| cstart / pagesize | Get Author Profile articles | Pagination. |
### Output
Every successful run streams records into the run's dataset. Download as JSON, CSV, XML, Excel, or HTML from the **Dataset** tab; consume programmatically via the Apify API or webhooks.
Example record:
```json
{
"record_type": "paper",
"title": "Attention Is All You Need",
"authors": "Vaswani, Ashish et al.",
"publication": "NeurIPS 2017",
"cited_by": 98234,
"year": 2017,
"link": "https://arxiv.org/abs/1706.03762",
"snippet": "The dominant sequence transduction models\u2026",
"cluster_id": "1234567890"
}
````
### Tips / Advanced options
- **Use `as_ylo` for literature reviews.** Bound by date — e.g. `as_ylo: 2020` for post-2020 ML papers.
- **Citation chart for academic impact.** Per-year citation counts — perfect for tenure-track impact reports.
- **BibTeX export for reference managers.** Get Paper Citation Formats → BibTeX → drop into Zotero / Mendeley.
- **Dedupe by `cluster_id`.** Same paper on arXiv + journal + preprint server — Google merges by cluster\_id.
### FAQ, Disclaimers, Support
#### What's `cluster_id`?
Google Scholar's internal paper identifier. All versions (arXiv, journal, preprint) of the same paper share a cluster\_id.
#### How do I find `author_id`?
Run Search Author Profiles first — each returned author has an `author_id`. Or grab it from the author's profile URL (`&user=…`).
#### Does this include PDF text?
No — only Scholar's metadata (title, authors, snippet, link). Download the PDF from `link` separately.
#### What's the citation count source?
Google Scholar's own `cited_by` count. Differs from Web of Science / Scopus / OpenCitations.
#### Disclaimer
This Actor scrapes public Google data only. You're responsible for compliance with Google's Terms of Service and any applicable data-protection laws (GDPR, CCPA, etc.) in your jurisdiction. ScrapeBadger does not store the scraped results — they are delivered directly to your Apify dataset.
#### Support
Something not working? Open a ticket in the **Issues** tab above — we triage within one business day. Full API reference: [docs.scrapebadger.com](https://docs.scrapebadger.com).
#### Powered by
[ScrapeBadger](https://scrapebadger.com) — Google-optimised residential proxy pool + browser-farm fallback, 99.7% uptime, unmetered bandwidth. No CAPTCHAs reach you.
# Actor input Schema
## `mode` (type: `string`):
Pick the Scholar call to run. Each mode uses different required inputs.
## `q` (type: `string`):
Search text (e.g. transformer neural networks). Required for Search Papers. Also used as the cluster\_id in Get Paper Citation Formats — paste the paper's cluster\_id from a Search Papers result.
## `mauthors` (type: `string`):
Author name to search (e.g. Geoffrey Hinton). Required for Search Author Profiles.
## `author_id` (type: `string`):
Google Scholar user ID (the user query parameter on a scholar.google.com profile URL, e.g. JicYPdAAAAAJ).
## `as_ylo` (type: `integer`):
Lower bound for publication year (e.g. 2020). Optional.
## `as_yhi` (type: `integer`):
Upper bound for publication year (e.g. 2024). Optional.
## `as_sdt` (type: `string`):
0 excludes patents (default). 7 includes patents and case law.
## `num` (type: `integer`):
Scholar paper results per page. 1–20.
## `hl` (type: `string`):
Google language code (e.g. en, es, de, fr).
## `cstart` (type: `integer`):
Offset for the author's article list (0-based).
## `pagesize` (type: `integer`):
How many articles to fetch per author-profile page.
## `max_pages` (type: `integer`):
How many pages of results to fetch. Each page = 1 API call (credits). Applies to Search Papers, Search Author Profiles, and Get Author Profile.
## Actor input object example
```json
{
"mode": "Search Papers",
"q": "transformer neural networks",
"as_sdt": "0",
"num": 10,
"hl": "en",
"cstart": 0,
"pagesize": 20,
"max_pages": 3
}
```
# Actor output Schema
## `dataset` (type: `string`):
No description
# 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 '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"q": "transformer neural networks"
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrape.badger/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 '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = { "q": "transformer neural networks" }
# Run the Actor and wait for it to finish
run = client.actor("scrape.badger/google-scholar-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"q": "transformer neural networks"
}' |
apify call scrape.badger/google-scholar-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrape.badger/google-scholar-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Google Scholar Scraper — Papers, Authors, Cites",
"description": "Scrape Google Scholar at scale: paper search with year range + language filters, author profile lookup (h-index, i10-index, interests, co-authors, full article list), citation formats (MLA, APA, Chicago, Harvard, Vancouver) with BibTeX / RIS / EndNote / RefWorks exports.",
"version": "1.0",
"x-build-id": "aO773nYJO9wEkdARg"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrape.badger~google-scholar-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrape.badger-google-scholar-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/scrape.badger~google-scholar-scraper/runs": {
"post": {
"operationId": "runs-sync-scrape.badger-google-scholar-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/scrape.badger~google-scholar-scraper/run-sync": {
"post": {
"operationId": "run-sync-scrape.badger-google-scholar-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"mode"
],
"properties": {
"mode": {
"title": "Mode",
"enum": [
"Search Papers",
"Search Author Profiles",
"Get Author Profile",
"Get Author Citation Chart",
"Get Paper Citation Formats"
],
"type": "string",
"description": "Pick the Scholar call to run. Each mode uses different required inputs.",
"default": "Search Papers"
},
"q": {
"title": "Query",
"type": "string",
"description": "Search text (e.g. transformer neural networks). Required for Search Papers. Also used as the cluster_id in Get Paper Citation Formats — paste the paper's cluster_id from a Search Papers result."
},
"mauthors": {
"title": "Author name query",
"type": "string",
"description": "Author name to search (e.g. Geoffrey Hinton). Required for Search Author Profiles."
},
"author_id": {
"title": "Author ID",
"type": "string",
"description": "Google Scholar user ID (the user query parameter on a scholar.google.com profile URL, e.g. JicYPdAAAAAJ)."
},
"as_ylo": {
"title": "Year from (inclusive)",
"minimum": 1900,
"maximum": 2100,
"type": "integer",
"description": "Lower bound for publication year (e.g. 2020). Optional."
},
"as_yhi": {
"title": "Year to (inclusive)",
"minimum": 1900,
"maximum": 2100,
"type": "integer",
"description": "Upper bound for publication year (e.g. 2024). Optional."
},
"as_sdt": {
"title": "Include patents",
"enum": [
"0",
"7"
],
"type": "string",
"description": "0 excludes patents (default). 7 includes patents and case law.",
"default": "0"
},
"num": {
"title": "Results per page",
"minimum": 1,
"maximum": 20,
"type": "integer",
"description": "Scholar paper results per page. 1–20.",
"default": 10
},
"hl": {
"title": "Language",
"type": "string",
"description": "Google language code (e.g. en, es, de, fr).",
"default": "en"
},
"cstart": {
"title": "Articles start offset",
"minimum": 0,
"type": "integer",
"description": "Offset for the author's article list (0-based).",
"default": 0
},
"pagesize": {
"title": "Articles per page",
"minimum": 1,
"maximum": 100,
"type": "integer",
"description": "How many articles to fetch per author-profile page.",
"default": 20
},
"max_pages": {
"title": "Max pages",
"minimum": 1,
"maximum": 20,
"type": "integer",
"description": "How many pages of results to fetch. Each page = 1 API call (credits). Applies to Search Papers, Search Author Profiles, and Get Author Profile.",
"default": 3
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```