# Google Scholar Scraper (`codingfrontend/google-scholar-scraper`) Actor Scrape academic papers, citations, and author profiles from Google Scholar - **URL**: https://apify.com/codingfrontend/google-scholar-scraper.md - **Developed by:** [codingfrontend](https://apify.com/codingfrontend) (community) - **Categories:** Developer tools, AI, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Google Scholar Scraper ### Features - **Academic Papers**: Extracts research papers and academic articles from Google Scholar search results - **Citations Count**: Captures how many times each paper has been cited by other works - **Author Information**: Records the names of all authors for each paper - **Publication Venue**: Extracts the journal or conference where the paper was published - **Publication Year**: Captures the year each paper was published - **PDF Links**: Collects direct PDF links when available for open-access papers - **Text Snippets**: Retrieves descriptive text snippets shown in Google Scholar results - **Date Range Filtering**: Filter papers by publication year range (yearFrom and yearTo) - **Sort Options**: Sort results by relevance or publication date - **Proxy Support**: Built-in Apify Proxy with residential proxies to avoid Scholar rate limiting ### Input Parameters | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | `query` | String | Yes | `"machine learning"` | Academic search query to look up on Google Scholar | | `maxItems` | Integer | No | `50` | Maximum number of results to retrieve (1–10000) | | `yearFrom` | Integer | No | — | Filter results published from this year onwards (e.g., 2020) | | `yearTo` | Integer | No | — | Filter results published up to this year (e.g., 2024) | | `sortBy` | String | No | `"relevance"` | Sort results by: `relevance` or `date` | | `proxyConfiguration` | Object | No | Apify Residential | Proxy settings for the scraper | #### Input Schema Example ```json { "query": "deep learning natural language processing", "maxItems": 100, "yearFrom": 2020, "yearTo": 2025, "sortBy": "date", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] } } ```` ### Output Schema The scraper outputs structured JSON data for each academic paper found on Google Scholar. #### Main Fields | Field | Type | Description | |-------|------|-------------| | `position` | Integer | Result position in search results | | `title` | String | Paper title | | `link` | String | URL to the paper | | `authors` | String | Paper authors | | `publication` | String | Publication venue (journal, conference, etc.) | | `year` | Integer | Publication year | | `citedBy` | Integer | Number of citations | | `snippet` | String | Text snippet from the paper | | `pdfLink` | String | Direct link to PDF if available | | `searchQuery` | String | The search query used | | `searchUrl` | String | Google Scholar search URL | | `scrapedAt` | String | ISO timestamp of when the data was scraped | #### Academic Paper Example ```json { "position": 1, "title": "Attention Is All You Need", "link": "https://arxiv.org/abs/1706.03762", "authors": "A Vaswani, N Shazeer, N Parmar, J Uszkoreit", "publication": "Advances in neural information processing systems, 2017", "year": 2017, "citedBy": 98450, "snippet": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder...", "pdfLink": "https://arxiv.org/pdf/1706.03762", "searchQuery": "deep learning natural language processing", "searchUrl": "https://scholar.google.com/scholar?q=deep+learning+natural+language+processing", "scrapedAt": "2025-01-15T10:30:00.000Z" } ``` # Actor input Schema ## `query` (type: `string`): The academic search query to look up on Google Scholar ## `maxItems` (type: `integer`): Maximum number of scholar results to retrieve ## `yearFrom` (type: `integer`): Filter results published from this year onwards (optional) ## `yearTo` (type: `integer`): Filter results published up to this year (optional) ## `sortBy` (type: `string`): Sort results by relevance or date ## `proxyConfiguration` (type: `object`): Proxy settings for the scraper. Use Apify residential proxies to avoid blocks. ## Actor input object example ```json { "query": "machine learning", "maxItems": 50, "sortBy": "relevance", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # 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 = { "query": "machine learning", "maxItems": 50, "sortBy": "relevance", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("codingfrontend/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 = { "query": "machine learning", "maxItems": 50, "sortBy": "relevance", "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("codingfrontend/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 '{ "query": "machine learning", "maxItems": 50, "sortBy": "relevance", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call codingfrontend/google-scholar-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=codingfrontend/google-scholar-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Scholar Scraper", "description": "Scrape academic papers, citations, and author profiles from Google Scholar", "version": "0.0", "x-build-id": "KPmbX2PbWVDE1gSb7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/codingfrontend~google-scholar-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-codingfrontend-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/codingfrontend~google-scholar-scraper/runs": { "post": { "operationId": "runs-sync-codingfrontend-google-scholar-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/codingfrontend~google-scholar-scraper/run-sync": { "post": { "operationId": "run-sync-codingfrontend-google-scholar-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "query" ], "properties": { "query": { "title": "Search Query", "type": "string", "description": "The academic search query to look up on Google Scholar", "default": "machine learning" }, "maxItems": { "title": "Maximum Items", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of scholar results to retrieve", "default": 50 }, "yearFrom": { "title": "Year From", "minimum": 1900, "maximum": 2030, "type": "integer", "description": "Filter results published from this year onwards (optional)" }, "yearTo": { "title": "Year To", "minimum": 1900, "maximum": 2030, "type": "integer", "description": "Filter results published up to this year (optional)" }, "sortBy": { "title": "Sort By", "enum": [ "relevance", "date" ], "type": "string", "description": "Sort results by relevance or date", "default": "relevance" }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for the scraper. Use Apify residential proxies to avoid blocks.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "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 } } } } } } } } } } ```