# Google Scholar Scraper (`masked_hacker/google-scholar-scraper`) Actor Scrape Google Scholar search results into structured academic records (title, authors, year, venue, citation count, PDF link, cluster id). - **URL**: https://apify.com/masked\_hacker/google-scholar-scraper.md - **Developed by:** [Masked Hacker](https://apify.com/masked_hacker) (community) - **Categories:** Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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: Papers, Citations & Authors Turn any [Google Scholar](https://scholar.google.com) search into a clean, structured dataset of academic papers. Search by keyword, author or exact phrase and get **title, authors, year, venue, publisher, citation count and PDF link** for every result, fully paginated. Perfect for **literature reviews, bibliometric analysis, citation tracking, and research landscape mapping** across any field. ### What you get - 🔎 **Search by keyword, author, or exact phrase**, with year-range filtering. - 📚 **Full bibliographic data per paper:** title, authors, year, venue and publisher. - 📈 **Citation count** and a direct link to the citing papers. - 📄 **Free PDF link** when Scholar shows one. - 🔗 **Related articles link** and Scholar's cluster ID for deduping paper versions. - 📊 Clean JSON / CSV / Excel export, ready for a spreadsheet or a pipeline. ### Pricing **Pay per event:** **$0.005 per result** returned, plus a small per-run start fee. You only pay for results you actually receive; no monthly subscription. ### Input | Field | Type | Description | |---|---|---| | `searchQueries` | string[] | Keyword searches to run, e.g. `graph neural networks`. Required. | | `exactPhrase` | string | Restrict every query to results containing this exact phrase. | | `authors` | string | Restrict every query to these author(s), e.g. `Y LeCun`. | | `yearFrom` / `yearTo` | int | Only include results published in this year range. | | `maxResultsPerQuery` | int | Stop paginating each query after this many results (default 100). | | `proxyConfiguration` | proxy | Residential proxy required. Scholar blocks datacenter IPs. | #### Example input ```json { "searchQueries": ["graph neural networks", "CRISPR gene editing"], "yearFrom": 2020, "maxResultsPerQuery": 100 } ```` ### Output One record per search result, in rank order for its query. | Field | Description | |---|---| | `title`, `authors`, `year` | Paper identity. | | `venue`, `publisher` | Journal, conference or publisher. | | `snippet` | Abstract excerpt shown under the result. | | `citationCount`, `citedByUrl` | Citation count and link to citing papers. | | `pdfUrl` | Direct link to a free PDF, when available. | | `resultUrl`, `relatedUrl`, `clusterId` | Paper link, related-articles link, and Scholar's cluster ID. | | `query`, `rank`, `scrapedAt` | Source query, result rank, and scrape time. | #### Example output ```json { "title": "Deep residual learning for image recognition", "authors": ["K He", "X Zhang", "S Ren"], "year": 2016, "venue": "Proceedings of the IEEE conference on computer vision", "citationCount": 194832, "pdfUrl": "https://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf", "resultUrl": "https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html", "query": "deep residual learning", "rank": 1 } ``` ### FAQ **Do I need a Google account or API key?** No. Just provide a search query. **How far back does pagination go?** As far as Scholar's own result pages go, up to `maxResultsPerQuery` per query. **Is this legal?** The actor collects only publicly available search-result data. You are responsible for complying with Google's terms and applicable law in your use of the data. # Actor input Schema ## `searchQueries` (type: `array`): Keyword searches to run on Google Scholar. Each entry is one search. ## `exactPhrase` (type: `string`): Optional. Restrict every query to results containing this exact phrase. ## `authors` (type: `string`): Optional. Restrict every query to these author(s), e.g. "Y LeCun". ## `yearFrom` (type: `integer`): Optional. Only include results published in or after this year. ## `yearTo` (type: `integer`): Optional. Only include results published in or before this year. ## `maxResultsPerQuery` (type: `integer`): Stop paginating each query after this many results. ## `proxyConfiguration` (type: `object`): Residential required — Scholar blocks datacenter IPs and rate-limits aggressively. ## Actor input object example ```json { "searchQueries": [ "graph neural networks", "CRISPR gene editing" ], "maxResultsPerQuery": 100, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # Actor output Schema ## `results` (type: `string`): All scraped academic records in the default dataset. # 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 = { "searchQueries": [ "large language models" ] }; // Run the Actor and wait for it to finish const run = await client.actor("masked_hacker/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 = { "searchQueries": ["large language models"] } # Run the Actor and wait for it to finish run = client.actor("masked_hacker/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 '{ "searchQueries": [ "large language models" ] }' | apify call masked_hacker/google-scholar-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=masked_hacker/google-scholar-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Scholar Scraper", "description": "Scrape Google Scholar search results into structured academic records (title, authors, year, venue, citation count, PDF link, cluster id).", "version": "0.0", "x-build-id": "Y2klU7oWH8riL7Sco" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/masked_hacker~google-scholar-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-masked_hacker-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/masked_hacker~google-scholar-scraper/runs": { "post": { "operationId": "runs-sync-masked_hacker-google-scholar-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/masked_hacker~google-scholar-scraper/run-sync": { "post": { "operationId": "run-sync-masked_hacker-google-scholar-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "searchQueries" ], "properties": { "searchQueries": { "title": "Search queries", "type": "array", "description": "Keyword searches to run on Google Scholar. Each entry is one search.", "items": { "type": "string" } }, "exactPhrase": { "title": "Exact phrase", "type": "string", "description": "Optional. Restrict every query to results containing this exact phrase." }, "authors": { "title": "Authors", "type": "string", "description": "Optional. Restrict every query to these author(s), e.g. \"Y LeCun\"." }, "yearFrom": { "title": "Year from", "minimum": 1900, "type": "integer", "description": "Optional. Only include results published in or after this year." }, "yearTo": { "title": "Year to", "minimum": 1900, "type": "integer", "description": "Optional. Only include results published in or before this year." }, "maxResultsPerQuery": { "title": "Max results per query", "minimum": 1, "type": "integer", "description": "Stop paginating each query after this many results.", "default": 100 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Residential required — Scholar blocks datacenter IPs and rate-limits aggressively.", "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 } } } } } } } } } } ```