# Google Scholar Scraper (`kawsar/google-scholar-scraper`) Actor Google Scholar scraper that collects paper titles, authors, citations, and PDF links from search results, so you get structured academic data without the manual work. - **URL**: https://apify.com/kawsar/google-scholar-scraper.md - **Developed by:** [Kawsar](https://apify.com/kawsar) (community) - **Categories:** Automation, Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.99 / 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.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 Google Scholar Scraper extracts academic paper data from Google Scholar search results. Give it a list of keywords and it returns paper titles, authors, citation counts, publication details, abstract snippets, and PDF links as structured JSON, ready for spreadsheets, databases, or research pipelines. ### What it does Google Scholar is the largest freely accessible index of academic literature, covering journal articles, conference papers, theses, books, and preprints across every discipline. This actor searches it programmatically and returns the results as clean, structured data. Each result includes the paper title, direct URL, authors, journal or conference name, publication year, how many times the paper has been cited, an abstract snippet, a link to the citing papers page, and a PDF link when one is publicly available. You can filter results by publication year range, choose the result language, and control how many pages to fetch per query. For bulk collection, pass a list of queries and each one runs independently up to its own item limit. ### Input | Field | Type | Default | Description | |---|---|---|---| | `queries` | string[] | — | List of search queries (keywords or phrases) | | `language` | string | `en` | Language code: `en`, `de`, `fr`, `zh`, etc. | | `startYear` | integer | — | Filter: only papers published on or after this year | | `endYear` | integer | — | Filter: only papers published on or before this year | | `pages` | integer | `10` | Pages to fetch per query (max 10, each page has up to 10 results) | | `maxItems` | integer | `10` | Maximum results to return **per query** (max 1000) | | `requestTimeoutSecs` | integer | `30` | Per-request timeout in seconds | #### How pagination works Google Scholar returns 10 results per page. With `pages: 10` and `maxItems: 100`, you get up to 100 results per query. With 5 queries, that is up to 500 total results. `maxItems` is a per-query cap, not a global cap. #### Example: single query ```json { "queries": ["machine learning"], "language": "en", "pages": 10, "maxItems": 100 } ```` #### Example: filter by year range ```json { "queries": ["climate change carbon capture"], "startYear": 2020, "endYear": 2024, "pages": 5, "maxItems": 50 } ``` #### Example: batch queries ```json { "queries": [ "deep learning image recognition", "reinforcement learning robotics", "transformer models NLP" ], "startYear": 2022, "pages": 10, "maxItems": 100 } ``` This fetches up to 100 results per query (300 total across 3 queries). #### Example: multilingual search ```json { "queries": ["maschinelles Lernen"], "language": "de", "pages": 3, "maxItems": 30 } ``` ### Output fields Each result is one row in the dataset: | Field | Type | Description | |---|---|---| | `query` | string | The search query that produced this result | | `page` | integer | Page number (0-indexed) | | `position` | integer | Position on the page (1–10) | | `paperTitle` | string | Title of the paper | | `url` | string | Direct link to the paper or its Google Scholar entry | | `authors` | string | Author names as listed on Google Scholar | | `publicationInfo` | string | Journal, conference, or publisher and year | | `year` | integer | Publication year (null if not found) | | `citedBy` | integer | Number of times this paper has been cited | | `citedByUrl` | string | Link to the Google Scholar page listing papers that cite this one | | `paperAbstract` | string | Abstract snippet shown on the search result | | `relatedArticlesUrl` | string | Link to related articles on Google Scholar | | `pdfUrl` | string | Direct PDF link (null if not publicly available) | | `scrapedAt` | string | ISO 8601 UTC timestamp of when this result was collected | | `error` | string | Error message if this result failed (null on success) | #### Example output record ```json { "query": "machine learning", "page": 0, "position": 1, "paperTitle": "Machine learning", "url": "https://books.google.com/books?id=ctM-EAAAQBAJ", "authors": "ZH Zhou", "publicationInfo": "2021 - books.google.com", "year": 2021, "citedBy": 3759, "citedByUrl": "https://scholar.google.com/scholar?cites=3387547533016043281", "paperAbstract": "Machine learning addresses the question of how to build computers that improve automatically through experience...", "relatedArticlesUrl": "https://scholar.google.com/scholar?q=related:EQ8shYj8Ai8J:scholar.google.com/", "pdfUrl": null, "scrapedAt": "2026-05-14T10:23:45.123456+00:00", "error": null } ``` ### Use cases #### Academic research and literature reviews Search a topic and collect the top-cited papers. Sort by `citedBy` to find the most influential work. Use `startYear`/`endYear` to scope a review to a specific period. ```json { "queries": ["CRISPR gene editing cancer therapy"], "startYear": 2018, "endYear": 2024, "pages": 10, "maxItems": 100 } ``` #### Citation tracking Monitor how many times a specific paper or author's work has been cited over time. Run the same query on a schedule and compare `citedBy` counts between runs. #### SEO and content research Find the most-cited academic sources for a topic before writing a long-form article or white paper. Use `paperAbstract` and `pdfUrl` to quickly assess relevance without opening each link. #### Competitive intelligence Track publication output in a specific subject area or from a specific institution. Feed the results into a database and query it over time to spot trends. #### Dataset building for NLP and ML Collect paper metadata at scale for training or evaluation datasets. The `paperAbstract` field gives you short English-language text samples across many topics. #### Grant and funding research Search for papers related to a funding call topic. Use `authors` and `publicationInfo` to identify active researchers and institutions in the space. ### Tips for better results - Use specific multi-word queries for more precise results: `"deep learning drug discovery"` returns more relevant papers than just `"deep learning"`. - Set `startYear` to filter out older work. Google Scholar's index goes back decades, and many queries surface results from the 1990s without a year filter. - Set `maxItems` to 100 and `pages` to 10 to collect the full first 100 results for each query. Google Scholar caps public access at around 100 results per search. - Use `language: "en"` to restrict results to English-language papers even when searching non-English topics. - For multi-author or multi-topic tracking, use the `queries` list with one query per author name, paper topic, or keyword cluster. ### Notes - Google Scholar returns up to 10 results per page and limits public access to roughly the first 10 pages (100 results) per query. - Year filtering uses Google Scholar's built-in `as_ylo` and `as_yhi` parameters and applies server-side. - Some results are book entries, patents, or citation-only records with no direct URL. The `url` field will be null for those. - PDF links appear when Google Scholar indexes a freely available version. Not all papers have one. - If a page returns no results, pagination stops early for that query and moves to the next one. - Results with scraping errors are pushed with an `error` field set so the rest of the run continues uninterrupted. # Actor input Schema ## `queries` (type: `array`): List of search queries to run in batch. Each query runs independently. ## `language` (type: `string`): Language code for search results (e.g. "en" for English, "de" for German). ## `startYear` (type: `integer`): Only return papers published on or after this year. ## `endYear` (type: `integer`): Only return papers published on or before this year. ## `pages` (type: `integer`): Number of result pages to fetch per query. Each page contains up to 10 results. ## `maxItems` (type: `integer`): Maximum number of results to return per query (not global). ## `requestTimeoutSecs` (type: `integer`): Per-request timeout in seconds. ## Actor input object example ```json { "queries": [ "machine learning", "climate change", "CRISPR gene editing" ], "language": "en", "startYear": 2020, "endYear": 2024, "pages": 10, "maxItems": 10, "requestTimeoutSecs": 30 } ``` # 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 = { "queries": [ "machine learning" ], "language": "en" }; // Run the Actor and wait for it to finish const run = await client.actor("kawsar/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 = { "queries": ["machine learning"], "language": "en", } # Run the Actor and wait for it to finish run = client.actor("kawsar/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 '{ "queries": [ "machine learning" ], "language": "en" }' | apify call kawsar/google-scholar-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=kawsar/google-scholar-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Scholar Scraper", "description": "Google Scholar scraper that collects paper titles, authors, citations, and PDF links from search results, so you get structured academic data without the manual work.", "version": "0.0", "x-build-id": "kpnfdwVBjoe8hgnyt" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/kawsar~google-scholar-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-kawsar-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/kawsar~google-scholar-scraper/runs": { "post": { "operationId": "runs-sync-kawsar-google-scholar-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/kawsar~google-scholar-scraper/run-sync": { "post": { "operationId": "run-sync-kawsar-google-scholar-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "queries": { "title": "Search queries (batch)", "type": "array", "description": "List of search queries to run in batch. Each query runs independently.", "items": { "type": "string" } }, "language": { "title": "Language", "type": "string", "description": "Language code for search results (e.g. \"en\" for English, \"de\" for German).", "default": "en" }, "startYear": { "title": "Start year", "minimum": 1900, "maximum": 2100, "type": "integer", "description": "Only return papers published on or after this year." }, "endYear": { "title": "End year", "minimum": 1900, "maximum": 2100, "type": "integer", "description": "Only return papers published on or before this year." }, "pages": { "title": "Pages per query", "minimum": 1, "maximum": 10, "type": "integer", "description": "Number of result pages to fetch per query. Each page contains up to 10 results.", "default": 10 }, "maxItems": { "title": "Max items per query", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of results to return per query (not global).", "default": 10 }, "requestTimeoutSecs": { "title": "Request timeout (seconds)", "minimum": 5, "maximum": 120, "type": "integer", "description": "Per-request timeout in seconds.", "default": 30 } } }, "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 } } } } } } } } } } ```