# Google Scholar Scraper (`easyapi/google-scholar-scraper`) Actor Powerful Google Scholar scraper collect up to 5000 scholarly results per run with flexible search options, citation filtering. Perfect for academic research, bibliometric analysis, and scientific trend tracking. πŸŽ“πŸ” - **URL**: https://apify.com/easyapi/google-scholar-scraper.md - **Developed by:** [EasyApi](https://apify.com/easyapi) (community) - **Categories:** Other - **Stats:** 387 total users, 17 monthly users, 100.0% runs succeeded, 8 bookmarks - **User rating**: 2.55 out of 5 stars ## Pricing from $4.99 / 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. 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 πŸŽ“πŸ” Effortlessly scrape academic data from Google Scholar with this powerful and flexible Actor! πŸš€ ### Features ✨ - πŸ“š Scrape scholarly articles, papers, and citations - πŸ”’ Retrieve up to 5000 results per run - 🌐 Support for multiple countries and languages - ⏳ Time-based filtering options - πŸ’Ύ Results saved directly to your Apify dataset ### Use Cases πŸ’‘ - πŸ§ͺ Academic research and literature reviews - πŸ“Š Bibliometric analysis and citation tracking - 🏫 University and research institution benchmarking - πŸ”¬ Trend analysis in scientific publications - πŸ‘¨β€πŸ”¬ Author and researcher profiling ### How It Works πŸ› οΈ 1. Set your search parameters in the input 2. The Actor fetch Google Scholar data 3. Results are processed and saved to your Apify dataset 4. Repeat until the desired number of results is reached or all available data is collected ### Input Parameters πŸ“ - `maxItems`: Maximum number of results to collect (100-5000) - `query`: Your search term - `gl`: Google country code (e.g., "us" for United States) - `hl`: Interface language (e.g., "en" for English) - `lr`: Language of search results (e.g., "lang_en" for English) - `cr`: Country restriction for search results - `nfpr`: No auto-correction of spelling (0 or 1) - `filter`: Whether to filter similar results (0 or 1) - `scholar_include_citations`: Include citations in results (true/false) - `scholar_year_min`: Minimum year for results - `scholar_year_max`: Maximum year for results - `scholar_patents_courts`: Filter for patents and court cases - `scisbd`: Scholar sort parameter (1 or 2) ### Output πŸ“Š The Actor provides detailed information about each scholarly result, including: - Title - Authors - Publication info - Number of citations - Link to the article - Brief summary or abstract - And more! Get started with Google Scholar Scraper today and unlock valuable academic insights for your research projects! πŸš€πŸŽ“ #### Input Example A full explanation of an input example in JSON. ```` { "maxItems": 100, "query": "rocket" } ``` #### Output sample The results will be wrapped into a dataset which you can always find in theΒ **Storage**Β tab. Here's an excerpt from the data you'd get if you apply the input parameters above: And here is the same data but in JSON. You can choose in which format to download your data: JSON, JSONL, Excel spreadsheet, HTML table, CSV, or XML. ``` \[ { "position": 1, "title": "Liquid-propellant rocket engine throttling: A comprehensive review", "link": "https://arc.aiaa.org/doi/abs/10.2514/1.49791", "domain": "arc.aiaa.org", "displayed\_link": "MJ Casiano, JR Hulka, V YangΒ - Journal of propulsion and power, 2010 - arc.aiaa.org", "pdf\_link": "https://ntrs.nasa.gov/api/citations/20090037061/downloads/20090037061.pdf", "snippet": "… and dynamics of an liquid-propellant rocket engine as well as difficulties and … rocket engine \nthrottling centered around engines from the United States. Several liquid-propellant rocket …", "cited\_by\_count": 253, "cited\_by\_link": "https://www.google.com/scholar?cites=7502999951619349366\&as\_sdt=2005\&sciodt=0,5\&hl=fr", "versions\_count": 11, "versions\_link": "https://www.google.com/scholar?cluster=7502999951619349366\&hl=fr\&as\_sdt=0,5" }, { "position": 2, "title": "Knowledge management–not rocket science", "link": "https://www.emerald.com/insight/content/doi/10.1108/13673270510590191/full/html", "domain": "www.emerald.com", "displayed\_link": "D CallΒ - Journal of Knowledge management, 2005 - emerald.com", "pdf\_link": "http://j.pelet.free.fr/publications/km/Knowledge\_management\_not\_rocket\_science.pdf", "snippet": "Purpose – To provide a broad understanding of knowledge management and how to \nsuccessfully implement knowledge management. Design/methodology/approach – Using a wide …", "cited\_by\_count": 198, "cited\_by\_link": "https://www.google.com/scholar?cites=1591768766360518117\&as\_sdt=2005\&sciodt=0,5\&hl=fr", "versions\_count": 7, "versions\_link": "https://www.google.com/scholar?cluster=1591768766360518117\&hl=fr\&as\_sdt=0,5" }, ... ] ```` # Actor input Schema ## `maxItems` (type: `integer`): Maximum number of scholarly results to be extracted. ## `query` (type: `string`): The search term to use ## `gl` (type: `string`): The Google country to use for the query ## `scholar_include_citations` (type: `boolean`): Whether to include citations in scholar results. ## `scholar_year_min` (type: `integer`): Minimum year for scholar results. ## `scholar_year_max` (type: `integer`): Maximum year for scholar results. ## `scholar_patents_courts` (type: `string`): Filter for patents and courts in scholar results. ## `scisbd` (type: `string`): Sort parameter for scholar results. ## `hl` (type: `string`): The Google UI language to return results ## `lr` (type: `string`): Limit the results to a specific language ## `cr` (type: `string`): Limit the results to a specific country ## `nfpr` (type: `integer`): Exclude results from auto-corrected queries (0 or 1) ## `filter` (type: `integer`): Enable/disable Similar Results and Omitted Results filters (0 or 1) ## Actor input object example ```json { "maxItems": 1000, "query": "rocket", "scholar_include_citations": true } ```` # 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 = { "maxItems": 1000, "query": "rocket" }; // Run the Actor and wait for it to finish const run = await client.actor("easyapi/google-scholar-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "maxItems": 1000, "query": "rocket", } # Run the Actor and wait for it to finish run = client.actor("easyapi/google-scholar-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "maxItems": 1000, "query": "rocket" }' | apify call easyapi/google-scholar-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=easyapi/google-scholar-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Scholar Scraper", "description": "Powerful Google Scholar scraper collect up to 5000 scholarly results per run with flexible search options, citation filtering. Perfect for academic research, bibliometric analysis, and scientific trend tracking. πŸŽ“πŸ”", "version": "0.0", "x-build-id": "GqGM8mHgPahOzq5DV" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/easyapi~google-scholar-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-easyapi-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/easyapi~google-scholar-scraper/runs": { "post": { "operationId": "runs-sync-easyapi-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/easyapi~google-scholar-scraper/run-sync": { "post": { "operationId": "run-sync-easyapi-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", "maxItems" ], "properties": { "maxItems": { "title": "Maximum Items", "minimum": 100, "maximum": 100000, "type": "integer", "description": "Maximum number of scholarly results to be extracted.", "default": 100 }, "query": { "title": "Search Query", "type": "string", "description": "The search term to use", "default": "rocket" }, "gl": { "title": "Google Country", "enum": [ "af", "al", "dz", "as", "ad", "ao", "ai", "aq", "ag", "ar", "am", "aw", "ac", "au", "at", "az", "bs", "bh", "bd", "bb", "by", "be", "bz", "bj", "bm", "bt", "bo", "ba", "bw", "bv", "br", "io", "bn", "bg", "bf", "bi", "kh", "cm", "ca", "cv", "cat", "ky", "cf", "td", "cl", "cn", "cx", "cc", "co", "km", "cg", "cd", "ck", "cr", "ci", "hr", "cy", "cz", "dk", "dj", "dm", "do", "tl", "ec", "eg", "sv", "gq", "er", "ee", "et", "fk", "fo", "fj", "fi", "fr", "gf", "pf", "tf", "ga", "gm", "ge", "de", "gh", "gi", "gr", "gl", "gd", "gp", "gu", "gt", "gn", "gw", "gy", "ht", "hm", "hn", "hk", "hu", "is", "in", "id", "iq", "ie", "il", "it", "jm", "jp", "jo", "kz", "ke", "ki", "kw", "kg", "la", "lv", "lb", "ls", "lr", "ly", "li", "lt", "lu", "mo", "mk", "mg", "mw", "my", "mv", "ml", "mt", "mh", "mq", "mr", "mu", "yt", "mx", "fm", "md", "mc", "mn", "ms", "ma", "mz", "na", "nr", "np", "nl", "an", "nc", "nz", "ni", "ne", "ng", "nu", "nf", "mp", "no", "om", "pk", "pw", "ps", "pa", "pg", "py", "pe", "ph", "pn", "pl", "pt", "pr", "qa", "re", "ro", "ru", "rw", "kn", "lc", "vc", "ws", "sm", "st", "sa", "sn", "cs", "sc", "sl", "sg", "sk", "si", "sb", "so", "za", "gs", "kr", "es", "lk", "sh", "pm", "sr", "sj", "sz", "se", "ch", "tw", "tj", "tz", "th", "tg", "tk", "to", "tt", "tn", "tr", "tm", "tc", "tv", "ug", "ua", "ae", "gb", "us", "um", "uy", "uz", "vu", "va", "ve", "vn", "vg", "vi", "wf", "eh", "ye", "zm", "zw" ], "type": "string", "description": "The Google country to use for the query" }, "scholar_include_citations": { "title": "Include citations", "type": "boolean", "description": "Whether to include citations in scholar results.", "default": true }, "scholar_year_min": { "title": "Minimum year", "minimum": 1900, "type": "integer", "description": "Minimum year for scholar results." }, "scholar_year_max": { "title": "Maximum year", "maximum": 2024, "type": "integer", "description": "Maximum year for scholar results." }, "scholar_patents_courts": { "title": "Patents and courts filter", "enum": [ "0", "1", "4" ], "type": "string", "description": "Filter for patents and courts in scholar results." }, "scisbd": { "title": "Scholar sort parameter", "enum": [ "1", "2" ], "type": "string", "description": "Sort parameter for scholar results." }, "hl": { "title": "UI Language", "enum": [ "af", "ak", "sq", "am", "ar", "hy", "az", "eu", "be", "bem", "bn", "bh", "bs", "br", "bg", "km", "ca", "chr", "ny", "zh-cn", "zh-tw", "co", "hr", "cs", "da", "nl", "en", "eo", "et", "ee", "fo", "tl", "fi", "fr", "fy", "gaa", "gl", "ka", "de", "el", "gn", "gu", "ht", "ha", "haw", "he", "iw", "hi", "hu", "is", "ig", "id", "ia", "ga", "it", "ja", "jw", "kn", "kk", "rw", "rn", "kg", "ko", "kri", "ku", "ckb", "ky", "lo", "la", "lv", "ln", "lt", "loz", "lg", "ach", "mk", "mg", "ms", "ml", "mt", "mi", "mr", "mfe", "mo", "mn", "sr-ME", "ne", "pcm", "nso", "no", "nn", "oc", "or", "om", "ps", "fa", "pl", "pt", "pt-br", "pt-pt", "pa", "qu", "ro", "rm", "nyn", "ru", "gd", "sr", "sh", "st", "tn", "crs", "sn", "sd", "si", "sk", "sl", "so", "es", "es-419", "su", "sw", "sv", "tg", "ta", "tt", "te", "th", "ti", "to", "lua", "tum", "tr", "tk", "tw", "ug", "uk", "ur", "uz", "vi", "cy", "wo", "xh", "yi", "yo", "zu" ], "type": "string", "description": "The Google UI language to return results" }, "lr": { "title": "Language Results", "enum": [ "lang_ar", "lang_hy", "lang_be", "lang_bg", "lang_ca", "lang_zh-cn", "lang_zh-tw", "lang_hr", "lang_cs", "lang_da", "lang_nl", "lang_en", "lang_eo", "lang_et", "lang_tl", "lang_fi", "lang_fr", "lang_de", "lang_el", "lang_iw", "lang_hu", "lang_is", "lang_id", "lang_it", "lang_ja", "lang_ko", "lang_lv", "lang_lt", "lang_no", "lang_fa", "lang_pl", "lang_pt", "lang_ro", "lang_ru", "lang_sr", "lang_sk", "lang_sl", "lang_es", "lang_sv", "lang_th", "lang_tr", "lang_uk", "lang_vi" ], "type": "string", "description": "Limit the results to a specific language" }, "cr": { "title": "Country Results", "enum": [ "af", "al", "dz", "as", "ad", "ao", "ai", "aq", "ag", "ar", "am", "aw", "ac", "au", "at", "az", "bs", "bh", "bd", "bb", "by", "be", "bz", "bj", "bm", "bt", "bo", "ba", "bw", "bv", "br", "io", "bn", "bg", "bf", "bi", "kh", "cm", "ca", "cv", "cat", "ky", "cf", "td", "cl", "cn", "cx", "cc", "co", "km", "cg", "cd", "ck", "cr", "ci", "hr", "cy", "cz", "dk", "dj", "dm", "do", "tl", "ec", "eg", "sv", "gq", "er", "ee", "et", "fk", "fo", "fj", "fi", "fr", "gf", "pf", "tf", "ga", "gm", "ge", "de", "gh", "gi", "gr", "gl", "gd", "gp", "gu", "gt", "gn", "gw", "gy", "ht", "hm", "hn", "hk", "hu", "is", "in", "id", "iq", "ie", "il", "it", "jm", "jp", "jo", "kz", "ke", "ki", "kw", "kg", "la", "lv", "lb", "ls", "lr", "ly", "li", "lt", "lu", "mo", "mk", "mg", "mw", "my", "mv", "ml", "mt", "mh", "mq", "mr", "mu", "yt", "mx", "fm", "md", "mc", "mn", "ms", "ma", "mz", "na", "nr", "np", "nl", "an", "nc", "nz", "ni", "ne", "ng", "nu", "nf", "mp", "no", "om", "pk", "pw", "ps", "pa", "pg", "py", "pe", "ph", "pn", "pl", "pt", "pr", "qa", "re", "ro", "ru", "rw", "kn", "lc", "vc", "ws", "sm", "st", "sa", "sn", "cs", "sc", "sl", "sg", "sk", "si", "sb", "so", "za", "gs", "kr", "es", "lk", "sh", "pm", "sr", "sj", "sz", "se", "ch", "tw", "tj", "tz", "th", "tg", "tk", "to", "tt", "tn", "tr", "tm", "tc", "tv", "ug", "ua", "ae", "gb", "us", "um", "uy", "uz", "vu", "va", "ve", "vn", "vg", "vi", "wf", "eh", "ye", "zm", "zw" ], "type": "string", "description": "Limit the results to a specific country" }, "nfpr": { "title": "No Autocorrect", "minimum": 0, "maximum": 1, "type": "integer", "description": "Exclude results from auto-corrected queries (0 or 1)" }, "filter": { "title": "Filter", "minimum": 0, "maximum": 1, "type": "integer", "description": "Enable/disable Similar Results and Omitted Results filters (0 or 1)" } } }, "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 } } } } } } } } } } ```