# 📄 Academic Paper Scraper — Research & Citations (`nexgendata/academic-paper-scraper`) Actor Scrape academic papers, research articles, citations, author profiles, and h-index data from Google Scholar. Extract abstracts, publication dates, journal names, and citation counts for literature reviews. - **URL**: https://apify.com/nexgendata/academic-paper-scraper.md - **Developed by:** [Stephan Corbeil](https://apify.com/nexgendata) (community) - **Categories:** Developer tools, AI - **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. 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 ## Academic Paper Scraper Scrape academic papers, research articles, citations, author profiles, and h-index data. Essential for literature reviews, research monitoring, and academic analytics. ### Why Use This Actor? This actor provides reliable, structured data extraction that you can integrate into your workflows via API, scheduled runs, or webhooks. All data is returned as clean JSON, ready for analysis, databases, or downstream processing. **Keywords:** academic papers, research articles, citations, author profiles, h-index ### Features - **Query** — Search query for academic papers - **Maxresults** — Maximum number of papers to return - **Year** — Filter by year (e.g., 2024) ### How to Use 1. **Configure inputs** — Set your search parameters in the Apify Console or via API 2. **Run the actor** — Click "Start" or trigger via API/scheduler 3. **Get results** — Download structured JSON data from the dataset #### API Integration ```bash curl "https://api.apify.com/v2/acts/nexgendata~academic-paper-scraper/runs" \ -X POST \ -H "Authorization: Bearer YOUR_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{}' ```` #### Scheduled Runs Set up automated runs on any schedule — hourly, daily, or weekly — using Apify's built-in scheduler. Perfect for monitoring and data pipelines. ### Output Format Results are stored in Apify datasets as structured JSON objects. Each run creates a new dataset that you can: - Download as JSON, CSV, or Excel - Access via REST API - Push to webhooks or integrations - Connect to Google Sheets, Slack, or Zapier ### Technical Details - Uses httpx for fast async HTTP requests - Leverages official APIs where available ### Integrations This actor works seamlessly with the Apify platform ecosystem: - **API access** — Full REST API for programmatic control - **Webhooks** — Get notified when runs complete - **Scheduler** — Automate recurring data collection - **Integrations** — Connect to Zapier, Make, Google Sheets, Slack, and more ### Support For questions, bug reports, or feature requests, open an issue on the actor's page or contact the developer through Apify. ### About nexgendata nexgendata builds reliable, production-ready data extraction tools on Apify. We focus on clean APIs, structured output, and developer-friendly documentation. # Actor input Schema ## `outputMode` (type: `string`): Choose output format. 'Research Tracker' adds citation analysis (tier ranking, citations/year velocity), author clustering, venue breakdown, publication trend timeline, and auto-generated insights about the research landscape. 'Raw Data' gives flat paper records. ## `query` (type: `string`): Search query for academic papers. Examples: 'large language models', 'CRISPR gene editing', 'climate change adaptation'. Works like Google Scholar. ## `maxResults` (type: `integer`): Number of papers to return. 25 for a quick overview, 100 for comprehensive landscape analysis. ## `year` (type: `string`): Filter by year (e.g., '2025') or year range (e.g., '2023-2025'). Leave empty for all years. ## Actor input object example ```json { "outputMode": "tracker", "query": "machine learning", "maxResults": 25, "year": "" } ``` # 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 = { "outputMode": "tracker", "query": "machine learning", "maxResults": 25, "year": "" }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/academic-paper-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 = { "outputMode": "tracker", "query": "machine learning", "maxResults": 25, "year": "", } # Run the Actor and wait for it to finish run = client.actor("nexgendata/academic-paper-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 '{ "outputMode": "tracker", "query": "machine learning", "maxResults": 25, "year": "" }' | apify call nexgendata/academic-paper-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/academic-paper-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "📄 Academic Paper Scraper — Research & Citations", "description": "Scrape academic papers, research articles, citations, author profiles, and h-index data from Google Scholar. Extract abstracts, publication dates, journal names, and citation counts for literature reviews.", "version": "0.0", "x-build-id": "x2IUU0NLV0E4sI4BI" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~academic-paper-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-academic-paper-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~academic-paper-scraper/runs": { "post": { "operationId": "runs-sync-nexgendata-academic-paper-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~academic-paper-scraper/run-sync": { "post": { "operationId": "run-sync-nexgendata-academic-paper-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "outputMode": { "title": "Output Mode", "enum": [ "tracker", "raw" ], "type": "string", "description": "Choose output format. 'Research Tracker' adds citation analysis (tier ranking, citations/year velocity), author clustering, venue breakdown, publication trend timeline, and auto-generated insights about the research landscape. 'Raw Data' gives flat paper records.", "default": "tracker" }, "query": { "title": "Research Topic or Keywords", "type": "string", "description": "Search query for academic papers. Examples: 'large language models', 'CRISPR gene editing', 'climate change adaptation'. Works like Google Scholar.", "default": "machine learning" }, "maxResults": { "title": "Maximum Papers", "type": "integer", "description": "Number of papers to return. 25 for a quick overview, 100 for comprehensive landscape analysis.", "default": 25 }, "year": { "title": "Publication Year Filter", "type": "string", "description": "Filter by year (e.g., '2025') or year range (e.g., '2023-2025'). Leave empty for all years.", "default": "" } } }, "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 } } } } } } } } } } ```