# OpenAlex Academic Paper Search (`lulzasaur/openalex-scraper`) Actor Search and retrieve academic papers, authors, and institutions from OpenAlex. Get citations, DOIs, abstracts, and publication data for 250M+ scholarly works. - **URL**: https://apify.com/lulzasaur/openalex-scraper.md - **Developed by:** [lulz bot](https://apify.com/lulzasaur) (community) - **Categories:** E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 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 ## OpenAlex Scraper - Academic Papers & Citations Scrape academic research data from [OpenAlex](https://openalex.org/), a free and open catalog of the world's scholarly papers, researchers, journals, and institutions. Covers 250M+ works. ### Features - **Works**: Search papers by keyword. Get titles, DOIs, citation counts, abstracts, open access status. - **Authors**: Search researchers. Get h-index, works count, citation counts, ORCID, affiliations. - **Institutions**: Search universities and organizations. Get works counts, homepage URLs, ROR IDs. - **Topics**: Search research topics. Get domain, field, subfield hierarchy. - Cursor-based pagination for large result sets - No API key required ### Modes | Mode | Description | |------|-------------| | `works` | Search academic papers and articles | | `authors` | Search researchers and scholars | | `institutions` | Search universities and organizations | | `topics` | Search research topics and fields | ### Input Example ```json { "mode": "works", "searchQuery": "transformer attention mechanism", "limit": 50 } ```` ### Output Fields (Works) | Field | Type | Description | |-------|------|-------------| | `title` | string | Paper title | | `doi` | string | Digital Object Identifier | | `publicationYear` | number | Year of publication | | `publicationDate` | string | Full publication date | | `citedByCount` | number | Number of citations | | `isOpenAccess` | boolean | Whether paper is open access | | `oaUrl` | string | Open access URL if available | | `abstract` | string | Reconstructed abstract | | `authors` | array | Authors with names, ORCIDs, institutions | | `concepts` | array | Associated concepts with relevance scores | | `type` | string | Work type (article, book-chapter, etc.) | | `sourceUrl` | string | OpenAlex URL | ### Output Fields (Authors) | Field | Type | Description | |-------|------|-------------| | `name` | string | Author display name | | `worksCount` | number | Total number of works | | `citedByCount` | number | Total citations | | `hIndex` | number | h-index | | `orcid` | string | ORCID identifier | | `institutions` | array | Affiliated institutions | ### Cost This actor charges $0.005 per result using Pay-Per-Event pricing. # Actor input Schema ## `mode` (type: `string`): What to search: works (papers), authors, institutions, or topics. ## `searchQuery` (type: `string`): Search query. For works: paper titles/abstracts. For authors: author names. For institutions: university/org names. ## `limit` (type: `integer`): Maximum number of results to return. ## `proxyConfiguration` (type: `object`): Optional proxy configuration. ## Actor input object example ```json { "mode": "works", "searchQuery": "machine learning", "limit": 50 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("lulzasaur/openalex-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("lulzasaur/openalex-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 '{}' | apify call lulzasaur/openalex-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lulzasaur/openalex-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenAlex Academic Paper Search", "description": "Search and retrieve academic papers, authors, and institutions from OpenAlex. Get citations, DOIs, abstracts, and publication data for 250M+ scholarly works.", "version": "1.0", "x-build-id": "tv76V1Sv6d7UuQiT6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lulzasaur~openalex-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lulzasaur-openalex-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lulzasaur~openalex-scraper/runs": { "post": { "operationId": "runs-sync-lulzasaur-openalex-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lulzasaur~openalex-scraper/run-sync": { "post": { "operationId": "run-sync-lulzasaur-openalex-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "mode": { "title": "Mode", "enum": [ "works", "authors", "institutions", "topics" ], "type": "string", "description": "What to search: works (papers), authors, institutions, or topics.", "default": "works" }, "searchQuery": { "title": "Search Query", "type": "string", "description": "Search query. For works: paper titles/abstracts. For authors: author names. For institutions: university/org names.", "default": "machine learning" }, "limit": { "title": "Max Results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of results to return.", "default": 50 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Optional proxy configuration." } } }, "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 } } } } } } } } } } ```