# OpenAlex Scraper - 250M+ Academic Papers, Citations & Authors (`antishock/openalex-academic-works-scraper`) Actor Extract papers, citations and authors from OpenAlex - 250M+ open works. Search by keyword, DOI, author or institution. Free API, no key needed. Returns metadata and citation counts. - **URL**: https://apify.com/antishock/openalex-academic-works-scraper.md - **Developed by:** [Ryan Zinburg](https://apify.com/antishock) (community) - **Categories:** Developer tools - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 Academic Works Scraper Apify Actor for exporting academic works from the public OpenAlex API. It searches papers by keyword and optional concept, year and open-access filters, reconstructs abstracts from `abstract_inverted_index`, and exports paper metadata, authors, citations, concepts, keywords and referenced works. ### Example input ```json { "searchQuery": "CRISPR gene editing", "maxResults": 10, "includeAbstract": true } ```` ### Output Each dataset item includes `oaId`, `doi`, `title`, `publicationYear`, `publicationDate`, reconstructed `abstract`, `citedByCount`, `isOpenAccess`, `openAccessUrl`, `journalName`, `authors`, `concepts`, `keywords`, `referencedWorksCount`, `referencedWorks`, and `type`. # Actor input Schema ## `searchQuery` (type: `string`): Keyword search query, e.g. machine learning healthcare or CRISPR gene editing. ## `filterConcept` (type: `string`): Optional OpenAlex concept ID filter, e.g. C154945302. ## `filterYear` (type: `string`): Optional year or range, e.g. 2024 or 2020-2024. ## `filterOpenAccess` (type: `boolean`): Only return open access works. ## `sortBy` (type: `string`): Sort works by citation count or publication date. ## `maxResults` (type: `integer`): Maximum number of works to export. ## `includeAbstract` (type: `boolean`): Reconstruct abstract text from OpenAlex abstract\_inverted\_index. ## Actor input object example ```json { "filterConcept": "", "filterYear": "", "filterOpenAccess": false, "sortBy": "cited_by_count", "maxResults": 100, "includeAbstract": 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("antishock/openalex-academic-works-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("antishock/openalex-academic-works-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 antishock/openalex-academic-works-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=antishock/openalex-academic-works-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenAlex Scraper - 250M+ Academic Papers, Citations & Authors", "description": "Extract papers, citations and authors from OpenAlex - 250M+ open works. Search by keyword, DOI, author or institution. Free API, no key needed. Returns metadata and citation counts.", "version": "0.1", "x-build-id": "1B1LtzZHXyrBxUqT4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/antishock~openalex-academic-works-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-antishock-openalex-academic-works-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/antishock~openalex-academic-works-scraper/runs": { "post": { "operationId": "runs-sync-antishock-openalex-academic-works-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/antishock~openalex-academic-works-scraper/run-sync": { "post": { "operationId": "run-sync-antishock-openalex-academic-works-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": [ "searchQuery" ], "properties": { "searchQuery": { "title": "Search query", "type": "string", "description": "Keyword search query, e.g. machine learning healthcare or CRISPR gene editing." }, "filterConcept": { "title": "OpenAlex concept ID", "type": "string", "description": "Optional OpenAlex concept ID filter, e.g. C154945302.", "default": "" }, "filterYear": { "title": "Publication year filter", "type": "string", "description": "Optional year or range, e.g. 2024 or 2020-2024.", "default": "" }, "filterOpenAccess": { "title": "Open access only", "type": "boolean", "description": "Only return open access works.", "default": false }, "sortBy": { "title": "Sort by", "enum": [ "cited_by_count", "publication_date" ], "type": "string", "description": "Sort works by citation count or publication date.", "default": "cited_by_count" }, "maxResults": { "title": "Maximum results", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of works to export.", "default": 100 }, "includeAbstract": { "title": "Include abstract", "type": "boolean", "description": "Reconstruct abstract text from OpenAlex abstract_inverted_index.", "default": true } } }, "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 } } } } } } } } } } ```