# OpenAlex Scholarly Works Scraper — Papers, Authors & Citations (`themineworks/openalex-scholarly-works`) Actor Search 250M+ scholarly papers from OpenAlex as clean structured JSON. Filter by topic, year, citations, open-access and type. Authors, venues, citations and abstracts. Zero charge on empty runs. - **URL**: https://apify.com/themineworks/openalex-scholarly-works.md - **Developed by:** [The Mine Works](https://apify.com/themineworks) (community) - **Categories:** Business, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% 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 Scholarly Works Scraper — Papers, Authors, Citations & Venues Search **250M+ scholarly works** from **OpenAlex** — the open index of the world's research — and get clean, structured JSON back. Filter by **topic, year, citation count, open-access status and work type**, and pull authors, institutions, venues, citations, concepts and reconstructed abstracts. Built for **research and competitive intelligence**, **literature reviews**, **bibliometrics**, **R&D landscaping**, and **AI/RAG pipelines** over scientific text. You only pay for works actually delivered. **Keywords:** OpenAlex API, scholarly data, research papers scraper, citation data, bibliometrics, literature review automation, academic search API, science intelligence, paper metadata. --- ### Why this actor OpenAlex is the free, open successor to Microsoft Academic Graph — a complete index of papers, authors, institutions, venues and citations. Its API is powerful but returns deeply structured records (nested authorships, inverted-index abstracts, scored concepts) and uses cursor pagination. This actor flattens all of that into **one clean record per work**: - **Full-text search** across titles, abstracts and fulltext. - **Filters** — publication year range, minimum citations, work type, open-access only. - **Flat, analysis-ready output** — title, DOI, year, type, authors, institutions, venue, citation count, open-access status, top concepts, language and a direct link. - **Reconstructed abstracts** — OpenAlex stores abstracts as an inverted index; this actor rebuilds readable abstract text on request. - **Fully open, no API key** — official OpenAlex API, joined to its fast "polite pool." --- ### What you can build with it - **R&D and competitive intelligence** — map who is publishing on a technology, where, and how often it is cited. - **Literature reviews** — assemble a filtered, citation-ranked corpus on a topic in seconds. - **Bibliometrics & trends** — quantify research output by year, institution or concept. - **Talent & partner scouting** — find the most-cited authors and institutions in a field. - **AI / RAG pipelines** — ingest titles and abstracts as grounded scientific context. --- ### Input | Field | Type | Default | Description | |---|---|---|---| | `searchTerm` | string | `large language models` | Full-text search. Empty = all. | | `fromYear` / `toYear` | integer | — | Publication-year bounds. | | `minCitations` | integer | — | Minimum citation count. | | `workType` | enum | — | `article`, `review`, `preprint`, `book`, `dataset`, etc. | | `openAccessOnly` | boolean | false | Only open-access works. | | `includeAbstract` | boolean | false | Reconstruct and include abstract text. | | `maxResults` | integer | 100 | Max works, most-cited first. | #### Example input ```json { "searchTerm": "perovskite solar cells", "fromYear": 2022, "minCitations": 25, "openAccessOnly": true, "includeAbstract": true, "maxResults": 200 } ```` *** ### Output Each work is one dataset record: ```json { "openalex_id": "https://openalex.org/W2064815984", "doi": "https://doi.org/10.1126/science.1231143", "title": "Multiplex Genome Engineering Using CRISPR/Cas Systems", "publication_year": 2013, "type": "article", "cited_by_count": 15686, "authors": ["Le Cong", "F. Ann Ran"], "author_institutions": ["Broad Institute", "MIT"], "venue": "Science", "is_open_access": false, "oa_status": "closed", "concepts": ["CRISPR", "Genome editing", "Cas9"], "language": "en", "openalex_url": "https://openalex.org/W2064815984", "scraped_at": "2026-06-10T15:00:00.000Z" } ``` `abstract` is included when `includeAbstract` is on. A final `{"_type": "summary"}` record reports how many works were returned and how many matched in total. *** ### Pricing **Your first 25 works are free — every Apify account, no card, no trial clock.** After that it is a flat **$0.002 per work delivered**. - First 25 works free per account (lifetime), then $0.002/work ($2 per 1,000) - **Zero charge on empty runs** — a search that matches nothing costs you nothing - No monthly minimum, no rental *** ### FAQ **Do I need an API key?** No. OpenAlex is fully open; this actor uses its fast polite pool. **How many works are indexed?** Over 250 million, across all fields of research. **Can I get abstracts?** Yes — turn on `includeAbstract` and the actor reconstructs readable abstract text from OpenAlex's inverted index. **How do I get the most-cited papers on a topic?** Just set `searchTerm` (and optionally `minCitations`); results are returned most-cited first. # Actor input Schema ## `searchTerm` (type: `string`): Full-text search across titles, abstracts and fulltext (e.g. crispr, large language models, perovskite solar). Leave empty to match all works in your other filters. ## `fromYear` (type: `integer`): Only works published in or after this year. ## `toYear` (type: `integer`): Only works published in or before this year. ## `minCitations` (type: `integer`): Only works cited at least this many times. ## `workType` (type: `string`): Filter by work type. Leave empty for all. ## `openAccessOnly` (type: `boolean`): Only return works that are open access. ## `includeAbstract` (type: `boolean`): Reconstruct and include each work's abstract text. Larger output. ## `maxResults` (type: `integer`): Maximum number of works to return, most cited first. ## Actor input object example ```json { "searchTerm": "large language models", "workType": "", "openAccessOnly": false, "includeAbstract": false, "maxResults": 25 } ``` # 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 = { "searchTerm": "large language models", "maxResults": 25 }; // Run the Actor and wait for it to finish const run = await client.actor("themineworks/openalex-scholarly-works").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 = { "searchTerm": "large language models", "maxResults": 25, } # Run the Actor and wait for it to finish run = client.actor("themineworks/openalex-scholarly-works").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 '{ "searchTerm": "large language models", "maxResults": 25 }' | apify call themineworks/openalex-scholarly-works --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=themineworks/openalex-scholarly-works", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenAlex Scholarly Works Scraper — Papers, Authors & Citations", "description": "Search 250M+ scholarly papers from OpenAlex as clean structured JSON. Filter by topic, year, citations, open-access and type. Authors, venues, citations and abstracts. Zero charge on empty runs.", "version": "0.1", "x-build-id": "zRMaSWcX3uELgtf3Q" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/themineworks~openalex-scholarly-works/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-themineworks-openalex-scholarly-works", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/themineworks~openalex-scholarly-works/runs": { "post": { "operationId": "runs-sync-themineworks-openalex-scholarly-works", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/themineworks~openalex-scholarly-works/run-sync": { "post": { "operationId": "run-sync-themineworks-openalex-scholarly-works", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchTerm": { "title": "Search term", "type": "string", "description": "Full-text search across titles, abstracts and fulltext (e.g. crispr, large language models, perovskite solar). Leave empty to match all works in your other filters." }, "fromYear": { "title": "From publication year", "type": "integer", "description": "Only works published in or after this year." }, "toYear": { "title": "To publication year", "type": "integer", "description": "Only works published in or before this year." }, "minCitations": { "title": "Minimum citations", "type": "integer", "description": "Only works cited at least this many times." }, "workType": { "title": "Work type", "enum": [ "", "article", "review", "book-chapter", "book", "dataset", "preprint", "dissertation", "report" ], "type": "string", "description": "Filter by work type. Leave empty for all.", "default": "" }, "openAccessOnly": { "title": "Open access only", "type": "boolean", "description": "Only return works that are open access.", "default": false }, "includeAbstract": { "title": "Include abstract", "type": "boolean", "description": "Reconstruct and include each work's abstract text. Larger output.", "default": false }, "maxResults": { "title": "Max works to return", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of works to return, most cited first.", "default": 100 } } }, "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 } } } } } } } } } } ```