# OpenAlex Scholarly Data Extractor (`xtracto/openalex-scholarly`) Actor Extract scholarly works, authors, institutions, journals, publishers, and funders from OpenAlex — one record per row. 316M+ works. Public data, no key. - **URL**: https://apify.com/xtracto/openalex-scholarly.md - **Developed by:** [xtractoo](https://apify.com/xtracto) (community) - **Categories:** Other - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.15 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 Scholarly Data Extractor Extract scholarly data at scale from **OpenAlex** — **works (papers)**, **authors**, **institutions**, **journals/sources**, **publishers**, **funders**, **topics**, and **concepts** — one record per row. Built for research-intelligence and R&D competitive analysis, bibliometrics, science-funding analysis, and academic data teams. --- ### Why use this actor - **Massive corpus** — 316M+ works (verified), plus ~95M authors and the full institution/journal/funder graph. - **One record per row**, with a flat header (`openalex_id`, `display_name`, `cited_by_count`, `publication_year`) plus the **full raw OpenAlex object** (authorships, institutions, open-access, IDs, …). - **No login, no key.** Add your email to join the faster "polite pool". ### Input | Field | Type | Description | | --- | --- | --- | | `entity` | **dropdown** | Works / Authors / Sources / Institutions / Publishers / Funders / Topics / Concepts. | | `filter` | text | OpenAlex filter, e.g. `publication_year:2026,is_oa:true`. | | `search` | text | Full-text keyword search. | | `mailto` | text | Your email — joins OpenAlex's faster pool (recommended). | | `perPage` | int | 1–200 (default 200). | | `maxItems` | int | 0 = all matching (up to the 100k cursor window). | `entity` is a **pick-list**. `filter`/`search` are free-text because OpenAlex's filter grammar is open-ended (documented at docs.openalex.org). ### Output Envelope + `recordType` (e.g. `WORK`, `AUTHOR`) + `entity_type` + flat header, then the raw OpenAlex object: ```json { "_input": "filter=publication_year:2026,is_oa:true", "_source": "S1-openalex", "_scrapedAt": "2026-06-03T10:00:00Z", "recordType": "WORK", "entity_type": "works", "openalex_id": "https://openalex.org/W...", "display_name": "...", "cited_by_count": 12, "publication_year": 2026, "doi": "https://doi.org/...", "authorships": [ "..." ], "open_access": { "is_oa": true, "...": "..." } } ```` ### How it works 1. Your entity choice and filters define what to pull. 2. The actor automatically pages through all matching results (up to 200 per request). 3. Each entity streams into the dataset. ### Known limits - Public data — no account needed, runs from any connection. Backs off on HTTP 429. Add `mailto` for the faster pool. - A single query streams up to **~100,000 results**; to pull a larger slice, narrow with `filter` (e.g. by year) and run per slice. - **Verified live 2026-06-03:** works total 315,965,530; `cursor=*` → `meta.next_cursor` confirmed. # Actor input Schema ## `entity` (type: `string`): Which OpenAlex entity to extract. ## `filter` (type: `string`): OpenAlex filter syntax, e.g. publication\_year:2026,is\_oa:true or authorships.institutions.country\_code:US. Leave blank for everything. ## `search` (type: `string`): Keyword search across the entity. ## `mailto` (type: `string`): Joins OpenAlex's faster 'polite pool'. e.g. you@example.com ## `perPage` (type: `integer`): How many records to fetch per request. ## `maxItems` (type: `integer`): Maximum records to return (0 = all matching). ## `proxyConfiguration` (type: `object`): Not required (public API). ## Actor input object example ```json { "entity": "works", "filter": "", "search": "machine learning", "mailto": "you@example.com", "perPage": 200, "maxItems": 0, "proxyConfiguration": { "useApifyProxy": false } } ``` # 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 = { "entity": "works", "filter": "", "search": "machine learning", "mailto": "you@example.com" }; // Run the Actor and wait for it to finish const run = await client.actor("xtracto/openalex-scholarly").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 = { "entity": "works", "filter": "", "search": "machine learning", "mailto": "you@example.com", } # Run the Actor and wait for it to finish run = client.actor("xtracto/openalex-scholarly").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 '{ "entity": "works", "filter": "", "search": "machine learning", "mailto": "you@example.com" }' | apify call xtracto/openalex-scholarly --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=xtracto/openalex-scholarly", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenAlex Scholarly Data Extractor", "description": "Extract scholarly works, authors, institutions, journals, publishers, and funders from OpenAlex — one record per row. 316M+ works. Public data, no key.", "version": "0.1", "x-build-id": "pHgpqgkwcrqnvysEu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/xtracto~openalex-scholarly/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-xtracto-openalex-scholarly", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/xtracto~openalex-scholarly/runs": { "post": { "operationId": "runs-sync-xtracto-openalex-scholarly", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/xtracto~openalex-scholarly/run-sync": { "post": { "operationId": "run-sync-xtracto-openalex-scholarly", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "entity" ], "properties": { "entity": { "title": "Entity type", "enum": [ "works", "authors", "sources", "institutions", "publishers", "funders", "topics", "concepts" ], "type": "string", "description": "Which OpenAlex entity to extract.", "default": "works" }, "filter": { "title": "OpenAlex filter (optional)", "type": "string", "description": "OpenAlex filter syntax, e.g. publication_year:2026,is_oa:true or authorships.institutions.country_code:US. Leave blank for everything.", "default": "" }, "search": { "title": "Full-text search (optional)", "type": "string", "description": "Keyword search across the entity.", "default": "" }, "mailto": { "title": "Your email (optional, recommended)", "type": "string", "description": "Joins OpenAlex's faster 'polite pool'. e.g. you@example.com", "default": "" }, "perPage": { "title": "Page size", "minimum": 1, "maximum": 200, "type": "integer", "description": "How many records to fetch per request.", "default": 200 }, "maxItems": { "title": "Max records (0 = all matching, up to the 100k cursor window)", "minimum": 0, "type": "integer", "description": "Maximum records to return (0 = all matching).", "default": 0 }, "proxyConfiguration": { "title": "Proxy", "type": "object", "description": "Not required (public API).", "default": { "useApifyProxy": false } } } }, "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 } } } } } } } } } } ```