# OpenAlex Research Intelligence (`taroyamada/openalex-research-intelligence`) Actor Extract publication, author, institution, source, citation, topic, DOI, and open-access signals from the official OpenAlex API. - **URL**: https://apify.com/taroyamada/openalex-research-intelligence.md - **Developed by:** [åĪŠéƒŽ åąąį”°](https://apify.com/taroyamada) (community) - **Categories:** Developer tools, Business, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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 Research Intelligence Extract research intelligence from the official OpenAlex API without browser scraping. Use search terms, OpenAlex work IDs, author IDs, institution IDs, or concept IDs to produce flattened dataset rows for works, authors, and publication sources. ### Inputs | Field | Default | Notes | |---|---:|---| | `searchTerms` | `[]` | Research queries for OpenAlex works. | | `workIds` | `[]` | OpenAlex work IDs or URLs. | | `authorIds` | `[]` | OpenAlex author IDs. | | `institutionIds` | `[]` | OpenAlex institution IDs. | | `conceptIds` | `[]` | OpenAlex concept IDs. | | `fromDate` / `toDate` | empty | Publication date filters in `YYYY-MM-DD`. | | `sort` | `cited_by_count:desc` | Citation, publication date, or relevance sort. | | `limitPerSource` | `25` | Works fetched per search/filter source. | | `maxWorks` | `100` | Global unique work cap. | | `includeAbstract` | `false` | Reconstruct abstract text from OpenAlex inverted index when available. | | `mailto` | empty | Optional polite-pool email parameter for OpenAlex. | | `delivery` | `dataset` | `dataset` or `webhook`. | | `dryRun` | `false` | Skip dataset/webhook delivery. | At least one of `searchTerms`, `workIds`, `authorIds`, `institutionIds`, or `conceptIds` is required. ### Dataset Rows `work_summary` - title, DOI, OpenAlex ID, publication year/date, type - citation count, open-access status, retraction flag - primary source, publisher, landing page, PDF URL - topics, concepts, countries, institutions `author_signal` - work ID/title, author name and OpenAlex ID - author order/position, corresponding author flag - affiliated institutions and countries `source_summary` - work ID/title, journal or source name - source type, ISSN, host organization, open-access source flag ### Example Input ```json { "searchTerms": ["large language models", "retrieval augmented generation"], "fromDate": "2024-01-01", "sort": "cited_by_count:desc", "limitPerSource": 10, "maxWorks": 20, "includeAbstract": false, "delivery": "dataset", "dryRun": false } ```` ### Local Development ```bash npm install npm test node src/index.js ``` `output/result.json` contains the full payload. Apify dataset delivery writes flattened rows. ### Limitations - OpenAlex coverage is broad but not identical to Crossref, PubMed, Semantic Scholar, or publisher APIs. - Citation counts and metadata can lag source publications. - Relevance sort is used only for search sources; non-search filters fall back to citation sort. - `includeAbstract` can increase payload size substantially. # Actor input Schema ## `searchTerms` (type: `array`): Research keywords to search in OpenAlex works. ## `workIds` (type: `array`): OpenAlex work IDs or URLs such as W2741809807 or https://openalex.org/W2741809807. ## `authorIds` (type: `array`): OpenAlex author IDs or URLs. Fetches works by author. ## `institutionIds` (type: `array`): OpenAlex institution IDs or URLs. Fetches works associated with the institution. ## `conceptIds` (type: `array`): OpenAlex concept IDs or URLs. Fetches works tagged to the concept. ## `fromDate` (type: `string`): Optional publication date floor, YYYY-MM-DD. ## `toDate` (type: `string`): Optional publication date ceiling, YYYY-MM-DD. ## `sort` (type: `string`): Sort order for OpenAlex work results. ## `limitPerSource` (type: `integer`): Maximum number of works to fetch for each source input. ## `maxWorks` (type: `integer`): Global cap on unique works emitted by one run. ## `includeAbstract` (type: `boolean`): When enabled, reconstruct abstract text from OpenAlex inverted indexes when available. ## `mailto` (type: `string`): Optional email sent as OpenAlex mailto query parameter. ## `timeoutMs` (type: `integer`): Per-request timeout in milliseconds. ## `delivery` (type: `string`): Send flattened rows to the dataset or POST the full payload to a webhook. ## `webhookUrl` (type: `string`): Required when delivery is webhook. ## `dryRun` (type: `boolean`): Run without saving dataset rows or sending a webhook. ## Actor input object example ```json { "searchTerms": [ "large language models", "retrieval augmented generation" ], "sort": "cited_by_count:desc", "limitPerSource": 25, "maxWorks": 100, "includeAbstract": false, "timeoutMs": 20000, "delivery": "dataset", "dryRun": 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 = { "searchTerms": [ "large language models", "retrieval augmented generation" ] }; // Run the Actor and wait for it to finish const run = await client.actor("taroyamada/openalex-research-intelligence").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`ðŸ’ū 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 = { "searchTerms": [ "large language models", "retrieval augmented generation", ] } # Run the Actor and wait for it to finish run = client.actor("taroyamada/openalex-research-intelligence").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("ðŸ’ū 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 '{ "searchTerms": [ "large language models", "retrieval augmented generation" ] }' | apify call taroyamada/openalex-research-intelligence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=taroyamada/openalex-research-intelligence", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenAlex Research Intelligence", "description": "Extract publication, author, institution, source, citation, topic, DOI, and open-access signals from the official OpenAlex API.", "version": "0.1", "x-build-id": "y9uZQR2dhwxxvwx77" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/taroyamada~openalex-research-intelligence/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-taroyamada-openalex-research-intelligence", "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/taroyamada~openalex-research-intelligence/runs": { "post": { "operationId": "runs-sync-taroyamada-openalex-research-intelligence", "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/taroyamada~openalex-research-intelligence/run-sync": { "post": { "operationId": "run-sync-taroyamada-openalex-research-intelligence", "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": { "searchTerms": { "title": "Search Terms", "type": "array", "description": "Research keywords to search in OpenAlex works.", "items": { "type": "string" } }, "workIds": { "title": "OpenAlex Work IDs", "type": "array", "description": "OpenAlex work IDs or URLs such as W2741809807 or https://openalex.org/W2741809807.", "items": { "type": "string" } }, "authorIds": { "title": "Author IDs", "type": "array", "description": "OpenAlex author IDs or URLs. Fetches works by author.", "items": { "type": "string" } }, "institutionIds": { "title": "Institution IDs", "type": "array", "description": "OpenAlex institution IDs or URLs. Fetches works associated with the institution.", "items": { "type": "string" } }, "conceptIds": { "title": "Concept IDs", "type": "array", "description": "OpenAlex concept IDs or URLs. Fetches works tagged to the concept.", "items": { "type": "string" } }, "fromDate": { "title": "From Publication Date", "type": "string", "description": "Optional publication date floor, YYYY-MM-DD." }, "toDate": { "title": "To Publication Date", "type": "string", "description": "Optional publication date ceiling, YYYY-MM-DD." }, "sort": { "title": "Sort", "enum": [ "cited_by_count:desc", "publication_date:desc", "publication_date:asc", "relevance_score:desc" ], "type": "string", "description": "Sort order for OpenAlex work results.", "default": "cited_by_count:desc" }, "limitPerSource": { "title": "Limit Per Source", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of works to fetch for each source input.", "default": 25 }, "maxWorks": { "title": "Max Works", "minimum": 1, "maximum": 500, "type": "integer", "description": "Global cap on unique works emitted by one run.", "default": 100 }, "includeAbstract": { "title": "Include Abstract", "type": "boolean", "description": "When enabled, reconstruct abstract text from OpenAlex inverted indexes when available.", "default": false }, "mailto": { "title": "Polite Pool Email", "type": "string", "description": "Optional email sent as OpenAlex mailto query parameter." }, "timeoutMs": { "title": "Request Timeout (ms)", "minimum": 1000, "maximum": 60000, "type": "integer", "description": "Per-request timeout in milliseconds.", "default": 20000 }, "delivery": { "title": "Delivery", "enum": [ "dataset", "webhook" ], "type": "string", "description": "Send flattened rows to the dataset or POST the full payload to a webhook.", "default": "dataset" }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "Required when delivery is webhook." }, "dryRun": { "title": "Dry Run", "type": "boolean", "description": "Run without saving dataset rows or sending a webhook.", "default": 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 } } } } } } } } } } ```