# OpenAlex Scholarly Works Tracker – Research Citation API (`deadwood_data_solutions/openalex-scholarly-works-tracker`) Actor Structured feed of scholarly works from OpenAlex: title, authors, institutions, journal, citations, open-access status and concepts. For research intelligence, competitive science, and bibliometrics. - **URL**: https://apify.com/deadwood\_data\_solutions/openalex-scholarly-works-tracker.md - **Developed by:** [K O](https://apify.com/deadwood_data_solutions) (community) - **Categories:** Developer tools, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 Scholarly Works Tracker – Research Paper & Citation API An **OpenAlex API** wrapper and research-intelligence feed that pulls scholarly works (papers, preprints, datasets) and normalizes each into a clean record: title, authors, institutions, venue, publication date, citation count, open-access status and research concepts. Use it to build a **research-intelligence tool**, a competitive-science monitor, a bibliometrics dataset, or an institution/topic publication feed — a free, structured alternative to closed citation databases. ### Who uses it University research offices and bibliometrics teams, R&D competitive-intelligence groups, scientific publishers, funders, and research-analytics products. ### Why this is worth charging for OpenAlex is an open catalog of 250M+ scholarly works, but its API returns deeply nested authorship/institution/concept objects and paginates by cursor. This actor flattens works into readable records with deduplicated authors and institutions, and delivers a topic- or institution-scoped, citation-aware feed you can monitor on a schedule — no per-seat citation-database license required. ### Output Each dataset item is one normalized record: ```json { "id": "https://openalex.org/W123", "doi": "https://doi.org/10.1/abc", "title": "Deep Widgets", "publicationDate": "2024-02-01", "publicationYear": 2024, "type": "article", "authors": [ "A. Lee", "B. Kim" ], "institutions": [ "MIT" ], "venue": "Nature", "citedByCount": 42, "isOpenAccess": true, "oaStatus": "gold", "concepts": [ "Machine learning" ], "referencedWorksCount": 3, "landingPageUrl": "https://nature.com/x", "source": "OpenAlex" } ```` ### Input | Field | Description | |---|---| | `search` | Full-text search across title/abstract/fulltext, e.g. 'graph neural networks'. | | `fromPublicationDate` | Only works published on or after this date. | | `toPublicationDate` | Only works published on or before this date. | | `institutionId` | OpenAlex institution ID to filter by, e.g. I136199984 (Harvard). | | `conceptId` | OpenAlex concept ID to filter by topic. | | `openAccessOnly` | Restrict to open-access works. | | `sort` | Sort expression, e.g. publication\_date:desc or cited\_by\_count:desc. | | `mailto` | Email for OpenAlex's polite pool (faster, courteous access). | | `pageSize` | Works per page (OpenAlex max 200). | | `maxPagesToScan` | Cursor pages per run. | | `onlyNewSinceLastRun` | Recommended for schedules — skips records already returned by a previous run using persisted state, so you're only charged for genuinely new items. | | `maxItems` | Stop after this many normalized records. | ### Pricing (Pay-Per-Event) - **`query`** — $0.01, charged once per run for the API query/poll. - **`work-record`** — $0.005 per normalized record pushed. This is the primary event. - `apify-actor-start` (Apify-managed) — covers baseline compute per run. A topic or institution monitor returns hundreds of works per run for a dollar or two. onlyNewSinceLastRun bills only newly-indexed works on a schedule. ### Source & reliability Data comes from the official `api.openalex.org` service using cursor pagination and the polite pool. No key or proxies required. Run `npm test` for the offline self-test covering the normalizer, including edge cases and missing-field handling. ### FAQ **Do I need an API key?** No — OpenAlex is open. Providing a contact email enables the faster polite pool. **Can I track an institution's output?** Yes. Set institutionId (and/or a search term) and schedule with onlyNewSinceLastRun for a rolling publication feed. *** *SEO keywords: OpenAlex API, scholarly works API, research paper scraper, citation data feed, bibliometrics API, academic literature monitor* # Actor input Schema ## `search` (type: `string`): Full-text search across title/abstract/fulltext, e.g. 'graph neural networks'. ## `fromPublicationDate` (type: `string`): Only works published on or after this date. ## `toPublicationDate` (type: `string`): Only works published on or before this date. ## `institutionId` (type: `string`): OpenAlex institution ID to filter by, e.g. I136199984 (Harvard). ## `conceptId` (type: `string`): OpenAlex concept ID to filter by topic. ## `openAccessOnly` (type: `boolean`): Restrict to open-access works. ## `sort` (type: `string`): Sort expression, e.g. publication\_date:desc or cited\_by\_count:desc. ## `mailto` (type: `string`): Email for OpenAlex's polite pool (faster, courteous access). ## `pageSize` (type: `integer`): Works per page (OpenAlex max 200). ## `maxPagesToScan` (type: `integer`): Cursor pages per run. ## `maxItems` (type: `integer`): Stop after this many normalized records have been pushed. Leave blank for no limit (bounded by pagination settings). ## `onlyNewSinceLastRun` (type: `boolean`): Recommended for scheduled runs. Uses persisted state to skip records already returned earlier, so a recurring schedule only charges for genuinely new items. ## Actor input object example ```json { "openAccessOnly": false, "pageSize": 100, "maxPagesToScan": 3, "onlyNewSinceLastRun": 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("deadwood_data_solutions/openalex-scholarly-works-tracker").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("deadwood_data_solutions/openalex-scholarly-works-tracker").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 deadwood_data_solutions/openalex-scholarly-works-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=deadwood_data_solutions/openalex-scholarly-works-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenAlex Scholarly Works Tracker – Research Citation API", "description": "Structured feed of scholarly works from OpenAlex: title, authors, institutions, journal, citations, open-access status and concepts. For research intelligence, competitive science, and bibliometrics.", "version": "1.0", "x-build-id": "0lvh4VH3kbl4mMe8P" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/deadwood_data_solutions~openalex-scholarly-works-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-deadwood_data_solutions-openalex-scholarly-works-tracker", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/deadwood_data_solutions~openalex-scholarly-works-tracker/runs": { "post": { "operationId": "runs-sync-deadwood_data_solutions-openalex-scholarly-works-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/deadwood_data_solutions~openalex-scholarly-works-tracker/run-sync": { "post": { "operationId": "run-sync-deadwood_data_solutions-openalex-scholarly-works-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "search": { "title": "Search query", "type": "string", "description": "Full-text search across title/abstract/fulltext, e.g. 'graph neural networks'." }, "fromPublicationDate": { "title": "From date (YYYY-MM-DD)", "type": "string", "description": "Only works published on or after this date." }, "toPublicationDate": { "title": "To date (YYYY-MM-DD)", "type": "string", "description": "Only works published on or before this date." }, "institutionId": { "title": "Institution ID", "type": "string", "description": "OpenAlex institution ID to filter by, e.g. I136199984 (Harvard)." }, "conceptId": { "title": "Concept ID", "type": "string", "description": "OpenAlex concept ID to filter by topic." }, "openAccessOnly": { "title": "Open access only", "type": "boolean", "description": "Restrict to open-access works.", "default": false }, "sort": { "title": "Sort", "type": "string", "description": "Sort expression, e.g. publication_date:desc or cited_by_count:desc." }, "mailto": { "title": "Contact email", "type": "string", "description": "Email for OpenAlex's polite pool (faster, courteous access)." }, "pageSize": { "title": "Per page", "minimum": 1, "maximum": 200, "type": "integer", "description": "Works per page (OpenAlex max 200).", "default": 100 }, "maxPagesToScan": { "title": "Max pages to scan", "minimum": 1, "maximum": 100, "type": "integer", "description": "Cursor pages per run.", "default": 3 }, "maxItems": { "title": "Maximum records to return", "minimum": 1, "type": "integer", "description": "Stop after this many normalized records have been pushed. Leave blank for no limit (bounded by pagination settings)." }, "onlyNewSinceLastRun": { "title": "Only return records not seen in a previous run", "type": "boolean", "description": "Recommended for scheduled runs. Uses persisted state to skip records already returned earlier, so a recurring schedule only charges for genuinely new items.", "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 } } } } } } } } } } ```