# OpenAlex Scholarly Works Scraper (`datamule/openalex-scholarly-works-scraper`) Actor Scrape scholarly works (papers, preprints, books), authors, institutions & journals from the free public OpenAlex API. Search + filter by author, institution, concept, year, type & open-access; sort; auto-paginate. Clean columns + raw. Pay per record. - **URL**: https://apify.com/datamule/openalex-scholarly-works-scraper.md - **Developed by:** [Datamule](https://apify.com/datamule) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.35 / 1,000 records This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 Works Scraper Turn the free, fully-open **[OpenAlex](https://openalex.org)** catalogue into clean, paginated dataset rows. OpenAlex is the open index of the world's scholarly research (~250M+ works, authors, institutions and journals) that filled the gap left by Microsoft Academic Graph's shutdown. This actor queries its **official public REST API** (`https://api.openalex.org`, no account, no API key) and returns one tidy record per result plus the complete raw object. Great for **literature reviews, bibliometrics, research-trend analysis, competitive intelligence, and building scholarly datasets**. ### What it scrapes - **Works** — papers, preprints, books, book chapters, datasets, dissertations, reviews… Each record has: OpenAlex id, DOI, title, publication year & date, type, language, author names + ids, affiliated institutions + countries, host venue (journal/repository), citation count, open-access status + URL, concepts/topics, referenced-works count, a reconstructed plain-text abstract, and a link to the OpenAlex page — plus the lossless `raw` work object. - **Authors / Institutions / Sources** — set **What to scrape** to one of these to resolve an entity (name, ids, ORCID/ROR/ISSN, works & citation counts, h-index) — handy for finding the id to plug back into a Works query. ### How to query - **Search term** — free text across titles/abstracts/full text (Works) or names (others). - **Structured Works filters** — author id, institution id, concept id, source id, work type, year range or exact year, open-access-only. All combined server-side (AND). - **Advanced raw filter** — any [OpenAlex filter](https://docs.openalex.org/api-entities/works/filter-works) expression, e.g. `cited_by_count:>100,has_doi:true`, AND-ed onto the above. - **Sort** — e.g. `cited_by_count:desc`, `publication_date:desc`. - **Max results** — the actor auto-paginates via OpenAlex's cursor (200/request), so this can be large. #### Example input ```json { "entityType": "works", "search": "machine learning", "fromYear": 2022, "openAccessOnly": true, "sort": "cited_by_count:desc", "maxResults": 500 } ```` Find a specific author's papers: ```json { "entityType": "works", "authorId": "A5105141183", "sort": "publication_date:desc" } ``` Look up an institution id: ```json { "entityType": "institutions", "search": "Harvard University" } ``` ### Output One dataset item per result. A no-match query returns 0 items (not an error). Every optional column is nullable, so records from different entity types share one schema; use the `raw` field for anything the normalized columns don't expose. ### Pricing **Pay per result** — you're charged one event per record returned. A query that matches nothing costs nothing. ### Notes - Uses OpenAlex's **polite pool** via a contact email in the request; set **Contact email** to your address for the best service. No key required either way. - Data © the OpenAlex project, released under **CC0**. Please cite OpenAlex where appropriate. - Not affiliated with or endorsed by OpenAlex. # Actor input Schema ## `entityType` (type: `string`): Which OpenAlex entity to query. Works = scholarly outputs (papers, preprints, books, datasets…) with full author/venue/citation metadata (the primary use case). Authors / Institutions / Sources return those entities (name, ids, works & citation counts) — useful for resolving an author/institution/journal id to feed back into a works query. ## `search` (type: `string`): Free-text search. For Works this matches the title, abstract and full text; for Authors/Institutions/Sources it matches their names. e.g. "machine learning", "CRISPR gene editing", "Harvard University". Leave empty to browse by filter/sort only. ## `workType` (type: `string`): Restrict Works to a single OpenAlex type, e.g. article, preprint, book, book-chapter, dataset, dissertation, review, report. Leave empty for all types. Ignored for non-Works entities. ## `fromYear` (type: `integer`): Only Works published on/after this year (inclusive). Combine with "To year" for a range. Ignored if "Exact publication year" is set, or for non-Works entities. ## `toYear` (type: `integer`): Only Works published on/before this year (inclusive). Ignored if "Exact publication year" is set, or for non-Works entities. ## `publicationYear` (type: `integer`): Only Works published in exactly this year. Overrides the From/To year range when set. Ignored for non-Works entities. ## `openAccessOnly` (type: `boolean`): When on, return only open-access Works (is\_oa:true). Ignored for non-Works entities. ## `authorId` (type: `string`): Restrict Works to those by a given author. Accepts an OpenAlex author id (e.g. A5105141183 or the full https://openalex.org/A5105141183 URL). Tip: run this actor with "Authors" first to find the id. Ignored for non-Works entities. ## `institutionId` (type: `string`): Restrict Works to those affiliated with a given institution (e.g. I136199984 for Harvard). Ignored for non-Works entities. ## `conceptId` (type: `string`): Restrict Works tagged with a given concept/topic (e.g. C519991488). Ignored for non-Works entities. ## `sourceId` (type: `string`): Restrict Works published in a given source — a journal or repository (e.g. S4306400194 for arXiv). Ignored for non-Works entities. ## `filter` (type: `string`): Optional raw OpenAlex filter string, AND-ed onto the structured filters above. Use OpenAlex's own filter grammar for anything not exposed as a field, e.g. "cited\_by\_count:>100,has\_doi:true" or "authorships.countries:US". See https://docs.openalex.org/api-entities/works/filter-works. For non-Works entities this is the only structured filter input. ## `sort` (type: `string`): How to order results, as "field:direction", e.g. "cited\_by\_count:desc" (most-cited first), "publication\_date:desc" (newest first), "works\_count:desc" (for authors/institutions). Leave empty for the default relevance order (or a plain listing when there's no search term). ## `maxResults` (type: `integer`): Maximum number of records to return. The actor paginates automatically via OpenAlex's cursor (200 per request), so this can be large. Each returned record is one billable item. ## `mailto` (type: `string`): Optional email address added to requests per OpenAlex's polite-pool convention for faster, more reliable service. No account or key is required. If omitted a neutral default is used. ## Actor input object example ```json { "entityType": "works", "search": "machine learning", "openAccessOnly": false, "sort": "cited_by_count:desc", "maxResults": 200 } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "search": "machine learning", "sort": "cited_by_count:desc", "maxResults": 200 }; // Run the Actor and wait for it to finish const run = await client.actor("datamule/openalex-scholarly-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 = { "search": "machine learning", "sort": "cited_by_count:desc", "maxResults": 200, } # Run the Actor and wait for it to finish run = client.actor("datamule/openalex-scholarly-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 '{ "search": "machine learning", "sort": "cited_by_count:desc", "maxResults": 200 }' | apify call datamule/openalex-scholarly-works-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datamule/openalex-scholarly-works-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenAlex Scholarly Works Scraper", "description": "Scrape scholarly works (papers, preprints, books), authors, institutions & journals from the free public OpenAlex API. Search + filter by author, institution, concept, year, type & open-access; sort; auto-paginate. Clean columns + raw. Pay per record.", "version": "0.1", "x-build-id": "KrJjSZwHFU4lCl7rf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datamule~openalex-scholarly-works-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datamule-openalex-scholarly-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datamule~openalex-scholarly-works-scraper/runs": { "post": { "operationId": "runs-sync-datamule-openalex-scholarly-works-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datamule~openalex-scholarly-works-scraper/run-sync": { "post": { "operationId": "run-sync-datamule-openalex-scholarly-works-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "entityType": { "title": "What to scrape", "enum": [ "works", "authors", "institutions", "sources" ], "type": "string", "description": "Which OpenAlex entity to query. Works = scholarly outputs (papers, preprints, books, datasets…) with full author/venue/citation metadata (the primary use case). Authors / Institutions / Sources return those entities (name, ids, works & citation counts) — useful for resolving an author/institution/journal id to feed back into a works query.", "default": "works" }, "search": { "title": "Search term", "type": "string", "description": "Free-text search. For Works this matches the title, abstract and full text; for Authors/Institutions/Sources it matches their names. e.g. \"machine learning\", \"CRISPR gene editing\", \"Harvard University\". Leave empty to browse by filter/sort only." }, "workType": { "title": "Work type (Works only)", "type": "string", "description": "Restrict Works to a single OpenAlex type, e.g. article, preprint, book, book-chapter, dataset, dissertation, review, report. Leave empty for all types. Ignored for non-Works entities." }, "fromYear": { "title": "From year (Works only)", "minimum": 1000, "type": "integer", "description": "Only Works published on/after this year (inclusive). Combine with \"To year\" for a range. Ignored if \"Exact publication year\" is set, or for non-Works entities." }, "toYear": { "title": "To year (Works only)", "minimum": 1000, "type": "integer", "description": "Only Works published on/before this year (inclusive). Ignored if \"Exact publication year\" is set, or for non-Works entities." }, "publicationYear": { "title": "Exact publication year (Works only)", "minimum": 1000, "type": "integer", "description": "Only Works published in exactly this year. Overrides the From/To year range when set. Ignored for non-Works entities." }, "openAccessOnly": { "title": "Open-access only (Works only)", "type": "boolean", "description": "When on, return only open-access Works (is_oa:true). Ignored for non-Works entities.", "default": false }, "authorId": { "title": "Author OpenAlex ID (Works only)", "type": "string", "description": "Restrict Works to those by a given author. Accepts an OpenAlex author id (e.g. A5105141183 or the full https://openalex.org/A5105141183 URL). Tip: run this actor with \"Authors\" first to find the id. Ignored for non-Works entities." }, "institutionId": { "title": "Institution OpenAlex ID (Works only)", "type": "string", "description": "Restrict Works to those affiliated with a given institution (e.g. I136199984 for Harvard). Ignored for non-Works entities." }, "conceptId": { "title": "Concept OpenAlex ID (Works only)", "type": "string", "description": "Restrict Works tagged with a given concept/topic (e.g. C519991488). Ignored for non-Works entities." }, "sourceId": { "title": "Source OpenAlex ID (Works only)", "type": "string", "description": "Restrict Works published in a given source — a journal or repository (e.g. S4306400194 for arXiv). Ignored for non-Works entities." }, "filter": { "title": "Advanced raw filter", "type": "string", "description": "Optional raw OpenAlex filter string, AND-ed onto the structured filters above. Use OpenAlex's own filter grammar for anything not exposed as a field, e.g. \"cited_by_count:>100,has_doi:true\" or \"authorships.countries:US\". See https://docs.openalex.org/api-entities/works/filter-works. For non-Works entities this is the only structured filter input." }, "sort": { "title": "Sort", "type": "string", "description": "How to order results, as \"field:direction\", e.g. \"cited_by_count:desc\" (most-cited first), \"publication_date:desc\" (newest first), \"works_count:desc\" (for authors/institutions). Leave empty for the default relevance order (or a plain listing when there's no search term)." }, "maxResults": { "title": "Max results", "minimum": 1, "type": "integer", "description": "Maximum number of records to return. The actor paginates automatically via OpenAlex's cursor (200 per request), so this can be large. Each returned record is one billable item.", "default": 200 }, "mailto": { "title": "Contact email (polite pool)", "type": "string", "description": "Optional email address added to requests per OpenAlex's polite-pool convention for faster, more reliable service. No account or key is required. If omitted a neutral default is used." } } }, "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 } } } } } } } } } } ```