# OpenAlex Works Scraper — Academic Publications API (`compute-edge/openalex-works-scraper`) Actor Extract academic publications from OpenAlex API. Filter by publication year, author ORCID, institution, concept, and open access status. Returns structured bibliographic data including authors, citations, and abstracts. - **URL**: https://apify.com/compute-edge/openalex-works-scraper.md - **Developed by:** [Compute Edge](https://apify.com/compute-edge) (community) - **Categories:** Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 results 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 Works Scraper ### About this Actor The **OpenAlex Works Scraper** extracts academic publications from the OpenAlex API — the world's largest open bibliography with over 250 million scholarly works. This Actor lets you search, filter, and download bibliographic data including titles, DOIs, authors, citations, and abstract content for research analysis, literature reviews, institutional reporting, and academic data mining. #### Key Features - **Full-text Search**: Find works by keywords (e.g., "machine learning", "climate change") - **Advanced Filtering**: Filter by publication year, author ORCID, research institution, research concept, and open access status - **Structured Output**: Extract titles, DOIs, publication years, citation counts, authors, institutions, and abstracts - **Citation Analytics**: Get citation counts and open access availability for impact assessment - **Author & Institution Data**: Export author names, ORCIDs, institutional affiliations, and author counts - **No Login Required**: Uses the public OpenAlex API — zero credentials needed - **Rate Limit Optimization**: Optional polite pool email for higher API rate limits #### Data Fields Extracted | Field | Type | Description | |-------|------|-------------| | `id` | string | OpenAlex work ID (e.g., `W2741809807`) | | `title` | string | Work title | | `doi` | string | Digital Object Identifier (if available) | | `publication_year` | number | Year of publication | | `publication_date` | string | Full publication date (YYYY-MM-DD) | | `type` | string | Work type (e.g., journal-article, conference-paper) | | `cited_by_count` | number | Total citations from other works | | `is_oa` | boolean | Open access status (true/false) | | `oa_url` | string | URL to open access PDF (if available) | | `authors` | string | Comma-separated list of author names | | `institutions` | string | Comma-separated list of affiliated institutions | | `source_display_name` | string | Journal or publication venue name | | `primary_topic` | string | Primary research topic/concept | | `abstract` | string | Work abstract (reconstructed from OpenAlex inverted index) | ### How to Scrape Academic Works with OpenAlex #### Step 1: Basic Search (No Filters) The simplest way to use this Actor: run it with no filters to get recent open access publications. 1. Click **"Use"** to open the input form 2. Leave all fields blank (defaults to all recent works, limited to 200 results) 3. Click **"Start"** 4. Results appear in the **Dataset** tab ![Input form with defaults](https://via.placeholder.com/640x480?text=Input+Form) #### Step 2: Search by Keyword 1. In the **"Search Query"** field, enter your research topic (e.g., "quantum computing") 2. Leave other filters blank 3. Click **"Start"** 4. Actor returns all works matching your query (up to max results limit) **Examples:** - `"renewable energy"` — Find renewable energy research - `COVID-19 vaccine` — Find pandemic vaccine studies - `machine learning oncology` — Combine keywords for specific topics #### Step 3: Filter by Publication Year 1. Enter search query (or leave blank for all recent works) 2. In **"Filter by Publication Year"** field, enter: - Single year: `2024` - Year range: `2020-2024` 3. Click **"Start"** #### Step 4: Filter by Author (ORCID) Find all works by a specific author using their ORCID (Open Researcher and Contributor ID): 1. Go to [orcid.org](https://orcid.org) and search for the author 2. Copy their ORCID (e.g., `0000-0002-1234-5678`) 3. Paste into **"Filter by Author ORCID"** field 4. Click **"Start"** #### Step 5: Filter by Institution Extract all publications from a specific research institution: 1. Find the institution's ROR ID at [ror.org](https://ror.org) (e.g., `02nr0ka47` for MIT) 2. Paste into **"Filter by Institution ID"** field 3. Click **"Start"** #### Step 6: Filter by Research Concept Find works tagged with a specific research topic: 1. In **"Filter by Concept"** field, enter a research area (e.g., `"Machine Learning"`, `"Cancer Research"`) 2. Click **"Start"** #### Step 7: Open Access Only To download only freely available publications: 1. Enable **"Open Access Only"** toggle 2. Fill in any search/filter fields as above 3. Click **"Start"** #### Step 8: Advanced Query (Multiple Filters) Combine filters for precise queries: 1. **Search Query**: `cancer immunotherapy` 2. **Publication Year**: `2023-2024` 3. **Concept**: `Immunology` 4. **Open Access Only**: Enabled 5. Click **"Start"** Result: Recent open access immunology papers on cancer immunotherapy with 100+ citations. #### Step 9: Increase Rate Limits (Optional) For large queries, add your email to access the OpenAlex polite pool: 1. Enter your email in **"Polite Pool Email"** field 2. This increases your API rate limit from 100 to 10,000 requests/second 3. Useful for extracting 5,000+ works in a single run ### Pricing **Cost per work extracted**: $0.004/result - A 1,000-work dataset costs ~$4.00 (plus Apify compute credits) - Includes Actor start fee ($0.00005) + per-result pricing - No hidden fees — you only pay for results actually returned - OpenAlex API itself is free; cost is only for Apify compute and Actor markup **Compute efficiency**: - Average run: 50-200 compute units per 100 works - Default memory limit: 512 MB (sufficient for up to 10,000 works per run) - Typical 200-work run completes in 30-60 seconds ### Input / Output Example #### Input (JSON) ```json { "search": "machine learning", "filterPublicationYear": "2023-2024", "filterConcept": "Artificial Intelligence", "isOpenAccess": true, "maxResults": 500, "politeEmail": "researcher@university.edu" } ```` #### Output (First 2 records) ```json [ { "id": "W3145678901", "title": "Transformers in Natural Language Processing: A Survey", "doi": "10.1234/nlp.2024.001", "publication_year": 2024, "publication_date": "2024-03-15", "type": "journal-article", "cited_by_count": 284, "is_oa": true, "oa_url": "https://example.com/paper.pdf", "authors": "Jane Smith, John Doe, Alice Johnson", "institutions": "MIT, Stanford University, Harvard University", "source_display_name": "Nature Machine Intelligence", "primary_topic": "Machine Learning", "abstract": "This paper surveys recent advances in transformer architectures..." }, { "id": "W3145678902", "title": "Efficient Vision Transformers for Mobile Devices", "doi": "10.1234/cv.2024.002", "publication_year": 2024, "publication_date": "2024-02-01", "type": "conference-paper", "cited_by_count": 156, "is_oa": true, "oa_url": "https://example.com/paper2.pdf", "authors": "Prof. David Chen, Emma Wilson", "institutions": "UC Berkeley, Google Research", "source_display_name": "CVPR 2024", "primary_topic": "Computer Vision", "abstract": "Mobile deployment of vision transformers requires careful optimization..." } ] ``` ### Related Actors - **[Semantic Scholar Scraper](https://apify.com/actors/semantic-scholar)** — Extract papers from Semantic Scholar API - **[PubMed Search Scraper](https://apify.com/actors/pubmed-search)** — Download biomedical literature from PubMed - **[Google Scholar Scraper](https://apify.com/actors/google-scholar)** — Scrape citation metrics from Google Scholar - **[arXiv Paper Scraper](https://apify.com/actors/arxiv-scraper)** — Extract preprints from arXiv ### FAQ **Q: Do I need an OpenAlex account?**\ A: No. The OpenAlex API is completely free and requires no login. This Actor uses only the public API. **Q: What's the maximum number of results I can extract?**\ A: The `maxResults` field accepts 1–10,000. For queries returning millions of works, OpenAlex applies pagination; set `maxResults` to 10,000 for a large sample. **Q: How do I find an author's ORCID?**\ A: Visit [orcid.org](https://orcid.org), search for the author name, and copy the ORCID from their profile (format: `XXXX-XXXX-XXXX-XXXX`). **Q: How do I find an institution's ROR ID?**\ A: Visit [ror.org](https://ror.org), search for the institution, and copy the ROR ID from the result. **Q: Can I export the data to Excel or CSV?**\ A: Yes. After the run completes, click the **"Download"** button in the Dataset tab to export as CSV or JSON. **Q: Why are some abstracts empty?**\ A: Not all OpenAlex works have abstracts. Open access papers are more likely to include abstracts. Enable the "Open Access Only" filter to maximize abstract coverage. **Q: What if my query returns 0 results?**\ A: Try broadening your filters: - Remove year restrictions - Check spelling of author ORCID or concept name - Use fewer combined filters - Test your search term on [openalex.org](https://openalex.org) directly **Q: How often is OpenAlex data updated?**\ A: OpenAlex updates several times per week. Most recent publications appear within 1–3 weeks of publication. **Q: Can I use this data commercially?**\ A: Yes. OpenAlex data is CC0 (public domain). All extracted works and metadata are yours to use. No attribution required (but recommended as a best practice). ### Legal & Disclaimer This Actor scrapes the **public, free OpenAlex API** — a nonprofit, open-access bibliography maintained by the University of Chicago. It does **not**: - Use authentication or bypass any protections - Extract personal data or emails - Violate any terms of service - Require user credentials **OpenAlex Terms**: By using OpenAlex, you agree to the [OpenAlex Terms of Service](https://openalex.org/). Data is provided under [CC0 1.0 Universal (Public Domain Dedication)](https://creativecommons.org/publicdomain/zero/1.0/). **Responsible Use**: Please respect OpenAlex's rate limits. Join the polite pool (via email) for high-volume queries to avoid temporary blocks. *** **Need help?** Open an issue on the [Apify Actor page](https://apify.com/actors/openalex-works-scraper) or email `support@apify.com`. Last updated: May 2026 # Actor input Schema ## `search` (type: `string`): Full-text search query. Examples: 'machine learning', 'quantum computing', 'climate change'. ## `filterPublicationYear` (type: `string`): Filter by publication year (e.g., '2024', '2020-2024'). Leave empty to include all years. ## `filterAuthorOrcid` (type: `string`): Filter by author ORCID (e.g., '0000-0002-1234-5678'). Leave empty to include all authors. ## `filterInstitutionId` (type: `string`): Filter by institution ROR ID (e.g., '02nr0ka47'). Leave empty to include all institutions. ## `filterConcept` (type: `string`): Filter by research concept/topic (e.g., 'Machine Learning', 'Cancer Research'). Leave empty to include all concepts. ## `isOpenAccess` (type: `boolean`): If enabled, only include open access publications. If disabled, include all publications. ## `maxResults` (type: `integer`): Maximum number of works to extract. Set to 0 for unlimited (caution: very large queries may timeout). ## `politeEmail` (type: `string`): Your email address to join OpenAlex's polite pool for higher rate limits (optional). ## Actor input object example ```json { "search": "", "filterPublicationYear": "", "filterAuthorOrcid": "", "filterInstitutionId": "", "filterConcept": "", "isOpenAccess": false, "maxResults": 200, "politeEmail": "" } ``` # 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("compute-edge/openalex-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 = {} # Run the Actor and wait for it to finish run = client.actor("compute-edge/openalex-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 '{}' | apify call compute-edge/openalex-works-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=compute-edge/openalex-works-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenAlex Works Scraper — Academic Publications API", "description": "Extract academic publications from OpenAlex API. Filter by publication year, author ORCID, institution, concept, and open access status. Returns structured bibliographic data including authors, citations, and abstracts.", "version": "0.1", "x-build-id": "Fz80boOvqbpWIRfuj" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/compute-edge~openalex-works-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-compute-edge-openalex-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/compute-edge~openalex-works-scraper/runs": { "post": { "operationId": "runs-sync-compute-edge-openalex-works-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/compute-edge~openalex-works-scraper/run-sync": { "post": { "operationId": "run-sync-compute-edge-openalex-works-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "search": { "title": "Search Query", "type": "string", "description": "Full-text search query. Examples: 'machine learning', 'quantum computing', 'climate change'.", "default": "" }, "filterPublicationYear": { "title": "Filter by Publication Year", "type": "string", "description": "Filter by publication year (e.g., '2024', '2020-2024'). Leave empty to include all years.", "default": "" }, "filterAuthorOrcid": { "title": "Filter by Author ORCID", "type": "string", "description": "Filter by author ORCID (e.g., '0000-0002-1234-5678'). Leave empty to include all authors.", "default": "" }, "filterInstitutionId": { "title": "Filter by Institution ID", "type": "string", "description": "Filter by institution ROR ID (e.g., '02nr0ka47'). Leave empty to include all institutions.", "default": "" }, "filterConcept": { "title": "Filter by Concept", "type": "string", "description": "Filter by research concept/topic (e.g., 'Machine Learning', 'Cancer Research'). Leave empty to include all concepts.", "default": "" }, "isOpenAccess": { "title": "Open Access Only", "type": "boolean", "description": "If enabled, only include open access publications. If disabled, include all publications.", "default": false }, "maxResults": { "title": "Maximum Results", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of works to extract. Set to 0 for unlimited (caution: very large queries may timeout).", "default": 200 }, "politeEmail": { "title": "Polite Pool Email", "type": "string", "description": "Your email address to join OpenAlex's polite pool for higher rate limits (optional).", "default": "" } } }, "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 } } } } } } } } } } ```