# OpenAlex Scholarly Works Scraper (`parseforge/openalex-scraper`) Actor Export academic works, authors, institutions, sources, and concepts from OpenAlexs open catalog of 250M+ scholarly records. Successor to Microsoft Academic Graph. Filter by author, concept, year, open access status, or affiliation. - **URL**: https://apify.com/parseforge/openalex-scraper.md - **Developed by:** [ParseForge](https://apify.com/parseforge) (community) - **Categories:** Other, Education, Automation - **Stats:** 13 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: 5.00 out of 5 stars ## 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. 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 ![ParseForge Banner](https://github.com/ParseForge/apify-assets/blob/ad35ccc13ddd068b9d6cba33f323962e39aed5b2/banner.jpg?raw=true) ## ๐ŸŽ“ OpenAlex Scholarly Works Scraper > ๐Ÿš€ **Export academic works, authors, institutions, and more from OpenAlex in seconds.** Filter by search query, entity type, or custom filters. No coding, no API keys required. > ๐Ÿ•’ **Last updated:** 2026-04-16 ยท **๐Ÿ“Š 30+ fields** ยท **๐Ÿ”„ Runs on Apify cloud or locally** ยท **๐Ÿ“ Export: JSON, CSV, Excel** The OpenAlex Scholarly Works Scraper connects to OpenAlex, the free and open catalog of **250M+ scholarly records** that succeeded Microsoft Academic Graph. It supports **7 entity types**: works, authors, institutions, sources, concepts, publishers, and funders. Each record includes **30+ structured fields** with titles, DOIs, citation counts, open access status, author details, institutional affiliations, and more. Whether you need **10 papers** for a quick lookup or **millions of records** for a large-scale bibliometric study, this tool handles it efficiently. Built for researchers conducting literature reviews, bibliometricians analyzing citation networks, university administrators tracking institutional output, and data teams building scholarly knowledge graphs. The scraper uses the OpenAlex API with support for free-text search and the full OpenAlex filter syntax. Providing a contact email puts your requests in the "polite pool" for faster processing. | **Target Audience** | **Use Cases** | |---|---| | Academic Researchers | Literature reviews, citation analysis | | Bibliometricians | Citation network mapping, impact studies | | University Administrators | Institutional output tracking | | Data Scientists | Knowledge graph construction, NLP corpus building | | Funding Agencies | Research output assessment, grant evaluation | | Library Scientists | Collection development, trend analysis | --- ### ๐Ÿ“‹ What the OpenAlex Scholarly Works Scraper does - ๐Ÿ“ **Extracts scholarly work metadata** including titles, abstracts, DOIs, publication dates, and citation counts for bibliometric analysis - ๐Ÿ‘ฅ **Collects author profiles** with names, ORCID IDs, institutional affiliations, and publication histories - ๐Ÿซ **Gathers institution data** including names, types, locations, and research output statistics - ๐Ÿ“ฐ **Pulls source information** for journals, conferences, and repositories with ISSN, publisher, and open access details - ๐Ÿ”— **Captures concept and topic data** for subject classification and research trend analysis - ๐Ÿ“Š **Tracks open access status** with OA type, OA URL, and license information for each work The scraper queries the OpenAlex API with your search terms and optional filters, handles cursor-based pagination, and processes results efficiently. The OpenAlex filter syntax supports field-level filtering like `publication_year:2024,is_oa:true,authorships.institutions.country_code:US` for precise targeting. > ๐Ÿ’ก **Why it matters:** OpenAlex is the largest free scholarly database, covering 250M+ works, 90M+ authors, and 100K+ institutions. This scraper gives you structured access to this data without writing API integration code. --- ### ๐ŸŽฌ Full Demo _๐Ÿšง Coming soon..._ --- ### โš™๏ธ Input
FieldTypeRequiredDescription
maxItemsintegerNoMaximum records to collect. Free users: limited to 10. Paid users: up to 1,000,000.
entitystringNoEntity type: works, authors, institutions, sources, concepts, publishers, or funders.
searchstringNoFree text search across titles, abstracts, and display names.
filterstringNoOpenAlex filter string (e.g., "publication_year:2024,is_oa:true").
emailstringNoContact email for OpenAlex "polite pool" (faster processing). Optional.
**Example 1: Search for machine learning papers** ```json { "entity": "works", "search": "machine learning", "maxItems": 100 } ```` **Example 2: Open access papers from US institutions in 2024** ```json { "entity": "works", "search": "climate change", "filter": "publication_year:2024,is_oa:true,authorships.institutions.country_code:US", "maxItems": 500, "email": "researcher@university.edu" } ``` > โš ๏ธ **Good to Know:** Providing your email address puts your requests in OpenAlex's "polite pool" for faster rate limits. The filter syntax supports dozens of fields. Free users are automatically limited to 10 items per run. *** ### ๐Ÿ“Š Output #### ๐Ÿงพ Schema | Emoji | Field | Type | Description | |---|---|---|---| | ๐Ÿ“ | title | string | Work title or entity display name | | ๐Ÿ†” | id | string | OpenAlex ID | | ๐Ÿ”— | doi | string | Digital Object Identifier (works) | | ๐ŸŒ | url | string | OpenAlex URL | | ๐Ÿ“… | publicationDate | string | Publication date (works) | | ๐Ÿ“… | publicationYear | number | Publication year | | ๐Ÿ‘ฅ | authors | array | Author names and affiliations | | ๐Ÿ“Š | citationCount | number | Total citations received | | ๐Ÿ“Š | citedByCount | number | Number of citing works | | ๐Ÿ“– | abstract | string | Article abstract (when available) | | ๐Ÿ“ฐ | source | string | Journal or venue name | | ๐Ÿ”“ | isOpenAccess | boolean | Whether the work is open access | | ๐Ÿ”“ | oaType | string | OA type (gold, green, bronze, hybrid) | | ๐Ÿ”— | oaUrl | string | URL to free version | | โš–๏ธ | license | string | License type | | ๐Ÿท๏ธ | concepts | array | Associated concepts/topics | | ๐Ÿซ | institutions | array | Author institutions | | ๐ŸŒ | countries | array | Author country codes | | ๐Ÿ“Š | referencedWorksCount | number | Number of references | | ๐Ÿ“Š | relatedWorksCount | number | Number of related works | | ๐Ÿ”ข | volume | string | Journal volume | | ๐Ÿ”ข | issue | string | Journal issue | | ๐Ÿ“„ | pages | string | Page range | | ๐Ÿท๏ธ | type | string | Work type (article, book, etc.) | | ๐Ÿ”ข | orcid | string | Author ORCID ID (authors entity) | | ๐Ÿซ | affiliation | string | Current affiliation (authors) | | ๐Ÿ“Š | worksCount | number | Total works (authors/institutions) | | ๐Ÿ“Š | hIndex | number | H-index (authors) | | ๐Ÿ“… | scrapedAt | string | Data collection timestamp | | โŒ | error | string | Error message if extraction failed | #### ๐Ÿ“ฆ Sample records
๐Ÿ“„ Record 1 - Machine Learning Paper ```json { "title": "Attention Is All You Need", "id": "W2741809809", "doi": "10.48550/arXiv.1706.03762", "publicationDate": "2017-06-12", "publicationYear": 2017, "authors": [ {"name": "Ashish Vaswani", "institution": "Google Brain"}, {"name": "Noam Shazeer", "institution": "Google Brain"} ], "citedByCount": 125000, "isOpenAccess": true, "oaType": "green", "concepts": ["Machine learning", "Attention mechanism", "Neural network"], "type": "article" } ```
๐Ÿ“„ Record 2 - Author Profile ```json { "title": "Geoffrey Hinton", "id": "A5023888391", "orcid": "0000-0001-5765-1234", "affiliation": "University of Toronto", "worksCount": 482, "citedByCount": 680000, "hIndex": 168, "concepts": ["Deep learning", "Neural networks", "Artificial intelligence"], "countries": ["CA"] } ```
๐Ÿ“„ Record 3 - Institution Record ```json { "title": "Massachusetts Institute of Technology", "id": "I63966007", "url": "https://openalex.org/I63966007", "worksCount": 295000, "citedByCount": 28000000, "countries": ["US"], "type": "education", "concepts": ["Computer science", "Engineering", "Physics"] } ```
*** ### โœจ Why choose this Actor | Feature | Details | |---|---| | ๐Ÿ“Š 250M+ records | Access the largest free scholarly database | | ๐Ÿ” 7 entity types | Works, authors, institutions, sources, concepts, publishers, funders | | ๐Ÿ”“ Open access tracking | OA status, type, URL, and license for every work | | ๐Ÿ“Š Citation metrics | Citation counts, h-index, and referenced works | | ๐Ÿ”ง Advanced filters | Full OpenAlex filter syntax for precise queries | | ๐Ÿ“ Multiple export formats | JSON, CSV, Excel for any workflow | | โšก Polite pool support | Provide email for faster processing | > ๐Ÿ“ˆ **Typical performance:** Collects **500+ records per minute** in polite pool mode. A dataset of 10,000 works takes roughly 20 minutes. *** ### ๐Ÿ“ˆ How it compares to alternatives | Feature | This Actor | Direct API Integration | Generic Scrapers | |---|---|---|---| | 30+ structured fields per record | โœ… | โœ… (requires coding) | Partial | | 7 entity types in one tool | โœ… | โœ… (requires coding) | โŒ | | No coding required | โœ… | โŒ | โŒ | | Export to CSV/JSON/Excel | โœ… | โŒ (raw JSON) | Partial | | Automatic pagination | โœ… | Manual | Partial | | Scheduled runs | โœ… | Custom setup | Partial | | Filter syntax support | โœ… | โœ… | โŒ | All the features of the OpenAlex API, without writing a single line of code. *** ### ๐Ÿš€ How to use 1. **Create a free Apify account** - [Sign up here](https://console.apify.com/sign-up?fpr=vmoqkp) (includes free credits) 2. **Open the OpenAlex Scholarly Works Scraper** - Navigate to the Actor page and click "Start" 3. **Choose your entity type** - Select works, authors, institutions, or another entity type 4. **Set your search and filters** - Enter a search query and optional OpenAlex filters 5. **Run and download** - Click "Start", wait for completion, then export as JSON, CSV, or Excel > โฑ๏ธ **First results appear in under 10 seconds.** A typical run of 100 records completes in about 30 seconds. *** ### ๐Ÿ’ผ Business use cases
**Academic Research** - Build citation network datasets - Track research trends by topic over time - Find collaborators at specific institutions - Monitor open access adoption in your field **University Administration** - Track institutional research output - Benchmark against peer institutions - Generate faculty publication reports - Monitor author h-indexes and citation impact
**Data Science & AI** - Build scholarly knowledge graphs - Create NLP training corpora from abstracts - Analyze collaboration patterns - Train topic classification models **Funding & Policy** - Assess research output for grant evaluation - Track funded research productivity - Analyze open access compliance rates - Map research activity by country and institution
*** *** ### ๐ŸŒŸ Beyond business use cases Data like this powers more than commercial workflows. The same structured records support research, education, civic projects, and personal initiatives.
#### ๐ŸŽ“ Research and academia - Empirical datasets for papers, thesis work, and coursework - Longitudinal studies tracking changes across snapshots - Reproducible research with cited, versioned data pulls - Classroom exercises on data analysis and ethical scraping #### ๐ŸŽจ Personal and creative - Side projects, portfolio demos, and indie app launches - Data visualizations, dashboards, and infographics - Content research for bloggers, YouTubers, and podcasters - Hobbyist collections and personal trackers
#### ๐Ÿค Non-profit and civic - Transparency reporting and accountability projects - Advocacy campaigns backed by public-interest data - Community-run databases for local issues - Investigative journalism on public records #### ๐Ÿงช Experimentation - Prototype AI and machine-learning pipelines with real data - Validate product-market hypotheses before engineering spend - Train small domain-specific models on niche corpora - Test dashboard concepts with live input
### ๐Ÿค– Ask an AI assistant about this scraper Open a ready-to-send prompt about this ParseForge actor in the AI of your choice: - ๐Ÿ’ฌ [**ChatGPT**](https://chat.openai.com/?q=How%20do%20I%20use%20the%20OpenAlex%20Scholarly%20Works%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - ๐Ÿง  [**Claude**](https://claude.ai/new?q=How%20do%20I%20use%20the%20OpenAlex%20Scholarly%20Works%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - ๐Ÿ” [**Perplexity**](https://perplexity.ai/search?q=How%20do%20I%20use%20the%20OpenAlex%20Scholarly%20Works%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - ๐Ÿ…’ [**Copilot**](https://copilot.microsoft.com/?q=How%20do%20I%20use%20the%20OpenAlex%20Scholarly%20Works%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) ### โ“ Frequently Asked Questions
๐Ÿ’ณ Do I need a paid Apify plan to run this actor? No. You can start right now on the free Apify plan, which includes **$5 in free monthly credit**. That is enough to run this actor several times and explore the output before committing to anything. Paid plans unlock higher limits, more concurrent runs, and larger datasets. [Create a free Apify account here](https://console.apify.com/sign-up?fpr=vmoqkp) to get started.
๐Ÿšจ What happens if my run fails or returns no results? Failed runs are not charged. If the source site changes, proxies get rate-limited, or a specific input matches nothing, re-run the actor or open our [contact form](https://tally.so/r/BzdKgA) and we will investigate. You can also check the run log in the Apify console to see why the run stopped.
๐Ÿ“ How many items can I scrape per run? Free users are limited to **10 items per run** so you can preview the output and confirm the actor works for your use case. Paid users can raise maxItems up to **1,000,000** per run. [Upgrade here](https://console.apify.com/sign-up?fpr=vmoqkp) if you need full scale.
๐Ÿ•’ How fresh is the data? Every run fetches live data at the moment of execution. There is no cache or delay: the records you get reflect what the source returned at that moment. Schedule the actor to maintain a rolling snapshot of the data you need.
๐Ÿง‘โ€๐Ÿ’ป Can I call this actor from my own code? Yes. Apify exposes every actor as a REST endpoint and ships first-class SDKs for [Node.js](https://docs.apify.com/sdk/js) and [Python](https://docs.apify.com/sdk/python). You can start a run, read the dataset, and handle webhooks from your own app in a few lines. All you need is your Apify API token.
๐Ÿ“ค How do I export the data? Every Apify dataset can be downloaded in one click from the console as CSV, JSON, JSONL, Excel, HTML, XML, or RSS. You can also pull results programmatically via the [Apify API](https://docs.apify.com/api/v2) or stream them into BigQuery, S3, and other destinations through built-in integrations.
๐Ÿ“… Can I schedule the actor to run automatically? Yes. Use the Apify scheduler to run the actor on any cadence, from hourly to monthly. Results are saved to your dataset and can be delivered to webhooks, email, Slack, cloud storage, or automation tools such as Zapier and Make. ***
### ๐Ÿ”Œ Automating OpenAlex Scholarly Works Scraper **Node.js** ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_API_TOKEN' }); const run = await client.actor("parseforge/openalex-scraper").call({ entity: "works", search: "machine learning", filter: "publication_year:2024,is_oa:true", maxItems: 200 }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items); ``` **Python** ```python from apify_client import ApifyClient client = ApifyClient("YOUR_API_TOKEN") run = client.actor("parseforge/openalex-scraper").call(run_input={ "entity": "works", "search": "machine learning", "filter": "publication_year:2024,is_oa:true", "maxItems": 200 }) items = list(client.dataset(run["defaultDatasetId"]).iterate_items()) print(items) ``` - ๐Ÿ“– [Apify API Reference](https://docs.apify.com/api/v2) - ๐Ÿ [Python API Client docs](https://docs.apify.com/api/client/python) - ๐ŸŸข [Node.js API Client docs](https://docs.apify.com/api/client/js) **Schedules:** Set up weekly or monthly runs with [Apify Schedules](https://docs.apify.com/platform/schedules) to track new publications, monitor citation growth, or maintain up-to-date researcher profiles. ### ๐Ÿ”Œ Integrate with any app - ๐Ÿ”— **Make (Integromat)** - Connect OpenAlex data to 1,000+ apps with visual workflows - ๐Ÿ”— **Zapier** - Trigger actions when new scholarly records match your criteria - ๐Ÿ”— **Slack** - Get notifications when new papers are published in your field - ๐Ÿ”— **Airbyte** - Sync scholarly data to your data warehouse - ๐Ÿ”— **GitHub** - Automate research data pipelines with GitHub Actions - ๐Ÿ”— **Google Drive** - Export scholarly data directly to Google Sheets *** ### ๐Ÿ”— Recommended Actors | Actor | Description | |---|---| | ๐Ÿ“š [PubMed Citation Scraper](https://apify.com/parseforge/pubmed-citation-scraper) | Extract citation data and metadata from PubMed biomedical literature | | ๐Ÿ“– [PLOS Journals Scraper](https://apify.com/parseforge/plos-journals-scraper) | Collect article data from PLOS ONE and other PLOS journals | | ๐Ÿงฌ [Crossref Scraper](https://apify.com/parseforge/crossref-scraper) | Collect DOI metadata and citation information from Crossref | | ๐Ÿ“ฐ [medRxiv Scraper](https://apify.com/parseforge/medrxiv-scraper) | Extract health sciences preprint data from medRxiv | | ๐Ÿ“„ [Semantic Scholar Scraper](https://apify.com/parseforge/semantic-scholar-scraper) | Query the Semantic Scholar API for academic paper data | > ๐Ÿ’ก **Pro Tip:** Use OpenAlex to find papers by topic, then cross-reference with the Crossref Scraper for detailed citation metadata and reference lists. *** **๐Ÿ†˜ Need Help?** [**Open our contact form**](https://tally.so/r/BzdKgA) and we will get back to you within 24 hours. For bug reports, feature requests, or integration help, we are here to assist. *** > **Disclaimer:** This Actor is provided as-is, without warranty. It is not affiliated with or endorsed by OpenAlex or OurResearch. Use it responsibly and in compliance with applicable terms of service. The authors are not responsible for how the collected data is used. Always verify data accuracy for critical applications. # Actor input Schema ## `maxItems` (type: `integer`): Free users: Limited to 10 items (preview). Paid users: Optional, max 1,000,000 ## `entity` (type: `string`): Which OpenAlex entity type to query. ## `search` (type: `string`): Free text search across titles, abstracts, and display names. ## `filter` (type: `string`): Optional OpenAlex filter string (e.g. 'publication\_year:2024,is\_oa:true,authorships.institutions.country\_code:us'). See https://docs.openalex.org/api/filter-works. ## `email` (type: `string`): Your email lets OpenAlex put your requests in the fast 'polite pool'. Leave empty to use the shared pool. ## Actor input object example ```json { "maxItems": 10, "entity": "works", "search": "machine learning" } ``` # Actor output Schema ## `overview` (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 = { "maxItems": 10, "entity": "works", "search": "machine learning" }; // Run the Actor and wait for it to finish const run = await client.actor("parseforge/openalex-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "maxItems": 10, "entity": "works", "search": "machine learning", } # Run the Actor and wait for it to finish run = client.actor("parseforge/openalex-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "maxItems": 10, "entity": "works", "search": "machine learning" }' | apify call parseforge/openalex-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=parseforge/openalex-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenAlex Scholarly Works Scraper", "description": "Export academic works, authors, institutions, sources, and concepts from OpenAlexs open catalog of 250M+ scholarly records. Successor to Microsoft Academic Graph. Filter by author, concept, year, open access status, or affiliation.", "version": "1.0", "x-build-id": "u2c9beyJSuVs4E4qh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/parseforge~openalex-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-parseforge-openalex-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/parseforge~openalex-scraper/runs": { "post": { "operationId": "runs-sync-parseforge-openalex-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/parseforge~openalex-scraper/run-sync": { "post": { "operationId": "run-sync-parseforge-openalex-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 1000000, "type": "integer", "description": "Free users: Limited to 10 items (preview). Paid users: Optional, max 1,000,000" }, "entity": { "title": "Entity", "enum": [ "works", "authors", "institutions", "sources", "concepts", "publishers", "funders" ], "type": "string", "description": "Which OpenAlex entity type to query." }, "search": { "title": "Search Query", "type": "string", "description": "Free text search across titles, abstracts, and display names." }, "filter": { "title": "OpenAlex Filter", "type": "string", "description": "Optional OpenAlex filter string (e.g. 'publication_year:2024,is_oa:true,authorships.institutions.country_code:us'). See https://docs.openalex.org/api/filter-works." }, "email": { "title": "Contact Email (Optional)", "type": "string", "description": "Your email lets OpenAlex put your requests in the fast 'polite pool'. 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