# OpenAlex Works Normalizer — Academic Paper Search & Enrichment (`wakey7dev/openalex-works-normalizer`) Actor Search 250M+ scholarly works from OpenAlex with intelligent normalization. Author name dedup, institution resolution, topic classification, citation impact tiers, and open-access filtering. Free API, no key required. Ideal for literature review, R\&D intelligence, and research analytics. - **URL**: https://apify.com/wakey7dev/openalex-works-normalizer.md - **Developed by:** [Chris Wakefield](https://apify.com/wakey7dev) (community) - **Categories:** AI, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ![Chris The Dev](https://raw.githubusercontent.com/chriswakefield87/appstore-screenshot-translator/main/assets/actor-banner.png) ## OpenAlex Works Normalizer — Academic Paper Search & Enrichment Search 250M+ scholarly works from OpenAlex with intelligent normalization and enrichment. Enter a keyword, author, institution, or field — get cleaned, deduplicated, and categorized results with author name resolution, institution dedup, topic classification, citation impact tiers, and open-access filtering. **Data source:** [OpenAlex](https://openalex.org) (free, no API key required) **License:** OpenAlex data is CC0 (public domain) --- ### Input Parameters | Parameter | Type | Required | Default | Description | |---|---|---|---|---| | `searchQuery` | string | No | `"deep learning medical imaging"` | Search by keyword, title, or abstract | | `authors` | string[] | No | `[]` | Filter by author name(s) | | `institutions` | string[] | No | `[]` | Filter by institution name(s) | | `fields` | string[] | No | `[]` | Filter by research field(s) (e.g. Medicine, Computer Science) | | `publicationYearStart` | integer | No | `2020` | Minimum publication year | | `publicationYearEnd` | integer | No | `2025` | Maximum publication year | | `minCitations` | integer | No | `0` | Minimum citation count filter | | `workType` | select | No | `All types` | Filter by publication type | | `openAccess` | select | No | `Any status` | Filter by OA status | | `maxResults` | integer | No | `50` | Max results (1–500) | | `sortBy` | select | No | `cited_by_count` | Sort by citations, relevance, or year | | `includeAbstract` | boolean | No | `false` | Include full abstract in output | ### Example Input ```json { "searchQuery": "machine learning drug discovery", "publicationYearStart": 2022, "publicationYearEnd": 2025, "minCitations": 10, "workType": "article", "openAccess": "gold", "maxResults": 25, "sortBy": "cited_by_count" } ```` ### Example Output (per work) ```json { "title": "Deep learning for drug discovery: a comprehensive review", "doi": "https://doi.org/10.1016/...", "openalexId": "https://openalex.org/W...", "publicationYear": 2024, "type": "article", "citedByCount": 342, "citationImpact": "Significant Impact", "source": "Nature Reviews Drug Discovery", "publisher": "Nature Publishing Group", "authorCount": 5, "institutionCount": 3, "countryCount": 2, "authors": [ { "name": "John M. Smith", "normalizedName": "John M. Smith", "institutions": ["MIT"], "countries": ["United States"] } ], "fields": ["Computer Science", "Medicine"], "fieldCategories": ["Computer Science and AI", "Health Sciences"], "oaStatus": "gold", "oaStatusDescription": "Open access published in a fully OA journal", "concepts": ["Deep learning", "Drug discovery", "Artificial intelligence"] } ``` ### Output Stores | Store Key | Contents | |---|---| | **OUTPUT** | Human-readable summary table with top results, field distribution, and country breakdown | | **RESULTS** | Full dataset items (normalized and enriched) | | **STATS** | Machine-readable statistics and distributions | ### Use Cases - **Literature review automation** — find high-impact papers on any topic with one query - **R\&D competitive intelligence** — discover what institutions and countries lead in a field - **Grant/funding research** — identify top-cited papers, trending topics, and key authors - **AI/ML data preparation** — build training datasets of scholarly metadata with deduplication - **Research analytics** — field distribution, country mapping, and OA status analysis - **Academic benchmarking** — compare institutional output across research areas ### Value-Add: What This Actor Normalizes - **Author names** — deduplicates across institutional affiliations (`"Hinton G."` → `"Geoffrey Hinton"`) - **Institutions** — resolves common aliases (`"MIT"` → `"Massachusetts Institute of Technology"`) - **Citation impact tiers** — classifies papers by citation count (Highly Influential, Significant Impact, etc.) - **Research fields** — maps OpenAlex field names to broad categories (e.g. `"Biochemistry"` → `"Life Sciences"`) - **Country names** — converts ISO codes to full country names - **OA status** — provides human-readable descriptions of open access status - **Title cleanup** — removes excessive whitespace and stray punctuation # Actor input Schema ## `searchQuery` (type: `string`): Search for works by keyword, title, abstract, or full text. Examples: 'machine learning cancer diagnosis', 'quantum computing'. Overrides other filters when set. ## `authors` (type: `array`): Filter by author name(s). Enter one per line. Partial matches supported. E.g. 'Geoffrey Hinton' or 'Hinton, Geoffrey'. ## `institutions` (type: `array`): Filter by institution name(s). Enter one per line. E.g. 'Massachusetts Institute of Technology', 'University of Cambridge'. ## `fields` (type: `array`): Filter by research field(s). Enter one per line. E.g. 'Medicine', 'Computer Science', 'Physics', 'Biology', 'Engineering'. ## `publicationYearStart` (type: `integer`): Minimum publication year (e.g. 2020). Leave empty for earliest available. ## `publicationYearEnd` (type: `integer`): Maximum publication year (e.g. 2025). Leave empty for current year. ## `minCitations` (type: `integer`): Only return works with at least this many citations. Useful for filtering high-impact research. ## `workType` (type: `string`): Filter by publication type. ## `openAccess` (type: `string`): Filter by open access status. ## `maxResults` (type: `integer`): Maximum number of works to return (1-500). ## `sortBy` (type: `string`): Sort results by relevance or impact. ## `includeAbstract` (type: `boolean`): Include work abstract in results. Useful for AI analysis but increases output size. ## Actor input object example ```json { "searchQuery": "deep learning medical imaging", "authors": [], "institutions": [], "fields": [], "publicationYearStart": 2020, "publicationYearEnd": 2025, "minCitations": 0, "workType": "", "openAccess": "", "maxResults": 50, "sortBy": "cited_by_count", "includeAbstract": false } ``` # Actor output Schema ## `results` (type: `string`): Complete normalized works with authors, topics, citations, and metadata. ## `summary` (type: `string`): Human-readable formatted summary of search results. ## `stats` (type: `string`): Machine-readable statistics about the search results. # 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 = { "searchQuery": "deep learning medical imaging", "authors": [], "institutions": [], "fields": [] }; // Run the Actor and wait for it to finish const run = await client.actor("wakey7dev/openalex-works-normalizer").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 = { "searchQuery": "deep learning medical imaging", "authors": [], "institutions": [], "fields": [], } # Run the Actor and wait for it to finish run = client.actor("wakey7dev/openalex-works-normalizer").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 '{ "searchQuery": "deep learning medical imaging", "authors": [], "institutions": [], "fields": [] }' | apify call wakey7dev/openalex-works-normalizer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=wakey7dev/openalex-works-normalizer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenAlex Works Normalizer — Academic Paper Search & Enrichment", "description": "Search 250M+ scholarly works from OpenAlex with intelligent normalization. Author name dedup, institution resolution, topic classification, citation impact tiers, and open-access filtering. Free API, no key required. Ideal for literature review, R&D intelligence, and research analytics.", "version": "1.0", "x-build-id": "mi5LKvd1VKKQKeXrq" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/wakey7dev~openalex-works-normalizer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-wakey7dev-openalex-works-normalizer", "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/wakey7dev~openalex-works-normalizer/runs": { "post": { "operationId": "runs-sync-wakey7dev-openalex-works-normalizer", "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/wakey7dev~openalex-works-normalizer/run-sync": { "post": { "operationId": "run-sync-wakey7dev-openalex-works-normalizer", "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": { "searchQuery": { "title": "Search Query", "type": "string", "description": "Search for works by keyword, title, abstract, or full text. Examples: 'machine learning cancer diagnosis', 'quantum computing'. Overrides other filters when set." }, "authors": { "title": "Author Names", "type": "array", "description": "Filter by author name(s). Enter one per line. Partial matches supported. E.g. 'Geoffrey Hinton' or 'Hinton, Geoffrey'.", "items": { "type": "string" } }, "institutions": { "title": "Institution Names", "type": "array", "description": "Filter by institution name(s). Enter one per line. E.g. 'Massachusetts Institute of Technology', 'University of Cambridge'.", "items": { "type": "string" } }, "fields": { "title": "Research Fields", "type": "array", "description": "Filter by research field(s). Enter one per line. E.g. 'Medicine', 'Computer Science', 'Physics', 'Biology', 'Engineering'.", "items": { "type": "string" } }, "publicationYearStart": { "title": "Year Start", "type": "integer", "description": "Minimum publication year (e.g. 2020). Leave empty for earliest available.", "default": 2020 }, "publicationYearEnd": { "title": "Year End", "type": "integer", "description": "Maximum publication year (e.g. 2025). Leave empty for current year.", "default": 2025 }, "minCitations": { "title": "Minimum Citations", "minimum": 0, "type": "integer", "description": "Only return works with at least this many citations. Useful for filtering high-impact research.", "default": 0 }, "workType": { "title": "Work Type", "enum": [ "", "article", "review", "book-chapter", "book", "preprint", "dataset", "dissertation" ], "type": "string", "description": "Filter by publication type.", "default": "" }, "openAccess": { "title": "Open Access Status", "enum": [ "", "gold", "green", "hybrid", "bronze", "diamond", "closed" ], "type": "string", "description": "Filter by open access status.", "default": "" }, "maxResults": { "title": "Maximum Results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of works to return (1-500).", "default": 50 }, "sortBy": { "title": "Sort By", "enum": [ "relevance_score", "cited_by_count", "publication_year" ], "type": "string", "description": "Sort results by relevance or impact.", "default": "cited_by_count" }, "includeAbstract": { "title": "Include Abstract", "type": "boolean", "description": "Include work abstract in results. Useful for AI analysis but increases output size.", "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 } } } } } } } } } } ```