# Crossref Scholar Scraper (`wakey7dev/crossref-scholar-scraper`) Actor Search 150M+ scholarly publications with author disambiguation, institution normalization, and subject classification. Powered by free Crossref API. - **URL**: https://apify.com/wakey7dev/crossref-scholar-scraper.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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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) ## Crossref Scholar Scraper Search scholarly publications across 150+ million works via the Crossref REST API with intelligent author disambiguation, institution normalization, and subject classification. ### What It Does - **Search** the Crossref database by keyword, author, affiliation, funder, or DOI - **Normalize** author names (J Smith → John Smith) and deduplicate - **Standardize** institution names (MIT → Massachusetts Institute of Technology) - **Classify** publications into broad subject areas (AI, medicine, physics, etc.) - **Output** clean, structured data ready for bibliometric analysis, literature reviews, or knowledge graphs ### Input Parameters | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `searchQuery` | string | ✅ | Keywords, author name, DOI, or institution to search | | `maxResults` | integer | No | Maximum results (1–1000, default: 50) | | `searchField` | string | No | `all`, `title`, `author`, `affiliation`, `funder`, or `doi` | | `filterType` | string | No | Filter by: `journal-article`, `proceedings-article`, `book`, `dissertation`, etc. | | `yearFrom` | string | No | Filter from year (e.g., `2020`) | | `yearTo` | string | No | Filter to year (e.g., `2024`) | | `normalizeAuthors` | boolean | No | Normalize and deduplicate author names (default: true) | | `normalizeAffiliations` | boolean | No | Normalize institution names (default: true) | | `emailAddress` | string | No | Your email for Crossref's Polite Pool (better rate limits) | ### Example Input ```json { "searchQuery": "large language model alignment", "maxResults": 25, "searchField": "all", "filterType": "journal-article", "yearFrom": "2023", "normalizeAuthors": true, "normalizeAffiliations": true } ```` ### Example Output Each result includes: - **DOI** and resolvable URL - **Title** and abstract - **Normalized authors** (deduplicated, name-expanded) - **Normalized affiliations** (standardized institution names) - **Normalized journal** name - **Subject areas** (AI, medicine, etc.) - **Citation count** (referenced-by count) - **Publication date**, type, publisher, funders, ISSN Plus aggregated statistics: top subject areas, most cited paper, publication type breakdown. ### Use Cases - **Academic researchers** — literature reviews, systematic reviews, citation analysis - **Bibliometric analysts** — impact metrics, research trend analysis, institution benchmarking - **University libraries** — publication tracking, open access monitoring - **AI/ML companies** — building knowledge graphs, training data curation - **Journal publishers** — monitoring publication metrics, competitor analysis - **Industry R\&D** — tracking scientific literature in specific domains ### Data Source Powered by the [Crossref REST API](https://api.crossref.org/) — a free, open API providing metadata for 150+ million scholarly works. No API key required. Uses the Polite Pool with configurable email for better rate limits. ### Pricing Pay-per-result: $2.00 per 1,000 results. You only pay for the data you retrieve. # Actor input Schema ## `searchQuery` (type: `string`): Keywords to search for (e.g. 'machine learning', 'climate change', author name, DOI). Supports Crossref query syntax. ## `maxResults` (type: `integer`): Maximum number of results to return (1-1000). ## `searchField` (type: `string`): Which field to search in. 'All' searches title, abstract, author, and more. ## `filterType` (type: `string`): Filter results by publication type. ## `yearFrom` (type: `string`): Filter publications from this year (e.g., 2020). Leave empty for no lower bound. ## `yearTo` (type: `string`): Filter publications up to this year (e.g., 2024). Leave empty for no upper bound. ## `normalizeAuthors` (type: `boolean`): Normalize author names and deduplicate (recommended). ## `normalizeAffiliations` (type: `boolean`): Normalize institution/affiliation names. ## `emailAddress` (type: `string`): Optional: Your email for Crossref's Polite Pool (better rate limits). Not required. ## Actor input object example ```json { "searchQuery": "machine learning", "maxResults": 50, "searchField": "all", "filterType": "", "normalizeAuthors": true, "normalizeAffiliations": true } ``` # Actor output Schema ## `results` (type: `string`): Complete dataset of normalized scholarly publications. ## `summary` (type: `string`): Human-readable table of search results. ## `stats` (type: `string`): Machine-readable statistics about the run. ## `authors` (type: `string`): Deduplicated list of normalized author names. ## `affiliations` (type: `string`): Deduplicated list of normalized institution names. # 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": "machine learning", "yearFrom": "", "yearTo": "", "emailAddress": "" }; // Run the Actor and wait for it to finish const run = await client.actor("wakey7dev/crossref-scholar-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 = { "searchQuery": "machine learning", "yearFrom": "", "yearTo": "", "emailAddress": "", } # Run the Actor and wait for it to finish run = client.actor("wakey7dev/crossref-scholar-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 '{ "searchQuery": "machine learning", "yearFrom": "", "yearTo": "", "emailAddress": "" }' | apify call wakey7dev/crossref-scholar-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=wakey7dev/crossref-scholar-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Crossref Scholar Scraper", "description": "Search 150M+ scholarly publications with author disambiguation, institution normalization, and subject classification. Powered by free Crossref API.", "version": "1.0", "x-build-id": "FqNbKpzPKTwNm2qb4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/wakey7dev~crossref-scholar-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-wakey7dev-crossref-scholar-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/wakey7dev~crossref-scholar-scraper/runs": { "post": { "operationId": "runs-sync-wakey7dev-crossref-scholar-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/wakey7dev~crossref-scholar-scraper/run-sync": { "post": { "operationId": "run-sync-wakey7dev-crossref-scholar-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "searchQuery" ], "properties": { "searchQuery": { "title": "Search Query", "type": "string", "description": "Keywords to search for (e.g. 'machine learning', 'climate change', author name, DOI). Supports Crossref query syntax." }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of results to return (1-1000).", "default": 50 }, "searchField": { "title": "Search Field", "enum": [ "all", "title", "author", "affiliation", "funder", "doi" ], "type": "string", "description": "Which field to search in. 'All' searches title, abstract, author, and more.", "default": "all" }, "filterType": { "title": "Filter by Type", "enum": [ "", "journal-article", "proceedings-article", "book", "book-chapter", "dissertation", "report", "dataset", "preprint" ], "type": "string", "description": "Filter results by publication type.", "default": "" }, "yearFrom": { "title": "Year From", "type": "string", "description": "Filter publications from this year (e.g., 2020). Leave empty for no lower bound." }, "yearTo": { "title": "Year To", "type": "string", "description": "Filter publications up to this year (e.g., 2024). Leave empty for no upper bound." }, "normalizeAuthors": { "title": "Normalize Authors", "type": "boolean", "description": "Normalize author names and deduplicate (recommended).", "default": true }, "normalizeAffiliations": { "title": "Normalize Affiliations", "type": "boolean", "description": "Normalize institution/affiliation names.", "default": true }, "emailAddress": { "title": "Email (Polite Pool)", "type": "string", "description": "Optional: Your email for Crossref's Polite Pool (better rate limits). Not required." } } }, "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 } } } } } } } } } } ```