# Crossref Scraper (`flamboyant_liner/crossref-scraper`) Actor Extract scholarly publication metadata from CrossRef. Get DOIs, citations, authors for 145M+ works. - **URL**: https://apify.com/flamboyant\_liner/crossref-scraper.md - **Developed by:** [Khrystyna Skotte](https://apify.com/flamboyant_liner) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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.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 ## CrossRef Scholarly Publications Scraper ### Overview Extract scholarly publication metadata from CrossRef API covering 145M+ academic works. Get DOIs, titles, authors, citations, journals, and publication dates. Search by keywords, author, ISSN, or date range. ### Features - Search by keywords to find specific results - Filter results by category or type - Export data in JSON, CSV, or Excel formats ### Use Cases - **Aggregate** - Aggregate academic papers and research data - **Build** - Build course catalogs and educational resource databases - **Track** - Track educational institution data and rankings - **Monitor** - Monitor academic publishing trends ### Input Parameters | Parameter | Type | Description | Default | |-----------|------|-------------|---------| | `query` | string | Search query for scholarly works | | | `doi` | string | Specific DOI to look up (e.g., 10.1038/nature12373) | | | `author` | string | Filter by author name | | | `publishedSince` | string | Filter works published after this date (YYYY-MM-DD) | | | `type` | string | Type of scholarly work | | | `mailto` | string | Your email for CrossRef's polite pool (faster rate limits) | | | `limit` | integer | Maximum number of works to return | `100` | ### Output Example Each result contains structured data like this: ```json { "doi": "Sample doi", "title": "Sample Education/Academic Result", "authors": [ "J. Smith", "A. Johnson" ], "type": "Standard", "containerTitle": "Sample containerTitle", "publisher": "Sample publisher", "publishedDate": "2025-01-15", "citedByCount": 127, "url": "https://example.com/item/12345", "abstract": "Detailed description of the item..." } ```` ### Pricing This actor uses pay-per-result pricing: - **$0.001** per result - **$1.00** per 1,000 results No monthly fees. You only pay for what you scrape. [Apify Free plan](https://apify.com/pricing) includes $5/month in platform credits. ### How to Run #### Apify Console 1. Go to the [CrossRef Scholarly Publications Scraper](https://apify.com/fortuitous_pirate/crossref-scraper) actor page 2. Configure your input parameters 3. Click **Start** and wait for the results 4. Download data in JSON, CSV, or Excel format #### API ```bash curl -X POST "https://api.apify.com/v2/acts/fortuitous_pirate~crossref-scraper/runs?token=YOUR_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{"maxItems": 10}' ``` #### Python SDK ```python from apify_client import ApifyClient client = ApifyClient("YOUR_API_TOKEN") run = client.actor("fortuitous_pirate/crossref-scraper").call( run_input={"maxItems": 10} ) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) ``` ### Integration Connect CrossRef Scholarly Publications Scraper with your existing tools and workflows: - **API access** - Programmatic access via [Apify API](https://docs.apify.com/api/v2) - **Webhooks** - Get notified when scraping completes - **Scheduling** - Set up recurring runs on any schedule - **Zapier / Make** - Connect with 5,000+ apps via [Apify integrations](https://apify.com/integrations) - **Python / Node.js SDKs** - Native client libraries for easy integration # Actor input Schema ## `query` (type: `string`): Search query for scholarly works ## `doi` (type: `string`): Specific DOI to look up (e.g., 10.1038/nature12373) ## `author` (type: `string`): Filter by author name ## `publishedSince` (type: `string`): Filter works published after this date (YYYY-MM-DD) ## `type` (type: `string`): Type of scholarly work ## `mailto` (type: `string`): Your email for CrossRef's polite pool (faster rate limits) ## `limit` (type: `integer`): Maximum number of works to return ## Actor input object example ```json { "limit": 10 } ``` # 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("flamboyant_liner/crossref-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("flamboyant_liner/crossref-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 flamboyant_liner/crossref-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=flamboyant_liner/crossref-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Crossref Scraper", "description": "Extract scholarly publication metadata from CrossRef. Get DOIs, citations, authors for 145M+ works.", "version": "1.0", "x-build-id": "YeH2MpiVI7aPUQOPc" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/flamboyant_liner~crossref-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-flamboyant_liner-crossref-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/flamboyant_liner~crossref-scraper/runs": { "post": { "operationId": "runs-sync-flamboyant_liner-crossref-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/flamboyant_liner~crossref-scraper/run-sync": { "post": { "operationId": "run-sync-flamboyant_liner-crossref-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "query": { "title": "Search Query", "type": "string", "description": "Search query for scholarly works" }, "doi": { "title": "DOI", "type": "string", "description": "Specific DOI to look up (e.g., 10.1038/nature12373)" }, "author": { "title": "Author Name", "type": "string", "description": "Filter by author name" }, "publishedSince": { "title": "Published Since", "type": "string", "description": "Filter works published after this date (YYYY-MM-DD)" }, "type": { "title": "Work Type", "enum": [ "", "journal-article", "book", "book-chapter", "proceedings-article", "dissertation", "dataset" ], "type": "string", "description": "Type of scholarly work" }, "mailto": { "title": "Email (for polite pool)", "type": "string", "description": "Your email for CrossRef's polite pool (faster rate limits)" }, "limit": { "title": "Maximum Results", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of works to return", "default": 10 } } }, "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 } } } } } } } } } } ```