# Cheap Crossref Scraper - Scholarly Articles, DOIs & Citations (`themineworks/crossref-scholarly-metadata`) Actor Cheapest Crossref scraper: scholarly articles, DOIs, authors & citations. $2/1,000 results, 25 free, pay-per-result, no subscription. Works in Claude, ChatGPT & any MCP-compatible AI agent. - **URL**: https://apify.com/themineworks/crossref-scholarly-metadata.md - **Developed by:** [The Mine Works](https://apify.com/themineworks) (community) - **Categories:** Business, Developer tools, MCP servers - **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.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 Metadata — Papers, DOIs & Citations Search **150M+ scholarly works** from **Crossref** — the authoritative DOI registry behind academic publishing — and get clean, structured JSON. Filter by topic, year, and work type; pull DOIs, titles, authors, journals, publishers, and citation counts. Built for **research, bibliometrics, publishing analytics, and AI/RAG over scientific literature**. No API key. **Keywords:** Crossref API, DOI lookup, scholarly metadata, citation data, bibliometrics, academic papers API, research data, publishing analytics. --- ### Why this actor Crossref is the registry that mints and resolves DOIs for most of the world's scholarly literature — journals, conference papers, books, datasets, preprints. Its REST API is excellent but returns deeply nested records and uses deep cursor pagination. This actor flattens it into one tidy record per work and handles paging for you. It pairs with our [OpenAlex actor](https://apify.com/themineworks/openalex-scholarly-works): Crossref is the publisher-registry view (DOIs, journals, citation counts); OpenAlex adds institutions and concepts. Use both to own research-data workflows. - **Full-text-ish search** across titles, authors, and metadata. - **Filters** — publication-year range and work type (journal-article, book-chapter, preprint…). - **Clean output** — DOI, title, year, authors, journal, publisher, citation count, ISSN, URL. - **Deep pagination** — pull a handful or tens of thousands. - **No API key** — official Crossref API; a contact email joins the faster "polite pool." --- ### What you can build - **Literature reviews** — assemble a filtered corpus on a topic with DOIs and citation counts. - **Bibliometrics** — quantify output by year, journal, or publisher. - **Reference enrichment** — resolve titles/DOIs to clean metadata for a citation manager or knowledge base. - **Publishing analytics** — track a journal's or publisher's output and impact. - **AI / RAG** — build a scholarly metadata layer for a research assistant. --- ### Input | Field | Type | Default | Description | |---|---|---|---| | `query` | string | `large language models` | Search across titles/authors/metadata. | | `fromYear` / `toYear` | integer | — | Publication-year bounds. | | `workType` | enum | — | `journal-article`, `book-chapter`, `proceedings-article`, `posted-content`, etc. | | `maxResults` | integer | 200 | Max works. | | `mailto` | string | — | Contact email for the polite pool (recommended). | #### Example ```json { "query": "perovskite solar cells", "fromYear": 2022, "workType": "journal-article", "maxResults": 1000, "mailto": "you@org.com" } ```` *** ### Output ```json { "doi": "10.1162/coli_a_00558", "title": "Large Language Models Are Biased Because They Are Large Language Models", "type": "journal-article", "year": 2025, "authors": ["Jane Doe", "John Smith"], "journal": "Computational Linguistics", "publisher": "MIT Press", "cited_by_count": 19, "issn": ["0891-2017"], "url": "https://doi.org/10.1162/coli_a_00558", "scraped_at": "2026-06-12T00:00:00.000Z" } ``` A final `{"_type":"summary"}` record reports total matches and how many were returned. *** ### Pricing **First 25 works free per account**, then **$0.002 per work** ($2 per 1,000). Zero charge on empty searches. No monthly rental, no API key. *** ### FAQ **Do I need a key?** No. Crossref is open; a contact email just gets you the faster polite pool. **How many works?** Over 150 million across journals, books, conferences, datasets, and preprints. **Difference from OpenAlex?** Crossref is the DOI/publisher registry (citation counts, journals); OpenAlex adds institutions, concepts, and open-access status. They complement each other. **Can I filter by year and type?** Yes — `fromYear`/`toYear` and `workType`. # Actor input Schema ## `query` (type: `string`): Search across titles, authors, and metadata (e.g. crispr, large language models). ## `fromYear` (type: `integer`): Only works published in or after this year. ## `toYear` (type: `integer`): Only works published in or before this year. ## `workType` (type: `string`): Filter by type (e.g. journal-article, book-chapter, proceedings-article). Leave empty for all. ## `maxResults` (type: `integer`): Maximum number of works to return. ## `mailto` (type: `string`): Crossref gives faster, more reliable service when you supply a contact email. Recommended. ## Actor input object example ```json { "query": "large language models", "workType": "", "maxResults": 25, "mailto": "" } ``` # 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 = { "query": "large language models", "maxResults": 25 }; // Run the Actor and wait for it to finish const run = await client.actor("themineworks/crossref-scholarly-metadata").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 = { "query": "large language models", "maxResults": 25, } # Run the Actor and wait for it to finish run = client.actor("themineworks/crossref-scholarly-metadata").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 '{ "query": "large language models", "maxResults": 25 }' | apify call themineworks/crossref-scholarly-metadata --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=themineworks/crossref-scholarly-metadata", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Cheap Crossref Scraper - Scholarly Articles, DOIs & Citations", "description": "Cheapest Crossref scraper: scholarly articles, DOIs, authors & citations. $2/1,000 results, 25 free, pay-per-result, no subscription. Works in Claude, ChatGPT & any MCP-compatible AI agent.", "version": "0.1", "x-build-id": "bl1cHMMHPsXlmpwOV" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/themineworks~crossref-scholarly-metadata/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-themineworks-crossref-scholarly-metadata", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/themineworks~crossref-scholarly-metadata/runs": { "post": { "operationId": "runs-sync-themineworks-crossref-scholarly-metadata", "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/themineworks~crossref-scholarly-metadata/run-sync": { "post": { "operationId": "run-sync-themineworks-crossref-scholarly-metadata", "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": [ "query" ], "properties": { "query": { "title": "Search query", "type": "string", "description": "Search across titles, authors, and metadata (e.g. crispr, large language models)." }, "fromYear": { "title": "From year", "type": "integer", "description": "Only works published in or after this year." }, "toYear": { "title": "To year", "type": "integer", "description": "Only works published in or before this year." }, "workType": { "title": "Work type", "enum": [ "", "journal-article", "book-chapter", "proceedings-article", "posted-content", "book", "dataset", "report" ], "type": "string", "description": "Filter by type (e.g. journal-article, book-chapter, proceedings-article). Leave empty for all.", "default": "" }, "maxResults": { "title": "Max works", "minimum": 1, "maximum": 50000, "type": "integer", "description": "Maximum number of works to return.", "default": 200 }, "mailto": { "title": "Contact email (polite pool)", "type": "string", "description": "Crossref gives faster, more reliable service when you supply a contact email. Recommended.", "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 } } } } } } } } } } ```