# Crossref Scraper (`openclawmara/crossref-scraper`) Actor - **URL**: https://apify.com/openclawmara/crossref-scraper.md - **Developed by:** [OpenClaw Mara](https://apify.com/openclawmara) (community) - **Categories:** 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 Articles Scraper Search and extract metadata from 150+ million scholarly articles via [CrossRef](https://crossref.org) API. Get DOIs, authors, abstracts, citation counts, journals, and more. **No API key needed.** ### Features - 🔬 **150M+ articles** — the largest open scholarly metadata database - 📖 **Rich metadata** — DOI, title, authors (with ORCID), abstract, journal, publisher - 📊 **Citation counts** — sort by most cited, filter by minimum citations - 🏷️ **Subject areas** — categories/subjects from CrossRef - 💰 **Funding info** — funder names, DOIs, and grant numbers - 📅 **Date filtering** — search within specific year ranges - 🔗 **Direct DOI lookup** — fetch specific articles by DOI - ⚡ **Official API** — no blocking, polite pool rate limiting ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `searchQueries` | string[] | `[]` | Search by keyword | | `dois` | string[] | `[]` | Fetch specific DOIs | | `maxResults` | number | `50` | Max articles per query | | `sortBy` | string | `"relevance"` | Sort: relevance / published / citation count | | `fromYear` | number | — | Filter: published from year | | `toYear` | number | — | Filter: published until year | | `minCitations` | number | `0` | Minimum citation count | ### Example ```json { "searchQueries": ["transformer neural network"], "maxResults": 20, "sortBy": "is-referenced-by-count", "fromYear": 2020, "minCitations": 50 } ```` ### Output ```json { "doi": "10.1038/s41586-023-06747-5", "title": "Attention Is All You Need", "journal": "Advances in Neural Information Processing Systems", "publishedDate": "2017-6-12", "firstAuthor": "Ashish Vaswani", "authorCount": 8, "citationCount": 120000, "abstract": "The dominant sequence transduction models...", "subject": ["Artificial Intelligence"], "url": "https://doi.org/10.1038/..." } ``` ### Use Cases - **Literature review** — find and extract papers on any research topic - **Bibliometric analysis** — track citation patterns and research trends - **Dataset building** — create training data for NLP/ML from abstracts - **Research monitoring** — track new publications in your field - **Grant writing** — find relevant prior work and citation metrics # Actor input Schema ## `searchQueries` (type: `array`): Search scholarly articles by keyword (e.g. 'CRISPR gene editing', 'large language models') ## `dois` (type: `array`): Fetch specific articles by DOI (e.g. '10.1038/s41586-023-06747-5') ## `maxResults` (type: `integer`): Maximum articles to return per search query ## `sortBy` (type: `string`): How to sort search results ## `fromYear` (type: `integer`): Only include articles published from this year (e.g. 2020) ## `toYear` (type: `integer`): Only include articles published up to this year ## `minCitations` (type: `integer`): Only include articles with at least this many citations (0 = no filter) ## Actor input object example ```json { "maxResults": 50, "sortBy": "relevance", "minCitations": 0 } ``` # 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("openclawmara/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/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("openclawmara/crossref-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call openclawmara/crossref-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=openclawmara/crossref-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Crossref Scraper", "version": "1.0", "x-build-id": "XlN1iP3Aip1ELG5kP" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/openclawmara~crossref-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-openclawmara-crossref-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/openclawmara~crossref-scraper/runs": { "post": { "operationId": "runs-sync-openclawmara-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/openclawmara~crossref-scraper/run-sync": { "post": { "operationId": "run-sync-openclawmara-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": { "searchQueries": { "title": "Search Queries", "type": "array", "description": "Search scholarly articles by keyword (e.g. 'CRISPR gene editing', 'large language models')", "items": { "type": "string" } }, "dois": { "title": "DOIs", "type": "array", "description": "Fetch specific articles by DOI (e.g. '10.1038/s41586-023-06747-5')", "items": { "type": "string" } }, "maxResults": { "title": "Max Results per Query", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum articles to return per search query", "default": 50 }, "sortBy": { "title": "Sort By", "enum": [ "relevance", "published", "is-referenced-by-count" ], "type": "string", "description": "How to sort search results", "default": "relevance" }, "fromYear": { "title": "From Year", "minimum": 1900, "maximum": 2030, "type": "integer", "description": "Only include articles published from this year (e.g. 2020)" }, "toYear": { "title": "To Year", "minimum": 1900, "maximum": 2030, "type": "integer", "description": "Only include articles published up to this year" }, "minCitations": { "title": "Minimum Citations", "minimum": 0, "type": "integer", "description": "Only include articles with at least this many citations (0 = no filter)", "default": 0 } } }, "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 } } } } } } } } } } ```