# CrossRef Scraper - Academic DOI & Metadata Extractor (`klondikeking/crossref-academic-scraper`) Actor Extract academic paper metadata, DOIs, authors, citations, and abstracts from CrossRef via the public REST API. No scraping needed - fast, reliable, and cost-effective for researchers and data scientists. - **URL**: https://apify.com/klondikeking/crossref-academic-scraper.md - **Developed by:** [Pierrick McD0nald](https://apify.com/klondikeking) (community) - **Categories:** Developer tools, Business - **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 Academic Scraper — DOI, Citation & Paper Metadata Extractor Extract academic paper metadata, DOIs, authors, citations, and abstracts from CrossRef via the official public REST API. This Actor is designed for researchers, data scientists, bibliometricians, and anyone who needs structured academic publication data at scale. CrossRef is the largest open scholarly metadata registry, indexing over 150 million works. This Actor queries that registry directly — no web scraping, no browser overhead, no proxy costs. Results are fast, clean, and reliable. ### Use Cases - **Literature review automation** — Build structured datasets of papers on any topic for systematic reviews or meta-analyses. - **Bibliometric research** — Collect citation counts, reference networks, and publisher distributions across disciplines. - **Journal quality analysis** — Filter by journal, publisher, or publication type to map the scholarly landscape. - **Research trend tracking** — Monitor emerging topics by querying new publications year over year. - **Reference management** — Export paper metadata with DOIs and URLs for import into Zotero, Mendeley, or EndNote. ### Input | Field | Type | Required | Description | |-------|------|----------|-------------| | `searchQuery` | String | Yes | Search terms to find academic works (e.g., "machine learning", "climate change") | | `maxItems` | Integer | No | Maximum results to extract, 1–1000 (default: 100) | | `workType` | String | No | Filter by type: journal-article, book-chapter, proceedings-article, book, dataset, etc. | | `publisher` | String | No | Filter by publisher name (partial match) | | `fromYear` | Integer | No | Only include works published on or after this year | | `toYear` | Integer | No | Only include works published on or before this year | | `sort` | String | No | Sort by relevance, published, updated, or deposited (default: relevance) | | `proxyConfiguration` | Object | No | Proxy settings (optional, API calls rarely need a proxy) | ### Output The Actor outputs a dataset where each item represents one academic work: ```json { "title": "Machine learning vs. neutrosophic machine learning", "doi": "10.1201/9781003606055-5", "url": "https://doi.org/10.1201/9781003606055-5", "type": "book-chapter", "publisher": "CRC Press", "publishedDate": "2025-03-26", "authors": ["Florentin Smarandache"], "abstract": "", "subjects": ["Computer Science", "Artificial Intelligence"], "referenceCount": 0, "citationCount": 12, "containerTitle": "Neutrosophic Machine Learning", "page": "", "volume": "", "issue": "", "issn": "" } ```` All fields are consistently typed. Missing values return empty strings or zeroes rather than nulls, making downstream processing easier. ### Pricing Pay per event: **$0.001 per extracted work**. Because this Actor uses the public CrossRef REST API directly, there are no proxy or browser costs. Margins exceed 95%, allowing us to pass those savings on to you at a highly competitive rate. ### Limitations - CrossRef abstracts are not always available; many works return an empty abstract field. - Author names are formatted as provided by publishers and may vary in completeness. - CrossRef rate limits apply; the Actor handles 429 responses with automatic retry and exponential backoff. - The API returns a maximum of 1,000 results per query due to CrossRef pagination limits. ### FAQ **Q: Do I need an API key?** A: No. CrossRef's public API does not require authentication for read operations. **Q: Can I filter by specific journals?** A: Yes. Use the `publisher` filter or post-process the `containerTitle` field in your downstream pipeline. **Q: Are citation counts real-time?** A: Citation counts are updated periodically by CrossRef and reflect their latest deposited data. ### Changelog - **v1.0.0** — Initial release with search, filtering, author extraction, and citation counts. # Actor input Schema ## `searchQuery` (type: `string`): Search terms to find academic works (e.g., machine learning, climate change, CRISPR) ## `maxItems` (type: `integer`): Maximum number of papers to extract (1-1000) ## `workType` (type: `string`): Filter by publication type (leave empty for all types) ## `publisher` (type: `string`): Filter by publisher name (optional, partial match) ## `fromYear` (type: `integer`): Only include works published on or after this year ## `toYear` (type: `integer`): Only include works published on or before this year ## `sort` (type: `string`): How to sort the results ## `proxyConfiguration` (type: `object`): Proxy settings for the Actor (optional, API calls usually do not need a proxy) ## Actor input object example ```json { "searchQuery": "machine learning", "maxItems": 100, "workType": "", "sort": "relevance", "proxyConfiguration": { "useApifyProxy": true } } ``` # Actor output Schema ## `dataset` (type: `string`): Dataset containing extracted academic works with metadata, DOIs, authors, and citations ## `stats` (type: `string`): Key-value store with execution statistics (works extracted, API calls, duration) # 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", "maxItems": 100, "workType": "", "publisher": "", "sort": "relevance", "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("klondikeking/crossref-academic-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", "maxItems": 100, "workType": "", "publisher": "", "sort": "relevance", "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("klondikeking/crossref-academic-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", "maxItems": 100, "workType": "", "publisher": "", "sort": "relevance", "proxyConfiguration": { "useApifyProxy": true } }' | apify call klondikeking/crossref-academic-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=klondikeking/crossref-academic-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "CrossRef Scraper - Academic DOI & Metadata Extractor", "description": "Extract academic paper metadata, DOIs, authors, citations, and abstracts from CrossRef via the public REST API. No scraping needed - fast, reliable, and cost-effective for researchers and data scientists.", "version": "1.0", "x-build-id": "S0m6lC9ylfHJfcaWZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/klondikeking~crossref-academic-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-klondikeking-crossref-academic-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/klondikeking~crossref-academic-scraper/runs": { "post": { "operationId": "runs-sync-klondikeking-crossref-academic-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/klondikeking~crossref-academic-scraper/run-sync": { "post": { "operationId": "run-sync-klondikeking-crossref-academic-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Search terms to find academic works (e.g., machine learning, climate change, CRISPR)" }, "maxItems": { "title": "Maximum Results", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of papers to extract (1-1000)", "default": 100 }, "workType": { "title": "Work Type", "enum": [ "", "journal-article", "book-chapter", "proceedings-article", "book", "dataset", "monograph", "report", "standard" ], "type": "string", "description": "Filter by publication type (leave empty for all types)", "default": "" }, "publisher": { "title": "Publisher", "type": "string", "description": "Filter by publisher name (optional, partial match)" }, "fromYear": { "title": "From Year", "minimum": 1900, "maximum": 2100, "type": "integer", "description": "Only include works published on or after this year" }, "toYear": { "title": "To Year", "minimum": 1900, "maximum": 2100, "type": "integer", "description": "Only include works published on or before this year" }, "sort": { "title": "Sort Order", "enum": [ "relevance", "published", "updated", "deposited" ], "type": "string", "description": "How to sort the results", "default": "relevance" }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for the Actor (optional, API calls usually do not need a proxy)" } } }, "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 } } } } } } } } } } ```