# CrossRef Scraper — Scholarly Works & Citations (`ponderable_hydrometer/crossref-scraper`) Actor Search 150M+ scholarly works from CrossRef — DOI, title, authors, journal, publisher, citations & dates. Filter by date & type. Free keyless API. - **URL**: https://apify.com/ponderable\_hydrometer/crossref-scraper.md - **Developed by:** [Ponderable Hydrometer](https://apify.com/ponderable_hydrometer) (community) - **Categories:** Developer tools, Automation, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.50 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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/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 ## CrossRef Scraper — Scholarly Works & Citations **Search CrossRef's catalogue of 150M+ scholarly works — journal articles, books, conference papers, datasets — and get rich, structured metadata.** DOI, title, authors with ORCID and affiliation, journal, publisher, publication date, citation count and subjects. Full CrossRef filter grammar, deep cursor paging, look up specific DOIs. Free keyless API. For research tools, bibliometrics, publishing workflows and literature reviews. ### What you get Per work: - **Identity** — `doi`, `title`, `type`, `url` - **Authors** — array of `{ given, family, name, orcid, affiliation[] }` - **Publication** — `containerTitle` (journal/book), `publisher`, `published` (YYYY-MM-DD), `year`, `volume`, `issue`, `page`, `issn[]`, `isbn[]` - **Impact** — `citedByCount`, `referencesCount` - **Content** — `subjects[]`, `abstract` (when available), `language` ### Output sample ```json { "doi": "10.1038/nature14539", "title": "Deep learning", "type": "journal-article", "authors": [ { "given": "Yann", "family": "LeCun", "name": "Yann LeCun", "orcid": "http://orcid.org/0000-0002-...", "affiliation": ["Facebook AI Research"] } ], "containerTitle": "Nature", "publisher": "Springer Science and Business Media LLC", "published": "2015-05-28", "year": 2015, "volume": "521", "issue": "7553", "page": "436-444", "issn": ["0028-0836", "1476-4687"], "isbn": [], "citedByCount": 68210, "referencesCount": 0, "subjects": ["Multidisciplinary"], "abstract": null, "language": "en", "url": "https://doi.org/10.1038/nature14539" } ```` ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `query` | string | — | Free-text search across all fields | | `queryBibliographic` | string | — | Combined title/author/year (good for citation matching) | | `queryAuthor` | string | — | Search by author name | | `queryTitle` | string | — | Search within work titles | | `filter` | string | — | Raw CrossRef filter, e.g. `from-pub-date:2024-01-01,type:journal-article,has-abstract:true` | | `sort` | string | — | e.g. `relevance`, `published`, `is-referenced-by-count`, `score`, `updated` | | `order` | string | `desc` | `desc` or `asc` | | `dois` | array | — | Look up specific DOIs | | `maxResults` | integer | `100` | Cap on works returned | ### Example input ```json { "query": "large language models", "filter": "from-pub-date:2024-01-01,type:journal-article", "sort": "is-referenced-by-count", "maxResults": 200 } ``` Look up specific DOIs with `{"dois":["10.1038/nature14539","10.1126/science.1231143"]}`. ### Why this actor - **Depth few match** — full author detail (ORCID + affiliation), citation counts, subjects and CrossRef's complete filter grammar. - **Whole result sets** — deep cursor paging, not just the first 1,000. - **Fast & polite** — a `mailto` User-Agent puts requests in CrossRef's faster "polite pool" with backoff on errors. ### Pricing Pay per result — **$1.50 per 1,000 results** (one result = one scholarly work). No subscription or platform fees; you pay only for the results you get. ### Notes & limits - Source is CrossRef's official free REST API — no key, no anti-bot. Abstracts are only present where the publisher deposited them. - Public metadata; you are responsible for compliant use. Not affiliated with CrossRef. ### Related actors - **arXiv Scraper** — preprints with abstracts and PDF links (pairs with CrossRef to cover preprints + published). - **Semantic Scholar Scraper** — papers, citations and influence metrics. - **Open Library Scraper** — books and ISBN metadata. # Actor input Schema ## `query` (type: `string`): Free-text search across all fields, e.g. "machine learning healthcare". ## `queryBibliographic` (type: `string`): Search title, author, year etc. together (good for citation matching). ## `queryAuthor` (type: `string`): Search by author name, e.g. "Hinton". ## `queryTitle` (type: `string`): Search within work titles. ## `filter` (type: `string`): Raw CrossRef filter, e.g. "from-pub-date:2024-01-01,type:journal-article,has-abstract:true". ## `sort` (type: `string`): e.g. relevance, published, is-referenced-by-count, score, updated. ## `order` (type: `string`): "desc" or "asc". ## `dois` (type: `array`): Look up specific DOIs, e.g. \["10.1038/nature14539"]. ## `maxResults` (type: `integer`): Cap on number of works returned. ## Actor input object example ```json { "query": "machine learning", "order": "desc", "maxResults": 100 } ``` # 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": "machine learning" }; // Run the Actor and wait for it to finish const run = await client.actor("ponderable_hydrometer/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 = { "query": "machine learning" } # Run the Actor and wait for it to finish run = client.actor("ponderable_hydrometer/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 '{ "query": "machine learning" }' | apify call ponderable_hydrometer/crossref-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ponderable_hydrometer/crossref-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "CrossRef Scraper — Scholarly Works & Citations", "description": "Search 150M+ scholarly works from CrossRef — DOI, title, authors, journal, publisher, citations & dates. Filter by date & type. Free keyless API.", "version": "0.1", "x-build-id": "C5URqMLfjJhT5QX6N" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ponderable_hydrometer~crossref-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ponderable_hydrometer-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/ponderable_hydrometer~crossref-scraper/runs": { "post": { "operationId": "runs-sync-ponderable_hydrometer-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/ponderable_hydrometer~crossref-scraper/run-sync": { "post": { "operationId": "run-sync-ponderable_hydrometer-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": "Query (general)", "type": "string", "description": "Free-text search across all fields, e.g. \"machine learning healthcare\"." }, "queryBibliographic": { "title": "Query — bibliographic", "type": "string", "description": "Search title, author, year etc. together (good for citation matching)." }, "queryAuthor": { "title": "Query — author", "type": "string", "description": "Search by author name, e.g. \"Hinton\"." }, "queryTitle": { "title": "Query — title", "type": "string", "description": "Search within work titles." }, "filter": { "title": "Filter (CrossRef syntax)", "type": "string", "description": "Raw CrossRef filter, e.g. \"from-pub-date:2024-01-01,type:journal-article,has-abstract:true\"." }, "sort": { "title": "Sort by", "type": "string", "description": "e.g. relevance, published, is-referenced-by-count, score, updated." }, "order": { "title": "Order", "enum": [ "desc", "asc" ], "type": "string", "description": "\"desc\" or \"asc\".", "default": "desc" }, "dois": { "title": "Specific DOIs", "type": "array", "description": "Look up specific DOIs, e.g. [\"10.1038/nature14539\"].", "items": { "type": "string" } }, "maxResults": { "title": "Max results", "minimum": 1, "type": "integer", "description": "Cap on number of works returned.", "default": 100 } } }, "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 } } } } } } } } } } ```