# Crossref Works Extractor (`xtracto/crossref-works`) Actor Extract scholarly publication metadata from Crossref — one work per row, with DOI, title, authors, publisher, type, dates, and references. 183M+ works. Public data, no key. - **URL**: https://apify.com/xtracto/crossref-works.md - **Developed by:** [xtractoo](https://apify.com/xtracto) (community) - **Categories:** Other, Lead generation - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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.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 Works Extractor Extract scholarly **publication metadata** from **Crossref** — one **work per row** — with DOI, title, authors, publisher, type, publication dates, license, and reference counts. Built for research and R&D intelligence, bibliometrics, publishing analytics, and reference/citation tooling. --- ### Why use this actor - **183M+ works** (verified) — pull the whole corpus or a slice by type, date, or query. - **One work per row**, with a flat header (`doi`, `title`, `type`, `publisher`, `is_referenced_by_count`) plus the **full raw Crossref object** (authors, references, license, funder, …). - **No login, no key.** Add your email to join Crossref's faster "polite pool". ### Input | Field | Type | Description | | --- | --- | --- | | `worksType` | **dropdown** | Journal article / Book chapter / Preprint / Dataset / … | | `fromPubDate` / `untilPubDate` | date | Publication-date window. | | `query` | text | Free-text search. | | `filter` | text | Extra Crossref filter (e.g. `has-orcid:true`), combined with the above. | | `mailto` | text | Your email — faster pool (recommended). | | `rows` | int | 1–1000 (default 1000). | | `maxItems` | int | 0 = all matching. | `worksType` is a **pick-list**; `query`/`filter` are free-text (Crossref's filter grammar is open-ended). ### Output — `WORK` Envelope + `recordType: "WORK"` + flat header, then the raw Crossref work: ```json { "_input": "type=journal-article; from=2024-01-01", "_source": "S1-crossref", "_scrapedAt": "2026-06-03T10:00:00Z", "recordType": "WORK", "doi": "10.1234/example", "title": "...", "type": "journal-article", "publisher": "...", "is_referenced_by_count": 12, "author": [ "..." ], "reference": [ "..." ] } ```` ### How it works 1. Your type, date, and keyword filters are applied to the search. 2. The actor automatically pages through all matching results (up to 1000 per request). 3. Each work streams into the dataset. ### Known limits - Public data — no account needed, runs from any connection. Backs off on HTTP 429. Add `mailto` for the faster pool. - **Verified live 2026-06-03:** total 183,058,502; `cursor=*` → `message.next-cursor` confirmed (200 in ~3.5s). # Actor input Schema ## `worksType` (type: `string`): Filter by Crossref work type (optional). ## `fromPubDate` (type: `string`): Optional lower bound on publication date. ## `untilPubDate` (type: `string`): Optional upper bound on publication date. ## `query` (type: `string`): Free-text search across works. ## `filter` (type: `string`): Additional Crossref filter, e.g. has-orcid:true,is-referenced-by-count:50. Combined with the fields above. ## `mailto` (type: `string`): Joins Crossref's faster 'polite pool'. ## `rows` (type: `integer`): How many records to fetch per request. ## `maxItems` (type: `integer`): Maximum records to return (0 = all matching). ## `proxyConfiguration` (type: `object`): Not required (public API). ## Actor input object example ```json { "worksType": "journal-article", "fromPubDate": "2024-01-01", "untilPubDate": "", "query": "machine learning", "filter": "", "mailto": "you@example.com", "rows": 1000, "maxItems": 0, "proxyConfiguration": { "useApifyProxy": false } } ``` # 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 = { "worksType": "journal-article", "fromPubDate": "2024-01-01", "untilPubDate": "", "query": "machine learning", "filter": "", "mailto": "you@example.com" }; // Run the Actor and wait for it to finish const run = await client.actor("xtracto/crossref-works").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 = { "worksType": "journal-article", "fromPubDate": "2024-01-01", "untilPubDate": "", "query": "machine learning", "filter": "", "mailto": "you@example.com", } # Run the Actor and wait for it to finish run = client.actor("xtracto/crossref-works").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 '{ "worksType": "journal-article", "fromPubDate": "2024-01-01", "untilPubDate": "", "query": "machine learning", "filter": "", "mailto": "you@example.com" }' | apify call xtracto/crossref-works --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=xtracto/crossref-works", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Crossref Works Extractor", "description": "Extract scholarly publication metadata from Crossref — one work per row, with DOI, title, authors, publisher, type, dates, and references. 183M+ works. Public data, no key.", "version": "0.1", "x-build-id": "sGAQtYxefrMfZZpD0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/xtracto~crossref-works/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-xtracto-crossref-works", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/xtracto~crossref-works/runs": { "post": { "operationId": "runs-sync-xtracto-crossref-works", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/xtracto~crossref-works/run-sync": { "post": { "operationId": "run-sync-xtracto-crossref-works", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "worksType": { "title": "Work type", "enum": [ "", "journal-article", "book-chapter", "proceedings-article", "posted-content", "dataset", "book", "monograph", "report", "dissertation", "standard", "reference-entry", "journal", "component", "peer-review", "other" ], "type": "string", "description": "Filter by Crossref work type (optional).", "default": "" }, "fromPubDate": { "title": "From publication date (YYYY-MM-DD)", "type": "string", "description": "Optional lower bound on publication date.", "default": "" }, "untilPubDate": { "title": "Until publication date (YYYY-MM-DD)", "type": "string", "description": "Optional upper bound on publication date.", "default": "" }, "query": { "title": "Search query (optional)", "type": "string", "description": "Free-text search across works.", "default": "" }, "filter": { "title": "Extra Crossref filter (optional)", "type": "string", "description": "Additional Crossref filter, e.g. has-orcid:true,is-referenced-by-count:50. Combined with the fields above.", "default": "" }, "mailto": { "title": "Your email (recommended)", "type": "string", "description": "Joins Crossref's faster 'polite pool'.", "default": "" }, "rows": { "title": "Rows per request", "minimum": 1, "maximum": 1000, "type": "integer", "description": "How many records to fetch per request.", "default": 1000 }, "maxItems": { "title": "Max works (0 = all matching)", "minimum": 0, "type": "integer", "description": "Maximum records to return (0 = all matching).", "default": 0 }, "proxyConfiguration": { "title": "Proxy", "type": "object", "description": "Not required (public API).", "default": { "useApifyProxy": false } } } }, "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 } } } } } } } } } } ```