# Crossref Scraper - DOI Metadata, Scholarly Works & Citations (`pink_comic/crossref-doi-scholarly-works-citation-metadata`) Actor

Search Crossref DOI and scholarly publication metadata by title, author, funder, journal, publisher, affiliation, ORCID, license and dates. Normalize source-linked works, authors, funders, references and mutable citation signals.

- **URL**: https://apify.com/pink\_comic/crossref-doi-scholarly-works-citation-metadata.md
- **Developed by:** [Ava Torres](https://apify.com/pink_comic) (community)
- **Categories:** Education, AI, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 crossref doi scholarly works scraper api - citatio evidence items

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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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 DOI Scholarly Works & Citation Metadata Scraper API

Search the public Crossref REST API by DOI, title, author, funder, journal, affiliation, ORCID, publisher, license and dates. Normalize source-linked scholarly works, authors, funders, publication dates, references, licenses and mutable citation signals.

### Default input

```json
{"bibliographicQuery":"machine learning","sortBy":"published_desc","maxResults":1,"maxCandidates":100}
```

The default costs at most **$0.0021**: `$0.0001` start + `$0.002` for one item.

### Important limits

Crossref metadata is depositor-supplied and can be incomplete, delayed, corrected, or inconsistent. Reference and `is-referenced-by` counts are incomplete, mutable discovery signals—not exhaustive citations, quality scores, endorsement, or proof of validity. Results are bounded to 10 cursor pages and 500 candidates. Use DOI links and primary publications for material decisions.

See the full README for filters, examples, output fields, pricing, and interpretation guidance.

# Actor input Schema

## `doi` (type: `string`):

Exact DOI, such as 10.1038/s41586-021-03819-2. Exact lookup bypasses broad search.

## `bibliographicQuery` (type: `string`):

Broad title, author, abstract, container, or citation text. The deterministic default finds recent machine-learning works.

## `titleQuery` (type: `string`):

Title-focused query.

## `authorQuery` (type: `string`):

Author-name query.

## `funderQuery` (type: `string`):

Funder-name query.

## `containerQuery` (type: `string`):

Journal, proceedings, book, or repository title query.

## `affiliationQuery` (type: `string`):

Author affiliation query.

## `type` (type: `string`):

Exact Crossref type, such as journal-article, proceedings-article, book-chapter, or posted-content.

## `publisher` (type: `string`):

Crossref publisher-name filter.

## `affiliation` (type: `string`):

Crossref affiliation filter.

## `orcid` (type: `string`):

ORCID identifier with or without https://orcid.org/.

## `licenseUrl` (type: `string`):

Exact Crossref license.url filter.

## `indexedFrom` (type: `string`):

Inclusive YYYY-MM-DD Crossref index date.

## `indexedUntil` (type: `string`):

Inclusive YYYY-MM-DD Crossref index date.

## `publishedFrom` (type: `string`):

Inclusive YYYY-MM-DD publication date. The default keeps the demo recent.

## `publishedUntil` (type: `string`):

Inclusive YYYY-MM-DD publication date. Update this bound for later publications.

## `minReferences` (type: `integer`):

Local filter within maxCandidates. Crossref reference counts are incomplete.

## `minCitations` (type: `integer`):

Local filter within maxCandidates. This mutable Crossref signal is not exhaustive citation coverage or a quality score.

## `sortBy` (type: `string`):

Optional sort results filter or result-control setting. See the README for exact source semantics, bounds, and examples.

## `maxResults` (type: `integer`):

Total paid output cap. The default costs at most $0.0021 including the $0.0001 start.

## `maxCandidates` (type: `integer`):

Bounds cursor pagination and local numeric filtering. Maximum 500 candidates and 10 pages.

## Actor input object example

```json
{
  "bibliographicQuery": "machine learning",
  "publishedFrom": "2025-01-01",
  "publishedUntil": "2026-07-30",
  "minReferences": 0,
  "minCitations": 0,
  "sortBy": "published_desc",
  "maxResults": 1,
  "maxCandidates": 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "bibliographicQuery": "machine learning",
    "maxResults": 1,
    "maxCandidates": 100
};

// Run the Actor and wait for it to finish
const run = await client.actor("pink_comic/crossref-doi-scholarly-works-citation-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "bibliographicQuery": "machine learning",
    "maxResults": 1,
    "maxCandidates": 100,
}

# Run the Actor and wait for it to finish
run = client.actor("pink_comic/crossref-doi-scholarly-works-citation-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 '{
  "bibliographicQuery": "machine learning",
  "maxResults": 1,
  "maxCandidates": 100
}' |
apify call pink_comic/crossref-doi-scholarly-works-citation-metadata --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=pink_comic/crossref-doi-scholarly-works-citation-metadata",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/c1ShzYKzpbQNWZF5R/builds/VZWaMMxI4tDDZoP4P/openapi.json