# Crossref DOI Search & Metadata Exporter (`archive-scout-labs/crossref-doi-metadata-exporter`) Actor

Search Crossref scholarly works and export clean DOI metadata for research, discovery, and citation analysis. Filter by query, work type, date, sort, and result count. Each result includes title, authors, publisher, journal, citations, subjects, licenses, references, ISSN, and DOI URL.

- **URL**: https://apify.com/archive-scout-labs/crossref-doi-metadata-exporter.md
- **Developed by:** [Archive Scout Labs](https://apify.com/archive-scout-labs) (community)
- **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.10 / 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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Crossref DOI Search & Metadata Exporter

Search Crossref works and export DOI, citation, author, publisher, journal, type, date, license, and reference metadata.

This HTTP-only Apify Actor uses the public Crossref REST API. It needs no customer login, cookie, proxy, or third-party API subscription.

### Quick start

1. Enter a focused query and optional filters.
2. Keep the result limit small for the first run.
3. Click **Start** and export the dataset as JSON, CSV, Excel, XML, RSS, or HTML.

```json
{
  "query": "machine learning",
  "maxItems": 25,
  "sort": "relevance",
  "order": "desc"
}
```

### Useful workflows

- Literature and prior-art discovery
- DOI enrichment for research datasets
- Citation and publisher monitoring

### Reliability and billing design

- Inputs and pagination are bounded to prevent accidental runaway jobs.
- Temporary rate limits and server errors are retried with exponential backoff.
- Unexpected source response shapes fail the run instead of producing a misleading empty success.
- Duplicate source records are removed within a run.
- Each successfully written dataset row is ready for a future pay-per-result event; source errors are never charged as results.
- The runtime stops cleanly when an Apify maximum-charge limit is reached.

### Data source and limitations

Source: https://api.crossref.org

This independent community Actor is not affiliated with or endorsed by the source publisher. It returns public records as supplied by the source and does not independently verify their accuracy. Source coverage, update cadence, field completeness, and rate limits can change. Users remain responsible for interpreting and using the data lawfully.

# Actor input Schema

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

Words to search across Crossref work metadata.

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

Optional Crossref type filter.

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

Optional ISO date.

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

Sort.

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

Order.

## `maxItems` (type: `integer`):

Maximum results.

## Actor input object example

```json
{
  "query": "machine learning",
  "type": "",
  "sort": "relevance",
  "order": "desc",
  "maxItems": 25
}
```

# Actor output Schema

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

Default dataset containing successful source records.

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("archive-scout-labs/crossref-doi-metadata-exporter").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("archive-scout-labs/crossref-doi-metadata-exporter").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).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 archive-scout-labs/crossref-doi-metadata-exporter --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,archive-scout-labs/crossref-doi-metadata-exporter"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/UqkQDAX8UkDmZT9Cp/builds/k6RuE1PGWahCjxHLz/openapi.json