# Crossref Scholarly Works Fetcher (`brunofin/crossref-scholarly-works-fetcher`) Actor

Fetch metadata for scholarly works (papers, articles, books) from the official Crossref REST API. Query by URL/DOI, keyword search, publication date range, work type, or ISSN. No API key required. Ideal for research pipelines, citation graphs, and LLM data prep.

- **URL**: https://apify.com/brunofin/crossref-scholarly-works-fetcher.md
- **Developed by:** [Bruno Finger](https://apify.com/brunofin) (community)
- **Categories:** Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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 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 Scholarly Works Fetcher

Fetch metadata for scholarly works (papers, articles, books, conference papers,
etc.) from the official **Crossref REST API**. No API key required.

### What it does

Crossref is a not-for-profit membership organisation that indexes metadata for
\~185 million scholarly works. This actor wraps the keyless
`https://api.crossref.org/works` endpoint so you can retrieve structured
metadata — titles, authors, publishers, publication dates, journal/ISNN,
citation counts, and source URLs — as rows in an Apify dataset.

### Input

| Field        | Type     | Default                 | Description                                                                 |
|--------------|----------|-------------------------|-----------------------------------------------------------------------------|
| `query`      | string   | `""`                    | Free-text search across title / abstract / keywords.                        |
| `doi`        | string   | `""`                    | A single DOI to resolve (e.g. `10.1038/nature12345`). Overrides query/filter.|
| `filter`     | string   | `""`                    | Crossref filter expression — e.g. `type:journal-article`, `from-pub-date:2024`, `issn:0028-0836`. |
| `maxWorks`   | integer  | `10` (1–100)            | Max works to return in search mode.                                         |

Only one of `doi`, `query`, or `filter` is needed. If all are empty the actor
defaults to fetching the 10 most recent journal-articles.

#### Examples

```json
{
  "query": "graph neural network",
  "maxWorks": 20
}
```

```json
{
  "doi": "10.1103/PhysRevLett.116.061102"
}
```

```json
{
  "filter": "from-pub-date:2024-01,type:journal-article",
  "maxWorks": 50
}
```

### Output

Each row in the dataset represents one scholarly work:

| Field          | Description                                           |
|----------------|-------------------------------------------------------|
| `doi`          | The DOI of the work.                                  |
| `title`        | Title of the work.                                    |
| `authors`      | Comma-separated author list with ORCID IDs if present.|
| `publisher`    | Publisher name.                                       |
| `published`    | Publication date (YYYY-MM-DD, YYYY-MM, or YYYY).      |
| `type`         | Work type (journal-article, book, book-chapter, etc.).|
| `citationCount`| Number of citing works (if available).                |
| `journal`      | Container / journal title.                            |
| `issn`         | ISSN(s) of the journal.                               |
| `url`          | Canonical URL for the work.                           |
| `error`        | Empty on success, populated if the work could not be fetched. |

### Billing

`PAY_PER_EVENT` per `scholarly-work-fetched` event — one charge per work emitted
to the dataset. Batch mode (`maxWorks`) lets a single run bill for up to 100 works.

### Notes

- The Crossref API is keyless and generous with rate limits (~no-auth requests).
- The actor sends a descriptive `User-Agent` header as required by the Crossref
  API policy.
- A proper email should be supplied in the User-Agent for production use; the
  current default is a placeholder.

# Actor input Schema

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

Free-text search across title, abstract, and keywords (e.g. "machine learning graph neural networks"). Used only when 'doi' is empty.

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

A single DOI to resolve (e.g. '10.1038/nature12345'). If provided, the actor fetches that single work and ignores query/filter. Leave empty to use search mode.

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

Crossref REST API filter expression for field-specific filtering. Examples: 'type:journal-article', 'from-pub-date:2024', 'type:book', 'issn:0028-0836'. Leave empty to use the default (most recent journal-articles). Ignored when 'doi' is provided.

## `maxWorks` (type: `integer`):

Maximum number of scholarly works to fetch in search mode (1-100). Each work is billed as a PAY\_PER\_EVENT charge.

## Actor input object example

```json
{
  "query": "",
  "doi": "",
  "filter": "",
  "maxWorks": 10
}
```

# Actor output Schema

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

No description

# 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("brunofin/crossref-scholarly-works-fetcher").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("brunofin/crossref-scholarly-works-fetcher").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 brunofin/crossref-scholarly-works-fetcher --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,brunofin/crossref-scholarly-works-fetcher"
        }
    }
}

```

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/0Aq3AKNijkguAIfcg/builds/ZTeNlZegpxEZNdgsR/openapi.json