# Crossref Funded Research (`realai_pl/crossref-funded-research`) Actor

Recent Crossref publications reporting funders and award numbers, linked by DOI.

- **URL**: https://apify.com/realai\_pl/crossref-funded-research.md
- **Developed by:** [Dawid Mańkowski](https://apify.com/realai_pl) (community)
- **Categories:** Developer tools, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.20 / 1,000 delivered funded research papers

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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 Funded Research

Find recent Crossref works that report funders and award numbers, with DOI links and publication metadata.

### Use and source

Follow grant-funded publications by topic for research intelligence or funding analysis.

Source: Crossref REST API (`https://api.crossref.org/works`).

### Example input

```json
{
  "queries": [
    "machine learning"
  ],
  "limit": 5,
  "onlyNew": true
}
```

Run this Actor on demand or on an Apify schedule. Each dataset row links to its source, and `collectedAt` records the UTC retrieval date and time. The `RUN_SUMMARY` output reports fetched and delivered counts.

### Output columns

`id`/`doi`, `title`, `journal`, `publisher`, `publishedDate`, `funders` (names and award numbers), `authors`, `type`, `sourceUrl`, `collectedAt`.

### Monitoring and coverage

Results depend on metadata deposited with Crossref and may be incomplete. `limit` applies per query; duplicate DOIs are removed. Only works reporting award numbers are returned.

With `onlyNew: true`, a named key-value store remembers up to 10,000 delivered fingerprints for the calling Apify user and `stateKey`. Reusing the key suppresses unchanged rows; choose a different key for an independent watchlist. Concurrent runs using the same key are unsupported. A zero-result run can still consume Apify compute and storage.

### Pricing

The PPE event `delivered-funded-paper` costs $0.0012 per delivered row. In a private Apify test on 2026-09-24, two delivered rows produced two matching events. A run capped at the price of one event delivered one of two fetched rows. No start or automatic dataset-item event is configured. Zero-output runs record no PPE events but still consume platform resources. Run costs vary with input and source response time.

# Actor input Schema

## `queries` (type: `array`):

Up to ten title queries; leave an empty string for newest funded papers.

## `limit` (type: `integer`):

Maximum number of funded papers to deliver per query, from 1 to 100.

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

Optional lower publication-date bound.

## `onlyNew` (type: `boolean`):

Remember delivered records across runs under the same state key.

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

Separate schedules by a distinct key; state is scoped by the Apify caller ID.

## Actor input object example

```json
{
  "queries": [
    "machine learning"
  ],
  "limit": 20,
  "onlyNew": true,
  "stateKey": "default"
}
```

# Actor output Schema

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

No description

## `summary` (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("realai_pl/crossref-funded-research").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("realai_pl/crossref-funded-research").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 realai_pl/crossref-funded-research --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,realai_pl/crossref-funded-research"
        }
    }
}
```

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/ddcUBCYVe4hnzy5KH/builds/zOdGfGsa27GiWe0JC/openapi.json