# Crossref Scholarly Research and DOI Metadata Radar (`adaptive_nightmare/crossref-academic-papers-radar`) Actor

Extract Crossref Scholarly Research and DOI Metadata data in clean JSON or CSV. Query by query, sort, maxPapers for records, engagement metrics, and metadata. Export datasets, run via API or Python/Node.js SDK, schedule runs, or integrate with webhooks with zero proxy fees.

- **URL**: https://apify.com/adaptive_nightmare/crossref-academic-papers-radar.md
- **Developed by:** [Darim Quraishi](https://apify.com/adaptive_nightmare) (community)
- **Categories:** Education, AI, Developer tools
- **Security:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$1.10 / 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 Scholarly Research and DOI Metadata Radar

Search and extract academic papers, preprint releases, digital object identifiers (DOIs), publication sources, author directories, and citation metrics from Crossref.

### Features

- Queries hundreds of millions of peer-reviewed journal articles, book chapters, conference proceedings, and preprints.
- Sorts papers by publication recency, search relevance, or citation counts to locate foundational literature.
- Extracts author directories, publishing houses, journal titles, ISSN numbers, and official DOI URLs.
- Provides clean metadata suitable for scientific literature review, automated citation indexing, and LLM fine-tuning.
- Runs without API keys, browser rendering, or proxy credits for high throughput and reliability.

### Input parameters

- Research Query: Keywords, academic topics, or specific research fields to search (for example: machine learning, gene editing). Default is artificial intelligence.
- Sort Order: Sort by publication date, search relevance, or total citation count. Default is published.
- Maximum Papers: Total number of scholarly records to collect. Default is 50, maximum is 200.

### Output dataset fields

Each dataset record contains:

- doi: Digital Object Identifier string (for example: 10.1157/13053466).
- title: Full title of the published paper or study.
- journalTitle: Name of the journal, conference, or publication venue.
- publisher: Publishing organization (Elsevier, IEEE, Springer Nature, Wiley).
- type: Item category such as journal-article, proceedings-article, or book-chapter.
- authors: Array of full author names.
- citationCount: Total recorded citations linking back to this work.
- publishedDate: ISO formatted timestamp of record creation or release.
- publishedYear: Year of publication.
- issn: International Standard Serial Number for the journal.
- url: Permanent resolving DOI link.
- query: Search keyword used to discover the paper.
- scrapedAt: UTC timestamp when the record was collected.

### Use cases

- Scientific research monitoring: Track newly published papers in fast-moving fields like medicine, AI, and materials science.
- Academic RAG pipelines: Build citation-backed retrieval systems for technical and biomedical applications.
- Trend intelligence: Identify trending research topics and influential authors based on citation velocity.
- Library curation: Aggregate journal article metadata for academic bibliographies and institutional repositories.

# Actor input Schema

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

Keywords, topic, or paper title to search (for example: large language models, crispr, quantum computing).

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

Sorting criteria for results.

## `maxPapers` (type: `integer`):

Total number of academic papers to retrieve.

## Actor input object example

```json
{
  "query": "artificial intelligence",
  "sort": "published",
  "maxPapers": 50
}
```

# 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("adaptive_nightmare/crossref-academic-papers-radar").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("adaptive_nightmare/crossref-academic-papers-radar").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 adaptive_nightmare/crossref-academic-papers-radar --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,adaptive_nightmare/crossref-academic-papers-radar"
        }
    }
}
```

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/zaTed78TRmAfGATG1/builds/AsFth9r8qJ6dLdRlk/openapi.json