# Scholarly Paper Metadata Search — Citation Counts & Links (`plaindata/scholarly-doi-metadata`) Actor

Search 150M+ scholarly works or look them up by DOI® name: title, journal, publisher, dates, citations, references, licence, full-text links. No author data, no abstracts. Data: Crossref REST API. Not affiliated with Crossref or the DOI Foundation.

- **URL**: https://apify.com/plaindata/scholarly-doi-metadata.md
- **Developed by:** [plaindata](https://apify.com/plaindata) (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 $1.50 / 1,000 result returneds

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

## Scholarly Paper & DOI Metadata Search — Citations

Search scholarly works or resolve a list of DOIs through the official Crossref REST API (data source) and get clean, flat rows: title, journal, publisher, work type, publication dates, **citation count**, reference count, ISSN/ISBN, volume/issue/pages, licence URLs, full-text links and funders. Built for literature reviews, citation checks and AI agents that need verified bibliographic data.

### Input

Search mode (this is the example input):

```json
{ "query": "large language models hallucination", "fromYear": 2024, "sort": "relevance", "maxItems": 50 }
```

DOI lookup mode (the query is ignored when DOIs are given):

```json
{ "dois": ["10.1145/3703155", "https://doi.org/10.1038/s41586-021-03819-2"] }
```

| Field | Meaning |
|---|---|
| `query` | Free-text bibliographic search (title, journal, keywords) |
| `dois` | DOIs or doi.org URLs to resolve exactly |
| `fromYear` / `untilYear` | Publication year range |
| `workType` | journal-article, proceedings-article, book-chapter, book, posted-content (preprints), dataset, report, dissertation |
| `sort` | relevance · is-referenced-by-count (most cited) · published (newest) |
| `maxItems` | Up to 2,000 rows per run (search mode) |

### Output (one row per work)

| Field | Example |
|---|---|
| `doi`, `url` | 10.1145/3703155 · https://doi.org/10.1145/3703155 |
| `title`, `subtitle` | A Survey on Hallucination in Large Language Models… |
| `containerTitle`, `shortContainerTitle` | ACM Transactions on Information Systems |
| `publisher`, `type` | Association for Computing Machinery (ACM) · journal-article |
| `publishedDate`, `publishedPrint`, `publishedOnline` | 2025-01-24 |
| `issn`, `isbn`, `volume`, `issue`, `page` | \["1046-8188", "1558-2868"] |
| `citedByCount`, `referencesCount` | 2273 · 391 |
| `authorCount` | 11 (number only — no names) |
| `licenseUrls`, `fullTextLinks`, `funders` | publisher licence / text-mining links, funder organisations |
| `source`, `scrapedAt` | data source note · UTC timestamp |

### What is deliberately not included

- **No personal data**: author and editor names, ORCID iDs and personal affiliations are not output (`authorCount` only).
- **No abstracts**: Crossref notes that some abstracts may be copyrighted by publishers or authors.
- No bulk dumps: at most 2,000 rows per run, for the query or DOIs you give.

### Data source and licence

- Crossref REST API (`api.crossref.org`), no sign-up or key. Crossref: "almost none of the metadata is subject to copyright" and "you may use it for any purpose".
- Polite use: one request at a time with at least 1.5 s between requests (Crossref public pool allows 1 request per second).
- Citation counts are Crossref's `is-referenced-by-count` (citations from other Crossref-registered works), not Google Scholar counts.

Not affiliated with or endorsed by Crossref.

# Actor input Schema

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

Free-text bibliographic search (title, journal, keywords), e.g. large language models hallucination. Leave empty when you only look up DOIs.

## `dois` (type: `array`):

DOIs or doi.org URLs, e.g. 10.1145/3703155. When given, each DOI is fetched exactly; the search query is ignored.

## `fromYear` (type: `integer`):

Earliest publication year.

## `untilYear` (type: `integer`):

Latest publication year.

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

Restrict to one Crossref work type.

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

Order of search results.

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

Upper bound on rows returned (search mode). Bulk dumps are not supported.

## Actor input object example

```json
{
  "query": "large language models hallucination",
  "fromYear": 2024,
  "sort": "relevance",
  "maxItems": 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 = {
    "query": "large language models hallucination",
    "fromYear": 2024,
    "maxItems": 50
};

// Run the Actor and wait for it to finish
const run = await client.actor("plaindata/scholarly-doi-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 = {
    "query": "large language models hallucination",
    "fromYear": 2024,
    "maxItems": 50,
}

# Run the Actor and wait for it to finish
run = client.actor("plaindata/scholarly-doi-metadata").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 '{
  "query": "large language models hallucination",
  "fromYear": 2024,
  "maxItems": 50
}' |
apify call plaindata/scholarly-doi-metadata --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,plaindata/scholarly-doi-metadata"
        }
    }
}
```

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/veMth0bTssjqrMAmB/builds/ybTt15pdL28JOT4D1/openapi.json