# Crossref Scholar Metadata Scraper (`wakey7dev/crossref-scholar-scraper`) Actor

Query Crossref for DOI metadata — titles, abstracts, authors, journals, citations, funding. 150M+ scholarly works. Academic research, bibliometrics, literature reviews.

- **URL**: https://apify.com/wakey7dev/crossref-scholar-scraper.md
- **Developed by:** [Chris Wakefield](https://apify.com/wakey7dev) (community)
- **Categories:** AI
- **Features:** Limited permissions
- **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/actors/running/actors-in-store.md#pay-per-usage

## 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

![Chris The Dev](https://raw.githubusercontent.com/chriswakefield87/appstore-screenshot-translator/main/assets/actor-banner.png)

## Crossref Scholar Scraper

Search scholarly publications across 150+ million works via the Crossref REST API with intelligent author disambiguation, institution normalization, and subject classification.

### What It Does

- **Search** the Crossref database by keyword, author, affiliation, funder, or DOI
- **Normalize** author names (J Smith → John Smith) and deduplicate
- **Standardize** institution names (MIT → Massachusetts Institute of Technology)
- **Classify** publications into broad subject areas (AI, medicine, physics, etc.)
- **Output** clean, structured data ready for bibliometric analysis, literature reviews, or knowledge graphs

### Input Parameters

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `searchQuery` | string | ✅ | Keywords, author name, DOI, or institution to search |
| `maxResults` | integer | No | Maximum results (1–1000, default: 50) |
| `searchField` | string | No | `all`, `title`, `author`, `affiliation`, `funder`, or `doi` |
| `filterType` | string | No | Filter by: `journal-article`, `proceedings-article`, `book`, `dissertation`, etc. |
| `yearFrom` | string | No | Filter from year (e.g., `2020`) |
| `yearTo` | string | No | Filter to year (e.g., `2024`) |
| `normalizeAuthors` | boolean | No | Normalize and deduplicate author names (default: true) |
| `normalizeAffiliations` | boolean | No | Normalize institution names (default: true) |
| `emailAddress` | string | No | Your email for Crossref's Polite Pool (better rate limits) |

### Example Input

```json
{
  "searchQuery": "large language model alignment",
  "maxResults": 25,
  "searchField": "all",
  "filterType": "journal-article",
  "yearFrom": "2023",
  "normalizeAuthors": true,
  "normalizeAffiliations": true
}
```

### Example Output

Each result includes:

- **DOI** and resolvable URL
- **Title** and abstract
- **Normalized authors** (deduplicated, name-expanded)
- **Normalized affiliations** (standardized institution names)
- **Normalized journal** name
- **Subject areas** (AI, medicine, etc.)
- **Citation count** (referenced-by count)
- **Publication date**, type, publisher, funders, ISSN

Plus aggregated statistics: top subject areas, most cited paper, publication type breakdown.

### Use Cases

- **Academic researchers** — literature reviews, systematic reviews, citation analysis
- **Bibliometric analysts** — impact metrics, research trend analysis, institution benchmarking
- **University libraries** — publication tracking, open access monitoring
- **AI/ML companies** — building knowledge graphs, training data curation
- **Journal publishers** — monitoring publication metrics, competitor analysis
- **Industry R\&D** — tracking scientific literature in specific domains

### Data Source

Powered by the [Crossref REST API](https://api.crossref.org/) — a free, open API providing metadata for 150+ million scholarly works. No API key required. Uses the Polite Pool with configurable email for better rate limits.

### Pricing

Pay-per-result: $2.00 per 1,000 results. You only pay for the data you retrieve.

# Actor input Schema

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

Keywords to search for (e.g. 'machine learning', 'climate change', author name, DOI). Supports Crossref query syntax.

## `maxResults` (type: `integer`):

Maximum number of results to return (1-1000).

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

Which field to search in. 'All' searches title, abstract, author, and more.

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

Filter results by publication type.

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

Filter publications from this year (e.g., 2020). Leave empty for no lower bound.

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

Filter publications up to this year (e.g., 2024). Leave empty for no upper bound.

## `normalizeAuthors` (type: `boolean`):

Normalize author names and deduplicate (recommended).

## `normalizeAffiliations` (type: `boolean`):

Normalize institution/affiliation names.

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

Optional: Your email for Crossref's Polite Pool (better rate limits). Not required.

## Actor input object example

```json
{
  "searchQuery": "machine learning",
  "maxResults": 50,
  "searchField": "all",
  "filterType": "",
  "yearFrom": "",
  "yearTo": "",
  "normalizeAuthors": true,
  "normalizeAffiliations": true,
  "emailAddress": ""
}
```

# Actor output Schema

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

Complete dataset of normalized scholarly publications.

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

Human-readable table of search results.

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

Machine-readable statistics about the run.

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

Deduplicated list of normalized author names.

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

Deduplicated list of normalized institution names.

# 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 = {
    "searchQuery": "machine learning",
    "yearFrom": "",
    "yearTo": "",
    "emailAddress": ""
};

// Run the Actor and wait for it to finish
const run = await client.actor("wakey7dev/crossref-scholar-scraper").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 = {
    "searchQuery": "machine learning",
    "yearFrom": "",
    "yearTo": "",
    "emailAddress": "",
}

# Run the Actor and wait for it to finish
run = client.actor("wakey7dev/crossref-scholar-scraper").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 '{
  "searchQuery": "machine learning",
  "yearFrom": "",
  "yearTo": "",
  "emailAddress": ""
}' |
apify call wakey7dev/crossref-scholar-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,wakey7dev/crossref-scholar-scraper"
        }
    }
}
```

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/13um6EYdcbhge2LUp/builds/FqNbKpzPKTwNm2qb4/openapi.json