# Semantic Scholar Research Normalizer (`wakey7dev/semantic-scholar-normalizer`) Actor

Search 200M+ scientific papers via Semantic Scholar API with normalized metadata — authors, citations, venues, and open access links. Free, no API key.

- **URL**: https://apify.com/wakey7dev/semantic-scholar-normalizer.md
- **Developed by:** [Chris Wakefield](https://apify.com/wakey7dev) (community)
- **Categories:** AI
- **Stats:** 2 total users, 1 monthly users, 0.0% 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?

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

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

## Semantic Scholar Research Normalizer

Search the **Semantic Scholar Academic Graph** and receive clean, deduplicated paper records from a corpus of more than 200 million scholarly works. This Actor turns inconsistent scholarly metadata into analysis-ready records with author identity deduplication, DOI normalisation, citation metrics, fields of study and open-access discovery.

**Free API. No API key required.**

### Features

- Topic, title, author and keyword search
- DOI and Semantic Scholar paper identifiers retained
- Deduplicates authors within each paper and papers by DOI or paper ID
- Normalizes titles, abstracts, venues and author names
- Includes citation, reference and influential-citation counts
- Identifies open-access PDF availability
- Human-readable OUTPUT table plus machine-readable STATS and full RESULTS

### Input parameters

| Parameter | Type | Description |
|---|---|---|
| `searchQuery` | string | Topic, title, author or keyword search |
| `maxResults` | integer | 1–100 papers, default 20 |
| `yearStart` | integer | Optional earliest publication year |
| `yearEnd` | integer | Optional latest publication year |

### Example input

```json
{"searchQuery":"large language models","maxResults":20,"yearStart":2020,"yearEnd":2026}
```

### Example output

```json
{"paperId":"...","title":"Attention Is All You Need","year":2017,"authors":[{"authorId":"...","name":"Ashish Vaswani","nameNormalized":"ashish vaswani"}],"citationCount":...,"doi":"10.48550/...","isOpenAccess":true,"fieldsOfStudy":["Computer Science"]}
```

### Use cases

- Systematic literature reviews and evidence discovery
- Research trend and citation analysis
- Prior-art and competitive intelligence
- Academic recommendation and knowledge graphs
- Open-access paper monitoring for libraries and research teams

### Data source attribution

Data is sourced from the [Semantic Scholar Academic Graph](https://www.semanticscholar.org/product/api), provided by the Allen Institute for AI. Please review and follow the source API's current rate limits and terms.

# Actor input Schema

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

Scientific topic, paper title, author or keyword search.

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

Maximum papers to return (1–100).

## `yearStart` (type: `integer`):

Optional earliest publication year.

## `yearEnd` (type: `integer`):

Optional latest publication year.

## Actor input object example

```json
{
  "searchQuery": "large language models",
  "maxResults": 20
}
```

# Actor output Schema

## `dataset` (type: `string`):

Normalized paper records.

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

Human-readable summary.

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

Machine-readable statistics.

# 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": "large language models"
};

// Run the Actor and wait for it to finish
const run = await client.actor("wakey7dev/semantic-scholar-normalizer").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": "large language models" }

# Run the Actor and wait for it to finish
run = client.actor("wakey7dev/semantic-scholar-normalizer").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": "large language models"
}' |
apify call wakey7dev/semantic-scholar-normalizer --silent --output-dataset

```

## MCP server setup

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

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/Z5rhy23jOnYOIDfzC/builds/Ck9IGRr1oQSNivudX/openapi.json