# Semantic Scholar Scraper — Papers & Citations (`ponderable_hydrometer/semantic-scholar-scraper`) Actor

Academic papers & authors from Semantic Scholar — citations, influence, venue, DOI/arXiv, open-access PDF & author metrics. Bulk search & batch lookup. No key.

- **URL**: https://apify.com/ponderable\_hydrometer/semantic-scholar-scraper.md
- **Developed by:** [Ponderable Hydrometer](https://apify.com/ponderable_hydrometer) (community)
- **Categories:** Developer tools, Automation, SEO tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.50 / 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/platform/actors/running/actors-in-store#pay-per-event

## 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

## Semantic Scholar Scraper — Papers, Citations & Authors

**Search 200M+ academic papers with citation counts and open-access PDFs — or batch-look-up by DOI/arXiv/PMID — and pull author metrics.** Sort by citations, filter by year and field. Free, keyless (optional key raises limits).

Built on the Semantic Scholar Graph API, using the **reliable bulk-search and batch endpoints** (not the heavily throttled plain search), so large runs actually complete.

### What you get

**Papers** (`type: "paper"`):

- `paperId`, `title`, `year`, `publicationDate`, `venue`
- **Impact** — `citationCount`, `influentialCitationCount`, `referenceCount`
- **Cross-IDs** — `doi`, `arxivId`, `pubMedId`
- `authors` (name + authorId), `fieldsOfStudy`, `publicationTypes`
- `openAccessPdf` (direct PDF URL when available), `abstract`, `url`

**Authors** (`type: "author"`) — `name`, `affiliations`, `homepage`, `paperCount`, `citationCount`, `hIndex`, `url`

### Output sample

```json
{
  "type": "paper",
  "paperId": "204e3073870fae3d05bcbc2f6a8e263d9b72e776",
  "title": "Attention Is All You Need",
  "year": 2017,
  "publicationDate": "2017-06-12",
  "venue": "Neural Information Processing Systems",
  "authors": [{ "name": "Ashish Vaswani", "authorId": "40348417" }],
  "citationCount": 98213,
  "influentialCitationCount": 15042,
  "referenceCount": 41,
  "doi": "10.5555/3295222.3295349",
  "arxivId": "1706.03762",
  "pubMedId": null,
  "fieldsOfStudy": ["Computer Science"],
  "publicationTypes": ["JournalArticle", "Conference"],
  "openAccessPdf": "https://arxiv.org/pdf/1706.03762",
  "abstract": "The dominant sequence transduction models…",
  "url": "https://www.semanticscholar.org/paper/204e3073870fae3d05bcbc2f6a8e263d9b72e776"
}
```

### Modes

- **paper-search** — search papers by `query`, `sort`, `year`, `fieldsOfStudy` (reliable bulk endpoint).
- **paper-detail** — batch-look-up by `ids` (S2 id, `DOI:…`, `ARXIV:…`, `PMID:…`).
- **author-search** — find authors by name (throttled pool — add a key).
- **author-papers** — all papers for given `authorIds` (throttled pool — add a key).

### Example input

```json
{
  "mode": "paper-search",
  "query": "large language models",
  "sort": "citationCount:desc",
  "year": "2020-2024",
  "maxResults": 100
}
```

By DOI/arXiv:

```json
{ "mode": "paper-detail", "ids": ["ARXIV:1706.03762", "ARXIV:2005.14165"] }
```

### Why this actor

- **Runs at scale** — uses the bulk/batch pool that isn't rate-throttled, unlike naive scrapers.
- **Impact metrics** — citation and influential-citation counts, not just titles.
- **Direct PDFs & cross-IDs** — open-access PDF URLs plus DOI/arXiv/PMID for joining.
- **Keyless** for papers; add an optional free key for author endpoints and higher throughput.

### Pricing

Pay per result — **$1.50 per 1,000 results** (one paper or author = one result). No subscription or platform fees.

### Related actors

- **arXiv Scraper** — preprints and metadata direct from arXiv.
- **CrossRef Scholarly Works Scraper** — works and citation links via CrossRef.
- **PubChem Scraper** — chemical compound data for chem/pharma research.

### Notes & limits

- Data is Semantic Scholar's official Graph API; the actor backs off on 429/5xx.
- **Author endpoints share a throttled anonymous pool** — provide a free API key (from semanticscholar.org/product/api) for reliable author results and higher limits.
- This actor is an independent tool, not affiliated with or endorsed by the Allen Institute for AI / Semantic Scholar.

# Actor input Schema

## `mode` (type: `string`):

paper-search, paper-detail (by id/DOI/arXiv), author-search, author-papers.

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

Search term for paper-search or author-search, e.g. "diffusion models", "Yoshua Bengio".

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

Sort bulk paper search, e.g. "citationCount:desc", "publicationDate:desc". Empty = relevance.

## `year` (type: `string`):

Restrict by year or range, e.g. "2023" or "2020-2024".

## `fieldsOfStudy` (type: `string`):

Filter by field, e.g. "Computer Science", "Medicine". Comma-separate multiple.

## `ids` (type: `array`):

Paper identifiers: Semantic Scholar id, or prefixed "DOI:10.1038/...", "ARXIV:1706.03762", "PMID:...".

## `authorIds` (type: `array`):

Semantic Scholar author IDs (get them from author-search).

## `apiKey` (type: `string`):

Optional key for reliable throughput (the anonymous pool is heavily shared-rate-limited). Apply free at semanticscholar.org/product/api. Kept secret.

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

Cap on papers/authors returned.

## Actor input object example

```json
{
  "mode": "paper-search",
  "query": "diffusion models",
  "maxResults": 100
}
```

# 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": "diffusion models"
};

// Run the Actor and wait for it to finish
const run = await client.actor("ponderable_hydrometer/semantic-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 = { "query": "diffusion models" }

# Run the Actor and wait for it to finish
run = client.actor("ponderable_hydrometer/semantic-scholar-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).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": "diffusion models"
}' |
apify call ponderable_hydrometer/semantic-scholar-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=ponderable_hydrometer/semantic-scholar-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/gcLFGlZl2sk1saWr7/builds/dLeR7FJrhNmTd0W89/openapi.json