# 🎓 Semantic Scholar Scraper — Papers, Citations & DOIs

**Use case:** 

No-code, cheap Semantic Scholar API. Scrape papers: title, abstract, authors, venue, year, citations, fields of study, DOI, arXiv & PDF — cheapest per item.

## Input

```json
{
  "query": "graph neural networks",
  "minCitationCount": 0,
  "openAccessOnly": false,
  "maxResults": 1000
}
```

## Output

```json
{
  "paperId": {
    "label": "Paper Id",
    "format": "text"
  },
  "title": {
    "label": "Title",
    "format": "text"
  },
  "abstract": {
    "label": "Abstract",
    "format": "text"
  },
  "year": {
    "label": "Year",
    "format": "number"
  },
  "publicationDate": {
    "label": "Publication Date",
    "format": "date"
  },
  "venue": {
    "label": "Venue",
    "format": "text"
  },
  "publicationTypes": {
    "label": "Publication Types",
    "format": "text"
  },
  "authors": {
    "label": "Authors",
    "format": "text"
  },
  "authorCount": {
    "label": "Author Count",
    "format": "number"
  },
  "citationCount": {
    "label": "Citation Count",
    "format": "number"
  },
  "referenceCount": {
    "label": "Reference Count",
    "format": "number"
  },
  "influentialCitationCount": {
    "label": "Influential Citation Count",
    "format": "number"
  },
  "fieldsOfStudy": {
    "label": "Fields Of Study",
    "format": "text"
  },
  "doi": {
    "label": "Doi",
    "format": "text"
  },
  "pmid": {
    "label": "Pmid",
    "format": "text"
  },
  "arxivId": {
    "label": "Arxiv Id",
    "format": "text"
  },
  "openAccessPdfUrl": {
    "label": "Open Access Pdf Url",
    "format": "link"
  },
  "url": {
    "label": "Url",
    "format": "link"
  }
}
```

## About this Actor

This example demonstrates how to use [Semantic Scholar Scraper — Papers, Citations & TLDRs](https://apify.com/hipersoft/semantic-scholar-scraper) with a specific input configuration. Visit the [Actor detail page](https://apify.com/hipersoft/semantic-scholar-scraper) to learn more, explore other use cases, and run it yourself.


## 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.
This Task's input is already configured above — use it as-is rather than inventing a new one.

- **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).
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- **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 full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/hipersoft/semantic-scholar-scraper.md

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).