# Computer Science Research Papers

**Use case:** 

Search Semantic Scholar for computer science research papers and export structured data ready for analysis. Each run returns title, abstract, authors, venue, DOI, arXiv ID, and citation counts for every paper matching your query. Filter by field of study, publication type, and year to build a focused CS papers dataset in CSV, JSON, or Excel.

## Input

```json
{
  "searchQueries": [
    "deep learning"
  ],
  "paperIds": [],
  "authorIds": [],
  "maxResults": 300,
  "yearFrom": 2015,
  "fieldsOfStudy": [
    "Computer Science"
  ],
  "publicationTypes": [
    "JournalArticle",
    "Conference"
  ],
  "openAccessOnly": false,
  "sortBy": "relevance",
  "includeAbstracts": true,
  "includeReferences": false,
  "includeCitations": false,
  "maxCitationsPerPaper": 50,
  "includeAuthorPapers": false
}
```

## Output

```json
{
  "recordType": {
    "label": "Type",
    "format": "text"
  },
  "title": {
    "label": "Title",
    "format": "text"
  },
  "authors": {
    "label": "Authors",
    "format": "array"
  },
  "year": {
    "label": "Year",
    "format": "number"
  },
  "venue": {
    "label": "Venue",
    "format": "text"
  },
  "citationCount": {
    "label": "Citations",
    "format": "number"
  },
  "referenceCount": {
    "label": "References",
    "format": "number"
  },
  "s2FieldsOfStudy": {
    "label": "Fields of study",
    "format": "array"
  },
  "fieldsOfStudy": {
    "label": "Fields (as published)",
    "format": "array"
  },
  "openAccessPdfUrl": {
    "label": "PDF",
    "format": "link"
  },
  "url": {
    "label": "Link",
    "format": "link"
  }
}
```

## About this Actor

This example demonstrates how to use [Semantic Scholar Scraper](https://apify.com/solidcode/semanticscholar-scraper.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/solidcode/semanticscholar-scraper.md) 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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- **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/solidcode/semanticscholar-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).