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Shanghai Ranking GRAS Access Skill

Extract university rankings from ShanghaiRanking's Global Ranking of Academic Subjects (GRAS).

Overview

This skill provides access to ShanghaiRanking's authoritative academic subject rankings, covering 55 subjects across 5 major fields from 2017 to 2025. The rankings evaluate universities based on five objective indicators:

• World-Class Faculty: Faculty awards and recognition
• World-Class Output: Research output volume
• High Quality Research: Research quality metrics
• Research Impact: Citation and impact metrics
• International Collaboration: International research partnerships

Available Functions

list_subjects

List all available academic subjects with their codes and available years.

Parameters:

• year (integer, optional): Reference year for available versions (default: 2025)

Example:

result = await execute({
    "function": "list_subjects",
    "year": 2024
})

Returns:

• List of subject categories (Natural Sciences, Engineering, Life Sciences, Medical Sciences, Social Sciences)
• Each subject's code, name, and available years
• Total subject count

get_rankings

Get university rankings for a specific academic subject.

Parameters:

• subject_code (string, required): Subject code (e.g., "AS0101" for Mathematics)
• year (integer, optional): Ranking year (default: 2024, range: 2017-2025)
• limit (integer, optional): Maximum results (default: 100)
• offset (integer, optional): Pagination offset (default: 0)

Example:

result = await execute({
    "function": "get_rankings",
    "subject_code": "AS0101",
    "year": 2024,
    "limit": 50
})

Returns:

• Subject information (code, name, category)
• List of universities with:
  • World rank
  • University name, code, logo URL
  • Country and country code
  • Total score
  • Individual indicator scores

search_universities

Search for universities by name within rankings.

Parameters:

• query (string, required): University name search query
• subject_code (string, optional): Filter to specific subject
• year (integer, optional): Ranking year (default: 2024)
• limit (integer, optional): Maximum results (default: 50)

Example:

# Search across all subjects
result = await execute({
    "function": "search_universities",
    "query": "Stanford",
    "year": 2024
})

# Search within a specific subject
result = await execute({
    "function": "search_universities",
    "query": "Oxford",
    "subject_code": "AS0210",  # Computer Science & Engineering
    "year": 2024
})

Subject Codes Reference

Natural Sciences

Engineering

Life Sciences

Medical Sciences

Social Sciences

Data Structure

Ranking Entry

{
  "rank": "1",
  "university": {
    "name": "Princeton University",
    "slug": "princeton-university",
    "code": "RI02848",
    "logo": "https://www.shanghairanking.com/logo/b1dad5288.png"
  },
  "country": "United States",
  "country_code": "us",
  "score": 388.8,
  "indicators": {
    "world_class_faculty": 80.9,
    "world_class_output": 194.6,
    "high_quality_research": 56.4,
    "research_impact": 43.0,
    "international_collaboration": 13.9
  }
}

Notes

• The ranking data is fetched from ShanghaiRanking's Nuxt.js static payload files
• Rankings typically include 300-500 universities per subject
• Some subjects may not be available for all years (e.g., Oceanography available from 2018)
• Rank ranges (e.g., "401-500") are used for lower-ranked institutions
• Scores may be null for institutions in rank ranges

Technical Details

• Data Source: ShanghaiRanking's GRAS website (www.shanghairanking.com)
• Data Format: JavaScript JSONP payloads parsed via Node.js
• Caching: Build hash is cached to minimize page fetches
• Rate Limiting: Consider implementing delays for bulk requests