DAMIcn/china-universities

China Universities Dataset 🎓

582 Chinese universities — names (English + 中文), city, province, category, 985/211/双一流 tags, logos, and ShanghaiRanking (软科中国大学排名) 2026 national ranks, scores, and institution-page links. Shipped as ready-to-use JSON · CSV · SQL, no scraping needed.

One import and you have a structured Chinese university database: searchable slugs, province→city geography, prestige tags, and up-to-date national rankings.

Why this exists

Every project touching Chinese higher-ed data ends up re-scraping the same ranking sites. I built this for a student-review platform and figured I'd save everyone the trouble — clean, merged, deduped, documented. If it helps you, a ⭐ means a lot.

Files

File	Format	Use it for
data/universities.json	JSON	JS/TS apps, APIs, static sites
data/universities.csv	CSV	Excel, pandas, quick analysis
data/universities.sql	PostgreSQL	Direct import (psql -f) — includes CREATE TABLE
scripts/scrape.mjs	Node 18+	Regenerate the raw ranking data any year

Data dictionary

Field	Type	Notes
slug	string	Canonical URL-friendly ID (tsinghua, ustc)
slug_aliases	string[]	Alternate slugs (e.g. tsinghua-university)
name / name_zh	string	English / 中文 names — 2026-current (13 recent renames applied)
city / province / country	string	English names; city→province hierarchy
category	string	comprehensive, stem, normal, agriculture, forestry (软科主榜 categories)
uni_type	string	public / private — curated, populated for a subset
languages_of_instruction	string[]	e.g. ["Chinese","English"] — curated subset
website	string	Official site — curated subset
logo_url	string	Official crest on ShanghaiRanking's CDN (hotlink — needs an HTTP Referer header to load)
shanghairanking.national_rank	int	软科主榜 national rank (1–573)
shanghairanking.score	float	Total score (published for ~top 500; null for 500+ tier)
shanghairanking.url	string	Deep link to the school's institution page
shanghairanking.tags	string[]	985, 211, 双一流 (Double First-Class) where applicable
shanghairanking.indicators	object	10-dimension sub-scores (办学层次/学科水平/师资/科研/国际竞争力…) — top ~100 schools only, keyed by indicator code

Quickstart

JavaScript / TypeScript

import unis from './data/universities.json' with { type: 'json' }
const top10 = unis.filter(u => u.shanghairanking.national_rank <= 10)
const elite985 = unis.filter(u => u.shanghairanking.tags.includes('985'))

Python / pandas

import pandas as pd
df = pd.read_csv('data/universities.csv')
df[df.province == 'Jiangsu'].sort_values('national_rank').head(10)

PostgreSQL

psql -d yourdb -f data/universities.sql

Sample row

{
  "slug": "tsinghua",
  "name": "Tsinghua University",
  "name_zh": "清华大学",
  "city": "Beijing", "province": "Beijing", "country": "China",
  "category": "comprehensive",
  "slug_aliases": ["tsinghua-university"],
  "shanghairanking": {
    "year": 2026, "national_rank": 1, "score": 1087.1,
    "url": "https://www.shanghairanking.cn/institution/tsinghua-university",
    "tags": ["双一流", "985", "211"]
  }
}

Coverage & honesty notes

• 573 / 582 unis carry a 软科 national rank — the rest are schools not on the 主榜 (they rank in 软科's specialist lists for medicine, finance, arts, etc., which this dataset doesn't cover yet).
• score is published only for the top ~500; lower-tier schools get national_rank ranges like 500+ (stored as null score).
• city is province-level coarse for a handful of rows (e.g. a few entries list city: "Anhui"). PRs fixing cities are very welcome.
• School names are the 2026 edition — some were renamed/upgraded in recent years (嘉兴学院→嘉兴大学, 常熟理工学院→苏州工学院…), which is a feature: this is fresher than most datasets floating around.

Regenerating

The ranking data comes from ShanghaiRanking's own published list — no credentials needed:

node scripts/scrape.mjs        # writes data/shanghairanking-2026.json (all 590 raw records)
node scripts/scrape.mjs 2025   # any edition year

The raw payload also carries per-school indicator sub-scores (indData, 10 dimensions) for the top ~100 schools — they're in the JSON if you want to go deeper.

Data, attribution & disclaimer

• Rankings, names, and scores are factual data published publicly by ShanghaiRanking (上海软科) — all credit belongs to them. institution_url links back to their site per school, and logo_url hotlinks their CDN rather than re-hosting.
• This repo is not affiliated with or endorsed by ShanghaiRanking or any listed university. University names/logos belong to their respective owners.
• I'm a student putting this together as a good-faith contribution so the community doesn't have to re-scrape the same pages — shared for research, education, and building useful things. If you're a rights holder and want anything changed or removed, open an issue and I'll sort it out promptly.
• Provided as-is, no warranty. Verify against the source before publishing anything critical.

License

Scripts: MIT (see LICENSE). The data itself is factual/public-domain-ish by nature — use it freely, and a link back here is appreciated but not required.

Keywords

china universities dataset · chinese university database · 中国大学排名 · 软科 shanghairanking 2026 · university rankings data · china higher education data · 985 211 双一流 list · chinese universities csv json sql

Use with 🤗 datasets / LLM agents

from datasets import load_dataset
ds = load_dataset("DAMIcn/china-universities")  # default config -> data/universities.csv
df = ds["train"].to_pandas()

No-install HTTP access — works for agents, notebooks, and the Dataset Viewer:

• Rows API: /static-proxy?url=https%3A%2F%2Fdatasets-server.huggingface.co%2Frows%3Fdataset%3DDAMIcn%2Fchina-universities%26amp%3Bconfig%3Ddefault%26amp%3Bsplit%3Dtrain%3C%2Fcode%3E%3C%2Fli%3E
• Search: /static-proxy?url=https%3A%2F%2Fdatasets-server.huggingface.co%2Fsearch%3Fdataset%3DDAMIcn%2Fchina-universities%26amp%3Bconfig%3Ddefault%26amp%3Bsplit%3Dtrain%26amp%3Bquery%3D985%3C%2Fcode%3E%3C%2Fli%3E
• Auto-converted Parquet: https://huggingface.co/api/datasets/DAMIcn/china-universities/parquet

Citation

@dataset{charradi_china_universities_2026,
  author  = {Charradi, Imad},
  title   = {China Universities Dataset},
  year    = {2026},
  url     = {https://huggingface.co/datasets/DAMIcn/china-universities}
}