collegeschat/university-information

收集全国各高校招生时不会写明,却会实实在在影响大学生活质量的要求与细节

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Summary Information

Updated 14 minutes ago
Added to GitGenius on September 13th, 2026
Created on June 25th, 2021
Open Issues & Pull Requests: 35 (+0)
GitHub issues: Enabled
Number of forks: 825
Total Stargazers: 5,089 (+0)
Total Subscribers: 10 (+0)

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Detailed Description

University Information is a crowdsourced database that collects practical details about Chinese universities that admissions materials omit but significantly affect student quality of life.

The project addresses the gap between official university marketing and the lived experience of students. It systematizes information about dormitory conditions, campus facilities, daily routines, and policies that prospective students rarely encounter during recruitment but encounter constantly once enrolled. The approach is community-driven: contributors share firsthand knowledge through discussions, and the collected data is published both on a web interface and as generated markdown files in a dedicated branch, making the information accessible for both browsing and contribution.

Prospective Chinese university students and their families should use this tool when evaluating which institution to attend. The project suits anyone making enrollment decisions who wants to understand practical living conditions beyond academic rankings. The database covers specific concerns including dormitory layouts and amenities, classroom and heating infrastructure, meal costs and quality, internet and power policies, transportation access, and enforcement of curfews and exercise requirements. This level of detail helps students anticipate whether a university's actual environment will match their expectations and needs.

The project maintains an active discussion forum for contributions and questions, with a separate generated branch for automatically produced documentation. The tool operates a dedicated web portal for querying collected information, indicating ongoing infrastructure maintenance. The project explicitly welcomes corrections and removals of inaccurate information, suggesting responsive moderation of the dataset.