迄今为止,中国知网数据库是中国大陆收录最全的数据库，拥有各地区的期刊论文，博硕学位论文、会议论文等重要文章。
中国知网数据库让研究中华语言、文化及文学的学生与教授，受益匪浅。一位使用中国知网多年的李同学即分享了她的经验。她说：“知网资料库齐全，搜查资料简易方便，或许是因为就读中文系的缘故，我在学校使用中国知网的频率十分高。大致来说，我在阅读这些额外材料时，不但增进知识，还同时了解海外学术的进展。”
除了中国知网外， 新加披国立教育学院图书馆还为学生及老师提供万方数据库。 陈家骏博士就此提道：“中国知网与万方，正好起互补的作用，让我们在搜查资料时更为便捷、迅速，十分利于学术的研究！”
有兴趣者可到新加坡国立教育学院图书馆，或是南洋理工大学图书馆查询。
要查找中文资源的同学及学者，可通过图书馆的主页搜寻，或是点击以下的网址链接。
中国知网：bit.ly/CNKI-database
万方数据：bit.ly/WanFang
---------------------------------------------------------------------------------------------------------------------------
Chinese Database CNKI Now Available
China National Knowledge Infrastructure (CNKI 中国知网) is the world’s most comprehensive online resource for accessing China’s intellectual output. The Chinese database features full-text articles in the fields of history, literature and philosophy.
This database which comprises: China Academic Journals Full-text Database (1994-), China Doctoral Dissertation Full-text Database (1984-), China Master’s Theses Full-text Database (1984-) and China Proceedings of Conference Full-text Database (1953-) is now available to NIE students and staff.
With an extensive coverage of resources available in the database, faculty and students undertaking studies and research on Chinese language, culture and literature will benefit greatly from it. Ms Lee, an NIE student, who has been using the database for many years, shared her experience with us.
“When I was pursuing my degree in Chinese, I had been a frequent user of CNKI. With its user-friendly searching capabilities, I had no problems locating the resources. The wide coverage helped me to deepen my content knowledge and understanding on the overseas academic development.”
Besides CNKI, NIE Library subscribes to Wanfang Data.
Dr Tan Kar Chun, a lecturer from Asian Languages and Cultures (ALC), thinks that “CNKI complements Wanfang Data. This makes searching of information even faster and more convenient especially for users doing academic research.”
To access the databases, please visit the following links:
CNKI: bit.ly/CNKI-database
Wanfang Data: bit.ly/WanFang
English version by Wong Yong Yeow