19th International CODATA Conference
Category: Open Communication
The experiences, progress and challenges to the practice of the Scientific Database’s (SDB) data sharing policy
Yu luqing (yulq@sdb.ac.cn), Xiao
Yun, Li Jianhui (lijh@sdb.ac.cn ), and
Wang Runqiang
Computer Network Information Center, Chinese Academy
of Sciences,
Chinese
SDB project has run for about 20 years, nowadays SDB
is a key project in the 10th Five-year Program of
In recently years, SDB faced more challenges to enhance data sharing in science communities, with the rapidly development of Internet technology and increasing requisitions of scientific data. It was schemed to formulate data sharing policy in the 10th Five-year Program, in order that enable researchers to share data resources across institutes and disciplines for remote and collaborative research.
SDB launched the scientific data sharing policy project in April 2003, then completed a report of the scientific data sharing policy study in November 2003, in addition brought forward the recommendatory data policy through thrice workshops. Sharing scientific data involves the rights and responsibilities of three key roles, which are data producer, supplier and end user. Recommendatory data policy covers data sharing principle, management mechanism, data classification, data collection, intellectual property, etc, which encourages institutes to use information technology to provide public access based on open and full principle among institutes. It’s emphasized that institutes as the producer have responsibility to enable end user to access data derived from publicly funded research, data supplier and end user cannot damage the rights and interests of data producer, but supplier would provide data service to end user by collecting and integrating data resources with the authorization of producer.
SDB will implement the recommendatory data sharing policy within memberships in 2004, which maybe receive greater challenges during its practice. Therefore, SDB will continue to make data policy perfect with several measures, including formulating detailed rules on subject, establishing the data quality assessment system, and even promoting the construction of national laws on scientific data policy.