Consideration on Data Quality Dimensions for Long-term Ecosystem Observation on Biotic Components
The Chinese Ecosystem Research Network (CERN) was established in 1988 under the auspices of the Chinese government and the World Bank Loan. Through years of effort, it is now well placed to address important issues, serving as a functional network to meet the needs of both the national and international ecological research. To improve scientific data quality, it is essential to construct a data quality dimension framework to provide continuous quality assessment and management. Data quality is the life of monitoring working. The costs of making incorrect scientific inferences based on faulty data can be substantial and far-reaching, and follow-on research may be critically jeopardized. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus primarily on accuracy, consistency and completeness, with no clear DQ framework and these dimensions having no clear description and measuring method. The purpose of this research is to provide a framework for the management of data quality as it applies to scientific data, specifically those generated by the fieldwork facilities and instrumentation that will populate the data centers of CERN, based on data quality theory. This paper develops a list of data quality dimensions that captures the aspects of data quality which are important for ecosystem long-term monitoring.
CERN Data Quality Ecosystem Quality dimensions Scientific data Information Quality
Dongxiu Wu Wenshan Wei Chuangye Song Ying Su
Institute of Botany (IB), Chinese Academy of Sciences (CAS), Sub-Center for Biology of Chinese Ecosy Institute of Botany (IB), Chinese Academy of Sciences (CAS), Sub-Center for Biology of Chinese Ecosy Information Quality Lab, Resource Sharing Promotion Center,Institute of Scientific and Technical Inf
国际会议
北京
英文
249-254
2010-11-27（万方平台首次上网日期，不代表论文的发表时间）