联系我们

论文查询结果	返回搜索

论文编号：	11779
作者编号：	2120183030
上传时间：	2020/6/21 10:36:46
中文题目：	科学数据归档服务质量评价指标体系构建研究
英文题目：	Construction of the Evaluation System for the Archiving Quality of Scientific Data
指导老师：	王芳
中文关键字：	科学数据；数据归档服务；服务质量评价；指标体系
英文关键字：	Scientific data; Data archiving service; Service quality evaluation; Evaluation system
中文摘要：	随着科学研究的大量开展，科学数据的数量也迅速增长。科学数据不仅是科学研究的重要成果，更为其他后续研究的开展提供了必要的数据基础。因此，科学数据的长期保存和合理利用成为近年来的热门研究问题。档案部门、数据中心、高校图书馆等机构也纷纷开展了科学数据归档服务的实践。为衡量科学数据归档服务的质量，本研究采用内容分析、比较研究和访谈等研究方法，通过理论分析、政策分析、案例研究、访谈编码形成了科学数据归档服务质量评价指标体系。 本研究从理论角度出发，结合数据管护生命周期理论、ISO质量管理原则以及科学数据管理成熟度模型，总结形成科学数据归档服务流程模型。其次对科学数据归档和管理相关的国内外政策进行收集分析，通过词频统计和归纳形成部分政策指标。再次，从国家基因组科学数据中心和英国数据档案馆的科学数据归档服务实践案例中比较分析，提取部分指标。最后，分别对科学数据归档服务者和服务对象进行访谈，通过访谈记录编码形成部分指标。 最终，结合上述研究过程形成了包含数据收集服务、数据处理服务、数据保存服务、数据使用服务4个一级指标，归档政策服务、归档范围鉴定、数据归档指南、培训指导服务、数据管理规范、数据规范性服务、数据完整性服务、数据真实性服务、数据审查服务、数据容错服务、基础设施建设、归档系统建设、数据传输服务、数据组织方式、存储安全性、数据利用服务、用户反馈服务和知识产权界定18个二级指标以及其下的39个三级指标的科学数据归档服务质量指标体系。指标体系的建立目的在于指导实践，本研究将其应用于北京大学管理科学数据中心实例的评价，检验指标的可行性并根据评分结果对评价对象提高科学数据归档服务质量给出分析与建议。图13幅，表12个，参考文献89篇。
英文摘要：	With the rapid development of scientific research, the amount of scientific data has also increased rapidly. Scientific data is not only an important product of scientific research, but also provides necessary foundation for the development of follow-up studies. Therefore, the long-term preservation and rational use of scientific data have become hot research topics in recent years. Archives, data centers, college libraries and other institutions have also started the practice of scientific data archiving services. In order to evaluate the quality of scientific data archiving services, this research adopts research methods such as content analysis, method of comparative study, and interviewing method. Through theory analysis, policy analysis, case studies and interview coding, an evaluation system for the archiving quality of scientific data is formed. This research is based on a theoretical perspective, combining data management life cycle theory, ISO quality management principles, and scientific data management maturity models to summarize a scientific data archiving service process model. Secondly, collect and analyze domestic and foreign policies related to scientific data archiving and management, and form some policy indicators through word frequency statistics and induction. Thirdly, from the comparative analysis of the scientific data archiving service practical cases of the National Genomics Data Center and the UK Data Archive, some indicators were extracted. Fourthly, interviews were conducted with scientific data archiving service providers and service objects. Some indicators were formed through the interview record coding. Finally, through the above research process, an evaluation system for the archiving quality of scientific data are formed, with 4 first-level indicators including data collection services, data processing services, data preservation services, data usage services, 18 second-level indicators including archiving policy services, archiving scope identification, data archiving guidelines, training guidance services, data management specifications, data normative services, data integrity services, data authenticity services, data review services, data fault tolerance services, infrastructure construction, archiving system construction, data transmission services, data organization methods, storage security, data utilization services, user feedback services, definition of intellectual property rights and 39 third-level indicators. The purpose of establishing the evaluation system is to guide the practice. This study applies it to the evaluation of the case of the Data Center for Management Science of Peking University to test the feasibility of the indicators. And give analysis and suggestions for the evaluation objects to improve the quality of scientific data archiving services according to the results of the evaluation. 13 figures, 12 tables and 89 references are included.
查看全文：	预览  下载(下载需要进行登录)