摘要
探究文化遗产领域科学数据复用的主要影响因素能够为文化遗产领域数据有效利用和管理提供参考。以DataCite中被复用的203个文化遗产科学数据集为研究对象,采用非参数检验、多元线性回归等方法,从数据自身属性、数据提供方、数据仓储三个维度,对文化遗产领域科学数据复用影响因素进行分析。研究发现,在控制时间跨度因素后,许可、类型、摘要长度、格式、出版商、资助方、质量管理是影响文化遗产领域科学数据复用的主要因素,具有正面影响,且影响程度依次递增。结合我国实际情况提出如下建议与启示:建立文化遗产元数据标准,提高数据描述信息的充分性;将数据复用纳入资助要求,推动文化遗产项目数据复用;构建文化遗产科学数据出版体系,完善数据出版中的同行评议;促进文化遗产数据质量管理,充分发挥数据仓储的作用。
To provide reference for the effective use and management of data in the field of cultural heritage,this paper explores the main factors affecting the reuse of scientific data in the field of cultural heritage by taking 203 cultural heritage scientific datasets reused in DataCite as the research object.Employing nonparametric test and multiple linear regression,this paper explores the influencing factors of scientific data reuse in the field of cultural heritage from three dimensions of data attribute,data provider and data repository.After controlling the time span factors,license,type,abstracts,formats,publishers,sponsor,and quality management are the main factors positively affecting the reuse of scientific data in the field of cultural heritage,and the degree of influence increases successively.Combining with the actual situation in China,this paper puts forward the following enlightenment:establishing cultural heritage metadata standards to improve the adequacy of data description information;incorporating data reuse into funding requirements to promote data reuse of cultural heritage projects;building a scientific data publishing system for cultural heritage and improving peer review in data publishing;promoting quality management of cultural heritage data and making full use of data repositories.
作者
饶梓欣
许鑫
Rao Zixin;Xu Xin(Faculty of Economics and Management,East China Normal University,Shanghai,200062;Social Survey and Data Center,East China Normal University,Shanghai,200241)
出处
《信息资源管理学报》
CSSCI
2023年第5期32-43,共12页
Journal of Information Resources Management
基金
国家社会科学基金重大项目“文化遗产智慧数据资源建设与服务研究”(21&ZD334)的研究成果之一。
关键词
数据复用
文化遗产
科学数据
数据质量管理
影响因素
Data reuse
Cultural heritage
Scientific data
Data quality management
Influencing factors