中国图书馆学报
中國圖書館學報
중국도서관학보
The Journal of The Library Science in China
2014年
4期
116~128
,共null页
数据管护 数字管护 数字保存 数字知识库 E-science 机构知识库
數據管護 數字管護 數字保存 數字知識庫 E-science 機構知識庫
수거관호 수자관호 수자보존 수자지식고 E-science 궤구지식고
Data curation. Digital curation. Digital preservation. Digital repository. E-science.Institutional repository.
数据管护(Data curation)可以促进科学数据共享,提高科学研究的质量。随着e-science的发展,数据管护正在引起科学家、大学与研究机构以及图书馆、档案馆等信息资源管理机构的重视。本文在总结已有成果的基础上,提出了一个细化的数据管护生命周期概念模型,包括6个阶段14个关键活动。以此为分析框架,对国外数据管护的研究、实践与教育进展进行全面梳理。研究发现:目前欧美国家在数据管护的投资立项、软件系统开发、新技术应用、数据质量评价以及教育与培训等方面取得了一定的进展,值得借鉴;同时也存在一些问题,如管护模型缺乏质量控制措施,大学与科研机构忽视数据政策的制定,人文学科与“小”学科对数据管护的认知不足,以及专业教育和职业培训严重欠缺等。图1。参考文献68。
數據管護(Data curation)可以促進科學數據共享,提高科學研究的質量。隨著e-science的髮展,數據管護正在引起科學傢、大學與研究機構以及圖書館、檔案館等信息資源管理機構的重視。本文在總結已有成果的基礎上,提齣瞭一箇細化的數據管護生命週期概唸模型,包括6箇階段14箇關鍵活動。以此為分析框架,對國外數據管護的研究、實踐與教育進展進行全麵梳理。研究髮現:目前歐美國傢在數據管護的投資立項、軟件繫統開髮、新技術應用、數據質量評價以及教育與培訓等方麵取得瞭一定的進展,值得藉鑒;同時也存在一些問題,如管護模型缺乏質量控製措施,大學與科研機構忽視數據政策的製定,人文學科與“小”學科對數據管護的認知不足,以及專業教育和職業培訓嚴重欠缺等。圖1。參攷文獻68。
수거관호(Data curation)가이촉진과학수거공향,제고과학연구적질량。수착e-science적발전,수거관호정재인기과학가、대학여연구궤구이급도서관、당안관등신식자원관리궤구적중시。본문재총결이유성과적기출상,제출료일개세화적수거관호생명주기개념모형,포괄6개계단14개관건활동。이차위분석광가,대국외수거관호적연구、실천여교육진전진행전면소리。연구발현:목전구미국가재수거관호적투자립항、연건계통개발、신기술응용、수거질량평개이급교육여배훈등방면취득료일정적진전,치득차감;동시야존재일사문제,여관호모형결핍질량공제조시,대학여과연궤구홀시수거정책적제정,인문학과여“소”학과대수거관호적인지불족,이급전업교육화직업배훈엄중흠결등。도1。삼고문헌68。
Data curation can promote the sharing of scientific data and increase the quality of research. With the development of e-science, data curation has received more and more attention from scientists, institutions and organizations for information resources management, such as libraries and archives. On the basis of summing up existing studies, this paper formulated a detailed data curation lifecycle model that comprises 6 stages with 14 key actions. Under this model, global advances in research, practice and education of data euration were combed. It is worth learning that European countries and the United States have made progress in project approval and investment on data curation, development of curating systems and application of new technology, data quality evaluation, education and training. Meanwhile, problems existing in data curation were also identified, such as lack of measures for quality control in curation models, ignorance of policy-making in universities and research institutions, shortage of related knowledge in humanities and "small" disciplines and insufficient education and training, etc. 1 fig. 68 refs.