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数据引证研究:进展与展望 被引量:42

Review on Data Citation in the Context of Big Data
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摘要 随着大数据时代的来临以及数据密集型科学研究范式的兴起,"数据引证"问题日益受到关注。本文对该领域的国际研究现状进行了梳理总结,研究发现:①对数据引证的知识计量研究,将推动文献计量学、信息计量学和科学计量学三者的合流,形成一个统一的新学科———知识计量学;②数据引证实践的现状不尽如人意,但已在诸如数据集标识系统建立等问题上取得了重要进展,统一规范化数据引证格式的趋势日益清晰;③数据引证现状评估与研究进展的追踪,数据引证索引的编纂、指标体系研究以及数据引证数据库的建立,基于数据引证行为、记录以及索引的分析,应是未来需重点突破的方向。 In the context of big data and data-intensive science, data citation now calls for special attention of ninny organi- zations and workshops worldwide. This paper surmnarizes international research on "data citation" in recent years, and gives some conclusions as follows: 1) "data citation" studies will bridge the gap among bibliometrics, informetics and scientometrics, and lead to the forming of knowmetrics; 2) "data citation" practices are far from satisfactory, but with some important advancement such as establishment of data set identifier systems, and the normalization of data citation practice is just on the way; 3) the most important research topics about data citation in the future include evaluating the progress of data citation, compiling data citation indices, building data reference database, and extending analysis based on data citation records and indices.
出处 《中国图书馆学报》 CSSCI 北大核心 2013年第1期112-118,共7页 Journal of Library Science in China
基金 国家自然科学基金青年项目“基于信息主权的国家核心竞争力保护与提升策略研究”(项目编号:70973037)的研究成果之一
关键词 数据引证 知识计量学 大数据 数据密集型科学 Data citation. Knownletrics. Big data. Data intensive science.
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参考文献14

  • 1White H. Citation analysis of data files use [J]. Library Trends, 1982, 31(3) : 467 -477.
  • 2Steve L. The age of big data[ N/OL]. The New York Times, [2012 -2 - 11 ] [2012 -8 -28]. http://forum, ccer. edu. cn/showtopic, aspx?topicid = 124765&page = end.
  • 3Jim G. On eScience-A transformed scientific method [C]//Tony H,Stewart T,Kirstin T. The Fourth Paradigm: Data- intensive Scientific Discovery. Redmond,WA: Microsoft Research, 2009 : 19 - 33.
  • 4Wallis J ,Borgman C. Who is responsible for data? An exploratory study of data authorship,ownership and responsibility [ C/OL]// Proceedings of the Annual Meeting of the American Society for Information Science and Technology, 2011, 48:1 -10. [2012 -08 -29]. http://dx. doi. org/10.1002/meet.2011.14504801188.
  • 5UCLA Library. Bridging data lifecycles : Tracking data use via data citation [ EB/OL ]. (2012 - 04 - 05 ) [2012 - 08 - 28]. http://library, ucar. edu/data_workshop/.
  • 6Palmer C,Weber N,Cragin M. The analytic potential of scientific data: Understanding re-use value[ C/OL]//Proceedings of the 74th Annual Meeting of the American Society for Information Science & Technology. Silver Spring, Mary. (2011 - 12 - 10) [2012 - 138 - 28 ]. http ://www. asis. org/asist2011/proceedings/submissions/174_FINAL SUBMIS- SION. pdf.
  • 7White House. Big data fact sheet [EB/OL]. (2012 -03 -29) [2012 -08 -28]. http://www, whitehouse, gov/sites/ defauh/files/micmsites/ostp/big_data fact sheet_final, pdf.
  • 8Clifford A. Jim Gray's fourth paradigm and the construction of the scientific record [ C ]//Tony H,Stewart T,Kirstin T. The Fourth Paradigm: Data-intensive Scientific Discovery. Redmond,WA: Microsoft Research, 2009: 177-183.
  • 9Egghe L,Rousseaur R. Introduction to informetrics: Quantitative methods in library,documentation and information Science [ M ]. Amsterdam,The Netherlands: Elsevier Science Publishers, 1990: 1 - 2.
  • 10Valerie E,Sarah W,Nicholas M,et al. Data citation in the wild [R/OL]. (2010-9 -13)[2012 -8 -28]. http://precedings, nature, com/documents/5452/version/1.

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