[Purpose/significance] This paper studies the core elements of knowledge innovation in research teams under data intensive research environment, and proposes a data capability model for knowledge innovation of research teams, identifying the ability structure in the ability factors. [Method/process] Through literature review, this paper summarizes the research dimensions of knowledge innovation capability of scientific research teams in traditional environment, analyzes the scientific research environment formed by the rapid development of science in the data intensive environment, sketchs 6 dimensions of the research team in the new environment, and then constructs a data capability model to promote the knowledge innovation.[Result/conclusion] The research team needs to have data awareness, data retrieval and data discovery capability, data organization, integration and management capability, data description and storage capability, knowledge and rules analysis from the data capability, data analysis and evaluation capability. These capabilities promote knowledge innovation in research teams in the form of stimulating knowledge discovery, knowledge management, knowledge integration, knowledge sharing and knowledge evaluation.
Du Xingye
,
Li He
,
Li Zhuozhuo
. Research on Data Capability Model Construction of Scientific Research Team Facing Knowledge Innovation[J]. Library and Information Service, 2018
, 62(4)
: 28
-36
.
DOI: 10.13266/j.issn.0252-3116.2018.04.004
[1] HEY T, TANSLEY S, TOLLE K. The fourth paradigm: data intensive scientific discovery [M].Washington:Microsoft Research,2009.
[2] 李立睿, 邓仲华. "互联网+"视角下面向科学大数据的数据素养教育研究[J]. 图书馆, 2016(11):92-96.
[3] 王晓文,沈思. 国外科研人员数据素养教育述评及启示[J]. 情报资料工作,2017(3):102-106.
[4] 唐五湘. 创新论[M]. 北京:中国盲文出版社,1999.
[5] ROGERS D M A. Knowledge innovation system: the common language [J].The journal of technology studies,1993:19(2): 2-8.
[6] NONAKA I, TOYAMA R, KONNO N. SECI, Ba, and leadership: a unified model of dynamic knowledge creation[J]. Long range planning, 2000, 33(1):5-34.
[7] 杨斌, 熊万玲, 游静. 基于知识转移的高校科研团队知识创新能力提升路径实证研究[J]. 情报理论与实践, 2011, 34(8):60-64.
[8] KHEDHAOURIA A, JAMAL A. Sourcing knowledge for innovation: knowledge reuse and creation in project teams[J]. Journal of knowledge management, 2015, 19(5):932-948.
[9] HONG J, LEE O K, SUH W. Creating knowledge within a team: a socio-technical interaction perspective[J]. Knowledge management research & practice, 2017, 15(1):23-33.
[10] 赵丽梅, 孙艳华. 面向知识创新的高校科研团队内部知识整合的特征与内涵研究[J]. 科技管理研究, 2015, 35(1):171-176.
[11] 刘岩芳, 袁永久. 面向知识创新的组织内部知识整合层级研究[J]. 情报科学, 2012(12):119-122.
[12] 郭艳丽,易树平. 基于知识位势的任务型团队知识创新模式研究[J]. 情报理论与实践, 2013, 36(1):20-24.
[13] 李纲, 巴志超. 科研团队中知识粘滞的影响因素研究[J]. 中国图书馆学报, 2017, 43(1):89-106.
[14] 王新春, 戚桂杰, 梁乙凯,等. 开放式创新社区组织知识创造能力提升研究[J]. 情报杂志, 2016, 35(3):203-206.
[15] 吴杨, 苏竣. 科研团队知识创新系统的复杂特性及其协同机制作用机理研究[J]. 科学学与科学技术管理, 2012, 33(1):156-165.
[16] 吴杨, 李晓强, 夏迪. 沟通管理在科研团队知识创新过程中的反馈机制研究[J]. 科技进步与对策, 2012, 29(1):7-10.
[17] 董春雨,薛永红. 数据密集型、大数据与"第四范式"[J]. 自然辩证法研究,2017,33(5):74-80,86.
[18] 邓仲华, 李志芳. 科学研究范式的演化——大数据时代的科学研究第四范式[J]. 情报资料工作, 2013, 34(4):19-23.
[19] 黄鑫, 邓仲华. 数据密集型科学研究的需求分析与保障[J]. 情报理论与实践, 2017, 40(2):66-70.
[20] 梁娜, 曾燕. 推进数据密集科学发现提升科技创新能力:新模式、新方法、新挑战——《第四范式:数据密集型科学发现》译著出版[J]. 中国科学院院刊, 2013(1):115-121.
[21] CARLSON J, JOHNSTON L,WESTRA B,et al. Developing an approach for data management education: a report from the data information literacy project[J].The international journal of digital curation, 2013,8(1):204-217.
[22] DCC curation lifecycle model[EB/OL].[2017-08-26].http://www.dcc.ac.uk/resources/curation-lifecycle-model.
[23] UK·data archive research data lifecycle[EB/OL].[2017-08-14].http://www.data-archive.ac.uk/create-manage/life-cycle.
[24] BOULTON R, CAMPBELL P, COLLINS B, et al. Science as an open enterprise [R]. London: Royal Society, 2012.
[25] Science 2.0 consultation: Royal Society response[EB/OL].[2017-06-14].https://royalsociety.org/~/media/policy/Publications/2014/science-2-0-consultation-response-290914.pdf.
[26] KOLTAY T.Data literacy for researchers and data librarians[J].Journal of librarianship & information science,2017, 49(1):3-14.
