科研大数据休眠:类型划分及消解机制研究  

Research on Dormancy for Big Data in Scientific Research:Classification and Resolution Mechanism

作　　者：冯晓 佟泽华[1] 丰佰恒 孙晓彬 石江瀚 Feng Xiao

机构地区：[1]山东理工大学信息管理研究院,山东淄博255049

出　　处：《情报理论与实践》2022年第4期8-16,7,共10页Information Studies:Theory & Application

基　　金：国家社会科学基金项目“数据生态视角下科研大数据协同治理研究”的成果之一,项目编号:19BTQ077。

摘　　要：[目的/意义]科研大数据开放共享是科技创新发展的关键,唤醒休眠科研大数据充分挖掘其价值潜能,对于促进数据资源及科研大数据生态的良性发展具有重要的理论与实践意义。[方法/过程]以生物休眠理论为基础,提出“科研大数据休眠”的概念,并对其发展特性进行分析,进而对其类型划分以及对应的消解机制和演化路径进行论述。[结果/结论]科研大数据休眠是以自发性、潜伏性、再生性、触发性、多线性为特征,以数据被动或主动休眠为类型,以数据激活、数据复苏、数据价值重塑的三阶模型(A-R-R机制)为消解机制,以减少数据被动休眠、促进数据复苏及价值重塑为目标的多路径、多层次、循环式动态演化过程。[Purpose/significance] Open sharing of scientific research big data is the key to the development of scientific and technological innovation.Awakening dormant scientific research big data and fully exploiting its value potential have important theoretical and practical significance for promoting the benign development of data resources and ecological research big data.[Method/process] Based on the biological dormancy theory,this paper puts forward the concept of “Dormancy for Scientific Research Big Data”,analyzes its development characteristics,and then discusses its classification,corresponding digestion mechanism and evolution path.[Result/conclusion] The research shows that the dormancy of scientific research big data is characterized by spontaneity,latency,regeneration,trigger and multilinear.It is a multi-path,multi-level and cyclic dynamic evolution process with the goal of reducing passive dormancy of data,promoting data recovery and value remodeling,and taking the third-order model(A-R-R mechanism) of data activation,data recovery and data value remodeling as the resolution mechanism.

关 键 词：科研大数据休眠 类型分析 消解机制 演化路径 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] G301[自动化与计算机技术—计算机科学与技术] G250.7[文化科学]