科学数据的开放共享推动了学术界对于其公共学术价值的认识和利用，大数据、科研基础设施和科研环境信息化使得科学研究向第四研究范式转型，科学数据复用为新的科学发现和知识创新提供了有效途径。科学数据复用研究受到学术界的关注，相关研究成果在近20年来日益丰富，但该学科领域的知识体系尚未建立。本研究以Web of Science核心合集数据库作为数据采集来源，运用HistCite和CiteSpace软件绘制知识图谱并结合文本内容分析，梳理了科学数据复用研究的发展态势、演进过程及研究结构，研究发现：科学数据复用研究经历了萌芽阶段（2006年前）、发展阶段（2007—2014年）和爆发阶段（2015年至今），主要包括基本内涵、共享与复用关系、数据复用影响因素、学科领域研究以及数据复用伦理等五个方面的研究主题。基于此，本研究从保障平台、理论基础、研究分支和方法工具四个层面构建科学数据复用研究的知识体系，并提出科学数据公共学术价值、科学数据复用行为及机制、科学数据复用评价及影响力、科学数据复用政策和领域科学数据复用研究等几个亟须深入开展的研究主题。本研究为今后开展科学数据复用的相关研究提供理论和实践指导。
〖HT5”H〗
Abstract
Data reuse, the reuse of scientific data to solve new research problems, accepts both the new interpretation of data explored by other researchers and the new test of original research data by researchers using other analysis technologies. Although big data, research infrastructure and informatization of the research environment are transforming scientific research into the fourth research paradigm, data reuse has provided an effective way for new scientific discovery and knowledge innovation. Its public value increases daily as a strategic resource of national scientific and technological innovation and scientific research infrastructure. The research of data reuse has received much attention in the past 20 years, but the knowledge system in this subject area has not yet been established and lacks proper planning and forward-looking prediction.
This study comprehensively uses the bibliometric methods and knowledge map analysis tools (such as HistCite and CiteSpace) to process and analyze the large-scale research literature data objectively and intuitively. Using the Web of Science database as the source of literature collection, we utilize the "data reuse", "data re-use", "data reusing", "reusing data", "reusing of data", "secondary data use", and "data re-usability" as the keywords and the deadline of data collection was March 20, 2021. This study involves 364 papers in sum finally.
The main findings and theoretical contributions of this study are as follows:
(1) The existing research on data reuse presents the development path, evolution process, driving factors, and research structure of "two main lines", "three stages", "four forces" and "five core fields". From the perspective of the development path, data reuse is mainly carried out along two main lines, which run through three evolutionary stages: germination (before 2006), development (2007-2014) and outbreak (2015-). From the keyword co-occurrence analysis, data reuse research has five core fields: basic theoretical research, data sharing and reuse relationship, user behavior and scientific research management, data reuse ethics, and data reuse in various disciplines.
(2) The knowledge system of data reuse research consists of four levels, including the guarantee platform layer, theoretical foundation layer, research branch layer and method tool layer. The development of digital scientific research and data infrastructure, the change of data behavior, scientific research evaluation, and the development of big data technology are the frontiers and growth points of developing four levels of knowledge systems and methods and tools. They also constitute the four driving forces for the in-depth development of scientific data reuse: the needs of big scientific research and the formation of a digital scientific research environment, the development of the data-intensive scientific discovery, the recognition of scientific data achievements, and the development of digital technology.
(3) The subsequent research on data reuse has an opportunity window for academic research in five aspects: public academic value of scientific data, behavior and mechanism of data reuse, influence of data reuse, policy of data reuse, and data reuse in the different fields. We expect the academic community to follow up continuously on these research topics and provide theoretical supports for practically improving scientific data reuse.
关键词
科学数据 /
数据复用 /
第四研究范式 /
引文分析 /
知识图谱
Key words
scientific data /
data reuse /
fourth research paradigm /
citation analysis /
knowledge map
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基金
国家社会科学基金重大项目：“数据科学对社会科学转型的重大影响研究”（17ZDA030）；中央高校基本科研业务费专项资金资助项目：“开放科学数据的学术价值及其影响力测度：一项社会调查数据来源的研究”（HIT.HSS.201841）；广西壮族自治区科协资助青年科技工作者专项课题：“大数据环境下广西科技工作者数据素养测评及培育策略研究”（桂科协〔2019〕ZB-13）；广西哲学社会科学规划研究课题：“大数据驱动下面向科研第四范式的高校图书馆应对策略研究”（17FTQ004）。
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