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.
Key words
scientific data /
data reuse /
fourth research paradigm /
citation analysis /
knowledge map
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