Specific topic areas may include (but are not limited to): Knowledge organization; Knowledge discovery and data mining; Knowledge integration and fusion; Semantic Web; Science of science; Bibliometrics and scientometrics; Analytic and diagnostic informetrics; Competitive intelligence; Predictive analysis; Social network analysis and metrics; Semantic and interactively analytic retrieval; Evidence-based policy analysis; Intelligent knowledge production; Knowledge-driven workflow management and decision-making; Knowledge-driven collaboration and its management; Domain knowledge infrastructure with knowledge fusion and analytics; Training for data & information scientists; Development of data and information services.
期刊介绍
The Journal of Data and Information Science, established in 2016, is an open-access, peer-reviewed journal governed by the Chinese Academy of Sciences (CAS) and published by the National Science Library of CAS (NSLC). Supported by NSLC, JDIS does not charge any publication fees. The Journal of Data and Information Science offers a platform for scientists across diverse fields to enhance the quantitative understanding of research activities through data-driven insights. It examines the internal mechanisms of research and its interplay with various social factors. The journal addresses universal issues that span diverse countries/regions and disciplines, including funding strategies, international collaboration models, career paths for scientists, trends in talent mobility, the evolution of disciplines, academic communication, research evaluation systems, technology transfer, the construction of research integrity, and the interactions between the scientific community and other societal systems. We encourage submissions from researchers across all domains to enrich this dialogue. In particular, JDIS puts special emphasis on research articles that aim to design, test, verify, or compare new theories, methods, or tools, in analysis of research profiles or developments. It requires that submitted manuscripts go far beyond merely statistically scientometric analysis using established methods and tools. Please note that, with the exception of topics in information science, JDIS does not consider for publication case studies that rely solely on bibliometric methods to provide descriptive overviews of subject field development.