The Journal of Data and Information Science (JDIS) focuses on data-based research oriented toward the exploration of scientific research and innovation. The main areas of interest are science of science, evidence-based policymaking, research evaluation, computational social science, and scientometrics/bibliometrics/altmetrics/ informetrics. Emphasis is given to research that focuses on data, analytics, and knowledge discovery, and supports decision making and science policy. This includes modeling, innovation, data security, media and communications, and social development. Topics may include studies of metadata or full content data, text or non-textural data, structured or non-structural data, domain-specific or cross-domain data, and dynamic or interactive data.

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. 

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