Data Science and Informetrics (DSI) covers the trends, scientific foundations, techniques and applications of the field. The journal publishes research papers, technical reports subject reviews, short comments and book reviews, providing a platform for data scientists, computer scientists, industry practitioners, and potential users of data science and analytics.

Topics covered include but are not limited to:

• Theory and mathematical foundations for data science and informetrics.

• Data analytics, knowledge discovery, machine learning, and deep learning, and intelligent processing of various data (including text, image, video, graph and network).

• Big data architecture, infrastructure, computing, matching, indexing, query processing, mapping, search, retrieval, interopera­bility, exchange and recommendation.

• Data science applications, intelligent services in scientific, business, governmental, cultural, behavioral, social and economic, health and medical, human, natural and artificial (including online/Web, cloud, IoT, mobile and social media) domains.

• Ethics, quality, privacy, safety and security, trust and risk on data science and big data.

• The convergence of bibliometrics, scientometrics, webometrics, altmetrics, informetrics and data science.

• Informetric problems using methods from other quantitative fields, such as mathematics, statistics, computer science, network science and data science.