Abstract

The guide introduces the background of current high-quality dataset construction from the policy, technology, and industry levels. It outlines the definition, characteristics, classification, construction entities, and "three major construction challenges" of high-quality datasets. It proposes the "five core elements" of artificial intelligence data engineering and the "three-step" strategy for enterprises to build high-quality datasets. It analyzes the core technologies for high-quality dataset construction and showcases practical examples of high-quality dataset construction in fields such as science, communications, transportation, towers, healthcare, and culture. Finally, it looks forward to the trends of future high-quality dataset construction from the perspectives of engineering capabilities, technological innovation, quality assessment, copyright compliance, and basic institutional innovation. It also provides suggestions for government departments and enterprises, offering strong support for the industry to advance high-quality dataset construction.