High−quality data acts as a pivotal driver for the intelligent transformation of research paradigms in materials science. However, the structural deficiencies in the current materials data production system not only lead to quality issues such as data fragmentation, siloing, format heterogeneity and incomplete records, but also result in an excessive transfer of quality control responsibilities to downstream users due to weak producer accountability and the absence of a systematic governance framework. Although users attempt to enhance data governance effectiveness by integrating domain knowledge into various corresponding methods, their limited insight into data production mechanisms hinders the generalizability of their governance strategies and the reliability of results. To this end, this paper focuses on the sustainable generation of high−quality materials data as the central theme, systematically examining the causes of data quality degradation across the data full lifecycle stages, and outlining a comprehensive materials data quality control framework. It aims to guide parties involved in data production and use to reevaluate the entire process from data acquisition and management to application, while providing referential insights for implementation pathways that promote efficient delivery of high−quality materials data, thereby laying foundational support for cultivating a materials data trusted ecosystem.

materials science  /  data quality control framework  /  trusted ecosystem  /  full lifecycle