Recently, the group standard titled "Specification for Cost Measurement of Information Technology Scientific Research Projects" (T/IQA 37—2026), spearheaded by the China Mobile Research Institute, was officially released through the Zhongguancun Zhilian Software Services Industry Quality Innovation Alliance. As the first cost measurement specification in the industry specifically targeting scientific research projects, this standard was jointly developed by 12 organizations, including relevant enterprises, universities, and research institutions, providing a unified framework for measuring costs in such projects. Previously, industry norms for cost measurement primarily applied to commercial application software, posing challenges as their models and methods were not fully adaptable to scientific research projects, leaving a gap in reference standards for cost evaluation in this domain. In its management practices for scientific research projects, the China Mobile Research Institute has consistently prioritized the scientific rigor and applicability of cost measurement methods, creatively exploring and developing a cost measurement model tailored to research initiatives, which has been systematically refined into the standard specifications. This standard clarifies the overall requirements, measurement methodologies, measurement processes, and application of results for cost measurement in scientific research projects, covering typical project types such as software development, hardware development, algorithm research, testing and evaluation, and soft science research. It applies to scenarios including cost estimation, budget preparation, and final settlement review of funds for information technology research projects, serving as a reference for industry regulatory authorities, research funding allocation and management bodies, research institutions, and third-party evaluation and audit agencies. The release and implementation of this standard will further shift cost measurement for research projects from reliance on experience-based judgment to model-driven estimation, reducing subjective discrepancies and enhancing the scientific basis, rationality, and compliance of research funding evaluations.