Coding Scheme for Profiles of Distinguished Sci-Tech Talents (Partial Sample, December 2024)

This dataset contains structured, coded multi-dimensional characteristic data for 847 distinguished sci-tech talents in China (partial sample). Through systematic coding, it associates a talent's election age (dependent variable) with their cultural capital, social capital, and symbolic capital (core independent variables), as well as control variables including gender, generational cohort, birth region, political status, and research field. The coding is derived from publicly available resumes, award records, and academic biographical information, following a clear set of classification and quantification rules (e.g., a 3-tier classification for institutional prestige, a 5-level scoring for awards, and a 4-segment division for generational cohorts). This dataset is suitable for quantitative research and heterogeneity analysis in areas such as the growth patterns of sci-tech talents, research evaluation systems, and the effectiveness of high-level talent policies.