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Description: PhD-trained scientists are essential contributors to the workforce in diverse employment sectors that include academia, industry, government, and non-profit organizations. Hence, best practices for training the future biomedical workforce are of national concern. Complementing coursework and laboratory research training, many institutions now offer professional training that enables career exploration and develops a broad set of skills critical to various career paths. The National Institutes of Health funded academic institutions to design innovative programming to enable this professional development through a mechanism known as Broadening Experiences in Scientific Training (BEST). Programming at the BEST awardee institutions included career panels, skill-building workshops, job-searching workshops, site visits, and internships. Because doctoral training is lengthy and requires focused attention on dissertation research, an initial concern was that students participating in additional complementary training activities might exhibit an increased time to degree or diminished research productivity. Metrics were analyzed from ten BEST awardee institutions to address this concern, using time to degree and publication records as measures of efficiency and productivity. Comparing doctoral students who participated to those who did not, results revealed that across these diverse academic institutions, there were no differences in time to degree or manuscript output. Furthermore, a few institutions demonstrated a positive correlation between participation in career and professional development activities and productivity. Our findings support the policy that doctoral students should participate in career and professional development opportunities that are intended to prepare them for a variety of diverse and important careers in the workforce. Note: Institutional data has been de-identified and each file corresponds to institutional labels consistent with publication manuscript (e.g., "Institution A" in manuscript corresponds to file "ZA TTD data deidentified"; Brandt et al, 2021). Columns for each institutional data set include: coded trainee ID; trainee participation data and dose-bin assigned; time to degree and/or defense; and number of publications (total, first author, and/or pub metric composite score).