Higher education plays a critical role in driving an innovative economy by
equipping students with knowledge and skills demanded by the workforce. While
researchers and practitioners have developed data systems to track detailed
occupational skills, such as those established by the U.S. Department of Labor
(DOL), much less effort has been made to document skill development in higher
education at a similar granularity. Here, we fill this gap by presenting a
longitudinal dataset of skills inferred from over three million course syllabi
taught at nearly three thousand U.S. higher education institutions. To
construct this dataset, we apply natural language processing to extract from
course descriptions detailed workplace activities (DWAs) used by the DOL to
describe occupations. We then aggregate these DWAs to create skill profiles for
institutions and academic majors. Our dataset offers a large-scale
representation of college-educated workers and their role in the economy. To
showcase the utility of this dataset, we use it to 1) compare the similarity of
skills taught and skills in the workforce according to the US Bureau of Labor
Statistics, 2) estimate gender differences in acquired skills based on
enrollment data, 3) depict temporal trends in the skills taught in social
science curricula, and 4) connect college majors' skill distinctiveness to
salary differences of graduates. Overall, this dataset can enable new research
on the source of skills in the context of workforce development and provide
actionable insights for shaping the future of higher education to meet evolving
labor demands especially in the face of new technologies.

高等教育在推动创新经济方面发挥着关键作用，通过为学生提供劳动力需求所需的知识和技能。本研究填补了一个空白，通过提供从近 3000 所美国高等教育机构的三百万门课程大纲中推断技能的纵向数据集，回答了如何在高等教育中记录技能发展的问题。这个数据集通过自然语言处理从课程描述中提取具体的工作场景活动（DOL 所用来描述职业的 DWAs），并聚合这些 DWAs 来为机构和学术专业创建技能概况。这个数据集可以用于研究技能来源、劳动力发展的背景，并为塑造未来高等教育以满足不断变化的劳动力需求提供可操作的见解。