A dataset of study program availability in German higher education between 1971 and 1996.

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-10-08 DOI:10.1038/s41597-025-06052-y

Boris Thome, Friederike Hertweck, Serife Yasar, Lukas Jonas, Stefan Conrad

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引用次数: 0

Abstract

Educational systems are dynamic. They shape human capital, technological and societal progress, and also economic growth. Higher education, in particular, fosters innovation, with varying fields of study contributing differently to this process. Yet, despite its importance, no dataset has previously documented the evolution of academic fields across higher education institutions in a specific country. Addressing this gap, we present the RWI-UNI-SUBJECTS¹ dataset, the first extensive collection of study opportunities across German higher education institutions between 1971 and 1996. The dataset originates from annual study guides by the German Federal Employment Agency for high school students. To extract the data, a custom-developed computer vision algorithm was used. We further enriched the dataset with administrative codes for fields, institutions, and districts, enabling seamless integration with additional datasets, such as social security data, official student statistics, or the National Educational Panel Study (NEPS). Covering a total of 105,307 study programs between 1971 and 1996, RWI-UNI-SUBJECTS¹ offers a valuable foundation for interdisciplinary research on education, innovation, and economic development.

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1971年至1996年间德国高等教育学习计划可用性数据集。

教育系统是动态的。它们影响着人力资本、技术和社会进步，也影响着经济增长。高等教育尤其能促进创新，不同的研究领域对这一过程的贡献不同。然而，尽管它很重要，但之前没有数据集记录了特定国家高等教育机构学术领域的演变。为了解决这一差距，我们提出了RWI-UNI-SUBJECTS1数据集，这是1971年至1996年间德国高等教育机构学习机会的第一个广泛收集。该数据来源于德国联邦就业局为高中生提供的年度学习指南。为了提取数据，使用了定制开发的计算机视觉算法。我们用领域、机构和地区的行政代码进一步丰富了数据集，实现了与其他数据集（如社会保障数据、官方学生统计数据或国家教育小组研究（NEPS））的无缝集成。RWI-UNI-SUBJECTS1涵盖了1971年至1996年间的105,307个研究项目，为教育、创新和经济发展的跨学科研究提供了宝贵的基础。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.