Publications

The project Monotoring Appointment Procedures (BerBeo) is a research project funded by the Federal Ministry of Education and Research (BMBF) and carried out by the German Centre for Higher Education Research and Science Studies GmbH (DZHW). The project analysed (procedural) quality assurance in appointment procedures for professors at German (state) universities through officers for appointment procedures. Officers for appointment procedures are defined as persons who are responsible for the procedural quality assurance of appointment procedures for professors and – depending on the implementation of their position – may also perform other tasks to support the decision-making [...] Full Abstract: https://doi.org/10.21249/DZHW:berbeo:1.0.0

On the basis of an analysis of maximally contrasting cases (universities, disciplines), it was investigated how the change in the appointment procedure affects the relationship between the actors involved and their enactment of institutional logics. Eight universities and four universities of applied sciences with different profiles were selected for this purpose. The analysis focussed on the subjects of sociology/social sciences (universities and universities of applied sciences), mechanical engineering (universities and universities of applied sciences), physics (universities) and medicine (universities) in order to cover subject-specific differences (with regard to tasks, [...] Full Abstract: https://doi.org/10.21249/DZHW:lib2016:2.0.0

The DZHW Scientists Survey 2023 is an online survey of full-time academic and artistic staff at German universities and equivalent institutions of higher education with the right to award doctorates. It is repeated at regular intervals as a trend study to explore the working and research conditions at German universities and equivalent institutions of higher education. The DZHW Scientists Survey 2023 was conducted from January to March 2023. The respondents therefore take a retrospective look at their working and research conditions during the Covid-19 pandemic and their current post-pandemic situation. The previous Scientists Surveys took place in 2010, 2016 and 2019/2020. [...] Full Abstract: https://doi.org/10.21249/DZHW:scs2023:1.0.0

Cardiovascular diseases continue to be the leading cause of mortality worldwide, claiming a significant number of lives each year. Despite the advancements in predictive models, including logistic regression, neural networks, and random forests, these techniques often lack transparency and interpretability, limiting their practical application in clinical settings. To address this challenge, this research introduces EPFHD-RARMING, an innovative approach designed to enhance the understanding and predictability of heart disease through the discovery of rare and meaningful patterns. EPFHD-RARMING utilizes rare association rule mining to [...] Full Abstract: https://www.nature.com/articles/s41598-024-69071-6#citeas