Publications

"The Student Survey in Germany" (2021) is a newly designed study that integrates three previously separate student surveys (Beuße et al., 2022): the Social Survey (Middendorff et al., 2017; Middendorff & Wallis, 2023), the Student Survey (Multrus et al., 2017; Multrus, 2021) and the survey "best - Studying with impairments" (Poskowsky et al., 2018; Unger et al., 2012). The study is designed as a cross-sectional survey with a long-term character. The new, integrated student survey thus continues the long tradition of the Social Survey and the Student Survey as important cross-sectional long-term observation studies for describing and analysing higher education and the student [...] Full Abstract: https://doi.org/10.21249/DZHW:sid2021:1.0.0

he project "VORAUS: Further developing established quality assurance formats: Which VORreservations and consensuses does a partially randomised SELECTION of research projects encounter in the scientific field?" was funded by the BMBF from April 2019 to March 2022. The study investigated reservations and consensus in the scientific field regarding partial randomisation. On this basis, it was clarified what is currently considered appropriate in science. Prior to a quantitative survey on the topic, the data from which is also available as a data package (DOI: https://doi.org/10.21249/DZHW:vorausquanti:1.0.0), problem-centred interviews were conducted with [...] Full Abstract: https://doi.org/10.21249/DZHW:vorausquali:1.0.0

Computational biology research faces a formidable challenge in the detection of complexes within protein-protein interaction (PPI) networks, critical for unraveling cellular processes, predicting functions of uncharacterized proteins, and diagnosing diseases. While evolutionary algorithms (EAs), particularly state-of-the-art methods, often partition PPI networks based on graph properties or biological semantics, their resilience to noisy or missing interactions remains an underexplored territory. In this paper, we propose a groundbreaking heuristic operator, termed "strong neighbor-node migration", specifically designed to elevate solution quality [...] Full Abstract: https://ieeexplore.ieee.org/document/10440281

Graph data models enable efficient storage, visualization, and analysis of highly interlinked data, by providing the benefits of horizontal scalability and high query performance. Clustering techniques, such as K-means, hierarchical clustering, are highly beneficial tools in data mining and machine learning to find meaningful similarities and differences between data points. Recent developments in graph data models, as well as clustering algorithms for graph data, have shown promising results in image segmentation, gene data analysis, etc. This has been primarily achieved through research and development of algorithms in the field of spectral theory, [...] Full abstract: https://doi.org/10.1007/s44163-024-00102-x