A comparison of methods for modeling soundscape dimensions based on different datasets

Soundscape studies vary considerably in study design, statistical methods, and model fit metrics used. Due to this confounding of data and methods, it is difficult to assess the suitability of statistical modelling techniques used in the literature. Therefore, five different methods and two performance metrics were applied to three existing soundscape datasets to model soundscape Pleasantness and Eventfulness based on seven acoustic and three sociodemographic predictors. Datasets differed in soundscape type (urban outdoor vs indoor), experimental setting (field- vs lab-based), size, and study design (site- vs person-centered). The fixed-effects and mixed-effects methods ranged from linear to nonlinear regression based on advanced machine learning approaches. Results showed that models performed better for Eventfulness than for Pleasantness in most cases, while performance as measured by the out-of-sample R2 was dependent on the total variance of the target, especially in both field studies with imbalanced targets and groups. Nonlinear methods consistently outperformed linear regression, with random forest and extreme gradient boosting performing particularly well, while the performance levels of all nonlinear methods remained comparable. Mixed-effects models provided a more generalized, albeit slightly smaller prediction performance when tested on unknown groups. Finally, this study motivates the use of cross-validation with special splitting for analyzing small imbalanced datasets

998 - Zweite Satzung zur Änderung der Prüfungsordnung (Studiengangspezifische Bestimmungen) für den ausbildungs- und berufsbegleitenden Bachelorstudiengang Taxation 3in1 an der Hochschule Düsseldorf vom 10.02.2025

Aufgrund der §§ 2 Abs. 4, 64 Abs. 1 des Gesetzes über die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell gültigen Fassung hat die Hochschule Düsseldorf die folgende Ordnung als Satzung erlassen

1004 - Dritte Satzung zur Änderung der Beitragsordnung der Studierendenschaft der Hochschule Düsseldorf vom 22.05.2025

Aufgrund des § 57 Abs. 1 des Gesetzes über die Hochschulen des Landes Nordrhein-Westfalen vom 16. September 2014 (GV. NRW. S. 547) in der aktuell geltenden Fassung gibt sich die Studierendenschaft der Hochschule Düsseldorf die folgende Satzung

1012 - Rahmenprüfungsordnung des Fachbereichs Architektur (RahmenPO) an der Hochschule Düsseldorf vom 08.07.2025

Aufgrund der §§ 2 Abs. 4, 64 Abs. 1 des Gesetzes über die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell gültigen Fas-sung hat die Hochschule Düsseldorf die folgende Rahmenprüfungsordnung als Satzung erlassen. Diese Ordnung gilt nur in Verbindung mit den studiengangspezifischen Bestimmungen der einzelnen Studiengänge im Fachbereich Architektur

1029 - Richtlinie über die Vergabe von Leistungsbezügen an der Hochschule Düsseldorf vom 09.10.2025

Gemäß der Verordnung über die Gewährung und Bemessung von Leistungsbezügen sowie über die Gewährung von Forschung- und Lehrzulagen für Hochschulbedienstete (Hochschul-Leistungsbezügeverordnung - HLeistBVO) vom 17. Dezember 2004 (GV. NRW. S. 790) in der aktuell geltenden Fassung hat das Präsidium der Hochschule Düsseldorf folgende Richtlinie erlassen

The Role of Universities of Applied Sciences in the Development of a national Research Data Infrastructure

The presentation highlights the role of Universities of Applied Sciences (UAS) in research and their potential for the development of a National Research Data Infrastructure (NFDI). To address the growing need for structured research data management (RDM) at UASs, the FDM@HAW network has emerged as a potential connector between NFDI and UAS landscape. It aims to raise awareness of NFDI services across a diverse UAS network, and articulate UAS-specific requirements back to NFDI. In doing so, it strengthens mutual integration and fosters targeted support. With over 200 institutions [1], UASs constitute a significant part of the German science system. Traditionally focused on teaching, UASs have become drivers of applied research and regional innovation. Their contributions to education and research make them essential actors in shaping RDM in Germany. Research at UASs benefits business, science, and society. In many federal states, the right to award doctorates further reinforces their research activities. Strengthening RDM competencies is important to ensure that valuable research data from UAS meet the FAIR principles. Furthermore, employers benefit from graduates equipped with strong data literacy. Despite their potential, UASs remain underrepresented in the NFDI (and partly in the RDM community). Low participation in NFDI consortia and limited association membership indicate a broader lack of engagement, largely due to low awareness of NFDI among UAS researchers and administrations [2, p.42] [3, p.6]. The NFDI structure report 2024 addresses this gap and calls for broader contact to UASs [4, p.27]. Structural challenges limit UAS engagement in RDM and the NFDI, including high teaching loads, minor resources, and partnerships prioritizing data protection over openness or commercial interests. In response, many UASs have implemented strategic networking in research, teaching, and infrastructure to overcome resource limitations. This approach also drives the development of RDM structures, often via projects or state-level initiatives. The Federal Ministry of Education and Research supports 14 projects [5] through its funding guideline for RDM at UASs. This initiative led to the establishment of the FDM@HAW network in 2023, connecting these projects. Since its formation, the network has engaged in continuous exchange on RDM development and contributed to events such as Love Data Week, the FORTRAMA conference [6], Volkswagen Foundation's Digital Skills in Science Week [7] and E-Science-Tage [8]. These activities have helped sharpen the network's focus, identify shared priorities and integrate new members. Members contribute ideas, tools and services developed in RDM projects or collaborations - such as the "NFDI Survival Kit" [9] and the "NFDI-N(HAW)igator" [10] - to promote RDM awareness and inform about NFDI on a national scale. In line with NFDI's mission to "improv[e] the possibilities for using data for science and society" [11], FDM@HAW seeks to disseminate knowledge of and strengthen competence in delving into these possibilities. The network provides valuable support to activate a target group that has largely been untapped. Close cooperation between NFDI and FDM@HAW will then contribute to the successful development of a nationwide RDM ecosystem. In the presentation, proposals for cooperation will be outlined. (The references [1]-[11] are given in the PDF abstract according to the template)