INDCOR white paper 4: Evaluation of Interactive Narrative Design For Complexity Representations

While a strength of Interactive Digital Narratives (IDN) is its support for multiperspectivity, this also poses a substantial challenge to its evaluation. Moreover, evaluation has to assess the system's ability to represent a complex reality as well as the user's understanding of that complex reality as a result of the experience of interacting with the system. This is needed to measure an IDN's efficiency and effectiveness in representing the chosen complex phenomenon. We here present some empirical methods employed by INDCOR members in their research including UX toolkits and scales. Particularly, we consider the impact of IDN on transformative learning and its evaluation through self-reporting and other alternatives.Comment: arXiv admin note: text overlap with arXiv:2010.1013

Introduction to Research Software Engineering

his course introduces selected fundamental methods and good practices from software engineering and highlights their value for research. Successful science is based on a systematic approach to research. But in practice, the development and use of research software often lacks methodology: no "laboratory journals" are being kept, the evaluation process is left undocumented, and the testing of results remains unstructured. Furthermore, reviews of self-written software barely occur. But thoroughly checked software and the documentation of its use is increasingly required for scientific publications. Researchers benefit from applying principles of software engineering to their practice

Stochastic Model Predictive Control Utilizing Bayesian Neural Networks

Integrating measurements and historical data can enhance control systems through learning-based techniques, but ensuring performance and safety is challenging. Robust model predictive control strategies, like stochastic model predictive control, can address this by accounting for uncertainty. Gaussian processes are often used but have limitations with larger models and data sets. We explore Bayesian neural networks for stochastic learning-assisted control, comparing their performance to Gaussian processes on a wastewater treatment plant model. Results show Bayesian neural networks achieve similar performance, highlighting their potential as an alternative for control designs, particularly when handling extensive data sets

Helmholtz Open Science Briefing. Helmholtz Open Science Forum Forschungssoftware. Report

Das Helmholtz Forum Forschungssoftware, welches gemeinsam von der Task Group Forschungssoftware des AK Open Science und dem HIFIS Software Cluster getragen wird, veranstaltete am 7. April 2022 ein Helmholtz Open Science Forum zum Thema Forschungssoftware. Die Veranstaltung wurde vom Helmholtz Open Science Office organisiert. Das virtuelle Forum widmete sich drei Aspekten beim offenen und nachhaltigen Umgang mit Forschungssoftware in der Helmholtz-Gemeinschaft: Policy, Praxis sowie Infrastrukturen und Tools. Die Veranstaltung war die zweite einer Reihe von Helmholtz Open Science Foren zum Thema. Die erste Veranstaltung fand im Mai 2021 unter dem Titel „Policies für Forschungssoftware“ statt. Vorliegender Bericht dokumentiert die Veranstaltung