In this article we compare the benefits for game design and development relative to the use of three Game User Research (GUR) methodologies (user interviews, game metrics, and psychophysiology) to assist in shaping levels for a 2-D platformer game. We illustrate how these methodologies help level designers make more informed decisions in an otherwise qualitative design process. GUR data sources were combined in pairs to evaluate their usefulness in small-scale commercial game development scenarios, as commonly used in the casual game industry. Based on the improvements suggested by each data source, three levels of a Super Mario clone were modified and the success of these changes was measured. Based on the results we conclude that user interviews provide the clearest indications for improvement among the considered methodologies while metrics and biometrics add different types of information that cannot be obtained otherwise. These findings can be applied to the development of 2-D games; we discuss how other types of games may differ from this. Finally, we investigate differences in the use of GUR methodologies in a follow-up study for a commercial game with children as players.
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In this paper we compare the effects of using three game user research methodologies to assist in shaping levels for a 2-D platformer game, and illustrate how the use of such methodologies can help level designers to make more informed decisions in an otherwise qualitative oriented design process. Game user interviews, game metrics and psychophysiology (biometrics) were combined in pairs to gauge usefulness in small-scale commercial game development scenarios such as the casual game industry. Based on the recommendations made by the methods, three sample levels of a Super Mario clone were improved and the opinions of a second sample of users indicated the success of these changes. We conclude that user interviews provide the clearest indications for improvement among the considered methodologies while metrics and biometrics add different types of information that cannot be obtained otherwise.
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Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
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"Rising Tides, Shifting Imaginaries: Participatory Climate Fiction-Making with Cultural Collections," is an transdisciplinary research project that merges information design, participatory art, and climate imaginaries to address the pressing challenge of climate change, particularly the rising sea levels in the Netherlands. The doctoral research project aims to reimagine human coexistence with water-based ecosystems by exploring and reinterpreting audiovisual collections from various archives and online platforms. Through a creative and speculative approach, it seeks to visualize existing cultural representations of Dutch water-based ecosystems and, with the help of generative AI, develop alternative narratives and imaginaries for future living scenarios. The core methodology involves a transdisciplinary process of climate fiction-making, where narratives from the collections are amplified, countered, or recombined. This process is documented in a structured speculative archive, encompassing feminist data visualizations and illustrated climate fiction stories. The research contributes to the development of Dutch climate scenarios and adaptation strategies, aligning with international efforts like the CrAFt (Creating Actionable Futures) project of the New European Bauhaus program. Two primary objectives guide this research. First, it aims to make future scenarios more relatable by breaking away from traditional risk visualizations. It adopts data feminist principles, giving space to emotions and embodiment in visualization processes and avoiding the presentation of data visualization as neutral and objective. Second, the project seeks to make scenarios more inclusive by incorporating intersectional and more-than-human perspectives, thereby moving beyond techno-optimistic approaches and embracing a holistic and caring speculative approach. Combining cultural collections, digital methodologies, and artistic research, this research fosters imaginative explorations for future living.