Multiverse analysis, a statistical method assessing varied results for the same phenomenon, is applied to bibliometrics to examine how team size influences disruptive research using an index to measure disruptiveness of research. Conflicting studies by Lingfei Wu (small teams favor disruption) and Alexander Petersen (contradictory findings) highlight challenges in comparing results due to differing models and index specifications. Metrics sonification is used to demonstrate the multiverse of results based on differing models and index specifications. By mapping team effect coefficients from 62 regression models to musical pitch in C major, the sonification reveals significant outcome variations tied to modeling assumptions and index definitions. The results show minimal practical relevance of team size effects, suggesting small and large teams contribute comparably to disruptive and developmental scientific progress. We are grateful to Marc Rossner – an English native speaker – who speaks the text. We are also grateful to Rouven Lazlo Haegner who mastered (optimized) the track for hearing on different platforms.

Metrics sonification of team size effects on disruptive research

I am active in two areas of sonification (parameter mapping): \\ sonification of scientometric data such as publication counts and citation impact data \\ sonification of exhibition visits such as ResetNow! at Haus der Kunst in Munich

Metrics sonification of team size effects on disruptive research

Metrics & Exhibition Sonification