Artículo

Music Ensemble: a large dataset on musicianship, cognition, and personality in musicians and nonmusicians

Talamini, Francesca ; Grassi, Massimo ; Altoè, Gianmarco ; Brattico, Elvira ; Caclin, Anne ; Carretti, Barbara ; Drai Zerbib, Véronique ; Ferreri, Laura ; Gambarota, Filippo ; Grahn, Jessica ; Guiotto Nai Fovino, Lucrezia ; Roccato, Marco ; Rodriguez Fornells, Antoni ; Swaminathan, Swathi ; Tillmann, Barbara ; Vuust, Peter ; Wilbiks, Jonathan ; Zentner, Marcel ; Aguilar, Karla ; Aryanto, Christ B. ; Assis Leite, Frederico C. ; Baldé, Aíssa M. ; Başkent, Deniz ; Bishop, Laura ; Rubinstein, Wanda Yanina Icon; von Bastian, Claudia C. ; Whiteford, Kelly L. ; Wood, Emily A. ; Worschech, Florian ; Zappa, Ana

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Fecha

11/2025

Editorial

Springer Nature

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Scientific Data

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eISSN

2052-4463

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Inglés

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Resumen

The Music Ensemble dataset is a large-scale, cross-national database that provides detailed information about the musical, cognitive, personality, and demographic profiles of young adult musicians and nonmusicians. Data were collected from 1438 participants (aged 18–30) across thirty-five research sites in Europe, North America, South America, and Australia. Participants completed an in-person, in-lab battery of objective tests, including measures of verbal, visuospatial and musical short-term memory, executive functions (updating component), nonverbal reasoning, verbal comprehension, and music perception skills. The battery also included standardized and custom self-report questionnaires assessing music sophistication, music reward, personality traits, socioeconomic status, and demographic characteristics. Music Ensemble was preregistered, and the research protocol followed a standardized procedure across sites. The dataset also includes a large subsample of musicians and nonmusicians that are pair-matched for age, gender, and education (678 pairs). It enables well-powered investigations into the relationship between musical expertise and individual differences in cognition, personality, and demographic variables. It is also suitable for training in statistical and psychometric methods.

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