Musicology, Data Science and MIR: Enablers and Barriers to Interdisciplinary and Multidisciplinary Research and Pedagogy

Cory McKay; María Elena Cuenca Rodríguez

doi:10.5334/tismir.299

Musicology, Data Science and MIR: Enablers and Barriers to Interdisciplinary and Multidisciplinary Research and Pedagogy

Transactions of the International Society for Music Information Retrieval

Volume 9 (2026): Issue 1

By: Cory McKay and María Elena Cuenca Rodríguez

Open Access

|Jun 2026

Abstract

This article explores enabling factors and barriers encountered when conducting interdisciplinary and multidisciplinary work on music, with a special focus on research involving musicology and data science in the context of music information retrieval/research (MIR). The results of a novel mixed‑methods survey of academics, professionals and students are presented. This study combines descriptive quantitative analysis with thematic analysis of open responses. Findings reveal common challenges, such as a range of communication problems, and highlight key enablers including shared goals, mutual respect and institutional support. These results are contextualised with a partial literature review of MIR publications focusing on musicology, followed by a more general discussion of enablers and barriers to successfully carrying out interdisciplinary and multidisciplinary work involving data science and music.

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Articles in this issue

DOI: https://doi.org/10.5334/tismir.299 | Journal eISSN: 2514-3298

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Language: English

Page range: 234 - 246

Submitted on: Jun 27, 2025

Accepted on: May 4, 2026

Published on: Jun 30, 2026

Published by: Ubiquity Press

In partnership with: Paradigm Publishing Services

Keywords:

musicology,

data science,

music information retrieval,

interdisciplinarity,

multidisciplinary research,

computational musicology,

pedagogy

© 2026 Cory McKay, María Elena Cuenca Rodríguez, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 9 (2026): Issue 1