Battle of the Bands: Trying to Identify Contributions to a Collaboration

In this article, we create and explore a dataset containing musical and lyrical features for tracks from three rock bands—Manchester Orchestra, The Front Bottoms, and All Get Out—all of whom contributed to a collaborative track, “Allentown.” We use the goal of disentangling the different contributions to “Allentown” to motivate learning logistic and multinomial regression. Furthermore, collecting data about what each band sounds like (using Essentia) and what the lyrics of each band read like (using the Bing Lexicon and the Linguistic Inquiry and Word Count software) provide opportunities for students to work with various technologies to collect and clean data, as well as create features from data. These data are approachable to students because of the context, and the overarching results match public comments about the collaboration, providing a real-world connection. We have successfully integrated these data into undergraduate and master’s level courses through classroom activities, assignments, and assessments, which are shared and discussed throughout the article.

Authors

Institutions

Publication Details

Journal
Journal of Statistics and Data Science Education
Published
2025-08-14
DOI
https://doi.org/10.1080/26939169.2025.2547611
Primary Topic
Music and Audio Processing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Battle of the Bands: Trying to Identify Contributions to a Collaboration

William Cipolli, Nicole M. Dalzell, Ciaran Evans, Roy Bower
Journal of Statistics and Data Science Education
Music and Audio Processing
article

Battle of the Bands: Trying to Identify Contributions to a Collaboration

William Cipolli, Nicole M. Dalzell, Ciaran Evans, Roy Bower
article en

Abstract

In this article, we create and explore a dataset containing musical and lyrical features for tracks from three rock bands—Manchester Orchestra, The Front Bottoms, and All Get Out—all of whom contributed to a collaborative track, “Allentown.” We use the goal of disentangling the different contributions to “Allentown” to motivate learning logistic and multinomial regression. Furthermore, collecting data about what each band sounds like (using Essentia) and what the lyrics of each band read like (using the Bing Lexicon and the Linguistic Inquiry and Word Count software) provide opportunities for students to work with various technologies to collect and clean data, as well as create features from data. These data are approachable to students because of the context, and the overarching results match public comments about the collaboration, providing a real-world connection. We have successfully integrated these data into undergraduate and master’s level courses through classroom activities, assignments, and assessments, which are shared and discussed throughout the article.

Journal of Statistics and Data Science Education
Colgate University (US), Wake Forest University (US), Furman University (US)
Openalex Percentile: Top 74%
Music and Audio Processing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Battle of the Bands: Trying to Identify Contributions to a Collaboration — William Cipolli, Nicole M. Dalzell, et al. · Journal of Statistics and Data Science Education (2025) | TGRS Research Map | TGRS