Learning Exercise
Analyzing Longitudinal Song-Title Structure with Open Aggregate Data
A 60–90 minute reproducible exercise in which learners examine annual song-title structure measures, interpret proportions and Wilson 95% intervals, distinguish aggregate trends from item-level claims, and evaluate catalog-coverage limitations.
- Material:
- Exploring Song-Title Structure and Change with Open Aggregate Data (1800–2026)
- Date last modified:
- October 24, 2026
- Created by:
- Kobi Eliraz
- License:
-
An open interactive research and teaching resource for examining how song-title structure changes over time. The study... see more
Exercise
1. Read the methodology and limitations on the canonical study page.
2. Download the annual aggregate CSV from the versioned Zenodo record.
3. Verify that each year is unique and that annual provenance counts reconcile with the annual record total.
4. Plot averageTitleTokens and oneTokenShare for 1945–2025. Exclude 2026 and state why.
5. Select one apparent period of change. Describe the pattern without claiming an unmeasured cultural cause.
6. Compare the chart with the reported confidence interval and discuss whether uncertainty changes the interpretation.
7. Submit the chart, a 300–500 word interpretation, one limitations paragraph, and the recommended dataset citation.
Technical Notes
The narrative is structured HTML. Aggregate data are available in CSV and JSON. Interactive visualizations require JavaScript, but the underlying measurements can be analyzed without the interactive interface.
Requirements
A current web browser, spreadsheet software or a Python/R environment capable of reading CSV files, and basic familiarity with descriptive statistics and plotting. No paid software or account is required.
Topics
music information retrieval; digital humanities; cultural analytics; data literacy; popular-music studies; longitudinal analysis; confidence intervals; open data
Type of Task
Learning Objectives
By the end of the exercise, learners will be able to: (1) distinguish an aggregate longitudinal trend from an item-level claim; (2) interpret yearly means, proportions, and Wilson 95% confidence intervals; (3) explain why the partial 2026 observation should be excluded from long-term trend inference; (4) identify at least two limitations created by catalog coverage and metadata normalization; and (5) reproduce one chart from the downloadable CSV and cite the versioned dataset correctly.
Assessment
30% reproducible data handling and correct year range; 25% accurate statistical interpretation; 20% treatment of uncertainty; 15% limitations and avoidance of causal overclaiming; 10% complete versioned citation.