| _version_ | 1866902094814904320 |
|---|---|
| author | Fresquet, Xavier |
| author_facet | Fresquet, Xavier |
| contents | <p class="p1">This repository contains the data, code, and visualizations supporting the article <span class="s1"><em>“The Algorithmic Piano: Canon, Performance, and Metadata in the Aria-MIDI Dataset.”</em></span></p> <p class="p1">The materials are derived from the Aria-MIDI dataset (Bradshaw & Colton, 2025), a large-scale corpus of MIDI files automatically transcribed from YouTube piano performances. The present repository does not redistribute the original dataset, but provides processed metadata, analytical outputs, and reproducible scripts used in this study.</p> <p class="p2">The repository is structured as follows:</p> <ul> <li><span class="s1"><strong>data/</strong></span>: processed metadata (CSV and JSON formats)</li> <li><span class="s1"><strong>code/</strong></span>: Python scripts and Jupyter notebooks used for analysis</li> <li><span class="s1"><strong>figures/</strong></span>: visualizations included in the publication</li> <li><span class="s1"><strong>README.md</strong></span>: documentation and usage instructions</li> </ul> <p class="p1">The analyses explore large-scale performance culture, platform-mediated canon formation, and algorithmic bias from a Digital Humanities perspective.</p> <p class="p4">This work was produced within the framework of the ANR project <span class="s1"><em>TSIA–MELODY</em></span> (<span class="s1"><em>Musicology Enhanced by Large Language Models and Deep Learning</em></span>), coordinated at Sorbonne Université (SCAI).</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19970386 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Data, Code, and Visualizations for the Aria-MIDI Digital Humanities Study Fresquet, Xavier <p class="p1">This repository contains the data, code, and visualizations supporting the article <span class="s1"><em>“The Algorithmic Piano: Canon, Performance, and Metadata in the Aria-MIDI Dataset.”</em></span></p> <p class="p1">The materials are derived from the Aria-MIDI dataset (Bradshaw & Colton, 2025), a large-scale corpus of MIDI files automatically transcribed from YouTube piano performances. The present repository does not redistribute the original dataset, but provides processed metadata, analytical outputs, and reproducible scripts used in this study.</p> <p class="p2">The repository is structured as follows:</p> <ul> <li><span class="s1"><strong>data/</strong></span>: processed metadata (CSV and JSON formats)</li> <li><span class="s1"><strong>code/</strong></span>: Python scripts and Jupyter notebooks used for analysis</li> <li><span class="s1"><strong>figures/</strong></span>: visualizations included in the publication</li> <li><span class="s1"><strong>README.md</strong></span>: documentation and usage instructions</li> </ul> <p class="p1">The analyses explore large-scale performance culture, platform-mediated canon formation, and algorithmic bias from a Digital Humanities perspective.</p> <p class="p4">This work was produced within the framework of the ANR project <span class="s1"><em>TSIA–MELODY</em></span> (<span class="s1"><em>Musicology Enhanced by Large Language Models and Deep Learning</em></span>), coordinated at Sorbonne Université (SCAI).</p> |
| title | Data, Code, and Visualizations for the Aria-MIDI Digital Humanities Study |
| url | https://doi.org/10.5281/zenodo.19970386 |