GitHub & OSF

Repositories

Open data, codebooks, analysis pipelines, and stimuli sets from my research — all in one place.

GitHub

Basslines-Bodies-BeehiveHaircuts

Examining the qualitative contents of participants' descriptions of music-influenced mental content (MIMC) while listening to music from 10 genres. Mixed-method qualitative analysis of 14,091 descriptions — codebook, data, stimuli, and co-occurrence network analysis included.

Qualitative thematic analysis Co-occurrence networks 10 music genres 14,091 free response MIMC descriptions

context-framed-listening

Python R NLP

Computational text analysis pipeline for my current paper on context-framed listening. Two complementary pipelines using MiniLM and TF-IDF to calculate the semantic similarity of free response music-influenced mental contents (MIMC) to same/different music clips, context cues, and/or musical genres.

Semantic similarity MiniLM TF-IDF

MUSIFEAST-17

Shiny app R Openly available stimuli

356 instrumental 30-second clips across 17 genres, with normative data from 701 UK & US participants (ages 18–75). Ratings of familiarity, enjoyment, emotional expression, genre recognition, and evoked thought types. Includes a Shiny/Plotly app for interactive exploration of the stimulus set.

Normative data 17 music genres 356 music stimuli clips 701 participants

✦ Interactive MUSIFEAST-17 Explorer

Plot any two normative variables against each other across all 356 clips — filter by genre, click any point to play its audio clip directly in your browser. Powered by Shiny + Plotly, running entirely client-side via WebAssembly. Allow ~30–60s for the R runtime to start up on first load.