### Large-scale iterated singing experiments reveal oral transmission mechanisms underlying music evolution Manuel Anglada-Tort, Peter M. C. Harrison, Harin Lee, and Nori Jacoby **This is a read-only repository for the data and code supporting the article, "Large-scale iterated singing experiments reveal oral transmission mechanisms underlying music evolution", published in [Current Biology](https://authors.elsevier.com/sd/article/S0960-9822(23)00243-9).** Anglada-Tort, M., Harrison, P. M. C., Lee, H., & Jacoby, N. (2023). Large-Scale Iterated Singing Experiments Reveal Oral Transmission Mechanisms Underlying Music Evolution. Current Biology. Online Advance Publication. DOI: https://doi.org/10.1016/j.cub.2023.02.070 ### Structure (zip file) Download and unzip the file, then: 1. **analysis:** code supporting the main analysis reported in the paper, organized by sections in the paper 2. **data**: all datasets used in the paper, including clean versions ready for analysis and raw datasets exported from PsyNet (e.g., see data/experiment1/batches/). We also include the code to generate each experiment in .../example_code/ 3. **features**: code to compute melodic features via bootstrapping and outcome results for each experiment (in csv) 4. **matlab**: code for all analysis using MATLAB, including peak finding and model simulations 5. **participants**: code to get demographic details from participants in all experiments 6. **peaks**: output and data used in peak finding analysis 7. **sample-size**: code to compute the sample-size analysis 8. **simulations**: code and output from all model simulations, including melody-transmission, production-perception, and singing transcription technology 9. **sing4me** Python package for the singing transcription technology, including jupyter notebook demos for testing the technology and code validation 10. **utils**: supporting code for stats, plotting, and general methods