Lessons learned in a large-scale project to digitize and computationally analyze musical scores

Abstract

Abstract Many areas of the digital humanities (DH) have the potential to benefit greatly from recent advances in machine learning, big data, and statistical analysis. These sophisticated techniques come with pitfalls, however, and their accidental misuse can lead to erroneous results. This article outlines in broad terms our experiences with a large-scale, long-term international project to digitize musical scores, automatically analyze them, and share the results with other researchers. It then describes our experiences in order to help other researchers in the DH avoid some of the missteps we and other DH researchers have made. In addition to issues associated with data mining, this article also discusses approaches to sharing data, software, and intermediate analyses such that they are accessible to other researchers in ways that encourage repeatability, verifiability, iterative refinement, creative exploration, and multidisciplinary collaboration.