We present the DTL1000 dataset, which was created in the “Dig That Lick” project and covers the history of recorded jazz with a sample of 1,750 improvisations extracted from 1,060 audio tracks. The dataset contains a mixture of collected (editorial metadata), manually annotated (structure, style), and automatically generated (main melody transcriptions of solos) data describing the recordings. The motivation for creating this dataset was the study of patterns in jazz improvisation, but there are many other applications for this resource. The accompanying paper presents the dataset creation process, data structure and contents with descriptive statistics and discusses the origin and process of the annotations, as well as general use cases and specifically the case of pattern analysis. These components and their combinations enable a number of use cases for jazz studies as well as algorithm development for music analysis. The DTL1000 dataset provides a rich resource for a variety of disciplines, and constitutes a contribution to a field where large datasets with rich annotations are scarce.The recorded legacy of jazz spans a century and provides a vast corpus of data documenting its development. Recent advances in digital signal processing and data analysis technologies enable automatic recognition of musical structures and their linkage through metadata to historical and social context. Automatic metadata extraction and aggregation give unprecedented access to large collections, fostering new interdisciplinary research opportunities. This project aims to develop innovative technological and music-analytical methods to gain fresh insight into jazz history by bringing together renowned scholars and results from several high-profile projects. Musicologists and computer scientists will together create a deeper and more comprehensive understanding of jazz in its social and cultural context. We exemplify our methods via a full cycle of analysis of melodic patterns, or "licks", from audio recordings to an aesthetically contextualised and historically situated understanding.

The study of jazz requires insights from, and feeds knowledge back into, African American Studies, Anthropology, Art History, Literary Studies, Music, Philosophy, Political Science, and Sociology. A thorough analysis of a century's worth of jazz recordings, and the practices the music entails, is now possible thanks to recent advances in the computational analysis of audio content, or Music Information Retrieval (MIR), and to progress in processing large datasets and information management with Semantic Web technologies. The former enables the automatic description of audio recordings in terms of high-level or structural musical aspects, and the latter allows such analyses to be linked to discographic metadata, distributed over multiple sites, describing performers and composers, listeners, performance venues, and production and consumption factors, and general historic, cultural and geographic information from external resources. These technologies can now facilitate access to large collections by researchers from the many disciplines interested in the evolution of musical expression.