Building upon a collection with functionality for discovery and analysis has been described by Lynch as a 'layered' approach to digital libraries. Meanwhile, as digital corpora have grown in size, their analysis is necessarily supplemented by automated application of computational methods, which can create layers of information as intricate and complex as those within the content itself. is combination of layers -aggregating homogeneous collections, specialised analyses, and new observations -requires a exible approach to systems implementation which enables pathways through the layers via common points of understanding, while simultaneously accommodating the emergence of previously unforeseen layers.In this paper we follow a Linked Data approach to build a layered digital library based on content from the Internet Archive Live Music Archive. Starting from the recorded audio and basic information in the Archive, we rst deploy a layer of catalogue metadata which allows an initial -if imperfect -consolidation of performer, song, and venue information. A processing layer extracts audio features from the original recordings, work ow provenance, and summary feature metadata. A further analysis layer provides tools for the user to combine audio and feature data, discovered and reconciled using interlinked catalogue and feature metadata from layers below.Finally, we demonstrate the feasibility of the system through an investigation of 'key typicality' across performances.is highlights the need to incorporate robustness to inevitable 'imperfections' when undertaking scholarship within the digital library, be that from mislabelling, poor quality audio, or intrinsic limitations of computational methods. We do so not with the assumption that a 'perfect' version can be reached; but that a key bene t of a layered approach is to allow accurate representations of information to be discovered, combined, and investigated for informed interpretation.

We report on the design, premiere and public evaluation of a multifaceted audience interface for a complex non-linear musical performance called Climb! which is particularly suited to being experienced more than once. This interface is designed to enable audiences to understand and appreciate the work, and integrates a physical instrument and staging, projected visuals, personal devices and an online archive. A public premiere concert comprising two performances of Climb! revealed how the audience reoriented to the second performance through growing understanding and comparison to the first. Using trajectories as an analytical framework for the audience 'journey' made apparent: how the trajectories of a single performance are embedded within the larger trajectories of a concert and the creative work as a whole; the distinctive demands of understanding and interpretation; and the potential of the archive in enabling appreciation across repeated performances.

A variety of digital data sources-including institutional and formal digital libraries, crowd-sourced community resources, and data feeds provided by media organisations such as the BBC-expose information of musicological interest, describing works, composers, performers, and wider historical and cultural contexts. Aggregated access across such datasets is desirable as these sources provide complementary information on shared real-world entities. Where datasets do not share identifiers, an alignment process is required, but this process is fraught with ambiguity and difficult to automate, whereas manual alignment may be time-consuming and error-prone. We address this problem through the application of a Linked Data model and framework to assist domain experts in this process. Candidate alignment suggestions are generated automatically based on textual and on contextual similarity. The latter is determined according to user-configurable weighted graph traversals. Match decisions confirming or disputing the candidate suggestions are obtained in conjunction with user insight and expertise. These decisions are integrated into the knowledge base, enabling further iterative alignment, and simplifying the creation of unified viewing interfaces. Provenance of the musicologist's judgement is captured and published, supporting scholarly discourse and counter-proposals. We present our implementation and evaluation of this framework, conducting a user study with eight musicologists. We further demonstrate the value of our approach through a case study providing aligned access to catalogue metadata and digitised score images from the British Library and other sources, and broadcast data from the BBC Radio 3 Early Music Show.

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The turn toward the digital has opened up previously difficult to access musical materials to wider musicological scholarship. Digital repositories provide access to publicly licensed score images, score encodings, textual resources, audiovisual recordings, and music metadata. While each repository reveals rich information for scholarly investigation, the unified exploration and analysis of separate digital collections remains a challenge. TROMPA-Towards Richer Online Music Public-domain Archives-addresses this through a knowledge graph interweaving composers, performers, and works described in established digital music libraries, facilitating discovery and combined access of complementary materials across collections. TROMPA provides for contribution of expert insights as citable, provenanced annotations, supporting analytical workflows and scholarly communication. Beyond scholars, the project targets four further user types: instrumental players; choir singers; orchestras; and music enthusiasts; with corresponding web applications providing specialised views of the same underlying knowledge graph. Thus, scholars' annotations provide contextual information to other types of users; while performers' rehearsal recordings and performative annotations, conductors' marked up scores, and enthusiasts' social discussions and listening behaviours, become available to scholarly analysis (per user consent). The knowledge graph is exposed as Linked Data, adhering to the FAIR principles of making data Findable, Accessible, Interoperable, and Re-usable, and supporting further interlinking, re-interpretation and re-use beyond the immediate scope of the project. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored.