Abstract Manuscripts are a crucial form of evidence for research into all aspects of premodern European history and culture, and there are numerous databases devoted to describing them in detail. This descriptive information, however, is typically available only in separate data silos based on incompatible data models and user interfaces. As a result, it has been difficult to study manuscripts comprehensively across these various platforms. To address this challenge, a team of manuscript scholars and computer scientists worked to create “Mapping Manuscript Migrations” (MMM), a semantic portal, and a Linked Open Data service. MMM stands as a successful proof of concept for integrating distinct manuscript datasets into a shared platform for research and discovery with the potential for future expansion. This paper will discuss the major products of the MMM project: a unified data model, a repeatable data transformation pipeline, a Linked Open Data knowledge graph, and a Semantic Web portal. It will also examine the crucial importance of an iterative process of multidisciplinary collaboration embedded throughout the project, enabling humanities researchers to shape the development of a digital platform and tools, while also enabling the same researchers to ask more sophisticated and comprehensive research questions of the aggregated data.

Abstract Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.

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.

Whether in the science lab or the music studio, we go in with a plan, we perform, and we make a record of that performance for distribution, consumption, and reuse. Both domains are increasingly data-intensive, with the adoption of new technology, and also socially intensive with democratised and growing citizen engagement. The music industry has embraced digital technology throughout the lifecycle from composition to consumption; scientific practice, and scholarly communication, are also undergoing transformation. Is the music industry more digital than science? We suggest that comparing and contrasting these two systems will provide insights of mutual benefit. Our investigation explores the notion of the Digital Music Object, analogous to the Research Object, for rich capture, sharing and reuse of both process and content.

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations — the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.

Existing semantic representations of music analysis encapsulate narrow sub-domain concepts and are frequently scoped by the context of a particular MIR task. Segmentation is a crucial abstraction in the investigation of phenomena which unfold over time; we present a Segment Ontology as the backbone of an approach that models properties from the musicological domain independently from MIR implementations and their signal processing foundations, whilst maintaining an accurate and complete description of the relationships linking them. This framework provides two principal advantages which we explore through several examples: a layered separation of concerns that aligns the model with the needs of the users and systems that consume and produce the data; and the ability to link multiple analyses of differing types through transforms to and from the Segment axis.

We present a proof-of-concept system that demonstrates the utility of linked data for enhancing the application of Music Information Retrieval (MIR) workflows, both when curating collections of music signal data for analysis, and publishing results that can be simply and readily correlated to these, and other, collection sets and Linked Data sources. The system includes: linked data implementations of a signal repository, collection builder, and results explorer, an extension to the my Experiment workflow sharing environment to include Meandre workflows, and support within my Experiment and Meandre to retrieve and persist resources from the linked data repositories. By way of example we gather and publish RDF describing signal collections derived from the country of an artist. Genre analysis over these collections and integration of collection and result metadata enables us to ask: "how country is my country?".