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This paper aims to examine how metadata taxonomies in embodied computing databases indicate context (e.g. a marketing context or an ethical context) and describe ways to track the evolution of the embodied computing industry over time through digital media archiving.

The authors compare the metadata taxonomies of two embodied computing databases by providing a narrative of their top-level categories. After identifying these categories, they describe how they structure the databases around specific themes.

The growing wearables market often hides complex sociotechnical tradeoffs. Marketing products like Vandrico Inc.’s Wearables Database frame wearables as business solutions without conveying information about the various concessions users make (about giving up their data, for example). Potential solutions to this problem include enhancing embodied computing literacy through the construction of databases that track media about embodied computing technologies using customized metadata categories. Databases such as FABRIC contain multimedia related to the emerging embodied computing market – including patents, interviews, promotional videos and news articles – and can be archived through user-curated collections and tagged according to specific themes (privacy, policing, labor, etc.). One of the benefits of this approach is that users can use the rich metadata fields to search for terms and create curated collections that focus on tradeoffs related to embodied computing technologies.

This paper describes the importance of metadata for framing the orientation of embodied computing databases and describes one of the first attempts to comprehensively track the evolution of embodied computing technologies, their developers and their diverse applications in various social contexts through media archiving.

Applied computational ontologies (ACOs) are increasingly used in data science domains to produce semantic enhancement and interoperability among divergent data. The purpose of this paper is to propose and implement a methodology for researching the sociotechnical dimensions of data-driven ontology work, and to show how applied ontologies are communicatively constituted with ethical implications.

The underlying idea is to use a data assemblage approach for studying ACOs and the methods they use to add semantic complexity to digital data. The author uses a mixed methods approach, providing an analysis of the widely used Basic Formal Ontology (BFO) through digital methods and visualizations, and presents historical research alongside unstructured interview data with leading experts in BFO development.

The author found that ACOs are products of communal deliberation and decision making across institutions. While ACOs are beneficial for facilitating semantic data interoperability, ACOs may produce unintended effects when semantically enhancing data about social entities and relations. ACOs can have potentially negative consequences for data subjects. Further critical work is needed for understanding how ACOs are applied in contexts like the semantic web, digital platforms, and topic domains. ACOs do not merely reflect social reality through data but are active actors in the social shaping of data.

The paper presents a new approach for studying ACOs, the social impact of ACO work, and describes methods that may be used to produce further applied ontology studies.

The chapter explores the developments in work on the history of quantification and sport, explaining how quantification in sport is generally understood, and then establishing what a sociological approach offers to scholars interested in exploring new expressions of these developments in biometrics and Big Data. It then outlines some potential directions scholars might pursue to further develop knowledge of these developments in the context of sport.

The chapter synthesizes existing literature from the sociology of quantification, sport sociology and quantification, and Big Data to provide historical, contemporary, and future oriented assessments of sport and datafication.

By situating a discussion of Big Data and biometrics in the context of sport, this chapter argues for the value of a sociological approach to these areas. The chapter engages prior work as a way to move scholars to challenge the assumed epistemological and political power of numbers for the way we engage sport.

The chapter argues for a number of future areas of study that may push the boundaries of existing research in the area.

The chapter provides a survey of the literature on sport, analytics, and Big Data as an impetus for future research into the importance of a sociological approach to these areas in the context of sport.

The Children's Hearings System is a Scottish welfare-based tribunal-based system in which decisions are made about the care and protection of children in conflict with the law and/or in need of additional care and protection. The Covid-19 pandemic resulted in the rapid implementation of a virtual Children's Hearings System. This system, which operated as the sole mechanism through which decisions were made between March and July 2020, continued to be used alongside in-person and hybrid Hearing formats for the duration of the pandemic. Early research into the use of virtual Hearings identified that their use presented significant barriers to participation, particularly in relation to the impacts of digital literacy and digital poverty. However, much of this research focused upon the experiences of adult participants in Hearings and failed to capture the experiences of children. In this chapter, we present findings from a qualitative study designed to explore the impact of virtual Hearings upon the participation and rights of children. In doing so, we demonstrate that virtual Hearings acted as both a barrier and facilitator of children's participation.

A definition of data called data as assemblage is presented. The definition accommodates different forms and meanings of data; emphasizes data subjects and data workers; and reflects the sociotechnical aspects of data throughout its lifecycle of creation and use. A scalable assemblage model describing the anatomy and behavior of data, datasets and data infrastructures is also introduced.

Data as assemblage is compared to common meanings of data. The assemblage model's elements and relationships also are defined, mapped to the anatomy of a US Census dataset and used to describe the structure of research data repositories.

Replacing common data definitions with data as assemblage enriches information science and research data management (RDM) frameworks. Also, the assemblage model is shown to describe datasets and data infrastructures despite their differences in scale, composition and outward appearance.

Data as assemblage contributes a definition of data as mutable, portable, sociotechnical arrangements of material and symbolic components that serve as evidence. The definition is useful in information science and research data management contexts. The assemblage model contributes a scale-independent way to describe the structure and behavior of data, datasets and data infrastructures and supports analyses and comparisons involving them.