As heritage-as-the-already-occurred folds into heritage-in-the-making practices, temporal and spatial fluidity is made more complex by digital mediation and particularly by Big Data. Such liveliness evokes ontological, epistemological and methodological challenges. Drawing on more-than-human theorizing, this article reframes the notion of data-bodies to advance data activist-oriented research in heritage. Focused primarily on women, it examines how their distributed agency and voice with respect to data practices and the makings of heritage could be amplified. I describe three methodological directions, influenced by feminist (...) work in critical data studies, which could be employed by researchers: attuning to and becoming with data, making data physical and changing narratives. From data-bodies to haunted data, performative data curation and mapping data-bodies, and attuning to data streams and re-voicing narratives, this article contributes to discussions of how to engage critically and creatively with the datafication of digital heritage practices, knowings and ontologies. (shrink)

In this article, we address the case of self-tracking as a practice in which two meaningful backgrounds (physical world and technological infrastructure) play an important role as the spatial dimension of human practices. Using a (post)phenomenological approach, we show how quantification multiplies backgrounds, while at the same time generating data about the user. As a result, we can no longer speak of a unified background of human activity, but of multiple dimensions of this background, which, additionally, is perceived as having (...) no pivotal role in the process, often being hidden, situated beyond human consciousness, or taken for granted. Consequently, the phenomenological experience of the background turns into a hermeneutic practice focused on the interpretation of representations and descriptions. By adopting a (post)phenomenological approach, we show the problems and limitations of quantification of human activities occurring in self-tracking and the theoretical problems associated with the scheme of human-technology relations. (shrink)

What are the challenges of turning data subjects into research participants—and how can we approach this task responsibly? In this paper, we develop a methodology for studying the lived experiences of people who are subject to automated scoring systems. Unlike most media technologies, automated scoring systems are designed to track and rate specific qualities of people without their active participation. Credit scoring, risk assessments, and predictive policing all operate obliquely in the background long before they come to matter. In doing (...) so, they constitute a problem not only for those subject to these systems but also for researchers who try to study their experience. Specifically, we identify three challenges that are distinct to studying experiences of automated scoring: limited awareness, embeddedness, and ongoing inquiry. Starting from the observation that coming to terms with one's position as a data subject constitutes a form of learning in its own right, we propose a research strategy called critical companionship. Originally articulated in the context of nursing research, critical companionship invites us to accompany a data subject over time, paying critical attention to how the participant's and the researcher's inquiries complicate and constitute each other. We illustrate the strengths and limitations of this methodology with materials from a recent study we conducted about people's credit repair practices and sketch a set of sensibilities for studying contemporary scoring systems from the margins. (shrink)

Since the inception of recorded music there has been a need for standards and reliability across sound formats and listening environments. The role of the audio mastering engineer is prestigious and akin to a craft expert combining scientific knowledge, musical learning, manual precision and skill, and an awareness of cultural fashions and creative labour. With the advent of algorithms, big data and machine learning, loosely termed artificial intelligence in this creative sector, there is now the possibility of automating human audio (...) mastering processes and radically disrupting mastering careers. The emergence of dedicated products and services in artificial intelligence-driven audio mastering poses profound questions for the future of the music industry, already having faced significant challenges due to the digitalization of music over the past decades. The research reports on qualitative and ethnographic inquiry with audio mastering engineers on the automation of their expertise and the potential for artificial intelligence to augment or replace aspects of their workflows. Investigating audio mastering engineers' awareness of artificial intelligence, the research probes the importance of criticality in their labour. The research identifies intuitive performance and critical listening as areas where human ingenuity and communication pose problems for simulation. Affective labour disrupts speculation of algorithmic domination by highlighting the pragmatic strategies available for humans to adapt and augment digital technologies. (shrink)

Digital data is an increasing and continual presence across the sites, activities and relationships of everyday life. In this article we explore what data presence means for the ways that the everyday is organised, sensed, and anticipated. While digital data studies have demonstrated how data is deeply entangled with the way in which everyday life is lived out and valued, at the same time our relationships with data are riddled with anxieties or small niggles or tricky trade-offs and their use (...) is often chaotic and muddled, part of the inevitable uncertainty about what will happen next. If the presence of data is part of the environments we inhabit, this raises the question of how and why data is valuable to us and what forms of hope and trust enable this value to further develop. (shrink)

This article adapts the ethnographic medium of the diary to develop a method for studying data and related data practices. The article focuses on the creation of one data diary, developed iteratively over three years in the context of a national centre for monitoring disasters and natural hazards in Brazil. We describe four points of focus involved in the creation of a data diary – spaces, interfaces, types and situations – before reflecting on the value of this method. We suggest (...) data diaries are able to capture the informal dimension of data-intensive organisations; enable empirical analysis of the specific ways that data intervene in the unfolding of situations; and as a document, data diaries can foster interdisciplinary and inter-expert dialogue by bridging different ways of knowing data. (shrink)

This paper is concerned with everyday data practices, considering how people record data produced through self-monitoring. The analysis unpacks the relationships between taking a measure, and making and reviewing records. The paper is based on an interview study with people who monitor their blood pressure and/or body mass index/weight. Animated by discussions of ‘data power’ which are, in part, predicated on the flow and aggregation of data, we aim to extend important work concerning the everyday constitution of digital data. In (...) the paper, we adopt and develop the idea of curation as a theory of attention. We introduce the idea of discerning work to characterise the skilful judgements people make about which readings they record, how readings are presented, and about the records they retain and those they discard. We suggest self-monitoring produces partial data, both in the sense that it embodies these judgements, and also because monitoring might be conducted intermittently. We also extend previous analyses by exploring the broad set of materials, digital and analogue, networked and not networked, involved in record keeping to consider the different ways these contributed to regulating attention to self-monitoring. By paying attention to which data is recorded and the occasions when data is not recorded, as well as the ways data is recorded, the research provides specificity to the different ways in which self-monitoring data may or may not flow or contribute to big data sets. We argue that ultimately our analysis contributes to nuancing our understanding of ‘data power’. (shrink)

The movements by national governments, funding agencies, universities, and research communities toward “open data” face many difficult challenges. In high-level visions of open data, researchers’ data and metadata practices are expected to be robust and structured. The integration of the internet into scientific institutions amplifies these expectations. When examined critically, however, the data and metadata practices of scholarly researchers often appear incomplete or deficient. The concepts of “accountability” and “transparency” provide insight in understanding these perceived gaps. Researchers’ primary accountabilities are (...) related to meeting the expectations of research competency, not to external standards of data deposition or metadata creation. Likewise, making data open in a transparent way can involve a significant investment of time and resources with no obvious benefits. This paper uses differing notions of accountability and transparency to conceptualize “open data” as the result of ongoing achievements, not one-time acts. (shrink)

Algorithms are increasingly affecting us in our daily lives. They seem to be everywhere, yet they are seldom seen by the humans dealing with the consequences that result from them. Yet, in recent theorisations, there is a risk that the algorithm is being given too much prominence. This article addresses the interaction between algorithmic outputs and the humans engaging with them by drawing on studies of two distinct empirical fields – self-quantification and audit controls of taxpayers. We explore recalibration as (...) a way to understand the practices and processes involved when, on the one hand, decisions are made based on results from algorithmic calculations in counting and accounting software, and on the other hand, when decisions are made based on human experience/knowledge. In particular, we are concerned with moments when an algorithmic output differs from expectations of ‘normalcy’ and ‘normativity’ in any given situation. This could be a ‘normal’ relation between sales and VAT deductions for a business, or a ‘normal’ number of steps one takes in a day, or ‘normative’ as it is according to the book, following guidelines and recommendations from other sources. In these moments, we argue that a process of recalibration occurs – an effortful moment where, rather than treat the algorithmic output as given, individuals’ tacit knowledge, experiences and intuition are brought into play to address the deviation from the normal and normative. (shrink)