This article traces an emergent tension in an interdisciplinary public health project called Weather Health and Air Pollution. The tension centered on two different kinds of data of air pollution: monitored and modeled data. Starting out with monitoring and modeling practices, the different ways in which they enacted air pollution are detailed. This multiplicity was problematic for the WHAP scientists, who were intent on working across disciplines, an initiative driven primarily by the epidemiologists who imbued the project with meaning and (...) value as the protagonists of “health.” To work collaboratively implies a stable, singular, and shared research object, however: one kind of data, one version of air pollution. In detailing two attempts by researchers to address the inadequacies of modeled and monitored data, this article explores the ways in which difference and multiplicity were negotiated and transformed. In doing so, this article suggests that it is the mobility and instability of data that are particularly fruitful for exploring the facilitation and enactment of new realities, while also making explicit the emergent problematics and partialities which inevitably result. (shrink)

While there are now an increasing number of studies that critically and rigorously engage with Big Data discourses and practices, these analyses often focus on social media and other forms of online data typically generated about users. This introduction discusses how environmental Big Data is emerging as a parallel area of investigation within studies of Big Data. New practices, technologies, actors and issues are concretising that are distinct and specific to the operations of environmental data. Situating these developments in relation (...) to the seven contributions to this special collection, the introduction outlines significant characteristics of environmental data practices, data materialisations and data contestations. In these contributions, it becomes evident that processes for validating, distributing and acting on environmental data become key sites of materialisation and contestation, where new engagements with environmental politics and citizenship are worked through and realised. (shrink)

This article provides an ethnographic account of how Big Data biology is produced, interpreted, debated, and translated in a Big Data-driven cancer clinical trial, entitled “Personalized OncoGenomics,” in Vancouver, Canada. We delve into epistemological differences between clinical judgment, pathological assessment, and bioinformatic analysis of cancer. To unpack these epistemological differences, we analyze a set of gazes required to produce Big Data biology in cancer care: clinical gaze, molecular gaze, and informational gaze. We are concerned with the interactions of these bodily (...) gazes and their interdependence on each other to produce Big Data biology and translate it into clinical knowledge. To that end, our central research questions ask: How do medical practitioners and data scientists interact, contest, and collaborate to produce and translate Big Data into clinical knowledge? What counts as actionable and reliable data in cancer decision-making? How does the explicability or translatability of genomic Big Data come to redefine or contradict medical practice? The article contributes to current debates on whether Big Data engenders new questions and approaches to biology, or Big Data biology is merely an extension of early modern natural history and biology. This ethnographic account will highlight how genomic Big Data, which underpins the mechanism of personalized medicine, allows oncologists to understand and diagnose cancer in a different light, but it does not revolutionize or disrupt medical oncology on an institutional level. Rather, personalized medicine is interdependent on different styles of thought, gaze, and practice to be produced and made intelligible. (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)