We develop the concept of ‘aesthetic practices’ to capture the work needed for population data to be disseminated via government data portals. Specifically, we look at the Census Hub of the European Statistical System and the Danish Ministry of Education’s Data Warehouse. These portals form part of open government data initiatives, which we understand as governing technologies. We argue that to function as such, aesthetic practices are required so that data produced at dispersed sites can be brought into relation and (...) projected as populations in forms such as bar charts, heat maps and tables. Two examples of aesthetic practices are analysed based on ethnographic studies we have conducted on the production of data for the Hub and Warehouse: metadata and data cleaning. Metadata enables data to come into relation by containing and accounting for the differences between data. Data cleaning deals with the indeterminacies and absences of data and involves algorithms to determine what values data can obtain so they can be brought into relation. We attend to how both aesthetic practices involve normative decisions that make absent what exceeds them: embodied knowledge that cannot or has not been documented as well as data that cannot meet the forms required of data portals. While these aesthetic practices are necessary to sustain data portals as ‘sites of projection,’ we also bring critical attention to their performative effects for knowing, enacting and governing populations. (shrink)

This paper examines how scientists conceptualize their research methodologies. Do scientists raise concerns about vague criteria and genuine uncertainties in experimental practice? If so, what sorts of issues do they identify as problematic? Do scientists acknowledge the presence of value judgments in scientific research, and do they reflect on the relation between epistemic and non-epistemic criteria for decisionmaking? We present findings from an analysis of qualitative interviews with 63 scientific researchers who talk about their views on good research practice. We (...) argue that analysts of science should care about scientists’ conceptualizations of the criteria and of the practical judgments that scientific inquiry involves. While scientists’ accounts of their own research methodologies alone do not give us a full picture of how science really works, they can point us to areas of concern. They can inspire and direct philosophical reflections about how science works. Throughout the interviews, the participating researchers provided specific examples from their own research contexts as illustrations of their methodological points. These examples reveal that scientists often struggle to evaluate the quality of their data, to figure out whether the available evidence confirms their hypothesis, whether a replication was successful, or to what extent they can rely on peer-reviewed papers. General ideas about good research methods do not directly translate into specific evaluation criteria or strategies that can guide research and help validate empirical data. (shrink)

This paper contributes to our understanding of farm data value chains with assistance from 54 semi-structured interviews and field notes from participant observations. Methodologically, it includes individuals, such as farmers, who hold well-known positionalities within digital agriculture spaces—platforms that include precision farming techniques, farm equipment built on machine learning architecture and algorithms, and robotics—while also including less visible elements and practices. The actors interviewed and materialities and performances observed thus came from spaces and places inhabited by, for example, farmers, crop (...) scientists, statisticians, programmers, and senior leadership in firms located in the U.S. and Canada. The stability of “the” artifacts followed for this project proved challenging, which led to me rethinking how to approach the subject conceptually. The paper is animated by a posthumanist commitment, drawing heavily from assemblage thinking and critical data scholarship coming out of Science and Technology Studies. The argument’s understanding of “chains” therefore lies on an alternative conceptual plane relative to most commodity chain scholarship. To speak of a data value chain is to foreground an orchestrating set of relations among humans, non-humans, products, spaces, places, and practices. The paper’s principle contribution involves interrogating lock-in tendencies at different “points” along the digital farm platform assemblage while pushing for a varied understanding of governance depending on the roles of the actors and actants involved. (shrink)

Open Science policies encourage researchers to disclose a wide range of outputs from their work, thus codifying openness as a specific set of research practices and guidelines that can be interpreted and applied consistently across disciplines and geographical settings. In this paper, we argue that this “one-size-fits-all” view of openness sidesteps key questions about the forms, implications, and goals of openness for research practice. We propose instead to interpret openness as a dynamic and highly situated mode of valuing the research (...) process and its outputs, which encompasses economic as well as scientific, cultural, political, ethical, and social considerations. This interpretation creates a critical space for moving beyond the economic definitions of value embedded in the contemporary biosciences landscape and Open Science policies, and examining the diversity of interests and commitments that affect research practices in the life sciences. To illustrate these claims, we use three case studies that highlight the challenges surrounding decisions about how––and how best––to make things open. These cases, drawn from ethnographic engagement with Open Science debates and semistructured interviews carried out with UK-based biologists and bioinformaticians between 2013 and 2014, show how the enactment of openness reveals judgments about what constitutes a legitimate intellectual contribution, for whom, and with what implications. (shrink)

In a recent editorial published in Nature, the journal's editors comment on a new automated software that has been used to check findings in psychology publications. The editors express concern with the way in which the anonymous fact-checkers have proceeded, but at the same time, they underscore the crucial role of peer criticism for scientific progress and insist: "self-correction is at the heart of science." Brief as it is, the editorial showcases that peer criticism and the application of norms of (...) good research practice are very thorny issues indeed.It is essential for the functioning of science that empirical findings, arguments, as well as methodological approaches are scrutinized... (shrink)