The governments of China, India, and the United Kingdom are unanimous in their belief that bioinformatics should supply the link between basic life sciences research and its translation into health benefits for the population and the economy. Yet at the same time, as ambitious states vying for position in the future global bioeconomy they differ considerably in the strategies adopted in pursuit of this goal. At the heart of these differences lies the interaction between epistemic change within the scientific community (...) itself and the apparatus of the state. Drawing on desk-based research and thirty-two interviews with scientists and policy makers in the three countries, this article analyzes the politics that shape this interaction. From this analysis emerges an understanding of the variable capacities of different kinds of states and political systems to work with science in harnessing the potential of new epistemic territories in global life sciences innovation. (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)

. Computational chemistry grew in a new era of “desktop modeling,” which coincided with a growing demand for modeling software, especially from the pharmaceutical industry. Parameterization of models in computational chemistry is an arduous enterprise, and we argue that this activity leads, in this specific context, to tensions among scientists regarding the epistemic opacity transparency of parameterized methods and the software implementing them. We relate one flame war from the Computational Chemistry mailing List in order to assess in detail the (...) relationships between modeling methods, parameterization, software and the various forms of their enclosure or disclosure. Our claim is that parameterization issues are an important and often neglected source of epistemic opacity and that this opacity is entangled in methods and software alike. Models and software must be addressed together to understand the epistemological tensions at stake. (shrink)

The Bermuda Principles for DNA sequence data sharing are an enduring legacy of the Human Genome Project. They were adopted by the HGP at a strategy meeting in Bermuda in February of 1996 and implemented in formal policies by early 1998, mandating daily release of HGP-funded DNA sequences into the public domain. The idea of daily sharing, we argue, emanated directly from strategies for large, goal-directed molecular biology projects first tested within the “community” of C. elegans researchers, and were introduced (...) and defended for the HGP by the nematode biologists John Sulston and Robert Waterston. In the C. elegans community, and subsequently in the HGP, daily sharing served the pragmatic goals of quality control and project coordination. Yet in the HGP human genome, we also argue, the Bermuda Principles addressed concerns about gene patents impeding scientific advancement, and were aspirational and flexible in implementation and justification. They endured as an archetype for how rapid data sharing could be realized and rationalized, and permitted adaptation to the needs of various scientific communities. Yet in addition to the support of Sulston and Waterston, their adoption also depended on the clout of administrators at the US National Institutes of Health and the UK nonprofit charity the Wellcome Trust, which together funded 90% of the HGP human sequencing effort. The other nations wishing to remain in the HGP consortium had to accommodate to the Bermuda Principles, requiring exceptions from incompatible existing or pending data access policies for publicly funded research in Germany, Japan, and France. We begin this story in 1963, with the biologist Sydney Brenner’s proposal for a nematode research program at the Laboratory of Molecular Biology at the University of Cambridge. We continue through 2003, with the completion of the HGP human reference genome, and conclude with observations about policy and the historiography of molecular biology. (shrink)

Knowledge-making practices in biology are being strongly affected by the availability of data on an unprecedented scale, the insistence on systemic approaches and growing reliance on bioinformatics and digital infrastructures. What role does theory play within data-intensive science, and what does that tell us about scientific theories in general? To answer these questions, I focus on Open Biomedical Ontologies, digital classification tools that have become crucial to sharing results across research contexts in the biological and biomedical sciences, and argue that (...) they constitute an example of classificatory theory. This form of theorizing emerges from classification practices in conjunction with experimental know-how and expresses the knowledge underpinning the analysis and interpretation of data disseminated online. (shrink)

The US National Institute of Health’s Human Microbiome Project aims to use genomic techniques to understand the microbial communities that live on the human body. The emergent field of microbiome science brought together diverse disciplinary perspectives and technologies, thus facilitating the negotiation of differing values. Here, we describe how values are conceptualized and negotiated within microbiome research. Analyzing discussions from a series of interdisciplinary workshops conducted with microbiome researchers, we argue that negotiations of epistemic, social, and institutional values were inextricable (...) from the reflective and strategic category work that defined and organized the microbiome as an object of study and a potential future site of biomedical intervention. Negotiating the divergence or tension between emerging scientific and regulatory classifications also activated “values levers” and opened up reflective discussions of how classifications embody values and how these values might differ across domains. These data suggest that scholars at the intersections of science and technology studies, ethics, and policy could leverage such openings to identify and intervene in the ways that ethical/regulatory and scientific/technical practices are coproduced within unfolding research. (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)

Genomics is increasingly considered a global enterprise – the fact that biological information can flow rapidly around the planet is taken to be important to what genomics is and what it can achieve. However, the large-scale international circulation of nucleotide sequence information did not begin with the Human Genome Project. Efforts to formalize and institutionalize the circulation of sequence information emerged concurrently with the development of centralized facilities for collecting that information. That is, the very first databases build for collecting (...) and sharing DNA sequence information were, from their outset, international collaborative enterprises. This paper describes the origins of the International Nucleotide Sequence Database Collaboration between GenBank in the United States, the European Molecular Biology Laboratory Databank, and the DNA Database of Japan. The technical and social groundwork for the international exchange of nucleotide sequences created the conditions of possibility for imagining nucleotide sequences as a “global” objects. The “transnationalism” of nucleotide sequence was critical to their ontology – what DNA sequences came to be during the Human Genome Project was deeply influenced by international exchange. (shrink)

ZusammenfassungGenomdaten, Kernstück der 2008 ausgerufenen Big Data-Revolution der Biologie, werden voll automatisiert sequenziert und analysiert. Der Wechsel von der manuellen Laborpraktik der Elektrophorese-Sequenzierung zu DNA-Sequenziermaschinen und softwarebasierten Analyseprogrammen vollzog sich zwischen 1982 und 1992. Erst dieser Wechsel ermöglichte die Flut an Daten, die mit der zweiten und dritten Generation der DNA-Sequenzierer erheblich zunimmt. Doch mit diesem Wechsel verändern sich auch die Validierungsstrategien der Genomdaten. Der Beitrag untersucht beides – die Automatisierung und die damit verbundene Validierungskultur – um ein Bild der (...) Komplexität der Datengenerierung und deren Datenpositivismus zu geben. Leitend ist dabei die Frage, ob dieser Datenpositivismus die Grundlage der aktuell angekündigten Big Data-Revolution der Molekularbiologie ist oder deren Datenhybris. (shrink)