What Difference Does Quantity Make? On the Epistemology of Big Data Biology

publication-status: Acceptedtypes: ArticleIs Big Data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of Big Data science does not lie in the sheer quantity of data involved, but rather in (1) the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community and (2) the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this article reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which Big Data needs to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analysing data given these developments, and the opportunities and worries associated with Big Data discourse and methods.Economic and Social Research CouncilES/F028180/1Leverhulme TrustRPG-2013-153European Union’s Seventh Framework Programme (FP7/2007-2013ERC grant agreement number 335925

Developing a feeling for error

This paper is based on ethnographic research of data practices in a public health project called Weather Health and Air Pollution. (All names are pseudonyms.) I examine two different kinds of practices that make air pollution data, focusing on how they relate to particular modes of sensing and articulating air pollution. I begin by describing the interstitial spaces involved in making measurements of air pollution at monitoring sites and in the running of a computer simulation. Specifically, I attend to a shared dimension of these practices, the checking of a numerical reading for error. Checking a measurement for error is routine practice and a fundamental component of making data, yet these are also moments of interpretation, where the form and meaning of numbers are ambiguous. Through two case studies of modelling and monitoring data practices, I show that making a ‘good’ (error free) measurement requires developing a feeling for the instrument–air pollution interaction in terms of the intended functionality of the measurements made. These affective dimensions of practice are useful analytically, making explicit the interaction of standardised ways of knowing and embodied skill in stabilising data. I suggest that environmental data practices can be studied through researchers’ materialisation of error, which complicate normative accounts of Big Data and highlight the non-linear and entangled relations that are at work in the making of stable, accurate data

Spatially Explicit Data: Stewardship and Ethical Challenges in Science

Scholarly communication is at an unprecedented turning point created in part by the increasing saliency of data stewardship and data sharing. Formal data management plans represent a new emphasis in research, enabling access to data at higher volumes and more quickly, and the potential for replication and augmentation of existing research. Data sharing has recently transformed the practice, scope, content, and applicability of research in several disciplines, in particular in relation to spatially specific data. This lends exciting potentiality, but the most effective ways in which to implement such changes, particularly for disciplines involving human subjects and other sensitive information, demand consideration. Data management plans, stewardship, and sharing, impart distinctive technical, sociological, and ethical challenges that remain to be adequately identified and remedied. Here, we consider these and propose potential solutions for their amelioration

Digital methodologies of education governance:Pearson plc and the remediation of methods

This article analyses the rise of software systems in education governance, focusing on digital methods in the collection, calculation and circulation of educational data. It examines how software-mediated methods intervene in the ways educational institutions and actors are seen, known and acted upon through an analysis of the methodological complex of Pearson Education’s Learning Curve data-bank and its Center for Digital Data, Analytics and Adaptive Learning. This calls for critical attention to the ‘social life’ of its methods in terms of their historical, technical and methodological provenance; their affordances to generate data for circulation within the institutional circuitry of Pearson and to its wider social networks; their capacity to configure research users’ interpretations; and their generativity to produce the knowledge to influence education policy decisions and pedagogic practices. The purpose of the article is to critically survey the digital methods being mobilized by Pearson to generate educational data, and to examine how its methodological complex acts to produce a new data-based knowledge infrastructure for education. The consequence of this shift to data-based forms of digital education governance by Pearson is a challenge to the legitimacy of the social sciences in the theorization and understanding of learning, and its displacement to the authority of the data sciences

Thinking about Design: Critical Theory of Technology and the Design Process

In this chapter we offer a framework for thinking about the design of technology. Our approach draws on critical perspectives from both social theory and science and technology studies (STS). We understand design to be the process of consciously shaping an artifact to adapt it to specific goals and environments. Ou

What Values in Design? The Challenge of Incorporating Moral Values into Design

Recently, there is increased attention to the integration of moral values into the conception, design, and development of emerging IT. The most reviewed approach for this purpose in ethics and technology so far is Value-Sensitive Design (VSD). This article considers VSD as the prime candidate for implementing normative considerations into design. Its methodology is considered from a conceptual, analytical, normative perspective. The focus here is on the suitability of VSD for integrating moral values into the design of technologies in a way that joins in with an analytical perspective on ethics of technology. Despite its promising character, it turns out that VSD falls short in several respects: (1) VSD does not have a clear methodology for identifying stakeholders, (2) the integration of empirical methods with conceptual research within the methodology of VSD is obscure, (3) VSD runs the risk of committing the naturalistic fallacy when using empirical knowledge for implementing values in design, (4) the concept of values, as well as their realization, is left undetermined and (5) VSD lacks a complimentary or explicit ethical theory for dealing with value trade-offs. For the normative evaluation of a technology, I claim that an explicit and justified ethical starting point or principle is required. Moreover, explicit attention should be given to the value aims and assumptions of a particular design. The criteria of adequacy for such an approach or methodology follow from the evaluation of VSD as the prime candidate for implementing moral values in design

Lit up and left dark: Failures of imagination in urban broadband networks

The design and deployment of urban broadband infrastructures inscribe particular imaginations of Internet access onto city streets. The different manifestations and locations of these networks, their uses, and access points often expose material excesses of urban broadband networks, as well as failures of Internet service providers, urban planners, and public officials to imagine the diverse ways that people incorporate Internet connection into their everyday lives. We approach the study of urban broadband networks through the juxtaposition of invisible networks that are buried under the streets and have always been “turned off” (dark fiber) versus hypervisible that are “turned on” and prominently displayed on city streets (LinkNYC). In our analysis of these two case studies, we critique themes of visibility and invisibility as indexes of power and access. Our findings are meant to provide a critical analysis of urban technology policy as well as theories of infrastructure, visibility, and access

The hidden architecture of higher education:Building a big data infrastructure for the ‘smarter university’

Universities are increasingly organized and managed through digital data. The collection, processing and dissemination of Higher Education data is enabled by complex new data infrastructures that include both human and nonhuman actors, all framed by political, economic and social contingencies. HE data infrastructures need to be seen not just as technical programs but as practical relays of political objectives to reform the sector. This article focuses on a major active data infrastructure project in Higher Education in the United Kingdom. It examines the sociotechnical networks of organizations, software programs, standards, dashboards and visual analytics technologies that constitute the infrastructure, and how these technologies are fused to governmental imperatives of market reform. The analysis foregrounds how HE is being reimagined through the utopian ideal of the ‘smarter university’ while simultaneously being reformed through the political project of marketization