Is 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 the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community and 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. (shrink)

What does the abundance of data and proliferation of data-making methods mean for the ordinary person, the person on the street? And, what could they come to mean? In this paper, we present an overview of a year-long project to examine just such questions and complicate, in some ways, what it is to ask them. The project is a collective exercise in which we – a mixture of social scientists, designers and makers – and those living and working on one (...) street in Cambridge, Tenison Road, are working to think through how data might be materialised and come to matter. The project aims to better understand the specificities and contingencies that arise when data is produced and used in place. Mid-way through the project, we use this commentary to give some background to the work and detail one or two of the troubles we have encountered in putting locally relevant data to work. We also touch on a methodological standpoint we are working our way into and through, one that we hope complicates the separations between subject and object in data-making and opens up possibilities for a generative refiguring of the manifold relations. (shrink)

This paper is based on ethnographic research of data practices in a public health project called Weather Health and Air Pollution. 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’ 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. (shrink)

A new generation of environmental satellites, the Sentinels, has recently been launched by the European Space Agency. Part of ESA’s Copernicus Programme, the sentinel mission has adopted an Open Data policy which intends to make different levels of data freely available via an online data hub. Sentinel data will support applications including land monitoring, emergency management and security and will thus form the evidence-base for a wide-range of local, regional, national and international decisions, from individual insurance claims to humanitarian interventions. (...) By providing ever more data streams for monitoring and managing the planet, the sentinels institute a novel mode of visibility in terms of resolution, coverage and frequency. At the same time, they are co-constitutive of the increasing datafication of the environment. In the entwinement of heavenly gaze and data visions, I want to recover the work of ‘data fictions’ in order to demystify notions of visibility and calculability associated with “earth observation” and trouble its unquestioned pursuit of total monitoring. (shrink)

Big Data has been variously defined in the literature. In the main, definitions suggest that Big Data possess a suite of key traits: volume, velocity and variety, but also exhaustivity, resolution, indexicality, relationality, extensionality and scalability. However, these definitions lack ontological clarity, with the term acting as an amorphous, catch-all label for a wide selection of data. In this paper, we consider the question ‘what makes Big Data, Big Data?’, applying Kitchin’s taxonomy of seven Big Data traits to 26 datasets (...) drawn from seven domains, each of which is considered in the literature to constitute Big Data. The results demonstrate that only a handful of datasets possess all seven traits, and some do not possess either volume and/or variety. Instead, there are multiple forms of Big Data. Our analysis reveals that the key definitional boundary markers are the traits of velocity and exhaustivity. We contend that Big Data as an analytical category needs to be unpacked, with the genus of Big Data further delineated and its various species identified. It is only through such ontological work that we will gain conceptual clarity about what constitutes Big Data, formulate how best to make sense of it, and identify how it might be best used to make sense of the world. (shrink)

Using a new materialist line of questioning that looks at the agential potentialities of water and its entanglements with Big Data and surveillance, this article explores how the recent Snowden revelations about the National Security Agency have reignited media scholars to engage with the infrastructures that enable intercepting, hosting, and processing immeasurable amounts of data. Focusing on the expansive architecture, location, and resource dependence of the NSA’s Utah Data Center, I demonstrate how surveillance and privacy can never be disconnected from (...) the material infrastructures that allow and render natural the epistemological state of mass surveillance. Specifically, I explore the NSA’s infrastructure and the million of gallons of water it requires daily to cool its servers, while located in one of the driest states in the US. Complicating surveillance beyond the NSA, as also already imbricated with various social media companies, this article questions the emplacement and impact of corporate data centers more generally, and the changes they are causing to the landscape and local economies. I look at how water is an intriguing and politically relevant part of the surveillance infrastructure and how it has been constructed as the main tool for activism in this case, and how it may eventually help transform the public’s conceptualization of Big Data, as deeply material. (shrink)

Despite all the attention to Big Data and the claims that it represents a “paradigm shift” in science, we lack understanding about what are the qualities of Big Data that may contribute to this revolutionary impact. In this paper, we look beyond the quantitative aspects of Big Data and examine it from a sociotechnical perspective. We argue that a key factor that distinguishes “Big Data” from “lots of data” lies in changes to the traditional, well-established “control zones” that facilitated clear (...) provenance of scientific data, thereby ensuring data integrity and providing the foundation for credible science. The breakdown of these control zones is a consequence of the manner in which our network technology and culture enable and encourage open, anonymous sharing of information, participation regardless of expertise, and collaboration across geographic, disciplinary, and institutional barriers. We are left with the conundrum—how to reap the benefits of Big Data while re-creating a trust fabric and an accountable chain of responsibility that make credible science possible. (shrink)

In this paper, we describe how critical data designers have created projects that ‘push back’ against the eclipse of environmental problems by dominant orders: the pioneering pollution database Scorecard, released by the US NGO Environmental Defense Fund in 1997; the US Environmental Protection Agency’s EnviroAtlas that brings together numerous data sets and provides tools for valuing ecosystem services; and the Houston Clean Air Network’s maps of real-time ozone levels in Houston. Drawing on ethnographic observations and interviews, we analyse how critical (...) data designers turn scientific data and findings into claims and visualisations that are meaningful in contemporary political terms. The skills of critical data designers cross scales and domains; they must identify problems calling for public consideration, and then locate, access, link, and create visualisations of data relevant to the problem. We conclude by describing hazards ahead in work to leverage Big Data to understand and address environmental problems. Critical data designers need to understand what counts as a societal problem in a particular context, what doesn’t, what is seen as connected and not, what is seen as ethically charged, and what is exonerated and discounted. Such recognition is produced through interpretive, ‘close reading’ of the historical moment in which they operate. (shrink)

Throughout much of his writing, Whitehead outlines a critique of what he termed the `bifurcation of nature'. This position divides the world into objective causal nature, on the one hand, with the perceptions of subjects on the other. On such a view, truth lies in a reality external to such subjects and it is the task of science to deliver clear and immediate access to this realm. Further, judgments about this external reality are the province of human subjects and it (...) is the task of philosophy to ascertain the validity or otherwise of these. This article outlines Whitehead's attempts to develop a conception of nature which avoids this premise; it also explains how he offers a way for contemporary theories to construct or engineer abstractions which go beyond notions of social construction or deconstruction. This article argues that not only does Whitehead offer a coherent alternative to such approaches but that the manner in which his major text is written reflects his commitment to the inherently constructed and constructing character of all existence. (shrink)

Place is a key concept in environmental studies and criticism. However, it is often overlooked as a dimension of situatedness in social studies of information. Rather, situatedness has been defined primarily as embodiment or social context. This paper explores place attachments in Big Data by adapting close and distant approaches for reading texts to examine the accessions data of the Arnold Arboretum, a living collection of trees, vines and shrubs established by Harvard University in 1872. Although it is an early (...) and unconventional example of the phenomenon, there are several reasons that the Arboretum is a useful site for investigating the relationship between Big Data and place. First, the category of place is embedded in a range of data fields used in the Arboretum’s records. Second, the Arboretum has long sought to be a place in which scientists and citizens alike can encounter large collections of data firsthand. Third, the place has shaped fluctuations in the daily production of data over the course of the Arboretum’s 144 year history. Furthermore, Arboretum data can help us see place in ways not necessarily tied to geolocation. Each of these place attachments suggests a different way in which data can be environmental: by being about, in, from, or generative of place. Taken together, these attachments offer a model for examining other data in relation to their environments. Moreover, the paper contends that rather than being detached from place, as prevailing discourses suggest, Big Data bring together more and further reaching place attachments than data sets of smaller sizes. (shrink)

The Snowden revelations and the emergence of ‘Big Data’ have rekindled questions about how security practices are deployed in a digital age and with what political effects. While critical scholars have drawn attention to the social, political and legal challenges to these practices, the debates in computer and information science have received less analytical attention. This paper proposes to take seriously the critical knowledge developed in information and computer science and reinterpret their debates to develop a critical intervention into the (...) public controversies concerning data-driven security and digital surveillance. The paper offers a two-pronged contribution: on the one hand, we challenge the credibility of security professionals’ discourses in light of the knowledge that they supposedly mobilize; on the other, we argue for a series of conceptual moves around data, human–computer relations, and algorithms to address some of the limitations of existing engagements with the Big Data-security assemblage. (shrink)

Today data art is a full-fledged and maturing artistic practice. Like painting, artists are creating new visuals and representations with data. Like sculpture, artists are recombining bits to build something new out of the commonplace. Like photography, artists are using data to mirror or reflect contemporary society. In my own practice for the last 15 years I have been using data to make works at the intersection of art, design and activism with a recent focus on environmental topics. It is (...) my belief that through improved representation of, access to and public involvement with data we can increase understanding of important issues and provoke behavioural and systemic change. In this paper, I will examine the evolution of my work using data as my medium as well as outline tactics for data art to promote change. (shrink)

Corporate carbon footprint data has become ubiquitous. This data is also highly promissory. But as this paper argues, such data fails both consumers and citizens. The governance of climate change seemingly requires a strong foundation of data on emission sources. Economists approach climate change as a market failure, where the optimisation of the atmosphere is to be evidence based and data driven. Citizens or consumers, state or private agents of control, all require deep access to information to judge emission realities. (...) Whether we are interested in state-led or in neoliberal ‘solutions’ for either democratic participatory decision-making or for preventing market failure, companies’ emissions need to be known. This paper draws on 20 months of ethnographic fieldwork in a Fortune 50 company’s environmental accounting unit to show how carbon reporting interferes with information symmetry requirements, which further troubles possibilities for contesting data. A material-semiotic analysis of the data practices and infrastructures employed in the context of corporate emissions disclosure details the situated political economies of data labour along the data processing chain. The explicit consideration of how information asymmetries are socially and computationally shaped, how contexts are shifted and how data is systematically straightened out informs a reflexive engagement with Big Data. The paper argues that attempts to automatise environmental accounting’s veracity management by means of computing metadata or to ensure that data quality meets requirements through third-party control are not satisfactory. The crossover of Big Data with corporate environmental governance does not promise to trouble the political economy that hitherto sustained unsustainability. (shrink)