P-hacking or data dredging involves manipulation of the research data in order to obtain a statistically significant result. The reasons behind P-hacking and the consequences of the same are discussed in the present manuscript.

Philosophy of dialectical materialism and logic.

Concern about the integrity of empirical research has arisen in recent years in the light of studies showing the vast majority of publications in academic journals report positive results, many of these results are false and cannot be replicated, and many positive results are the product of data dredging and the application of flexible data analysis practices coupled with selective reporting. While a number of potential solutions have been proposed, the effects of these are poorly understood and (...) empirical evaluation of each would take many years. We propose that methods from the systems sciences be used to assess the effects, both positive and negative, of proposed solutions to the problem of declining research integrity such as study registration, Registered Reports, and open access to methods and data. In order to illustrate the potential application of systems science methods to the study of research integrity, we describe three broad types of models: one built on the characteristics of specific academic disciplines; one a diffusion of research norms model conceptualizing researchers as susceptible, “infected” and recovered; and one conceptualizing publications as a product produced by an industry comprised of academics who respond to incentives and disincentives. (shrink)

While the common procedure of statistical significance testing and its accompanying concept of p-values have long been surrounded by controversy, renewed concern has been triggered by the replication crisis in science. Many blame statistical significance tests themselves, and some regard them as sufficiently damaging to scientific practice as to warrant being abandoned. We take a contrary position, arguing that the central criticisms arise from misunderstanding and misusing the statistical tools, and that in fact the purported remedies themselves risk damaging science. (...) We argue that banning the use of p-value thresholds in interpreting data does not diminish but rather exacerbates data-dredging and biasing selection effects. If an account cannot specify outcomes that will not be allowed to count as evidence for a claim—if all thresholds are abandoned—then there is no test of that claim. The contributions of this paper are: To explain the rival statistical philosophies underlying the ongoing controversy; To elucidate and reinterpret statistical significance tests, and explain how this reinterpretation ameliorates common misuses and misinterpretations; To argue why recent recommendations to replace, abandon, or retire statistical significance undermine a central function of statistics in science: to test whether observed patterns in the data are genuine or due to background variability. (shrink)

The crisis of replication has led many to blame statistical significance tests for making it too easy to find impressive looking effects that do not replicate. However, the very fact it becomes difficult to replicate effects when features of the tests are tied down actually serves to vindicate statistical significance tests. While statistical significance tests, used correctly, serve to bound the probabilities of erroneous interpretations of data, this error control is nullified by data-dredging, multiple testing, and other (...) biasing selection effects. Arguments claiming to vitiate statistical significance tests attack straw person variants of tests that commit well-known fallacies and misinterpretations. There is a tension between popular calls for preregistration – arguably, one of the most promising ways to boost replication – and accounts that downplay error probabilities: Bayes Factors, Bayesian posteriors, likelihood ratios. By underscoring the importance of error control for well testedness, the replication crisis points to reformulating tests so as to avoid fallacies and report the extent of discrepancies that are and are not indicated with severity. (shrink)

The Fourth Industrial Revolution based on digitalization, the development of AI, robotics, big data, and increasing automation is dredging up older debates on the end of human work. This article contributes to this debate arguing that these changing circumstances represent an opportunity to advance a renewed consideration of human work. By emphasizing its most distinctively human dimensions, including gratuitousness, relationality, and meaningfulness, we propose the articulation of a social model that recognizes relational goods as a specific contribution of (...) human work in an attempt to overcome the state and the market's monopoly of public life built around the dichotomy between the private and the public. Such a social model is based on the concept of circular subsidiarity, which represents a departure from traditional notions of subsidiarity by promoting a dynamic and reciprocal interplay between three essential societal spheres: the state, the market, and organized civil society, all of which have their role to fulfill by providing security, efficiency, and relational goods. Ultimately, this article suggests that circular subsidiarity can lead to a more inclusive and equitable social model by acknowledging how relational goods not only humanize civil society but also sustain the functioning of both the market and the state. (shrink)

ArgumentThe Thematic Apperception Test was a projective psychological test created by Harvard psychologist Henry A. Murray and his lover Christina Morgan in the 1930s. The test entered the nascent intelligence service of the United States during the Second World War due to its celebrated reputation for revealing the deepest aspects of an individual's unconscious. It subsequently spread as a scientifically objective research tool capable not only of dredging the unconscious depths, but also of determining the best candidate for a (...) management position, the psychological complexes of human nature, and the unique characteristics of a culture. Two suppositions underlie the utility of the test. One is the power of narrative. The test entails a calculated abuse of the subjects tested, based on their inability to interpret their own narrative. The form of the test requires that a subject fail to decipher the coded, unconscious meaning their narrative reveals. Murray believed the interpretation of a subject's narrative and the projection contained therein depended exclusively on the psychologist. This view of interpretation stems from the seemingly more reasonable belief of nineteenth-century Romantic thinkers that a literary text serves as a proxy for an author's deepest self. The TAT also supposes that there is something beyond consciousness closely resembling a psychoanalytic unconscious, which also has clear precedents in nineteenth-century German thought. Murray's views on literary interpretation, his view of psychology as well as the continuing prevalence of the TAT, signals a nineteenth-century concept of self that insists “on relations of depth and surface, inner and outer life”. It is clear the hermeneutic practice of Freud's psychoanalysis, amplified in Jung, drew on literary conceptions of the unconscious wider than those of nineteenth-century psychology. (shrink)

In this paper I examine third wave leminism in the hopes of shedding light on its relationship to the concurrent contemporary backlash against leminism. I investigate this by attempting to answer two questions. First, given the nature of the first and second waves, is the third wave appropriately so called? I tentatively conclude that it is not. Second, I ask whether the issue of identity, which is central to third wave analysis, is addressed well by third wavers. I suggest that (...) there are serious problems with the rejection of identity politics that characterizes much third wave feminism, particularly in the repudiation of second wave feminism that seems to accompany it. I conclude that, at best, the third wave seems unprepared to light the present backlash and, at worst, it appears to be a part of it. (shrink)

We face reality presented with the data of conscious experience and nothing else. The project of early modern philosophy was to build a complete theory of the world from this starting point, with no cheating. Crucial to this starting point is the data of conscious sensory experience – sense data. Attempts to avoid this project often argue that the very idea of sense data is confused. But the sense-data way of talking, the sense-data language, (...) can be freed from every blemish using ideas from contemporary metaontology. We can adopt a sense-data framework that vindicates the traditional claims of sense-data theories and leads to plausible theories of perception and color. We can, we should, and in a sense, we must. Yet when we do, we face the traditional problem of external world skepticism, head-on. The real challenge of skepticism is forced upon us; it cannot honestly be avoided using externalist tricks, burden of proof shifting, or other razzle dazzle. But the challenge can be met: we are rational to posit the external world as the best explanation of the many synchronic and diachronic patterns over our sense-data. This paper argues for all of these points and ends with a plea for analytic empiricism – a traditional sense-data version of empiricism that uses all of the tools of analytic philosophy while avoiding the more questionable doctrines of the Vienna Circle (phenomenalism, verificationism). Analytic empiricism is our last best hope for completing the great philosophical project of early modern philosophy. (shrink)

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to “mental privacy.” In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other well-understood (...) class='Hi'>data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum’s “contextual integrity” theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. (shrink)

Nowadays, protecting trust in social sciences also means engaging in open community dialogue, which helps to safeguard robustness and improve efficiency of research methods. The combination of open data, open review and open dialogue may sound simple but implementation in the real world will not be straightforward. However, in view of Begley and Ellis’s (2012) statement that, “the scientific process demands the highest standards of quality, ethics and rigour,” they are worth implementing. More importantly, they are feasible to work (...) on and likely will help to restore plausibility to social sciences research. Therefore, I feel it likely that the triplet of open data, open review and open dialogue will gradually emerge to become policy requirements regardless of the research funding source. (shrink)

We have seen through many examples that data science and artificial intelligence can reinforce structural inequalities like sexism and racism. Data is power, and that power is distributed unequally. This book offers a vision for a feminist data science that can challenge power and work towards justice. This book takes a stand against a world that benefits some (including the authors, two white women) at the expense of others. It seeks to provide concrete steps for data (...) scientists seeking to learn how feminism can help them work towards justice, and for feminists seeking to learn how their own work can carry over to the growing field of data science. It is addressed to professionals in all fields where data-driven decisions are being made, as well as to communities that want to better understand the data that surrounds them. It is written for everyone who seeks to better understand the charts and statistics that they encounter in their day-to-day lives, and for everyone who seeks to better communicate the significance of such charts and statistics to others. This is an example-driven book written with a broad audience of scholars, students, and practitioners in mind. It offers a way of thinking about data, both their uses and their limits, that is informed by direct experience, by a commitment to action, and by the ideas associated with intersectional feminist thought. (shrink)

We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the pragmatic-representational view of data. On the PR view, data are representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness for particular purposes. Some important implications of the PR view for data assessment, related to misrepresentation, context-sensitivity, and complementary use, are highlighted. The PR view provides (...) insight into the common but little-discussed practices of iteratively reusing and repurposing data, which result in many datasets’ having a phylogeny—an origin and complex evolutionary history—that is relevant to their evaluation and future use. We relate these insights to the open-data and data-rescue movements, and highlight several future avenues of research that build on the PR view of data. (shrink)

Digital personal data is increasingly framed as the basis of contemporary economies, representing an important new asset class. Control over these data assets seems to explain the emergence and dominance of so-called “Big Tech” firms, consisting of Apple, Microsoft, Amazon, Google/alphabet, and Facebook. These US-based firms are some of the largest in the world by market capitalization, a position that they retain despite growing policy and public condemnation—or “techlash”—of their market power based on their monopolistic control of personal (...) data. We analyse the transformation of personal data into an asset in order to explore how personal data is accounted for, governed, and valued by Big Tech firms and other political-economic actors. However, our findings show that Big Tech firms turn “users” and “user engagement” into assets through the performative measurement, governance, and valuation of user metrics, rather than extending ownership and control rights over personal data per se. We conceptualize this strategy as a form of “techcraft” to center attention on the means and mechanisms that Big Tech firms deploy to make users and user data measurable and legible as future revenue streams. (shrink)

While the term “reactivity” has come to be associated with specific phenomena in the social sciences, having to do with subjects’ awareness of being studied, this paper takes a broader stance on this concept. I argue that reactivity is a ubiquitous feature of the psychological subject matter and that this fact is a precondition of experimental research, while also posing potential problems for the experimenter. The latter are connected to the worry about distorted data and experimental artifacts. But what (...) are experimental artifacts and what is the most productive way of dealing with them? In this paper, I approach these questions by exploring the ways in which experimenters in psychology simultaneously exploit and suppress the reactivity of their subject matter in order to produce experimental data that speak to the question or subject matter at hand. Highlighting the artificiality of experimental data, I raise the question of what distinguishes a genuine experimental result from an experimental artifact. My analysis construes experimental results as the outcomes of inferences from the data that take material background assumptions as auxiliary premises. Artifacts occur when one or more of these background assumptions are false, such that the data do not reliably serve the purposes they were generated for. I conclude by laying out the ways in which my analysis of data quality is relevant to, and informed by, recent debates about the replicability of experimental results. (shrink)

Between 2006 and 2011, humanity accumulated 1,600 EB of data. As a result of this growth, there is now more data produced than available storage. This article explores the problem of “Big Data,” arguing for an epistemological approach as a possible solution to this ever-increasing challenge.

This article provides a history of private sector tracking technologies, examining how the advent of commercial surveillance centered around a logic of data capitalism. Data capitalism is a system in which the commoditization of our data enables an asymmetric redistribution of power that is weighted toward the actors who have access and the capability to make sense of information. It is enacted through capitalism and justified by the association of networked technologies with the political and social benefits (...) of online community, drawing upon narratives that foreground the social and political benefits of networked technologies. I examine its origins in the wake of the dotcom bubble, when technology makers sought to develop a new business model to support online commerce. By leveraging user data for advertising purposes, they contributed to an information environment in which every action leaves behind traces collected by companies for commercial purposes. Through analysis of primary source materials produced by technology makers, journalists, and business analysts, I examine the emergence of data capitalism between the mid-1990s and mid-2000s and its central role in the contemporary information economy. (shrink)

This paper provides a restatement and defense of the data/ phenomena distinction introduced by Jim Bogen and me several decades ago (e.g., Bogen and Woodward, The Philosophical Review, 303–352, 1988). Additional motivation for the distinction is introduced, ideas surrounding the distinction are clarified, and an attempt is made to respond to several criticisms.

This article extends property-owning democracy to the digital realm and introduces “data-owning democracy,” a new political economic regime characterized by the wide distribution of data as capital among citizens. Drawing on republican theory and acknowledging data's unique role in the digital economy, it proposes a two-tier model that combines different modes of data ownership and corresponding rights. The first layer of “data-owning democracy” is characterized by a digital public infrastructure that enables citizens to collectively generate (...) data and have a say in how their citizen data are used. In the second layer, individuals automatically receive machine-readable copies of their data whenever they are generated—a slightly more advanced form of the European Union's existing right to data portability (Art. 20). With its focus on empowerment, data-owning democracy is designed to be complementary to existing data protection regulations. It also illustrates how political theory more broadly, and republican theory specifically, can be instructive for specifying the normative components of a new political economy dealing with questions of empowerment and digital rights. (shrink)

Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal (...) knowledge is necessary for the project, both as a source for handling complexity and as an output for meeting the project’s goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data. (shrink)

Data science, and the related field of big data, is an emerging discipline involving the analysis of data to solve problems and develop insights. This rapidly growing domain promises many benefits to both consumers and businesses. However, the use of big data analytics can also introduce many ethical concerns, stemming from, for example, the possible loss of privacy or the harming of a sub-category of the population via a classification algorithm. To help address these potential ethical (...) challenges, this paper maps and describes the main ethical themes that were identified via systematic literature review. It then identifies a possible structure to integrate these themes within a data science project, thus helping to provide some structure in the on-going debate with respect to the possible ethical situations that can arise when using data science analytics. (shrink)

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine (...) learning in particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the ‘new’ biology is substantially diminished if compared to the ‘old’ biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. (shrink)

It has been recently suggested that if the Extended Mind thesis is true, mental privacy might be under serious threat. In this paper, I look into the details of this claim and propose that one way of dealing with this emerging threat requires that data ontology be enriched with an additional kind of data—viz., mental data. I explore how mental data relates to both data and metadata and suggest that, arguably, and by contrast with these (...) existing categories of informational content, mental data should not be merely legally protected. Rather, if we value mental privacy as we know it, technological measures should be employed to ensure that one’s mental data are practically—not just legally—impossible for others to obtain. (shrink)

Robert John Ackermann deals decisively with the problem of relativism that has plagued post-empiricist philosophy of science. Recognizing that theory and data are mediated by data domains (bordered data sets produced by scientific instruments), he argues that the use of instruments breaks the dependency of observation on theory and thus creates a reasoned basis for scientific objectivity. Originally published in 1985. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from (...) the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905. (shrink)

The first few years of the 21st century were characterised by a progressive loss of privacy. Two phenomena converged to give rise to the data economy: the realisation that data trails from users interacting with technology could be used to develop personalised advertising, and a concern for security that led authorities to use such personal data for the purposes of intelligence and policing. In contrast to the early days of the data economy and internet surveillance, the (...) last few years have witnessed a rising concern for privacy. As bad data practices have come to light, citizens are starting to understand the real cost of using online digital technologies. Two events stamped 2018 as a landmark year for privacy: the Cambridge Analytica scandal, and the implementation of the European Union’s General Data Protection Regulation (GDPR). The former showed the extent to which personal data has been shared without data subjects’ knowledge and consent and many times for unacceptable purposes, such as swaying elections. The latter inaugurated the beginning of robust data protection regulation in the digital age. Getting privacy right is one of the biggest challenges of this new decade of the 21st century. The past year has shown that there is still much work to be done on privacy to tame the darkest aspects of the data economy. As data scandals continue to emerge, questions abound as to how to interpret and enforce regulation, how to design new and better laws, how to complement regulation with better ethics, and how to find technical solutions to data problems. The aim of the research project Data, Privacy, and the Individual is to contribute to a better understanding of the ethics of privacy and of differential privacy. The outcomes of the project are seven research papers on privacy, a survey, and this final report, which summarises each research paper, and goes on to offer a set of reflections and recommendations to implement best practices regarding privacy. (shrink)

Data subject rights provide data controllers with obligations that can help with transparency, giving data subjects some control over their personal data. To date, a growing number of researchers have used these data subject rights as a methodology for data collection in research studies. No one, however, has gathered and analysed different academic research studies that use data subject rights as a methodology for data collection. To this end, we conducted a systematic (...) literature review that searched, compiled, and analysed 32 academic studies that use data subject rights as a data collection method. We find that the right of access is the most commonly-used data subject right by researchers, most studies are interested in measuring data subject rights compliance, and that a variety of difficulties exist in conducting research studies with data subject rights. We conclude that researchers should explore other data subject rights for alternative purposes, ease the process of exercising data subject rights, and improve the scalability of these studies. (shrink)

Critical Data Studies explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on (...) individuals’ daily lives. CDS questions the many assumptions about Big Data that permeate contemporary literature on information and society by locating instances where Big Data may be naively taken to denote objective and transparent informational entities. In this introduction to the Big Data & Society CDS special theme, we briefly describe CDS work, its orientations, and principles. (shrink)

Moving away from the strong body of critique of pervasive ‘bad data’ practices by both governments and private actors in the globalized digital economy, this book aims to paint an alternative, more optimistic but still pragmatic picture of the datafied future. The authors examine and propose ‘good data’ practices, values and principles from an interdisciplinary, international perspective. From ideas of data sovereignty and justice, to manifestos for change and calls for activism, this collection opens a multifaceted conversation (...) on the kinds of futures we want to see, and presents concrete steps on how we can start realizing good data in practice. (shrink)

Has the rise of data-intensive science, or ‘big data’, revolutionized our ability to predict? Does it imply a new priority for prediction over causal understanding, and a diminished role for theory and human experts? I examine four important cases where prediction is desirable: political elections, the weather, GDP, and the results of interventions suggested by economic experiments. These cases suggest caution. Although big data methods are indeed very useful sometimes, in this paper’s cases they improve predictions either (...) limitedly or not at all, and their prospects of doing so in the future are limited too. (shrink)

This paper investigates some metaphysical and epistemological assumptions behind Bogen and Woodward’s data-to-phenomena inferences. I raise a series of points and suggest an alternative possible Kantian stance about data-to-phenomena inferences. I clarify the nature of the suggested Kantian stance by contrasting it with McAllister’s view about phenomena as patterns in data sets.

A growing awareness of the lack of reproducibility has undermined society’s trust and esteem in social sciences. In some cases, well-known results have been fabricated or the underlying data have turned out to have weak technical foundations.

ABSTRACT:Technological advances introduce the possibility that, in the future, firms will be able to use big-data analysis to discover and offer consumers their individual reservation price. This can generate some interesting benefits, such as a better state of affairs in terms of equality of both welfare and resources, as well as increased social welfare. However, these benefits are countered by considerations of relational equality. This article takes up the market-failures approach as its basis to demonstrate what is wrong with (...) using big data to personalize prices. The article offers an improvement to the market-failures approach and argues that what is wrong with using big data to personalize prices is that it unfairly undermines consumers’ ability to benefit from the market, which is the very point of having a market. (shrink)

The collection and circulation of data is now a central element of increasingly more sectors of contemporary capitalism. This article analyses data as a form of capital that is distinct from, but has its roots in, economic capital. Data collection is driven by the perpetual cycle of capital accumulation, which in turn drives capital to construct and rely upon a universe in which everything is made of data. The imperative to capture all data, from all (...) sources, by any means possible influences many key decisions about business models, political governance, and technological development. This article argues that many common practices of data accumulation should actually be understood in terms of data extraction, wherein data is taken with little regard for consent and compensation. By understanding data as a form capital, we can better analyse the meaning, practices, and implications of datafication as a political economic regime. (shrink)

Purpose The information society has developed rapidly since the end of the twentieth century. Many countries (including Australia) have been looking at ways to protect their citizens against the variety of risks associated with the continued evolution of the internet. The Australian Federal Government in 2013 proposed data retention as one possible method of protecting Australian society and aiding law enforcement agencies to investigate and prosecute cyber-crime. Design/methodology/approach The aim of this paper is to consider the issue of (...) class='Hi'>data retention from a stakeholder’s perspective by analysing the public submissions garnered by the Australian Federal Government and identify the key issues and concerns that were raised by these stakeholders. The paper used a qualitative approach to undertake theme analysis. Findings The paper shows the concerns and wishes that different stakes holders have regarding data retention within Australia. Originality/value This is a unique study into implementation of data retention at a national level, in terms of the paper focussing on Australia. (shrink)

Research data should be made readily available. A robust data-sharing plan, led by the principal investigator of the research project, requires considerable administrative and operational resources. Because external support for data sharing is minimal, principal investigators should consider engaging existing institutional information experts, such as librarians and information systems personnel, to participate in data-sharing efforts.

New data-driven technologies yield benefits and potentials, but also confront different agents and stakeholders with challenges in retaining control over their data. Our goal in this study is to arrive at a clear picture of what is meant by data sovereignty in such problem settings. To this end, we review 341 publications and analyze the frequency of different notions such as data sovereignty, digital sovereignty, and cyber sovereignty. We go on to map agents they concern, in (...) which context they appear, and which values they allude to. While our sample reveals a considerable degree of divergence and an occasional lack of clarity about intended meanings of data sovereignty, we propose a conceptual grid to systematize different dimensions and connotations. Each of them relates in some way to meaningful control, ownership, and other claims to data articulated by a variety of agents ranging from individuals to countries. Data sovereignty alludes to a nuanced mixture of normative concepts such as inclusive deliberation and recognition of the fundamental rights of data subjects. (shrink)

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine (...) learning in particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the ‘new’ biology is substantially diminished if compared to the ‘old’ biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. (shrink)

This article examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines. In particular, it critically explores new forms of empiricism that declare ‘the end of theory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities and computational social sciences that propose radically different ways to make (...) sense of culture, history, economy and society. It is argued that: Big Data and new data analytics are disruptive innovations which are reconfiguring in many instances how research is conducted; and there is an urgent need for wider critical reflection within the academy on the epistemological implications of the unfolding data revolution, a task that has barely begun to be tackled despite the rapid changes in research practices presently taking place. After critically reviewing emerging epistemological positions, it is contended that a potentially fruitful approach would be the development of a situated, reflexive and contextually nuanced epistemology. (shrink)

In this article, we introduce and demonstrate the concept-metaphor of broken data. In doing so, we advance critical discussions of digital data by accounting for how data might be in processes of decay, making, repair, re-making and growth, which are inextricable from the ongoing forms of creativity that stem from everyday contingencies and improvisatory human activity. We build and demonstrate our argument through three examples drawn from mundane everyday activity: the incompleteness, inaccuracy and dispersed nature of personal (...) self-tracking data; the data cleaning and repair processes of Big Data analysis and how data can turn into noise and vice versa when they are transduced into sound within practices of music production and sound art. This, we argue is a necessary step for considering the meaning and implications of data as it is increasingly mobilised in ways that impact society and our everyday worlds. (shrink)