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)

Data-intensive science comes with increased risks concerning quality and reliability of data, and while trust in science has traditionally been framed as a matter of scientists being expected to adhere to certain technical and moral norms for behaviour, emerging discourses of open science present openness and transparency as substitutes for established trust mechanisms. By ensuring access to all available information, quality becomes a matter of informed judgement by the users, and trust no longer seems (...) necessary. This strategy does not, however, take into consideration the networks of professionals already enabling data-intensive science by providing high-quality data. In the life sciences, biological data- and knowledge bases managed by expert biocurators have become crucial for data-intensive research. In this paper, I will use the case of biocurators to argue that openness and transparency will not diminish the need for trust in data-intensive science. On the contrary, data-intensive science requires a reconfiguration of existing trust mechanisms in order to include those who take care of and manage scientific data after its production. (shrink)

Recent claims, mainly from computer scientists, concerning a largely automated and model-free data-intensive science have been countered by critical reactions from a number of philosophers of science. The debate suffers from a lack of detail in two respects, regarding the actual methods used in data-intensive science and the specific ways in which these methods presuppose theoretical assumptions. I examine two widely-used algorithms, classificatory trees and non-parametric regression, and argue that these are theory-laden in (...) an external sense, regarding the framing of research questions, but not in an internal sense concerning the causal structure of the examined phenomenon. With respect to the novelty of data-intensive science, I draw an analogy to exploratory as opposed to theory-directed experimentation. (shrink)

In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual (...) shifts. First, we discuss the EXPOsOMICS project, an attempt to integrate genomic and environmental data which suggests a reframing of the boundaries between external and internal environments. Second, we explore the MEDMI platform, whose efforts to combine health, environmental and climate data instantiate a reframing and expansion of environmental exposure. Third, we illustrate how extracting epidemiological insights from extensive social data collected by the CIDACS institute yields innovative attributions of causal power to environmental factors. Identifying these shifts highlights the benefits and opportunities of new environmental data, as well as the challenges that such tools bring to understanding and fostering health. It also emphasises the constraints that data selection and accessibility pose to scientific imagination, including how researchers frame key concepts in health-related research. (shrink)

Life sciences research is increasingly international and data-intensive. Researchers work in multi-jurisdictional teams or formally established research consortia to exchange data and conduct research using computation of multiple sources and volumes of data at multiple sites and through multiple pathways. Despite the internationalization and data intensification of research, the same ethics review process as applies to single-site studies in one country tends to apply to multi-site studies in multiple countries. Because of the standard requirement for (...) multi-jurisdictional or multi-site ethics review, international research projects are subjected to multiple ethics reviews of the same research protocol. Consequently, the reviews may be redundant and resource-consuming, whilst the opinions delivered by ethics committees may be inconsistent both within and across jurisdictions. In this article, we present findings based on interviews conducted with international experts in research ethics on the topic of et... (shrink)

This chapter argues for the importance of considering conceptual and normative commitments when addressing questions of responsible practice in data-intensive agricultural research and development. We consider genetic gain-focused plant breeding strategies that envision a data-intensive mode of breeding in which genomic, environmental and socio-economic data are mobilised for rapid crop variety development. Focusing on socio-economic data linkage, we examine methods of product profiling and how they accommodate gendered dimensions of breeding in the field. Through (...) a comparison with participatory breeding methods, we argue that the conceptual commitments underpinning current methods of integrating socioeconomic data into calculations of genetic gain can preclude the achievement of key social development goals, and that better engagement with participatory approaches can help address this problem. We conclude by identifying three key avenues towards a data-intensive approach to plant breeding that utilises the diverse sources of relevant evidence available, including socio-economic data, and maximises the chance of developing sustainable and responsible strategies and research practices in this domain: (1) reliable, long-term management of data infrastructures; (2) ongoing critical analysis of the conceptual foundations of specific strategies; and (3) regular transdisciplinary consultations including expertise in the social studies of agricultural science as well as participatory breeding techniques. (shrink)

We present two case studies from contemporary biology in which we observe conflicts between established and emerging approaches. The first case study discusses the relation between molecular biology and systems biology regarding the explanation of cellular processes, while the second deals with phylogenetic systematics and the challenge posed by recent network approaches to established ideas of evolutionary processes. We show that the emergence of new fields is in both cases driven by the development of high-throughput data generation technologies and (...) the transfer of modeling techniques from other fields. New and emerging views are characterized by different philosophies of nature, i.e. by different ontological and methodological assumptions and epistemic values and virtues. This results in a kind of conflict we call “epistemic competition” that manifests in two ways: On the one hand, opponents engage in mutual critique and defense of their fundamental assumptions. On the other hand, they compete for the acceptance and integration of the knowledge they provide by a broader scientific community. Despite an initial rhetoric of replacement, the views as well as the respective audiences come to be seen as more clearly distinct during the course of the debate. Hence, we observe—contrary to many other accounts of scientific change—that conflict results in the formation of new niches of research, leading to co-existence and perceived complementarity of approaches. Our model thus contributes to the understanding of the pluralization of the scientific landscape. (shrink)

Data-intensive techniques, now widely referred to as 'big data', allow for novel ways to address complexity in science. I assess their impact on the scientific method. First, big-data science is distinguished from other scientific uses of information technologies, in particular from computer simulations. Then, I sketch the complex and contextual nature of the laws established by data-intensive methods and relate them to a specific concept of causality, thereby dispelling the popular myth that (...) big data is only concerned with correlations. The modeling in data-intensive science is characterized as 'horizontal'—lacking the hierarchical, nested structure familiar from more conventional approaches. The significance of the transition from hierarchical to horizontal modeling is underlined by a concurrent paradigm shift in statistics from parametric to non-parametric methods. (shrink)

I argue for the causal character of modeling in data-intensive science, contrary to widespread claims that big data is only concerned with the search for correlations. After discussing the concept of data-intensive science and introducing two examples as illustration, several algorithms are examined. It is shown how they are able to identify causal relevance on the basis of eliminative induction and a related difference-making account of causation. I then situate data-intensive modeling (...) within a broader framework of an epistemology of scientific knowledge. In particular, it is shown to lack a pronounced hierarchical, nested structure. The significance of the transition to such “horizontal” modeling is underlined by the concurrent emergence of novel inductive methodology in statistics such as non-parametric statistics. Data-intensive modeling is well equipped to deal with various aspects of causal complexity arising especially in the higher level and applied sciences. (shrink)

Biomedical deployments of data science capitalise on vast, heterogeneous data sources. This promotes a diversified understanding of what counts as evidence for health-related interventions, beyond the strictures associated with evidence-based medicine. Focusing on COVID-19 transmission and prevention research, I consider the epistemic implications of this diversification of evidence in relation to: (1) experimental design, especially the revival of natural experiments as sources of reliable epidemiological knowledge; and (2) modelling practices, particularly the recognition of transdisciplinary expertise as crucial (...) to developing and interpreting data models. Acknowledging such shifts in evidential, experimental and modelling practices helps avoid harmful applications of data-intensive methods. (shrink)

In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in Section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In Section 3, I argue that these methodological claims (...) are in tension with a prominent account of scientific method, often called “Inference to the Best Explanation”. Later on, in Section 3, I consider an argument against IBE that will be congenial to proponents of Big Data, namely, the argument due to Roche and Sober Analysis, 73:659–668, that “explanatoriness is evidentially irrelevant.” This argument is based on Bayesianism, one of the most prominent general accounts of theory-confirmation. In Section 4, I consider some extant responses to this argument, especially that of Climenhaga Philosophy of Science, 84:359–368,. In Section 5, I argue that Roche and Sober’s argument does not show that explanatory reasoning is dispensable. In Section 6, I argue that there is good reason to think explanatory reasoning will continue to prove indispensable in scientific practice. Drawing on Cicero’s oft-neglected De Divinatione, I formulate what I call the “Ciceronian Causal-nomological Requirement”, which states, roughly, that causal-nomological knowledge is essential for relying on correlations in predictive inference. I defend a version of the CCR by appealing to the challenge of “spurious correlations,” chance correlations which we should not rely upon for predictive inference. In Section 7, I offer some concluding remarks. (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)

The ethical and social implications of data mining, algorithmic curation and automation in the context of social media have been of heightened concern for a range of researchers with interests in digital media in recent years, with particular concerns about privacy arising in the context of mobile and locative media. Despite their wide adoption and economic importance, mobile dating apps have received little scholarly attention from this perspective – but they are intense sites of data generation, algorithmic processing, (...) and cross-platform data-sharing; bound up with competing cultures of production, exploitation and use. In this paper, we describe the ways various forms of data are incorporated into, and emerge from, hook-up apps’ business logics, socio-technical arrangements, and cultures of use to produce multiple and intersecting data cultures. We propose a multi-layered research agenda for critical and empirical inquiry into this field, and suggest appropriate conceptual and methodological frameworks for exploring the social and political challenges of data cultures. (shrink)

Increasingly, in data-intensive areas of the life sciences, experimental results are being described in algorithmically useful ways with the help of ontologies. Such ontologies are authored and maintained by scientists to support the retrieval, integration and analysis of their data. The proposition to be defended here is that ontologies of this type – the Gene Ontology (GO) being the most conspicuous example – are a part of science. Initial evidence for the truth of this proposition (which (...) some will find self-evident) is the increasing recognition of the importance of empirically-based methods of evaluation to the ontology development work being undertaken in support of scientific research. The ontologies created by scientists must, of course, be associated with implementations satisfying the requirements of software engineering. But these ontologies are not themselves engineering artifacts, and to conceive them as such brings grievous consequences. Rather, we shall argue, ontologies such as the GO are comparable to scientific theories, to scientific databases, or to scientific journal publications. Such a view implies a radically new conception of what is involved in the authoring, maintenance and application of ontologies in scientific contexts, and therewith also a radically new approach to the evaluation of ontologies and to the training of ontologists. (shrink)

Big data biology—bioinformatics, computational biology, systems biology (including ‘omics’), and synthetic biology—raises a number of issues for the philosophy of science. This article deals with several such: Is data-intensive biology a new kind of science, presumably post-reductionistic? To what extent is big data biology data-driven? Can data ‘speak for themselves?’ I discuss these issues by way of a reflection on Carl Woese’s worry that “a society that permits biology to become an engineering (...) discipline, that allows that science to slip into the role of changing the living world without trying to understand it, is a danger to itself.” And I argue that scientific perspectivism, a philosophical stance represented prominently by Giere, Van Fraassen, and Wimsatt, according to which science cannot as a matter of principle transcend our human perspective, provides the best resources currently at our disposal to tackle many of the philosophical issues implied in the modeling of complex, multilevel/multiscale phenomena. (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)

In the past few years, the ethical ramifications of AI technologies have been at the center of intense debates. Considerable attention has been devoted to understanding how a morally responsible practice of data science can be promoted and which values have to shape it. In this context, ethics and moral responsibility have been mainly conceptualized as compliance to widely shared principles. However, several scholars have highlighted the limitations of such a principled approach. Drawing from microethics and the virtue (...) theory tradition, in this paper, we formulate a different approach to ethics in data science which is based on a different conception of “being ethical” and, ultimately, of what it means to promote a morally responsible data science. First, we develop the idea that, rather than only compliance, ethical decision-making consists in using certain moral abilities, which are cultivated by practicing and exercising them in the data science process. An aspect of virtue development that we discuss here is moral attention, which is the ability of data scientists to identify the ethical relevance of their own technical decisions in data science activities. Next, by elaborating on the capability approach, we define a technical act as ethically relevant when it impacts one or more of the basic human capabilities of data subjects. Therefore, rather than “applying ethics”, data scientists should cultivate ethics as a form of reflection on how technical choices and ethical impacts shape one another. Finally, we show how this microethical framework concretely works, by dissecting the ethical dimension of the technical procedures involved in data understanding and preparation of electronic health records. (shrink)

Background Moral distress has negative effects on physical and mental health. However, there is little information about nurses’ coping strategies reducing moral distress. Aim The purpose of this study was to investigate the coping strategies of intensive care unit nurses reducing moral distress in Iran. Study design This is a qualitative study with a content analysis approach. Participants and research context The research sample consisted of nurses working in intensive care units of teaching hospitals affiliated to Tehran University (...) of Medical Sciences. Samples were selected among eligible nurses by purposive sampling. Data were obtained through 17 in-depth, individual, and semi-structured interviews with 17 nurses. Graneheim and Lundman’s (2004) 6-step content analysis method was used to analyze the data. Data management was also performed by MAXQDA software version 20. The COREQ checklist was used to report the study. Ethical considerations This project was approved by the Ethics Committee of Tehran University of Medical Sciences. All ethical guidelines in research were followed. Finding The data analysis resulted in the formation of two main categories of desirable coping strategies (with two subcategories of compensation and rejuvenation) and less desirable coping strategies (with three subclasses of indifference over time, escape and concealment). Discussion The intensive care unit nurses in dealing with ethical problems first try to solve the problem through discussion, but when they fail to resolve it peacefully, they resort to several coping strategies. Factors, such as increasing experience, lack of support from hospital managers and officials, poor communication between colleagues, the need to maintain hierarchy, fear of reprimand, and a sense of powerlessness, changed the nurses’ preferred strategies. It is important for managers to provide a blame/punishment-free atmosphere for expression of ethical experiences; a supportive atmosphere in which staff can engage in ethical discussions without fear of punishment. They should also provide opportunities for rest, rejuvenation, and adequate training for their employees. (shrink)

The era of ‘big data’ studies and computational social science has recently given rise to a number of realignments within and beyond the social sciences, where otherwise distinct data formats – digital, numerical, ethnographic, visual, etc. – rub off and emerge from one another in new ways. This article chronicles the collaboration between a team of anthropologists and sociologists, who worked together for one week in an experimental attempt to combine ‘big’ transactional and ‘small’ ethnographic data (...) formats. Our collaboration is part of a larger cross-disciplinary project carried out at the Danish Technical University, where high-resolution transactional data from smartphones allows for recordings of social networks amongst a freshman class. With a parallel deployment of ethnographic fieldwork among the DTU students, this research set-up raises a number of questions concerning how to assemble disparate ‘data-worlds’ and to what epistemological and political effects? To address these questions, a specific social event – a lively student party – was singled out from the broader DTU dataset. Our experimental collaboration used recordings of Bluetooth signals between students’ phones to visualize the ebb and flow of social intensities at the DTU party, juxtaposing these with ethnographic field-notes on shifting party atmospheres. Tracing and reflecting on the process of combining heterogeneous data, the article offers a concrete case of how a ‘stitching together’ of digital and ethnographic data-worlds might take place. (shrink)

The large-scale and intensive collection and analysis of digital data (commonly called “Big Data”) has become a common, popular, and consensual research method for the social sciences, as the automation of data collection, mathematization of analysis, and digital objectification reinforce both its efficiency and truth-value. This article opens with a critical review of the literature on data collection and analysis, and summarizes current ethical discussions focusing on these technologies. A semiotic model of data production (...) and circulation is then introduced to problematize the view that digital data has ceased to stand for a formalization method (a possible kind of representation among others), and effectively “becomes the world itself” (a direct presentation of the world outperforming all other modes of representation). Following Charles Sanders Peirce’s semiotics and pragmaticist philosophy, we characterize digitalization as a hypersymbolic semiotic process, and we highlight the naturalization of meaning, the illusion of iconicity, and rhetorical efficiency on which data’s truth value relies within the context of its large-scale, profit-driven, and results-oriented research uses. This outlines some epistemological and ethical implications of data’s visualization, use, and authority, and indicates avenues for critical semiotics of contemporary data science and analysis. (shrink)

BackgroundEthics review is the process of assessing the ethics of research involving humans. The Ethics Review Committee (ERC) is the key oversight mechanism designated to ensure ethics review. Whether or not this governance mechanism is still fit for purpose in the data-driven research context remains a debated issue among research ethics experts.Main textIn this article, we seek to address this issue in a twofold manner. First, we review the strengths and weaknesses of ERCs in ensuring ethical oversight. Second, we (...) map these strengths and weaknesses onto specific challenges raised by big data research. We distinguish two categories of potential weakness. The first category concerns persistent weaknesses, i.e., those which are not specific to big data research, but may be exacerbated by it. The second category concerns novel weaknesses, i.e., those which are created by and inherent to big data projects. Within this second category, we further distinguish between purview weaknesses related to the ERC’s scope (e.g., how big data projects may evade ERC review) and functional weaknesses, related to the ERC’s way of operating. Based on this analysis, we propose reforms aimed at improving the oversight capacity of ERCs in the era of big data science.ConclusionsWe believe the oversight mechanism could benefit from these reforms because they will help to overcome data-intensive research challenges and consequently benefit research at large. (shrink)

Intense interspecies engagements are central to the work of ecologists, as they seek to understand our rapidly changing world. To explore researcher-bird engagements in ecological fieldwork, we use a lens of care. Taking as a starting point the widely shared photos of bird-in-the-hand that portray situations where individual birds become sources of data about populations, we show the significance of complex care work in ethically and epistemically loaded moments. Crucial knowledge about survival, biodiversity loss and animal welfare emerges at (...) the intersection of multispecies care for individuals, for populations and for knowledge infrastructures. We put forth the concept of intermittent care to explain the changing concerns for individual birds here-and-now or for populations of the future. Attention to care therefore helps unravel how ecological knowing depends on entanglements between humans, nature, and technologies that take shape through a constant negotiation of different strands and matters of multispecies care. (shrink)

This paper investigates epistemological and ethical implications of the growingavailability of direct-to-consumer genetic testing for the science and society. Direct-toconsumer genetic testing is characterized as the genetic testing sold directly to consumerswithout any assistance from professionals. By offering empowerment and control, companiesconvince consumers to sequence their genome by granting the company access to theirgenetic data in exchange to results that are not always accurate. To which extent doconsumers properly understand the results of their genetic testing? Are consumers aware (...) ofthe possible implications for disclosing genetic information to private companies? I addressthese questions in two steps: firstly, I discuss epistemological implications of thedevelopment of Genomics, understood as a data-intensive science, by delving into thetheoretical commitments of the concept of gene and the notion of circular causality.Secondly, I reflect upon the privacy risks of taking direct-to-consumer genetic testing in adata-rich world. Finally, I draw some conclusions on the possible consequences of direct-toconsumer genetic testing by suggesting key-concepts that may help to clarify the limits andscope of genetic testing. (shrink)

Ethicists differ considerably in their reasons for using empirical data. This paper presents a brief overview of four traditional approaches to the use of empirical data: “the prescriptive applied ethicists,” “the theorists,” “the critical applied ethicists,” and “the particularists.” The main aim of this paper is to introduce a fifth approach of more recent date (i.e. “integrated empirical ethics”) and to offer some methodological directives for research in integrated empirical ethics. All five approaches are presented in a table (...) for heuristic purposes. The table consists of eight columns: “view on distinction descriptive-prescriptive sciences,” “location of moral authority,” “central goal(s),” “types of normativity,” “use of empirical data,” “method,” “interaction empirical data and moral theory,” and “cooperation with descriptive sciences.” Ethicists can use the table in order to identify their own approach. Reflection on these issues prior to starting research in empirical ethics should lead to harmonization of the different scientific disciplines and effective planning of the final research design. Integrated empirical ethics (IEE) refers to studies in which ethicists and descriptive scientists cooperate together continuously and intensively. Both disciplines try to integrate moral theory and empirical data in order to reach a normative conclusion with respect to a specific social practice. IEE is not wholly prescriptive or wholly descriptive since IEE assumes an interdepence between facts and values and between the empirical and the normative. The paper ends with three suggestions for consideration on some of the future challenges of integrated empirical ethics. (shrink)

This article will describe two forthcoming art/science projects, ‘Heliosphere’ (Grant, Kin, Matthias) and ‘The Sun Project’ (Grant, Kin, Matthias). Each project materializes data from solar flare activity by tracking the solar wind across the Earth. Using information streamed from satellites orbiting the Earth’s atmosphere and from terrestrial detectors, the Sun’s activity will be visualized and sonified at sites across the Earth. The article also describes the research context undertaken in developing the work including solar physics, anthropology the history (...) of science. (shrink)

Berichte zur Wissenschaftsgeschichte, Volume 45, Issue 3, Page 373-383, September 2022.

Berichte zur Wissenschaftsgeschichte, Volume 45, Issue 3, Page 373-383, September 2022.

Background:Maintaining patient’s dignity in intensive care units is difficult because of the unique conditions of both critically-ill patients and intensive care units.Objectives:The aim of this study was to uncover the cultural factors that impeded maintaining patients’ dignity in the cardiac surgery intensive care unit.Research Design:The study was conducted using a critical ethnographic method proposed by Carspecken.Participants and research context:Participants included all physicians, nurses and staffs working in the study setting. Data collection methods included participant observations, formal (...) and informal interviews, and documents assessment. In total, 200 hours of observation and 30 interviews were performed. Data were analyzed to uncover tacit cultural knowledge and to help healthcare providers to reconstruct the culture of their workplace.Ethical Consideration:Ethical approval for the study from Ethics committee of Isfahan University of Medical Sciences was obtained.Findings:The findings of the study fell into the following main themes: “Presence: the guarantee for giving enough attention to patients’ self-esteem”, “Instrumental and objectified attitudes”, “Adherence to the human equality principle: value-action gap”, “Paternalistic conduct”, “Improper language”, and “Non-interactive communication”. The final assertion was “Reductionism as a major barrier to the maintaining of patient’s dignity”.Discussion:The prevailing atmosphere in subculture of the CSICU was reductionism and paternalism. This key finding is part of the biomedical discourse. As a matter of fact, it is in contrast with dignified care because the latter necessitate holistic attitudes and approaches.Conclusion:Changing an ICU culture is not easy; but through increasing awareness and critical self-reflections, the nurses, physicians and other healthcare providers, may be able to reaffirm dignified care and cure in their therapeutic relationships. (shrink)

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)

As data-intensive and computational science become increasingly established as the dominant mode of conducting scientific research, visualisations of data and of the outcomes of science become increasingly prominent in mediating knowledge in the scientific arena. This position piece advocates that more attention should be paid to the epistemological role of visualisations beyond their being a cognitive aid to understanding, but as playing a crucial role in the formation of evidence for scientific claims. The new generation (...) of computational and informational visualisations and imaging techniques challenges the philosophy of science to re-think its position on three key distinctions: the qualitative/quantitative distinction, the subjective/objective distinction, and the causal/non-causal distinction. (shrink)

Background Informed consent is one of the key principles of conducting research involving humans. When research participants give consent, they perform an act in which they utter, write or otherwise provide an authorisation to somebody to do something. This paper proposes a new understanding of the informed consent as a compositional act. This conceptualisation departs from a modular conceptualisation of informed consent procedures. Methods This paper is a conceptual analysis that explores what consent is and what it does or does (...) not do. It presents a framework that explores the basic elements of consent and breaks it down into its component parts. It analyses the consent act by first identifying its basic elements, namely: a) data subjects or legal representative that provides the authorisation of consent; b) a specific thing that is being consented to; and c) specific agent(s) to whom the consent is given. Results This paper presents a framework that explores the basic elements of consent and breaks it down into its component parts. It goes beyond only providing choices to potential research participants; it explains the rationale of those choices or consenting acts that are taking place when speaking or writing an authorisation to do something to somebody. Conclusions We argue that by clearly differentiating the goals, the procedures of implementation, and what is being done or undone when one consent, one can better face the challenges of contemporary data-intensive biomedical research, particularly regarding the retention and use of data. Conceptualising consent as a compositional act enhances more efficient communication and accountability and, therefore, could enable more trustworthy acts of consent in biomedical science. (shrink)

What makes some firms more innovative than others and what determines the source of these innovations are questions that are still not adequately answered due to the complex, often esoteric, nature of the innovation process. This paper considers the effect of one externally oriented strategy (extent of formal inter-organizational linkages) and one internally oriented strategy (degree of knowledge intensity) on overall levels of innovativeness and the source of these innovations. Using data collected from firms operating in the bioscience-technology industries (...) in both New York and Utah, our results suggest that both of these strategies have a significant effect upon innovation levels and the source of innovation (internally versus externally stimulated). Interestingly, there were few firms that undertook both of these strategies simultaneously and thus the proposed interaction effect where increased absorptive capacity (on the basis of knowledge intensity) would make external linkages even more valuable could not be discerned. (shrink)

This article presents a conceptual and methodological framework to study heritage-based tribalism in Big Data ecologies by combining approaches from the humanities, social and computing sciences. We use such a framework to examine how ideas of human origin and ancestry are deployed on Twitter for purposes of antagonistic ‘othering’. Our goal is to equip researchers with theory and analytical tools for investigating divisive online uses of the past in today’s networked societies. In particular, we apply notions of heritage, othering (...) and neo-tribalism, and both data-intensive and qualitative methods to the case of people’s engagements with the news of Cheddar Man’s DNA on Twitter. We show that heritage-based tribalism in Big Data ecologies is uniquely shaped as an assemblage by the coalescing of different forms of antagonistic othering. Those that co-occur most frequently are the ones that draw on ‘Views on Race’, ‘Trust in Experts’ and ‘Political Leaning’. The framings of the news that were most influential in triggering heritage-based tribalism were introduced by both right- and left-leaning newspaper outlets and by activist websites. We conclude that heritage-themed communications that rely on provocative narratives on social media tend to be labelled as political and not to be conducive to positive change in people’s attitudes towards issues such as racism. (shrink)

Intensive Science and Virtual Philosophy cuts to the heart of the philosophy of Gilles Deleuze and of today's science wars.At the start of the 21st Century, ...

Background: Nurses face challenges regarding professional identify. Being unaware of these challenges and not owning positive professional identify leads to a lack of self-confidence. Thus, nurses face problems in interpersonal communication and lose their attachment to their profession. Few studies have engaged with impediments to forming positive professional identity in relation to intensive care nurses. Objective: The purpose of this study is to investigate the impediments to forming positive professional identity in nurses working in intensive care unit. Research (...) design: In this study, the conventional content analysis method was used in order to obtain the impediments to forming positive professional identities in nurses. Data were collected through 15 semi-structured interviews and 2 focus group interviews from March 2015 to June 2016. Purposive sampling was used and participants were recruited until data saturation was reached; they were then analyzed using the conventional content analysis method. Participants and research context: In total, 24 intensive care unit nurses from one hospital in Northern Iran were selected through purposive sampling. Ethical consideration: This study was approved by the Ethics Committee of Shiraz University of Medical Sciences. Findings: Four main themes of professional identity challenges were extracted from the analysis of the data: neglect of professional status of nursing; distrust of nursing knowledge; unprofessional performance; and low professional attraction. Discussion and conclusion: Nurses stated that the neglect of the professional status of nursing and distrust of nursing knowledge have always been a hindrance in forming a positive professional identity. They also mentioned that unprofessional performance and low professional attraction are the other influencing factors in this regard. Thus, detecting these factors can guide nurses and their managers towards creating positive professional identity, and as a result, will improve their job satisfaction, professional advancement, and durability. (shrink)

Background Big data and AI applications now play a major role in many health contexts. Much research has already been conducted on ethical and social challenges associated with these technologies. Likewise, there are already some studies that investigate empirically which values and attitudes play a role in connection with their design and implementation. What is still in its infancy, however, is the comparative investigation of the perspectives of different stakeholders. Methods To explore this issue in a multi-faceted manner, we (...) conducted semi-structured interviews as well as focus group discussions with patients and clinicians. These empirical methods were used to gather interviewee’s views on the opportunities and challenges of medical AI and other data-intensive applications. Results Different clinician and patient groups are exposed to medical AI to differing degrees. Interviewees expect and demand that the purposes of data processing accord with patient preferences, and that data are put to effective use to generate social value. One central result is the shared tendency of clinicians and patients to maintain individualistic ascriptions of responsibility for clinical outcomes. Conclusions Medical AI and the proliferation of data with import for health-related inferences shape and partially reconfigure stakeholder expectations of how these technologies relate to the decision-making of human agents. Intuitions about individual responsibility for clinical outcomes could eventually be disrupted by the increasing sophistication of data-intensive and AI-driven clinical tools. Besides individual responsibility, systemic governance will be key to promote alignment with stakeholder expectations in AI-driven and data-intensive health settings. (shrink)

Biomedical research is increasingly data intensive and computational, and “big data science” is migrating into the clinical arena. Unfortunately, ethicists, regulators, and policy‐makers have barely begun to explore the ethical, legal, and social issues raised by the variety of analytical and computational approaches in use and under development in biology and medicine. Most scholarship concerning big data bioscience has focused on privacy, a vitally important consideration but not the only one. Among the issues raised by (...) new computational technologies are questions about safety and safety assessment, justice, and how to obtain proper informed consent. These technologies also raise a myriad of regulatory issues that could influence the probability of translating new assays or computational tools to the clinical or public health spheres. (shrink)

Before the EU General Data Protection Regulation entered into force in May 2018, we witnessed an intense struggle of actors associated with data-dependent fields of science, in particular health-related academia and biobanks striving for legal derogations for data reuse in research. These actors engaged in a similar line of argument and formed issue alliances to pool their collective power. Using descriptive coding followed by an interpretive analysis, this article investigates the argumentative repertoire of these actors and (...) embeds the analysis in ethical debates on data sharing and biobank-related data governance. We observe efforts to perform a paradigmatic shift of the discourse around the General Data Protection Regulation-implementation away from ‘protecting data’ as key concern to ‘protecting health’ of individuals and societies at large. Instead of data protection, the key risks stressed by health researchers became potential obstacles to research. In line, exchange of information with data subjects is not a key concern in the arguments of biobank-related actors and it is assumed that patients want ‘their’ data to be used. We interpret these narratives as a ‘reaction’ to potential restrictions for data reuse and in line with a broader trend towards Big Data science, as the very idea of biobanking is conceptualized around long-term use of readily prepared data. We conclude that a sustainable implementation of biobanks needs not only to comply with the General Data Protection Regulation, but must proactively re-imagine its relation to citizens and data subjects in order to account for the various ways that science gets entangled with society. (shrink)

Aim To synthesize qualitative research on perspectives and understandings of Intensive Care Unit (ICU) patients, family members, and staff regarding respect and dignity in ICU, in order to explore the connotations and meanings of respect and dignity in ICU. Design A qualitative meta-synthesis. Methods The Chinese and English databases were systematically searched, including PubMed, Web of Science, CINAHL, Embase, Cochrane Library, CNKI, Wangfang Data, VIP, and CBM from each database’s inception to July 22, 2023. Studies were critically (...) appraised using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Qualitative Research. Qualitative data were extracted, summarized, and meta-synthesized. (PROSPERO: CRD42023447218). Results A total of 9 studies from 6 countries were included in the meta-synthesis. Thirty-six main themes and 67 sub-themes were extracted, which were eventually integrated into 9 categories and 4 themes: (1) integrity of humanity; (2) autonomy; (3) equality; (4) environmental support. Conclusion To maintain patient dignity, it is necessary to create an environment of respect within the ICU where healthcare professionals uphold the concept of preserving human integrity and respect patients' autonomy and equality. Healthcare professionals need to value the dignity of ICU patients and treat them as unique individuals during treatment and care. Hospital managers should also strive to create a respectful environment to provide environmental support for dignity care implementation. (shrink)

The present study specifically focuses on science teachers’ views about scientific inquiry and their use of scientific inquiry in their lesson plans, which were prepared at a professional development workshop designed for better utilization of science centers (SCs). As an impact evaluation research, qualitative data was collected from 41 purposively selected volunteer science teachers. The project team provided the participants with intense instruction in inquiry, and fostered them to learn nature of science and nature of (...) scientific inquiry explicitly. The participants designed lesson plans that integrate school science curricula with exhibits at SCs before and after the workshop. An open-ended questionnaire about the views about scientific inquiry (VASI) was administered before and after the workshop, and teachers’ post-lesson plans were analyzed to detect the presence of scientific inquiry aspects. The majority of teachers exhibited improved views about scientific inquiry based on the VASI instrument. Also, lesson plan analyses indicated that teachers, who showed more improvement in VASI, included more scientific inquiry (SI) elements in their post-lesson plans. It was observed that science teachers’ lesson plans are limited in terms of teaching science in line with real scientific inquiries in SCs to make students learn about the nature of scientific inquiry while learning science. Only two groups embedded SI properly in the SC-oriented lesson plans, and teachers rather used inquiry-based methods of teaching (e.g., argumentation, predict-observe-explain) and process skills (e.g., questioning, explanations). Accordingly, further studies are suggested to develop a specific pedagogical content knowledge framework for teaching with/in SCs. (shrink)

Since the 1970s and more rigorously since the 1990s, many countries have regulated data protection and privacy laws in order to ensure the safety and privacy of personal data. First, a comparison is made of different acts regarding genetic information that are in force in the EU, the USA, and China. In Turkey, changes were adopted only recently following intense debates. This study aims to explore the experts’ opinions on the regulations of the health information systems, data (...) security, privacy, and confidentiality in Turkey, with a particular focus on genetic data, which is more sensitive than other health data as it is a permanent identifier that is inherited to next of kin and shared with other family members. Two focus groups with 18 experts and stakeholders were conducted, discussing topics such as central data collection, legalized data sharing, and the management of genetic information in health information systems. The article concludes that the new Turkish personal data protection law is problematic as the frame of collectible data is wide-ranging, and the exceptions are extensive. Specific laws or articles dedicated to genetic data that also overlook the dimension of discrimination based on genetic differences in Turkey should be taken into consideration. In broader terms, it is intended to put up for discussion that in addition to ethical aspects, economic aspects and legal aspects of health should be included in the discussion to be carried out within the framework of socio-political analyses with culture-specific approaches and cross-culture boundaries simultaneously. (shrink)

In the life sciences, there is an ongoing discussion about a perceived ‘reproducibility crisis’. However, it remains unclear to which extent the perceived lack of reproducibility is the consequence of issues that can be tackled and to which extent it may be the consequence of unrealistic expectations of the technical level of reproducibility. Large-scale, multi-institutional experimental replication studies are very cost- and time-intensive. This Perspective suggests an alternative, complementary approach: meta-research using sociological and philosophical methodologies to examine researcher trust (...) in data. An improved understanding of the criteria used by researchers to judge data reliability will provide crucial, initial evidence on the actual scale of the reproducibility crisis and on measures to tackle it. (shrink)

This paper argues that the difference between contemporary software intensive scientific practice and more traditional non-software intensive varieties results from the characteristically high conditionality of software. We explain why the path complexity of programs with high conditionality imposes limits on standard error correction techniques and why this matters. While it is possible, in general, to characterize the error distribution in inquiry that does not involve high conditionality, we cannot characterize the error distribution in inquiry that depends on software. (...) Software intensive science presents distinctive error and uncertainty modalities that pose new challenges for the epistemology of science. (shrink)

The use of molecules and reactions as evidence, markers and/or traits for evolutionary processes has a history more than a century long. Molecules have been used in studies of intra-specific variation and studies of similarity among species that do not necessarily result in the analysis of phylogenetic relations. Promoters of the use of molecular data have sustained the need for quantification as the main argument to make use of them. Moreover, quantification has allowed intensive statistical analysis, as a (...) condition and a product of increasing automation. All of these analyses are subject to the methodological anxiety characteristic of a community in search of objectivity. It is in this context that scientists compared and evaluated protein and nucleic acid sequence data with other types of molecular data – including immunological, electrophoretic and hybridization data. This paper argues that by looking at long-term historical processes, such as the use of molecular evidence in evolutionary biology, we gain valuable insights into the history of science. In that sense, it accompanies a growing concern among historians for big-pictures of science that incorporate the fruitful historical research on local cases of the last decades. (shrink)

The controversy over imageless thought versus picture thinking , with the recent reconsideration of model-based reasoning in the physical sciences is briefly examined. The main focus of the article is on the role of instrumentally elicited images in the sciences, especially in the physical sciences, with special reference to optics, experimental particle physics and observational astronomy, against the background of the civilization of digital images, though to some degree every scientific discipline is implicated. Imaging, today chiefly in the mode of (...) electronic digital visual imaging, reaches into every phase of scientific inquiry, observational, experimental, simulational, even in mathematical research. The combination of algorithms and image-intensive science with the plethora of big data results in an epistemic pattern of "mathematical imagism". The epistemological issues regarding the image-intensiveness as of data-intensiveness of scientific research deserve further probing, in pursuance of the discussions of the ideal of mechanical objectivity in machine vision versus trained judgment in the selection and assessment of scientific images: as for now tacitly we depend on conventions regarding what we called "warranted imageability". (shrink)

Definition of the problem In medicine, as well as in other disciplines, computer science expertise is becoming increasingly important. This requires a culture of interdisciplinary assessment, for which medical ethics committees are not well prepared. The use of big data and artificial intelligence (AI) methods (whether developed in-house or in the form of “tools”) pose further challenges for research ethics reviews. Arguments This paper describes the problems and suggests solving them through procedural changes. Conclusion An assessment that is (...) interdisciplinary from the outset appears to be more suitable than having two commissions with different expertise. However, this would require that the composition of medical ethics committees be altered. In addition, the article recommends initial measures to be taken during research ethics reviews of big data and AI projects in order to consolidate the review process and ensure standardization of the criteria. (shrink)

Big dreams are rare but highly memorable dream experiences that make a strong and lasting impact on the dreamer's waking awareness. Moving far beyond "I forgot to study and the finals are today" and other common scenarios, such dreams can include vivid imagery, intense emotions, fantastic characters, and an uncanny sense of being connected to forces beyond one's ordinary dreaming mind. In Big Dreams, Kelly Bulkeley provides the first full-scale cognitive scientific analysis of such dreams, putting forth an original theory (...) about their formation, function, and meaning.Big dreams have played significant roles in religious and cultural history, but because of their infrequent occurrence and fantastical features, they have rarely been studied in light of modern science. We know a great deal about the religious manifestations of big dreams throughout history and around the world, but until now that cross-cultural knowledge has never been integrated with scientific research on their psychological roots in the brain-mind system. In Big Dreams, Bulkeley puts a classic psychological thesis to the scientific test by clarifying and improving it with better data, sharper analysis, and a broader evolutionary framework. He brings evidence from multiple sources, shows patterns of similarity and difference, questions prior assumptions, and provides predictive models that can be applied to new sets of data. The notion of a connection between dreaming and religion has always been intuitively compelling; Big Dreams transforms it into a solid premise of religious studies and brain-mind science. Combining evidence from religious studies, psychology, anthropology, evolutionary biology, and neuroscience, Big Dreams makes a compelling argument that big dreams are a primal wellspring of religious experience. They represent an innate, neurologically hard-wired capacity of our species that regularly provokes greater self-awareness, creativity, and insight into the existential challenges and spiritual potentials of human life. (shrink)