Deep neural networks have become increasingly successful in applications from biology to cosmology to social science. Trained DNNs, moreover, correspond to models that ideally allow the prediction of new phenomena. Building in part on the literature on ‘eXplainable AI’, I here argue that these models are instrumental in a sense that makes them non-explanatory, and that their automated generation is opaque in a unique way. This combination implies the possibility of an unprecedented gap between discovery and explanation: When unsupervised models (...) are successfully used in exploratory contexts, scientists face a whole new challenge in forming the concepts required for understanding underlying mechanisms. (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)

This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in (...) making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research. (shrink)

Despite an enormous philosophical literature on models in science, surprisingly little has been written about data models and how they are constructed. In this paper, I examine the case of how paleodiversity data models are constructed from the fossil data. In particular, I show how paleontologists are using various model-based techniques to correct the data. Drawing on this research, I argue for the following related theses: first, the ‘purity’ of a data model is not a measure of its epistemic reliability. (...) Instead it is the fidelity of the data that matters. Second, the fidelity of a data model in capturing the signal of interest is a matter of degree. Third, the fidelity of a data model can be improved ‘vicariously’, such as through the use of post hoc model-based correction techniques. And, fourth, data models, like theoretical models, should be assessed as adequate for particular purposes. (shrink)

There is an ongoing methodological debate in philosophy of science concerning the use of case studies as evidence for and/or against theories about science. In this paper, I aim to make a contribution to this debate by taking an empirical approach. I present the results of a systematic survey of the PhilSci-Archive, which suggest that a sizeable proportion of papers in philosophy of science contain appeals to case studies, as indicated by the occurrence of the indicator words “case study” and/or (...) “case studies.” These results are confirmed by data mined from the JSTOR database on research articles published in leading journals in the field: Philosophy of Science, the British Journal for the Philosophy of Science (BJPS), and the Journal for General Philosophy of Science (JGPS), as well as the Proceedings of the Biennial Meeting of the Philosophy of Science Association (PSA). The data also show upward trends in appeals to case studies in articles published in Philosophy of Science, the BJPS, and the JGPS. The empirical work I have done for this paper provides philosophers of science who are wary of the use of case studies as evidence for and/or against theories about science with a way to do philosophy of science that is informed by data rather than case studies. (shrink)

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...) argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)

In recent years, critical scholarship has drawn attention to increasing power differentials between corporations that use data and people whose data is used. A growing number of scholars see digital data and information commons as a way to counteract this asymmetry. In this paper I raise two concerns with this argument: First, because digital data and information can be in more than one place at once, governance models for physical common-pool resources cannot be easily transposed to digital commons. Second, not (...) all data and information commons are suitable to address power differentials. In order to create digital commons that effectively address power asymmetries we must pay more systematic attention to the issue of exclusion from digital data and information commons. Why and how digital data and information commons exclude, and what the consequences of such exclusion are, decide whether commons can change power asymmetries or whether they are more likely to perpetuate them. (shrink)

In current philosophical discussions on evidence in the medical sciences, epidemiology has been used to exemplify a specific version of evidential pluralism. According to this view, known as the Russo–Williamson Thesis, evidence of both difference-making and mechanisms is produced to make causal claims in the health sciences. In this paper, I present an analysis of data and evidence in epidemiological practice, with a special focus on research on the exposome, and I cast doubt on the extent to which evidential pluralism (...) holds in this case. I start by focusing on the claim that molecular data allows for the production of mechanistic evidence. On the basis of a close look at the ways in which molecular data is used in exposome research, I caution against interpretations in terms of mechanistic evidence. Secondly, I expand my critical remarks on the thesis by addressing the conditions under which data is categorised as evidence in exposome research. I argue that these show that the classification of a dataset as a type of evidence is dependent on the ways in which the data is used. This is in contrast with the approach of evidential pluralism, where evidence is classified in different types on the basis of its intrinsic properties. Finally, I come back to what I consider the core of the thesis and suggest that the epidemiological research analysed in the paper indicates different interpretations of evidential pluralism and its applicability in the health sciences. (shrink)

We introduce a new type of pluralism about biological function that, in contrast to existing, demonstrates a practical integration among the term’s different meanings. In particular, we show how to generalize Sandra Mitchell’s notion of integrative pluralism to circumstances where multiple epistemic tools of the same type are jointly necessary to solve scientific problems. We argue that the multiple definitions of biological function operate jointly in this way based on how biologists explain the evolution of protein function. To clarify how (...) our account relates to existing views, we introduce a general typology for monist and pluralist accounts along with standardized criteria for judging which is best supported by evidence. (shrink)

The idea that ambiguity can be productive in data science remains controversial. Efforts to make scientific publications and data intelligible to computers generally assume that accommodating multiple meanings for words, known as polysemy, undermines reasoning and communication. This assumption has nonetheless been contested by historians, philosophers, and social scientists, who have applied qualitative research methods to demonstrate the generative and strategic value of polysemy. Recent quantitative results from linguistics have also shown how polysemy can actually improve the efficiency of human (...) communication. I present a new conceptual typology based on a synthesis of prior research about the aims, norms, and circumstances under which polysemy arises and is evaluated. The typology supports a contextual pluralist view of polysemy’s value for scientific research practices: polysemy does both substantial positive and negative work in science, but its utility is context-sensitive in ways that are often overlooked by the norms people have formulated to regulate its use, including prior scholars researching polysemy. I also propose that historical patterns in the use of partial synonyms, i.e. terms with overlapping meanings, provide an especially promising phenomenon for integrative research addressing these issues. (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)

Despite wide recognition that speculation is critical for successful science, philosophers have attended little to it. When they have, speculation has been characterized in narrowly epistemic terms: a hypothesis is speculative due to its (lack of) evidential support. These ‘evidence-first’ accounts provide little guidance for what makes speculation productive or egregious, nor how to foster the former while avoiding the latter. I examine how scientists discuss speculation and identify various functions speculations play. On this basis, I develop a ‘function-first’ account (...) of speculation. This analysis grounds a richer discussion of when speculation is egregious and when it is productive, based in both fine-grained analysis of the speculation’s purpose, and what I call the ‘epistemic situation’ scientists face. (shrink)

Whether we live in a world of autonomous things, or a world of interconnected processes in constant flux, is an ancient philosophical debate. Modern biology provides decisive reasons for embracing the latter view. How does one understand the practices and outputs of science in such a dynamic, ever-changing world - and particularly in an emergency situation such as the COVID-19 pandemic, where scientific knowledge has been regarded as bedrock for decisive social interventions? We argue that key to answering this question (...) is to consider the role of the activity of reification within the research process. Reification consists in the identification of more or less stable features of the flux, and treating these as constituting stable things. As we illustrate with reference to biological and biomedical research on COVID-19, reification is a necessary component of any process of inquiry and comes in at least two forms: means reification, when researchers create objects meant to capture features of the world, or phenomena, in order to be able to study them; and target reification, when researchers infer an understanding of phenomena from an investigation of the epistemic objects created to study them. We note that both objects and phenomena are dynamic processes and argue that have no reason to assume that changes in objects and phenomena track one another. We conclude that failure to acknowledge these forms of reification and their epistemic role in scientific inquiry can have dire consequences for how the resulting knowledge is interpreted and used. (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)

In this paper we argue that the difference between standard measurement and proxy measurement in paleoclimatology should not be understood in terms of ‘directness’. Measurements taken by climatologists to be paradigmatically non-proxy exhibit the kinds of indirectness that are thought to separate them proxy measurement. Rather, proxy measurements and standard measurements differ in how they account for confounding causal factors. Measurements are ‘proxy’ to the extent that the measurements require vicarious controls, while measurements are not proxy, but rather ‘standard’, to (...) the extent that the measurement apparatus provides sufficient physical control on its own. Guided by this account of proxy measurement, we then consider some of the challenges specific to historical proxy measurement and how it is that historical scientists address these challenges. Historical scientists rely on significant causal dependencies and naturally preserved signals to mitigate the influence of destructive processes over time. (shrink)

When radiocarbon dating techniques were applied to archaeological material in the 1950s they were hailed as a revolution. At last archaeologists could construct absolute chronologies anchored in temporal data backed by immutable laws of physics. This would make it possible to mobilize archaeological data across regions and time-periods on a global scale, rendering obsolete the local and relative chronologies on which archaeologists had long relied. As profound as the impact of 14C dating has been, it has had a long and (...) tortuous history now described as proceeding through three revolutions, each of which addresses distinct challenges of capturing, processing and packaging radiogenic data for use in resolving chronological puzzles with which archaeologists has long wrestled. In practice, mobilizing radiogenic data for archaeological use is a hard-won achievement; it involves multiple transformations that, at each step of the way, depend upon a diverse array of technical expertise and background knowledge. I focus on strategies of triangulation and traceability that establish the integrity of these data and their relevance as anchors for evidential reasoning in archaeology. (shrink)

Philosophers of science are well aware that theories are underdetermined by data. But what about the data? Scientific data are selected and processed representations or pieces of nature. What is useless context and what is valuable specimen, as well as how specimens are processed for study, are not obvious or predetermined givens. Instead, they are decisions made by scientists and other research workers, such as technicians, that produce different outcomes for the data. Vertebrate fossils provide a revealing case of this (...) data-processing, because they are embedded in rock that often matches the fossils’ color and texture, requiring an expert eye to judge where the fossil/context interface is. Fossil preparators then permanently define this interface by chiseling away the material they identify as rock. As a result, fossil specimens can emerge in multiple possible forms depending on the preparator’s judgment, skill, and chosen tools. A prepared fossil then is not yet data but potential data, following Leonelli’s relational framework in which data are defined as evidence that scientists have used to support a proposed theory. This paper draws on ethnographic evidence to assess how scientists overcome this underdetermination of specimens, as potential data, in addition to the underdetermination of theories and of data, to successfully construct specimen-based knowledge. Among other strategies, paleontology maintains a division of labor between data-makers and theory-makers. This distinction serves to justify the omission of preparators’ nonstandard, individualized techniques from scientific publications. This separation has benefits for both scientists and technicians; however, it restricts knowledge production by preventing scientists from understanding how the pieces of nature they study were processed into researchable specimens. (shrink)

This essay considers how scholarly approaches to the development of molecular biology have too often narrowed the historical aperture to genes, overlooking the ways in which other objects and processes contributed to the molecularization of life. From structural and dynamic studies of biomolecules to cellular membranes and organelles to metabolism and nutrition, new work by historians, philosophers, and STS scholars of the life sciences has revitalized older issues, such as the relationship of life to matter, or of physicochemical inquiries to (...) biology. This scholarship points to a novel molecular vista that opens up a pluralist view of molecularizations in the twentieth century and considers their relevance to current science. (shrink)

Debate about the epistemic prowess of historical science has focused on local underdetermination problems generated by a lack of historical data; the prevalence of information loss over geological time, and the capacities of scientists to mitigate it. Drawing on Leonelli’s recent distinction between ‘phenomena-time’ and ‘data-time’ I argue that factors like data generation, curation and management significantly complexifies and undermines this: underdetermination is a bad way of framing the challenges historical scientists face. In doing so, I identify circumstances of epistemic (...) scarcity where underdetermination problems are particularly salient, and discuss cases where legacy data—data generated using differing technologies and systems of practice—are drawn upon to overcome underdetermination. This suggests that one source of overcoming underdetermination is our knowledge of science’s past. Further, data-time makes agnostic positions about the epistemic fortunes of scientists working under epistemic scarcity more plausible. But agnosticism seems to leave philosophers without much normative grip. So, I sketch an alternative approach: focusing on the strategies scientists adopt to maximize their epistemic power in light of the resources available to them. (shrink)

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

This editorial critically engages with the understanding of openness by attending to how notions of presence and absence come bundled together as part of efforts to make open. This is particularly evident in contemporary discourse around data production, dissemination, and use. We highlight how the preoccupations with making data present can be usefully analyzed and understood by tracing the related concerns around what is missing, unavailable, or invisible, which unvaryingly but often implicitly accompany debates about data and openness.

We undeniably live in an information age—as, indeed, did those who lived before us. After all, as the cultural historian Robert Darnton pointed out: ‘every age was an age of information, each in its own way’ (Darnton 2000: 1). Darnton was referring to the news media, but his insight surely also applies to the sciences. The practices of acquiring, storing, labeling, organizing, retrieving, mobilizing, and integrating data about the natural world has always been an enabling aspect of scientific work. Natural (...) history and its descendant discipline of biological taxonomy are prime examples of sciences dedicated to creating and managing systems of ordering data. In some sense, the idea of biological taxonomy as an information science is commonplace. Perhaps it is because of its self-evidence that the information science perspective on taxonomy has not been a major theme in the history and philosophy of science. The botanist Vernon Heywood once pointed out that historians of biology, in their ‘preoccupation with the development of the sciences of botany and zoology… [have] diverted attention from the role of taxonomy as an information science’ (Heywood 1985: 11). More specifically, he argued that historians had failed to appreciate how principles and practices that can be traced to Linnaeus constituted ‘a change in the nature of taxonomy from a local or limited folk communication system and later a codified folk taxonomy to a formal system of information science [that] marked a watershed in the history of biology’ (ibid.). A similar observation could be made about twentieth-century philosophy of biology, which mostly skipped over practical and epistemic questions about information management in taxonomy. The taxonomic themes that featured in the emerging philosophy of biology literature in the second half of the twentieth century were predominantly metaphysical in orientation. This is illustrated by what has become known as the ‘essentialism story’: an account about the essentialist nature of pre- Darwinian taxonomy that used to be accepted by many historians and philosophers, and which stimulated efforts to document and interpret shifts in the metaphysical understanding of species and (natural) classification (Richards 2010; Winsor 2003; Wilkins 2009). Although contemporary debates in the philosophy of taxonomy have moved on, much discussion continues to focus on conceptual and metaphysical issues surrounding the nature of species and the principles of classification. Discussions centring on whether species are individuals, classes, or kinds have sprung up as predictably as perennials. Raucous debates have arisen even with the aim of accommodating the diversity of views: is monism, pluralism, or eliminativism about the species category the best position to take? In addition to these, our disciplines continue to interrogate what is the nature of these different approaches to classification: are they representational or inferential roles of different approaches to classification (evolutionary taxonomy, phenetics, phylogenetic systematics)? While there is still much to learn from these discussions—in which we both actively participate—our aim with this topical collection has been to seek different entrypoints and address underexposed themes in the history and philosophy of taxonomy. We believe that approaching taxonomy as an information science prompts new questions and can open up new philosophical vistas worth exploring. A twenty-first century information science turn in the history and philosophy of taxonomy is already underway. In scientific practice and in daily life it is hard to escape the imaginaries of Big Data and the constant threats of being ‘flooded with data’. In the life sciences, these developments are often associated with the socalled bioinformatics crisis that can hopefully be contained by a new, interdisciplinary breed of bioinformaticians. These new concepts, narratives, and developments surrounding the centrality of data and information systems in the biological and biomedical sciences have raised important philosophical questions about their challenges and implications. But historical perspectives are just as necessary to judge what makes our information age different from those that preceded us. Indeed, as the British zoologist Charles Godfray has often pointed out, the piles of data that are being generated in contemporary systematic biology have led to a second bioinformatics crisis, the first being the one that confronted Linnaeus in the mid-18th century (Godfray 2007). Although our aim is to clear a path for new discussions of taxonomy from an information science-informed point of view, we continue where others in the history, philosophy, and sociology of science have already trod. We believe that an appreciation of biological taxonomy as an information science raises many questions about the philosophical, theoretical, material, and practical aspects of the use and revision of biological nomenclatures in different local and global communities of scientists and citizen scientists. In particular, conceiving of taxonomy as an information science directs attention to the temporalities of managing an accumulating data about classified entities that are themselves subject to revision, to the means by which revision is accomplished, and to the semantic, material, and collaborative contexts that mediate the execution of revisions. (shrink)

Philosophers of science often make reference — whether tacitly or explicitly — to the notion of a scientific community. Sometimes, such references are useful to make our object of analysis tractable in the philosophy of science. For others, tracking or understanding particular features of the development of science proves to be tied to notions of a scientific community either as a target of theoretical or social intervention. We argue that the structure of contemporary scientific research poses two unappreciated, or at (...) least underappreciated, challenges to this concept of the “scientific community” in the philosophy of science. In particular, we will present two case studies from robotics research, broadly construed, which show that (1) the boundedness of the scientific community is threatened when private citizens can develop scientific and technological advances at minimal expense (democratization), and (2) the discreteness of scientific research programs is threatened by the complexly interrelated environment of contemporary scientific work (interconnectivity). Taken together, the extent of democratization and interconnectivity present a significant challenge for any practically oriented philosophy of science, one which we hope will be taken on directly by philosophers in the future. (shrink)

Many decades ago Patrick Suppes argued rather convincingly that theoretical hypotheses are not confronted with the direct, raw results of an experiment, rather, they are typically compared with models of data. What exactly is a data model however? And how do the interactions of particles at the subatomic scale give rise to the huge volumes of data that are then moulded into a polished data model? The aim of this paper is to answer these questions by presenting a detailed case (...) study of the construction of data models at the LHCb for testing Lepton Flavour Universality in rare decays of B-mesons. The close examination of the scientific practice at the LHCb leads to the following four main conclusions: raw data in their pure form are practically useless for the comparison of experimental results with theory, and processed data are in some cases epistemically more reliable, real and simulated data are involved in the co-production of the final data model and cannot be easily distinguished, theory-ladenness emerges at three different levels depending on the scope and the purpose for which background theory guides the overall experimental process and the overall process of acquiring and analysing data in high energy physics is too complicated to be fully captured by a generic methodological description of the experimental practice. (shrink)

A cognitive ethnography of how bioengineering scientists create innovative modeling methods. In this first full-scale, long-term cognitive ethnography by a philosopher of science, Nancy J. Nersessian offers an account of how scientists at the interdisciplinary frontiers of bioengineering create novel problem-solving methods. Bioengineering scientists model complex dynamical biological systems using concepts, methods, materials, and other resources drawn primarily from engineering. They aim to understand these systems sufficiently to control or intervene in them. What Nersessian examines here is how cutting-edge bioengineering (...) scientists integrate the cognitive, social, material, and cultural dimensions of practice. Her findings and conclusions have broad implications for researchers in philosophy, science studies, cognitive science, and interdisciplinary studies, as well as scientists, educators, policy makers, and funding agencies. In studying the epistemic practices of scientists, Nersessian pushes the boundaries of the philosophy of science and cognitive science into areas not ventured before. She recounts a decades-long, wide-ranging, and richly detailed investigation of the innovative interdisciplinary modeling practices of bioengineering researchers in four university laboratories. She argues and demonstrates that the methods of cognitive ethnography and qualitative data analysis, placed in the framework of distributed cognition, provide the tools for a philosophical analysis of how scientific discoveries arise from complex systems in which the cognitive, social, material, and cultural dimensions of problem-solving are integrated into the epistemic practices of scientists. Specifically, she looks at how interdisciplinary environments shape problem-solving. Although Nersessian’s case material is drawn from the bioengineering sciences, her analytic framework and methodological approach are directly applicable to scientific research in a broader, more general sense, as well. (shrink)

It is often thought that the vindication of experimental work lies in its capacity to be revelatory of natural systems. I challenge this idea by examining laboratory experiments in ecology. A central task of community ecology involves combining mathematical models and observational data to identify trophic interactions in natural systems. But many ecologists are also lab scientists: constructing microcosm or ‘bottle’ experiments, physically realizing the idealized circumstances described in mathematical models. What vindicates such ecological experiments? I argue that ‘extrapolationism’, the (...) view that ecological lab work is valuable because it generates truths about natural systems, does not exhaust the epistemic value of such practices. Instead, bottle experiments also generate ‘understanding’ of both ecological dynamics and empirical tools. Some lab work, then, aids theoretical understanding, as well as targeting hypotheses about nature. 1Introduction2Trophic Interactions and Observational Techniques3Cryptic Dynamics in Bottle Experiments4Extrapolationism 4.1Ecological possibility and actuality4.2Ecological heterogeneity5Understanding 5.1The epistemic good of understanding5.2Bottle experiments as understanding-generators5.3How understanding travels6Conclusion. (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)

Criticism of big data has focused on showing that more is not necessarily better, in the sense that data may lose their value when taken out of context and aggregated together. The next step is to incorporate an awareness of pitfalls for aggregation into the design of data infrastructure and institutions. A common strategy minimizes aggregation errors by increasing the precision of our conventions for identifying and classifying data. As a counterpoint, we argue that there are pragmatic trade-offs between precision (...) and ambiguity that are key to designing effective solutions for generating big data about biodiversity. We focus on the importance of theory-dependence as a source of ambiguity in taxonomic nomenclature and hence a persistent challenge for implementing a single, long-term solution to storing and accessing meaningful sets of biological specimens. We argue that ambiguity does have a positive role to play in scientific progress as a tool for efficiently symbolizing multiple aspects of taxa and mediating between conflicting hypotheses about their nature. Pursuing a deeper understanding of the trade-offs and synthesis of precision and ambiguity as virtues of scientific language and communication systems then offers a productive next step for realizing sound, big biodiversity data services. (shrink)

Open Science policies encourage researchers to disclose a wide range of outputs from their work, thus codifying openness as a specific set of research practices and guidelines that can be interpreted and applied consistently across disciplines and geographical settings. In this paper, we argue that this “one-size-fits-all” view of openness sidesteps key questions about the forms, implications, and goals of openness for research practice. We propose instead to interpret openness as a dynamic and highly situated mode of valuing the research (...) process and its outputs, which encompasses economic as well as scientific, cultural, political, ethical, and social considerations. This interpretation creates a critical space for moving beyond the economic definitions of value embedded in the contemporary biosciences landscape and Open Science policies, and examining the diversity of interests and commitments that affect research practices in the life sciences. To illustrate these claims, we use three case studies that highlight the challenges surrounding decisions about how––and how best––to make things open. These cases, drawn from ethnographic engagement with Open Science debates and semistructured interviews carried out with UK-based biologists and bioinformaticians between 2013 and 2014, show how the enactment of openness reveals judgments about what constitutes a legitimate intellectual contribution, for whom, and with what implications. (shrink)

Are we entering a major new phase of modern science, one in which our standard, human modes of reasoning and understanding, including heuristics, have decreasing value? The new methods challenge human intelligibility. The digital revolution inspires such claims, but they are not new. During several historical periods, scientific progress has challenged traditional concepts of reasoning and rationality, intelligence and intelligibility, explanation and knowledge. The increasing intelligence of machine learning and networking is a deliberately sought, somewhat alien intelligence. As such, it (...) challenges the traditional, heuristic foresight of expert researchers. Nonetheless, science remains human-centered in important ways—and yet many of our ordinary human epistemic activities are alien to ourselves. This fact has always been the source of “the discovery problem”. It generalizes to the problem of understanding expert scientific practice. Ironically, scientific progress plunges us ever deeper into complexities beyond our grasp. But how is progress possible without traditional realism and the intelligibility realism requires? Pragmatic flexibility offers an answer. (shrink)

Bio-ontologies are digital frameworks for handling biological and biomedical data. They consist of theoretical entities and relations with explicitly defined logical structures and precise definitions, whose purpose is to provide a shared language for representing information to be distributed and integrated across diverse scientific contexts. It is tempting to view bio-ontologies as clear and formal expressions of a scientific community’s ontological commitments about their domain of inquiry, and to view their integration as tantamount to the metaphysical unification of science that (...) some philosophers have envisaged. However, I argue that the local, practical, social and technological factors that influence their design prevent us from straightforwardly reading metaphysical conclusions from them. I discuss these complications and suggest how they can be overcome, revealing more general lessons for the development of a well-founded scientific metaphysics. (shrink)

The collection and classification of data into meaningful categories is a key step in the process of knowledge making. In the life sciences, the design of data discovery and integration tools has relied on the premise that a formal classificatory system for expressing a body of data should be grounded in consensus definitions for classifications. On this approach, exemplified by the realist program of the Open Biomedical Ontologies Foundry, progress is maximized by grounding the representation and aggregation of data on (...) settled knowledge. We argue that historical practices in systematic biology provide an important and overlooked alternative approach to classifying and disseminating data, based on a principle of coordinative rather than definitional consensus. Systematists have developed a robust system for referring to taxonomic entities that can deliver high quality data discovery and integration without invoking consensus about reality or “settled” science. (shrink)

I propose an approach that expands philosophical views of scientific change, on the basis of an analysis of contemporary biomedical research and recent developments in the philosophy of scientific change. Focusing on the establishment of the exposome in epidemiology as a case study and the role of data as a context for contrasting views on change, I discuss change at conceptual, methodological, material, and social levels of biomedical epistemology. Available models of change provide key resources to discuss this type of (...) change, but I present the need for an approach that models transfer, alignment, and influence as key processes of change. I develop this as a pragmatic approach to scientific change, where processes might change substantially depending on specific circumstances, thus contributing to and complementing the debate on a crucial epistemological issue. (shrink)

This paper explores what it takes for research laboratories to produce valuable knowledge in academic institutions marked by the coexistence of multiple evaluative frameworks. Drawing upon ethnographic fieldwork carried out in two UK-based epigenetics research laboratories, I examine the set of practices through which research groups intertwine knowledge production with the making of scientific, health, and wealth value. This includes building and maintaining a portfolio of valuable resources, such as expertise, scientific credibility, or data, and turning these resources into assets (...) by carefully organizing and managing their value. Laboratories then put these assets to productive use within and outside their labs toward the creation or extraction of value. I identify two models for producing value within academic science: a commodity-based model whereby laboratories mobilize their assets to produce results, which can be converted into publications for the accumulation of credibility capital, and a rentier model of accumulation whereby laboratories own valuable assets, which they rent out to others outside their lab against revenue. Following recent developments in Science and Technology Studies on value production in the bioeconomy, I argue that the concepts of asset and rent are essential analytical tools for getting to grips with the origins of value within academic science. (shrink)

We provide a novel articulation of the epistemic peril of p-hacking using three resources from philosophy: predictivism, Bayesian confirmation theory, and model selection theory. We defend a nuanced position on p-hacking: p-hacking is sometimes, but not always, epistemically pernicious. Our argument requires a novel understanding of Bayesianism, since a standard criticism of Bayesian confirmation theory is that it cannot represent the influence of biased methods. We then turn to pre-analysis plans, a methodological device used to mitigate p-hacking. Some say that (...) pre-analysis plans are epistemically meritorious while others deny this, and in practice pre-analysis plans are often violated. We resolve this debate with a modest defence of pre-analysis plans. Further, we argue that pre-analysis plans can be epistemically relevant even if the plan is not strictly followed—and suggest that allowing for flexible pre-analysis plans may be the best available policy option. (shrink)

Philosophers of science are increasingly arguing for and addressing the need to do work that is socially and scientifically engaged. However, we currently lack well-developed frameworks for thinking about how we should engage other expert communities and what the epistemic benefits are of doing so. In this paper, I draw on Collins and Evans' concept of ‘interactional expertise’ – the ability to speak the language of a discipline in the absence of an ability to practice – to consider the epistemic (...) benefits that can arise when philosophers engage scientific communities. As Collins and Evans argue, becoming an interactional expert requires that one ‘hang out’ with members of the relevant expert community in order to learn crucial tacit knowledge needed to speak the language. Building on this work, I argue that acquiring interactional expertise not only leads to linguistic fluency, but it also confers several ‘socio-epistemic’ benefits such as the opportunity to cultivate trust with scientific communities. These benefits can improve philosophical work and facilitate the broader uptake of philosophers' ideas, enabling philosophers to meet a variety of epistemic goals. As a result, having at least some philosophers of science acquire interactional expertise via engagement will likely enhance the diversity of epistemic capacities for philosophy of science as a whole. For some philosophers of science, moreover, the socio-epistemic benefits identified here may be more important than the ability to speak the language of a discipline, suggesting the need for a broader analysis of interactional expertise, which this paper also advances. (shrink)

One of the key features of the contemporary data economy is the widespread circulation of data and its interoperability. Critical data scholars have analysed data repurposing practices and other factors facilitating the travelling of data. While this approach focused on flows provides great potential, in this article we argue that it tends to overlook questions of attachment and belonging. Drawing upon ethnographic fieldwork within a Danish data-linkage infrastructure, and building upon insights from archival science, we discuss the work of data (...) practitioners enabling the repurposing of pathology samples extracted from patients for the conduct of ‘personal medicine’ – our term to discuss the so-called old-fashioned treatment of patients – towards personalised medicine. This first involves ‘getting to know’ the tissues and unpacking their previous uses and meanings, then detaching them from their original source to extract data from such tissues and making them flow towards a new container where they can be worked on and connected with other data. As data practitioners make these tissues travel, transforming them into research data, they organise the attachments of data to new agendas, persons and places. Crucially, in our case, we observe the prominence of national attachments, whereby managing tissues and data in and out of containers involves tying them to the nation to serve its interests. We thus expose how the building of data linkage infrastructures entails more than the accumulation and curation of data, but also involves crafting meanings, futures and belonging to specific communities and territories. (shrink)

There are many tangled normative and technical questions involved in evaluating the quality of software used in epidemiological simulations. In this paper we answer some of these questions and offer practical guidance to practitioners, funders, scientific journals, and consumers of epidemiological research. The heart of our paper is a case study of the Imperial College London covid-19 simulator, set in the context of recent work in epistemology of simulation and philosophy of epidemiology.

In science and technology studies today, there is a troubling tendency to portray actors in the biosciences as “cultural dopes” and technology as having monolithic qualities with predetermined outcomes. To remedy this analytical impasse, this article introduces the concept styles of valuation to analyze how actors struggle with valuing technology in practice. Empirically, this article examines how actors in a bioscientific laboratory struggle with valuing the properties and qualities of algorithms in a high-throughput setting and identifies the copresence of several (...) different styles. The question that the actors struggle with is what different configurations of algorithms, devices, and humans are “good bioscience,” that is, what do the actors perform as a good distribution of agency between algorithms and humans? A key finding is that algorithms, robots, and humans are valued in multiple ways in the same setting. For the actors, it is not apparent which configuration of agency and devices is more authoritative nor is it obvious which skills and functions should be redistributed to the algorithms. Thus, rather than tying algorithms to one set of values, such as “speed,” “precision,” or “automation,” this article demonstrates the broad utility of attending to the multivalence of algorithms and technology in practice. (shrink)

AI technology is capturing the African imaginations as a gateway to progress and prosperity. There is a growing interest in AI by different actors across the continent including scientists, researchers, humanitarian and aid organizations, academic institutions, tech start-ups, and media organizations. Several African states are looking to adopt AI technology to capture economic growth and development opportunities. On the other hand, African researchers highlight the gap in regulatory frameworks and policies that govern the development of AI in the continent. They (...) argue that this could lead to AI technology exacerbating problems of inequalities and injustice in the continent. However, most of the literature on AI ethics is biased toward Euro-American perspectives and lack the understanding of how AI development is apprehended in the Global South, and particularly Africa. Drawing on the case study of the first African Master’s in Machine Intelligence program, this paper argues for looking beyond the question of ethics in AI and examining AI governance issues through the analytical lens of the raciality of computing and the political economy of technoscience to understand AI development in Africa. By doing so, this paper seeks a different theorization for AI ethics from the South that is based on lived experiences of those in the margins and avoids the framings of technological futures that simplistically pathologize or celebrate Africa. (shrink)

In contrast to China’s efforts to upgrade its system of governance around a stupefying amount of data collection and electronic scoring, countries committed to democracy and human rights did not upgrade their systems. Instead, those countries ended up with surveillance capitalism. It is vital for the survival of those ideas about governance to perform such an upgrade. This paper aims to contribute to that goal. I propose a framework of epistemic actorhood in terms of four roles and characterize digital lifeworlds (...) and what matters about them both in terms of how they fit in with Max Tegmark’s distinctions among stages of life and in terms of how they generate their own episteme, the data episteme, with its immense possibilities of infopower. Epistemic rights that strengthen existing human rights – as part of a fourth generation of rights – are needed to protect epistemic actorhood in those roles. In the long run, we might well need a new kind of right, the right to the exercise of genuinely human intelligence. (shrink)

In this paper, I explore how bio-hybrid forms can be created and combined starting from organic forms. The thesis put forward is epistemological: the combinatorial practice of bionics, biomimetics, biorobotics, and all design strategies inspired by nature is not based on a kind of biomimetic inspiration, i. e., on a kind of imitation of nature, but on a practice of translation. To develop this thesis, I focus on the practices of contemporary biorobotics, first examining the practice of translating natural forms (...) into technical artefacts as developed by Raoul Heinrich Francé at the beginning of the 20th century, secondly, analysing the production of robots capable of replicating complex locomotion systems and, finally, investigating the interaction between robots and living organisms (fish). In the last part of the paper, I reflect on the philosophical payoff and broader conditions of possibility for this translational practice. I discuss when and to what extent a translation of biological forms into biotechnical ones is acceptable and point out the notion of form that underlies this practice. Moreover, I draw attention to the need to philosophically investigate what happens between different domains of knowledge – and especially between science and technology. This article is thus an invitation to philosophers to develop a philosophy in the interstices of knowledge production. (shrink)

It is now well-appreciated by philosophers that contemporary large-scale ‘-omics’ studies in biology stand in non-trivial relationships to more orthodox hypothesis-driven approaches. These relationships have been clarified by Ratti (2015); however, there remains much more to be said regarding how an important field of genomics cited in that work—‘genome-wide association studies’ (GWAS)—fits into this framework. In the present article, we propose a revision to Ratti’s framework more suited to studies such as GWAS. In the process of doing so, we introduce (...) to the philosophical literature novel exploratory experiments in (phospho)proteomics, and demonstrate how these experiments interplay with the above considerations. (shrink)

This article documents how biomedical researchers in the United Kingdom understand and enact the idea of “openness.” This is of particular interest to researchers and science policy worldwide in view of the recent adoption of pioneering policies on Open Science and Open Access by the U.K. government—policies whose impact on and implications for research practice are in need of urgent evaluation, so as to decide on their eventual implementation elsewhere. This study is based on 22 in-depth interviews with U.K. researchers (...) in systems biology, synthetic biology, and bioinformatics, which were conducted between September 2013 and February 2014. Through an analysis of the interview transcripts, we identify seven core themes that characterize researchers’ understanding of openness in science and nine factors that shape the practice of openness in research. Our findings highlight the implications that Open Science policies can have for research processes and outcomes and provide recommendations for enhancing their content, effectiveness, and implementation. (shrink)

A major approach to cancer research in the late twentieth century was to search for genes that, when altered, initiated the development of a cell into a cancerous state or failed to stop this development. But as researchers acquired the capacity to sequence tumors and incorporated the resulting data into databases, it became apparent that for many tumors no genes were frequently altered and that the genes altered in different tumors in the same tissue type were often distinct. To address (...) this heterogeneity problem, many researchers looked to a higher level of organization—to mechanisms in which gene products participated. They proposed to reduce heterogeneity by recognizing that multiple gene alterations affect the same mechanism and that it is the altered mechanism that is responsible for the cell developing one or more hallmarks of cancer. I examine how mechanisms figure in this research and focus on two heuristics researchers use to integrate proteins into mechanisms, one focusing on pathways and one focusing on clusters in networks. (shrink)

“Ancient DNA Research” is the practice of extracting, sequencing, and analyzing degraded DNA from dead organisms that are hundreds to thousands of years old. Today, many researchers are interested in adapting state-of-the-art molecular biological techniques and high-throughput sequencing technologies to optimize the recovery of DNA from fossils, then use it for studying evolutionary history. However, the recovery of DNA from fossils has also fueled the idea of resurrecting extinct species, especially as its emergence corresponded with the book and movie Jurassic (...) Park in the 1990s. In this paper, I use historical material, interviews with scientists, and philosophical literature to argue that the search for DNA from fossils can be characterized as a data-driven and celebrity-driven practice. Philosophers have recently argued the need to seriously consider the role of data-driven inquiry in the sciences, and likewise, this history highlights the need to seriously consider the role of celebrity in shaping the kind of research that gets pursued, funded, and ultimately completed. On this point, this history highlights that the traditional philosophical and scientific distinctions between data-driven and hypothesis-driven research are not always useful for understanding the process and practice of science. Consequently, I argue that the celebrity status of a particular research practice can be considered as a “serious epistemic strategy” that researchers, as well as editors and funders, employ when making choices about their research and publication processes. This interplay between celebrity and methodology matters for the epistemology of science. (shrink)

Contrary to the traditional claim that needs for unambiguous communication about animal and plant species are best served by a single set of names ruled by international Codes, I suggest that a more diversified system is required, especially to cope with problems emerging from aggregation of biodiversity data in large databases. Departures from Linnaean nomenclature are sometimes intentional, but there are also other, less obvious but widespread forms of not Code-compliant grey nomenclature. A first problem is due to the circumstance (...) that the Codes are intended to rule over the way names are applied to species and other taxonomic units, whereas users of taxonomy need names to be applied to specimens. For different reasons, it is often impossible to refer a specimen with certainty to a named species, and in those cases an open nomenclature is employed. Second, molecular taxonomy leads to the discovery of clusters of gene sequence diversity not necessarily equivalent to the species recognized and named by taxonomists. Those clusters are mostly indicated with informal names or formulas that challenge comparison between different publications or databases. In several instances, it is not even clear if a formula refers to an individual voucher specimen, or is a provisional species name. The use of non-Linnaean names and formulas must be revised and strengthened by fixing standard formats for the different kinds of objects or hypotheses and providing permanent association of ‘grey names’ with standardized source information such as author and year. In the context of a broad-scope revisitation of aims and scope of scientific nomenclature, it may be worth rethinking if natural objects like plant galls and lichens, although other than the ‘single-entity’ objects traditionally covered by biological classifications, may nevertheless deserve taxonomic names. (shrink)

This essay argues for the importance of an organismic perspective in plant biology and considers some of its implications. These include an increased attention to plant-environment interaction and an emphasis on integrated approaches. Furthermore, this essay contextualizes the increased emphasis on the concept of organism in recent years and places the concept in a longer history. Recent developments in biology and worsening environmental crises have led researchers to study plant responses to changing environments with whole plant approaches that situate plants (...) in their environments, emphasizing the intricate and dynamic interaction between them. This renewed attention to the organism recalls the debates of the early twentieth century, when organicism was one of the three main frameworks in biology (along with vitalism and mechanism). Some scholars see this renewed importance today as a “return” of this earlier period. This essay argues that including insights from plant biology will benefit philosophy of biology research that examines the concept of organism and organicism now and in earlier periods. A comprehensive account of the concept of organism should involve a botanical conception of the organism as well as a zoological one (which is more frequently considered). Although this essay does not aim to present a conceptual analysis, it presents examples of how an organismic perspective can be useful for understanding concepts (such as phenotype, stress, etc.) and research processes (such as experiment set-ups, data processes, etc.) in plant biology. Philosophy of biology investigations that aim at a comprehensive understanding of the concept of organism can benefit greatly from examinations of cases in plant biology, both now and in the past. (shrink)