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)

Recent work in epistemology and philosophy of science has argued that understanding is an important cognitive state that philosophers should seek to analyse. This paper offers a new perspective on understanding by looking to work in philosophy of mind and cognitive science. Understanding is normally taken to be inside the head. I argue that this view is mistaken. Often, understanding is a state that criss-crosses brain, body and world. To support this claim, I draw on extended cognition, a burgeoning framework (...) in cognitive science that stresses the crucial role played by tools, material representations and the wider environment in our cognitive processes. I defend an extended view of understanding against likely objections and argue that it has important consequences for questions concerning the nature of understanding and its relationship to explanation. (shrink)

Epistemic diversity is the ability or possibility of producing diverse and rich epistemic apparati to make sense of the world around us. In this paper we discuss whether, and to what extent, different conceptions of knowledge—notably as ‘justified true belief’ and as ‘distributed and embodied cognition’—hinder or foster epistemic diversity. We then link this discussion to the widespread move in science and philosophy towards monolingual disciplinary environments. We argue that English, despite all appearance, is no Lingua Franca, and we give (...) reasons why epistemic diversity is also deeply hindered is monolingual contexts. Finally, we sketch a proposal for multilingual academia where epistemic diversity is thereby fostered. (shrink)

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)

Large part of contemporary science is in fact technoscience, in the sense that it crucially depends on several technologies for the generation, collection, and analysis of data. This prompts a re-examination of the relations between science and technologies. In this essay, I advance the view that we’d better move beyond the ‘subordination view’ and the ‘instrumental’ view. The first aims to establish the primacy of science over technology, and the second uses technology instrumentally to support a realist position about theoretical (...) entities. I suggest that we should instead concentrate on how science and technology interact. This will reveal that technology has a poietic character, namely it actively partakes in the production of knowledge. But this poietic character can only be understood within the cognitive activity of scientific communities. Current research in molecular epidemiology, notably the projects funded within the ‘European exposome initiative’, serves as a motivation for such discussion and as an illustration of the claims made. (shrink)

We offer a formal treatment of the semantics of both complete and incomplete mistrustful or distrustful information transmissions. The semantics of such relations is analysed in view of rules that define the behaviour of a receiving agent. We justify this approach in view of human agent communications and secure system design. We further specify some properties of such relations.

This paper aims to clarify the consequences of new scientific and philosophical approaches for the practical-theoretical framework of modern developmental biology. I highlight normal development, and the instructive-permissive distinction, as key parts of this framework which shape how variation is conceptualised and managed. Furthermore, I establish the different dimensions of biological variation: the units, temporality and mode of variation. Using the analytical frame established by this, I interpret a selection of examples as challenges to the instructive-permissive distinction. These examples include (...) the phenomena of developmental plasticity and transdifferentiation, the role of the microbiome in development, and new methodological approaches to standardisation and the assessment of causes. Furthermore, I argue that investigations into organismal development should investigate the effects of a wider range of kinds of variation including variation in the units, modes and temporalities of development. I close by examining various possible opportunities for producing and using normal development free of the assumptions of the instructive-permissive distinction. These opportunities are afforded by recent developments, which include new ways of producing standards incorporating more natural variation and being based on function rather than structure, and the ability to produce, store, and process large quantities of data. (shrink)

With increasing publication and data production, scientific knowledge presents not simply an achievement but also a challenge. Scientific publications and data are increasingly treated as resources that need to be digitally ‘managed.’ This gives rise to scientific Knowledge Management : second-order scientific work aiming to systematically collect, take care of and mobilise first-hand disciplinary knowledge and data in order to provide new first-order scientific knowledge. We follow the work of Leonelli, Efstathiou and Hislop in our analysis of the use of (...) KM in semantic systems biology. Through an empirical philosophical account of KM-enabled biological research, we argue that KM helps produce new first-order biological knowledge that did not exist before, and which could not have been produced by traditional means. KM work is enabled by conceiving of ‘knowledge’ as an object for computational science: as explicated in the text of biological articles and computable via appropriate data and metadata. However, these founded knowledge concepts enabling computational KM risk focusing on only computationally tractable data as knowledge, underestimating practice-based knowing and its significance in ensuring the validity of ‘manageable’ knowledge as knowledge. (shrink)

We have two theses about scientific knowledge in the age of computation. Our general claim is that scientific Knowledge Management practices emerge as second-order practices whose aim is to systematically collect, take care of and mobilise first-hand disciplinary knowledge and data. Our specific thesis is that knowledge management practices are transforming biological research in at least three ways. We argue that scientific Knowledge Management a. operates with founded concepts of biological knowledge as explicated and computable, b. enables new outputs and (...) ways of knowing within biology, and c. risks enforcing objectivist epistemologies of knowledge as some one objective thing. (shrink)

Just like atomic physics seventy years ago, when it was realized that chain reaction could lead to medical applications as well as to the creation of atomic weapons, the life sciences have entered a grey zone. “Advances in biotechnology […]” a 2003 CIA document stated, “have the potential to create a much more dangerous biological warfare threat […] Engineered biological agents could be much worse than any disease known to man”. As sociologists of science have noted, contemporary life sciences have (...) dual use potential as they can be used to foster beneficial applications, for instance in health care, or generate harm, either intentionally or not, and possibly on an unprecedented.. (shrink)

Dominantní pozitivistická epistemologie dat v současné vědě, která staví na metafyzickém realismu a ideálu mechanické objektivity, trpí řadou trhlin. Tyto nedostatky byly identifikovány v mnohých kritických ohlasech, které vedou k nové problematizaci ustáleného konceptu dat. Mezi kritizovanými aspekty tohoto konceptu se opakují varování ohledně zasazenosti dat do kontextu jejich tvorby, jejich zprostředkovanost nebo otevřenost manipulacím. Funkce dat v posledních letech nabývá na důležitosti kvůli stoupající datové náročnosti vědy, a proto je dříve neproblematickému konceptu dat věnováno více pozornosti. Objevují se také (...) alternativní přístupy k epistemologii dat, z nichž tato práce představuje směr konstruktivistický a rétorický. Prezentovaný text lze díky své kompilativní povaze, čerpající z kritické literatury zabývající se epistemologií dat, pojmout jako syntézu a rekonfiguraci existujících myšlenek k danému tématu. Je možné ho rovněž přijmout jako jeden z příspěvků k rétorické argumentaci v diskurzu dat současné vědy. (shrink)

Model organism databases are used extensively for knowledge retrieval and knowledge sharing among biologists. With the invention of genome sequencing and protein profiling technologies, large amount of molecular data provides practical insights into the molecular study of model organisms. The knowledge-intensive characteristic of model organism databases provides a reference point for the comparative study of other species. In this paper, I argue that algorithms could be used to facilitate cross-species research. I emphasize the epistemic significance of algorithms in the integration (...) of data for cross-species research. I examine (1) how algorithms guide data integration in model organism databases; and (2) the importance of algorithms for the use of model organism database in the cross-species research. I argue that an extrapolation from the stored data to other species is possible in virtue of the fact that algorithms can facilitate two modes of data integration—viz., inter-level and cross-species integration. Lastly, I examine the implication of the data integration role of an algorithm in light of mechanistic explanation. (shrink)

Neuroimaging technology is the most widely used tool to study human cognition. While originally a promising tool for mapping the content of cognitive theories onto the structures of the brain, recently developed tools for the analysis, handling and sharing of data have changed the theoretical landscape of cognitive neuroscience. Even with these advancements philosophical analyses of evidence in neuroimaging remain skeptical of the promise of neuroimaging technology. These views often treat the analysis techniques used to make sense of data produced (...) in a neuroimaging experiment as one, attributing the inferential limitations of analysis pipelines to the technology as a whole. Situated against the neuroscientists own critical assessment of their methods and the limitations of those methods, this skepticism appears based on a misunderstanding of the role data analysis techniques play in neuroimaging research. My project picks up here, examining how data analysis techniques, such as pattern classification analysis, are used to assess the evidential value of neuroimaging data. The project takes the form of three papers. In the first I identify the use of multiple data analysis techniques as an important aspect of the data interpretation process that is overlooked by critics. In the second I develop an account of inferences in neuroimaging research that is sensitive to this use of data analysis techniques, arguing that interpreting neuroimaging data is a process of isolating and explaining a variety of data patterns. In the third I argue that the development and uptake of new techniques for analyzing data must be accompanied by changes in research practices and standards of evidence if they are to promote knowledge generation. My approach to this work is both traditionally philosophical, insofar as it involves reading and analyzing the work of philosophers and neuroscientists, and embedded insofar as most of the research was conducted while engaged in attending lab meetings and participating in the work of those scientists whose work is the object of my research. (shrink)

In addition to his experiments on selection in pure lines, Wilhelm Johannsen performed less well-known hybridisation experiments with beans. This article describes these experiments and discusses Johannsen’s motivations and interpretations, in the context of developments in early genetics. I will show that Johannsen first presented the hybridisation experiments as an additional control for his selection experiments. The latter were dedicated to investigating heredity with respect to debates concerning the significance of natural selection of continuous variation for evolution. In the course (...) of the establishment of a Mendelian research program after 1900, the study of heredity gained increasing independence from questions of evolution, and focused more on the modes and mechanisms of heredity. Further to their role as control experiments, Johannsen also saw his hybridisation experiments as contributing to the Mendelian program, by extending the scope of the principles of Mendelian inheritance to quantitative characters. Towards the end of the first decade of genetics, Johannsen revisited his experiments to illustrate the many–many relationship between genes and characters, at a time when that relationship appeared increasingly complex, and the unit-character concept, accordingly, became inadequate. For the philosophy of science, the example shows that experiments can have multiple roles in a research programme, and can be interpreted in the light of questions other than those that motivated the experiments in the first place. (shrink)

This article examines the use of scientific arguments in negotiations on the status of Digital Sequence Information (DSI), focusing on the Convention on Biological Diversity (CBD), the International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA), and the Pandemic Influenza Preparedness Framework (PIP). DSI is a placeholder term used in negotiations on the dematerialization of genetic resources: the ability to sequence “physical” genetic resources and use this “intangible” information, which radically changes research practices. The CBD (among other instruments) (...) establishes rules for Access to the Genetic Resource and the Fair and Equitable Sharing of Benefits from their utilization (ABS). This applies to “physical” genetic resources, but it is not clear for DSI. Indeed, different legal interpretations and political narrative are conflicting over the integration of DSI into these legal frameworks. This article explores how science is used in these negotiations, particularly in its rhetorical and epistocratic dimensions. The methodology combines an interdisciplinary approach (legal technique, philosophy of law and science) and a comparative discourse analysis: on the terminology; the inclusion of DSI in the definition of genetic material; and the inclusion of DSI in ABS systems. While scientific arguments play a crucial role in this technical issue, this article shows that scientific arguments can be used to support political positions (under the guise of objectivity and neutrality), and that this use of scientific arguments is not consistent, even contradictory (between PIP and CBD/ITPGRFA). -/- . (shrink)