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)

Berichte zur Wissenschaftsgeschichte, Volume 45, Issue 3, Page 415-427, September 2022.

Berichte zur Wissenschaftsgeschichte, Volume 45, Issue 3, Page 415-427, September 2022.

The purpose of this study is to examine how trends in the use of images in modern life science journals have changed since the spread of computer-based visual and imaging technology. To this end, a new classification system was constructed to analyze how the graphics of a scientific journal have changed over the years. The focus was on one international peer-reviewed journal in life sciences, Cell, which was founded in 1974, whereby 1725 figures and 160 tables from the research articles (...) in Cell were sampled. The unit of classification was defined as a graphic and the figures and tables were divided into 5952 graphics. These graphics were further classified into hierarchical categories, and the data in each category were aggregated every five years. The following categories were observed: data graphics, explanation graphics, and hybrid graphics. Data graphics increased by more than sixfold between 1974 and 2014, and some types of data graphics including mechanical reproduction images and bar charts displayed notable changes. The representation of explanatory graphics changed from hand-painted illustrations to diagrams of Bezier-curves. It is suggested that in addition to the development of experimental technologies such as fluorescent microscopy and big data analysis, continuously evolving application software for image creation and researchers’ motivation to convince reviewers and editors have influenced these changes. (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)

Reactivity in qualitative data collection occurs when a researcher generates data about a situation with reactivity, that is, a situation in which the ongoing research affects the research participants such that they, say, diverge from their routines when the researcher is present, or tell the researcher what they think she wants to hear. In qualitative research, there are two basic approaches to reactivity. The traditional position maintains that data should ideally be collected in situations without any reactivity. In other words, (...) good data are reactivity free. By contrast, the more recent view holds that data from situations with reactivity are fine as long as the researcher is aware of the occurring reactivity so that she can take it into account when interpreting her data. In this fashion, good data are reactivity transparent. In this paper, I first spell out and defend the more recent approach to reactivity. I argue that qualitative data are reactivity transparent when conjoined with true reactivity assumptions and that, thus supplemented, data are informative about social life independently of its being studied. Next, I examine various issues raised by the requirement to put forth true reactivity assumptions. Lastly, I use my discussion of reactivity transparency as a basis for providing a framework for thinking about good qualitative data. (shrink)

ZusammenfassungDer Beitrag nimmt den heute allgegenwärtigen Begriff „Daten“ als historische Kategorie in den Blick. Er geht der langsamen Verbreitung des Wortes unter Statistikern im 19. Jahrhundert nach und untersucht die materielle Kultur derjenigen Konzepte und Praktiken, die mit seiner Verwendung einhergingen. Am Beispiel der preußischen Volkszählung legt der Beitrag mit diesem Vorgehen bislang unbeachtete Genealogien datengetriebener Forschung frei: Nicht erst Computerspezialisten des 20. und 21. Jahrhunderts, sondern Wissenschaftler des 19. Jahrhunderts machten sich den Begriff für die Produktion streng abstrahierter, numerischer (...) Information zunutze. Sie entwickelten hochflexible Aufbereitungsverfahren von Massenerhebungen, lange bevor Maschinen wie das Hollerith-System oder der Computer die Kompilierung per Hand ersetzten. Bei der preußischen Volkszählung war das Werkzeug dieser „Verdatung“ die sogenannte Zählkarte, ein beweglicher Papierträger, der ein komplexes System der Auswertung erzwang. Der Beitrag rekonstruiert die Logistik dieses Verfahrens und verfolgt die durch ganz Berlin zirkulierenden Papiermassen. Er analysiert die Organisation der meist weiblichen, in Heimarbeit beschäftigten Auszähler und ermisst die sozialen und intellektuellen Anforderungen, die erforderlich wurden, um die auf den beweglichen Zählkarten eingeschriebenen Informationen möglichst schnell und fehlerlos in numerische Aggregate umzuwandeln. Der Beitrag argumentiert, dass die Verdatung nicht nur in der Bevölkerungsstatistik zu neuen Darstellungsformen und Erkenntnissen führte, sondern auch die Forschungspraktiken etlicher anderer „baconischer“ Wissenschaften prägte. Von der Nationalökonomie über die Taxonomie bis zur Paläontologie stärkte das Sehen und Verarbeiten von „Daten“ historische Sensibilitäten und führte zu überraschenden, epochalen Einsichten. (shrink)

This paper proposes an outline for a typology of the different forms that scientific objects can take in the life sciences. The first section discusses preparations (or specimens)—a form of scientific object that accompanied the development of modern biology in different guises from the seventeenth century to the present: as anatomical–morphological specimens, as microscopic cuts, and as biochemical preparations. In the second section, the characteristics of models in biology are discussed. They became prominent from the end of the nineteenth century (...) onwards. Some remarks on the role of simulations—characterising the life sciences of the turn from the twentieth to the twenty-first century—conclude the paper. -/- . (shrink)

Recently, biologists have argued that data - driven biology fosters a new scientific methodology; namely, one that is irreducible to traditional methodologies of molecular biology defined as the discovery strategies elucidated by mechanistic philosophy. Here I show how data - driven studies can be included into the traditional mechanistic approach in two respects. On the one hand, some studies provide eliminative inferential procedures to prioritize and develop mechanistic hypotheses. On the other, different studies play an exploratory role in providing useful (...) generalizations to complement the procedure of prioritization. Overall this paper aims to shed light on the structure of contemporary research in molecular biology. (shrink)

This paper proposes an account of scientific data that makes sense of recent debates on data-driven and ‘big data’ research, while also building on the history of data production and use particularly within biology. In this view, ‘data’ is a relational category applied to research outputs that are taken, at specific moments of inquiry, to provide evidence for knowledge claims of interest to the researchers involved. They do not have truth-value in and of themselves, nor can they be seen as (...) straightforward representations of given phenomena. Rather, they are fungible objects defined by their portability and prospective usefulness as evidence. (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)

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)

This paper discusses what it means and what it takes to integrate data in order to acquire new knowledge about biological entities and processes. Maureen O’Malley and Orkun Soyer have pointed to the scientific work involved in data integration as important and distinct from the work required by other forms of integration, such as methodological and explanatory integration, which have been more successful in captivating the attention of philosophers of science. Here I explore what data integration involves in more detail (...) and with a focus on the role of data-sharing tools, like online databases, in facilitating this process; and I point to the philosophical implications of focusing on data as a unit of analysis. I then analyse three cases of data integration in the field of plant science, each of which highlights a different mode of integration: inter-level integration, which involves data documenting different features of the same species, aims to acquire an interdisciplinary understanding of organisms as complex wholes and is exemplified by research on Arabidopsis thaliana; cross-species integration, which involves data acquired on different species, aims to understand plant biology in all its different manifestations and is exemplified by research on Miscanthus giganteus; and translational integration, which involves data acquired from sources within as well as outside academia, aims at the provision of interventions to improve human health and is exemplified by research on Phytophtora ramorum. Recognising the differences between these efforts sheds light on the dynamics and diverse outcomes of data dissemination and integrative research; and the relations between the social and institutional roles of science, the development of data-sharing infrastructures and the production of scientific knowledge. (shrink)

Scientific classification has long been recognized as involving a specific style of reasoning and doing research, and as occasionally affecting the development of scientific theories. However, the role played by classificatory activities in generating theories has not been closely investigated within the philosophy of science. I argue that classificatory systems can themselves become a form of theory, which I call classificatory theory, when they come to formalize and express the scientific significance of the elements being classified. This is particularly evident (...) in some of the classification practices used in contemporary experimental biology, such as bio-ontologies used to classify genomic data and typologies used to classify “normal” stages of development in developmental biology. In this paper, I explore some characteristics of classificatory theories and ways in which they differ from other types of scientific theories and other components of scientific epistemology, such as models and background assumptions. (shrink)

Following Hans-Jörg Rheinberger’s epistemological concept we show how a generic element of synthetic biology, the “biological switch”, can be integrated into an experimental system. Here synthetic biology is assumed to be a technoscience. Hence, the biological switch becomes a technoscientific research object. Consequently, the experimental system has to be analyzed in a technoscientific experimental setting, showing differences in comparison with the former. To work out the specific properties of the technoscientific experimental system, biological switching behavior is compared with the scientific (...) research object laser light in its classical setting. For the analyses, both the laser light and bistability, enabling a biological switch, are considered as epistemic things connected by the same theoretical concept of phase transitions. The so-called Schlögl model is used to model both biological switching behavior and induced emission of radiation and becomes an epistemic thing in itself. It becomes clear that the answer, whether one is dealing with the emission of laser light or with bistable switching behavior, is linked to the perspective taken. The technoscientific orientation towards applications and the development of basic scientific theories require different perspectives on one and the same epistemic thing, here also represented by the model. The research objects of synthetic biology as a technoscience thus also enter into the corresponding experimental systems as techno-epistemic objects. Their analysis leads to a more complete understanding of what constitutes synthetic biology. (shrink)

This paper uses several case studies to suggest that (1) two prominent definitions of data do not on their own capture how scientists use data and (2) a novel perspectival account of data is needed. It then outlines some key features of what this account could look like. Those prominent views, the relational and representational, do not fully capture what data are and how they function in science. The representational view is insensitive to the scientific context in which data are (...) used. The relational account does not fully account for the empirical nature of data and how it is possible for data to be evidentially useful. The perspectival account surmounts these problems by accommodating a representational element to data. At the same time, data depend upon the epistemic context because they are the product of situated and informed judgements. (shrink)

Recent studies of scholarly data work argue that whatever researchers handle as data and, in particular, what researchers consider as potential evidence for supporting claims, counts as data. In this article I extend the relational approach towards data to the various ways of dealing with data in the course of a single research project. Relying on an example from ecology, I argue that data gain presence for and occupy researchers in manifold ways: for example, as a promise, desire or pressure, (...) or as a problem of trust and strength. In this way we notice that data, in the eyes of researchers, have more than one mode of existence, each of which is linked with its own expectations, challenges and actions. And we notice that these modes of existence not only coexist in the framework of a single project, but can also compete with each other. (shrink)

Data production in experimental sciences depends on localised experimental systems, but the epistemic properties of data transcend the contingencies of the processes that produce them. Philosophers often believe that experimental systems instantiate but do not produce the epistemic properties of data. In this paper, we argue that experimental systems' local functioning entails intrinsic capacities to produce the epistemic properties of data. We develop this idea by applying Derrida's model of arche-writing to study a case of theory-oriented experimental practice. Derrida's model (...) relativises or dissolves the conceptual distinction between the moment of data production and a subsequent moment of data dissemination. It thus has consequences for understanding both data production (despite being intrinsically local, data production a priori generates transferrable and modellable information) and data dissemination (when modelling information, researchers needs to refer this information to the context of its production). We study a case of data production in a non-exploratory experimental system designed to test a pre-existing hypothesis in visual neuroscience. A case of theory-oriented experimental practice should allow us to identify the autonomous functioning of experimental systems in data production more clearly, insofar as it allows us to study the limits of pre-existing theory in the activities of these systems. We suggest that pre-existing concepts, hypotheses and theories condition the relevance but not the production of experimental data. (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)

The objective of this dissertation is to write the first pages of the biography of paleobiological data. I will focus on i) the genesis of this kind of data as it emerged in German stratigraphy and paleontology between the mid 19th and the early 20th centuries and ii) how the conceptualization of the paleontological data was reformulated and taken as the starting point for studying the patterns of the diversity of life in deep time between the 1940s and 70s. This (...) is the turning point that marks the ‘coming into being’ of specifically paleobiological data. I will reconstruct in detail how it was possible to transform an incomplete and misleading material object — the fossil record that can be admired in a museum —into a no longer misleading source of knowledge. I will namely describe the genesis of the paleobiological practice by retracing the encounter of two different approaches to the fossil record between the mid 19th and early 20th centuries in German academia and their later diffusion into English-speaking countries. (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)