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)

Is Big Data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of Big Data science does not lie in the sheer quantity of data involved, but rather in the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community and the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments (...) generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this article reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which Big Data needs to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analysing data given these developments, and the opportunities and worries associated with Big Data discourse and methods. (shrink)

Knowledge-making practices in biology are being strongly affected by the availability of data on an unprecedented scale, the insistence on systemic approaches and growing reliance on bioinformatics and digital infrastructures. What role does theory play within data-intensive science, and what does that tell us about scientific theories in general? To answer these questions, I focus on Open Biomedical Ontologies, digital classification tools that have become crucial to sharing results across research contexts in the biological and biomedical sciences, and argue that (...) they constitute an example of classificatory theory. This form of theorizing emerges from classification practices in conjunction with experimental know-how and expresses the knowledge underpinning the analysis and interpretation of data disseminated online. (shrink)

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)

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)

Scientific knowledge production is currently affected by the dissemination of data on an unprecedented scale. Technologies for the automated production and sharing of vast amounts of data have changed the way in which data are handled and interpreted in several scientific domains, most notably molecular biology and biomedicine. In these fields, the activity of data gathering has become increasingly technology-driven, with machines such as next generation genome sequencers and mass spectrometers generating billions of data points within hours, and with little (...) need for human supervision. Given the relative ease and low costs with which datasets can be produced (that is, once a laboratory has been able to afford .. (shrink)

It is time to escape the constraints of the Systematics Wars narrative and pursue new questions that are better positioned to establish the relevance of the field in this time period to broader issues in the history of biology and history of science. To date, the underlying assumptions of the Systematics Wars narrative have led historians to prioritize theory over practice and the conflicts of a few leading theorists over the less-polarized interactions of systematists at large. We show how shifting (...) to a practice-oriented view of methodology, centered on the trajectory of mathematization in systematics, demonstrates problems with the common view that one camp straightforwardly “won” over the other. In particular, we critique David Hull’s historical account in Science as a Process by demonstrating exactly the sort of intermediate level of positive sharing between phenetic and cladistic theories that undermines their mutually exclusive individuality as conceptual systems over time. It is misleading, or at least inadequate, to treat them simply as holistically opposed theories that can only interact by competition to the death. Looking to the future, we suggest that the concept of workflow provides an important new perspective on the history of mathematization and computerization in biology after World War II. (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 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 article considers the temporal dimension of data processing and use and the ways in which it affects the production and interpretation of knowledge claims. I start by distinguishing the time at which data collection, dissemination, and analysis occur from the time in which the phenomena for which data serve as evidence operate. Building on the analysis of two examples of data reuse from modeling and experimental practices in biology, I then argue that Dt affects how researchers select and interpret (...) data as evidence and identify and understand phenomena. (shrink)

The collection and dissemination of data on human and nonhuman organisms has become a central feature of 21st-century biology and has been endorsed by funding agencies in the United States and Europe as crucial to translating biological research into therapeutic and agricultural innovation. Large molecular data sets, often referred to as “big data,” are increasingly incorporated into digital databases, many of which are freely accessible online. These data have come to be seen as resources that play a key role in (...) mediating global market exchange, thus achieving a prominent social and economic status well beyond science itself. At the same time, calls to make all such data publicly and freely available have garnered strength and visibility, most prominently in the form of the Open Data movement. I discuss these developments by considering the conditions under which data journey across the communities and institutions implicated in globalized biology and biomedicine, and what this indicates about how Internet-based communication and the use of online databases affect scientific research and its role within contemporary society. (shrink)

Many biologists appeal to the so-called Krogh principle when justifying their choice of experimental organisms. The principle states that “for a large number of problems there will be some animal of choice, or a few such animals, on which it can be most conveniently studied”. Despite its popularity, the principle is often critiqued for implying unwarranted generalizations from optimal models. We argue that the Krogh principle should be interpreted in relation to the historical and scientific contexts in which it has (...) been developed and used. We interpret the Krogh Principle as a heuristic, i.e., as a recommendation to approach biological problems through organisms where a specific trait or physiological mechanism is expected to be most distinctively displayed or most experimentally accessible. We designate these organisms “Krogh organisms.” We clarify the differences between uses of model organisms and non-standard Krogh organisms. Among these is the use of Krogh organisms as “negative models” in biomedical research, where organisms are chosen for their dissimilarity to human physiology. Importantly, the representational scope of Krogh organisms and the generalizability of their characteristics are not fixed or assumed but explored through experimental studies. Research on Krogh organisms is steeped in the comparative method characteristic of zoology and comparative physiology, in which studies of biological variation produce insights into general physiological constraints. Accordingly, we conclude that the Krogh principle exemplifies the advantages of studying biological variation as a strategy to produce generalizable insights. (shrink)

A powerful idea put forward in the recent philosophy of science literature is that scientific models are best understood as instruments, tools or, more generally, artifacts. This idea has thus far been developed in combination with the more traditional representational approach: accordingly, current artifactualist accounts treat models as representational tools. But artifactualism and representationalism are independent views, and adopting one does not require acceptance of the other. This paper argues that a leaner version of artifactualism, free of representationalist assumptions, is (...) both desirable and viable. Taking seriously the idea that models are artifacts can help us philosophically to make sense of how and why scientific modeling works even without reference to representation. (shrink)

Patient-derived xenografts are currently promoted as new translational models in precision oncology. PDXs are immunodeficient mice with human tumors that are used as surrogate models to represent specific types of cancer. By accounting for the genetic heterogeneity of cancer tumors, PDXs are hoped to provide more clinically relevant results in preclinical research. Further, in the function of so-called “mouse avatars”, PDXs are hoped to allow for patient-specific drug testing in real-time. This paper examines the circulation of knowledge and bodily material (...) across the species boundary of human and personalized mouse model, historically as well as in contemporary practices. PDXs raise interesting questions about the relation between animal model and human patient, and about the capacity of hybrid or interspecies models to close existing translational gaps. We highlight that the translational potential of PDXs not only depends on representational matching of model and target, but also on temporal alignment between model development and practical uses. Aside from the importance of ensuring temporal stability of human tumors in a murine body, the mouse avatar concept rests on the possibility of aligning the temporal horizons of the clinic and the lab. We examine strategies to address temporal challenges, including cryopreservation and biobanking, as well as attempts to speed up translation through modification and use of faster developing organisms. We discuss how featured model virtues change with precision oncology, and contend that temporality is a model feature that deserves more philosophical attention. (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)

In this opening chapter, we review the literature on information quality. Our major aim is to introduce the issues, and trace some of the history of the debates, with a view to situating the chapters in this volume – whose authors come from different disciplines – to help make them accessible to readers with different backgrounds and expertise. We begin in this section by tracing some influential analyses of IQ in computer science. This is a useful basis for examining some (...) examples of developing work on IQ in section two. We look at some cases for applying IQ in section three, and conclude with some discussion points in section four. (shrink)

Despite the burgeoning interest in new and more complex accounts of the organism-environment dyad by biologists and philosophers, little attention has been paid in the resulting discussions to the history of these ideas and to their deployment in disciplines outside biology—especially in the social sciences. Even in biology and philosophy, there is a lack of detailed conceptual models of the organism-environment relationship. This volume is designed to fill these lacunae by providing the first multidisciplinary discussion of the topic of organism-environment (...) interaction. It brings together scholars from history, philosophy, psychology, anthropology, medicine, and biology to discuss the common focus of their work: entangled life, or the complex interaction of organisms and environments. (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)

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

The governments of China, India, and the United Kingdom are unanimous in their belief that bioinformatics should supply the link between basic life sciences research and its translation into health benefits for the population and the economy. Yet at the same time, as ambitious states vying for position in the future global bioeconomy they differ considerably in the strategies adopted in pursuit of this goal. At the heart of these differences lies the interaction between epistemic change within the scientific community (...) itself and the apparatus of the state. Drawing on desk-based research and thirty-two interviews with scientists and policy makers in the three countries, this article analyzes the politics that shape this interaction. From this analysis emerges an understanding of the variable capacities of different kinds of states and political systems to work with science in harnessing the potential of new epistemic territories in global life sciences innovation. (shrink)

Contrary to concerns of some critics, we present evidence that biomedical research is not dominated by a small handful of model organisms. An exhaustive analysis of research literature suggests that the diversity of experimental organisms in biomedical research has increased substantially since 1975. There has been a longstanding worry that organism‐centric funding policies can lead to biases in experimental organism choice, and thus negatively impact the direction of research and the interpretation of results. Critics have argued that a focus on (...) model organisms has unduly constrained the diversity of experimental organisms. The availability of large electronic databases of scientific literature, combined with interest in quantitative methods among philosophers of science, presents new opportunities for data‐driven investigations into organism choice in biomedical research. The diversity of organisms used in NIH‐funded research may be considerably lower than in the broader biomedical sciences, and may be subject to greater constraints on organism choice. (shrink)

Many philosophers are skeptical about the scientific value of the concept of biological information. However, several have recently proposed a more positive view of ascribing information as an exercise in scientific modeling. I argue for an alternative role: guiding empirical data collection for the sake of theorizing about the evolution of semantics. I clarify and expand on Bergstrom and Rosvall’s suggestion of taking a “diagnostic” approach that defines biological information operationally as a procedure for collecting empirical cases. The more recent (...) modeling-based accounts still perpetuate a theory-centric view of scientific concepts, which motivated philosophers’ misplaced skepticism in the first place. (shrink)

Philosophers of science have recently focused on the scientific activity of modeling phenomena, and explicated several of its properties, as well as the activities embedded into it. A first approach to modeling has been elaborated in terms of representing a target system: yet other epistemic functions, such as producing data or detecting phenomena, are at least as relevant. Additional useful distinctions have emerged, such as the one between phenomenological and mechanistic models. In biological sciences, besides mathematical models, models now come (...) in three forms: in vivo, in vitro and in silico. Each has been investigated separately, and many specific problems they raised have been laid out. Another relevant distinction is disciplinary: do models differ in significant ways according to the discipline involved—medicine or biology, evolutionary biology or earth science? Focusing on either this threefold distinction or the disciplinary boundaries reveals that they might not be sufficient from a philosophical perspective. On the contrary, focusing on the interaction between these various kinds of models, some interesting forms of explanation come to the fore, as is exemplified by the papers included in this issue. On the other hand, a focus on the use of models, rather than on their content, shows that the distinction between biological and medical models is theoretically sound. (shrink)

ZusammenfassungGenomdaten, Kernstück der 2008 ausgerufenen Big Data-Revolution der Biologie, werden voll automatisiert sequenziert und analysiert. Der Wechsel von der manuellen Laborpraktik der Elektrophorese-Sequenzierung zu DNA-Sequenziermaschinen und softwarebasierten Analyseprogrammen vollzog sich zwischen 1982 und 1992. Erst dieser Wechsel ermöglichte die Flut an Daten, die mit der zweiten und dritten Generation der DNA-Sequenzierer erheblich zunimmt. Doch mit diesem Wechsel verändern sich auch die Validierungsstrategien der Genomdaten. Der Beitrag untersucht beides – die Automatisierung und die damit verbundene Validierungskultur – um ein Bild der (...) Komplexität der Datengenerierung und deren Datenpositivismus zu geben. Leitend ist dabei die Frage, ob dieser Datenpositivismus die Grundlage der aktuell angekündigten Big Data-Revolution der Molekularbiologie ist oder deren Datenhybris. (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)

Biologists who work on the pig (_Sus scrofa_) take advantage of its similarity to humans by constructing the inferential and material means to traffic data, information and knowledge across the species barrier. Their research has been funded due to its perceived value for agriculture and medicine. Improving selective breeding practices, for instance, has been a driver of genomics research. The pig is also an animal model for biomedical research and practice, and is proposed as a source of organs for cross-species (...) transplantation: xenotransplantation. Genomics research has informed transplantation biology, which has itself motivated developments in genomics. Both have generated models of correspondences between the genomes of pigs and humans. Concerning genomics, I detail how researchers traverse species boundaries to develop representations of the pig genome, alongside ensuring that such representations are sufficiently porcine. In transplantation biology, the representations of the genomes of humans and pigs are used to detect and investigate immunologically-pertinent differences between the two species. These key differences can then be removed, to ‘humanise’ donor pigs so that they can become a safe and effective source of organs. In both of these endeavours, there is a tension between practices that ‘humanise’ the pig (or representations thereof) through using resources from human genomics, and the need to ‘dehumanise’ the pig to maintain distinctions for legal, ethical and scientific reasons. This paper assesses the ways in which this tension has been managed, observing the differences between its realisations across comparative pig genomics and transplantation biology, and considering the consequences of this. (shrink)

Computational modeling is one of the primary approaches to constructing protein–protein interfaces in the laboratory. The algorithm-driven computational protein design has been successfully applied to the construction of functional proteins with improved binding affinity and increased thermostability. It is intriguing how a computational protein modeling approach can construct and shape the reality of new functional proteins from scratch. I articulate an account of abstraction and exploration-driven strategies in this computational endeavor. I aim to show that how a computational modelling approach, (...) which is laden with mathematics and algorithms, can have a constructive force on the target protein. (shrink)

Model organisms are a living form of scientific models. Despite the widespread use of model organisms in scientific research, the actual representational relationship between model organisms and their target species is often poorly characterized in the context of cross-species research. Many model organisms do not represent the target species adequately, let alone accurately. This is partly due to the complex and emergent life phenomena in the organism, and partly due to the fact that a model organism is always taken to (...) represent a broad range of diverse organisms. More often than not, model organisms are taken as a reference point for an extrapolation to be made to the unknown characteristics of other species. I propose to view model organisms as analogue simulators which represent the emergent phenomenon in the context of cross-species research. A model organism represents a wide range of species by simulating their molecular microstates which underlie various emergent phenomena. I show that although model organisms represent the target species inadequately at many levels of complexity, they have epistemic values as a simulator in virtue of which the emergent phenomenon can be modeled dynamically, a virtue that is hardly attainable by non-dynamic models. (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)

Philosophers of science have recently focused on the scientific activity of modeling phenomena, and explicated several of its properties, as well as the activities embedded into it. A first approach to modeling has been elaborated in terms of representing a target system: yet other epistemic functions, such as producing data or detecting phenomena, are at least as relevant. Additional useful distinctions have emerged, such as the one between phenomenological and mechanistic models. In biological sciences, besides mathematical models, models now come (...) in three forms: in vivo, in vitro and in silico. Each has been investigated separately, and many specific problems they raised have been laid out. Another relevant distinction is disciplinary: do models differ in significant ways according to the discipline involved—medicine or biology, evolutionary biology or earth science? Focusing on either this threefold distinction or the disciplinary boundaries reveals that they might not be sufficient from a philosophical perspective. On the contrary, focusing on the interaction between these various kinds of models, some interesting forms of explanation come to the fore, as is exemplified by the papers included in this issue. On the other hand, a focus on the use of models, rather than on their content, shows that the distinction between biological and medical models is theoretically sound. (shrink)

We demonstrate a heterogeneity of representation types for breast cancer phenotypes and stress that the characterisation of a tumour phenotype often includes parameters that go beyond the representation of a corresponding empirically observed tumour, thus reflecting significant functional features of the phenotypes as well as epistemic interests that drive the modes of representation. Accordingly, the represented features of cancer phenotypes function as epistemic vehicles aiding various classifications, explanations, and predictions. In order to clarify how the plurality of epistemic motivations can (...) be integrated on a formal level, we give a distinction between six categories of human agents as individuals and groups focused around particular epistemic interests. We analyse the corresponding impact of these groups and individuals on representation types, mapping and reasoning scenarios. Respecting the plurality of representations, related formalisms, expressivities and aims, as they are found across diverse scientific communities, we argue for a pluralistic ontology integration. Moreover, we discuss and illustrate to what extent such a pluralistic integration is supported by the distributed ontology language DOL, a meta-language for heterogeneous ontology representation that is currently under standardisation as ISO WD 17347 within the OntoIOp (Ontology Integration and Interoperability) activity of ISO/TC 37/SC 3. We particularly illustrate how DOL supports representations of parthood on various levels of logical expressivity, mapping of terms, merging of ontologies, as well as non-monotonic extensions based on circumscription allowing a transparent formal modelling of the normal/abnormal distinction in phenotypes. (shrink)