The kinds of models discussed in this paper function as measuring instruments. We will concentrate on two necessary steps for measurement: (1) the search of a mathematical representation of the phenomenon; (2) this representation should cover an invariant relationship between the properties of the phenomenon to be measured and observable accociated attributes of a measuring instrument. Therefore, the measuring instrument should function as a nomological machine. However, invariant relationships are not necessarily ceteris paribus regularities, but could also occur when the (...) influence of the environment is negligible. Then we are able to achieve accurate measurements outside the laboratory. (shrink)

Systems biology has provided new resources for discovering and reasoning about mechanisms. In addition to generating databases of large bodies of data, systems biologists have introduced platforms such as Cytoscape to represent protein–protein interactions, gene interactions, and other data in networks. Networks are inherently flat structures. One can identify clusters of highly connected nodes, but network representations do not represent these clusters as at a higher level than their constituents. Mechanisms, however, are hierarchically organized: they can be decomposed into their (...) parts and their activities can be decomposed into component operations. A potent bridge between flat networks and hierarchical mechanisms is provided by biological ontologies, both those curated by hand such as Gene Ontology and those extracted directly from databases such as Network Extracted Ontology. I examine several examples in which by applying ontologies to networks, systems biologists generate new hypotheses about mechanisms and characterize these novel strategies for developing mechanistic explanations. (shrink)

Systems biology provides alternatives to the strategies to developing mechanistic explanations traditionally pursued in cell and molecular biology and much discussed in accounts of mechanistic explanation. Rather than starting by identifying a mechanism for a given phenomenon and decomposing it, systems biologists often start by developing cell-wide networks of detected connections between proteins or genes and construe clusters of highly interactive components as potential mechanisms. Using inference strategies such as ‘guilt-by-association’, researchers advance hypotheses about functions performed of these mechanisms. I (...) examine several examples of research on budding yeast, first on what are taken to be enduring networks and subsequently on networks that change as cells perform different activities or respond to different external conditions. (shrink)

A lot of attention has been devoted to the study of discoveries in high energy physics, but less on measurements aiming at improving an existing theory like the standard model of particle physics, getting more precise values for the parameters of the theory or establishing relationships between them. This paper provides a detailed and critical study of how measurements are performed in recent HEP experiments, taking examples from differential cross section measurements with the ATLAS detector at the LHC. This study (...) will be used to provide an elucidation of the concept of event used in HEP, in order to determine what constitutes an observation and what does not. It will highlight the essential place taken by theory-ladenness in order to produce observational facts, and will show how uncertainty and sensitivity estimates constitute an operational approach to robustness, inside the practice of science, avoiding potential circularity problem traditionally implied by theory-ladenness. This is in contrast to robustness analyses typically considered in the literature. A careful analysis of systematic uncertainty estimates and of statistical tests used to set empirical conclusions from the observations will however demonstrate that quantitative statements obtained from these statistical tests cannot be more than simple guiding arguments for the production of knowledge, but do not determine it. This indicates that the frontier between theory and observation is blurry and that the dichotomy theory-experiment should be revised. (shrink)

This paper aims to identify the key characteristics of model organisms that make them a specific type of model within the contemporary life sciences: in particular, we argue that the term “model organism” does not apply to all organisms used for the purposes of experimental research. We explore the differences between experimental and model organisms in terms of their material and epistemic features, and argue that it is essential to distinguish between their representational scope and representational target. We also examine (...) the characteristics of the communities who use these two types of models, including their research goals, disciplinary affiliations, and preferred practices to show how these have contributed to the conceptualization of a model organism. We conclude that model organisms are a specific subgroup of organisms that have been standardized to fit an integrative and comparative mode of research, and that it must be clearly distinguished from the broader class of experimental organisms. In addition, we argue that model organisms are the key components of a unique and distinctively biological way of doing research using models.Keywords: Experimental organism; Genetics; Model organism; Modeling; Philosophy of biology; Representation. (shrink)

Although cases are central to the epistemic practices utilized within clinical medicine, they appear to be limited in their ability to provide evidence about causal relations because they provide detailed accounts of particular patients without explicit filtering of those attributes most likely to be relevant for explaining the phenomena observed. This paper uses a series of recent case reports to explore the role of cases in casual attribution in medical diagnosis. It is argued that cases are brought together by practitioners (...) to generate causal attributions and testable predictions using a manipulability view of causation. (shrink)

Ludwik Fleck, Edmund Husserl : on the historicity of scientific knowledge -- Gaston Bachelard : the concept of "phenomenotechnique" -- Georges Canguilhem : epistemological history -- Pisum : Carl Correns's experiments on Xenia, 1896-99 -- Eudorina : Max Hartmann's experiments on biological regulation in protozoa, 1914-21 -- Ephestia : Alfred Kähn's experimental design for a developmental physiological -- Genetics, 1924-45 -- Tobacco mosaic virus : virus research at the Kaiser Wilhelm Institutes for Biochemistry and Biology, 1937-45 -- The concept of (...) the gene : molecular biological perspectives -- The liquid scintillation counter : traces of radioactivity -- The concept of information : the writings of François Jacob -- Intersections -- Preparations -- The economy of the scribble. (shrink)

Archaeological facts have a perplexing character; they are often seen as less likely to “lie,” capable of bearing tangible, material witness to actual conditions of life, actions and events, but at the same time they are notoriously fragmentary and enigmatic, and disturbingly vulnerable to dispersal and attrition. As Trouillot (1995) argues for historical inquiry, the identification, selection, interpretation and narration of archaeological facts is a radically constructive process. Rather than conclude on this basis that archaeological facts and fictions are indistinguishable, (...) I identify a number of strategies that archaeologists rely on to make discerning use of “legacy” data – archaeological data recovered and curated over for decades, even centuries, often for very different purposes than those that animate contemporary archaeological inquiry. These include source criticism, secondary retrieval, repositioning and recontextualizing these data in ways that can, sometimes radically shift the “facts” associated with them. The construction of critical genealogies of these facts – the travels and transformations of the material, interpretive and narrative facts of archaeology – is a crucial condition for the successful exploitation of these epistemic possibilities. (shrink)

The use of multiple means of determination to “triangulate” on the existence and character of a common phenomenon, object, or result has had a long tradition in science but has seldom been a matter of primary focus. As with many traditions, it is traceable to Aristotle, who valued having multiple explanations of a phenomenon, and it may also be involved in his distinction between special objects of sense and common sensibles. It is implicit though not emphasized in the distinction between (...) primary and secondary qualities from Galileo onward. It is arguably one of several conceptions involved in Whewell’s method of the “consilience of inductions” (Laudan 1971) and is to be found in several places in Peirce. (From M. Brewer and B. Collins, eds., (1981); Scientific Inquiry in the Social Sciences (a festschrift for Donald T. Campbell), San Francisco: Jossey-Bass, pp. 123–162.). (shrink)

This book explores the diverse understandings of the archaeological record in both historical and contemporary perspective, while also serving as a guide to reassessing current views. Gavin Lucas argues that archaeological theory has become both too fragmented and disconnected from the particular nature of archaeological evidence. The book examines three ways of understanding the archaeological record - as historical sources, through formation theory, and as material culture - then reveals ways to connect these three domains through a reconsideration of archaeological (...) entities and archaeological practice. Ultimately, Lucas calls for a rethinking of the nature of the archaeological record and the kind of history and narratives written from it. (shrink)

Many people assume that the claims of scientists are objective truths. But historians, sociologists, and philosophers of science have long argued that scientific claims reflect the particular historical, cultural, and social context in which those claims were made. The nature of scientific knowledge is not absolute because it is influenced by the practice and perspective of human agents. Scientific Perspectivism argues that the acts of observing and theorizing are both perspectival, and this nature makes scientific knowledge contingent, as Thomas Kuhn (...) theorized forty years ago. Using the example of color vision in humans to illustrate how his theory of “perspectivism” works, Ronald N. Giere argues that colors do not actually exist in objects; rather, color is the result of an interaction between aspects of the world and the human visual system. Giere extends this argument into a general interpretation of human perception and, more controversially, to scientific observation, conjecturing that the output of scientific instruments is perspectival. Furthermore, complex scientific principles—such as Maxwell’s equations describing the behavior of both the electric and magnetic fields—make no claims about the world, but models based on those principles can be used to make claims about specific aspects of the world. Offering a solution to the most contentious debate in the philosophy of science over the past thirty years, Scientific Perspectivism will be of interest to anyone involved in the study of science. (shrink)

Darwinism, Democracy, and Race examines the development and defence of an argument that arose at the boundary between anthropology and evolutionary biology in twentieth-century America. In its fully articulated form, this argument simultaneously discredited scientific racism and defended free human agency in Darwinian terms. The volume is timely because it gives readers a key to assessing contemporary debates about the biology of race. By working across disciplinary lines, the book's focal figures--the anthropologist Franz Boas, the cultural anthropologist Alfred Kroeber, the (...) geneticist Theodosius Dobzhansky, and the physical anthropologist Sherwood Washburn--found increasingly persuasive ways of cutting between genetic determinist and social constructionist views of race by grounding Boas's racially egalitarian, culturally relativistic, and democratically pluralistic ethic in a distinctive version of the genetic theory of natural selection. Collaborators in making and defending this argument included Ashley Montagu, Stephen Jay Gould, and Richard Lewontin. Darwinism, Democracy, and Race will appeal to advanced undergraduates, graduate students, and academics interested in subjects including Philosophy, Critical Race Theory, Sociology of Race, History of Biology and Anthropology, and Rhetoric of Science. (shrink)

"Scientific Research in the Era of Big Data" - this book was also published in French (Mimesis) in 2019 and in Portuguese in 2022 (FIOCRUZ editors).

This work fulfills the need for a conceptual and technical framework to improve understanding of Information Quality (IQ) and Information Quality standards. The meaning and practical implementation of IQ are addressed, as it is relevant to any field where there is a need to handle data and issues such as accessibility, accuracy, completeness, currency, integrity, reliability, timeliness, usability, the role of metrics and so forth are all a part of Information Quality. -/- In order to support the cross-fertilization of theory (...) and practice, the latest research is presented in this book. The perspectives of experts from beyond the origins of IQ in computer science are included: library and information science practitioners and academics, philosophers of information, of engineering and technology, and of science are all contributors to this volume. -/- The chapters in this volume are based on the work of a collaborative research project involving the Arts and Humanities Research Council and Google and led by Professor Luciano Floridi, University of Oxford. -/- This work will be of interest to anyone handling data, including those from commercial, public, governmental and academic organizations. The expert editors’ contributions introduce issues of interest to scientists, database curators and philosophers, even though the issues may be disguised in the language and examples common to a different discipline. (shrink)

Material traces of the past are notoriously inscrutable; they rarely speak with one voice, and what they say is never unmediated. They stand as evidence only given a rich scaffolding of interpretation which is, itself, always open to challenge and revision. And yet archaeological evidence has dramatically expanded what we know of the cultural past, sometimes demonstrating a striking capacity to disrupt settled assumptions. The questions we address in Evidential Reasoning are: How are these successes realized? What gives us confidence (...) in the credibility and robustness, the trustworthiness, of the evidential claims based on archaeological data? And, what constitute best practices in building evidential claims, critically scrutinizing them and putting them to work in archaeological contexts? Rather than retreat to abstractions about how how science operates in the ideal, we approach this question by interrogating a number of close-to-the-ground case studies with the aim of teasing out the wisdom embodied in archaeological practice. The cases we consider – of fieldwork, strategies for working with old evidence, and the role of external resources – illuminate the role of various types of inferential scaffolding and bring into focus practice-grounded epistemic norms that we believe serve archaeologists better than the all-or-nothing ideals of truth and objectivity that dominate programmatic debate. (shrink)

Archaeological data are shadowy in a number of senses. Not only are they notoriously fragmentary but the conceptual and technical scaffolding on which archaeologists rely to constitute these data as evidence can be as constraining as it is enabling. A recurrent theme in internal archaeological debate is that reliance on sedimented layers of interpretative scaffolding carries the risk that “preunderstandings” configure what archaeologists recognize and record as primary data, and how they interpret it as evidence. The selective and destructive nature (...) of data capture in archeology further suggests that there may be little scope for putting “legacy” data to work in new ways. And yet archaeologists have been strikingly successful in mining old datasets for new insights. I situate these concerns in the broader context of debate about the epistemic standing of the historical sciences, and then consider three strategies by which archaeologists address the challenges posed by legacy data. The first two – secondary retrieval and recontextualization – are a matter of reconfiguring the scaffolding that underpins evidential reasoning. The third turns on redeploying old data in the context of computational models that support the experimental simulation of the cultural systems and contexts under study. (shrink)

The vagaries of evidential reasoning in archaeology are notorious: the material traces that comprise the archaeological record are fragmentary and profoundly enigmatic, and the inferential gap that archaeologists must cross to constitute them as evidence of the cultural past is a peren­nial source of epistemic anxiety. And yet we know a great deal about the cultural past, including vast reaches of the past for which this material record is our only source of evidence. The contents of this record stand as (...) evidence only under interpretation, but however much a construct it is, archaeological evidence has a striking capacity to disrupt settled assump­tions, redirecting inquiry and expanding interpretive horizons in direc­tions no one could have anticipated. It is this capacity for constraining inference and interpretation that I am concerned to understand. I outline a model of evidential reasoning based on archaeological practice that integrates insights drawn from philosophical theories of confirmation, model building and hypothesis testing. Given growing interest in the uses of material evidence in fields that had been resolutely text-based, the archaeological principles of evidential reasoning may have much wider reach than this particular social/historical discipline. (shrink)

This paper provides a restatement and defense of the data/ phenomena distinction introduced by Jim Bogen and me several decades ago (e.g., Bogen and Woodward, The Philosophical Review, 303–352, 1988). Additional motivation for the distinction is introduced, ideas surrounding the distinction are clarified, and an attempt is made to respond to several criticisms.

Noah Rosenberg et al.'s 2002 article “Genetic Structure of Human Populations” reported that multivariate genomic analysis of a large cell line panel yielded reproducible groupings (clusters) suggestive of individuals' geographical origins. The paper has been repeatedly cited as evidence that traditional notions of race have a biological basis, a claim its authors do not make. Critics of this misinterpretation have often suggested that it follows from interpreters' personal biases skewing the reception of an objective piece of scientific writing. I contend, (...) however, that the article itself to some degree facilitates this misrepresentation. I analyze in detail several verbal and visual features of the original article that may predispose aspects of its racial interpretation; and, tracing the arguments of one philosopher and one popular science writer, I show how these features are absorbed, transformed into arguments for a biological basis of race, and re-attributed to the original. The essay demonstrates how even slight ambiguities can enable the misappropriation of scientific writing, unintentionally undermining the authors' stated circumspection on the relationship between cluster and race. (shrink)

This paper builds on previous work that investigated anticancer drugs as ‘informed materials’, i.e., substances that undergo an informational enrichment that situates them in a dense relational web of qualifications and measurements generated by clinical experiments and clinical trials. The paper analyzes the recent transformation of anticancer drugs from ‘informed’ to ‘informing material’. Briefly put: in the post-genomic era, anti-cancer drugs have become instruments for the production of new biological, pathological, and therapeutic insights into the underlying etiology and evolution of (...) cancer. Genomic platforms characterize individual patients’ tumors based on their mutational landscapes. As part of this new approach, drugs targeting specific mutations transcend informational enrichment to become tools for informing their targets, while also problematizing the very notion of a ‘target’. In other words, they have become tools for the exploration of cancer pathways and mechanisms. While several studies in the philosophy and history of biomedicine have called attention to the heuristic relevance and experimental use of drugs, few have investigated concrete instances of this role of drugs in clinical research. (shrink)

Debiasing procedures are experimental methods aimed at correcting errors arising from the cognitive biases of the experimenter. We discuss two of these methods, the predesignation rule and randomization, showing to what extent they are open to the experimenter’s regress: there is no metarule to prove that, after implementing the procedure, the experimental data are actually free from biases. We claim that, from a contractarian perspective, these procedures are nonetheless defensible since they provide a warrant of the impartiality of the experiment: (...) we only need proof that the result has not been intentionally manipulated for prima facie acceptance. (shrink)

We argue that ‘locality’, perhaps the most mundane term in ecology, holds a basic ambiguity: two concepts of space—nomothetic and idiographic—which are both necessary for a rigorous resurvey to “the same” locality in the field, are committed to different practices with no common measurement. A case study unfolds the failure of the standard assumption that an exogenous grid of longitude and latitude, as fine‐grained as one wishes, suffices for revisiting a species locality. We briefly suggest a scale‐dependent “resolution” for this (...) replication problem, since it has no general, rational solution. *Received January 2008; revised April 2009. †To contact the authors, please write to: Ayelet Shavit, Department of Interdisciplinary Studies, Tel‐Hai Academic College, Upper Galilee, 12210 Israel; e‐mail: [email protected] . James Griesemer, Department of Philosophy, University of California, Davis, One Shields Avenue, Davis, CA 95616; e‐mail: [email protected] . Biodiversity is largely a matter of real estate. And, as with other real estate, location is everything. (Kiester at el. 1996 ). (shrink)

In a new introduction, the authors describe how science and its social context were understood when this book was first published, and how the study of the history of science has changed since then.

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)

A number of recent discussions comparing computer simulation and traditional experimentation have focused on the significance of “materiality.” I challenge several claims emerging from this work and suggest that computer simulation studies are material experiments in a straightforward sense. After discussing some of the implications of this material status for the epistemology of computer simulation, I consider the extent to which materiality (in a particular sense) is important when it comes to making justified inferences about target systems on the basis (...) of experimental results. (shrink)

In a formal theory of induction, inductive inferences are licensed by universal schemas. In a material theory of induction, inductive inferences are licensed by facts. With this change in the conception of the nature of induction, I argue that the celebrated “problem of induction” can no longer be set up and is thereby dissolved. Attempts to recreate the problem in the material theory of induction fail. They require relations of inductive support to conform to an unsustainable, hierarchical empiricism.

Contrary to formal theories of induction, I argue that there are no universal inductive inference schemas. The inductive inferences of science are grounded in matters of fact that hold only in particular domains, so that all inductive inference is local. Some are so localized as to defy familiar characterization. Since inductive inference schemas are underwritten by facts, we can assess and control the inductive risk taken in an induction by investigating the warrant for its underwriting facts. In learning more facts, (...) we extend our inductive reach by supplying more localized inductive inference schemes. Since a material theory no longer separates the factual and schematic parts of an induction, it proves not to be vulnerable to Hume's problem of the justification of induction. (shrink)

This paper addresses the problem of how scientific knowledge, which is always locally generated, becomes accepted in other sites. The analysis suggests that there are a small number of strategies that enable scientists to resituate knowledge and that these strategies are generic: they are not restricted to specific disciplines or modes of doing science but rather are found in a variety of different forms across the sciences.

Big Data has been variously defined in the literature. In the main, definitions suggest that Big Data possess a suite of key traits: volume, velocity and variety, but also exhaustivity, resolution, indexicality, relationality, extensionality and scalability. However, these definitions lack ontological clarity, with the term acting as an amorphous, catch-all label for a wide selection of data. In this paper, we consider the question ‘what makes Big Data, Big Data?’, applying Kitchin’s taxonomy of seven Big Data traits to 26 datasets (...) drawn from seven domains, each of which is considered in the literature to constitute Big Data. The results demonstrate that only a handful of datasets possess all seven traits, and some do not possess either volume and/or variety. Instead, there are multiple forms of Big Data. Our analysis reveals that the key definitional boundary markers are the traits of velocity and exhaustivity. We contend that Big Data as an analytical category needs to be unpacked, with the genus of Big Data further delineated and its various species identified. It is only through such ontological work that we will gain conceptual clarity about what constitutes Big Data, formulate how best to make sense of it, and identify how it might be best used to make sense of the world. (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)

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)

The use of big data to investigate the spread of infectious diseases or the impact of the built environment on human wellbeing goes beyond the realm of traditional approaches to epidemiology, and includes a large variety of data objects produced by research communities with different methods and goals. This paper addresses the conditions under which researchers link, search and interpret such diverse data by focusing on “data mash-ups”—that is the linking of data from epidemiology, biomedicine, climate and environmental science, which (...) is typically achieved by holding one or more basic parameters, such as geolocation, as invariant. We argue that this strategy works best when epidemiologists interpret localisation procedures through an idiographic perspective that recognises their context-dependence and supports a critical evaluation of the epistemic value of geolocation data whenever they are used for new research purposes. Approaching invariants as strategic constructs can foster data linkage and re-use, and support carefully-targeted predictions in ways that can meaningfully inform public health. At the same time, it explicitly signals the limitations in the scope and applicability of the original datasets incorporated into big data collections, and thus the situated nature of data linkage exercises and their predictive power. (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)

Community databases have become crucial to the collection, ordering and retrieval of data gathered on model organisms, as well as to the ways in which these data are interpreted and used across a range of research contexts. This paper analyses the impact of community databases on research practices in model organism biology by focusing on the history and current use of four community databases: FlyBase, Mouse Genome Informatics, WormBase and The Arabidopsis Information Resource. We discuss the standards used by the (...) curators of these databases for what counts as reliable evidence, acceptable terminology, appropriate experimental set-ups and adequate materials (e.g., specimens). On the one hand, these choices are informed by the collaborative research ethos characterising most model organism communities. On the other hand, the deployment of these standards in databases reinforces this ethos and gives it concrete and precise instantiations by shaping the skills, practices, values and background knowledge required of the database users. We conclude that the increasing reliance on community databases as vehicles to circulate data is having a major impact on how researchers conduct and communicate their research, which affects how they understand the biology of model organisms and its relation to the biology of other species. (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)

Despite all the attention to Big Data and the claims that it represents a “paradigm shift” in science, we lack understanding about what are the qualities of Big Data that may contribute to this revolutionary impact. In this paper, we look beyond the quantitative aspects of Big Data and examine it from a sociotechnical perspective. We argue that a key factor that distinguishes “Big Data” from “lots of data” lies in changes to the traditional, well-established “control zones” that facilitated clear (...) provenance of scientific data, thereby ensuring data integrity and providing the foundation for credible science. The breakdown of these control zones is a consequence of the manner in which our network technology and culture enable and encourage open, anonymous sharing of information, participation regardless of expertise, and collaboration across geographic, disciplinary, and institutional barriers. We are left with the conundrum—how to reap the benefits of Big Data while re-creating a trust fabric and an accountable chain of responsibility that make credible science possible. (shrink)