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)

ABSTRACT This essay analyzes how academic institutions, government agencies, and the nascent biotech industry contested the legal ownership of recombinant DNA technology in the name of the public interest. It reconstructs the way a small but influential group of government officials and university research administrators introduced a new framework for the commercialization of academic research in the context of a national debate over scientific research's contributions to American economic prosperity and public health. They claimed that private ownership of inventions arising (...) from public support would provide a powerful means to liberate biomedical discoveries for public benefit. This articulation of the causal link between private ownership and the public interest, it is argued, justified a new set of expectations about the use of research results arising from government or public support, in which commercialization became a new public obligation for academic researchers. By highlighting the broader economic and legal shifts that prompted the reconfiguration of the ownership of public knowledge in late twentieth‐century American capitalism, the essay examines the threads of policy‐informed legal ideas that came together to affirm private ownership of biomedical knowledge as germane to the public interest in the coming of age of biotechnology and genetic medicine. (shrink)

This article follows shorebirds—migratory animals that have gone from game to nongame animals over the course of the past century in North America—as a way to track modern field biology, bureaucratic institutions, and the valuation of wildlife. Doing so allows me to make interrelated arguments about the history of wildlife management and science. The first is to note the endurance of observation-based natural history methods in field biology over the long twentieth century and the importance of these methods for the (...) persistent contribution of amateurs. The second major line of argument advances the historical significance of scientific, government bureaucracies as sites of natural knowledge production. Historians of biology and ecology have tended to stress scientists with institutional homes in universities, museums, and at land-grant field stations—particularly as various forms of field biology became professionalized over the twentieth century. In contrast, migratory animals like shorebirds, whether under the auspices of the US Biological Survey or the contemporary Fish and Wildlife Service, were primarily studied and conserved by biologists in bureaucratic agencies. Mid- to low-level bureaucrats, along with avocational birders, have mainly been responsible for developing what we know about shorebird migration, behavior, and life history. And third, shorebirds foreground the importance of bureaucratic context for the valuation of nature, from their economic value to agriculture in the early twentieth century to their value as rare, endangered species in the twentyfirst. (shrink)

The use of molecules and reactions as evidence, markers and/or traits for evolutionary processes has a history more than a century long. Molecules have been used in studies of intra-specific variation and studies of similarity among species that do not necessarily result in the analysis of phylogenetic relations. Promoters of the use of molecular data have sustained the need for quantification as the main argument to make use of them. Moreover, quantification has allowed intensive statistical analysis, as a condition and (...) a product of increasing automation. All of these analyses are subject to the methodological anxiety characteristic of a community in search of objectivity. It is in this context that scientists compared and evaluated protein and nucleic acid sequence data with other types of molecular data – including immunological, electrophoretic and hybridization data. This paper argues that by looking at long-term historical processes, such as the use of molecular evidence in evolutionary biology, we gain valuable insights into the history of science. In that sense, it accompanies a growing concern among historians for big-pictures of science that incorporate the fruitful historical research on local cases of the last decades. (shrink)

Throughout the twentieth century calls to modernize natural history motivated a range of responses. It was unclear how research in natural history museums would participate in the significant technological and conceptual changes that were occurring in the life sciences. By the 1960s, the Museum of Vertebrate Zoology at the University of California, Berkeley, was among the few university-based natural history museums that were able to maintain their specimen collections and support active research. The MVZ therefore provides a window to the (...) modernization of natural history. This paper concentrates on the directorial transitions that occurred at the MVZ between 1965 and 1971. During this period, the MVZ had four directors: Alden H. Miller (Director 1940–1965), an ornithologist; Aldo Starker Leopold (Acting Director 1965–1966), a conservationist and wildlife biologist; Oliver P. Pearson (Director 1966–1971), a physiologist and mammalogist; and David B. Wake (Director 1971–1998), a morphologist, developmental biologist, and herpetologist. The paper explores how a diversity of overlapping modernization strategies, including hiring new faculty, building infrastructure to study live animals, establishing new kinds of collections, and building modern laboratories combined to maintain collections at the MVZ’s core. The paper examines the tensions between the different modernization strategies to inform an analysis of how and why some changes were institutionalized while others were short-lived. By exploring the modernization of collections-based research, this paper emphasizes the importance of collections in the transformation of the life sciences. (shrink)

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

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)

Genomics is increasingly considered a global enterprise – the fact that biological information can flow rapidly around the planet is taken to be important to what genomics is and what it can achieve. However, the large-scale international circulation of nucleotide sequence information did not begin with the Human Genome Project. Efforts to formalize and institutionalize the circulation of sequence information emerged concurrently with the development of centralized facilities for collecting that information. That is, the very first databases build for collecting (...) and sharing DNA sequence information were, from their outset, international collaborative enterprises. This paper describes the origins of the International Nucleotide Sequence Database Collaboration between GenBank in the United States, the European Molecular Biology Laboratory Databank, and the DNA Database of Japan. The technical and social groundwork for the international exchange of nucleotide sequences created the conditions of possibility for imagining nucleotide sequences as a “global” objects. The “transnationalism” of nucleotide sequence was critical to their ontology – what DNA sequences came to be during the Human Genome Project was deeply influenced by international exchange. (shrink)

The fossil record is paleontology’s great resource, telling us virtually everything we know about the past history of life. This record, which has been accumulating since the beginning of paleontology as a professional discipline in the early nineteenth century, is a collection of objects. The fossil record exists literally, in the specimen drawers where fossils are kept, and figuratively, in the illustrations and records of fossils compiled in paleontological atlases and compendia. However, as has become increasingly clear since the later (...) twentieth century, the fossil record is also a record of data. Paleontologists now routinely abstract information from the physical fossil record to construct databases that serve as the basis for quantitative analysis of patterns in the history of life. What is the significance of this distinction? While it is often assumed that the orientation towards treating the fossil record as a record of data is an innovation of the computer age, it turns out that nineteenth century paleontology was substantially “data driven.” This paper traces the evolution of data practices and analyses in paleontology, primarily through examination of the compendia in which the fossil record has been recorded over the past 200 years. I argue that the transition towards conceptualizing the fossil record as a record of data began long before the emergence of the technologies associated with modern databases (such as digital computers and modern statistical methods). I will also argue that this history reveals how new forms of visual representation were associated with the transition from seeing the fossil record as a record of objects to one of data or information, which allowed paleontologists to make new visual arguments about their data. While these practices and techniques have become increasingly sophisticated in recent decades, I will show that their basic methodology was in place over a century ago, and that, in a sense, paleontology has always been a “data driven” science. (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)

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine learning in (...) particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the ‘new’ biology is substantially diminished if compared to the ‘old’ biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. (shrink)

From the 1980s onwards, the Roslin Institute and its predecessor organizations faced budget cuts, organizational upheaval and considerable insecurity. Over the next few decades, it was transformed by the introduction of molecular biology and transgenic research, but remained a hub of animal geneticists conducting research aimed at the livestock-breeding industry. This paper explores how these animal geneticists embraced genomics in response to the many-faceted precarity that the Roslin Institute faced, establishing it as a global centre for pig genomics research through (...) forging and leading the Pig Gene Mapping Project (PiGMaP); developing and hosting resources, such as a database for genetic linkage data; and producing associated statistical and software tools to analyse the data. The Roslin Institute leveraged these resources to play a key role in further international collaborations as a hedge against precarity. This adoption of genomics was strategically useful, as it took advantage of policy shifts at the national and European levels towards funding research with biotechnological potential. As genomics constitutes a set of infrastructures and resources with manifold uses, the development of capabilities in this domain also helped Roslin to diversify as a response to precarity. (shrink)

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine learning in (...) particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the ‘new’ biology is substantially diminished if compared to the ‘old’ biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. (shrink)

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)

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)

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)

In 1947, the Cornell psychiatrist Keeve Brodman and a handful of colleagues began developing what would become one of the most widely used health questionnaires of its time—the Cornell Medical Index (CMI). A rigidly standardized form, the CMI presented 195 yes-no questions designed to capture the health status of “the total patient.” Over the following decades, Brodman’s project of standardizing medical history taking gradually evolved into a project of mathematizing and computerizing diagnosis: out of the CMI grew the Medical Data (...) Screen (MDS), one of the first computerized methods of deriving diagnoses from patient data. This essay follows the life course of these tools through the second half of the twentieth century. It argues for a genealogy of biomedical computing and computerized diagnosis that takes more seriously the continuities between computer-based digital practices and paper-based analog ones. The computerized MDS evolved from, and rested upon, paper data practices associated with questionnaires and surveys. The interlocking histories of the CMI and the MDS prompt a reconsideration of the material and temporal parameters within which the history of computerized medicine has conventionally been understood. (shrink)

Two different kinds of collections have been used to anticipate influenza pandemics: viral strains and bird specimens. These collections have been organized in museums and data banks to fill the gaps when specimens were decaying or when viral strains were missing. This article asks how collecting practices changed when such collections integrated specimens from China, considered a reservoir of influenza viruses and bird species, following a recurrent critical trope that Chinese specimens were missing. The article shows that techniques for hunting (...) viruses and birds were used to accumulate information so as to mitigate the global threat of species extinction. It suggests that in sentinel territories, museums transform samples and specimens into signs and images of future catastrophic events. (shrink)

In his highly regarded history of the rise of clinical trials in America, HarryMarks describes how their widespread adoption resulted largely fromthe efforts of ‘therapeutic reformers’ who sought to replace the individualexpertise of clinicians with the ‘science of controlled experiment’. Thetransition described by Marks resembles in many respects the transition fromthe ‘truth-to-nature’ objectivity of individual experts to a ‘mechanical’ formof objectivity portrayed by Daston and Galison. In particular,Marks details the passage from a regime of trust in expertise and experts to (...) aregime based on the mechanical generation of data, the elimination ofhuman judgement and the adoption of a ‘view from nowhere’ where individual and group idiosyncrasies are systematically suppressedthrough recourse to quantitative methods.[...]. (shrink)

In his highly regarded history of the rise of clinical trials in America, HarryMarks describes how their widespread adoption resulted largely fromthe efforts of ‘therapeutic reformers’ who sought to replace the individualexpertise of clinicians with the ‘science of controlled experiment’. Thetransition described by Marks resembles in many respects the transition fromthe ‘truth-to-nature’ objectivity of individual experts to a ‘mechanical’ formof objectivity portrayed by Daston and Galison. In particular,Marks details the passage from a regime of trust in expertise and experts to (...) aregime based on the mechanical generation of data, the elimination ofhuman judgement and the adoption of a ‘view from nowhere’ where individual and group idiosyncrasies are systematically suppressedthrough recourse to quantitative methods.[...]. (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)

“Practice” has become a ubiquitous term in the history of science, and yet historians have not always reflected on its philosophical import and especially on its potential connections with ethics. In this essay, we draw on the work of the virtue ethicist Alasdair MacIntyre to develop a theory of “communal practices” and explore how such an approach can inform the history of science, including allegations about the corruption of science by wealth or power; consideration of scientific ethics or “moral economies”; (...) the role of values in science; the ethical distinctiveness of scientific vocations; and the relationship between history of science and the practice of science itself. (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)

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

This paper describes the activities of amateur plant breeders and their application of various methods and technologies derived from genetics research over the course of the twentieth century. These ranged from selection and hybridization to more interventionist approaches such as radiation treatment to induce genetic mutations and chemical manipulation of chromosomes. I argue that these activities share characteristics with twenty-first-century do-it-yourself (DIY) biology (a recent upswing in amateur experimental biology) as well as other amateur science and technology of the twentieth (...) century. The characterization of amateur plant breeding as amateur experimental biology offers a corrective to a dominant narrative within the history of biology, in which the turn to experimental research in the early twentieth century is thought to have served as an obvious dividing line between amateur and professional activities. Considered alongside other better-known amateur efforts, it also suggests that we might gain something by taking a more unified approach to the study of amateur science and technology. (shrink)

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

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)

Evo-Devo exhibits a plurality of scientific “cultures” of practice and theory. When are the cultures acting—individually or collectively—in ways that actually move research forward, empirically, theoretically, and ethically? When do they become imperialistic, in the sense of excluding and subordinating other cultures? This chapter identifies six cultures – three /styles/ (mathematical modeling, mechanism, and history) and three /paradigms/ (adaptationism, structuralism, and cladism). The key assumptions standing behind, under, or within each of these cultures are explored. Characterizing the internal structure of (...) the cultures is necessary for understanding how they collaborate or compete, and how they are fragmented or integrated, in the rich interdisciplinary /trading zone/ (Galison 1997) of Evo-Devo. Evo-Devo is an important example of how science can progress through a radical plurality of perspectives and cultures. (shrink)