Repertoires: How to Transform a Project into a Research Community

Scientific inertia in animal-based research in biomedicine

Despite aspirations to substitute animal experimentation with alternative methods and recent progress in the area of non-animal approaches, such as organoïds and organ(s)-on-a-chip technologies, there is no extensive replacement of animal-based research in biomedicine. In this paper, I will analyse this state of affairs with reference to key institutional and socio-epistemic barriers for the development and use of non-animal approaches in the context of biomedical research in Europe. I will argue that there exist several factors that inhibit change in this context. In particular, there is what I call "scientific inertia", i.e. a certain degree of conservatism in scientific practice regarding the development and use of non-animal approaches to replace animal experimentation. This type of inertia is facilitated by socio-epistemic characteristics of animal-based research in the life sciences and is a key factor in understanding the status quo in biomedical research. The underlying reasons for scientific inertia have not received sufficient attention in the literature to date because the phenomenon transcends traditional disciplinary boundaries in the study of animal experimentation. This paper addresses this issue and seeks to contribute to a better understanding of scientific inertia by using a methodology that looks at the interplay of institutional, epistemic, and regulatory aspects of animal-based research.

Data management challenges for artificial intelligence in plant and agricultural research

Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.

Data management challenges for artificial intelligence in plant and agricultural research

Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.

What Is New about the Exposome? Exploring Scientific Change in Contemporary Epidemiology

In this commentary, I discuss the scientific changes brought by the exposome, asking what is new about this approach and line of research. I place the exposome in a historical perspective, by analyzing the conditions under which the exposome has been conceived, developed and established in the context of contemporary epidemiological research. I argue that the exposome has been developed by transferring approaches, methods and conceptualizations from other lines of research in the life and health sciences. I thus discuss the conceptual and methodological innovations of the exposome as a result of the merging and adaptation of these elements for new uses and purposes. On this basis, I argue that the novelty of the exposome should be seen in incremental rather than revolutionary terms and, in this sense, the exposome shares significant elements with other projects and repertoires in postgenomics. I conclude by discussing the consequences of this analysis for the potential limitations and future development of exposome research.

The plurality of assumptions about fossils and time

A research community must share assumptions, such as about accepted knowledge, appropriate research practices, and good evidence. However, community members also hold some divergent assumptions, which they-and we, as analysts of science-tend to overlook. Communities with different assumed values, knowledge, and goals must negotiate to achieve compromises that make their conflicting goals complementary. This negotiation guards against the extremes of each group's desired outcomes, which, if achieved, would make other groups' goals impossible. I argue that this diversity, as a form of value pluralism, regularly influences scientific practice and can make scientific evidence and knowledge more useful and more reliable. As an example, I examine vertebrate paleontology laboratories, which house a variety of workers with different training and priorities, particularly about the meaning of time. Specifically, scientists want to study fully prepared fossils immediately, conservators want to preserve fossils for future use (such as by not preparing them), and preparators mediate between the other groups' conflicting goals. After all, one cannot study a fossil encased in rock, and one cannot remove that rock without removing information from that specimen. In response, these coworkers articulate their assumptions in everyday deliberations about how scientific evidence should be made and used. I argue that this exchange of assumptions is crucial for a research community to achieve mutually beneficial compromises that benefit current and future knowledge construction.

How Does One "Open" Science? Questions of Value in Biological Research

Open Science policies encourage researchers to disclose a wide range of outputs from their work, thus codifying openness as a specific set of research practices and guidelines that can be interpreted and applied consistently across disciplines and geographical settings. In this paper, we argue that this "one-size-fits-all" view of openness sidesteps key questions about the forms, implications, and goals of openness for research practice. We propose instead to interpret openness as a dynamic and highly situated mode of valuing the research process and its outputs, which encompasses economic as well as scientific, cultural, political, ethical, and social considerations. This interpretation creates a critical space for moving beyond the economic definitions of value embedded in the contemporary biosciences landscape and Open Science policies, and examining the diversity of interests and commitments that affect research practices in the life sciences. To illustrate these claims, we use three case studies that highlight the challenges surrounding decisions about how--and how best--to make things open. These cases, drawn from ethnographic engagement with Open Science debates and semistructured interviews carried out with UK-based biologists and bioinformaticians between 2013 and 2014, show how the enactment of openness reveals judgments about what constitutes a legitimate intellectual contribution, for whom, and with what implications.

Silent performances: Are "repertoires" really post-Kuhnian?

Ankeny and Leonelli (2016) propose "repertoires" as a new way to understand the stability of certain research programs as well as scientific change in general. By bringing a more complete range of social, material, and epistemic elements into one framework, they position their work as a correction to the Kuhnian impulse in philosophy of science and other areas of science studies. I argue that this "post-Kuhnian" move is not complete, and that repertoires maintain an internalist perspective. Comparison with an alternative framework, the "sociotechnical imaginaries" of Jasanoff and Kim (2015), illustrates precisely which elements of practice are externalized by Ankeny and Leonelli. Specifically, repertoires discount the role of audience, without whom the repertoires of science are unintelligible, and lack an explicit place for ethical and political imagination, which provide meaning for otherwise mechanical promotion of particular research programs. This comparison reveals, I suggest, two distinct modes of scholarship, one internalist and the other critical. While repertoires can be modified to meet the needs of critical STS scholars and to completely reject Kuhn's internalism, whether or not we do so depends on what we want our scholarship to achieve.

Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting.

Generations of interdisciplinarity in bioinformatics

Bioinformatics, a specialism propelled into relevance by the Human Genome Project and the subsequent -omic turn in the life science, is an interdisciplinary field of research. Qualitative work on the disciplinary identities of bioinformaticians has revealed the tensions involved in work in this "borderland." As part of our ongoing work on the emergence of bioinformatics, between 2010 and 2011, we conducted a survey of United Kingdom-based academic bioinformaticians. Building on insights drawn from our fieldwork over the past decade, we present results from this survey relevant to a discussion of disciplinary generation and stabilization. Not only is there evidence of an attitudinal divide between the different disciplinary cultures that make up bioinformatics, but there are distinctions between the forerunners, founders and the followers; as inter/disciplines mature, they face challenges that are both inter-disciplinary and inter-generational in nature.

Collaboration, interdisciplinarity, and the epistemology of contemporary science

Over the last decades, science has grown increasingly collaborative and interdisciplinary and has come to depart in important ways from the classical analyses of the development of science that were developed by historically inclined philosophers of science half a century ago. In this paper, I shall provide a new account of the structure and development of contemporary science based on analyses of, first, cognitive resources and their relations to domains, and second of the distribution of cognitive resources among collaborators and the epistemic dependence that this distribution implies. On this background I shall describe different ideal types of research activities and analyze how they differ. Finally, analyzing values that drive science towards different kinds of research activities, I shall sketch the main mechanisms underlying the perceived tension between disciplines and interdisciplinarity and argue for a redefinition of accountability and quality control for interdisciplinary and collaborative science.