Sabina Leonelli

University of Exeter | UoE · Department of Sociology and Philosophy

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 (1) the prominence and status acquired by data as commodity and recognised output, bo...

Argument We examine the criteria used to validate the use of nonhuman organisms in North-American alcohol addiction research from the 1950s to the present day. We argue that this field, where the similarities between behaviors in humans and non-humans are particularly difficult to assess, has addressed questions of model validity by transforming th...

The collection and dissemination of data on human and non-human organisms has become a central feature of 21 st 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 datasets, often referred to as 'big data...

The consultation of internet databases and the related use of computer software to retrieve, visualise and model data have become key components of many areas of scientific research. This paper focuses on the relation of these developments to understanding the biology of organisms, and examines the conditions under which the evidential value of dat...

For more than fifty years, international aid for agricultural research has been shaped by an unusual partnership: an ad-hoc consortium of national governments, foreign aid agencies, philanthropies, United Nations agencies, and international financial institutions, known as CGIAR. Formed in 1971 following the initial celebration of the so-called Gre...

In this article, I will explore how the underlying research values of ‘openness’ and ‘mutual responsiveness’, which are central to open science practices, can be integrated into a new ethos of science. Firstly, I will revisit Robert Merton's early contribution to this issue, examining whether the ethos of science should be understood as a set of no...

Qualitative research provides rigorous methods not only for investigating behavioral or social issues, but can also be used for exploring epistemic issues related to science and its practices. There is growing scholarly awareness that important aspects of science can be best understood through qualitative analyses and cannot be captured using more...

How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be inc...

Biomedical deployments of data science capitalise on vast, heterogeneous data sources. This promotes a diversified understanding of what counts as evidence for health-related interventions, beyond the strictures associated with evidence-based medicine. Focusing on COVID-19 transmission and prevention research, I consider the epistemic implications...

The Open Science [OS] movement aims to foster the wide dissemination, scrutiny and re-use of research components for the good of science and society. This Element examines the role played by OS principles and practices within contemporary research and how this relates to the epistemology of science. After reviewing some of the concerns that have pr...

We analyse ongoing efforts to share genomic data about SARS-COV-2 through a comparison of the characteristics of the Global Initiative on Sharing All Influenza Data and the Covid-19 Data Portal with respect to the representativeness and governance of the research data therein. We focus on data and metadata on genetic sequences posted on the two inf...

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-dimen...

A reader can expect the abstract, paper and keywords to discuss descriptions of evidence, classification schema, seizure rules and more generally the data frictions, constraints and limitations associated with the processing of digital forensic evidence involving children in England. The widespread availability and use of digital devices both enabl...

This chapter argues for the importance of considering conceptual and normative commitments when addressing questions of responsible practice in data-intensive agricultural research and development. We consider genetic gain-focused plant breeding strategies that envision a data-intensive mode of breeding in which genomic, environmental and socio-eco...

This chapter provides a framing for this volume by reviewing the significance and the organisational, technical and social opportunities and challenges related to plant data linkage. We review what “responsible practice” means in relation to the plant environments being documented, the infrastructures used to circulate data, the institutions involv...

This paper examines classification practices in the domain of plant data semantics, and particularly methods used to label plant traits to foster the collection, management, linkage and analysis of data about crops across locations—which crucially inform research and interventions on plants and agriculture. The efforts required to share data place...

Accelerating the rate of genetic gain has in recent years become a key objective in plant breeding for the Global South, building on the availability of new data technologies and bridging biological interest in crop improvement with economic interest in enhancing the cost efficiency of breeding programs. This paper explains the concept of genetic g...

In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We ar...

I argue that Open Science as currently conceptualised and implemented does not take sufficient account of epistemic diversity within research. I use three case studies to exemplify how Open Science threatens to privilege some forms of inquiry over others, thus exasperating divides within and across systems of practice, and overlooking important sou...

Whether we live in a world of autonomous things, or a world of interconnected processes in constant flux, is an ancient philosophical debate. Modern biology provides decisive reasons for embracing the latter view. How does one understand the practices and outputs of science in such a dynamic, ever-changing world - and particularly in an emergency s...

Research, innovation, and progress in the life sciences are increasingly contingent on access to large quantities of data. This is one of the key premises behind the “open science” movement and the global calls for fostering the sharing of personal data, datasets, and research results. This paper reports on the outcomes of discussions by the panel...

Post COVID-19 Implications for Genetic Diversity and Genomics Research & Innovation: A Call for Governance and Research Capacity http://www.fao.org/3/cb5573en/cb5573en.pdf

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 whic...

The paper problematises the reliability and ethics of using social media data, such as sourced from Twitter or Instagram, to carry out health-related research. As in many other domains, the opportunity to mine social media for information has been hailed as transformative for research on well-being and disease. Considerations around the fairness, r...

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-dimen...

This paper mobilizes a transnational approach to intervene in the unfolding history of the Covid-19 pandemic, advocating for nationally based, interdependent initiatives that push back against the fragmentation of national responses and, eventually, national protectionism. Focusing on the governance of digital technologies for data sharing, and usi...

We investigate how technology ‘co-development’ (between researchers, stakeholders and local communities) is framed in practice by those developing gene drive mosquitos for malaria eradication. Our case study focuses on UK and Mali-based researchers planning to undertake the first field trials in Mali of gene drive mosquitos for malaria control. Whi...

Huge amounts of genomic data produced by researchers around the world undermine data-centred discovery and therapeutic development. This paper considers how researchers make decisions about the actionability of specific datasets and the conditions that allow such data to be trusted. We discuss the case of COSMIC, a leading cancer genomics database...

What are the priorities for data science in tackling COVID-19, and in which ways can big data analysis inform and support responses to the outbreak? It is imperative for data scientists to spend time and resources scoping, scrutinizing, and questioning the possible scenarios of use of their work—particularly given the fast-paced knowledge productio...

This Element presents a philosophical exploration of the concept of the ‘model organism’ in contemporary biology. Thinking about model organisms enables us to examine how living organisms have been brought into the laboratory and used to gain a better understanding of biology and to explore the research practices, commitments, and norms underlying...

Successful collaborative research is dependent on excellent ideas and innovative experimental approaches as well as the provision of appropriate support networks. Collaboration requires venues, infrastructures, training facilities and, perhaps most importantly, a sustained commitment to working together as a community. These activities do not occur...

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain an...

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 resea...

This chapter considers and compares the ways in which two types of data, economic observations and phenotypic data in plant science, are prepared for use as evidence for claims about phenomena such as business cycles and gene-environment interactions. We focus on what we call “cleaning by clustering” procedures, and investigate the principles under...

The introduction discusses the idea of data journeys and its characteristics as an investigative tool and theoretical framework for this volume and broader scholarship on data. Building on a relational and historicized understanding of data as lineages, it reflects on the methodological and conceptual challenges involved in mapping, analyzing and c...

In the last three decades of the twentieth century, scientists working in coral reef biology documented unprecedented and extensive changes and degradation of reefs worldwide. This chapter investigates the evolution of coral reef biology research during this critical period, focusing on the emergence and use in the field of an “infection repertoire...

We investigate translation in biomedicine by exploring how researchers supported by the British Pharmacological Society's Integrative Pharmacology Fund (IPF) have responded to increasing translational aspirations within pre-clinical animal research. The IPF sought to enhance institutional capacities, collaborative practices, and personal skills wit...

Despite August Krogh’s famous admonition that a ‘convenient’ organism exists for every biological problem, we argue that appeals to ‘convenience’ are not sufficient to capture reasoning about organism choice. Instead, we offer a detailed analysis based on empirical data and philosophical arguments for a working set of twenty criteria that interact...

How did data get so big? Through political, social and economic interests, shows Sabina Leonelli, in the fourth essay on how the past 150 years have shaped the science system, marking Nature’s anniversary. How did data get so big? Through political, social and economic interests, shows Sabina Leonelli.

The availability of big data has the potential to transform many areas of the life sciences and usher in new ways of doing research. Here, I argue that big data biology also raises fundamental questions in the philosophy of science: for example, what is a good dataset, and how can reliable knowledge be extracted from big data? Collaborations betwee...

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 phenotypin...

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 unw...

See full text here: http://philsci-archive.pitt.edu/14352/

This paper analyses the role of information security (IS) in shaping the dissemination and re-use of biomedical data, as well as the embedding of such data in material, social and regulatory landscapes of research. We consider data management practices adopted by two UK-based data linkage infrastructures: the Secure Anonymised Information Linkage,...

Global Access to Research Software: The Forgotten Pillar of Open Science Implementation.

This introduction outlines the contents of the special collection “Open Data and Africa”, which documents the goals and aspirations associated with Open Data means in Africa today: what opportunities they offer, what challenges they pose and what the implications follow from the increasing political and institutional support for this concept.

Openness is a buzzword these days. Governments, software, and even humans are furnished with the adjective ‘open’. And this adjective is not a quiet and modest bystander, but a demanding parole: To be open means to be transparent, responsible, accountable, inclusive. To be open is to be good. What does this mean for science? If we understand openn...

A heated debate surrounds the significance of reproducibility as an indicator for research quality and reliability, with many commentators linking a "crisis of reproducibility" to the rise of fraudulent, careless and unreliable practices of knowledge production. Through the analysis of discourse and practices across research fields, I point out tha...

To manage the transition to the open access (OA) model of scholarly publishing, we need to understand better what enables, encourages and inhibits the adoption of OA publishing among scientists, and to appreciate individual differences within disciplines. The study adopts a psychological perspective to elucidate motivations, capabilities and opport...

This paper 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 (Data time, or Dt) from the time in which the phenomena for which data serve as evidence operate...

Poor provision of information and communication technologies in low/middle-income countries represents a concern for promoting open data. This is often framed as a 'digital divide' and addressed through initiatives that increase the availability of information and communication technologies to researchers based in low-resourced environments, as wel...

Big data refers to large, complex, potentially linkable data from diverse sources, ranging from the genome and social media, to individual health information and the contributions of citizen science monitoring, to large-scale long-term oceanographic and climate modeling and its processing in innovative and integrated “data mashups.” Over the past f...

This paper reflects on the relation between international debates around data quality assessment and the diversity characterising research practices, goals and environments within the life sciences. Since the emergence of molecular approaches, many biologists have focused their research, and related methods and instruments for data production, on t...

This editorial critically engages with the understanding of openness by attending to how notions of presence and absence come bundled together as part of efforts to make open. This is particularly evident in contemporary discourse around data production, dissemination, and use. We highlight how the preoccupations with making data present can be use...

This chapter examines the challenges involved in disseminating, integrating and analyzing large datasets collected within both clinical and research settings. I highlight the technical, ethical and epistemic concerns underlying attempts to portray and use big data as revolutionary tools for producing biomedical knowledge and related interventions....

We propose a framework to describe, analyze, and explain the conditions under which scientific communities organize themselves to do research, particularly within large-scale, multidisciplinary projects. The framework centers on the notion of a research repertoire, which encompasses well-aligned assemblages of the skills, behaviors, and material, s...

The distributed and global nature of data science creates challenges for evaluating the quality, import and potential impact of the data and knowledge claims being produced. This has significant consequences for the management and oversight of responsibilities and accountabilities in data science. In particular, it makes it difficult to determine w...

The Open Science (OS) movement promises nothing less than a revolution in the availability of scientific knowledge around the globe. By removing barriers to online data and encouraging publication in Open Access formats and Open Data archives, OS seeks to expand the role, reach and value of research. The promises of OS imply a set of expectations a...

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 sidestep...

This article documents how biomedical researchers in the United Kingdom understand and enact the idea of “openness.” This is of particular interest to researchers and science policy worldwide in view of the recent adoption of pioneering policies on Open Science and Open Access by the U.K. government—policies whose impact on and implications for res...

Scientific journals have long acted as a stabilizing force in academia, by defining scientific communities, demarcating subfields and showcasing their key insights. Stability derives not least from the structure of a scientific paper, which imposes order on the ever-shifting processes of data

Improving laboratory animal science and welfare requires both new scientific research and insights from research in the humanities and social sciences. Whilst scientific research provides evidence to replace, reduce and refine procedures involving laboratory animals (the '3Rs'), work in the humanities and social sciences can help understand the soc...

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This paper proposes an account of scientific data that makes sense of recent debates on data-driven 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 kno...

How effectively communities of scientists come together and cooperate is crucial both to the quality of research outputs and to the extent to which such outputs integrate insights, data, and methods from a variety of fields, laboratories, and locations around the globe. This essay is focused on the ensemble of material and social conditions that ma...

At the turn of the millennium, the Human Genome Project and the upcoming publication of the human genome sequence promised to open an entirely new approach to healthcare, based on the genotype of the individual. This approach was dubbed personalised medicine (PM). However, the analysis of sequencing results revealed that the complexity of the biolo...

The open science (OS) movement has been seen as an important facilitator for public participation in science. This has been underpinned by the assumption that widespread and free access to research outputs leads to (1) better and more efficient science, (2) economic growth, in particular for small and medium-sized enterprises wishing to capitalise...

We examine the criteria used to validate the use of nonhuman organisms in North-American alcohol addiction research from the 1950s to the present day. We argue that this field, where the similarities between behaviors in humans and non-humans are particularly difficult to assess, has addressed questions of model validity by transforming the situate...

The use of online databases to collect and disseminate data is typically portrayed as crucial to the management of 'big science'. At the same time, databases are not deemed successful unless they facilitate the re-use of data towards new scientific discoveries, which often involves engaging with several highly diverse and inherently unstable resear...

Despite the clear demand for open data sharing, its implementation within plant science is still limited. This is, at least in part, because open data-sharing raises several unanswered questions and challenges to current research practices. In this commentary, some of the challenges encountered by plant researchers at the bench when generating, int...

This paper examines the notion of "translational research", which has become a dominant form of the institutionalization and practice of contemporary biomedicine, as an entry point into theorizing questions of knowledge, value and their articulations. We are interested in locating translational research in a conjuncture that is marked, on the one h...

This article explains the key role of model organisms within contemporary research, while at the same time acknowledging their limitations as biological models. We analyse the epistemic and social characteristics of model organism biology as a form of "big science", which includes the development of large, centralised infrastructures, a shared etho...

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...

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 classifi...