The Role of Bio-Ontologies in Data-Driven Research: A Philosophical Perspective

This project aims to reach a philosophical understanding of the role played by theory in the practices of data dissemination and re-use that characterise data-driven research. Bio-ontologies have the potential to play the epistemic role of theories in this context, insofar as they (1) express the knowledge underlying data-driven research and (2) guide such research towards future discoveries

Global data for local science: Assessing the scale of data infrastructures in biological and biomedical research

publication-status: Acceptedtypes: ArticleThe 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 research communities. This paper examines the tensions encountered by database developers in their efforts to foster both the global circulation and the local adoption of data. I focus on two prominent attempts to build data infrastructures in the fields of plant science and cancer research over the last decade: The Arabidopsis Information Resource and the Cancer Biomedical Informatics Grid. I show how curators’ experience of the diverse and dynamic nature of biological research led them to envision databases as catering primarily for local, rather than global, science; and to structure them as platforms where methodological and epistemic diversity can be expressed and explored, rather than denied or overcome. I conclude that one way to define the scale of data infrastructure is to consider the range and scope of the biological and biomedical questions which it helps to address; and that within this perspective, databases have a larger scale than the science that they serve, which tends to remain fragmented into a wide variety of specialised projects

Documenting the emergence of bio-ontologies: or, why researching bioinformatics requires HPSSB.

This paper reflects on the analytic challenges emerging from the study of bioinformatic tools recently created to store and disseminate biological data, such as databases, repositories, and bio-ontologies. I focus my discussion on the Gene Ontology, a term that defines three entities at once: a classification system facilitating the distribution and use of genomic data as evidence towards new insights; an expert community specialised in the curation of those data; and a scientific institution promoting the use of this tool among experimental biologists. These three dimensions of the Gene Ontology can be clearly distinguished analytically, but are tightly intertwined in practice. I suggest that this is true of all bioinformatic tools: they need to be understood simultaneously as epistemic, social, and institutional entities, since they shape the knowledge extracted from data and at the same time regulate the organisation, development, and communication of research. This viewpoint has one important implication for the methodologies used to study these tools; that is, the need to integrate historical, philosophical, and sociological approaches. I illustrate this claim through examples of misunderstandings that may result from a narrowly disciplinary study of the Gene Ontology, as I experienced them in my own research

The Time of Data: Time-Scales of Data Use in the Life Sciences

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 (Phenomena time, or Pt). Building on the analysis of two examples of data re-use from modelling and experimental practices in biology, I then argue that Dt affects how researchers (1) select and interpret data as evidence and (2) identify and understand phenomena

Without urgent action big and open data may widen existing inequalities and social divides

The juncture of big and open data informs areas as diverse as artificial intelligence, agriculture, and public health, and promises to transform our ability to tackle global challenges. However, Sabina Leonelli highlights three major concerns over how big and open data are currently managed: the unsustainable nature of the digital data landscape; the quality and credibility of the data themselves; and how data sources currently represent only privileged – typically English-speaking – individuals and communities, with little representation from less visible and more vulnerable groups. These challenges can be overcome, but to do so requires significant investment in key data governance priorities

Data Interpretation in the Digital Age

publication-status: Acceptedtypes: ArticleThe 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 data posted online is assessed and interpreted by the researchers who access them, in ways that underpin and guide the use of those data to foster discovery. I consider the types of knowledge required to interpret data as evidence for claims about organisms, and in particular the relevance of knowledge acquired through physical interaction with actual organisms to assessing the evidential value of data found online. I conclude that familiarity with research in vivo is crucial to assessing the quality and significance of data visualised in silico; and that studying how biological data are disseminated, visualised, assessed and interpreted in the 2 digital age provides a strong rationale for viewing scientific understanding as a social and distributed, rather than individual and localised, achievement

Classificatory Theory in Biology

publication-status: Acceptedtypes: ArticleAuthor's version of a paper subsequently published in Biological Theory. Please cite the published version by following the DOI link.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.Economic and Social Research Council (ESRC

Why the Current Insistence on Open Access to Scientific Data? Big Data, Knowledge Production and the Political Economy of Contemporary Biology

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

What Counts as Scientific Data? A Relational Framework

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.Max Planck Institute for the History of Science (project “Sciences of the Archive”)European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013

Centralising Labels to Distribute Data: The Regulatory Role of Genomic Consortia.

publication-status: Publishe