This is one of the most important books published in science studies over the last few years, and generalist readers should not be put off by its seemingly narrow topic, namely the actualization and validation of the vast mass of biological data encoded in digital formats, so that it may be 'shared' amongst relevant scientists. One should take the subtitle ... Read moreseriously: the world is bloated with attempts by philosophers to clarify the fine points of empiricism in science, but almost none of that achieves anything near the level of insight and enlightenment achieved in this short book. Perhaps more surprisingly, few authors prove capable of grasping the profound transformations happening in the political economy of science with greater sensitivity than Leonelli. Her pungent observations deserve the attention of anyone interested in the meaning and significance of the 'Open Science' movement.
Metascience At the core of the book is a philosophical view of data, which is seen as a relational category, applicable to portable (though not immutable) research outputs that have or are expected to have evidential value for knowledge claims. A key feature of data-centrism is then, for Leonelli, the recognition that this evidential value is underdetermined, i.e. that we cannot say in advance what claims any given data may bear upon. This prospective and open nature of data has a number of consequences on how it is to be dealt with, which much of the book sets out to explore. . . . It offers very interesting epistemological views about data, its usage, and the challenges in its dissemination; it does an excellent job in revealing the often less visible work of curators and of the social and conceptual structures underlying broad efforts at data annotation, organization, and dissemination; and it represents an important resource for scholars interested in these issues.
History and Philosophy of the Life Sciences Data-Centric Biology is a rich and carefully argued book that nicely integrates social and philosophical perspectives . . . a pathbreaking contribution
Isis Going far beyond the epistemic concerns that preoccupy many philosophers, Data-Centric Biology brilliantly shows readers the practices that make data informative and meaningful, the biocurators who carefully attend to data's forms, and the social, economic, and political resources on which our systems of Big Data Sciences depend. Leonelli is a leader in this area of scholarship, commanding a vast comprehensive knowledge of the historical, philosophical, and social studies of the life sciences and the data practices that sustain them.
Mike Fortun, Rensselaer Polytechnic Institute The first critical book-length study of data centrism in the life sciences and beyond, Data-Centric Biology sheds new and surprising light on the phenomenon of big data. Analytically competent, historically informed, sociologically sensitive, this book is a brilliant and successful demonstration of what bringing together philosophy, history, and social studies of science can achieve.
Hans-J rg Rheinberger, Max Planck Institute for the History of Science The dynamic, procedural themes running throughout Leonelli's book usefully shift our philosophical focus from the abstracta of understanding how science works from a frozen perspective to a more useful analysis of how scientists in fact get the job done (when they do) and
more critically
how they might do it better.
BJPS Review of Books In Data-Centric Biology: A Philosophical Study, Sabina Leonelli provides the first book-length study of what is novel about data-centric research in the life sciences. . . . Her book is a laudable achievement in integrating insights from a methodologically permissive and pluralistic outlook.
Quarterly Review of Biology Has there been a shift in what data means that is key to understanding the future of science? Sabina Leonelli's new book, Data-Centric Biology: A Philosophical Study, argues for an important and fruitful answer outside the comfort zone of many philosophers. Along the way, she also delivers valuable insights into expanding efforts to standardize, automate, and communicate how scientists handle, share, reproduce, interpret, and store data.
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