Over the last two decades, digital access to data has revolutionized research methods and ways of doing science in the biological and biomedical fields. Prominent scientists have characterized this shift as leading to a new, data-intensive paradigm for research, encompassing innovative ways to produce, store, disseminate, and interpret huge masses of data. In this book Sabina Leonelli explores the epistemological challenges this poses to how life is researched and understood. By following how data travels across research contexts, and the role played by standards, theories, models, and human agency in shaping their evidential value, she shows the conditions under which digitally available data further our understanding of life. Turning to how the characteristics of data-intensive science bear on philosophical debates, Leonelli explores the shifting criteria for what counts as scientific evidence and how data are transformed into new knowledge. In short, she argues that a philosophical characterization of how data and knowledge move from one context to another is of fundamental importance to a productive philosophical understanding of contemporary scientific practices.
Data and Society: A Critical Introduction investigates the growing importance of data as a technological, social, economic and scientific resource. It explains how data practices have come to underpin all aspects of human life and explores what this means for those directly involved in handling data. The book fosters informed debate over the role of data in contemporary society explains the significance of data as evidence beyond the "Big Data" hype spans the technical, sociological, philosophical and ethical dimensions of data provides guidance on how to use data responsibly includes data stories that provide concrete cases and discussion questions. Grounded in examples spanning genetics, sport and digital innovation, this book fosters insight into the deep interrelations between technical, social and ethical aspects of data work.
This Element proposes to frame openness in the Open Science [OS] movement as the effort to establish judicious connections among systems of practice, predicated on a process-oriented view of research as a tool for effective and responsible agency. This title is also available as Open Access on Cambridge Core.
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 this understanding. We contend that model organisms are key components of a distinctive way of doing research. We focus on what makes model organisms an important type of model, and how the use of these models has shaped biological knowledge, including how model organisms represent, how they are used as tools for intervention, and how the representational commitments linked to their use as models affect the research practices associated with them. This title is available as Open Access on Cambridge Core.
Experts examine new modeling strategies for the interpretation of biological data and their integration into the conceptual framework of theoretical biology, detailing approaches that focus on morphology, development, behavior, or evolution. Abstract and conceptual models have become an indispensable tool for analyzing the flood of highly detailed empirical data generated in recent years by advanced techniques in the biosciences. Scientists are developing new modeling strategies for analyzing data, integrating results into the conceptual framework of theoretical biology, and formulating new hypotheses. In Modeling Biology, leading scholars investigate new modeling strategies in the domains of morphology, development, behavior, and evolution. The emphasis on models in the biological sciences has been accompanied by a new focus on conceptual issues and a more complex understanding of epistemological concepts. Contributors to Modeling Biology discuss models and modeling strategies from the perspectives of philosophy, history, and applied mathematics. Individual chapters discuss specific approaches to modeling in such domains as biological form, development, and behavior. Finally, the book addresses the modeling of these properties in the context of evolution, with a particular emphasis on the emerging field of evolutionary developmental biology (or evo-devo). Contributors Giorgio A. Ascoli, Chandrajit Bajaj, James P. Collins, Luciano da Fontoura Costa, Kerstin Dautenhahn, Nigel R. Franks, Scott Gilbert, Marta Ibañes Miguez, Juan Carlos Izpisúa-Belmonte, Alexander S. Klyubin, Thomas J. Koehnle, Manfred D. Laubichler, Sabina Leonelli, James A. R. Marshall, George R. McGhee Jr., Gerd B. Müller, Chrystopher L. Nehaniv, Karl J. Niklas, Lars Olsson, Eirikur Palsson, Daniel Polani, Diego Rasskin Gutman, Hans-Jörg Rheinberger, Alexei V. Samsonovich, Jeffrey C. Schank, Harry B. M. Uylings, Jaap van Pelt, Iain Werry
Experts examine new modeling strategies for the interpretation of biological data and their integration into the conceptual framework of theoretical biology, detailing approaches that focus on morphology, development, behavior, or evolution.
In recent decades, there has been a major shift in the way researchers process and understand scientific data. Digital access to data has revolutionized ways of doing science in the biological and biomedical fields, leading to a data-intensive approach to research that uses innovative methods to produce, store, distribute, and interpret huge amounts of data. In Data-Centric Biology, Sabina Leonelli probes the implications of these advancements and confronts the questions they pose. Are we witnessing the rise of an entirely new scientific epistemology? If so, how does that alter the way we study and understand life—including ourselves? Leonelli is the first scholar to use a study of contemporary data-intensive science to provide a philosophical analysis of the epistemology of data. In analyzing the rise, internal dynamics, and potential impact of data-centric biology, she draws on scholarship across diverse fields of science and the humanities—as well as her own original empirical material—to pinpoint the conditions under which digitally available data can further our understanding of life. Bridging the divide between historians, sociologists, and philosophers of science, Data-Centric Biology offers a nuanced account of an issue that is of fundamental importance to our understanding of contemporary scientific practices.
Data and Society: A Critical Introduction investigates the growing importance of data as a technological, social, economic and scientific resource. It explains how data practices have come to underpin all aspects of human life and explores what this means for those directly involved in handling data. The book fosters informed debate over the role of data in contemporary society explains the significance of data as evidence beyond the "Big Data" hype spans the technical, sociological, philosophical and ethical dimensions of data provides guidance on how to use data responsibly includes data stories that provide concrete cases and discussion questions. Grounded in examples spanning genetics, sport and digital innovation, this book fosters insight into the deep interrelations between technical, social and ethical aspects of data work.
This Element proposes to frame openness in the Open Science [OS] movement as the effort to establish judicious connections among systems of practice, predicated on a process-oriented view of research as a tool for effective and responsible agency. This title is also available as Open Access on Cambridge Core.
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 this understanding. We contend that model organisms are key components of a distinctive way of doing research. We focus on what makes model organisms an important type of model, and how the use of these models has shaped biological knowledge, including how model organisms represent, how they are used as tools for intervention, and how the representational commitments linked to their use as models affect the research practices associated with them. This title is available as Open Access on Cambridge Core.
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.