Data Centric Biology

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.

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.

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 research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research.

Individuals are things that everybody knows—or thinks they do. Yet even scholars who practice or analyze the biological sciences often cannot agree on what an individual is and why. One reason for this disagreement is that the many important biological individuality concepts serve very different purposes—defining, classifying, or explaining living structure, function, interaction, persistence, or evolution. Indeed, as the contributors to Biological Individuality reveal, nature is too messy for simple definitions of this concept, organisms too quirky in the diverse ways they reproduce, function, and interact, and human ideas about individuality too fraught with philosophical and historical meaning. Bringing together biologists, historians, and philosophers, this book provides a multifaceted exploration of biological individuality that identifies leading and less familiar perceptions of individuality both past and present, what they are good for, and in what contexts. Biological practice and theory recognize individuals at myriad levels of organization, from genes to organisms to symbiotic systems. We depend on these notions of individuality to address theoretical questions about multilevel natural selection and Darwinian fitness; to illuminate empirical questions about development, function, and ecology; to ground philosophical questions about the nature of organisms and causation; and to probe historical and cultural circumstances that resonate with parallel questions about the nature of society. Charting an interdisciplinary research agenda that broadens the frameworks in which biological individuality is discussed, this book makes clear that in the realm of the individual, there is not and should not be a direct path from biological paradigms based on model organisms through to philosophical generalization and historical reification.

Databases have revolutionized nearly every aspect of our lives. Information of all sorts is being collected on a massive scale, from Google to Facebook and well beyond. But as the amount of information in databases explodes, we are forced to reassess our ideas about what knowledge is, how it is produced, to whom it belongs, and who can be credited for producing it. Every scientist working today draws on databases to produce scientific knowledge. Databases have become more common than microscopes, voltmeters, and test tubes, and the increasing amount of data has led to major changes in research practices and profound reflections on the proper professional roles of data producers, collectors, curators, and analysts. Collecting Experiments traces the development and use of data collections, especially in the experimental life sciences, from the early twentieth century to the present. It shows that the current revolution is best understood as the coming together of two older ways of knowing—collecting and experimenting, the museum and the laboratory. Ultimately, Bruno J. Strasser argues that by serving as knowledge repositories, as well as indispensable tools for producing new knowledge, these databases function as digital museums for the twenty-first century.

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.

Biology, medicine and biochemistry have become data-centric fields for which Deep Learning methods are delivering groundbreaking results. Addressing high impact challenges, Deep Learning in Biology and Medicine provides an accessible and organic collection of Deep Learning essays on bioinformatics and medicine. It caters for a wide readership, ranging from machine learning practitioners and data scientists seeking methodological knowledge to address biomedical applications, to life science specialists in search of a gentle reference for advanced data analytics.With contributions from internationally renowned experts, the book covers foundational methodologies in a wide spectrum of life sciences applications, including electronic health record processing, diagnostic imaging, text processing, as well as omics-data processing. This survey of consolidated problems is complemented by a selection of advanced applications, including cheminformatics and biomedical interaction network analysis. A modern and mindful approach to the use of data-driven methodologies in the life sciences also requires careful consideration of the associated societal, ethical, legal and transparency challenges, which are covered in the concluding chapters of this book.

This work offers unique insight into how biology is evolving to meet the needs of data-intensive science. It examines the history of computational science and lessons learned, discusses the evolution of biology into a data-driven science, and addresses the challenges and complexities of modern, data-driven biology. Exploring the core technological and scientific challenges, including algorithmic and infrastructural requirements, the book analyzes common themes and differences between biology and other data-intensive sciences and highlights the future directions of the field and the skill sets that will be critical for future success.