Chapter 1. A note on logic and the methodology of data-driven science (Hosni and Landes) -- Chapter 2. Pure Inductive Logic (Vencovska) -- Chapter 3. Where do we stand on maximal entropy? (Williamson) -- Chapter 4. Probability logic and statistical relational artificial intelligence (Weitkamper) -- Chapter 5. An Overview of the Generalization Problem (Facciuto) -- Chapter 6. The Logic of DNA Identification (Zabell) -- Chapter 7. Reasoning With and About Bias (Manganini and Primiero) -- Chapter 8. Knowledge Representation, Scientific Argumentation and Non-monotonic Logic (Landes et al) -- Chapter 9. Reasoning with Data in the framework of a Quantum Approach to Machine Learning (Chiara et al).

This book calls for a rethinking of logic as the core methodological tool for scientific reasoning in the context of a steadily increasing emphasis on data-centered science. To do so it provides a state-of-the-art presentation of the role logic can have in making the most of the current opportunities while making explicit the key challenges opened up by the data-driven age of scientific research. Particular attention is given to the following four core fields and applications: Reasoning with correlations (medical, life-science applications); logics for statistical inference (machine learning, and societal applications thereof); reasoning with evidence (defining good evidence); causal reasoning (forensic reasoning). The book collects contributions from key logicians, methodologists and scientists. This multidisciplinary perspective benefits both scientists and logicians interested in data-driven science. Scientists are introduced to logics that go beyond classical and thus are applicable to reasoning with data; Logicians have a change to focus on the potential applications of their methods and techniques to pressing scientific problems. This book is, therefore, of interest to scientists and logicians working on data-centered science.