The chapter from "The Fourth Paradigm: Data-Intensive Scientific Discovery" edited by Tony Hey, Stewart Tansley, and Kristin Tolle, discusses the emergence of a new scientific paradigm based on data-intensive computing. The authors argue that this paradigm is analogous to the invention of the printing press, marking a significant shift in how scientific research is conducted. They highlight the challenges and opportunities presented by the vast volume of data generated by modern scientific instruments and computational models, emphasizing the need for better tools for data capture, curation, and analysis. Key points include: - The division between data mining and theory creation in scientific research. - The importance of long-term data provenance and community access. - The need for generic tools to support the entire research cycle, from data capture to publication. - The role of digital libraries in storing and managing scientific data and documents. - The impact of new technologies like cloud computing and multicore processors on scientific research. - The importance of interdisciplinary cooperation and training in data-intensive science. - The need for funding to support the development of data-intensive tools and infrastructure. The chapter also includes a detailed transcript of Jim Gray's talk, where he outlines his vision for the fourth paradigm of scientific research, emphasizing the need for better tools and infrastructure to manage and analyze the growing volume of scientific data.The chapter from "The Fourth Paradigm: Data-Intensive Scientific Discovery" edited by Tony Hey, Stewart Tansley, and Kristin Tolle, discusses the emergence of a new scientific paradigm based on data-intensive computing. The authors argue that this paradigm is analogous to the invention of the printing press, marking a significant shift in how scientific research is conducted. They highlight the challenges and opportunities presented by the vast volume of data generated by modern scientific instruments and computational models, emphasizing the need for better tools for data capture, curation, and analysis. Key points include: - The division between data mining and theory creation in scientific research. - The importance of long-term data provenance and community access. - The need for generic tools to support the entire research cycle, from data capture to publication. - The role of digital libraries in storing and managing scientific data and documents. - The impact of new technologies like cloud computing and multicore processors on scientific research. - The importance of interdisciplinary cooperation and training in data-intensive science. - The need for funding to support the development of data-intensive tools and infrastructure. The chapter also includes a detailed transcript of Jim Gray's talk, where he outlines his vision for the fourth paradigm of scientific research, emphasizing the need for better tools and infrastructure to manage and analyze the growing volume of scientific data.