Bits of Power

Bits of Power
Author: Committee on Issues in the Transborder Flow of Scientific Data
Publisher: National Academies Press
Total Pages: 250
Release: 1997-08-21
Genre: Computers
ISBN: 0309523567

Since Galileo corresponded with Kepler, the community of scientists has become increasingly international. A DNA sequence is as significant to a researcher in Novosibirsk as it is to one in Pasadena. And with the advent of electronic communications technology, these experts can share information within minutes. What are the consequences when more bits of scientific data cross more national borders and do it more swiftly than ever before? Bits of Power assesses the state of international exchange of data in the natural sciences, identifying strengths, weaknesses, and challenges. The committee makes recommendations about access to scientific data derived from public funding. The volume examines: Trends in the electronic transfer and management of scientific data. Pressure toward commercialization of scientific data, including the economic aspects of government dissemination of the data. The implications of proposed changes to intellectual property laws and the role of scientists in shaping legislative and legal solutions. Improving access to scientific data by and from the developing world. Bits of Power explores how these issues have been addressed in the European Community and includes examples of successful data transfer activities in the natural sciences. The book will be of interest to scientists and scientific data managers, as well as intellectual property rights attorneys, legislators, government agencies, and international organizations concerned about the electronic flow of scientific data.

Artificial Intelligence, Big Data and Data Science in Statistics

Artificial Intelligence, Big Data and Data Science in Statistics
Author: Ansgar Steland
Publisher: Springer Nature
Total Pages: 378
Release: 2022-11-15
Genre: Mathematics
ISBN: 3031071557

This book discusses the interplay between statistics, data science, machine learning and artificial intelligence, with a focus on environmental science, the natural sciences, and technology. It covers the state of the art from both a theoretical and a practical viewpoint and describes how to successfully apply machine learning methods, demonstrating the benefits of statistics for modeling and analyzing high-dimensional and big data. The book’s expert contributions include theoretical studies of machine learning methods, expositions of general methodologies for sound statistical analyses of data as well as novel approaches to modeling and analyzing data for specific problems and areas. In terms of applications, the contributions deal with data as arising in industrial quality control, autonomous driving, transportation and traffic, chip manufacturing, photovoltaics, football, transmission of infectious diseases, Covid-19 and public health. The book will appeal to statisticians and data scientists, as well as engineers and computer scientists working in related fields or applications.