Ian Foster

Dennis B. Gannon

The emergence of powerful, always-on cloud utilities has transformed how consumers interact with information technology, enabling video streaming, intelligent personal assistants, and the sharing of content. Businesses, too, have benefited from the cloud, outsourcing much of their information technology to cloud services. Science, however, has not fully exploited the advantages of the cloud. Could scientific discovery be accelerated if mundane chores were automated and outsourced to the cloud? Leading computer scientists Ian Foster and Dennis Gannon argue that it can, and in this book offer a guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The book surveys the technology that underpins the cloud, new approaches to technical problems enabled by the cloud, and the concepts required to integrate cloud services into scientific work. It covers managing data in the cloud, and how to program these services; computing in the cloud, from deploying single virtual machines or containers to supporting basic interactive science experiments to gathering clusters of machines to do data analytics; using the cloud as a platform for automating analysis procedures, machine learning, and analyzing streaming data; building your own cloud with open source software; and cloud security. The book is accompanied by a website, Cloud4SciEng.org, that provides a variety of supplementary material, including exercises, lecture slides, and other resources helpful to readers and instructors.

Cloud Computing for Science and Engineering

Ian D. L. Foster

A. M. Gurnell

Bruce Webb

Bruce W. Webb

Eminent contributors present papers essential to a greater understanding of solute and sediment behaviour, the methods of analysis and interpretation of rating curves, the impact of urbanisation, the assessment of spatial patterns of process at the drainage basin and the global scales, sediment sources and sinks, radionuclide studies and much more.

Sediment and Water Quality in River Catchments

Kevin Pettman

Where does Rooney stand in the order of World Strikers? Is Cech better than Casillas? Where does Gerrard rate against Vieira? Using its unique form guide, the MOTD Annual answers these crucial questions and many more, running down the top 50 deadliest strikers, the 50 meanest defenders, the 50 classiest midfielders and the top 25 keepers in the world. Also including the most detailed guide around to the Premiership clubs, plus interviews with top players, footballing insights from the MOTD presenters, and a guide to Euro 2008, it offers incredible value and genuinely new and exclusive information for discerning football fans. Everything that you would expect from a sporting brand as mighty as Match of the Day. The MOTD Annual was conceived two years ago by one-man band Terry Pratt, a former publisher of current football annual market-leader Match. With the Match Annual starting to look tired, but still hitting sales of 250,000, Terry spotted an open goal. He drafted in old colleague and former Match managing editor Chris Hunt, and together they created a far superior product and launched it under a much stronger brand. Terry was rewarded with 250% sales growth from year one to two, and third place in the Football Annual bestsellers chart, behind only Match and Shoot. After this excellent launch, and with the same team now packaging the Annual for BBC Books, we are perfectly placed to take it all the way to the top.

Match of the Day 2010

Julia Lane

Ron S. Jarmin

Frauke Kreuter

Rayid Ghani

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations. Features: Takes an accessible, hands-on approach to handling new types of data in the social sciences Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes Illustrates social science and data science principles through real-world problems Links computer science concepts to practical social science research Promotes good scientific practice Provides freely available workbooks with data, code, and practical programming exercises, through Binder and GitHub New to the Second Edition: Increased use of examples from different areas of social sciences New chapter on dealing with Bias and Fairness in Machine Learning models Expanded chapters focusing on Machine Learning and Text Analysis Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner.

Data Science Methods and Tools for Research and Practice

Big Data and Social Science

This book constitutes the thoroughly referred post-proceedings of the International Provenance and Annotation Workshops, IPAW 2006, held in Chicago, Il, USA in May 2006. The 26 revised full papers presented together with two keynote papers were carefully selected for presentation during two rounds of reviewing and improvement. The papers are organized in topical sections.

International Provenance and Annotation Workshop, IPAW 2006, Chicago, Il, USA, May 3-5, 2006, Revised Selected Papers

Provenance and Annotation of Data

A Comprehensive Guide to the Terms Most Commonly Used in Printing, Paper, Typesetting, Origination, Finishing, Point of Sale, Premiums and Promotions Etc

Dictionary of Printing Terms

Carl Kesselman

"The Grid" is an emerging infrastructure that will fundamentally change the way people think about and use computing. The editors reveal the revolutionary impact of large-scale resource sharing and virtualization within science and industry, and the intimate relationships between organization and resource sharing structures.

Blueprint for a New Computing Infrastructure

The Grid

Florian Krobb

Arthur Schnitzler durchlebte eine Zeitenwende: der Erste Weltkrieg verwandelte Osterreich von einem multinationalen Imperium in einen deutschsprachigen Kleinstaat. Beides, das Vorher und das Nachher, markiert Schnitzlers Zeitgenossenschaft. Er lebte in einer geistesgeschichtlichen und literarischen Umbruchsphase. Der burgerlich-liberale Konsens, der Schnitzlers Milieu charakterisierte, wurde in seinen Grunduberzeugungen herausgefordert. Wissenschaftliche und technische Errungenschaften kennzeichnen Schnitzlers Zeitgenossenschaft. Arthur Schnitzler lived through a time of profound political, social and intellectual change: the First World War transformed Austria from a huge multi-national empire into a small Alpine republic; the liberal middle-class consensus which characterised the author's personal background began to disintegrate during this time, and new departures not only in science and technology but also in literary styles and conventions posed new challenges to a politically involved and acutely socially aware modern writer.

Zeitgenossenschaften

Arthur Schnitzler

Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.

Ian Foster

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