Data-Intensive Science

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ISBN 9781439881392
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Features

  • Provides a comprehensive overview of how technical capabilities can be leveraged to enable scientific discovery
  • Offers a path forward for facilitating data-intensive science
  • Demonstrates the impact of data-intensive science through case studies highlighting current best practices

Summary

Data-intensive science has the potential to transform scientific research and quickly translate scientific progress into complete solutions, policies, and economic success. But this collaborative science is still lacking the effective access and exchange of knowledge among scientists, researchers, and policy makers across a range of disciplines. Bringing together leaders from multiple scientific disciplines, Data-Intensive Science shows how a comprehensive integration of various techniques and technological advances can effectively harness the vast amount of data being generated and significantly accelerate scientific progress to address some of the world’s most challenging problems.

In the book, a diverse cross-section of application, computer, and data scientists explores the impact of data-intensive science on current research and describes emerging technologies that will enable future scientific breakthroughs. The book identifies best practices used to tackle challenges facing data-intensive science as well as gaps in these approaches. It also focuses on the integration of data-intensive science into standard research practice, explaining how components in the data-intensive science environment need to work together to provide the necessary infrastructure for community-scale scientific collaborations.

Organizing the material based on a high-level, data-intensive science workflow, this book provides an understanding of the scientific problems that would benefit from collaborative research, the current capabilities of data-intensive science, and the solutions to enable the next round of scientific advancements.

Table of Contents

What Is Data-Intensive Science?, Terence Critchlow and Kerstin Kleese van Dam

Where Does All the Data Come From?, Geoffrey Fox, Tony Hey, and Anne Trefethen

Data-Intensive Grand Challenge Science Problems
Large-Scale Microscopy Imaging Analytics for In Silico Biomedicine, Joel Saltz, Fusheng Wang, George Teodoro, Lee Cooper, Patrick Widener, Jun Kong, David Gutman, Tony Pan, Sharath Cholleti, Ashish Sharma, Daniel Brat, and Tahsin Kurc

Answering Fundamental Questions about the Universe, Eric S. Myra and F. Douglas Swesty

Materials of the Future: From Business Suits to Space Suits, Mark F. Horstemeyer

Case Studies
Earth System Grid Federation: Infrastructure to Support Climate Science Analysis as an International Collaboration: A Data-Driven Activity for Extreme-Scale Climate Science, Dean N. Williams, Ian T. Foster, Bryan Lawrence, and Michael Lautenschlager

Data-Intensive Production Grids, Bob Jones and Ian Bird 

EUDAT: Toward a Pan-European Collaborative Data Infrastructure, D. Lecarpentier, J. Reetz, and P. Wittenburg

From Challenges to Solutions
Infrastructure for Data-Intensive Science: A Bottom-Up Approach, Eli Dart and William Johnston

A Posteriori Ontology Engineering for Data-Driven Science, Damian D.G. Gessler, Cliff Joslyn, and Karin Verspoor

Transforming Data into the Appropriate Context, Bill Howe

Bridging the Gap between Scientific Data Producers and Consumers: A Provenance Approach, Eric G. Stephan, Paulo Pinheiro, and Kerstin Kleese van Dam

In Situ Exploratory Data Analysis for Scientific Discovery, Kanchana Padmanabhan, Sriram Lakshminarasimhan,  Zhenhuan Gong, John Jenkins, Neil Shah, Eric Schendel, Isha Arkatkar, Rob Ross, Scott Klasky, and Nagiza F. Samatova

Interactive Data Exploration, Brian Summa, Attilay Gyulassy, Peer-Timo Bremer, and Valerio Pascucci

Linked Science: Interconnecting Scientific Assets, Tomi Kauppinen, Alkyoni Baglatzi, and Carsten Keßler

Summary and Conclusions, Terence Critchlow and Kerstin Kleese van Dam

Index

Author Bio(s)