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Data-Intensive Science.

Author: Terence Critchlow
Publisher: Hoboken : CRC Press, 2013.
Series: Chapman & Hall/CRC Computational Science.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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,  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Terence Critchlow
ISBN: 9781439881415 1439881413 9781439881392 1439881391 9781299710511 1299710514
OCLC Number: 845257674
Description: 1 online resource (446 pages).
Contents: What Is Data-Intensive Science?, Terence Critchlow and Kerstin Kleese van DamWhere Does All the Data Come From?, Geoffrey Fox, Tony Hey, and Anne Trefethen Data-Intensive Grand Challenge Science ProblemsLarge-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 KurcAnswering Fundamental Questions about the Universe, Eric S. Myra and F. Douglas SwestyMaterials of the Future: From Business Suits to Space Suits, Mark F. HorstemeyerCase StudiesEarth 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 LautenschlagerData-Intensive Production Grids, Bob Jones and Ian Bird EUDAT: Toward a Pan-European Collaborative Data Infrastructure, D. Lecarpentier, J. Reetz, and P. WittenburgFrom Challenges to SolutionsInfrastructure for Data-Intensive Science: A Bottom-Up Approach, Eli Dart and William JohnstonA Posteriori Ontology Engineering for Data-Driven Science, Damian D.G. Gessler, Cliff Joslyn, and Karin VerspoorTransforming 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 DamIn 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. SamatovaInteractive Data Exploration, Brian Summa, Attilay Gyulassy, Peer-Timo Bremer, and Valerio PascucciLinked Science: Interconnecting Scientific Assets, Tomi Kauppinen, Alkyoni Baglatzi, and Carsten KesslerSummary and Conclusions, Terence Critchlow and Kerstin Kleese van DamIndex
Series Title: Chapman & Hall/CRC Computational Science.

Abstract:

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 significan.

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"This nicely integrated collection of contributions is an attempt to familiarize readers with this challenging aspect of science in the 21st century. The editors draw a picture of the future of Read more...

 
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