In this 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.…mehr
In this 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.
Terence Critchlow is the chief scientist of the Scientific Data Management Group in the Computational Sciences and Mathematics Division of the Pacific Northwest National Laboratory (PNNL), where he leads projects on data analysis, data dissemination, and workflow system. A senior member of IEEE and ACM, Dr. Critchlow earned a PhD in computer science from the University of Utah. His research focuses on large-scale data management, metadata, data analysis, online analytical processing, data integration, data dissemination, and scientific workflows. Kerstin Kleese van Dam is an associate division director and lead of the Scientific Data Management Group at PNNL. In 2006, she received the British Female Innovators and Inventors Silver Award for the effective management of scientific data. Her research focuses on data management and analysis in extreme-scale environments.
Inhaltsangabe
What Is Data-Intensive Science? Where Does All the Data Come From? Data-Intensive Grand Challenge Science Problems: Large-Scale Microscopy Imaging Analytics for In Silico Biomedicine. Answering Fundamental Questions about the Universe. Materials of the Future: From Business Suits to Space Suits. 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. Data-Intensive Production Grids. EUDAT: Toward a Pan-European Collaborative Data Infrastructure. From Challenges to Solutions: Infrastructure for Data-Intensive Science: A Bottom-Up Approach. A Posteriori Ontology Engineering for Data-Driven Science. Transforming Data into the Appropriate Context. Bridging the Gap between Scientific Data Producers and Consumers: A Provenance Approach. In Situ Exploratory Data Analysis for Scientific Discovery. Interactive Data Exploration. Linked Science: Interconnecting Scientific Assets. Summary and Conclusions. Index.
What Is Data-Intensive Science? Where Does All the Data Come From? Data-Intensive Grand Challenge Science Problems: Large-Scale Microscopy Imaging Analytics for In Silico Biomedicine. Answering Fundamental Questions about the Universe. Materials of the Future: From Business Suits to Space Suits. 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. Data-Intensive Production Grids. EUDAT: Toward a Pan-European Collaborative Data Infrastructure. From Challenges to Solutions: Infrastructure for Data-Intensive Science: A Bottom-Up Approach. A Posteriori Ontology Engineering for Data-Driven Science. Transforming Data into the Appropriate Context. Bridging the Gap between Scientific Data Producers and Consumers: A Provenance Approach. In Situ Exploratory Data Analysis for Scientific Discovery. Interactive Data Exploration. Linked Science: Interconnecting Scientific Assets. Summary and Conclusions. Index.
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