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. Br
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.