Guide to e-Science

<p>Part I: Sharing and Open Research</p><p>Implementing a Grid / Cloud e-Science Infrastructure for Hydrological Sciences</p><p>The German Grid Initiative: Current State and Future Perspectives</p><p>Democratizing Resource-Intensive e-Science Through Peer-to-Peer Grid Computing</p><p>Peer4Peer: E-science Communities for Overlay Network and Grid Computing Research</p><p>Part II: Data-Intensive e-Science</p><p>A Multi-Disciplinary, Model-Driven, Distributed Science Data System Architecture</p><p>An Integrated Ontology Management and Data Sharing Framework for Large-Scale Cyberinfrastructure</p><p>Part III: Collaborative Research</p><p>An e-Science Cyberinfrastructure for Solar-enabled Water Production and Recycling</p><p>e-Science Infrastructure Interoperability Guide: The Seven Steps Towards Interoperability for e-Science</p><p>Trustworthy Distributed Systems Through Integrity-Reporting</p><p>An Intrusion Diagnosis Perspective on Cloud Computing</p><p>Part IV: Research Automation, Reusability, Reproducibility and Repeatability</p><p>Conventional Workflow Technology for Scientific Simulation</p><p>Facilitating E-Science Discovery Using Scientific Workflows on the Grid</p><p>Concepts and Algorithms of Mapping Grid-Based Workflows to Resources Within an SLA Context</p><p>Orchestrating e-Science with the Workflow Paradigm: Task-Based Scientific Workflow Modelling and Performing</p><p>Part V: e-Science: Easy Science</p><p>Face Recognition using Global and Local Salient Features</p><p>OGSA-Based SOA for Collaborative Cancer Research: System Modelling and Generation</p><p>e-Science: The Way Leading to Modernization of Sciences and Technologies</p>