Integrating Data Management and Collaborative Sharing with Computational Science Research ProcessesKerstin Kleese van Dam (Pacific Northwest National Laboratory, USA), Mark James (University of California San Diego, USA) and Andrew M. Walker (University of Bristol, UK)
Copyright © 2012. 33 pages.
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DOI: 10.4018/978-1-61350-116-0.ch021 Sample PDFCite
MLA
Kleese van Dam, Kerstin, Mark James and Andrew M. Walker. "Integrating Data Management and Collaborative Sharing with Computational Science Research Processes." Handbook of Research on Computational Science and Engineering: Theory and Practice. IGI Global, 2012. 506-538. Web. 19 May. 2013. doi:10.4018/978-1-61350-116-0.ch021
APA
Kleese van Dam, K., James, M., & Walker, A. M. (2012). Integrating Data Management and Collaborative Sharing with Computational Science Research Processes. In J. Leng, & W. Sharrock (Eds.), Handbook of Research on Computational Science and Engineering: Theory and Practice (pp. 506-538). Hershey, PA: Engineering Science Reference. doi:10.4018/978-1-61350-116-0.ch021
Chicago
Kleese van Dam, Kerstin, Mark James and Andrew M. Walker. "Integrating Data Management and Collaborative Sharing with Computational Science Research Processes." In Handbook of Research on Computational Science and Engineering: Theory and Practice, ed. J. Leng and Wes Sharrock, 506-538 (2012), accessed May 19, 2013. doi:10.4018/978-1-61350-116-0.ch021
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  | | TopAbstractThis chapter describes the key principles and components of a good data management system, provides real world examples of how these can be successfully integrated with scientific research processes and enable successful data sharing, provides an outlook on future developments, and discusses lessons learned. We conclude with a short section on how to get started for those whose interest has been piqued. TopComplete Chapter List
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Kerstin Kleese van Dam (Pacific Northwest National Laboratory, USA), Mark James (University of California San Diego, USA), Andrew M. Walker (University of Bristol, UK)
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TopKey Terms in this ChapterSemantic Technologies: Encoding of meaning separately from data files, content files, and application codes to enable machines as well as people to understand, share and reason with them at execution time Data Management: All disciplines related to managing data as a valuable resource, here in particular we refer to Scientific Data Management, the management of storage, access, usage, lifecycle, content and meaning for scientific data High Performance Computing: The use of parallel processing for running advanced application programs efficiently, reliably and quickly on leadership class computer systems Collaborative: The term refers here both to working practices and supporting tools that allow and further the joint working of researchers with common interests that are often in geographically distributed locations. Data Curation: The preservation and management of scientific data specifically for continuous reuse by identified, dedicated user groups. Metadata: Data about Data Data Lifecycle Management: Effective management and exploitation of data from its creation until it is obsolete. |
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