Shuo, Liang, Holmes, Violeta, Antoniou, Grigoris and Higgins, Joshua (2015) iCurate: A Research Data Management System. In: Multi-disciplinary Trends in Artificial Intelligence. Lecture Notes in Computer Science (9426). Springer, Fuzhou, China,, pp. 39-47. ISBN ISBN 978-3-319-26181-2

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Abstract

Scientific research activities generate a large amount of data, which varies in format, volume, structure and ownership. Although there are revision control systems and databases developed for data archiving, the traditional data management methods are not suitable for High Performance Computing (HPC) systems. The files in such systems do not have semantic annotations and cannot be archived and managed for public dissemination.
We have proposed and developed a Research Data Management (RDM)system, iCurate', which provides easy-to-use RDM facilities with semantic annotations. The system incorporates Metadata Retrieval, Departmental Archiving, Workflow Management System, Meta data Validation and Self Inferencing. The `i' emphasises the user-oriented design. iCurate will support researchers by annotating their data in a clearer and machine readable way from its production to publication for the future reuse

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