Conference Paper

iCurate: A Research Data Management System

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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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... Approaches for extracting metadata on an application level especially for HPC are harder to find. One of them is iCurate, the research data management system at the University of Huddersfield, UK [36]. It is tailored to HPC data and offers an automated metadata extraction. ...
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The deluge of dark data is about to happen. Lacking data management capabilities, especially in the field of supercomputing, and missing data documentation (i.e., missing metadata annotation) constitute a major source of dark data. The present work contributes to addressing this challenge by presenting ExtractIng, a generic automated metadata extraction toolkit. Existing metadata information of simulation output files scattered through the file system, can be aggregated, parsed and converted to the EngMeta metadata model. Use cases from computational engineering are considered to demonstrate the viability of ExtractIng. The evaluation results show that the metadata extraction is simulation-code independent in the sense that it can handle data outputs from various fields of science, is easy to integrate into simulation workflows and compatible with a multitude of computational environments.
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Conference Paper
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... They introduce persistent identifiers to reference the data. iCurate is a data management system that is not based on OAIS but has a strong focus on the management layer and metadata [17]: Annotation, retrieval, and validation of metadata should be possible. The system is adapted to the HPC workflow, that means users can specify already in the Portable Batch System (PBS) file some metadata which is added to the output files when the job is complete. ...
Conference Paper
This paper targets the challenges of research data management with a focus on High Performance Computing (HPC) and simulation data. Main challenges are discussed: The Big Data qualities of HPC research data, technical data management, organizational and administrative challenges. Emerging from these challenges, requirements for a feasible HPC research data management are derived and an alternative data life cycle is proposed. The requirement analysis includes recommendations which are based on a modified OAIS architecture: To meet the HPC requirements of a scalable system, metadata and data must not be stored together. Metadata keys are defined and organizational actions are recommended. Moreover, this paper contributes by introducing the role of a Scientific Data Manager, who is responsible for the institution’s data management and taking stewardship of the data.
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New checklist for a data management plan
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