ICurate: A Research Data Management System

Shuo Liang; Violeta Holmes; Grigoris Antoniou; Joshua Higgins

doi:10.1007/978-3-319-26181-2_4

ICurate: A Research Data Management System

Shuo Liang, Violeta Holmes, Grigoris Antoniou, Joshua Higgins

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

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.

Original language	English
Title of host publication	Multi-disciplinary Trends in Artificial Intelligence
Subtitle of host publication	9th International Workshop, MIWAI 2015, Proceedings
Editors	Antonis Bikakis, Xianghan Zheng
Publisher	Springer Verlag
Pages	39-47
Number of pages	9
ISBN (Electronic)	9783319261812
ISBN (Print)	9783319261805
DOIs	https://doi.org/10.1007/978-3-319-26181-2_4
Publication status	Published - 29 Nov 2015
Event	9th International Workshop on Multi-Disciplinary Trends in Artificial Intelligence - Fuzhou, China Duration: 13 Nov 2015 → 15 Nov 2015 Conference number: 9 http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=43492&copyownerid=31599 (Link to Conference Information)

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9426
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	9th International Workshop on Multi-Disciplinary Trends in Artificial Intelligence
Abbreviated title	MIWAI 2015
Country/Territory	China
City	Fuzhou
Period	13/11/15 → 15/11/15
Internet address	http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=43492&copyownerid=31599 (Link to Conference Information)

Access to Document

10.1007/978-3-319-26181-2_4Licence: Other

http://eprints.hud.ac.uk/id/eprint/31633/Licence: Unspecified

Cite this

Liang, S., Holmes, V., Antoniou, G., & Higgins, J. (2015). ICurate: A Research Data Management System. In A. Bikakis, & X. Zheng (Eds.), Multi-disciplinary Trends in Artificial Intelligence: 9th International Workshop, MIWAI 2015, Proceedings (pp. 39-47). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9426). Springer Verlag. https://doi.org/10.1007/978-3-319-26181-2_4

Liang, Shuo ; Holmes, Violeta ; Antoniou, Grigoris et al. / ICurate : A Research Data Management System. Multi-disciplinary Trends in Artificial Intelligence: 9th International Workshop, MIWAI 2015, Proceedings. editor / Antonis Bikakis ; Xianghan Zheng. Springer Verlag, 2015. pp. 39-47 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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, {\textquoteleft}iCurate{\textquoteright}, 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 {\textquoteleft}i{\textquoteright} 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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author = "Shuo Liang and Violeta Holmes and Grigoris Antoniou and Joshua Higgins",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Liang, S, Holmes, V, Antoniou, G & Higgins, J 2015, ICurate: A Research Data Management System. in A Bikakis & X Zheng (eds), Multi-disciplinary Trends in Artificial Intelligence: 9th International Workshop, MIWAI 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9426, Springer Verlag, pp. 39-47, 9th International Workshop on Multi-Disciplinary Trends in Artificial Intelligence, Fuzhou, China, 13/11/15. https://doi.org/10.1007/978-3-319-26181-2_4

ICurate : A Research Data Management System. / Liang, Shuo; Holmes, Violeta; Antoniou, Grigoris et al.

Multi-disciplinary Trends in Artificial Intelligence: 9th International Workshop, MIWAI 2015, Proceedings. ed. / Antonis Bikakis; Xianghan Zheng. Springer Verlag, 2015. p. 39-47 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9426).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - ICurate

T2 - 9th International Workshop on Multi-Disciplinary Trends in Artificial Intelligence

AU - Liang, Shuo

AU - Holmes, Violeta

AU - Antoniou, Grigoris

AU - Higgins, Joshua

N1 - Conference code: 9

PY - 2015/11/29

Y1 - 2015/11/29

N2 - 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.

AB - 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.

KW - Big data

KW - Data curation

KW - Humancomputer interaction

KW - Linked Open Data

KW - Research Data Management

KW - Research support

KW - Semantic web

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U2 - 10.1007/978-3-319-26181-2_4

DO - 10.1007/978-3-319-26181-2_4

M3 - Conference contribution

AN - SCOPUS:84952323240

SN - 9783319261805

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 39

EP - 47

BT - Multi-disciplinary Trends in Artificial Intelligence

A2 - Bikakis, Antonis

A2 - Zheng, Xianghan

PB - Springer Verlag

Y2 - 13 November 2015 through 15 November 2015

ER -

Liang S, Holmes V, Antoniou G, Higgins J. ICurate: A Research Data Management System. In Bikakis A, Zheng X, editors, Multi-disciplinary Trends in Artificial Intelligence: 9th International Workshop, MIWAI 2015, Proceedings. Springer Verlag. 2015. p. 39-47. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-26181-2_4

ICurate: A Research Data Management System

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