Automated Quality Evaluation for a More Effective Data Peer Review

Dusterhus, A.; Hense, A.. 2014 Automated Quality Evaluation for a More Effective Data Peer Review. Data Science Journal, 13. 67-78. https://doi.org/10.2481/dsj.14-009

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Official URL: http://dx.doi.org/10.2481/dsj.14-009

Abstract/Summary

A peer review scheme comparable to that used in traditional scientific journals is a major element missing in bringing publications of raw data up to standards equivalent to those of traditional publications. This paper introduces a quality evaluation process designed to analyse the technical quality as well as the content of a dataset. This process is based on quality tests, the results of which are evaluated with the help of the knowledge of an expert. As a result, the quality is estimated by a single value only. Further, the paper includes an application and a critical discussion on the potential for success, the possible introduction of the process into data centres, and practical implications of the scheme.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.2481/dsj.14-009
ISSN:	1683-1470
Additional Keywords:	Data peer review, Data publication, Quality evaluation, Statistical quality assurance, Meteorological data
Date made live:	11 Jun 2014 14:14 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/507423

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.2481/dsj.14-009

ISSN:

1683-1470

Additional Keywords:

Data peer review, Data publication, Quality evaluation, Statistical quality assurance, Meteorological data

Date made live:

11 Jun 2014 14:14 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/507423