Düsterhus, André and Hense, Andreas (2014) Automated quality evaluation for a more effective data peer review. Data Science Journal, 13. pp. 67-78. ISSN 1683-1470
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Abstract
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: | Article |
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Keywords: | Data peer review; Data publication; Quality evaluation; Statistical quality assurance; Meteorological data; |
Academic Unit: | Faculty of Social Sciences > Geography |
Item ID: | 12288 |
Identification Number: | 10.2481/dsj.14-009 |
Depositing User: | André Düsterhus |
Date Deposited: | 30 Jan 2020 12:32 |
Journal or Publication Title: | Data Science Journal |
Publisher: | Ubiquity Press |
Refereed: | Yes |
Related URLs: | |
URI: | https://mu.eprints-hosting.org/id/eprint/12288 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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