Abstract
The complexity of modern biological database management systems indicates the need of integrated metadata repositories for harmonized and high-quality assured data processing. Such systems should allow for the derivation of specific producer-oriented indicators monitoring the quality of the final datasets and statistics provided to the end-users. In this paper, we offer a quality assurance and assessment framework for biological dataset management from both the producers’ and users’ perspective. In order to assist the producers in high-quality end-results, we consider the integration of a process-oriented data/metadata model enriched with quality declaration metadata, like quality indicators, for the entire process of dataset management. With the automatic manipulation of both data and “quality” metadata, we assure standardization of processes and error detection and reduction. Regarding the user assessment of final results, we discuss trade-offs among certain quality components (such as accuracy, timeliness, relevance, comparability, etc.) and offer indicative user-oriented quality indicators.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arah O.A., Westert G.P., and Hurst J. et al. (2006). Conceptual framework for the OECD Health Care Quality Indicators project. International Journal for Quality in Health Care, Vol. 18, pp. 5–13.
Eurostat (2009). Quality in Statistics – ESS quality and performance indicators – 2009 progress report, accessed in CIRCA database, members only library.
IMF (2003). Data Quality Assessment Framework. Internet: http://dsbb.imf.org/vgn/images/ pdfs/dqrs_factsheet.pdf, accessed October 12th, 2009.
Legido-Quigley H., McKee M., Nolte E., and Glinos I.A. (2008). Assuring the Quality of Health Care in the European Union: A Case for Action, World Health Organization: Copenhagen, ISBN 9289071931.
Linden H., and Papageorgiou H. (2004). Standard Quality Indicators. European Conference on Quality and Methodology in Official Statistics: Mainz, Germany. Also published in Statistical Research Reference Material, Japan Statistical Research Institute, Hosei University, No 93, pp. 61–86. Tokyo, Japan. ISSN 0288-8734, 2006.
Mainz J. (2003). Defining and classifying clinical indicators for quality improvement. International Journal for Quality in Health Care, Vol. 15, pp. 523–530.
OECD (2003). Quality dimensions, core values for OECD statistics and procedures for planning and evaluating statistical activities. Internet: http://www.oecd.org/dataoecd/26/38/21687665.pdf, accessed October 12th, 2009.
Vardaki M., and Papageorgiou H. (2007). Statistical data and metadata quality assessment, in Handbook of Research on Public Information Technology, eds. G.D. Garson and M. Khosrow-Pour, Vol. 2, IGI Global, USA, pp. 604–614.
Vardaki M., Papageorgiou H., and Pentaris F. (2009). A statistical metadata model for clinical trials’ data management. Computer Methods and Programs in Biomedicine, Vol. 95, pp. 129–145.
Acknowledgment
This research was partially funded by the University of Athens, Special Account for Research Grants, Grant no. 70/4/8758.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this paper
Cite this paper
Vardaki, M., Papageorgiou, H. (2010). On Quality Assurance and Assessment of Biological Datasets and Related Statistics. In: Arabnia, H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol 680. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5913-3_11
Download citation
DOI: https://doi.org/10.1007/978-1-4419-5913-3_11
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-5912-6
Online ISBN: 978-1-4419-5913-3
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)