Managing Information Quality in e-Science: A Case Study in Proteomics

  • Paolo Missier
  • Alun Preece
  • Suzanne Embury
  • Binling Jin
  • Mark Greenwood
  • David Stead
  • Al Brown
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3770)

Abstract

We describe a new approach to managing information quality (IQ) in an e-Science context, by allowing scientists to define the quality characteristics that are of importance in their particular domain. These preferences are specified and classified in relation to a formal IQ ontology, intended to support the discovery and reuse of scientists’ quality descriptors and metrics. In this paper, we present a motivating scenario from the biological sub-domain of proteomics, and use it to illustrate how the generic quality model we have developed can be expanded incrementally without making unreasonable demands on the domain expert who maintains it.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aebersold, R., Mann, M.: Mass spectrometry-based proteomics. Nature 422, 198–207 (2003)CrossRefGoogle Scholar
  2. 2.
    Baader, F., Horrocks, I., Sattler, U.: Description Logics. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, pp. 3–28. Springer, Heidelberg (2004)Google Scholar
  3. 3.
    English, L.: Improving Data Warehouse and Business Information Quality. Wiley, Chichester (1999)Google Scholar
  4. 4.
    Brazma, A., et al.: Minimum Information about a Microarray Experiment (MIAME) — Toward Standards for Microarray Data. Nature Genetics 29, 365–371 (2001)CrossRefGoogle Scholar
  5. 5.
    Missier, P., Embury, S., Greenwood, M., Preece, A., Jin, B.: An ontology-based approach to handling information quality in e-science. In: Proc 4th e-Science All Hands Meeting (2005)Google Scholar
  6. 6.
    Pandey, A., Mann, M.: Proteomics to study genes and genomes. Nature 405, 837–846 (2000)CrossRefGoogle Scholar
  7. 7.
    Patterson, S.D., Aebersold, R.H.: Proteomics: the first decade and beyond. Nature Genetics 33(Supplement), 311–323 (2003)CrossRefGoogle Scholar
  8. 8.
    Redman, T.C.: Data Quality for the Information Age. Artech House (1996)Google Scholar
  9. 9.
    Wang, R.Y., Ziad, M., Lee, Y.W.: Data Quality. Advances in Database Systems. Kluwer Academic Publishers, Dordrecht (2001)MATHGoogle Scholar
  10. 10.
    Wand, Y., Wang, R.: Anchoring data quality dimensions in ontological foundations. Communications of the ACM 39(11) (1996)Google Scholar
  11. 11.
    Wang, R.Y., Strong, D.M.: Beyond accuracy: What data quality means to data consumers. Journal of Management Information System 12(4) (1996)Google Scholar
  12. 12.
    Wroe, C., Stevens, R., Goble, C., Roberts, A., Greenwood, M.: A suite of DAML+OIL ontologies to describe bioinformatics web services and data. International Journal of Cooperative Information Systems 12(2), 197–224 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Paolo Missier
    • 1
  • Alun Preece
    • 2
  • Suzanne Embury
    • 1
  • Binling Jin
    • 2
  • Mark Greenwood
    • 1
  • David Stead
    • 3
  • Al Brown
    • 3
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUK
  2. 2.Computing ScienceUniversity of AberdeenAberdeenUK
  3. 3.Molecular and Cell BiologyUniversity of AberdeenAberdeenUK

Personalised recommendations