1. Modelling and computing the quality of information in e-science Paolo Missier, Suzanne Embury, Mark Greenwood School of Computer Science University of Manchester, UK Alun Preece, Binling Jin Department of Computing Science University of Aberdeen, UK http://www.qurator.org Aberdeen, 24/1/07

4. Taxonomy for data quality dimensions

7. Correctness in biology - examples No false positives: Every protein in the output is actually present in the cell sample Generate peptides peak lists, match peak lists (eg Imprint) Qualitative proteomics: Protein identification No false positives, no false negatives Microarray data analysis Transcriptomics: Gene expression report (up/down-regulation) Functional annotation f for p correct if function f can reliably be attributed to p Manual curation Uniprot protein annotation Correctness Creation process Data type

10. Example: protein identification Data output Protein identification algorithm “ Wet lab” experiment Protein Hitlist Protein function prediction Correct entry  true positive This evidence is independent of the algorithm / SW package It is readily available and inexpensive to obtain Evidence : mass coverage (MC) measures the amount of protein sequence matched Hit ratio (HR) gives an indication of the signal to noise ratio in a mass spectrum ELDP reflects the completeness of the digestion that precedes the peptide mass fingerprinting

11. Correctness of protein identification Estimator function: (computes a score rather than a probability) PMF score = (HR x 100) + MC + (ELDP x 10) Prediction performance – comparing 3 models: ROC curve: True positives vs false positives

19. Example: original proteomics workflow Taverna workflow Quality flow embedding point

20. Example: embedded quality workflow

21. Interactive conditions / actions

22. Generic quality process pattern Collect evidence - Fetch persistent annotations - Compute on-the-fly annotations <variables <var variableName=&quot; Coverage “ evidence=&quot; q:Coverage &quot;/> <var variableName=&quot; PeptidesCount “ evidence=&quot; q:PeptidesCount &quot;/> </variables> Evaluate conditions Execute actions <action> <filter> <condition> ScoreClass in {``q:high'', ``q:mid''} and Coverage > 12 </condition> </filter> </action> Compute assertions Classifier Classifier Classifier <QualityAssertion serviceName=&quot; PIScoreClassifier &quot; serviceType=&quot; q:PIScoreClassifier &quot; tagSemType=&quot; q:PIScoreClassification &quot; tagName=&quot; ScoreClass &quot; Persistent evidence

23. A semantic model for quality concepts Quality “upper ontology” (OWL) Evidence annotations are class instances Quality evidence types Evidence Meta-data model (RDF)

24. Main taxonomies and properties assertion-based-on-evidence: QualityAssertion  QualityEvidence is-evidence-for: QualityEvidence  DataEntity Class restriction: MassCoverage   is-evidence-for . ImprintHitEntry Class restriction: PIScoreClassifier   assertion-based-on-evidence . HitScore PIScoreClassifier   assertion-based-on-evidence . Mass Coverage

25. The ontology-driven user interface Detecting inconsistencies: no annotators for this Evidence type Detecting inconsistencies: Unsatisfied input requirements for Quality Assertion

26. Qurator architecture

27. Quality-aware query processing