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Towards the Discovery of Person-Level Data (SemStats, ISWC 2013) [2013.10]
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Towards the Discovery of Person-Level Data (SemStats, ISWC 2013) [2013.10]

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  • 1. Towards the Discovery of Person-Level Data International Workshop on Semantic Statistics 22 October 2013, Sydney, Australia Thomas Bosch1, Benjamin Zapilko1, Joachim Wackerow1, Arofan Gregory2 1GESIS – Leibniz Institute for the Social Sciences, Germany {first name.last name}@gesis.org 2Open Data Foundation, USA agregory@opendatafoundation.org
  • 2. Why DDI as Linked Data?
  • 3. Overview class overview question 0..* 0..* Question 0..* 0..* Variable analysisUnit variable 0..1 1..* 0..* 0..* skos:Concept AnalysisUnit question 0..* containsVariable 0..1 Instrument Questionnaire 1..* 1..* product Study universe 0..* analysisUnit inGroup 0..1 0..* universe 0..* «union» StudyGroup 0..* 1 1..* skos:Concept Universe 1 universe universe 1 0..* 1..* dcat:Dataset LogicalDataSet 0..*
  • 4. Use Cases
  • 5. Where to search for specific data?
  • 6. What microdata according to specific metadata exists?
  • 7. What datasets are associated with the microdata?
  • 8. What aggregated data according to specific metadata exists?
  • 9. What datasets are associated with the aggregated data?
  • 10. From which microdata datasets is the aggregated dataset derived?
  • 11. What summary statistics does a variable have?
  • 12. What category statistics does a variable representation have?
  • 13. What microdata datasets are created by the research institute 'GESIS'
  • 14. Conclusion
  • 15. Thank you for your attention…
  • 16. Backup Slides
  • 17. Why DDI as Linked Data? • Users discover data in the Linked Open Data Cloud using DDI metadata • Users can search for data • Data providers publish searchable / accessable metadata • The Discovery specification contains the most important DDI concepts for the discovery purpose • We integrated well elaborated vocabularies • We use SW technologies
  • 18. Overview • Study • LogicalDataSet: the dataset where we save the actual data • Universe: for whom is the study applied to? (e.g. all women in Germany) • Analysis Unit (e.g. persons or households) • Instrument: How do we want to measure? (e.g. 'What is your sex?') • Concept: What do we want to measure? • Variable: Where do we save what we measured? (e.g. sex)
  • 19. Future Work • Physical description of rectangular data especially CSV data – We integrate this description in discovery • How originate aggregated data on the basis of microdata? – Aggregation method is described in the form machines can process it – We see a need that this area should be explored further in order to describe the relationship between aggregate data and microdata more detailed
  • 20. Acknowledgements 26 experts from the statistical community and the Linked Data community coming from 12 different countries contributed to this work. They were participating in the events mentioned below. • 1st workshop on 'Semantic Statistics for Social, Behavioural, and Economic Sciences: Leveraging the DDI Model for the Linked Data Web' at Schloss Dagstuhl - Leibniz Center for Informatics, Germany in September 2011 • Working meeting in the course of the 3rd Annual European DDI Users Group Meeting (EDDI11) in Gothenburg, Sweden in December 2011 • 2nd workshop on 'Semantic Statistics for Social, Behavioural, and Economic Sciences: Leveraging the DDI Model for the Linked Data Web' at Schloss Dagstuhl - Leibniz Center for Informatics, Germany in October 2012 • Working meeting at GESIS - Leibniz Institute for the Social Sciences in Mannheim, Germany in February 2013