DC 2012 - Leveraging the DDI Model for Linked Statistical Data in the Social, Behavioural, and Economic Sciences
1. Leveraging the DDI Model for Linked Statistical Data
in the Social, Behavioural, and Economic Sciences
DC 2012
05.09.2012
Thomas Bosch Richard Cyganiak
GESIS – Leibniz Institute for the Social Digital Enterprise Research Institute,
Sciences, Germany Ireland
thomas.bosch@gesis.org richard@cyganiak.de
Joachim Wackerow Benjamin Zapilko
GESIS – Leibniz Institute for the Social GESIS – Leibniz Institute for the Social
Sciences, Germany Sciences, Germany
joachim.wackerow@gesis.org benjamin.zapilko@gesis.org
3. What is DDI?
• DDI (Data Documentation Initiative)
• Established international standard for the documentation and
management of data from the social, behavioral, and economic
sciences
• Data model for statistical data
• Supports the entire research data lifecycle
• Focus on microdata
• Structured high quality metadata
• enable secondary analysis without the need to contact the primary
researcher
• Enables the re-use of metadata of existing studies for
designing new studies
• Currently specified in XML Schema
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4. How was the DDI Ontology developed?
• DDI subset
• of the most important DDI elements
• Use cases
• Experts in the statistics domain formulated use cases which are seen
as most significant to solve frequent problems
• Most important use case: discover microdata connected with multiple
studies
• Leverage existing DDI-XML docs to DDI-RDF automatically
• Direct mapping
• Generic mapping (Bosch and Mathiak, 2011)
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5. Why DDI as Linked Data?
• Currently no such ontology available
• To increase visibility of data holdings using mainstream Web
technologies
• To open DDI to the Linked Data community
• To process DDI-RDF by RDF tools
• To link DDI-RDF to other RDF data
• To better identify opportunities for merging datasets
• To enable inferencing
• To research microdata within the LOD cloud
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6. What other metadata standards
vocabularies are used?
• Dublin Core Metadata Element Set, Version 1.1
• DCMI Metadata Terms
• SKOS
• SDMX RDF Data Cube Vocabulary
• ISO/IEC 11179
• ISO 19115
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7. Discovery Use Case
• Which studies are connected with a specific coverage consisting of the 3
dimensions: time, country, and subject?
• What questions with a specific question text are contained in the study
questionnaire?
• What questions are connected with a concept with a specific label?
• What questions are combined with a variable with an associated coverage
consisting of the 3 dimensions time, country, and subject?
• What concepts are linked to particular variables or questions?
• What representation does a specific variable have?
• What codes and what categories are part of this representation?
• What variable label does a variable with a particular variable name have?
• What‘s the maximum value of a certain variable?
• What are the absolute and relative frequencies of a specific code?
• What data files contain the entire dataset?
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25. Acknowledgements
• Archana Bidargaddi (NSD - Norwegian Social Science Data Services, Norway)
• Franck Cotton (INSEE - Institut National de la Statistique et des Études
Économiques, France)
• Richard Cyganiak (DERI - Digital Enterprise Research Institute, Ireland)
• Daniel Gilman (BLS - Bureau of Labor Statistics, USA)
• Marcel Hebing (SOEP - German Socio-Economic Panel Study, Germany)
• Larry Hoyle (University of Kansas, USA)
• Jannik Jensen (DDA - Danish Data Archive, Denmark)
• Stefan Kramer (CISER - Cornell Institute for Social and Economic Research, USA)
• Amber Leahey (Scholars Portal Project - University of Toronto, Canada)
• Abdul Rahim (Metadata Technologies Inc., USA)
• John Shepherdson (UK Data Archive, UK)
• Dan Smith (Algenta Technologies Inc., USA)
• Humphrey Southall (Department of Geography, UK Portsmouth University, UK)
• Wendy Thomas (MPC - Minnesota Population Center, USA)
• Johanna Vompras (University Bielefeld Library, Germany)
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