|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Metadata Creation, Transformation and Discovery for Social Science Data Management: The DAMES Project Infrastructure|
|Authors:||Blum, Jesse Michael|
Tan, Koon Leai Larry
Turner, Kenneth J
|Citation:||Blum JM, Warner G, Jones S, Lambert P, Dawson A, Tan KLL & Turner KJ (2009) Metadata Creation, Transformation and Discovery for Social Science Data Management: The DAMES Project Infrastructure, IASSIST Quarterly, 33 (1), pp. 23-30.|
|Abstract:||This paper discusses the use of metadata, underpinned by DDI (Data Documentation Initiative), to support social science data management. Social science data management refers broadly to the discovery, preparation, and manipulation of social science data for the purposes of research and analysis. Typical tasks include recoding variables within a dataset, and linking data from different sources. A description is given of the DAMES project (Data Management through e-Social Science), a UK project which is building resources and services to support quantitative social science data management activities. DAMES provides generic facilities for performing (and recording) operations on data. Specific resources include support for analysis through micro-simulation, and support for access to specialist data on occupations, educational qualifications, measures of ethnicity and immigration, social care, and mental health. The DAMES project tools and services can generate, use, transform, and search metadata that describe social science datasets (including microdata from social survey datasets and aggregate-level macrodata). On DAMES, these metadata are described by various standards including DDI Version 2, DDI Version 3, JSDL (Job Submission Definition Language), and the purpose-designed JFDL (Job Flow Definition Language). The paper describes how DAMES uses metadata with a range of resources that are integrated with a job execution infrastructure, a Web portal, and a tool for data fusion.|
|Rights:||The publisher has granted permission for use of this article in this Repository. The article was first published in IASSIST Quarterly by International Association for Social Science Information Services and Technology.|
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