1. DAMES - Data Management through e-Social Science 1 DAMES: Data Management through e-Social Science NCeSS Research Node University of Stirling / University of Glasgow 2008-2011

2. DAMES - Data Management through e-Social Science 2 Rationale Data management refers to operations on data widely performed by social science researchers Matching data files together Cleaning data Operationalising variables Why is e-Social Science relevant? Dealing with distributed, heterogeneous datasets Generic data requirements / provisions Lack of previous systematic standards (e.g. metadata; security; citation procedures; resources to review/obtain suitable data)

3. DAMES - Data Management through e-Social Science 3 Rationale A substantial social science need for improved standards and resources in data management In practice, social researchers often spend more time on data management than any other part of the research process A methodology of data management is relevant to social science literatures on harmonisation, comparability Data access / collection Data Management Data Analysis UK Data Archive Qualidata Flagship social surveys Office for National Statistics Administrative data Specialist academic outputs DAMES ONS support ESDS support NCRM workshops Essex summer school ESRC RDI initiatives CQeSS

4. DAMES - Data Management through e-Social Science 4 DAMES Node – major objectives Case studies, provision and support for data management in the social sciences 4 social science themes 1)Grid Enabled Specialist Data Environments occupations; education; ethnicity 2)Micro-simulation on social care data 3)Linking e-Health and social science databases 4)Training and interfaces for data management support Underlying computer science research themes –Metadata; data abstraction; data fusion; workflow modelling; data security

5. DAMES - Data Management through e-Social Science 5 DAMES – social science contributions Work and stratification Understanding occupational inequalities Understanding vocational and academic qualifications Data on immigrant and autochthonous populations Ageing and social care Social care needs and requirements Microsimulation for an ageing population Health inequalities New resources relating social and economic data and health patterns

6. DAMES - Data Management through e-Social Science 6 Node investigators University of Stirling Social scienceComputer Science Dr Paul Lambert (sociology)Prof. Ken Turner Dr Vernon Gayle (sociology)Dr Simon Jones Prof. Alison Bowes (sociology/social policy) Dr Margaret Maxwell (sociology of health) University of Glasgow (National e-Science Centre) Prof. David Bell (economics)Prof. Richard Sinnott

7. DAMES - Data Management through e-Social Science 7 Example: Grid Enabled Occupational Data Environment (GEODE)

8. DAMES - Data Management through e-Social Science 8 DAMES - Overview Driven by social science needs for both introductory and advanced data management operations