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DAMES: Data Management through e-Social Science . NCeSS Research Node University of Stirling / University of Glasgow 2008-2011. Rationale . ‘Data management’ refers to operations on data widely performed by social science researchers Matching data files together ‘Cleaning’ data

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dames data management through e social science

DAMES: Data Management through e-Social Science

NCeSS Research Node

University of Stirling / University of Glasgow 2008-2011

DAMES - Data Management through e-Social Science

rationale
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)

DAMES - Data Management through e-Social Science

rationale1
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’

DAMES

ONS support

ESDS support

UK Data Archive

Qualidata

Flagship social surveys

Office for National Statistics

Administrative data

Specialist academic outputs

NCRM workshops

Essex summer school

ESRC RDI initiatives

CQeSS

Data Management

Data access / collection

Data Analysis

DAMES - Data Management through e-Social Science

dames node major objectives
DAMES Node – major objectives

Case studies, provision and support for data management in the social sciences

4 social science themes

  • Grid Enabled Specialist Data Environments
    • occupations; education; ethnicity
  • Micro-simulation on social care data
  • Linking e-Health and social science databases
  • Training and interfaces for data management support

Underlying computer science research themes

    • Metadata; data abstraction; data fusion; workflow modelling; data security

DAMES - Data Management through e-Social Science

dames social science contributions
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

DAMES - Data Management through e-Social Science

node investigators
Node investigators

DAMES - Data Management through e-Social Science

example grid enabled occupational data environment geode
Example: Grid Enabled Occupational Data Environment (GEODE)

DAMES - Data Management through e-Social Science

dames overview
DAMES - Overview
  • Driven by social science needs for both introductory and advanced data management operations

DAMES - Data Management through e-Social Science