Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/3102
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Enabling Quantitative Data Analysis through e-Infrastructure
Authors: Tan, Koon Leai Larry
Lambert, Paul
Turner, Kenneth J
Blum, Jesse Michael
Gayle, Vernon
Jones, Simon
Sinnott, Richard
Warner, Guy
Contact Email: sbj@cs.stir.ac.uk
Keywords: Data Management
Quantitative Data
e-Infrastructure
Workflows
Metadata
Issue Date: Nov-2009
Publisher: Sage
Citation: Tan KLL, Lambert P, Turner KJ, Blum JM, Gayle V, Jones S, Sinnott R & Warner G (2009) Enabling Quantitative Data Analysis through e-Infrastructure, Social Science Computer Review, 27 (4), pp. 539-552.
Abstract: This paper discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. We highlight how a number of activities which are central to quantitative data analysis, referred to as 'data management', can benefit from e-infrastructural support. We conclude by discussing how these issues are relevant to the DAMES (Data Management through e-Social Science) research Node, an ongoing project that aims to develop e-Infrastructural resources for quantitative data analysis in the social sciences.
Type: Journal Article
URI: http://hdl.handle.net/1893/3102
DOI Link: http://dx.doi.org/10.1177/0894439309332647
Rights: The final, definitive version of this article has been published in the Journal, Social Science Computer Review, 27/4, 2009, © SAGE Publications, Inc., 2009 by SAGE Publications, Inc. at the Social Science Computer Review page: http://ssc.sagepub.com/ on SAGE Journals Online: http://online.sagepub.com/
Affiliation: University of Stirling
Sociology/Social Pol&Criminology
Computing Science - CSM Dept
Computing Science and Mathematics
Sociology/Social Pol&Criminology
Computing Science - CSM Dept
University of Glasgow
Computing Science - CSM Dept

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