DataSHIELD - New Directions and Dimensions - Wilson+2017-published.pdf (1.52 MB)
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DataSHIELD – New Directions and Dimensions

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journal contribution
posted on 25.01.2018, 09:39 by Rebecca C. Wilson, Oliver W. Butters, Demetris Avraam, James Baker, Jonathan A. Tedds, Andrew Turner, Madeleine Murtagh, Paul R. Burton
In disciplines such as biomedicine and social sciences, sharing and combining sensitive individual-level data is often prohibited by ethical-legal or governance constraints and other barriers such as the control of intellectual property or the huge sample sizes. DataSHIELD (Data Aggregation Through Anonymous Summary-statistics from Harmonised Individual-levEL Databases) is a distributed approach that allows the analysis of sensitive individual-level data from one study, and the co-analysis of such data from several studies simultaneously without physically pooling them or disclosing any data. Following initial proof of principle, a stable DataSHIELD platform has now been implemented in a number of epidemiological consortia. This paper reports three new applications of DataSHIELD including application to post-publication sensitive data analysis, text data analysis and privacy protected data visualisation. Expansion of DataSHIELD analytic functionality and application to additional data types demonstrate the broad applications of the software beyond biomedical sciences.

History

Citation

Data Science Journal, 2017, 16 (21), pp. 1–21

Author affiliation

/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGY/School of Medicine/Department of Health Sciences

Version

VoR (Version of Record)

Published in

Data Science Journal

Publisher

Ubiquity Press

eissn

1683-1470

Acceptance date

05/04/2017

Copyright date

2017

Available date

25/01/2018

Publisher version

https://datascience.codata.org/articles/10.5334/dsj-2017-021/

Language

en

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