DataSHIELD - New directions and dimensions

Rebecca C. Wilson*, Oliver W. Butters, Demetris Avraam, James Baker, Jonathan A. Tedds, Andrew Turner, Madeleine Murtagh, Paul R. Burton

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

18 Citations (Scopus)
340 Downloads (Pure)


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 ÐataSHIELD 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.

Original languageEnglish
Article number21
JournalData Science Journal
Publication statusPublished - 19 Apr 2017


  • Data privacy
  • Distributed data
  • Sensitive data


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