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

doi:10.5334/dsj-2017-021

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

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Abstract

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 language	English
Article number	21
Journal	Data Science Journal
Volume	16
DOIs	https://doi.org/10.5334/dsj-2017-021
Publication status	Published - 19 Apr 2017

Keywords

Data privacy
Distributed data
Sensitive data

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abstract = "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 {\DH}ataSHIELD 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.",

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AU - Baker, James

AU - Tedds, Jonathan A.

AU - Turner, Andrew

AU - Murtagh, Madeleine

AU - Burton, Paul R.

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N2 - 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.

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DataSHIELD - New directions and dimensions

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