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DataSHIELD – new directions and dimensions

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journal contribution
posted on 2023-06-09, 05:50 authored 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

Publication status

  • Published

File Version

  • Published version

Journal

Data Science Journal

ISSN

1683-1470

Publisher

Committee on Data for Science and Technology

Issue

21

Volume

16

Page range

1-21

Department affiliated with

  • History Publications

Research groups affiliated with

  • Sussex Humanities Lab Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2017-04-19

First Open Access (FOA) Date

2017-04-19

First Compliant Deposit (FCD) Date

2017-04-19

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