660-2935-1-PB.pdf (2.39 MB)
DataSHIELD – new directions and dimensions
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 BurtonIn 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 JournalISSN
1683-1470Publisher
Committee on Data for Science and TechnologyExternal DOI
Issue
21Volume
16Page range
1-21Department affiliated with
- History Publications
Research groups affiliated with
- Sussex Humanities Lab Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2017-04-19First Open Access (FOA) Date
2017-04-19First Compliant Deposit (FCD) Date
2017-04-19Usage metrics
Categories
No categories selectedLicence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC