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dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system

Authors :
Soumya Banerjee
Tom R. P. Bishop
Source :
BMC Research Notes, Vol 15, Iss 1, Pp 1-6 (2022)
Publication Year :
2022
Publisher :
BMC, 2022.

Abstract

Abstract Objective Platforms such as DataSHIELD allow users to analyse sensitive data remotely, without having full access to the detailed data items (federated analysis). While this feature helps to overcome difficulties with data sharing, it can make it challenging to write code without full visibility of the data. One solution is to generate realistic, non-disclosive synthetic data that can be transferred to the analyst so they can perfect their code without the access limitation. When this process is complete, they can run the code on the real data. Results We have created a package in DataSHIELD (dsSynthetic) which allows generation of realistic synthetic data, building on existing packages. In our paper and accompanying tutorial we demonstrate how the use of synthetic data generated with our package can help DataSHIELD users with tasks such as writing analysis scripts and harmonising data to common scales and measures.

Details

Language :
English
ISSN :
17560500
Volume :
15
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Research Notes
Publication Type :
Academic Journal
Accession number :
edsdoj.783da531054f45cc80c8659c5d91e715
Document Type :
article
Full Text :
https://doi.org/10.1186/s13104-022-06111-2