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dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system

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...

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Detalles Bibliográficos
Autores principales: Banerjee, Soumya, Bishop, Tom R. P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235208/
https://www.ncbi.nlm.nih.gov/pubmed/35761417
http://dx.doi.org/10.1186/s13104-022-06111-2
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author Banerjee, Soumya
Bishop, Tom R. P.
author_facet Banerjee, Soumya
Bishop, Tom R. P.
author_sort Banerjee, Soumya
collection PubMed
description 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.
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spelling pubmed-92352082022-06-28 dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system Banerjee, Soumya Bishop, Tom R. P. BMC Res Notes Research Note 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. BioMed Central 2022-06-27 /pmc/articles/PMC9235208/ /pubmed/35761417 http://dx.doi.org/10.1186/s13104-022-06111-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Note
Banerjee, Soumya
Bishop, Tom R. P.
dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system
title dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system
title_full dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system
title_fullStr dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system
title_full_unstemmed dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system
title_short dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system
title_sort dssynthetic: synthetic data generation for the datashield federated analysis system
topic Research Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235208/
https://www.ncbi.nlm.nih.gov/pubmed/35761417
http://dx.doi.org/10.1186/s13104-022-06111-2
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