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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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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. |
format | Online Article Text |
id | pubmed-9235208 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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