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dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system.

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New dsSynthetic package generates realistic synthetic data for DataSHIELD users. This enables code development and data harmonization without direct access to sensitive information, improving federated analysis.

Keywords:
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Area of Science:

  • Bioinformatics
  • Data Science
  • Statistical Computing

Background:

  • Federated analysis platforms like DataSHIELD enable remote analysis of sensitive data, but limit direct data visibility for users.
  • This lack of visibility poses challenges for code development and data harmonization tasks.

Purpose of the Study:

  • To develop a method for generating realistic, non-disclosive synthetic data within the DataSHIELD environment.
  • To facilitate code development and data preparation for users working with sensitive data remotely.

Main Methods:

  • Development of the dsSynthetic R package for DataSHIELD.
  • Utilizing existing packages to generate synthetic data that mimics the characteristics of real data.

Main Results:

  • The dsSynthetic package successfully generates realistic synthetic data.
  • Demonstrated utility of synthetic data for tasks including writing analysis scripts and harmonizing data.

Conclusions:

  • Synthetic data generated by dsSynthetic can effectively support DataSHIELD users in developing and refining analysis code.
  • This approach enhances the usability of federated analysis platforms by mitigating data access limitations.