Synthetic Data for Sharing and Exploration in High-Performance Sport: Considerations for Application.

ABSTRACT:

Background

Synthetic data represent alternative data sources generated using mathematical procedures to address specific issues in research and practice. Synthetic data have emerging applications in clinical and medical data contexts and may assist in overcoming privacy issues to help support open science practice.

Objective

The present study discusses the applicability of an established synthetic data generation process using sequential tree-based algorithms (synthpop package in R) in the context of athlete monitoring data in sport, with the aim of providing an educational primer and discussion for potential application of these methods when exploring issues in the field sports and exercise sciences.

Methods

The software package in R, synthpop, was used in seven simu

SUBMITTER: Warmenhoven J 

PROVIDER: S-EPMC12460485 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature