useR! 2024

Statistical method development is partly driven through applications and the complexities of real world datasets. But we all know that sharing these datasets is often difficult because of legal, ethical or practical concerns, thus making the creation of synthetic data closely reproducing the real world data an attractive option circumventing such issues. Similarly, generating realistic data is important for method comparison studies that are crucial for establishing the evidence base for statistical methods.

Yet there seems to be little consensus on how to actually code data generators. As a first step to make coding of simulations more accessible, we provide a systematic scoping review of existing R packages to support data generation (results publicly available on osf). We will also include our own package that aims to complement the existing ecosystem by building a library of interesting data generators derived from real-world datasets.

A single tool is not enough to fit all needs, so we will discuss how these tools help you to support open science principles by facilitating sharing of data from your own research, or by generating data for your own methods development.