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An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences
Styliani-Christina Fragkouli1,2, Somya Iqbal3, Lisa Crossman4,5
1Department of Biology, National and Kapodistrian University of Athens, Athens 15772, Greece.
NAR Genomics and Bioinformatics
|February 13, 2026
Summary
Synthetic data (SD) is crucial for life sciences, but robust evaluation metrics are lacking. This review highlights the need for standardized methods to ensure the reliability and trustworthiness of artificial datasets for scientific discovery.
Area of Science:
- Life Sciences
- Computational Biology
- Bioinformatics
Background:
- Synthetic data (SD) addresses data scarcity and privacy in life sciences.
- Artificial datasets aid in developing and validating computational methods.
- Adoption of SD relies on rigorous evaluation metrics for fidelity and reliability.
Purpose of the Study:
- To systematically review current practices for assessing synthetic data evaluation metrics.
- To identify gaps in the evaluation of SD across distinct life sciences domains.
Main Methods:
- Systematic literature review following PRISMA guidelines.
- Examination of six critical domains related to SD assessment.
- Analysis of practices for evaluating synthetic datasets in life sciences.
Main Results:
- SD generation methods are evolving rapidly.
- Systematic evaluation of SD is frequently overlooked.
- Lack of standardized evaluation hinders comparison, validation, and trust in SD.
Conclusions:
- There is an urgent need for robust, standardized evaluation approaches for SD.
- Standardized metrics will enhance confidence and guide responsible implementation of SD.
- This review lays groundwork for domain-specific, interoperable standards for SD.
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