Manuscripts that use synthetic data are likely to raise practical questions for journals before they raise policy questions for publishers.
Synthetic data is data generated to resemble real-world data. It may be produced through statistical models, simulations, privacy-preserving methods, or AI systems that learn patterns from an existing dataset. In research, it can help protect sensitive records, fill gaps in limited datasets, model rare cases, or test early hypotheses when real data cannot be shared.
For editors and reviewers, the first question is not whether synthetic data was used. It is what role it played in the article.
Was it used only to test a method? Was it used to replace sensitive source data? Did it support a figure, table, or statistical claim? Were conclusions drawn from the synthetic dataset itself, or from results later checked against real-world data? These distinctions matter because they affect what can reasonably be reviewed.
A standard data availability statement may not be enough. Authors should make clear what source data or assumptions shaped the synthetic data, how it was generated, what checks were run, what limitations remain, and which parts of the article depend on it. If an AI tool was used, the submission should say where it was used and whether the output was validated.
The same clarity is useful for production and platform teams. Data statements, supplementary files, ethics notes, funding requirements, and metadata records all become easier to manage when the manuscript describes the synthetic dataset consistently.
This does not mean peer reviewers must audit every data-generation step. It means the manuscript should give them enough information to judge whether the method supports the claim being made.
Publishers can help by making the workflow more explicit. Submission systems can include prompts for synthetic data use. Journal instructions can distinguish between simulated data, privacy-preserving synthetic data, and AI-generated datasets. Reviewer forms can ask whether the role of synthetic data is clear. Editorial teams can flag papers that need data-methods expertise before review is complete.
The need becomes more practical as articles move through AI-enabled discovery systems. Published work is now indexed, summarized, mined, recommended, and reused across platforms. If the role of synthetic data is unclear at publication, that uncertainty may travel with the article into later reviews, datasets, policy briefs, or research workflows.
Synthetic data has useful applications in research and publishing. The immediate task is not to treat it as exceptional every time it appears. The task is to ensure that authors describe its role clearly enough for editors, reviewers, and readers to understand what was generated, what was checked, and what can be relied on. Know more
Knowledgespeak Editorial Team
