ArticleeLife2020
A synthetic dataset primer for the biobehavioural sciences to promote reproducibility and hypothesis generation.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed.
- Synthetic Data Generation and Nonparametric Techniques for Assessing Multivariate Similarity to Address Small-Sample Size Challenges.bioRxiv : the preprint server for biology · 2026Article
- Article
- What Do Asexual Women Want? A Propensity Score Matching Study of Preferred Relationship Options and Ideal Partner Preferences.Archives of sexual behavior · 2026Article
- Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models.BMC medical informatics and decision making · 2025Article
- Toward New Directions in Human Biology: A Roadmap for Anthropological Causal Inference With Observational Data.American journal of human biology : the official journal of the Human Biology Council · 2025Article
- Magnitude and Impact of Hallucinations in Tabular Synthetic Health Data on Prognostic Machine Learning Models: Validation Study.Journal of medical Internet research · 2025Article
- Synthetic Data for Sharing and Exploration in High-Performance Sport: Considerations for Application.Sports medicine (Auckland, N.Z.) · 2025Article
- Transparency in epidemiological analyses of cohort data a case study of the Norwegian mother, father, and child cohort study (MoBa).BMC medical research methodology · 2025Article
- Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study.Journal of medical Internet research · 2025Article
- An evaluation of synthetic data augmentation for mitigating covariate bias in health data.Patterns (New York, N.Y.) · 2024Article
- An evaluation of the replicability of analyses using synthetic health data.Scientific reports · 2024Article
- Releasing synthetic data from the Avon Longitudinal Study of Parents and Children (ALSPAC): Guidelines and applied examples.Wellcome open research · 2024Article
- Responsibilities for receiving and using individual participant data.Cochrane evidence synthesis and methods · 2023Article
- Evaluating the Utility and Privacy of Synthetic Breast Cancer Clinical Trial Data Sets.JCO clinical cancer informatics · 2023Article
- Techniques to produce and evaluate realistic multivariate synthetic data.Scientific reports · 2023Article
- A comparison of synthetic data generation and federated analysis for enabling international evaluations of cardiovascular health.Scientific reports · 2023Article
- Open Science Practices in Communication Sciences and Disorders: A Survey.Journal of speech, language, and hearing research : JSLHR · 2023Article
- Estimating social bias in data sharing behaviours: an open science experiment.Scientific data · 2023Article
- A method for generating synthetic longitudinal health data.BMC medical research methodology · 2023Article
- The relationship between psycho-environmental characteristics and wellbeing in non-spending players of certain mobile games.Royal Society open science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Open research data provide considerable scientific, societal, and economic benefits. However, disclosure risks can sometimes limit the sharing of open data, especially in datasets that include sensitive details or information from individuals with rare disorders. This article introduces the concept of synthetic datasets, which is an emerging method originally developed to permit the sharing of confidential census data. Synthetic datasets mimic real datasets by preserving their statistical properties and the relationships between variables. Importantly, this method also reduces disclosure risk to essentially nil as no record in the synthetic dataset represents a real individual. This practical guide with accompanying R script enables biobehavioural researchers to create synthetic datasets and assess their utility via the
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.