Open data exploitation

Introduction

ImportantProblem statement

The last 3 years have seen an explosion in published manuscripts analyzing open-access health datasets, in many cases presenting misleading or biologically implausible findings. There is a growing evidence base to suggest that this is due in part to artificial intelligence-assisted and formulaic workflows, and publishers are responding by discouraging submissions employing open-access health datasets (Spick et al. 2026).

Excess publications from open datasets. Reproduced from Spick et al. (2026) under CC BY 4.0 license.

Key resources

Note“Explosion of formulaic research articles”

Analysis of excess publications based on the NHANES database by Suchak et al. (2025).

Note“Quantifying new threats”

Analysis of excess papers from 9 open datasets by Spick et al. (2026).

Note“Dramatic increases in redundant publications”

Analysis of publications duplicating the same analyses of the NHANES dataset by Maupin, Suchak, et al. (2025).

Note“Safeguarding open science from exploitative practices”

Potential remedies proposed by Maupin, Spick, et al. (2025).

Bibliography

Maupin, Danny, Matt Spick, and Nophar Geifman. 2025. “Safeguarding Open Science from Exploitative Practices.” PLOS Medicine 22 (12): e1004851. https://doi.org/10.1371/journal.pmed.1004851.

Maupin, Danny, Tulsi Suchak, Adrian Barnett, and Matt Spick. 2025. “Dramatic Increases in Redundant Publications in the Generative AI Era.” BMC Medicine 24 (1): 29. https://doi.org/10.1186/s12916-025-04569-y.

Spick, Matt, Anthony Onoja, Charlie Harrison, Stefan Stender, Jennifer Byrne, and Nophar Geifman. 2026. “Quantifying New Threats to Health and Biomedical Literature Integrity from Rapidly Scaled Publications and Problematic Research.” Journal of Clinical Epidemiology 193 (May): 112203. https://doi.org/10.1016/j.jclinepi.2026.112203.

Suchak, Tulsi, Anietie E. Aliu, Charlie Harrison, Reyer Zwiggelaar, Nophar Geifman, and Matt Spick. 2025. “Explosion of Formulaic Research Articles, Including Inappropriate Study Designs and False Discoveries, Based on the NHANES US National Health Database.” PLOS Biology 23 (5): e3003152. https://doi.org/10.1371/journal.pbio.3003152.