A new study reveals that people are willing to sacrifice thousands of dollars to protect their privacy. By analyzing a legal reform that allowed personal injury plaintiffs to remain anonymous in court, the researchers found that insurance settlements increased by 12–17% once the risk of public exposure of sensitive health and financial information was removed. The findings provide rare real-world evidence that privacy has substantial economic value.

Public biomedical databases such as NHANES, UK Biobank, FAERS, the Global Burden of Disease Study, and FinnGen have become a focus of the research-integrity debate, following a rapid rise in papers built from broadly accessible datasets. A correspondence published in Medicine Discovery argues that the quality of public database research rests on the study process, while the database name provides little information about scientific quality, and that treating a familiar database name as a marker of low quality risks confusing the infrastructure with the production model that exploits it. Citing a scientometric analysis of 36 health datasets — nine of which showed publication patterns consistent with rapid, formulaic expansion, producing 23,005 indexed publications in 2025 and an estimated excess of 11,577 publications above the prior trend — the authors argue that the same NHANES files support careful epidemiology, surveillance, and methods development as well as formulaic work, and that the recent surge emerged long after the survey architecture and its public documentation were established. They describe how generative AI has changed the economics of secondary data research, distinguishing researcher-directed assistance from more autonomous workflows that extend into question formulation and analytical planning, and warning of a plausible risk of automated hypothesis fishing. The correspondence proposes three actions for editorial policy: a short dataset-specific methods note for heavily reused resources; stronger transparency around analysis code, preregistration, and AI use; and editorial screening focused on study conduct rather than data provenance. 

A new LMU study shows: Three-quarters of the estimated global potential for new forests to capture CO₂ is threatened by significant social and institutional challenges.

Kyushu University researchers have developed an AI-based method to measure how closely corporate disclosures match the ESG issues that matter financially to each industry. Applied to Japan’s food and beverage sector, it showed that companies place greater emphasis on social-related topics, while environmental disclosure remains relatively limited. Priorities also shifted as reporting requirements changed. The study offers insights for investors, firms, and standard setters seeking to evaluate and design effective sustainability reporting regimes.