Data sharing statements in five high-impact anesthesiology journals (2020-2023): a cross-sectional study

Area of Science:

• Anesthesiology
• Medical Informatics
• Open Science

Background:

• Data sharing is fundamental for research transparency and reproducibility.
• Anesthesiology research is evolving, necessitating an understanding of current data sharing practices.
• Assessing data sharing statements (DSS) in high-impact journals informs open science initiatives.

Purpose of the Study:

• To evaluate the presence, quality, and accessibility of DSS in anesthesiology research articles.
• To analyze trends in DSS implementation across five high-impact anesthesiology journals from 2020 to 2023.
• To identify factors influencing the inclusion of DSS in published research.

Main Methods:

• A cross-sectional study of 1,123 articles from five top-ranked anesthesiology journals (2020-2023).
• Duplicate screening using Rayyan and data extraction via a structured Google Form.
• Utilized a large language model (ChatGPT, GPT-4) for thematic analysis of DSS, with manual validation.

Main Results:

• DSS prevalence varied significantly by journal and year, with some journals showing minimal adoption (<8%).
• Government-funded studies were more likely to include DSS (β=0.734, P=0.047).
• Conditional data availability was the most common DSS type (74%), and only 14% of contacted authors shared data upon request.

Conclusions:

• Data sharing statements are underutilized in leading anesthesiology journals.
• Enhanced journal policies, funder mandates, and educational initiatives are recommended to improve data sharing.
• Findings may not be generalizable due to the limited journal sample.