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Transparency and Reproducibility in the Adolescent Brain Cognitive Development (ABCD) Study
Daniel A Lopez1,2, Carlos Cardenas-Iniguez3, Punitha Subramaniam4
1Department of Psychiatry, Oregon Health & Science University, Portland, Oregon.
Medrxiv : the Preprint Server for Health Sciences
|June 10, 2024
Summary
Improving data transparency in the Adolescent Brain Cognitive Development (ABCD) Study is crucial for scientific reproducibility. This study found that higher data completeness scores correlate with higher journal impact factors, emphasizing the need for better reporting practices.
Area of Science:
- Neuroscience
- Developmental Psychology
- Data Science
Background:
- Scientific transparency is vital for trust, yet data use and reporting can obscure findings.
- Poor data practices undermine the reliability of scientific research.
- The Adolescent Brain Cognitive Development (ABCD) Study is a large-scale initiative collecting valuable data.
Purpose of the Study:
- To assess current data use and reporting practices in publications utilizing ABCD Study data.
- To identify areas for improvement in transparency and reproducibility.
- To provide actionable recommendations for future research.
Main Methods:
- A systematic review of 549 articles published from 2017-2023 using ABCD Study data.
- Data extraction using over 30 items to evaluate data use and reporting completeness.
- Calculation of a Level of Completeness (LOC) score and correlation analysis with journal impact factor.
Main Results:
- Analytic scripts were shared in only 30% of articles; missing data details were reported inconsistently (60% for participants, 38% for variables).
- While race/ethnicity variables were common (78%), their justification was infrequent (41%).
- Higher LOC scores significantly correlated with higher journal impact factors (p < 0.0001).
Conclusions:
- Significant opportunities exist to enhance transparency and reproducibility in ABCD Study research.
- Adopting recommended practices can improve the rigor and impact of future studies.
- Clearer reporting of data handling, missing data, and analytic methods is essential.
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