Abstract：
BACKGROUND: Transparency can build trust in the scientific process, but scientific findings can be undermined by poor and obscure data use and reporting practices. The purpose of this work is to report how data from the Adolescent Brain Cognitive Development (ABCD) Study has been used to date, and to provide practical recommendations on how to improve the transparency and reproducibility of findings.
METHODS: Articles published from 2017 to 2023 that used ABCD Study data were reviewed using more than 30 data extraction items to gather information on data use practices. Total frequencies were reported for each extraction item, along with computation of a Level of Completeness (LOC) score that represented overall endorsement of extraction items. Univariate linear regression models were used to examine the correlation between LOC scores and individual extraction items. Post hoc analysis included examination of whether LOC scores were correlated with the logged 2-year journal impact factor.
RESULTS: There were 549 full-length articles included in the main analysis. Analytic scripts were shared in 30 % of full-length articles. The number of participants excluded due to missing data was reported in 60 % of articles, and information on missing data for individual variables (e.g., household income) was provided in 38 % of articles. A table describing the analytic sample was included in 83 % of articles. A race and/or ethnicity variable was included in 78 % of reviewed articles, while its inclusion was justified in only 41 % of these articles. LOC scores were highly correlated with extraction items related to examination of missing data. A bottom 10 % of LOC score was significantly correlated with a lower logged journal impact factor when compared to the top 10 % of LOC scores (β=-0.77, 95 % -1.02, -0.51; p-value < 0.0001).
CONCLUSIONS: These findings highlight opportunities for improvement in future papers using ABCD Study data to readily adapt analytic practices for better transparency and reproducibility efforts. A list of recommendations is provided to facilitate adherence in future research.
METHODS: Articles published from 2017 to 2023 that used ABCD Study data were reviewed using more than 30 data extraction items to gather information on data use practices. Total frequencies were reported for each extraction item, along with computation of a Level of Completeness (LOC) score that represented overall endorsement of extraction items. Univariate linear regression models were used to examine the correlation between LOC scores and individual extraction items. Post hoc analysis included examination of whether LOC scores were correlated with the logged 2-year journal impact factor.
RESULTS: There were 549 full-length articles included in the main analysis. Analytic scripts were shared in 30 % of full-length articles. The number of participants excluded due to missing data was reported in 60 % of articles, and information on missing data for individual variables (e.g., household income) was provided in 38 % of articles. A table describing the analytic sample was included in 83 % of articles. A race and/or ethnicity variable was included in 78 % of reviewed articles, while its inclusion was justified in only 41 % of these articles. LOC scores were highly correlated with extraction items related to examination of missing data. A bottom 10 % of LOC score was significantly correlated with a lower logged journal impact factor when compared to the top 10 % of LOC scores (β=-0.77, 95 % -1.02, -0.51; p-value < 0.0001).
CONCLUSIONS: These findings highlight opportunities for improvement in future papers using ABCD Study data to readily adapt analytic practices for better transparency and reproducibility efforts. A list of recommendations is provided to facilitate adherence in future research.
摘要：
背景：透明度可以在科学过程中建立信任，但是科学发现可能会被不良和晦涩的数据使用和报告实践所破坏。这项工作的目的是报告迄今为止如何使用青少年大脑认知发育（ABCD）研究的数据，并就如何提高调查结果的透明度和可重复性提供切实可行的建议。
方法：2017年至2023年发表的使用ABCD研究数据的文章使用30多个数据提取项目进行了审查，以收集有关数据使用实践的信息。报告了每个提取项目的总频率，以及表示提取项目总体认可的完整性水平(LOC)分数的计算。使用单变量线性回归模型来检查LOC评分与单个提取项目之间的相关性。事后分析包括检查LOC得分是否与记录的2年期刊影响因子相关。
结果：主要分析中包含549篇全长文章。分析脚本在30％的完整文章中共享。60%的文章报告了因数据缺失而被排除在外的参与者数量，以及单个变量缺失数据的信息(例如，家庭收入)在38%的文章中提供。83%的文章中包含了描述分析样本的表格。78％的评论文章中包括种族和/或种族变量，而其中只有41%的文章是合理的。LOC评分与缺失数据检查相关的提取项目高度相关。与LOC得分的前10%相比，LOC得分的后10%与较低的日志影响因子显著相关(β=-0.77,95%-1.02,-0.51;p值<0.0001)。
结论：这些发现强调了在未来的论文中改进的机会，这些论文使用ABCD研究数据来轻松调整分析实践，以实现更好的透明度和可重复性。提供了一系列建议，以促进未来研究的坚持。
方法：2017年至2023年发表的使用ABCD研究数据的文章使用30多个数据提取项目进行了审查，以收集有关数据使用实践的信息。报告了每个提取项目的总频率，以及表示提取项目总体认可的完整性水平(LOC)分数的计算。使用单变量线性回归模型来检查LOC评分与单个提取项目之间的相关性。事后分析包括检查LOC得分是否与记录的2年期刊影响因子相关。
结果：主要分析中包含549篇全长文章。分析脚本在30％的完整文章中共享。60%的文章报告了因数据缺失而被排除在外的参与者数量，以及单个变量缺失数据的信息(例如，家庭收入)在38%的文章中提供。83%的文章中包含了描述分析样本的表格。78％的评论文章中包括种族和/或种族变量，而其中只有41%的文章是合理的。LOC评分与缺失数据检查相关的提取项目高度相关。与LOC得分的前10%相比，LOC得分的后10%与较低的日志影响因子显著相关(β=-0.77,95%-1.02,-0.51;p值<0.0001)。
结论：这些发现强调了在未来的论文中改进的机会，这些论文使用ABCD研究数据来轻松调整分析实践，以实现更好的透明度和可重复性。提供了一系列建议，以促进未来研究的坚持。
