The growing availability of large-population human biomedical datasets provides researchers with unique opportunities to conduct rigorous and impactful studies on brain and behavioral development, allowing for a more comprehensive understanding of neurodevelopment in diverse populations. However, the patterns observed in these datasets are more likely to be influenced by upstream structural inequities (that is, structural racism), which can lead to health disparities based on race, ethnicity and social class. This paper addresses the need for guidance and self-reflection in biomedical research on conceptualizing, contextualizing and communicating issues related to race and ethnicity. We provide recommendations as a starting point for researchers to rethink race and ethnicity choices in study design, model specification, statistical analysis and communication of results, implement practices to avoid the further stigmatization of historically minoritized groups, and engage in research practices that counteract existing harmful biases.

大量人类生物医学数据集的不断增加为研究人员提供了独特的机会，可以对大脑和行为发育进行严格且有影响力的研究，从而可以更全面地了解不同人群的神经发育。然而，在这些数据集中观察到的模式更有可能受到上游结构性不平等（即结构性种族主义）的影响，这可能导致基于种族、民族和社会阶层的健康差异。本文讨论了生物医学研究中关于种族和民族相关问题的概念化、情境化和沟通问题的指导和自我反思的需要。我们提供建议作为起点，让研究人员重新思考研究设计、模型规范、统计分析和结果交流中的种族和族裔选择，实施实践以避免历史上少数群体的进一步污名化，并从事抵消现有有害物质的研究实践。偏见。