Large, open datasets for human connectomics research: Considerations for reproducible and responsible data use

被引:33
|
作者
Laird, Angela R. [1 ]
机构
[1] Florida Int Univ, Dept Phys, Miami, FL 33199 USA
基金
美国国家卫生研究院;
关键词
Connectomics; Large open datasets; Neuroimaging data sharing; Reproducible analytics; CULTURAL NEUROSCIENCE; SOCIAL DETERMINANTS; NEUROIMAGING DATA; WIDE ASSOCIATION; BRAIN; DISCRIMINATION; ACCULTURATION; METAANALYSIS; INNOVATION; STRESS;
D O I
10.1016/j.neuroimage.2021.118579
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Large, open datasets have emerged as important resources in the field of human connectomics. In this review, the evolution of data sharing involving magnetic resonance imaging is described. A summary of the challenges and progress in conducting reproducible data analyses is provided, including description of recent progress made in the development of community guidelines and recommendations, software and data management tools, and initiatives to enhance training and education. Finally, this review concludes with a discussion of ethical conduct relevant to analyses of large, open datasets and a researcher's responsibility to prevent further stigmatization of historically marginalized racial and ethnic groups. Moving forward, future work should include an enhanced emphasis on the social determinants of health, which may further contextualize findings among diverse population-based samples. Leveraging the progress to date and guided by interdisciplinary collaborations, the future of connectomics promises to be an impressive era of innovative research, yielding a more inclusive understanding of brain structure and function.
引用
收藏
页数:15
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