Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA; email:
Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, and Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland 21218, USA.
Annu Rev Neurosci. 2020 Jul 8;43:441-464. doi: 10.1146/annurev-neuro-100119-110036. Epub 2020 Apr 13.
As acquiring bigger data becomes easier in experimental brain science, computational and statistical brain science must achieve similar advances to fully capitalize on these data. Tackling these problems will benefit from a more explicit and concerted effort to work together. Specifically, brain science can be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgrounds and expertise. This perspective can be applied across modalities and scales and enables collaborations across previously siloed communities.
随着实验脑科学获取更大数据变得更加容易，计算和统计脑科学必须取得类似的进展，才能充分利用这些数据。解决这些问题将得益于更加明确和一致的合作努力。具体来说，通过利用社区驱动工具的力量，可以进一步使脑科学民主化，这些工具既由具有不同背景和专业知识的许多不同的人构建，也使这些人受益。这种观点可以应用于各种模态和规模，并实现以前孤立社区之间的协作。
