Department of Psychology, University of Virginia, Charlottesville, Virginia, USA.
School of Data Science, University of Virginia, Charlottesville, Virginia, USA.
Big Data. 2021 Jun;9(3):153-187. doi: 10.1089/big.2020.0065. Epub 2020 Nov 18.
Brain scientists are now capable of collecting more data in a single experiment than researchers a generation ago might have collected over an entire career. Indeed, the brain itself seems to thirst for more and more data. Such digital information not only comprises individual studies but is also increasingly shared and made openly available for secondary, confirmatory, and/or combined analyses. Numerous web resources now exist containing data across spatiotemporal scales. Data processing workflow technologies running via cloud-enabled computing infrastructures allow for large-scale processing. Such a move toward greater openness is fundamentally changing how brain science results are communicated and linked to available raw data and processed results. Ethical, professional, and motivational issues challenge the whole-scale commitment to data-driven neuroscience. Nevertheless, fueled by government investments into primary brain data collection coupled with increased sharing and community pressure challenging the dominant publishing model, large-scale brain and data science is here to stay.
脑科学家现在能够在一次实验中收集到比前一代人在整个职业生涯中可能收集到的更多的数据。事实上,大脑本身似乎渴望更多的数据。这些数字信息不仅包括单个研究,而且还越来越多地被共享,并为二次、确认和/或组合分析而公开提供。现在有许多网络资源包含跨越时空尺度的数据。通过云计算基础设施运行的数据处理工作流程技术允许进行大规模处理。这种向更大开放性的转变从根本上改变了大脑科学研究结果的交流方式,以及与可用原始数据和处理结果的联系方式。伦理、专业和激励问题挑战着对数据驱动的神经科学的全面投入。然而,政府对原始大脑数据收集的投资增加,加上数据共享和社区压力的增加,挑战了主导的出版模式,大规模的大脑和数据科学已经成为现实。