Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who “give” their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.

中文翻译：

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神经影像学研究中的数据共享和隐私问题：机遇、障碍、挑战和床下的怪物

协作网络和数据共享计划正在扩大科学进步的机会。这些举措提高了科学的透明度，让外部研究小组有机会复制、复制和扩展研究成果。此外，更大的数据集提供了识别个体亚组内同质模式的机会，其中这些模式可能被较小样本中神经生物学测量的异质性所掩盖。然而，数据共享和数据汇集计划并非没有挑战，尤其是新法律乍一看似乎对开放科学计划限制性很大。有趣的是，其中一些新法律的关键是什么（即欧盟的 s 一般数据保护条例）是，它们为那些出于研究目的“提供”数据的人提供了更大的数据控制。因此，数据共享中最重要的因素是允许参与者就他们希望如何使用他们的数据做出明智的决定，并在特定国家的法律范围内，遵循参与者的意愿。该框架包括获得彻底的知情同意并允许参与者确定他们希望共享数据的程度，许多道德和法律障碍被简化为床底下的怪物。在这份手稿中，我们讨论了数据共享的许多选项和障碍，从完全开放到联合学习，再到完全封闭。重要的是，我们强调数据共享、隐私、