Enabling the sharing of neuroimaging data through well-defined intermediate levels of visibility.
Enabling the sharing of neuroimaging data through well-defined intermediate levels of visibility.
复制标题
通过明确定义的中间可见性级别实现神经影像数据的共享。
DOI：
10.1016/j.neuroimage.2004.03.048
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Cornett,Todd
中科院分区：
文献类型：
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作者：
Smith,Kenneth;Jajodia,Sushil;Swarup,Vipin;Hoyt,Jeffrey;Hamilton,Gail;Faatz,Donald;Cornett,Todd
神经影像数据的共享为科学带来了巨大的好处，然而，共享数据的数据所有者面临着大量的保管责任，例如确保数据集在新的共享环境中得到正确解释，保护人类研究参与者的身份和隐私，以及维护理解的使用顺序。如果选择广泛共享或根本不共享，结果往往是不共享，因为数据所有者无法控制其暴露于与数据共享相关的风险。在这种情况下，数据共享是通过为数据所有者提供定义良好的中间数据可见性级别来实现的，逐步向公共可见性发展。在本文中，我们定义了一个新的和通用的数据共享模型，结构化共享社区（SSC），满足这一要求。代表合作协议、联盟成员、研究组织和其他附属关系的任意可见性级别通过允许的信息流的明确路径被构造成策略空间。操作使用户和应用程序能够管理数据的可见性并强制执行访问权限和限制。我们展示了如何在现实的神经信息学架构中实现策略空间，并具有可接受的正确性保证，并简要描述了开源实现工作。
The sharing of neuroimagery data offers great benefits to science, however, data owners sharing their data face substantial custodial responsibilities, such as ensuring data sets are correctly interpreted in their new shared context, protecting the identity and privacy of human research participants, and safeguarding the understood order of use. Given choices of sharing widely or not at all, the result will often be no sharing, due to the inability of data owners to control their exposure to the risks associated with data sharing. In this context, data sharing is enabled by providing data owners with well-defined intermediate levels of data visibility, progressing incrementally toward public visibility. In this paper, we define a novel and general data sharing model, Structured Sharing Communities (SSC), meeting this requirement. Arbitrary visibility levels representing collaborative agreements, consortium memberships, research organizations, and other affiliations are structured into a policy space through explicit paths of permissible information flow. Operations enable users and applications to manage the visibility of data and enforce access permissions and restrictions. We show how a policy space can be implemented in realistic neuroinformatic architectures with acceptable assurance of correctness, and briefly describe an open source implementation effort.