CRCNS: US-France Data Sharing Proposal: Lowering the barrier of entry to network neuroscience

网络神经科学领域已经开发出强大的分析工具来研究大脑网络， 有望加深我们对大脑网络在健康，疾病， 发展和认知。尽管有广泛的兴趣，但仍存在障碍，阻止这些工具具有 更广泛的影响。这些问题包括：（1）共享和记录软件的非标准化实践，（2）冗长的 从一种方法第一次被引入到它变得公开可用的延迟，以及（3）理论上的差距 知识和理解导致不正确，由于错误而延迟，以及报告结果中的错误。 这些障碍最终减缓了神经科学发现的速度，并阻碍了应用领域的进展。 为了克服这些挑战，我们将使用开放科学方法和云计算， 网络神经科学工具的可用性。我们将使用“brainlife.io“平台共享这些工具， 将被打包成独立的、标准化的、可复制的应用程序，与 用户社区，并集成到现有的brainlife.io分析管道。应用程序也将伴随 通过链接到主要来源，深入的教程和文档，以及通过示例， 强调它们的正确用法，并提供解决方案，以减少可能的陷阱。 在标准化和包装网络神经科学工具作为应用程序，这项拟议的研究将从事 新一代的神经科学家，为他们提供强大的新的，并导致新的发现。二是 拟议的研究将有助于不断增长的建模分析套件，可以修改，以适应专门的 目的最后，Brainlife.io平台将作为支持神经科学的基础设施的一部分。 research.总之，这些进展将为网络神经科学研究带来新的机遇， 进一步刺激其增长，同时增加与神经科学其他领域的协同作用。

The field of network neuroscience has developed powerful analysis tools for studying brain networks and holds promise for deepening our understanding of the role played by brain networks in health, disease, development, and cognition. Despite widespread interest, barriers exist that prevent these tools from having broader impact. These include (1) unstandardized practices for sharing and documenting software, (2) long delays from when a method is first introduced to when it becomes publicly available, and (3) gaps in theoretic knowledge and understanding leading to incorrect, delays due to mistakes, and errors in reported results. These barriers ultimately slow the rate of neuroscientific discovery and stall progress in applied domains. To overcome these challenges, we will use open science methods and cloud-computing, to increase the availability of network neuroscience tools. We will use the platform "brainlife.io" for sharing these tools, which will be packaged into self-contained, standardized, reproducible Apps, shared with and modified by a community of users, and integrated into existing brainlife.io analysis pipelines. Apps will also be accompanied by links to primary sources, in-depth tutorials, and documentation, and worked-through examples, highlighting their correct usage and offering solutions for mitigating possible pitfalls. In standardizing and packaging network neuroscience tools as Apps, this proposed research will engage a new generation of neuroscientists, providing them powerful new and leading to new discoveries. Second, the proposed research will contribute growing suite of modeling analysis that can be modified to suit specialized purposes. Finally, the Brainlife.io platform will serve as part of the infrastructure supporting neuroscience research. Altogether, these advances will lead to new opportunities in network neuroscience research and further stimulate its growth while increasing synergies with other domains in neuroscience.