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

CRCNS：美法数据共享提案：降低网络神经科学的准入门槛

• 批准号: 9916138
• 负责人: Danielle Smith Bassett
• 金额: $ 21.51万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-16 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9916138
• 关键词: Address; Aging; Biophysics; Brain; Cloud Computing; Cognition; Communities; Complex Analysis; Computer software; Data; Data Set; Development; Disease; Documentation; Ecosystem; Education; Elements; France; Funding; Generations; Graph; Growth; Instruction; Knowledge; Language; Lead; Libraries; Link; Literature; Machine Learning; Mathematics; Methods; Modeling; Neurosciences; Pathway Analysis; Play; Process; Reporting; Reproducibility; Research; Research Personnel; Resources; Role; Running; Science; Sociology; Standardization; Structure; Study models; System; Techniques; Time; Training; analysis pipeline; brain computer interface; cloud based; cyber infrastructure; data sharing; experience; innovation; insight; learning strategy; network architecture; network models; neuroimaging; open data; prevent; relating to nervous system; statistics; tool

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.

网络神经科学领域已经开发了用于研究大脑网络和的强大分析工具 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，但存在障碍，可以防止这些工具具有 更广泛的影响。其中包括（1）共享和记录软件的非标准化实践，（2）长 何时首先引入公开可用的方法的延迟，以及（3）理论中的差距 知识和理解导致错误，由于错误引起的延迟以及报告结果错误。 这些障碍最终会减慢应用域中神经科学发现和失速进展的速度。 为了克服这些挑战，我们将使用开放的科学方法和云计算，以增加 网络神经科学工具的可用性。我们将使用平台“ Brainlife.io”共享这些工具，这些工具 将包装成独立，标准化，可重复的应用程序，并与 用户社区，并集成到现有的brainlife.io分析管道中。应用程序也将伴随 通过指向主要来源，深入的教程和文档的链接，以及工作示例， 突出显示其正确的用法并提供解决可能陷阱的解决方案。 在标准化和包装网络神经科学工具中，这项拟议的研究将与 新一代的神经科学家为他们提供了强大的新事物，并带来了新发现。第二， 拟议的研究将贡献不断增长的建模分析套件，可以修改以适合专业 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 研究。总之，这些进步将为网络神经科学研究带来新的机会， 进一步刺激其生长，同时与神经科学中其他领域的协同作用增加。