Neuroimaging Informatics Tools and Resources Collaboratory: Outreach, Infrastructure and Maintenance

摘要 神经成像工具和资源合作实验室（NITRC：www.nitrc.org）是一个建立和奖励- 获奖（2009年Excellence.gov奖和2015年HHS创新奖）的信息库和支持， 与广泛的神经成像领域及其开发人员和用户社区相关的资源。 托管在NITRC Resources（NITRC-R）上的资源包括软件、硬件和数据集等。 资产NITRC的目的是为神经科学研究人员提供一个完整的解决问题的方案 寻找、开发和分享神经影像学和神经信息学软件工具， 大规模的静息状态和结构成像数据集，并以高分辨率操作软件和数据， 性能计算环境。NITRC将继续成为研究人员可以识别 软件和数据，并将它们无缝地交付给基于云的计算资源进行处理。这 将通过执行旨在提供的目标来实现：1）内容和用户维护， 已建立的NITRC-R、IR和CE环境; 2）完整的基础设施维护 开发和部署环境; 3）现有领域的内容扩展， 新的领域，适合NITRC的支持;和4）外展，用户文档，培训和支持， 建立NITRC用户基础。这一持续努力将大大加强高绩效的交付， 大规模数据集正在成为神经科学研究的支柱， 通过加强资源共享，提高出版物的可复制性。 !

Abstract The NeuroImaging Tools and Resources Collaboratory (NITRC: www.nitrc.org) is an established and award- winning (2009 Excellence.gov award and 2015 HHS Innovates award) repository of information and support for resources relevant to a broad set of neuroimaging domains and their communities of developers and users. Resources hosted on NITRC Resources (NITRC-R) include software, hardware, and datasets, among other assets. The purpose of NITRC is to provide neuroscience researchers with a complete solution to the problem of finding, developing, and sharing neuroimaging and neuroinformatics software tools, finding and sharing large-scale resting state and structural imaging datasets, and manipulating the software and the data in high- performance computing environments. NITRC will continue to be a framework where researchers can identify software and data and seamlessly deliver them to a cloud-based computational resource for processing. This will be accomplished through the execution of aims designed to provide: 1) Content and user maintenance for the established NITRC-R, IR and CE environments; 2) infrastructure maintenance for the complete development and deployment environments; 3) content expansion in the existing domains, and identification of new areas that are amenable to NITRC support; and 4) outreach, user documentation, training, and support for the established NITRC user base. This continued effort will greatly enhance the delivery of high-performance computing to the large-scale datasets that are becoming the mainstay of neuroscience research and promote more reproducible publication practices through enhanced resource sharing. !