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)是一个历史悠久的奖项- 荣获(2009年度卓越政府奖和2015年度HHS创新奖)信息和支持存储库 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。 NITRC资源(NITRC-R)上托管的资源包括软件、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问题解决方案 查找、开发和共享神经成像和神经信息学软件工具、查找和共享 大规模静息状态和结构成像数据集，并在高分辨率下处理软件和数据。 性能计算环境。NITRC将继续成为研究人员可以确定 软件和数据，并无缝地将它们提供给基于云的计算资源进行处理。这 将通过执行旨在提供：1)内容和用户维护的目标来实现 已建立的NITRC-R、IR和CE环境；2)为 开发和部署环境；3)现有域中的内容扩展，并确定 可接受NITRC支持的新领域；以及4)外展、用户文档、培训和支持 已建立的NITRC用户基础。这一持续的努力将极大地增强高性能的交付 向正在成为神经科学研究和推广中流砥柱的大规模数据集进行计算 通过加强资源共享，提高出版做法的可再现性。 好了！

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. !