ReproNim: A Center for Reproducible Neuroimaging Computation

ReproNim：可复制神经成像计算中心--总体 摘要：在过去的二十年里，庞大的技术、计算和社会基础设施 它的出现改变了信息收集和知识收集的方式，涉及到科学的各个方面。 神经成像作为一门学科，正准备利用这些新技术和基础设施来 改进科学运行方式。由于收集的数据集本质上很大且复杂， 神经成像研究，以及在研究中积累的大量共享数据和工具 社区，我们需要降低有效的门槛：使用数据；描述数据和流程；共享 以及随后对集体的“大数据”的再利用。数据的聚合和分析方法的重复使用 在解决人们对当今许多神经成像技术的可复制性和威力的担忧方面变得至关重要 学习。这个可再生性问题的严重性表明，我们产生和 该领域的报告知识是有序的。 我们的BTRC资源，ReproNim：可复制神经成像计算中心，寻求继续 推动神经成像研究方式的转变。通过协调发展 技术和培训(每一项都支持一套全面的数据管理工具和技能， 分析和利用支持基础研究和临床活动的框架)，我们的首要任务 目标是提高神经影像科学的重复性，扩大我们国家的价值 投资于神经成像研究，同时使这一过程更轻松、更高效地 调查人员。重复性是科学进步的关键，因为目前的文献包含大量 错误结论的数量(由于权力有限、出版偏见和偶尔出错)。给出了一个 神经成像研究，很难区分假阳性和真阳性结果，因为 数据很难汇总，准确的方法也很难复制或重复使用。为了推动这一领域的发展 以一种拥抱可再现性的方式进行分析和出版，整个中心将拥有 以下目标：a)提供一个可重复使用的分析系统，其组成部分包括数据和 软件发现(R&D 1)、标准化工作流描述的实施和开发 这些工作流程的结果的机器可读标记和存储(tr&d 2)以及开发 执行选项，便于在多个计算环境中运行，并降低 规模和可靠性(R&D 3)；B)与协作和服务用户社区合作，我们部署、 使用从软件到软件的各种用例来测试和验证可重现的分析系统 开发人员到支持原始数据的归档和重用以及 在多种不同的应用中促进可重复性临床研究(及其出版)的衍生结果 C)向社区提供培训和教育，以促进继续使用和发展 神经影像研究中的可重复性和概括性框架。

ReproNim: A Center for Reproducible Neuroimaging Computation - Overall Summary: Over the last two decades a vast technological, computational and societal infrastructure has emerged transforming how information is collected and knowledge is gathered in all facets of science. Neuroimaging, as a discipline, is uniquely poised to exploit these new technologies and infrastructure to improve the way science is performed. Given the intrinsically large and complex data sets collected in neuroimaging research, coupled with the extensive array of shared data and tools amassed in the research community, we need to lower the barriers for efficient: use of data; description of data and process; sharing and subsequent reuse of the collective ‘big’ data. Aggregation of data and reuse of analytic methods have become critical in addressing concerns about the replicability and power of many of today’s neuroimaging studies. The magnitude of this reproducibility issue indicates that a paradigm shift in the way we generate and report knowledge in this field is in order. Our BTRC resource, ReproNim: A Center for Reproducible Neuroimaging Computation, seeks to continue to drive a shift in the way neuroimaging research is performed. Through the coordinated development of technology and training, (each of which supports a comprehensive set of tools and skills in data management, analysis and utilization of frameworks in support of both basic research and clinical activities), our overarching goal is to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research, while making the process easier and more efficient for investigators. Reproducibility is critical to scientific advancement because the current literature contains large numbers of erroneous conclusions (due to limited power, publication bias and occasionally mistakes). Given a neuroimaging study, it is exceedingly difficult to discern between false positive and true positive findings as data is hard to aggregate, and exact methods are hard to replicate or reuse. In order to advance the field in terms of analysis and publication in a way that embraces reproducibility, the overall Center will have the following aims: A) Deliver a reproducible analysis system comprised of components that include data and software discovery (TR&D 1), implementation of standardized workflow description and development of machine-readable markup and storage of the results of these workflows (TR&D 2) and development of execution options that facilitates operation in multiple computational environments and reduces barriers to scale and reliability (TR&D 3); B) Working with a community of Collaborative and Service users, we deploy, test and validate the reproducible analysis system with a wide variety of use cases ranging from software developers to applied scientists that support the archiving and reuse of raw data and the archival and reuse of derived results to promote reproducible clinical research (and its publication) in multiple different application areas; and C) Provide training and education to the community to foster continued use and development of the reproducible and generalizable framework in neuroimaging research.