ReproNim: A Center for Reproducible Neuroimaging Computation

ReproNim：可重复神经影像计算中心

• 批准号: 10482411
• 负责人: David Nelson Kennedy
• 金额: $ 117.83万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10482411
• 关键词: Address; Adopted; Adoption; Affect; Age; Archives; Area; BRAIN initiative; Basic Science; Big Data; Biomedical Engineering; Brain; Clinical; Clinical Research; Collaborations; Communities; Complement; Computer software; Coupled; Data; Data Aggregation; Data Analyses; Data Analytics; Data Set; Development; Discipline; Disease; Elements; Environment; Face; Feedback; First Independent Research Support and Transition Awards; Fostering; Goals; Human; Individual; Infrastructure; Institution; International; Investments; Knowledge; Literature; Methods; Mission; Modeling; Patients; Phase; Population; Procedures; Process; Publication Bias; Publications; Readability; Reporting; Reproducibility; Research; Research Personnel; Research Subjects; Research Training; Resources; Role; Science; Scientist; Services; Software Tools; Standardization; Statistical Data Interpretation; Structure; System; Systems Analysis; Technology; Testing; Time; Training; Training Support; Training and Education; United States National Institutes of Health; Work; analytical method; biomedical imaging; clinical application; complex data; computerized data processing; data acquisition; data archive; data management; data modeling; data reuse; data sharing; data tools; design; improved; informatics infrastructure; innovation; neuroimaging; new technology; open data; open source; operation; prevent; research study; response; skills; social; success; technology development; technology research and development; tool

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.

ReproNim：可再现神经成像计算中心-总体 摘要：在过去的二十年里，一个庞大的技术，计算和社会基础设施， 它的出现改变了科学各个方面的信息收集和知识收集方式。 神经影像学，作为一门学科，是独一无二的准备利用这些新技术和基础设施， 改善科学的执行方式。由于收集的数据集本身就很大，而且很复杂， 神经影像学研究，再加上广泛的共享数据和研究中积累的工具， 社区，我们需要降低有效的障碍：数据的使用;数据和过程的描述;共享 以及随后对集体“大”数据的再利用。数据的聚合和分析方法的重用 在解决当今许多神经成像的可复制性和功能方面变得至关重要 问题研究这种可重复性问题的严重性表明，我们生成和 报告在这方面的知识是有序的。 我们的BTRC资源，ReproNim：可再现神经成像计算中心，寻求继续 来推动神经影像学研究方式的转变。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， 分析和利用框架，以支持基础研究和临床活动），我们的总体 我们的目标是提高神经影像科学的可重复性，扩大我们国家的价值。 投资神经影像学研究，同时使这一过程更容易，更有效， investigators.生殖对科学进步至关重要，因为目前的文献包含大量 错误结论的数量（由于效力有限、发表偏倚和偶尔的错误）。给予 在神经影像学研究中，很难区分假阳性和真阳性结果， 数据很难聚合，精确的方法也很难复制或重用。为了推进这一领域， 分析和出版的方式，包括再现性，整个中心将有 A）提供由包括数据的组件组成的可再现的分析系统， 软件发现（TR&D 1），标准化工作流程描述的实施和 机器可读的标记和存储这些工作流程的结果（TR&D 2）， 执行选项，便于在多个计算环境中操作，并减少了 规模和可靠性（TR&D 3）; B）与协作和服务用户社区合作，我们部署， 测试和验证可再现的分析系统，包括各种各样的用例， 从开发人员到应用科学家，支持原始数据的存档和重用，以及 衍生结果，以促进在多种不同应用中的可重复临床研究（及其出版物） C）向社区提供培训和教育，以促进持续使用和发展 神经影像学研究中可重复和可推广的框架。