Center for Reproducible Neuroimaging Computation (CRNC)

 描述(由申请人提供)：在过去的二十年里，出现了一个庞大的技术、计算和社会基础设施，并改变了收集信息和收集科学各方面知识的方式。在医学界内，为了响应NIH的众多数据共享倡议和任务，以及通过基层的努力，该社区成功地积累了大量的共享数据。在科学过程中，方法应该是可重复的。在科学经济学中，数据和方法应该最大限度地重复使用，以最大限度地提高数据获取投资的科学回报。数据和分析处理方法的重复使用已经成为人们日益关注的焦点，人们越来越关注当今许多科学研究的可复制性和威力。这个重复性问题的严重性表明，对于我们如何从越来越多的公共和私人神经成像资源库中生成和报告知识，可能需要进行范式转变。这些因素阻碍了科学发现，并最终损害了所有利益攸关方，包括调查人员本身、他们的同行和同事、他们的机构以及他们的资助机构。我们建议的BTRC资源，可重复神经成像计算中心(CRNC)，寻求实现神经成像研究方式的转变。通过开发支持一套全面的数据管理、分析和利用框架的技术来支持基础研究和临床活动，我们的总体目标是提高神经成像科学的重复性，并扩大我们国家对神经成像研究的投资的价值。重复性至关重要，因为目前的文献包含大量错误的结论(由于能力有限、假阳性、发表偏见和偶尔出错)。鉴于一项神经成像研究，由于数据很难汇总，准确的方法也很难复制，因此很难区分假阳性和真阳性的发现。为了在分析和出版方面以一种包容可再现性的方式推动该领域的发展，整个中心将有以下目标：a)提供一个可重复使用的分析系统，其构成部分包括数据和软件发现(tr&d1)、实施标准化的工作流程描述、开发机器可读的标记和存储这些工作流程的结果(tr&d2)以及开发可促进在多种计算环境中运作并减少规模和可靠性障碍的执行办法(tr&d3)；B)与合作者和服务用户社区合作，使用从软件开发人员到应用科学家的各种用例部署、测试和验证可重复使用的分析系统，以支持原始数据的存档和重复使用以及衍生结果的存档和重复使用，以在多个不同的应用领域促进可重复临床研究(及其出版)；以及c)向社区提供培训和教育，以促进可重复使用框架在神经成像研究中的持续使用和发展。

 DESCRIPTION (provided by applicant): Over the last two decades a vast technological, computational and societal infrastructure has emerged and transformed how information is collected and knowledge is gathered in all facets of science. Within the medical community, in response to numerous NIH data sharing initiatives and mandates, as well as through grassroots efforts, the community has succeeded in accumulating an extensive array of shared data. In the scientific process, methods should be reproducible. In the economics of science, data and methods should be maximally reusable in order to maximize the scientific return on the data acquisition investment. Data and analytic processing methods reuse have become a focal point in a growing concern about the replicability and power of many of today's scientific studies. The magnitude of this reproducibility issue indicates that a paradigm shift may be in order as to how we generate and report knowledge from our mounting public and private neuroimaging repositories. These factors impede scientific discovery and is ultimately a disservice to all the stakeholders, including the investigators themselves, their peers and colleagues, their institution, and their funding agencies. Our proposed BTRC resource, the Center for Reproducible Neuroimaging Computation (CRNC), seeks to implement a shift in the way neuroimaging research is performed. Through the development of technology that supports a comprehensive set of data management, analysis and utilization 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. Reproducibility is critical because the current literature contains large numbers of erroneous conclusions (due to limited power, false positive, 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. 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 collaborator and service users, 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 framework in neuroimaging research.