Nipype: Dataflows for Reproducible Biomedical Research

项目摘要 随着神经影像数据的巨大增长，对可用的、自动化的、 和强大的数据分析工具。Nipype是一个成熟的Python库，用于高效灵活地分析大脑 成像“数据。其可重复使用的工作流可以组合来自一组不同的现有软件包的算法 以产生可重现的结果。这项提议的目标是进一步增强可用性、功能性和 Nipype的互操作性，并扩大其传播。这将增加研究人员和 临床医生，提高其对生物医学研究的影响，并解决其目前的许多限制。更易于使用 自动化工具可以减少错误，导致更快的生物医学发现，并促进从 从长椅到床边。从软件工程的角度来看，目标是提供一个设计良好、跨平台、 和可扩展的数据流计算解决方案，直观且易于使用。 我们建议在当前广泛的功能集的基础上构建一个交互式的、直观的基于Web的平台 与现有数据库、软件和其他工作流服务互操作的Nipype。结果将是一个 可泛化、可伸缩、可扩展且经过测试的基础设施，最大限度地减少复杂的编程接口 到更易于使用的Web应用程序。Nipype仍将保留其在此Web背后的可扩展插件架构- 基于平台，允许继续包含新的软件包和算法，并在 多个平台。用户将能够对他们的数据使用最合适的分析策略。这 平台不仅允许继续使用熟悉的软件，还提供对最新软件的即时接触 用于数据分析的软件工具。对于分析，用户将可以访问完整的来源，允许其他人 重现他们的脚步。我们将与NeuroVault和NeuroSynth互动，提供无缝过渡 在数据、处理、共享和解释结果之间。最后，要保持这样一个开放和协作的 我们将通过实践研讨会和网络研讨会对用户和开发人员进行培训，鼓励他们 扩大生态系统的优势，以实现高效和可重复的分析。 虽然该架构最初将部署在脑成像社区内，但我们将采用通用的 标准，以确保与更大的生物医学成像社区的互操作性。通过继续参与 用户社区和扩展研究计算的生态系统，该项目将降低 在大数据集上进行简单高效的计算，目标是更快地开发治疗方案。

Project Summary With the tremendous increase of neuroimaging data, there is a corresponding demand for usable, automated, and robust data analysis tools. Nipype is a mature Python library for efficient and flexible analysis of Big “brain imaging” Data. Its reusable workflows can combine algorithms from a diverse set of existing software packages to generate reproducible results. The goal of this proposal is to further enhance the usability, functionality, and interoperability of Nipype and to widen its dissemination. This will increase its use by researchers and clinicians, boost its impact on biomedical research, and address many of its current limitations. Easier-to-use automation tools can reduce errors, lead to faster biomedical discoveries, and facilitate the transition from bench to bedside. From a software engineering standpoint, the goal is to offer a well-designed, cross-platform, and extensible dataflow computing solution that is intuitive and easy to use. We propose to build an interactive and intuitive web-based platform on top of the current extensive feature set of Nipype that interoperates with existing databases, software, and other workflow services. The result will be a generalizable, scalable, extensible, and tested infrastructure that minimizes complex programming interfaces to easier-to-use web applications. Nipype will still retain its extensible plugin architecture behind this web- based platform to allow continued inclusion of new software packages and algorithms, and execution on multiple platforms. Users will be able to use the most appropriate analysis strategies for their data. This platform will not only allow continued use of familiar software, but provide immediate exposure to the latest software tools for data analyses. For analysis, users will have access to complete provenance allowing others to reproduce their steps. We will interact with NeuroVault and NeuroSynth to provide a seamless transition between data, processing, sharing, and interpreting results. Finally, to sustain such an open and collaborative effort, we will train users and developers through hands-on workshops and webinars, encouraging them to take advantage of an expanding ecosystem for efficient and reproducible analysis. While the architecture will be initially deployed within the brain imaging community, we will adopt common standards to ensure interoperability with the greater biomedical imaging community. By continuing to engage the user community and extending the ecosystem for research computing, the project will lower the barrier for easy and efficient computation on large datasets, with the goal of faster development of treatment options.