Nipype: Dataflows for Reproducible Biomedical Research

Nipype：可重复生物医学研究的数据流

• 批准号: 9053094
• 负责人: Satrajit Sujit Ghosh
• 金额: $ 71.25万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-15 至 2019-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9053094
• 关键词: Address; Adopted; Adoption; Algorithms; Architecture; Automation; Back; Big Data to Knowledge; Biomedical Research; Brain; Brain imaging; Code; Communities; Complex; Computer software; Coupled; Data; Data Analyses; Data Provenance; Data Set; Data Sources; Databases; Development; Diagnosis; Diagnostic; Documentation; Ecosystem; Education; Educational workshop; Ensure; Exposure to; Galaxy; Goals; Image Analysis; Imagery; Imaging technology; Lead; Libraries; Licensing; Methods; Online Systems; Output; Pythons; Reproducibility; Research; Research Infrastructure; Research Personnel; Resources; Services; Software Engineering; Software Tools; Solid; Staging; Synapses; System; Testing; Training; Validation; Visual; Work; base; bench to bedside; bioimaging; clinical application; computerized data processing; cost; data access; data management; design; flexibility; graphical user interface; improved; interoperability; nervous system disorder; neuroimaging; open source; prognostic; programs; repository; software development; symposium; therapy development; tool; usability; web app; web services

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.

项目概要 随着神经影像数据的巨大增加，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“大脑” 成像”数据。其可重复使用的工作流程可以结合来自各种现有软件包的算法 产生可重复的结果。该提案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，并解决其当前的许多局限性。更易于使用 自动化工具可以减少错误，加快生物医学发现，并促进从 长凳到床边。从软件工程的角度来看，目标是提供一个精心设计的、跨平台的、 直观且易于使用的可扩展数据流计算解决方案。 我们建议在当前广泛的功能集之上构建一个交互式且直观的基于网络的平台 Nipype 与现有数据库、软件和其他工作流服务进行互操作。结果将是 可通用、可伸缩、可扩展且经过测试的基础架构，可最大程度地减少复杂的编程接口 更易于使用的 Web 应用程序。 Nipype 仍将在该网络背后保留其可扩展插件架构- 基于平台，允许继续包含新的软件包和算法，并在 多个平台。用户将能够对其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，而且可以立即接触最新的软件 用于数据分析的软件工具。为了进行分析，用户将可以访问完整的出处，从而允许其他人 重现他们的步骤。我们将与 NeuroVault 和 NeuroSynth 互动以提供无缝过渡 数据、处理、共享和解释结果之间的关系。最后，为了维持这种开放和协作的氛围 我们将通过实践研讨会和网络研讨会来培训用户和开发人员，鼓励他们采取 利用不断扩大的生态系统进行高效且可重复的分析。 虽然该架构最初将部署在脑成像社区内，但我们将采用常见的 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 用户社区并扩展研究计算的生态系统，该项目将降低使用门槛 对大型数据集进行简单高效的计算，目标是更快地开发治疗方案。