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.

项目摘要 随着神经影像数据的巨大增加，对可用，自动化的需求有相应的需求 和强大的数据分析工具。 NIPYPE是一个成熟的Python库，用于对大脑的有效和灵活分析 成像”数据。其可重复使用的工作流可以结合来自现有软件包的潜水员的算法 产生可重复的结果。该提案的目的是进一步提高可用性，功能和 NIPYPE的互操作性及其传播。这将增加研究人员的使用和 临床医生增强其对生物医学研究的影响，并解决其当前许多局限性。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，并促进过渡 长凳到床边。从软件工程的角度来看，目标是提供精心设计的跨平台， 以及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互动和直观的基于网络的平台 与现有数据库，软件和其他工作流服务相互互动的NIPYPE的。结果将是 可推广，可扩展，可扩展和测试的基础架构，可最大程度地减少复杂的编程接口 更易于使用Web应用程序。 NIPYPE仍将保留其可扩展的插件架构 基于基于新的软件包和算法的平台，并执行 多个平台。用户将能够为其数据使用最合适的分析策略。这 平台不仅可以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，用户可以访问完整的出处，使其他人 复制他们的步骤。我们将与Neurovault和Neurosynth进行互动，以提供无缝的过渡 在数据，处理，共享和解释结果之间。最后，维持这样的开放和协作 努力，我们将通过动手研讨会和网络研讨会培训用户和开发人员，鼓励他们参加 扩展的生态系统的优势，以进行有效和可再现的分析。 虽然该体系结构最初将部署在大脑成像社区中，但我们将采用共同 确保与更大的生物医学成像社区互操作性的标准。通过继续参与 用户社区并扩展了用于研究计算的生态系统，该项目将降低 在大型数据集上易于高效的计算，目的是更快地开发治疗选择。