Nipype: Dataflows for Reproducible Biomedical Research

项目总结

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.