Efficient and reproducible execution from data collection to processing

研发项目3：从数据收集到处理的高效和可重复执行(DO) 摘要：ReproNim项目旨在改变神经成像实践，使研究更有效率 并以这样一种方式有效，因此也使其可重现。随着更多的数据、元数据和计算 神经成像社区可以使用资源、工具和框架来管理数据和 确保对所有数字科学对象进行一致控制的处理工作流 变得越来越重要。这些工具应有助于获得有效的结果，同时确定其出处。 并最大限度地减少对人工管理和干预的需要；它们不应妨碍 研究。在这个技术研究和开发项目，研发3，我们建立了新的方法，如 以及采用现有工具并对其作出贡献，以使数据收集和分析的许多阶段自动化， 有效利用研究人员可用的本地或远程计算资源。特别是， 我们的目标是：1)通过收集和表示数据，实现“进行(执行)实验”的自动化， 元数据，以及神经成像采集的所有阶段的来源，包括 对于质量保证和对可能的混杂因素进行适当的核算可能很重要，例如 音频/视频刺激、生理记录、实验设计细节。自动集成 成像和非成像数据不仅使研究更高效、更省力，还使 收集和共享的数据更加全面、准确和可重现。2)使计算资源 (GPU、本地高性能计算中心和云计算资源) 可有效地供研究人员执行所需的数据转换(转换，分析， 等)。在协调执行时，我们将记录详细的出处信息，以便重新执行 研究过程的任何阶段，并将其提供给研究人员和生成的 结果。有效利用计算资源和收集详细的出处将有助于 前沿分析工作流的试验和应用，同时减少必要的技术 技术诀窍。3)维护、支持和扩展现有的ReproNim及相关软件和数据资源 我们和我们的合作伙伴已经向社区公开提供了。这一努力将得到以下补充 针对不同用户体验级别和用例的培训模块和支持。确保这样的连续性 工具、计算环境和数据资源的可用性和强健操作对于 旨在支持高效和可重复计算的努力。我们将与以下机构合作开展这项工作 其他ReproNim技术研发项目，我们的协作和服务项目，以及 整个神经成像社区。这项工作将自动化和方便的接口复杂 技术，同时促进使用既定的数据标准和来源记录，降低 神经影像科学家提升知识所需的技术专长。

TR&D Project 3: Efficient and reproducible execution from data collection to processing (DO) SUMMARY: The ReproNim project seeks to transform neuroimaging practice, to make research more efficient and effective in such a way that also makes it reproducible as a result. As more data, metadata, and computing resources become available to the neuroimaging community, tools and frameworks for managing data and processing workflows that ensure consistent control over all of the digital objects of science become increasingly important. Such tools should assist in obtaining valid results while establishing their provenance and minimizing the need for manual curation and intervention; they should not get in the way of doing research. In this Technology Research and Development Project, TR&D 3, we establish new approaches, as well as adopt and contribute back to existing tools, to automate many stages of data collection and analysis, making efficient use of local or remote computing resources that are available to the researchers. In particular, we aim to 1) Automate “Doing (execution of) an experiment” through collection and representation of data, metadata, and provenance across all stages of a neuroimaging acquisition, including all the data types that could be important for quality assurance and proper accounting for possible confounding factors, such as audio/video stimuli, physiological recordings, details of the experimental design. Automated integration of imaging and non-imaging data not only makes research more efficient and labor saving, it also makes collected and shared data more comprehensive, accurate, and reproducible. 2) Make computational resources (GPUs, local High Performance Computing centers, and cloud computing resources) conveniently and efficiently available to researchers to perform execution of needed data transformations (conversion, analysis, etc.). While orchestrating execution we will record detailed provenance information, sufficient for re-execution of any stage of the research process, and make it available to the researcher alongside with the produced results. Efficient use of computational resources and collection of detailed provenance will facilitate experimentation and application of bleeding edge analysis workflows, while reducing necessary technological know-how. 3) Maintain, support, and extend existing ReproNim and related software and data resources that we and our partners have made available openly to the community. This effort will be complemented by training modules and support for different user experience levels and use cases. Ensuring such continuity in availability and robust operation of tools, computing environments, and data resources is essential for any effort aiming to support efficient and reproducible computation. We will carry out this work in collaboration with the other ReproNim technology research and development projects, our collaborative and service projects, and the neuroimaging community at large. This work will automate and conveniently interface complex technologies while facilitating use of established data standards and provenance recording, lowering the technological expertise necessary for neuroimaging scientists to advance knowledge.