Efficient and reproducible execution from data collection to processing

从数据收集到处理的高效且可重复的执行

• 批准号: 10482426
• 负责人: David Nelson Kennedy
• 金额: $ 29.57万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10482426
• 关键词: Accounting; Adopted; Affect; Age; Archives; Back; Brain; Clinical Research; Cloud Computing; Collaborations; Collection; Communities; Complement; Complex; Computer software; Custom; Data; Data Analyses; Data Collection; Data Discovery; Data Provenance; Data Reporting; Data Sources; Descriptor; Development; Disease; Ensure; Environment; Event; Experimental Designs; Feedback; Harvest; Heterogeneity; High Performance Computing; Image; Infrastructure; Intervention; Investigation; Investments; Knowledge; Maintenance; Manuals; Measures; Metadata; Monitor; Physiological; Population; Process; Reporting; Reproducibility; Research; Research Personnel; Resources; Running; Savings; Science; Scientist; Services; Software Tools; Standardization; Stimulus; Structure; Technology; Testing; Time; Training Activity; Training Support; Validity of Results; Work; analysis pipeline; base; comparative; computational platform; computer infrastructure; computerized data processing; computing resources; data curation; data management; data resource; data sharing; data standards; design; digital; exhaustion; experience; experimental study; flexibility; graphical user interface; improved; neuroimaging; novel strategies; operation; phenotypic data; quality assurance; research study; side effect; software development; synergism; technology research and development; tool; web interface

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.

TR&D项目3：从数据收集到处理的高效和可重复执行（DO） ReproNim项目旨在改变神经成像实践，使研究更有效 并且以这样的方式有效，从而也使其结果是可再现的。随着越来越多的数据、元数据和计算 神经影像学社区可以获得资源、管理数据的工具和框架， 确保对所有科学数字对象进行一致控制的处理工作流程， 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 最大限度地减少人工管理和干预的需要;它们不应该妨碍 research.在这个技术研究和开发项目中，TR&D 3，我们建立了新的方法， 以及采用现有工具并对其作出贡献，使数据收集和分析的许多阶段自动化， 有效地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）通过数据的收集和表示来自动化“进行（执行）实验”， 元数据和起源，包括所有数据类型， 对于质量保证和适当考虑可能的混杂因素可能很重要，例如 音频/视频刺激，生理记录，实验设计的细节。自动集成 成像和非成像数据不仅使研究更加有效和节省劳动力， 收集和共享的数据更加全面、准确和可复制。2)使计算资源 (GPUs、本地高性能计算中心和云计算资源）， 有效地提供给研究人员来执行所需的数据转换（转换，分析， 等）。在编排执行时，我们将记录详细的出处信息，足以重新执行 研究过程的任何阶段，并将其与生产一起提供给研究人员。 结果有效利用计算资源和收集详细的出处将有助于 实验和应用前沿分析工作流程，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向社会公开提供的信息。这一努力将得到以下方面的补充： 针对不同用户体验级别和用例的培训模块和支持。确保这种连续性， 工具、计算环境和数据资源的可用性和稳健运行对于任何 致力于支持高效和可重复的计算。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，我们的合作和服务项目，以及 神经影像学界。这项工作将自动化和方便地接口复杂 技术，同时促进使用既定的数据标准和来源记录，降低 神经影像科学家推进知识所需的技术专长。