DataJoint SciOps: A Managed Service for Neuroscience Data Workflows

DataJoint SciOps：神经科学数据工作流的托管服务

• 批准号: 10651888
• 负责人: Dimitri Yatsenko
• 金额: $ 103.98万
• 依托单位: VATHES INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10651888
• 关键词: Acute; Address; Adopted; Adoption; Agreement; Algorithms; Archives; Automation; Brain; Budgets; Cloud Computing; Collaborations; Collection; Communities; Computer software; Cost Control; Custom; Data; Data Analyses; Data Collection; Data Protection; Data Science; Development; Disease; Effectiveness; Elements; Engineering; Ensure; Environment; Fostering; Generations; Grant; Health; High Performance Computing; Infrastructure; International; Internet; Joints; Life Cycle Stages; Marketing; Methods; Neurosciences; Neurosciences Research; Occupations; Online Systems; Performance; Phase; Physics; Population Heterogeneity; Process; Productivity; Publishing; Quality Control; Reporting; Reproducibility; Research; Resolution; Resources; Role; Schedule; Science; Scientist; Secure; Self Management; Services; Small Business Innovation Research Grant; Speed; Standardization; System; Technology; United States National Institutes of Health; Universities; Visualization; Work; automated analysis; cloud based; cloud platform; commercialization; complex data; computer infrastructure; cost; data hosting; data infrastructure; data management; data pipeline; data repository; design; experimental study; flexibility; graphical user interface; instrument; member; neuroinformatics; neurophysiology; neurotechnology; open data; open source; operation; platform as a service; programs; satisfaction; software as a service; tool

Project summary This SBIR proposal aims to address current challenges in data-driven neuroscience by implementing DataJoint SciOps: a commercial service to help research labs implement computational workflows for data-intensive science experiments. This turn-key service will organize secure data pipelines and automate analysis jobs based on scalable cloud infrastructure while keeping the entire process transparent and reproducible. Progress in neuroscience relies on analyzing vast amounts of complex data recorded by new generations of neurotechnologies. To analyze this data, research teams develop advanced algorithms and share them as open-source software. These software toolchains require advanced computing infrastructure and operations, posing a set of engineering hurdles to manage the particular experiment workflows for data entry, acquisition, analysis, sharing, and publishing. DataJoint SciOps is made possible by the DataJoint Elements program (NIH Grant U24 NS116470), which provides a collection of community-curated software modules for building standardized computational workflows. These designs integrate best-in-class open-source analysis software from leading research teams and provide integrations with neuroscience infrastructure projects. DataJoint SciOps will effectively serve as the commercial extension of DataJoint Elements by providing computing infrastructure, hosting, and a managed service with subject-matter expert support and customization services. This Direct-to-Phase II commercialization project will develop and validate a comprehensive managed service for executing data-centric neuroscience projects with robust automated processes for data management and analysis (Aim 1). A cloud-based software-as-a-service platform will streamline the service to enable scaling to hundreds of labs through standardization, self-service, and process automation (Aim 2). In the process, DataJoint will partner with Johns Hopkins University's Applied Physics Lab to integrate the platform with neuroinformatics resources and provide collaboration interfaces (Aim 3). Jointly, the teams will ensure the transparency and reproducibility of the managed workflows and integrate it with other data infrastructure programs in the U.S. and internationally. With several thousand neuroscience research groups seeking to adopt advanced neurotechnology instruments and analysis tools, the commercially operated DataJoint SciOps service will lower the technological and organizational barriers for efficient and reproducible research. 1

项目摘要 该SBIR提案旨在通过实施数据连接来解决数据驱动的神经科学的当前挑战 Sciops：一种用于帮助研究实验室的商业服务 实验。这项旋转服务将基于可扩展组织组织安全的数据管道和自动分析作业 云基础架构，同时保持整个过程透明且可重复。神经科学的进展依赖 在分析的新一代神经技术记录的大量复杂数据中。为了分析这些数据， 研究团队开发了高级算法并将其作为开源软件共享。这些软件工具链 需要高级计算基础架构和操作，构成一组工程障碍来管理 用于数据输入，获取，分析，共享和发布的特定实验工作流动。数据连接科学 通过数据连接元素程序（NIH Grant U24 NS116470）使得可能 社区策划的软件模块，用于构建标准化计算工作流量。这些设计集成了 领先研究团队的一流开源分析软件，并与神经科学提供一体化 基础设施项目。 Data Koint Sciops将有效地充当数据连接元素的商业扩展 通过提供计算基础架构，托管和托管服务，并提供主题专家支持和 定制服务。 这个直接到相位的II商业化项目将开发并验证一项全面的托管服务 使用强大的自动化流程来执行以数据为中心的神经科学项目进行数据管理和分析 （目标1）。基于云的软件即服务平台将简化该服务，以缩放到数百个 通过标准化，自助服务和过程自动化实验（AIM 2）。在此过程中，数据连接将合作 使用约翰·霍普金斯大学的应用物理实验室，将平台与神经信息资源和 提供协作界面（AIM 3）。共同的团队将确保透明度和复制 托管工作流动，并将其与美国和国际的其他数据基础架构计划集成在一起。 有数千个神经科学研究小组寻求采用先进的神经技术工具，并 分析工具，商业操作的数据连接科学服务将降低技术和组织 有效和可重复研究的障碍。 1