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: 10547509
• 负责人: Dimitri Yatsenko
• 金额: $ 108.46万
• 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/10547509
• 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; 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; United States National Institutes of Health; Universities; Visualization; Work; automated analysis; base; cloud based; cloud platform; commercialization; complex data; computer infrastructure; cost; data hosting; data infrastructure; data management; data pipeline; data repository; data tools; 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DataJoint来解决数据驱动神经科学的当前挑战。 SciOps：帮助研究实验室为数据密集型科学实施计算工作流程的商业服务 实验这项交钥匙服务将组织安全的数据管道，并基于可扩展的 云基础设施，同时保持整个过程的透明性和可复制性。神经科学的进步依赖于 分析新一代神经技术记录的大量复杂数据。为了分析这些数据， 研究团队开发先进的算法，并将其作为开源软件共享。这些软件工具链 需要先进的计算基础设施和操作，造成了一系列工程障碍，以管理 特定的实验工作流程用于数据输入、获取、分析、共享和发布。DataJoint SciOps DataJoint Elements计划（NIH Grant U24 NS116470）使之成为可能，该计划提供了一系列 社区策划的软件模块，用于构建标准化的计算工作流程。这些设计整合了 来自领先研究团队的一流开源分析软件，并提供与神经科学的集成 基础设施项目DataJoint SciOps将有效地作为DataJoint Elements的商业扩展 通过提供计算基础设施、托管和托管服务以及主题专家支持， 定制服务。 这个直接进入第二阶段的商业化项目将开发和验证一个全面的管理服务， 使用强大的自动化数据管理和分析流程执行以数据为中心的神经科学项目 (Aim 1）。基于云的软件即服务平台将简化服务， 通过标准化、自助服务和流程自动化（目标2），在此过程中，DataJoint将与 与约翰霍普金斯大学应用物理实验室合作，将该平台与神经信息学资源整合， 提供协作接口（目标3）。各小组将共同确保 托管工作将其与美国和国际上的其他数据基础设施计划进行整合。 数千个神经科学研究小组寻求采用先进的神经技术仪器， 分析工具，商业运营的DataJoint SciOps服务将降低技术和组织成本， 有效和可重复研究的障碍。 1