Data Science Core

数据科学核心

• 批准号: 10678800
• 负责人: Liam M Paninski
• 金额: $ 21.3万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10678800
• 关键词: Brain; Data Science Core; International; Laboratories; Sorting - Cell Movement

Summary/Abstract (Data Science Core B) This project—and the International Brain Laboratory (IBL) more generally—represents a tightly closed loop of experiment, theory, and data analysis. This loop depends critically on sophisticated, scalable, and robust data science resources and methods. This core will provide these resources and methods. First, this core will extend the existing IBL data architecture to handle the new datasets that will be collected as part of this project (Aim 1). IBL already uses this data architecture daily to collect experimental data (including behavioral video and Neuropixels recordings) and metadata; automatically preprocess and analyze the data; automatically transfer the data to a central server; and share the results within the collaboration and externally. This core will extend this architecture to handle the new experiments and data types (calcium imaging, functional ultrasound imaging, optogenetic perturbations, in situ sequencing) to be pursued here. Second, this Core will apply and refine sophisticated data-analysis algorithms directly related to the project’s scientific goals, and serve these algorithms publicly as open-source tools to the broader community (Aim 2). IBL already has good working pipelines in place to preprocess (spike sort) Neuropixels datasets. During the proposed project, this core will continue to refine and improve these spike-sorting pipelines, incorporate new pipelines to handle additional large data types (calcium imaging and in situ sequencing data) that are not currently in place in the IBL infrastructure, and support development of methods for analyzing large-scale multi- neuronal recordings from multiple brain areas over multiple experiments. All analytical and data-architecture tools will be versioned, open-source, and immediately available for use and development by other laboratories. A major synergistic aspect of IBL is that these pipelines will be heavily internally tested by many users with a wide variety of expertise across multiple labs. These tools will also be served on the Neuroscience Cloud Analysis as a Service platform to facilitate reproducible, easy usage. We thus expect the availability of these new tools to have an immediate and broad impact on the field.

摘要/摘要（数据科学核心 B） 这个项目——以及更广泛的国际大脑实验室（IBL）——代表了一个紧密闭环 实验、理论和数据分析。该循环关键取决于复杂、可扩展且可靠的数据 科学资源和方法。这个核心将提供这些资源和方法。 首先，该核心将扩展现有的 IBL 数据架构以处理将要发布的新数据集。 作为该项目（目标 1）的一部分收集。 IBL 已经每天使用此数据架构来收集实验数据 （包括行为视频和 Neuropixels 记录）和元数据；自动预处理和分析 数据；自动将数据传输到中央服务器；并在合作中分享成果 外部。该核心将扩展该架构以处理新的实验和数据类型（钙成像、 功能超声成像、光遗传学扰动、原位测序）将在这里进行。 其次，该核心将应用和完善与数据直接相关的复杂数据分析算法。 项目的科学目标，并将这些算法作为开源工具公开提供给更广泛的社区 （目标 2）。 IBL 已经拥有良好的工作管道来预处理（尖峰排序）Neuropixels 数据集。期间 在拟议的项目中，该核心将继续细化和改进这些尖峰排序管道，纳入新的 用于处理其他大型数据类型（钙成像和原位测序数据）的管道 目前已在 IBL 基础设施中使用，并支持开发用于分析大规模多 多次实验中多个大脑区域的神经元记录。所有分析和数据架构 工具将进行版本控制、开源，并立即可供其他实验室使用和开发。 IBL 的一个主要协同方面是，这些管道将由许多具有丰富经验的用户进行严格的内部测试。 多个实验室的广泛专业知识。这些工具也将在神经科学云上提供 分析即服务平台，促进可重复且易于使用。因此，我们期望这些资源的可用性 新工具对该领域产生直接和广泛的影响。