C2: Data Science

C2：数据科学

• 批准号: 10705969
• 负责人: Carlos D Brody
• 金额: $ 72.81万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-08 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10705969
• 关键词: Acceleration; Adopted; Adoption; Algorithms; Anatomy; Automation; BRAIN initiative; Behavior; Behavioral; Calcium; Code; Collaborations; Collection; Communities; Computers; Consumption; Data; Data Analyses; Data Collection; Data Science; Data Science Core; Decision Making; Documentation; Electron Microscopy; Electrophysiology (science); Ensure; Feedback; Funding; Geometry; Guidelines; Human; Human Resources; Image; Infrastructure; Laboratories; Manuals; Metadata; Methods; Modeling; Mus; Nature; Neuroanatomy; Neurosciences; Operant Conditioning; Optics; Persons; Process; Publications; Rattus; Reproducibility; Research; Research Personnel; Research Project Grants; Rodent; Short-Term Memory; Software Tools; Standardization; System; Time; Training; Work; computational pipelines; computerized data processing; computerized tools; data exchange; data handling; data integration; data interoperability; data pipeline; data reuse; data sharing; data standards; distributed archives; experience; improved; light microscopy; light weight; meetings; multiple datasets; neural; neural circuit; neuromechanism; neurophysiology; open-access repositories; optogenetics; pharmacologic; programs; segmentation algorithm; sharing platform; tool; user-friendly; virtual reality; web app; web-based tool

Project Summary/Abstract: Core 2, Data Science The Data Science Core will facilitate and standardize data collection and analysis for all research projects within this U19 program. In particular, we will develop processes and systems for collecting, organizing, and analyzing behavioral, imaging, electrophysiology, and neural manipulation data. To benefit the broader neuroscience community, we will adopt shared data and metadata formats and make our pipelines publicly available, with DataJoint as the common framework for scientific data pipelines and the Neurodata without Borders format to share large raw data. This platform will facilitate collaborative analysis of datasets by multiple researchers within the project, and make our analyses reproducible and extensible by others. We will make our code and data public in easy-to-find, open-access repositories, such as the BRAIN Initiative’s Distributed Archives for Neurophysiology Data Integration and Github. Our use of these common data standards will make the data interoperable and reusable, thus ensuring that our data publications adhere to FAIR guidelines. The core’s first aim will be to provide standardized computational pipelines for neurophysiological and behavioral data. We already have standardized data pipelines for collection of virtual-reality behavioral data and preprocessing of mesoscope imaging and Neuropixels electrophysiology recordings. We now propose to extend this effort to all data generated by the collaboration, via three new initiatives. First, we will construct a shared platform, accessed by modular, user-friendly web apps, to support virtual-reality and operant-conditioning tasks. Second, we will extend our preprocessing pipeline for electrophysiology and calcium imaging data to support several state-of-the-art segmentation algorithms. Automation of preprocessing, data transfer between systems, and standardization of manual curation steps will make analyses faster and easier, enabling more effective and reproducible processing of neurophysiological data. Third, we will develop infrastructure to support perturbations during behavior, including optogenetic, pharmacological, and physical manipulations. The core’s second aim will be to document the system, train users, and disseminate our computational tools and workflows. This effort will alleviate burdens on researchers, accelerate research by promoting standard software tools, increase adoption of standardized pipelines, and facilitate reuse of our data by others. To facilitate training and use of these pipelines, we will develop integrated web-based tools that allow world-wide access and control of local data processing. The modular nature of these tools will make them useful to and popular with the broader neuroscience community. We will provide continuous, in-person training to all researchers and technicians, including yearly tutorials with external consultants. Together, these methods for automating and standardizing data handling, when shared with the broader community, will improve the reproducibility, reusability, and interoperability of data across the field of neuroscience.

项目摘要/摘要：核心 2，数据科学 数据科学核心将促进和标准化所有研究项目的数据收集和分析 特别是在这个 U19 计划中，我们将开发用于收集、组织和收集的流程和系统。 分析行为、成像、电生理学和神经操作数据，使更广泛的人受益。 神经科学社区，我们将采用共享数据和元数据格式，并公开我们的管道 可用，以 DataJoint 作为科学数据管道的通用框架，而 Neurodata 则无需 共享大量原始数据的边界格式将促进多个数据集的协作分析。 项目中的研究人员，并使我们的分析可被其他人复制和扩展。 代码和数据在易于查找、开放访问的存储库中公开，例如 BRAIN Initiative 的分布式存储库 我们对这些通用数据标准的使用将使神经生理学数据集成档案和 Github 成为可能。 数据可互操作和可重用，从而确保我们的数据发布遵守 FAIR 准则。 该核心的首要目标是为神经生理学和神经生理学提供标准化的计算管道。 我们已经拥有用于收集虚拟现实行为数据的标准化数据管道。 我们现在建议对介观成像和神经像素电生理学记录进行预处理。 通过三项新举措，我们将这一努力扩展到协作生成的所有数据。 共享平台，通过模块化、用户友好的网络应用程序访问，以支持虚拟现实和 其次，我们将扩展电生理学和操作条件反射任务的预处理流程。 钙成像数据支持多种最先进的自动化分割算法。 预处理、系统之间的数据传输以及手动管理步骤的标准化将使 分析更快、更容易，从而能够更有效和可重复地处理神经生理学数据。 第三，我们将开发基础设施来支持行为过程中的扰动，包括光遗传学、 药理学和物理操作。 核心的第二个目标是记录系统、培训用户并传播我们的计算成果 这项工作将减轻研究人员的负担，通过促进研究加速研究。 标准软件工具，增加标准化管道的采用，并促进其他人重用我们的数据。 为了促进这些管道的培训和使用，我们将开发基于网络的集成工具，允许 这些工具的模块化特性将使它们能够在全球范围内访问和控制本地数据。 我们将提供持续的现场培训，对更广泛的神经科学界有用并受到欢迎。 向所有研究人员和技术人员提供这些方法，包括每年与外部顾问一起进行的教程。 用于自动化和标准化数据处理，当与更广泛的社区共享时，将改善 神经科学领域数据的可重复性、可重用性和互操作性。