Data Science Core

数据科学核心

• 批准号: 10546506
• 负责人: Karel Svoboda
• 金额: $ 107.97万
• 依托单位: ALLEN INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10546506
• 关键词: Adult; Algorithmic Analysis; Algorithms; Anatomy; Architecture; Atlases; Behavior; Behavioral; Brain; Cell Nucleus; Collaborations; Collection; Communities; Complex; Computer software; Data; Data Analyses; Data Science; Data Science Core; Data Set; Databases; Development; Dimensions; Ecosystem; Electrodes; Electrophysiology (science); Fluorescent in Situ Hybridization; Geography; Image; Immunohistochemistry; Individual; Infrastructure; Ingestion; Knowledge; Link; Location; Logic; Maps; Measurement; Metadata; Modality; Modeling; Modernization; Molecular; Neuroanatomy; Paper; Physiology; Process; Quality Control; Reagent; Recording of previous events; Reproducibility; Research; Research Project Grants; Resolution; Science; Site; Slice; Software Engineering; Standardization; Stimulus; Synapses; System; Thalamic structure; analysis pipeline; cell type; cloud based; complement resource; complex data; computational pipelines; computer infrastructure; data format; data infrastructure; data pipeline; data repository; data sharing; data standards; design; extracellular; flexibility; improved; in vivo; interoperability; large scale data; large scale simulation; metadata standards; meter; multimodality; neural circuit; neurophysiology; open data; open source; optogenetics; parallelization; repository; single molecule; tool; transcriptomics; voltage; web interface

Summary, Data Science Core The Data Science Core (DSC) serves multiple purposes related to moving, storing, analyzing, and sharing data. The data sets collected by this collaboration will be very large (PB scale) and multi-modal, including: Brain-wide, mesoscale anatomy (Project 1-4); spatial transcriptomics (Project 2, Molecular Science Core); large-scale in vivo electrophysiology (Project 3, 4); brain slice synaptic physiology and voltage imaging (Project 3); behavior (Project 4); large-scale simulations of neural circuits (Project 5). Implementing algorithms to extract knowledge from these large-scale and complex data sets demands professional data science and software practices. The DSC will configure the infrastructure to efficiently share data and analysis pipelines in the cloud. Individual research projects also require support for implementing data analysis algorithms and related software engineering. The DSC will implement and refine analysis algorithms so that these can be applied to data at scale. In addition to discoveries, research papers, and reagents, data itself is a major product of our proposed research. This team is not only committed to open science but has a history of delivering. Anatomical, molecular, neurophysiological and behavioral data will be made available in widely-used repositories in standardized data formats. The DSC will support the sharing of data within the team and with the scientific community at large. The thalamus is a collection of nuclei that has been traditionally segmented using low-dimensional information, including cytoarchitecture and single-channel immunohistochemistry. The existing segmentations (i.e. anatomical atlases) are not sufficient to describe the rich functional architecture of the thalamus. We therefore need to register all measurements precisely to thalamic sub-regions, agnostic to current notions of intra-thalamic boundaries. A major task for the DSC is to register anatomical (Project 1, 2), molecular (Project 2, MSC) and neurophysiological measurements (Projects 3, 4) at the highest possible resolution to a standardized reference atlas (the Allen Common Coordinate Framework, CCF). Localization of all measurements within the thalamus is an important step in discovering the architecture and functional logic of the thalamus. The DSC will implement efficient and accurate workflows for spatial alignment of all data in the CCF. The DSC will subscribe to the following principles: i) implement best practices with respect to FAIR (Findable, Accurate, Interoperable, Reusable) data principles; ii) reuse and extend community data standards; iii) rely on existing data repositories as available; and iv) reuse and build on existing open source software ecosystems.

摘要，数据科学核心 数据科学核心 (DSC) 具有与移动、存储、分析和共享数据相关的多种用途。 此次合作收集的数据集将非常大（PB规模）和多模式，包括：全脑、 介观解剖学（项目 1-4）；空间转录组学（项目 2，分子科学核心）；大规模体内 电生理学（项目3、4）；脑切片突触生理学和电压成像（项目3）；行为（项目 4);神经回路的大规模模拟（项目 5）。实施算法以从中提取知识 大规模且复杂的数据集需要专业的数据科学和软件实践。 DSC 将 配置基础设施以在云中高效共享数据和分析管道。个人研究 项目还需要实现数据分析算法和相关软件工程的支持。这 DSC 将实施和完善分析算法，以便这些算法可以大规模应用于数据。此外 发现、研究论文和试剂，数据本身就是我们提出的研究的主要产品。这支球队 不仅致力于开放科学，而且拥有交付的历史。解剖学、分子学、神经生理学 行为数据将以标准化数据格式在广泛使用的存储库中提供。 DSC 将支持团队内部以及与整个科学界的数据共享。 丘脑是细胞核的集合，传统上使用低维信息进行分割， 包括细胞结构和单通道免疫组织化学。现有的细分（即 解剖图谱）不足以描述丘脑丰富的功能结构。我们因此 需要将所有测量精确地记录到丘脑子区域，与当前丘脑内的概念无关 边界。 DSC 的主要任务是注册解剖学（项目 1、2）、分子（项目 2、MSC）和 以尽可能最高的分辨率进行标准化参考的神经生理学测量（项目 3、4） 图集（艾伦通用坐标框架，CCF）。丘脑内所有测量的定位是 发现丘脑的结构和功能逻辑的重要一步。 DSC 将实施 高效、准确的工作流程，用于 CCF 中所有数据的空间对齐。 DSC 将遵守以下原则： i) 实施有关 FAIR 的最佳实践（Findable、 准确、可互操作、可重用）数据原则； ii) 重用和扩展社区数据标准； iii) 依赖 现有可用的数据存储库； iv) 重用并构建现有的开源软件生态系统。