Data Science Core

数据科学核心

• 批准号: 10294399
• 负责人: Karel Svoboda
• 金额: $ 60.48万
• 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/10294399
• 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; 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; Time; base; cell type; cloud based; complement resource; complex data; computational pipelines; computer infrastructure; data analysis pipeline; data format; data infrastructure; data repository; data sharing; data standards; design; extracellular; flexibility; improved; in vivo; interoperability; large scale data; large scale simulation; metadata standards; multimodality; neural circuit; neurophysiology; open data; open source; optogenetics; 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）服务于与移动、存储、分析和共享数据相关的多种目的。 此次合作收集的数据集将非常大（PB规模），并且是多模式的，包括： 中尺度解剖学（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） 4）;神经电路的大规模模拟（项目5）。实现算法从这些数据中提取知识 大规模和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，以高效地共享云中的数据和分析管道。个人研究 项目还需要支持实施数据分析算法和相关软件工程。的 DSC将实施和完善分析算法，以便这些算法可以应用于大规模数据。除了 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，而且有着交付的历史。解剖分子神经生理学 行为数据将以标准化的数据格式在广泛使用的储存库中提供。的DSC 将支持在小组内部以及与整个科学界分享数据。 丘脑是传统上使用低维信息分割的核团的集合， 包括细胞结构和单通道免疫组织化学。现有的分割（即， 解剖图谱）不足以描述丘脑丰富的功能结构。因此我们 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的主要任务是登记解剖学（项目1，2），分子（项目2，MSC）和 神经生理学测量（项目3，4）在尽可能高的分辨率标准化参考 艾伦共同坐标框架（CCF）。丘脑内所有测量的定位是 这是发现丘脑结构和功能逻辑的重要一步。DSC将实施 为CCF中所有数据的空间对齐提供高效准确的工作流程。 DSC将遵循以下原则：i）实施与FAIR（可查找， 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 现有的数据存储库（如可用）;以及iv）重用并构建现有的开源软件生态系统。