Data Science Resource Core

数据科学资源核心

• 批准号: 10438689
• 负责人: Liam M Paninski
• 金额: $ 56.4万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10438689
• 关键词: Address; Algorithmic Analysis; Algorithms; Anatomy; Archives; Benchmarking; Calcium; Cells; Communities; Complex; Computer software; Data; Data Analyses; Data Science; Data Science Core; Data Storage and Retrieval; Electrophysiology (science); Experimental Designs; Factor Analysis; Goals; Image; Infrastructure; Knowledge; Laboratories; Location; Metadata; Methods; Modality; Modeling; Neurons; Neurosciences; Population; Reproducibility; Research; Research Personnel; Research Project Grants; Savings; Signal Transduction; Standardization; Stimulus; Structure; Theoretical model; Time; Training; analysis pipeline; cell type; data analysis pipeline; data management; data sharing; data streams; data tools; denoising; design; experimental study; imaging approach; improved; in vivo; iterative design; multimodality; neural circuit; open source; open source tool; optogenetics; preference; relating to nervous system; response; simulation; software development; spatiotemporal; theories; tool; transcriptome sequencing

SUMMARY The major theme of this proposal is a tightly closed loop of experiment, theory, and data analysis. Sophisticated, scalable data science methods are a critical component of this loop. The Data Science Core serves two primary purposes. First, we will apply and refine sophisticated data analysis algorithms directly related to the project’s scientific goals. This project will generate massive streams of data from multiple recording and simulation modalities: whole-cell electrophysiology and anatomy, large-scale calcium imaging, spatiotemporally-complex optogenetic perturbations, RNA sequencing images, in addition to massive simulations of networks of spiking neurons. A correspondingly major effort is needed to manage this data, to distill it into new scientific knowledge, and to design new experiments, theoretical analyses, and simulations to close the theory-experiment-analysis loop. This will entail the application and iterative refinement of algorithms for preprocessing the data (e.g., taking calcium imaging video and extracting demixed and denoised neural activity from each cell visible in the field of view); aligning, registering, and performing statistical inferences on data across multiple modalities (e.g, calcium imaging, optogenetic stimulation, and seqFISH); functionally characterizing the stimulus preferences and correlation structure of the activity in the observed cells; and developing closed-loop optimal experimental design methods to obtain richer, more informative data. Second, this Core will build a collaborative infrastructure allowing the multiple laboratories in this project to act as one: sharing data and analysis tools, and closely integrating theorists and experimentalists. This infrastructure will: be completely open source; build on current efforts to standardize neuroscience data; be modular and extensible to allow for rapid iterative improvement of each stage of the algorithmic pipeline; enforce automatic archiving and recording of algorithmic metadata describing versioning and parameter choices for easy searchability and reproducibility; and allow for straightforward benchmarking. As we develop these practices and tools for data and analysis pipeline sharing, we will make them immediately available to the community. Thus we will provide a model platform for vastly improving reproducibility, keeping analysis pipelines up to date as improved methods are developed, and most importantly saving researchers from re- developing and re-implementing analysis software and data storage/sharing solutions. We aim to make it easy for groups of labs anywhere in the world to unite and crack large-scale neural circuits. This will transform the way neuroscience is done.

概括 该提案的主题是实验、理论和数据分析的紧密闭环。 复杂的、可扩展的数据科学方法是这个循环的关键组成部分。 数据科学核心有两个主要目的。首先，我们将应用和完善复杂的数据分析 与项目的科学目标直接相关的算法。该项目将产生海量数据流 来自多种记录和模拟模式：全细胞电生理学和解剖学、大规模 钙成像、时空复杂光遗传学扰动、RNA 测序图像，此外 尖峰神经元网络的大规模模拟。需要付出相应的巨大努力来管理这一问题 数据，将其提炼成新的科学知识，并设计新的实验、理论分析和 模拟以形成理论-实验-分析的闭环。这将需要应用和迭代细化 用于预处理数据的算法（例如，拍摄钙成像视频并提取分层和 视野中可见的每个细胞的去噪神经活动）；对齐、注册和执行 对多种模式（例如钙成像、光遗传学刺激和 序列FISH);从功能上描述刺激偏好和活动的相关结构 观察到的细胞；开发闭环优化实验设计方法以获得更丰富、更多 信息性数据。 其次，该核心将建立一个协作基础设施，使该项目中的多个实验室能够采取行动 合一：共享数据和分析工具，紧密结合理论家和实验家。这 基础设施将： 完全开源；以当前神经科学数据标准化工作为基础；是 模块化和可扩展，允许算法管道的每个阶段快速迭代改进； 强制自动归档和记录描述版本控制和参数的算法元数据 易于搜索和再现的选择；并允许直接进行基准测试。随着我们的发展 这些用于数据和分析管道共享的实践和工具，我们将立即将它们提供给 社区。因此，我们将提供一个模型平台，大大提高可重复性，保持分析 随着改进方法的开发，管道不断更新，最重要的是，使研究人员免于重新设计 开发和重新实施分析软件和数据存储/共享解决方案。我们的目标是让一切变得简单 世界各地的实验室团体可以联合并破解大规模神经回路。这将改变 神经科学的完成方式。