Data Science Resource Core

摘要 这项提议的主要主题是实验、理论和数据分析的紧密闭合循环。 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，我们将应用和改进复杂的数据分析 算法与该项目的科学目标直接相关。该项目将产生海量数据流 来自多种记录和模拟方式：全细胞电生理学和解剖学，大规模 钙成像、时空复杂的光遗传扰动、RNA测序图像 对尖峰神经元网络的大规模模拟。需要做出相应的重大努力来管理这一点 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 模拟以结束理论-实验-分析循环。这将需要应用程序和迭代求精 用于对数据进行预处理的算法(例如，获取钙成像视频并提取分离的和 从视野中可见的每个细胞中去除噪声的神经活动)；对齐、配准和执行 对多种模式(例如，钙成像、光遗传刺激和 SeqFISH)；在功能上表征活动的刺激偏好和关联结构 以及开发闭环最优试验设计方法，以获得更丰富、更多的 信息量大的数据。 其次，该核心将构建一个协作基础设施，允许该项目中的多个实验室采取行动 一体：共享数据和分析工具，将理论家和实验者紧密结合在一起。这 基础设施将：完全开源；建立在当前标准化神经科学数据的努力基础上； 模块化和可扩展，以便快速迭代改进算法流水线的每一阶段； 强制自动存档和记录描述版本和参数的算法元数据 易于搜索和重现的选择；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，我们将立即提供给 社区。因此，我们将提供一个模型平台，极大地提高重复性，保持分析 随着改进方法的发展，最新的管道被开发出来，最重要的是使研究人员不必重新 开发和重新实施分析软件和数据存储/共享解决方案。我们的目标是让这一切变得容易 让世界上任何地方的实验室联合并破解大规模神经电路。这将改变 神经科学是怎么做的。

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.