Data Science Core

数据科学核心

• 批准号: 9983197
• 负责人: Hyunjune SEBASTIAN SEUNG
• 金额: $ 45.41万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-28 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9983197
• 关键词: Adopted; Algorithms; Anatomy; Area; Behavior; Behavioral; Brain; Calcium; Cloud Computing; Computer software; Computers; Consumption; Data; Data Analyses; Data Science; Data Science Core; Documentation; Electron Microscopy; Electrophysiology (science); Engineering; Funding; Human; Human Resources; Image; Individual; Ingestion; Internet; Language; Maintenance; Metadata; Methods; Mind; Online Systems; Process; Publications; Reproducibility; Research; Research Personnel; Research Project Grants; Research Support; Running; Science; Series; Services; Short-Term Memory; Software Engineering; Software Tools; Sorting - Cell Movement; Source Code; Standardization; System; Time; Training; Visualization software; Work; Writing; cognitive ability; computational pipelines; computerized data processing; computerized tools; connectome data; convolutional neural network; data format; data reuse; data sharing; experimental study; image processing; interoperability; light microscopy; multiple datasets; neural circuit; neuromechanism; neurophysiology; relating to nervous system; relational database; repository; software development; tool; training project; virtual

Project Summary: Core 2, Data Science Working memory, the ability to temporarily hold multiple pieces of information in mind for manipulation, is central to virtually all cognitive abilities. This multi-component research project aims to comprehensively dissect the neural circuit mechanisms of this ability across multiple brain areas. In doing so, it will generate an extremely large quantity of data, from multiple types of experiments, which will then need to be integrated together. The Data Science Core will support the individual research projects in discovering relationships among behavior, neural activity, and neural connectivity. The Core will create a standardized computational pipeline and human workflow for preprocessing of calcium-imaging data. The pipeline will run either on local computers or in cloud computing services, and users will interact with it through a web browser. The preprocessing will incorporate existing image-processing algorithms, such as Constrained Nonnegative Matrix Factorization and convolutional networks. In addition, the Core will build a data science platform that stores behavior, neural activity, and neural connectivity in a relational database that is queried by the DataJoint language. Diverse analysis tools will be integrated into DataJoint, enabling the robust maintenance of data-processing chains. This data-science platform will facilitate collaborative analysis of datasets by multiple researchers within the project, and make the analyses reproducible and extensible by other researchers. We will develop effective methods for training and otherwise disseminating our computational tools and work flows. Finally, the Core will make raw data, derived data, and analyses available to the public upon publication via the data-science platform, source-code repositories, and web-based visualization tools. To facilitate the conduct of this research, the creation of software tools, and the reuse of the data by others after the primary research has concluded, the project will adopt shared data and metadata formats using the HDF5 implementation of the Neurodata without Borders format. Data will be made public in accord with the FAIR guiding principles — findable by a DOI and/or URL, accessible through a RESTful web API, and interoperable and reusable due to DataJoint and the Neurodata Without Borders format for data and metadata. These tools will allow the researchers within the project to store, manipulate, and analyze their data efficiently and to share it with other researchers as needed.

项目摘要：核心 2，数据科学 工作记忆是指暂时记住多条信息以进行操作的能力 这个多组成部分的研究项目旨在全面了解所有认知能力的核心。 剖析这种能力跨多个大脑区域的神经回路机制，它将产生一个 来自多种类型实验的大量数据，然后需要整合 数据科学核心将支持各个研究项目发现关系。 行为、神经活动和神经连接之间的关系将创建一个标准化的计算。 用于预处理钙成像数据的管道和人工工作流程管道将在本地运行。 计算机或云计算服务中，用户将通过网络浏览器与其进行交互。 预处理将结合现有的图像处理算法，例如约束非负矩阵 此外，Core 将构建一个存储数据科学平台。 DataJoint 查询的关系数据库中的行为、神经活动和神经连接 DataJoint将集成多种分析工具，实现稳健的维护。 该数据科学平台将促进多个数据集的协作分析。 项目中的研究人员，并使其他研究人员可以重复和扩展分析。 将开发有效的方法来培训和传播我们的计算工具， 最后，核心将向公众提供原始数据、派生数据和分析。 通过数据科学平台、源代码存储库和基于网络的可视化工具进行发布。 促进这项研究的进行、软件工具的创建以及其他人在研究后对数据的重用 初步研究已经结束，该项目将采用使用 HDF5 的共享数据和元数据格式 实施 Neurodata without Borders 格式。数据将按照 FAIR 公开。 指导原则——可通过 DOI 和/或 URL 查找、通过 RESTful Web API 访问且可互操作 由于数据和元数据的 DataJoint 和 Neurodata Without Borders 格式，这些工具可以重复使用。 将允许项目内的研究人员有效地存储、操作和分析他们的数据并共享 根据需要与其他研究人员一起进行。