Data Science Core

项目概要：核心2，数据科学 工作记忆是一种在头脑中暂时保存多条信息以进行操作的能力， 对几乎所有认知能力都至关重要这一多方面的研究项目旨在全面 在多个大脑区域中剖析这种能力的神经回路机制。在这样做的时候，它将产生一个 来自多种类型实验的大量数据，然后需要将其整合 一起数据科学核心将支持个人研究项目发现关系 行为、神经活动和神经连接之间的联系。核心将创建一个标准化的计算 用于钙成像数据预处理的流水线和人工工作流。管道将在当地运行， 在计算机或云计算服务中，用户将通过Web浏览器与它进行交互。的 预处理将结合现有的图像处理算法，如约束非负矩阵 因子分解和卷积网络。此外，Core还将建立一个数据科学平台， 数据联合查询的关系数据库中的行为、神经活动和神经连接 语言各种分析工具将被集成到DataJoint中，从而能够对 数据处理链这个数据科学平台将促进多个数据集的协作分析。 研究人员在项目中，并使分析可复制和可扩展的其他研究人员。我们 将开发有效的方法来培训和传播我们的计算工具， 工作流程。最后，核心将向公众提供原始数据、衍生数据和分析， 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发布。到 促进这项研究的进行，软件工具的创建，以及其他人在之后重新使用数据 初步研究已经结束，该项目将采用共享数据和元数据格式，使用HDF 5 神经数据无国界格式的实现。数据将根据展会的雅阁公开 指导原则-可通过DOI和/或URL查找，可通过RESTful Web API访问，并且可互操作 由于数据和元数据的DataJoint和Neurodata Without Borders格式，这些工具 将允许项目中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。

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.