Data Science Core

项目摘要：核心2，数据科学 工作记忆，即在脑海中暂时记住多个信息以进行操作的能力，是 几乎是所有认知能力的核心。这一多组分研究项目旨在全面 剖析这种能力在多个大脑区域的神经回路机制。在这样做时，它将生成一个 来自多种类型实验的极其大量的数据，然后需要集成这些数据 在一起。数据科学核心将支持发现关系的个别研究项目 在行为、神经活动和神经连通性之间。核心将创建一个标准化的计算 钙成像数据预处理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，用户将通过网络浏览器与之互动。这个 预处理将结合现有的图像处理算法，如约束非负矩阵 因式分解和卷积网络。此外，核心将建立一个数据科学平台，存储 DataJoint查询的关系数据库中的行为、神经活动和神经连接 语言。各种分析工具将集成到DataJoint中，从而实现强大的维护 数据处理链。这个数据科学平台将促进对数据集的协作分析， 项目内的研究人员，并使分析可由其他研究人员重现和扩展。我们 将开发有效的方法来培训和传播我们的计算工具和 工作流程。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、源代码储存库和基于网络的可视化工具进行出版。至 促进这项研究的开展、软件工具的创建以及其他人在此之后重新使用数据 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 无边界神经数据格式的实施。数据将按照交易会的要求公开。 指导原则-可通过DOI和/或URL找到，可通过REST风格的Web API访问，并且可互操作 由于DataJoint和Neurodata Without Borders格式的数据和元数据，因此可以重复使用。这些工具 将允许项目中的研究人员高效地存储、操作和分析他们的数据并共享 如果需要，它将与其他研究人员合作。

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.