Data-Science Core

数据科学核心

• 批准号: 10400145
• 负责人: Jan Drugowitsch
• 金额: $ 53.02万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10400145
• 关键词: Architecture; Behavioral; Brain; Charge; Code; Collaborations; Communities; Computer Models; Computer software; Data; Data Analyses; Data Science Core; Data Scientist; Data Sources; Data Storage and Retrieval; Development; Documentation; Ensure; Experimental Designs; Factor Analysis; Future; Gaussian model; Goals; Information Technology; Infrastructure; International; Joints; Label; Laboratories; Metadata; Methods; Modeling; Morphologic artifacts; Nature; Neurosciences; Population; Principal Component Analysis; Process; Publishing; Reproducibility; Research; Research Personnel; Resources; Software Tools; Source Code; Standardization; Statistical Models; Stimulus; Structure; Techniques; Testing; Time; Variant; Work; computer infrastructure; data access; data cleaning; data exchange; data sharing; data standards; experience; experimental study; improved; member; multidisciplinary; neural model; neuromechanism; neurophysiology; open source; relating to nervous system; repository; searchable database; theories; tool; voltage

Project Summary This application proposes to use theory-driven experimental design, with advanced techniques for neural recording, data analysis, and computational modeling, to investigate the neural mechanisms, circuits, and representations underlying the perceptual process of causal inference in space and time. The multidisciplinary nature of the proposed work requires close collaboration among consortium members. The Data Science Core will facilitate this collaboration and provide the tools necessary to handle and analyze the large-scale neural data collected in the proposed experiments. To achieve these goals, the Data Science Core will rely on existing infrastructure, open standards, and open-source software as much as possible. Aim 1 will establish a unified data standard, and data exchange and storage infrastructure, using the architecture established by the International Brain Laboratory, which stores metadata in a relational, searchable database, and experimental and processed data on a separate file server. Github will enable joint development, exchange, and documentation of the code underlying data preprocessing, processing, and analysis. To relate data to models, voltages recorded experimentally must be transformed into standardized spike times and counts, without artifacts or confounds. Aim 2 will develop a principled, transparent, and reproducible pipeline for this preprocessing and apply it to all neurophysiological data generated in Projects B and C. The first stage will eliminate electrical and behavioral artifacts and convert voltages into spike times and local field potentials. The second stage will use a statistical model of neural activity to identify and label potential outliers. This pipeline will produce annotated and cleaned data in a standardized format that can be used to perform reliable analyses, model fitting, and hypothesis tests. Aim 3 will combine cutting-edge methods and convert them to software tools that can be reliably applied to new data. Most of this effort will be applied to variants of latent- state discovery techniques that jointly fit the influence of stimuli, model-driven hypothesized latent states, and unobserved latent states such as slow fluctuations. The central work of this aim is to implement those tools, help the team apply them to the data generated by the collaboration, and refine them for public use. Aim 4 is to share the experimental data with the wider research community by uploading the relevant portions of the data to public and freely accessible repositories. Code, documentation, and use cases will be made public on Github. The use of standard data structures, open standards, and open-source software will ensure barrier-free access, ease of use, and reproducibility for neuroscience researchers. With the help of a full-time data scientist hired to manage these efforts, the Data Science Core will build on established data storage and analysis standards and methods to produce cleaned and standardized data that our consortium can use to close the loop between theory and experiments. By sharing code, use cases, and data with other researchers, this project will also improve and extend these resources for future use by others.

项目摘要 本申请建议使用理论驱动的实验设计，与先进的技术，神经 记录，数据分析和计算建模，以研究神经机制，电路， 在空间和时间的因果推理的感知过程的表征。多学科 拟议工作的性质要求合作体成员之间密切合作。数据科学核心 将促进这种合作，并提供必要的工具来处理和分析大规模的神经网络。 在实验中收集的数据。为了实现这些目标，数据科学核心将依赖于 尽可能利用现有的基础设施、开放标准和开源软件。目标1将建立一个 统一的数据标准，以及数据交换和存储基础设施，使用 国际脑实验室，它将元数据存储在一个关系型的、可搜索的数据库中， 并在另一个文件服务器上处理数据GitHub将支持联合开发、交换和 数据预处理、处理和分析的基础代码文档。要将数据与模型关联， 实验记录的电压必须转换成标准化的尖峰时间和计数， 艺术品或混淆品AIM 2将为此开发一个有原则的、透明的、可复制的管道 预处理并将其应用于项目B和C中生成的所有神经生理数据。第一阶段将 消除电气和行为伪影，并将电压转换为尖峰时间和局部场电位。的 第二阶段将使用神经活动的统计模型来识别和标记潜在的离群值。这条管道 将以标准化格式生成带注释和经过清理的数据， 分析、模型拟合和假设检验。Aim 3将联合收割机结合尖端的方法，并将其转化为 可以可靠地应用于新数据的软件工具。这项工作的大部分将应用于潜在的变体- 状态发现技术，共同适应刺激的影响，模型驱动的假设潜在状态， 未观察到的潜在状态，如缓慢波动。这一目标的中心工作是执行这些工具， 帮助团队将它们应用到协作生成的数据中，并将其细化以供公众使用。目标4： 通过上传数据的相关部分，与更广泛的研究社区共享实验数据 到公共和自由访问的存储库。代码、文档和用例将在 GitHub.使用标准数据结构、开放标准和开放源码软件将确保无障碍 神经科学研究人员的访问，易用性和可重复性。在全职数据科学家的帮助下， 为了管理这些工作，数据科学核心将建立在现有的数据存储和分析基础上 标准和方法，以产生清洁和标准化的数据，我们的财团可以用来关闭 理论与实验之间的循环。通过与其他研究人员共享代码、用例和数据， 该项目还将改进和扩大这些资源，供其他方面今后使用。