Data Science Core

数据科学核心

• 批准号: 10231066
• 负责人: WOLFGANG LOSERT
• 金额: $ 39.09万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10231066
• 关键词: Adopted; Behavior; Behavioral; Biological Models; Brain; Calcium; Code; Communities; Complex; Data; Data Aggregation; Data Analyses; Data Collection; Data Science; Data Science Core; Documentation; Ensure; Etiology; Experimental Designs; Goals; Grant; Human Resources; Image; Image Analysis; Infrastructure; Laboratories; Laboratory Study; Language; Light; Linear Models; Link; Methods; Modality; Modeling; Neurons; Neurosciences; Optics; Output; Pattern; Process; Programming Languages; Pythons; Quality Control; Reproducibility; Research; Research Project Grants; Resolution; Scientist; Sensory; Site; Software Design; Software Engineering; Standardization; Statistical Data Interpretation; Stimulus; Structure; System; Technology; Testing; Time; Training; Uncertainty; Validation; Work; base; computer science; data acquisition; data complexity; data format; data framework; data handling; data hub; data modeling; data sharing; data standards; design; dynamic system; experience; experimental study; graphical user interface; high dimensionality; improved; member; operation; optical imaging; optogenetics; prototype; quality assurance; relating to nervous system; sensory input; sensory system; software infrastructure; spatiotemporal; theories; tool; two-photon

Two-photon calcium imaging combined with holographic optogenetic excitation offers a powerful new way to read and control neuronal codes, with the potential to directly link spatiotemporal activity at single-neuron resolution to behavior. The volume of data produced in such experiments is very large, involving sensory inputs, acquired images, and behavioral outputs. Another contributor to data complexity are the holographic light patterns produced that allow for optogenetic control of neuronal groups. Neither data nor analysis approaches are standardized for this technology yet. The Data Science Resource Core will be essential to enable the experimental studies proposed in these research projects, which span multiple laboratories and sensory modalities. The three key aims of the Data Science Core will be (1) to facilitate data aggregation, (2) to develop quality control on data acquisition and analysis, and (3) to enable theory guided experimental design. The first aim is to standardize aggregation of data. We will deploy a new prototype Brain Platform graphical user interface using data formats from the emerging Neurodata Without Borders structure. The Core will work with all team members to integrate existing code written in multiple languages into the Brain Platform, which will thus serve as local hubs for data aggregation and sharing. Once implemented, training materials will be developed for broad dissemination of the Brain Platform The second aim is to ensure the quality of data and analysis. Analysis codes from all team groups that is made available through the Brain Platform will be regularly validated against each other and against ground-truth data obtained in the technology core and other baseline data. Additionally, statistical analysis approaches will be developed and implemented to enable power calculations for model inference. The third aim is to enable theory guided experiment design and validation. This aim also will support data analysis based on high-dimensional inference, including Granger causality, network criticality and stability analysis, and intersection information. Together, the three aims of the Data Science Core — standardized data aggregation, quality-controlled analysis, and theory guided experimental design — will support robust, reliable and reproducible acquisition and analysis of neural and behavioral data from experiments that use two-photon calcium imaging combined with holographic optogenetic stimulation.

双光子钙成像与全息光生激发相结合提供了一种强有力的新方法 读取和控制神经元编码，有可能直接联系单个神经元的时空活动 行为的解决方案。这种实验产生的数据量非常大，涉及到感官 输入、获取的图像和行为输出。数据复杂性的另一个贡献者是全息 产生的光图案允许对神经元组进行光遗传控制。既不是数据也不是分析 这项技术的方法还没有标准化。数据科学资源核心将是 支持这些研究项目中提出的实验研究，这些研究项目跨越多个实验室和 感官模式。数据科学核心的三个主要目标将是(1)促进数据聚合，(2) 发展数据采集和分析的质量控制；(3)实现理论指导的实验设计。 第一个目标是使数据聚合标准化。我们将部署一个新的原型大脑平台图形化 使用来自新兴的无国界神经数据结构的数据格式的用户界面。核心将起作用 所有团队成员将以多种语言编写的现有代码集成到Brain平台中，该平台 因此，将作为数据聚合和共享的本地枢纽。一旦实施，培训材料将被 为广泛传播大脑平台而开发 第二个目标是确保数据和分析的质量。来自所有团队小组的分析代码 通过大脑平台提供的信息将定期相互验证，并对照地面事实进行验证 在技术核心中获得的数据和其他基线数据。此外，统计分析方法将 将被开发和实施，以实现模型推理的功率计算。 第三个目标是实现理论指导的实验设计和验证。这一目标也将支持数据 基于高维推理的分析，包括格兰杰因果关系、网络临界性和稳定性 分析和交集信息。 总而言之，数据科学核心的三个目标-标准化数据聚合，质量控制 分析和理论指导的实验设计-将支持稳健、可靠和可重复的获取 并对结合使用双光子钙成像的实验中的神经和行为数据进行分析 通过全息光遗传刺激。