Data Science Core

数据科学核心

• 批准号: 10456141
• 负责人: WOLFGANG LOSERT
• 金额: $ 39.13万
• 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 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10456141
• 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 verification; 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遗传激发相结合提供了一种强有力的新方法