Data Science Core
数据科学核心
基本信息
- 批准号:10516499
- 负责人:
- 金额:$ 40.58万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-08-01 至 2027-07-31
- 项目状态:未结题
- 来源:
- 关键词:AlgorithmsAnatomyAnimalsArchivesArtificial IntelligenceAttentionBlood VolumeBrainBrain imagingCharacteristicsClassificationCodeCollaborationsComputer softwareDataData FilesData ScienceData Science CoreData ScientistData SetData Storage and RetrievalData StoreElectroencephalographyGoalsHumanImageIndividualInformation RetrievalInfrastructureInvestigationLeadLearningLinkLiquid substanceMagnetic Resonance ImagingMeasurementMeasuresMetadataMethodsModernizationMusNoiseParticipantPatternPublicationsReadingRecording of previous eventsResearchResourcesRunningSamplingScientistShapesSleepSoftware ToolsSuggestionSystemTimeTrainingUncertaintyUniversitiesVariantViralVisualizationWorkWritinganatomic imagingbasecerebrospinal fluid flowcode developmentcomputational platformdata accessdata infrastructuredata sharingdata toolsdoctoral studentexperienceexperimental studyfile formathemodynamicsimage processingimprovedin vivoinnovationinstrumentlarge datasetsmachine learning algorithmmathematical modelmembermultimodal datamultimodalityneural circuitneuroregulationnovelopen sourceorganizational structureprogramsrelating to nervous systemrepositorysearchable databasesignal processingsimulationsolutestatistical and machine learningsupercomputersynergismtooltwo-photonuser-friendlyvirus core
项目摘要
Abstract, Data Science Core
The goal of this proposal is to characterize and quantify how neural circuits control cerebrospinal fluid (CSF) flow
and solute clearance during sleep and wake, in both mice and humans. Achieving that goal will require combining
multimodal data from mathematical models and various experiments in both species. The Data Science Core
will provide the essential infrastructure and produce innovative data methods to enable powerful synergy among
the Projects. First, because the Projects will produce enormous data sets, and because the data will be
multimodal (MRI, two-photon imaging, simulation results, EEG, and more), productive collaboration will require
careful attention to storing, organizing, processing, analyzing, and internally sharing the data. Aim 1 is to provide
data infrastructure and staffing for seamless integration of all Projects, for synergistic code development and
sharing, and for rapid analysis of multimodal data via efficient workflows, always leveraging existing best
practices. Second, discovering the causal links among neural control, CSF flow, and solute clearance will require
tools and analyses that do not yet exist. Aim 2 is to build transformative tools leveraging quantitative, data-driven
methods to measure CSF flow, its drivers, their neural control mechanisms, and the resulting efflux. Third, the
novel data and tools produced in Projects 1-4 will have potential to advance the field, enabling a range of new
scientific questions, especially if they are disseminated widely. Aim 3 is to document the software tools we
develop, annotate the data we produce, and share both publicly. The Data Science Core will build on existing
methods and tools previously developed by the co-PIs, take advantage of best practices in the field, and combine
the expertise of experienced data scientists to build novel tools. Documented code will be shared via GitHub.
We will use a two-tiered system for sharing data internally, in which each Project will leverage their existing data
workflows and keep some data locally; meanwhile, data shared among Projects will be stored on the BlueHive
supercomputer at the University of Rochester, available to all U19 participants. We will develop a clear
organizational structure for shared data, storing metadata in sidecar files and implementing a searchable
database to facilitate collaboration and interaction among the Projects. We will keep a searchable stock list of
tools provided by the Virus Core, along with delivery times and histories. Annotated data will be shared publicly
via repositories upon publication. A full-time data scientist will be employed to lead day-to-day Core activities,
along with two PhD students.
摘要,数据科学核心
这项提议的目标是描述和量化神经回路如何控制脑脊液(Csf)流动。
以及睡眠和清醒时的溶质清除,在老鼠和人类中都是如此。实现这一目标将需要结合
来自两个物种的数学模型和各种实验的多模式数据。数据科学的核心
将提供必要的基础设施和产生创新的数据方法,以实现强大的协同
这些项目。首先,因为这些项目将产生巨大的数据集,而且数据将是
多模式(MRI、双光子成像、模拟结果、EEG等),需要高效协作
仔细注意数据的存储、组织、处理、分析和内部共享。目标1是提供
数据基础设施和人员配备,实现所有项目的无缝集成、协同代码开发和
共享,并通过高效的工作流快速分析多模式数据,始终利用现有的最佳
练习。其次,发现神经控制、脑脊液流动和溶质清除之间的因果联系将需要
尚不存在的工具和分析。目标2是构建利用量化、数据驱动的变革性工具
方法测量脑脊液流量、其驱动因素、神经控制机制以及由此产生的外排。第三,
项目1-4中产生的新数据和工具将有可能推动该领域的发展,使一系列新的
科学问题,特别是如果它们广泛传播的话。目标3是记录我们的软件工具
开发、注释我们生成的数据,并公开共享这两个数据。数据科学核心将建立在现有基础上
方法和工具,利用该领域的最佳实践,并结合
经验丰富的数据科学家的专业知识来构建新的工具。记录的代码将通过GitHub共享。
我们将使用两层系统在内部共享数据,其中每个项目将利用其现有数据
工作流并在本地保留一些数据;同时,项目之间共享的数据将存储在BlueHave上
罗切斯特大学的超级计算机,所有U19的参与者都可以使用。我们将制定一项明确的
共享数据的组织结构,将元数据存储在SideCar文件中,并实现可搜索的
数据库,以促进项目之间的协作和互动。我们将保留一份可搜索的库存清单
病毒核心提供的工具,以及交付时间和历史记录。带注解的数据将公开共享
在发布时通过存储库。将聘请一名全职数据科学家领导核心的日常活动,
还有两名博士生。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Douglas H Kelley其他文献
Hydraulic resistance of three-dimensional pial perivascular spaces in the brain
大脑三维软脑膜血管周围空间的液压阻力
- DOI:
10.21203/rs.3.rs-3411983/v1 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
K. Boster;Jiatong Sun;Jessica K. Shang;Douglas H Kelley;John H. Thomas - 通讯作者:
John H. Thomas
Douglas H Kelley的其他文献
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{{ truncateString('Douglas H Kelley', 18)}}的其他基金
Project 1: Modeling brain-state-dependent fluid flow and clearance in mice and humans
项目 1:模拟小鼠和人类大脑状态依赖性液体流动和清除
- 批准号:
10673158 - 财政年份:2022
- 资助金额:
$ 40.58万 - 项目类别:
Project 1: Modeling brain-state-dependent fluid flow and clearance in mice and humans
项目 1:模拟小鼠和人类大脑状态依赖性液体流动和清除
- 批准号:
10516501 - 财政年份:2022
- 资助金额:
$ 40.58万 - 项目类别:
CRCNS: Waste-clearance flows in the brain measured using physics-informed neural network
CRCNS:使用物理信息神经网络测量大脑中的废物清除流量
- 批准号:
10706594 - 财政年份:2022
- 资助金额:
$ 40.58万 - 项目类别:
CRCNS: Waste-clearance flows in the brain measured using physics-informed neural network
CRCNS:使用物理信息神经网络测量大脑中的废物清除流量
- 批准号:
10613222 - 财政年份:2022
- 资助金额:
$ 40.58万 - 项目类别:
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