A Confocal Fluorescence Microscopy Brain Data Archive
A Confocal Fluorescence Microscopy Brain Data Archive
批准号:
10451277
负责人:
Marcel P Bruchez
金额:
$100.96万
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
未结题
起止时间:
2017-09-19 至 2027-05-31
关键词:
ArchivesAtlasesBRAIN initiativeBrainBrain imagingCellsCensusesCollaborationsCollectionCommunitiesComputer softwareDataData SetDepositionEcosystemEnvironmentFluorescence MicroscopyFunding AgencyGoalsHigh Performance ComputingHumanImageIndividualInfrastructureLinkLocationMachine LearningMeta-AnalysisMetadataMicroscopeMicroscopyMorphologyMusNeuronsNeurosciencesNeurosciences ResearchOutcomePerformancePoliciesPrimatesProcessProviderResearchResearch PersonnelResourcesRetrievalRoleScientistSecureServicesSpecimenSpeedStandardizationSystemTimeTrainingUnited States National Institutes of HealthVisualizationVisualization softwarearchive dataarchived databrain sizecomputerized toolscomputing resourcescostcost effectivedata archivedata curationdata exchangedata explorationdata harmonizationdata reusedata sharingdata sharing networksexpectationexperimental studyflexibilityimage archival systemimprovedmeetingsmicroscopic imagingnovelpetabyterepositorytranscriptomics
中文摘要
项目总结
英文摘要
PROJECT SUMMARY
Brain image data collected by the neuroscience community will continue to grow
dramatically in the next 5-10 years as the push to image physically larger volumes, such as
whole brains from humans and other primates, becomes a focus. The scientific community, with
the support of NIH data sharing policies, expect this valuable data to be shared and made
available to the community as rapidly as possible.
The Brain Image Library (BIL) is a public resource serving the neuroscience community by
providing a persistent scalable repository for sharing microscopy data generated by brain
researchers. Importantly, raw data can be archived with BIL, and all post-processing and
analysis can be completed at BIL and rapidly made publicly available. BIL maximizes data
reusability by providing rapid access to a range of community-specific software suites and user-
specific software which can quickly access a variety of high-performance computational
resources including GPU, CPU, high-RAM and machine learning nodes. This one-stop access
encourages the reuse of data by making it easily accessible to a variety of scientists and
promotes transparency and scientific discovery as the outcomes of any meta-analysis can be
made public and tied directly to the original data content.
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会议论文
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批准号:10002308
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依托单位:
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批准号:8865632
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财政年份:2013
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批准号:8698418
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财政年份:2013
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财政年份:2013
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财政年份:2009
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负责人:Marcel P Bruchez
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依托单位:
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项目类别:
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财政年份:2000
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负责人:Marcel P Bruchez
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依托单位:
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财政年份:2000
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依托单位:
海外基金