CRCNS: US-France Data Sharing Proposal: Lowering the barrier of entry to network neuroscience
CRCNS: US-France Data Sharing Proposal: Lowering the barrier of entry to network neuroscience
批准号:
10019389
负责人:
Danielle Smith Bassett
金额:
$21.86万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-16 至 2022-06-30
关键词:
AddressAgingBehaviorBiophysicsBrainClinicalCloud ComputingCognitionCommunitiesComplex AnalysisComputer softwareDataData SetDevelopmentDiseaseDocumentationEcosystemEducationElementsFranceFundingGenerationsGraphGrowthHealthInfrastructureInstructionKnowledgeLanguageLeadLibrariesLinkLiteratureMathematicsMethodsModelingNeurosciencesNeurosciences ResearchPathway AnalysisPlayProcessReportingReproducibilityResearchResearch PersonnelResourcesRoleRunningScienceSociologySourceStandardizationStructureStudy modelsSystemTechniquesTimeTrainingWorkanalysis pipelinebrain computer interfacecloud basedcognitive processcyber infrastructuredata sharingexperienceinnovationinsightinterestmachine learning methodnetwork architecturenetwork modelsneuroimagingopen datapreventrelating to nervous systemstatisticssupport networksynergismtool
中文摘要
点击翻译按钮获取中文摘要
英文摘要
The field of network neuroscience has developed powerful analysis tools for studying brain networks and
holds promise for deepening our understanding of the role played by brain networks in health, disease,
development, and cognition. Despite widespread interest, barriers exist that prevent these tools from having
broader impact. These include (1) unstandardized practices for sharing and documenting software, (2) long
delays from when a method is first introduced to when it becomes publicly available, and (3) gaps in theoretic
knowledge and understanding leading to incorrect, delays due to mistakes, and errors in reported results.
These barriers ultimately slow the rate of neuroscientific discovery and stall progress in applied domains.
To overcome these challenges, we will use open science methods and cloud-computing, to increase the
availability of network neuroscience tools. We will use the platform "brainlife.io" for sharing these tools, which
will be packaged into self-contained, standardized, reproducible Apps, shared with and modified by a
community of users, and integrated into existing brainlife.io analysis pipelines. Apps will also be accompanied
by links to primary sources, in-depth tutorials, and documentation, and worked-through examples,
highlighting their correct usage and offering solutions for mitigating possible pitfalls.
In standardizing and packaging network neuroscience tools as Apps, this proposed research will engage a
new generation of neuroscientists, providing them powerful new and leading to new discoveries. Second, the
proposed research will contribute growing suite of modeling analysis that can be modified to suit specialized
purposes. Finally, the Brainlife.io platform will serve as part of the infrastructure supporting neuroscience
research. Altogether, these advances will lead to new opportunities in network neuroscience research and
further stimulate its growth while increasing synergies with other domains in neuroscience.
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