Data Management and Analysis Core
Data Management and Analysis Core
批准号:
10559544
负责人:
Emily Hohmeiser Griffith
金额:
$16.74万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-03-03 至 2025-01-31
关键词:
AddressBioinformaticsBiological AssayCenter for Translational Science ActivitiesCollaborationsCommunicationComputer softwareComputing MethodologiesDataData AnalysesData CollectionData Coordinating CenterData Management ResourcesData ScienceData ScientistDatabasesDedicationsDerivation procedureDevelopmentDocumentationElementsEnvironmental HealthExperimental DesignsExtravasationFAIR principlesFamilyFosteringGoalsHealthHuman ResourcesIndividualInterdisciplinary CommunicationLeadershipLearningMethodsModelingMonitorOntologyPoly-fluoroalkyl substancesProcessPublic HealthPublishingQuality ControlReproducibilityResearchResearch PersonnelResourcesRunningSamplingScienceScientistSignal Recognition ParticleSolidStandardizationStructureSuperfundTrainingTraining ActivityVisualVisualizationWorkcheminformaticscomputational toxicologycomputer infrastructurecomputerized toolscomputing resourcesdata accessdata exchangedata integrationdata interoperabilitydata managementdata qualitydata sharingdata standardsdata streamsdesignenvironmental chemicalexperiencemembernext generationnovelpreventprogramsquality assurancesoftware developmenttranslational impactuser-friendlyvulnerable community
中文摘要
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英文摘要
ABSTRACT
DMAC
The Data Management and Analysis Core (DMAC) of our NC State SRP Center proposal is designed to integrate
results from all data streams into a true synthesis that advances environmental and public health related to per-
and polyfluoroalkyl substances (PFAS). The first activity (Specific Aim) of the DMAC in establishing Center-wide
data management and integration was the development of a Comprehensive Data Management Plan (cDMP).
The cDMP was designed to instantiate FAIR (Findable, Accessible, Interoperable, Reusable) principles in
managing Center data and was used for the coordinated development of individual project/core DMPs. A key
element of our cDMP is the derivation of an ontology of data types, which recognizes that data have common
elements that cross disciplinary (as well as project-specific) boundaries. This formalization of connections
between nominally different data streams and assignment of individual points-of-contact for each type will
establish the DMAC as a resource for operationalizing data integration. Subsequent DMAC Specific Aims
establish processes for monitoring data analysis, coordinate analysis through shared data structures and
associated software, provide means for visualization and sharing of results, and coordinate data-centric training
activities. Thus, the DMAC will enable synthesis not possible from singular Projects/Cores alone, through
coordination amongst projects and cores, fostering data sharing and interoperability, and providing formal data
quality assurance and quality control.
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