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DATA LIFECYCLE STAGETOPICSUBTOPICRESPONSIBLEINFLUENCECOMMENTS
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ENVISION Review of the overall strategies and drivers of an organization’s research data program.Data Governance—
Strategic/Qualitative		Identification of goals and roles
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Vision and/or policy
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Data management organization
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Organizational values, including DEI
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Data management value proposition
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Needs assessment
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Purpose and value of data
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Organization intent regarding FAIR data
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End-use support
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Stewardship
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Data Governance—Legal and Regulatory Compliance Privacy
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Ethics
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Safety and security assurance
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Inventory
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Risk assessment
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Risk mitigation and management
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Sharing/licensing
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Social license for use and reuse
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Jurisdiction for sharing and reuse
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Data Culture and Reward StructureRoles and responsibilities
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Recognition of data management
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Value of data workers
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Promotion and tenure
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Integrity of research and data
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FAIR data principles
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Maintainance of FAIR data
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Incentives and impact for sharing and reuse
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Disincentives for sharing and reuse
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CARE and ethics
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Education and Workforce DevelopmentWorkforce skills inventory
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Workforce preparedness in new and advanced technologies
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Data management training
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HR’s role in workforce development and training
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Promotional paths and career development
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Resources—Allocation and SustainabilitySources of funding
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Long-term funding
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Staffing
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Community Engagement Stakeholder communities
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Modes of engagement
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Partners/partnerships
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Engagement across knowledge domains and sectors
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Inclusivity in interactions
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Data services and the beneficiaries
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PLAN The tactical management positioning in an organization for effective research data management throughout the research data lifecycle.Chain of Control (Custody of Data)Roles and responsibilities
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Implementation authority
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Centralized inventory of services, groups, and resources
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Provenance
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Financial Aspects of PlanningFunding models for provisioning resources
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Funding sources
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Decision-making tools to assess costs
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Cost-benefit analysis
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Cost breakdown by lifecycle stage
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Downstream lifecycle costs, e.g., technology refresh, infrastructure maintenance
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Staffing and training
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Data Management PlanningWritten data management plans (DMPs)
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Purpose/intent of research study and context of anticipated data use
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Specification of data objects, metadata, analysis tools, and workflows throughout the lifecycle
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Machine-readable DMPs
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Linkage of DMPs to administrative records
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Data organization (e.g., database, repository) to facilitate future access
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Data management expertise and training
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Data ObjectsQuantitative and qualitative
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Measurements, including images, audio recordings, photos/videos
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Observations
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Surveys
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Software
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Models
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Documentation (text)
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Specimens (physical samples)
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Presentations
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FAIROrganizational support for making data more FAIR
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Identification of methods/guidelines vis-à-vis FAIR principles
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Data/Metadata ConsiderationsCriteria for selection of data/metadata
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Nature of data/metadata required
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Intended extent of FAIRness
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Methods to capture and store data/metadata
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Metadata schema
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Data ArchitectureDesign
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Processing operations
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Workflows
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Models
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LIMS
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Hosting and storage, cloud storage
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Configuration management
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Interoperability among different architectures
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Security
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Existing standards
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Hardware and Software InfrastructureOrganizational research needs
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Tools to support data-related processes
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Models that connect infrastructure to data processes and workflow
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Interoperability
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Persistent instrument identifiers
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Sustainability of data vis-à-vis obsolete infrastructure
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Security and privacy considerations
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Staff expertise and support staff
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Research Data StandardsCriteria, i.e., general vs. domain-specific standards
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Sources of standards/guidelines for data/metadata
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Quality standards
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Community-based standards/conventions