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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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A Conceptual Enterprise Framework for Managing Scientific Data Stewardship.

Ge Peng1,2, Jeffrey L Privette2, Curt Tilmes3

  • 1North Carolina State University, Cooperative Institute for Climate and Satellites - North Carolina (CICS-NC), US.

Data Science Journal
|October 26, 2020
PubMed
Summary
This summary is machine-generated.

A new data-centric framework, based on the Plan-Do-Check-Act cycle, helps scientific data centers manage stewardship activities. This approach aids compliance verification and reporting for digital research data preservation and reuse.

Keywords:
Domain StewardsEnterprise FrameworkInformation ManagementMaturity MatrixOpen DataPDCA-cycleResearch DataScientific Data Stewardship

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Area of Science:

  • Data Science
  • Information Science
  • Geospatial Science

Background:

  • Scientific data stewardship is crucial for long-term preservation and reuse of digital research data, ensuring data trustworthiness for decision-making.
  • Federal directives and scientific guidelines necessitate formal stewardship approaches for compliance verification and reporting.
  • Existing stewardship frameworks are often too costly, lengthy, and may not address federal requirements or geospatial data specifics.

Purpose of the Study:

  • To propose a data-centric conceptual enterprise framework for managing scientific data stewardship activities.
  • To provide a systematic and holistic approach for data centers to meet stewardship requirements.
  • To support continual improvement and compliance with federal, agency, and user mandates.

Main Methods:

  • Development of a data-centric conceptual enterprise framework.
  • Application of the Plan-Do-Check-Act (PDCA) cycle philosophy.
  • Integration of maturity assessment models for quantitative evaluation.

Main Results:

  • The proposed framework offers a systematic and integrated approach to data stewardship.
  • It enables quantitative evaluation of stewardship activities through maturity assessments.
  • Facilitates informed decision-making for continuous improvement and compliance.

Conclusions:

  • The data-centric PDCA-based framework provides an effective and efficient solution for scientific data stewardship.
  • It addresses the limitations of current frameworks, particularly for federal compliance and geospatial data.
  • Supports data centers in achieving and maintaining compliance with diverse requirements.