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A mixed-methods protocol to develop and validate a stewardship maturity matrix for human genomic data in the cloud.

Vasiliki Rahimzadeh1, Ge Peng2, Mildred Cho3

  • 1Baylor College of Medicine, One Baylor Plaza, Houston, TX, (United States).

Frontiers in Genetics
|October 31, 2022
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Summary

This study introduces a stewardship maturity matrix (SMM) to help cloud repositories manage human genomic data access. It provides tools for tracking stewardship goals and aligning them with stakeholder values in genomic data science.

Keywords:
DelphiELSI (ethicaland social implications)clouddata governancehuman genomicslegalstewardship

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Area of Science:

  • Genomic Data Science
  • Cloud Computing
  • Data Management

Background:

  • The cloud will increasingly host human genomic and health datasets.
  • Repository managers need practical tools to assess data stewardship and stakeholder alignment.
  • Current methods lack robust frameworks for evaluating stewardship progress.

Purpose of the Study:

  • To develop and test a stewardship maturity matrix (SMM) for cloud-based human genomic data repositories.
  • To provide repository managers with tools for assessing stewardship outcomes and tracking progress.
  • To align data stewardship practices with diverse stakeholder values.

Main Methods:

  • A mixed-methods protocol combining Delphi survey methods and SMM modeling.
  • Adaptation of SMM modeling from earth and planetary sciences for genomic data.
  • Integration of quantitative and qualitative data analysis for enhanced rigor.

Main Results:

  • A protocol for developing and implementing an SMM for genomic data repositories.
  • Identification of key stewardship outcomes valued by stakeholders.
  • A framework for evaluating and improving data stewardship systems.

Conclusions:

  • The developed SMM protocol offers a practical approach to enhance data stewardship in cloud repositories.
  • This mixed-methods approach improves the evaluation of data stewardship systems.
  • The protocol better aligns stewardship practices with the values of stakeholders in genomic data science.