Background: The qualitative data risk assessment matrix proposed by Mayernik et al. (2020) provides a crucial foundation for identifying threats to scientific data preservation. However, its qualitative nature limits its utility for cross-collection comparison and systematic resource allocation. Objective: This paper presents a methodology for transforming the qualitative risk matrix into a quantitative Data Risk Score (DRS), enabling objective prioritization of data preservation efforts. Methods: We employed a two-stage Delphi method with 30 international data stewardship experts to assign weights to the 21 risk factors and 10 categorization methods from the original framework. These weights were integrated into a scoring algorithm. Results: The resulting DRS was validated against three case studies: a modern genomic repository, a legacy social science archive, and a distributed ecological network. The score effectively discriminated risk levels between collections and provided a transparent basis for prioritization. Conclusion: The Data Risk Score operationalizes the conceptual risk framework, providing repositories, funders, and institutions with an actionable metric to guide preservation strategy and investment.
Background: The qualitative data risk assessment matrix proposed by Mayernik et al. (2020) provides a crucial foundation for identifying threats to scientific data preservation. However, its qualitative nature limits its utility for cross-collection comparison and systematic resource allocation. Objective: This paper presents a methodology for transforming the qualitative risk matrix into a quantitative Data Risk Score (DRS), enabling objective prioritization of data preservation efforts. Methods: We employed a two-stage Delphi method with 30 international data stewardship experts to assign weights to the 21 risk factors and 10 categorization methods from the original framework. These weights were integrated into a scoring algorithm. Results: The resulting DRS was validated against three case studies: a modern genomic repository, a legacy social science archive, and a distributed ecological network. The score effectively discriminated risk levels between collections and provided a transparent basis for prioritization. Conclusion: The Data Risk Score operationalizes the conceptual risk framework, providing repositories, funders, and institutions with an actionable metric to guide preservation strategy and investment.
