Risk Assessment for Scientific Data

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.5334/dsj-2020-010. This is version 2 of this Preprint.

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Matt Mayernik, Kelsey Breseman, Robert R. Downs, Ruth Duerr, Alexis Garretson, Sophie Hou


This is a preprint draft of the paper that was officially published in the Data Science Journal. Please quote from the published version: http://doi.org/10.5334/dsj-2020-010. Abstract: Ongoing stewardship is required to keep data collections and archives in existence. Scientific data collections may face a range of risk factors that could hinder, constrain, or limit current or future data use. Identifying such risk factors to data use is a key step in preventing or minimizing data loss. This paper presents an analysis of data risk factors that scientific data collections may face, and a data risk assessment matrix to support data risk assessments to help ameliorate those risks. The goals of this work are to inform and enable effective data risk assessment by: a) individuals and organizations who manage data collections, and b) individuals and organizations who want to help to reduce the risks associated with data preservation and stewardship. The data risk assessment framework presented in this paper provides a platform from which risk assessments can begin, and a reference point for discussions of data stewardship resource allocations and priorities.




Earth Sciences, Library and Information Science, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Social and Behavioral Sciences



Published: 2020-01-10 00:30

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