Reproducibility Needs Reshape Scientific Data Governance
Paul Meijer, Yousef Aggoune, Madeline Ambrose, Aldan Beaubien, James, Harvey, Nicole Howard, Neelima Inala, Ed Johnson, Autumn Kelsey, Melissa, Kinsey, Jessica Liang, Paul Mariz, Stark Pister, Sathya Subramanian, Vitalii, Tereshchenko, Anne Vetto
TL;DR
Effective scientific data governance must integrate reproducibility practices to enhance data utility, inform policies, and support research lifecycle management.
Contribution
This paper highlights the importance of embedding reproducibility into data governance to improve scientific research practices.
Findings
Reproducibility informs data governance policies.
Integrated approach enhances research lifecycle management.
Software systems support data reuse and retention policies.
Abstract
Scientific data governance should prioritize maximizing the utility of data throughout the research lifecycle. Research software systems that enable analysis reproducibility inform data governance policies and assist administrators in setting clear guidelines for data reuse, data retention, and the management of scientific computing needs. Proactive analysis reproducibility and data governance are integral and interconnected components of research lifecycle management.
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Taxonomy
TopicsResearch Data Management Practices