Shifting sensitive data management practices at the University of Melbourne: A delicate balance

Dr Kimberley D’costa¹, Dr  Helena Lynn¹

¹University Of Melbourne, Parkville, Australia

Inappropriate management of sensitive research data has the potential to cause significant harm to research participants, researchers, and institutions. Challenges also exist due to a complex and evolving environment with changing regulatory mandates, developing technology solutions, emerging cybersecurity threats and new research methods.

We identified the need to improve sensitive data management at the University of Melbourne. After reviewing the national landscape, we sought to develop a mechanism for researchers to assess data sensitivity and implement improved management practices.

The University of Melbourne ‘Research Data Classification Framework’ was published in early 2022. It presents a consistent approach to the classification and management of sensitive research data.

Key characteristics include:

1. Functional differences between different classes of sensitivity,
2. Synthesis of compliance obligations and specific data protection safeguards into a simple and actionable format, and
3. Holistic guidance to enhance data handling and sharing practices across research disciplines.

The Framework offers significant improvements to researcher experience, improves research integrity and transparency, mitigates risk, and enables more effective strategic planning for infrastructure and services.

In this talk, we will discuss:

• The approach of co-design with academic and professional experts,
• Key considerations identified and the rationale for decisions made,
• Our planned engagement roadmap to ensure uptake and effect cultural change,
• Integration with existing research processes, and
• Mechanisms for evaluation and reiterative improvement.

Additionally, we will reflect on how our methodology aligns with the recommendations of Australian Research Data Common’s Institutional Underpinnings Program and encourages contemporary practices such as open data publication. The Research Data Classification Framework is currently accessible by only internal staff and students; however, this content will be shared upon request.

Biography:

Kim is a Project Officer with the University of Melbourne’s Research Data Management (RDM) Program. Since commencement in 2019, the Program has made significant advancement towards its’ goal of refining and implementing vital tools, resources, and support to manage research data in a rapidly evolving regulatory and technological environment. The RDM Program is part of the University’s broader Petascale Campus Initiative.