Managing data across the research lifecycle
Macquarie University has established a framework and a series of initiatives to support research data management (RDM), helping researchers meet their responsibilities, comply with regulations and rise to global expectations.
In 2019, the National Health and Medical Research Council (NHMRC), the Australian Research Council (ARC) and Universities Australia released the Management of Data and Information in Research: A guide supporting the Australian Code for the Responsible Conduct of Research. The guide established that data management across all stages of the data lifecycle is an integral component of research integrity and is a shared responsibility between researchers and institutions.
Macquarie University has invested in initiatives and a framework to support RDM that went into effect in September 2021.
RDM Policy suite
The RDM Policy suite underpins a comprehensive set of expected practices at Macquarie University for managing data across the research lifecycle. The suite includes the:
The significant changes to previous practices associated with the policy involve use of appropriate RDM practices including:
- expectation of high-quality data management planning
- classification of data by sensitivity, with appropriate security measures applied for each level
- a requirement to use University-supported research data systems and platforms.
Further information on the framework and factors that drive it is available to students and staff.
Execution of the early phases of Macquarie University’s RDM Framework implementation coincided with participation in the Australian Research Data Council (ARDC) Institutional Underpinnings program’s Culture Change project. Within this project, ARDC supported a case study of RDM culture change in graduate research students at Macquarie University. Read more about the University's involvement in the project, including outputs and resources associated with the release and implementation of Macquarie's RDM Framework in 2021 and 2022.
Planning the curation of research data is an essential element of research integrity. Best practice data management will increase research openness, integrity, impact, and reproducibility.
Completing a data management plan (DMP)
A DMP articulates how research data will be handled throughout the data lifecycle, adhering to FAIR (Fineable, Accessible, Interoperable, Reusable) and CARE (Collective benefit, Authority to control, Responsibility, Ethics) principles if research is related to Indigenous peoples and studies. For all research projects, a DMP should be completed at project planning stage before any data is collected.
Macquarie University has an institutional formal DMP form in FoRA (Forms for Research Applications). Requirement to complete the formal DMP is being progressively rolled out by cohort. For current requirement information, see the DMP page.
Researchers are required to assess and articulate the sensitivity of their data. Macquarie University recognises three sensitivity levels:
- general
- sensitive
- highly sensitive.
The sensitivity level dictates what platforms can be used to collect, share, analyse, and archive research data, as well as security precautions that need to be taken. Understanding how to manage your sensitive data safely and securely is essential, especially on devices, applications and platforms used and when setting sensitivity labels on applications and documents.
See the Research Data Sensitivity, Security and Storage Guidelines and use the Data Sensitivity Calculator tool to determine your data's classification.
Planning the curation of research data is an essential element of research integrity. Best practice data management will increase research openness, integrity, impact, and reproducibility.
To ensure everyone has the same base knowledge of research data management best practices, our training module and library guide present essential information on topics including:
- data management planning
- storage and security
- naming and metadata
- repositories
- best practices, including the FAIR and CARE principles.
Learning about RDM
We have a number of resources available to both staff and students, including:
- RDM Online, an online training module that provides foundational training on the principles of good research data management
- Graduate Research Students and Supervisors are required to complete the training module
- Non-Macquarie University staff or students can view the module on DreSA
- the LibGuide, which gives an overview of the best approaches for collecting, storing and disseminating research data at Macquarie
- the RDM website, providing researchers with a range of additional information and self-help resources
- training sessions on data management planning and data platforms.
Macquarie University provides access to supported platforms suitable for collecting, managing, sharing, analysing, archiving and publishing research data. These platforms meet legal and ethical requirements and allow for essential security controls that must be applied to different data sensitivity levels.
Non-supported platforms
If Macquarie-supported platform options do not meet needs of researchers, they should request review of non-supported platforms by submitting an Applying for Interim approval of a non-supported platform form to ensure the platform is secure and appropriate for the type of data being handled.
Using Generative AI, Large Language Models (LLM) or services in research
We provide guidance and advice on engaging with generative AI or LLM tools/services appropriately and securely. This includes information on what models and services are acceptable for use. Researchers should review Macquarie University’s official guidance notes on:
Macquarie University staff and students should visit the RDM website for more information and self-help resources.
For internal questions and requests for specialised assistance, contact the RDM team at research.data.management@mq.edu.au to generate a help ticket.
For external questions, contact the RDM team at RDM-comms@mq.edu.au.