How a planned approach to data collection, storage and dissemination can help you troubleshoot potential problems early and maximise your impact.
At the design phase of a research project, researchers should undertake a process of decision-making and documentation of key research data management activities.
It is common overseas for funding agencies to require a formal data plan as part of the funding application process.
While this is not yet common practice in Australia, it is likely in the near future that the Australian Research Council and the National Health and Medical Research Council will require greater evidence of data planning.
The Australian Code for Responsible Conduct of Research (pdf 508kb) requires aspects of data management such as ownership, ethics, and retention and disposal to be well-documented by researchers.
In many cases, these documentation requirements are met through mechanisms like funding applications and agreements, contracts, employment agreements, memoranda of understanding, and human ethics applications and management procedures in the academic unit. Other requirements - for example, secure storage and backup of digital data - are not currently well-documented and this can cause problems as personnel, technologies and research processes change over time.
Monash University encourages all researchers (including higher degree students) to undertake data planning at the start of each research project.
Any individual or group that wants to self-assess data management practices and try to improve them might also benefit from a data planning process.
The Library provides a research data planning checklist:
This checklist will guide researchers through a comprehensive planning process that covers all the aspects of data management covered by the Code for Responsible Conduct of Research and the Monash University Research Data Management Policy.
The checklist has been designed so that you can easily identify which policies, procedures and guidelines apply, and find out where to go within the University for further advice. Support to complete the checklist is also offered as part of the Library's research data advice and planning services.
A data planning process ensures that all aspects of data management are holistically explored at the start of a project. Short-term and long-term aims can be balanced, so that decisions made early in a project do not negatively impact on the ability to find and use the research data in future.
Effective management of data provides researchers with many benefits, including:
A data planning process is particularly important in the context of collaborative research projects. Researchers may identify areas of potential difficulty or conflict, and these can be resolved with colleagues and collaborators before they escalate into issues. Clarifying ownership of data, and ensuring early agreement on technical standards and frameworks across institutions, are an important part of establishing trust and ensuring that a project runs smoothly.
Currently there is no internal requirement for Monash University researchers to centrally lodge a data management plan.
Researchers should retain a copy of the data management plan for their own records, and can use it as a discussion document when talking to collaborators, services providers and research administrators.
Given that international policy agencies and research funding councils are increasing their emphasis on data planning, the University is likely to consider making a formal data management plan a requirement for applying for internal research funding and for confirming PhD candidature. By building data planning into research projects now, researchers will be well-prepared for any future changes to policy and processes at Monash University and in the wider research environment.
Data planning is an ongoing process: researchers should review their plan regularly during the life of a research project as data management requirements will evolve. Researchers may want to review plans annually, or when there are changes in technology, personnel and research methodologies that have implications for how the research data will be managed.