Publication and Dissemination of Research Outputs
A11.5.2 "Researchers and institutions have an obligation to care for and maintain research data in accordance with the Australian Code for the Responsible Conduct of Research (2007). The ARC considers data management planning an important part of the responsible conduct of research and strongly encourages the depositing of data arising from a Project in an appropriate publicly accessible subject and/or institutional repository."
ARC Instructions and FAQs
"Management of Data" section in ARC grant applications.
Changes to ARC funding rules can be seen in the Funding Rules for schemes under the Discovery Program and Linkage Program. Further guidance is also available through the Instructions to Applicants and Frequently Asked Questions for each scheme.
What information am I required to provide in relation to Management of Data in the Project Description?
In line with responsibilities outlined in the Australian Code for Responsible Conduct of Research (2007) and international best practice, the ARC has updated wording in relation to the management of data.
The ARC does not mandate open data. However, researchers are encouraged to consider the ways in which they can best manage, store, disseminate and re-use data generated through ARC-funded research. The Project Description requires researchers to articulate briefly their plans for the management of data generated through the proposed Project. In answering this question researchers need not include extensive detail of the physical or technological infrastructure.
Answers should focus on plans to make data as openly accessible as possible for the purposes of verification and for the conduct of future research by others. Where it may not be appropriate for data to be disseminated or re-used, justification may be provided.
Further information and resources on managing data are available on the Australian National Data Service (ANDS) website at http://www.ands.org.au/.
Is it sufficient to answer the Management of Data section in the Project Description by noting that I will comply with my institution’s requirements?
No. Whilst the ARC recognises that some institutions may have infrastructure and/or processes in place for storing, managing and sharing data and that these are valuable resources, to take into account the differences that may exist between institutions, disciplines and research projects, researchers are encouraged to highlight specific plans for the management of their research data in this section.
The Management of Data section in the Project Description aims to encourage consideration of ARC-funded research data at both an individual and institutional level, in accordance with the responsibilities outlined in the Australian Code for Responsible Conduct of Research (2007). Researchers, in consultation with their institutions, are best placed to consider the management and future potential of their research data. This approach allows individuals to take into account the differences that may exist between disciplines and research projects as well utilise institutional resources and support available.
Details of compliance with institutional requirements should be included in this section, provided that they are supported by a description specific to the data arising from the individual research Project.
Help at the University of Melbourne
The ARC is making a clear statement about the expectation for research data management planning. It is considered best international practice at this stage to scope your research data management needs for your proposed project. We have developed a Data Planning Scoping Checklist to assist you in this process.
When completing the "Management of Data" section we suggest that you might focus on just these four aspects of your data during the project and after the completion of your project:
- Re-use arrangements
In addressing these aspects of your data, you should consider incorporating some of the following keywords in application:
Compliance, central storage; post-project plan for data; accessibility; sharing; reuse; ethics; confidentiality; what data will you use or create; formats; volume; policy; code; data publication; data citation; data verification.