Subject guides: Research Data Management (RDM): What is Research Data Management

Research data management (RDM) is about collecting, caring for, using, preserving and sharing the data supporting your research. It encompasses all practices and actions to ensure that research data are secure, sustainable, easy to find, understand and reuse.

Key elements of RDM include:

how to store your data and back them up effectively so that they are protected against corruption and loss
how to organise your data, using meaningful file names and logical folder structures and applying version control to modified files
how to document your data, so that you (and others) can understand what the data are, how they were collected/generated, and how they have been processed and analysed
how to process personal and confidential data, to ensure that sensitive data is protected
how to preserve and share your data so that they can be consulted and re-used by other researchers, usually by using suitable data repositories

RDM starts with data management planning. A number of funders ask for a data management plan (DMP) to be completed as part of a grant application, and it is always advisable to create a DMP for any research project involving the collection of primary data.

RDM is especially important when applied to primary data, i.e. new data collected or generated in the research activity. Because these are new and, in many cases, are essential to validating your research findings, it is important to ensure they are properly curated from the beginning.

While you are not responsible for the preservation and sharing of secondary data that you use in your research, you will still need to consider a number of issues, including: how and on what terms are the data to be accessed and used; where and how any copies of data will be stored; and whether the data provider allows copies of the data or derived data to be distributed.

Importance of RDM

Helps to maintain reliable and accurate data throughout the research process
Facilitates data reproducibility and reuse after completion and validation of results, making the best use of public resources
Helps to provide clarity for everyone involved – who owns the data, who is responsible for managing the data during and at the end of the research
Communicates research results responsibly, enabling others to replicate outcomes and reducing duplicate data collection
Helps to increase the impact and visibility of your research
Enhances data security and minimizes the risk of data loss
Meets publisher and funder requirements for access to data
Well-described and shared data can increase collaboration, citation and opportunities for funding.

Meets organisational custodial and curatorial responsibilities for research data
Helps researchers to comply with Institutional data management and storage policies.

Enables compliance with the Australian Code for the Responsible Conduct of Research
Ensures that researchers retain data arising from funded outputs, sharing where possible

Benefits of RDM

Implementing good RDM practices takes effort and time. But it also yields significant benefits for yourself, the research community, and society at large.

No wonder RDM is increasingly considered an essential part of good research practice!

Enhances the visibility, citations and impact of your research
Enhances data security and minimising your risk of losing valuable data
Increased your research efficiency
Opportunities for collaboration
Career recognition
Decreases risk of non-compliance

Facilitates data finding and re-use
Prevents duplication of effort by enabling others to use your data
Ensures research data and records are accurate, complete, authentic and reliable
Enables new research and new insights into the data
Valuable data is protected
Supports and reinforces research quality, integrity and reproducibility

Efficient use of public resources
Better quality research can lead to better decision making
Opportunities for citizen science
Increased transparency and trust in science

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