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Research Data Management (RDM) involves making decisions about how you will collect and look after your digital and physical research data, and putting those decisions into action. It covers:
Watch this video to get a brief overview of RDM.
Research data is anything that forms the basis of your research output. The types and forms of research data vary between disciplines, however, this general definition is helpful:
Research data: Data are facts, observations, or experiences on which an argument, theory or test is based. Data may be numerical, descriptive or visual. Data may be raw or analysed, experimental or observational. Data includes: laboratory notebooks; field notebooks; primary research data (including research data in hardcopy or in computer readable form); questionnaires; audiotapes; videotapes; models; photographs; films; test responses. Research collections may include slides; artefacts; specimens; samples.
Source: University of Melbourne Management of Research Data and Records Policy
Provenance information about the data might also be included: the how, when, where it was collected and with what (for example, instrument). The software code used to generate, annotate or analyse the data may also be included.
Here is Massey University Library's guidance and advice for good research data management.
Manage Your Research Data
Publish and Share Your Research Data
Preserve Your Research Data
Updates on Massey University RDM initiatives are posted on the eResearch Community Blog.
Some universities use a research data life cycle. The University of Sydney's research data life cycle looks like this:
The University of Sydney https://library.sydney.edu.au/research/data-management/research-data-management.html
Watch this 4-minute cartoon to learn about some of the problems that can arise in the absence of good data management practices.
Increased research efficiency: you can find, understand and use data when you need it
Risk reduction: protects your data against loss, deterioration or privacy and copyright breaches
Quality: helps to verify research findings over time
Reputation: shared data enhances research visibility and increases citations
Compliance support: complies with ethical codes, data protection laws, journal requirements, funder policies.
Poor management can lead to catastrophes like the loss of data, or the violation of people’s privacy.
Page authorised by University Librarian
Last updated on Monday 27 November 2017
Research Data Management Librarian
Guidance, training opportunities and practical support.
Research Development Team
Data management (funder retention requirements) advice for current and future research projects.
Contact a Subject Librarian
Personal research help by email, phone, or appointment.
These guidelines are informed by information provided under open licenses by other organisations including: