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Introduction to Research Data Management (RDM)
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:
- Data organisation and description
- Storage and backup
- How you make it available to others (where appropriate).
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 means data in the form of facts, observations, images, computer program results, recordings, measurements or experiences on which an argument, theory, test or hypothesis, or another research output is based. Data may be numerical, descriptive, visual or tactile. It may be raw, cleaned or processed, and may be held in any format or media.
Source: The Queensland University of Technology Management of research data policy.
Also include any information that gives the research data context, so it can be understood and reused. For example: how, when, where it was collected, instruments used, software code used to generate, annotate or analyse the data etc.
Research Data Management at Massey University
Here is Massey University Library's guidance and advice for good research data management.
Manage Your Research Data
- Create Your Data Management Plan
- Describe Your Data
- Organise Your Data
- Store Your Data in Digital Form
- Store Your Data in Physical Form
Publish and Share Your Research Data
- Publish and Share Your Data
- Ownership of and Access to Your Data [including Cultural Research Data]
- Discovery, Citation, and Impact of Your Data
Preserve Your Research Data
Updates on Massey University RDM initiatives are posted on the eResearch Community Blog.
Research Data Life Cycle
Some universities use a research data life cycle. The University of Sydney's research data life cycle looks like this:
The Benefits of Good Research Data Management
Increased research efficiency: you can find, understand and use data when you need it
Risk management: 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: comply 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.
Watch this 4-minute cartoon to learn about some of the problems that can arise in the absence of good data management practices.
Page authorised by University Librarian
Last updated on Monday 11 January 2021
Manage Your Research Data Tool Kit
- Create & Manage Data (UK Data Archive)
- Data Management Plans (Digital Curation Centre)
- Digital Storage Options
- Disciplinary Metadata
- Guide to Writing "Readme" Style Metadata
Contact a Subject Librarian
Your first point of contact in the library for RDM guidance, training opportunities and practical support. Subject librarians also provide personal research help by email, phone, or appointment.
Research Development Team
Data management (funder retention requirements) advice for current and future research projects.
These guidelines are informed by information provided under open licenses by other organisations including:
- Archives New Zealand CC BY 3.0
- Australian National Data Service (ANDS) CC BY 4.0 (content adapted)
- Cornell University Library CC BY 4.0
- Digital Curation Centre (DCC) CC BY 4.0
- Lincoln University
- Massachusetts Institute of Technology (MIT) Libraries CC BY-NC 2.0
- Mantra: Research Data Management Training CC BY 4.0
- National Archives of Australia
- University of Oxford fact sheet: Research data management CC BY 3.0
- Research Data Oxford University
- UCLA Library CC BY 4.0
- University of Auckland Centre for eResearch (CC-BY)
- University of Sydney (Under terms of Copyright Disclaimer)