Research data management


The data that Curtin researchers create has an incredible value.

  • It’s valuable to you - you’re building your research and publications on it
  • It’s valuable to the discipline you’re working in - every discipline moves forward by building on shared information
  • It’s valuable to the university - it’s a critical output of every research project, which aids Curtin’s reputation

As with all things of great value, there is a high cost associated with it.

  • It takes time and effort to develop the skills and knowledge required to conduct research
  • It takes time to go through the whole research process
  • It takes financial resources to complete the research

Because of this balance of value and cost, it’s in the interests of the researcher and the university to ensure that the maximum benefit is obtained by research conducted - by following practices described in this guide and managing research data well, researchers can help ensure their research has the greatest impact and benefit possible.

Types of data

Research data is any documentation, in any format, of findings, observations or outcomes created through the research process. This definition is broad by necessity - the range of research activity at Curtin is very broad. Each different field and discipline have their own ways of collecting and using data; each research question will require different data; and each research project will create different forms of data.

You data could be:

  • numerical data in tabular format
  • an unstructured text document
  • physical samples taken from field studies
  • digital files for specific software
  • sensitive personal information on research subjects
  • publicly shared datasets

Whatever form your data takes, it’s it’s important to understand that proper handling will improve your impact and strengthen the validity of your research results.


Since 2015 people have used the acronym “F.A.I.R.” to describe qualities that research data can have which maximises how beneficial it can be.

This acronym stands for:

F - Findable: Data can be more findable by: properly describing what the data is; putting it in a permanent and easily searchable place; and making it easy for humans and computers to search for it.

A - Accessible: Data can be more accessible by: using non-proprietary, standardised and automated methods to supply the data to those who want or need it; letting others know how they can get the data; and letting others know if the data is no longer available.

I - Interoperable: Data can be more interoperable by: storing and providing the data in widely-used and accessible file formats; describing the data using standard terms (vocabularies) that are relevant and widely known; and describing if it relates to other data and what exactly that relationship is.

R - Reusable: Data can be more reusable by: making it clear how the data was collected or if there are validity concerns; making any conditions of reuse clear in license readable to humans and machines; and meeting the standards used within the relevant research community.


​Data ownership refers to the intellectual property rights over the data created through research, and may also define ongoing roles around data management and use. Ownership of research is a complex issue that may involve the principal investigator, the sponsoring institution, the funding agency, and any participating human subjects. Clarifying data ownership and intellectual property rights is an important part of data management as this will ultimately decide who has control and rights over the data and can influence how the research data is managed, how it can be reused in the future and who has responsibility for these issues.

Due to complications around research funding agreements, collaborative projects, ethical guidelines, shared datasets and institutional policies, data ownership can be confusing. If there are no formal agreements or guidelines, you should clarify the ownership of the data and the implications as soon as possible and keep this information in writing, the same way you would with an authorship agreement. These discussions could include parties such as:

  • Funding bodies
  • HDR Supervisors
  • Principal Investigators
  • Co-authors
  • Project members
  • Other collaborating institutions/research bodies

In general, Curtin students retain ownership of their data, as outlined in the Intellectual Property Policy. Curtin staff should refer to the Intellectual Property Policy, the Intellectual Property Procedures and the Research Data and Primary Materials Policy. Any staff who are considering making their code openly licenced should consult with the Commercialisation Team and the Curtin Intellectual Property Policy and Procedures.

Any researcher conducting research in collaboration with an organisation external to Curtin should obtain a written agreement outlining the ownership of the research data. This agreement may also include details around particular storage and access requirements and who is responsible for meeting those requirements.


The following policy and procedure documents identify and outline the various roles and responsibilities around Research Data Management at Curtin and in Australia.

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