The data that Curtin researchers create has an incredible value.
As with all things of great value, there is a high cost associated with it.
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.
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.
Your data could be:
Whatever form your data takes, it’s important to understand that proper handling will improve your impact and strengthen the validity of your research results.
The acronym FAIR is used to describe qualities that research data can have which maximises how beneficial it can be. They describes how research outputs should be organised so they can be findable, accessible, interporable and reusable. Major international and national funding bodies, including the ARC and NHMRC, promote FAIR data to maximise the integrity and impact of their research investment.
The FAIR principles are designed to support knowledge discovery and innovation both by humans and machines. Making your research data FAIR can increase visibility and impact of yourself and your work, maximise potential from your data assets, and improve the reproducibility of your research. Following the FAIR guiding principles will also strengthen your research data management strategy. Even if you don’t intend to share your data with anyone yet, you will most likely reuse your own data.
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.
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.
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.
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.
8 steps to make your data more FAIR (Printable Guide) [PDF, 144kB]
A one-page guide to make your research data more findable, accessible, interoperable, and resuable.
Make your data FAIR: workshop recording [00:55:20]
A recording of the “Make your data FAIR” workshop from ResBaz Perth 2021.
ARDC’s FAIR Data Guidelines [00:31:51]
This page explains in further detail what the acronym means, benefits of making data FAIR and gives some useful resources.
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:
The following policy and procedure documents identify and outline the various roles and responsibilities around Research Data Management at Curtin and in Australia.
Introduction to Research Data Management [00:08:03]
A brief video from the University of Leicester giving a simple and well explained introduction to RDM.
Australian Research Data Commons: Data Management
The ARDC (previously ANDS) helps institutions around Australia to organise and manage their research data, enabling researchers to promote and share it with other researchers, and locate other research data useful to them.
Curtin webpage that provides an overview of eResearch tools, resources, and services available to Curtin researchers.