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, interoperable 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.
1. Describe your research data to maximise discovery.
Consider the information you will need to accurately interpret and reuse a dataset in your discipline, then include this detailed information with your data. Make use of keywords, controlled vocabularies and a thesaurus to maximise its chance of being found online.
2. Give your research data a unique identifier.
A Digital Object Identifier (DOI) can be used to cite your data or link it to your author identifier (ORCID), institution, grant, publications, and project, providing a full picture of your body of works.
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.
3. Register your research data online.
You may choose to publish your data openly or only publish the metadata to a data repository. Either option can make your data more FAIR.
4. Define access rules, protocols and requesting.
If your data is not available openly, define access rules and protocols, and describe how access can be requested. Make sure to keep your contact details up to date. If you no longer have custodianship of the data, don’t forget to update the access information.
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.
5. Use open, unencrypted, uncompressed forms.
Use open, non-proprietary file formats and share your data in its unencrypted and uncompressed form. Include software and source code where possible to ensure others can reuse and reproduce your research result.
6. Adopt common metadata schema and data description standards.
These allow inter-disciplinary interoperability, data exchange, and machine readability.
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.
7. Apply a licence and explain how your data can be reused.
Creative Commons licenses are widely used and easy to understand. Bear in mind that a Creative Commons non-derivative license is not appropriate for data as while it allows access, it prohibits adoption and reuse.
8. Obtain participant consent.
If your data contains personal information, ensure that you plan for obtaining participants consent so that you can make a de-identified version of the data available.
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 reusable.
FAIR Principles More detailed information from the GO FAIR Initative on how data can be made more FAIR.
How to make your data FAIR In-depth information with case studies on specific approaches, methods and techniques for making data more FAIR.
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:
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 Curtin 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 Commercialisation team works closely with Curtin researchers who develop novel concepts or inventions to guide and assess the commercial viability and the best method of bringing it to market.
The following policy and procedure documents identify and outline the various roles and responsibilities around Research Data Management at Curtin and in Australia.
Intellectual Property Procedures
Research Data and Primary Materials Policy
This policy outlines the research data obligations for researchers and for various university support areas, teams and departments.
This policy outlines the other policy documents across the university that help ensure high quality research is conducted in a responsible manner.
The Australia Code for the Responsible Conduct of Research
The Code applies to researchers at all Australian Universities and research bodies and outlines the broad requirements of researchers and institutions.
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 eResearch
Curtin webpage that provides an overview of eResearch tools, resources, and services available to Curtin researchers.