Discovery and reuse
This section aligns with the Discovery stage of the Research Data Lifecycle.
Sharing research data supports the discovery of new knowledge, improves transparency, and enables collaboration. Findable and accessible research data that can be cited, reused, and built upon benefits the wider research community.
This section provides guidance on how to find datasets, cite them properly, and apply the FAIR (Findable, Accessible, Interoperable, Reusable) principles.
Find and access data
Published datasets can be discovered through a range of repositories and data catalogues. Some repositories provide open access to full datasets, while others provide only metadata with access conditions or contact details.
Click on the sections below to explore a range of data repositories.
Major Australian Repositories
| Repository | Description |
|---|---|
| Research Data Australia (RDA) |
National data discovery service managed by the Australian Research Data Commons (ARDC).
|
| Australian Data Archive (ADA) | National service for the collection and preservation of digital research data |
| data.gov.au | Australian Government public data |
| Aboriginal and Torres Strait Islander Data Archive (ATSIDA) | Australian Indigenous research data |
| Australian Ocean Data Network (AODN) | Ocean and marine data |
| CSIRO Data Access Portal | Multidisciplinary research data and software |
| PARADISEC | Endangered languages and cultural data from Pacific and Regional areas |
Many of these platforms also accept data deposits. For publishing guidance, read the page on Share and publish.
Selected international and disciplinary repositories
| Repository | Description |
|---|---|
| Dryad | Biosciences datasets linked to publications |
| Gene Expression Omnibus (GEO) | National service for the collection and preservation of digital research data |
| Inter-university Consortium for Political and Social Research (ICPSR) | Social and behavioural data |
| Global Biodiversity Information Facility | Biodiversity data |
| Climate Change Knowledge Portal | Climate and environmental data |
| The Digital Archaeological Record | Archaeology and heritage data |
| Open Resources and Tools for Language (ORTOLANG) | French linguistics |
| WHO Global Health Observatory Data Repository | International health statistics |
| International Food Policy Research Institute | Food policy |
| National Climatic Data Centre | Environmental science data |
| NASA Space Science Data Coordinated Archive | Space science data |
Government repositories
Tip
If you’re preparing to share your own data to support reuse and citation, refer to the Share and publish page.
Cite and reuse data
Data citation is a growing practice in scholarly literature. Citing datasets gives credit to data creators, supports research transparency, and enables others to locate the data you’ve used.
Citing data
Published datasets should be cited in your reference list. A typical data citation includes:
Author(s) (Year): Title of dataset. Publisher or repository. DOI (if available)
Example:
Smith, J., & Lee, R. (2023): Survey data on coastal erosion in NSW. University of Example. https://doi.org/10.1234/example.doi
Tip
The Australian Research Data Commons (ARDC) provides further guidance on data citation practices and standards: ARDC: Data Citation.
Reusing data
When reusing datasets in your research, make sure to:
- Check the licence and comply with any conditions of reuse.
- Acknowledge the source through proper citation.
- Where applicable, include data reuse information in your ethics or research integrity documentation.
FAIR data principles
The FAIR principles are internationally recognised guidelines that support the effective discovery and reuse of research data:
Findable
Data should be described with rich metadata and registered in searchable resources.
Accessible
Data and metadata should be retrievable via open, standard protocols.
Interoperable
Data should use shared standards and vocabularies to enable integration with other datasets.
Reusable
Data should be well-described, with clear usage licences and provenance information.
Applying the FAIR principles makes your data more valuable, both within your discipline and across the broader research ecosystem. FAIR does not necessarily mean data is open, but that it is well-managed and accessible under appropriate conditions.
For more detail, visit the ARDC: Making data FAIR.