The data used or generated in your research project is a key research output. Managing it can save time, reduce risk, and increase the impact of your work. The Library provides guidance on planning and implementing effective research data management practices.
What is research data?
The UTS Research Policy defines research data as anything which can be "used to validate research findings and/or used to enable reproduction of the research". This includes the data, sources, evidence, and research materials used or generated during research, in both physical and digital formats.
Research data management
Research data management refers to how you manage the digital and physical data generated or used during your research project. It covers collecting, organising, storing, and managing access to data during the project, as well as planning for archiving, sharing and publishing it.
Good data management can help you:
- reduce risk and protect against data loss
- support research integrity and reproducibility
- enable collaboration, engagement, and impact
- meet publisher and funder requirements.
Research data management plans
A research data management plan (RDMP) outlines how data will be managed in your project. It typically covers:
- what data will be created
- what policies will apply to the data
- who owns the data and who will have access to it
- what data management practices will be used
- what facilities and equipment are required
- any ethical, commercial, or cultural sensitivities around the data and how risks will be managed
- who will be responsible for each of these activities
If you are using AI tools to collect, generate, or process data, record this in your RDMP as part of your broader project documentation. Depending on the project, you may also need to address AI use in ethics applications, consent materials, data management procedures, or contractual arrangements. AI tools should only be used where consistent with data classification, ethics approvals, and contractual obligations. See the UTS Use of AI in Research Guidelines for guidance on the responsible use of AI use across the research lifecycle.
A RDMP links your project to your research data, and records ownership and responsibilities. RDMPs are living documents and should be updated as your project changes.
Stash is the UTS research data management platform and is where you will create your RDMP. Stash can also be used to link to available workspaces, create a data record for preservation, and publish data where needed.
To begin, log in to Stash and select Create RDMP.
Funder requirements for research data
Many funding bodies require researchers to explain how data produced during a project will be managed. This may include storage, access, sensitive or restricted data, reuse arrangements, and whether data will be shared or published.
If your project is funded, check the grant guidelines, funding agreement and relevant funder policies for any specific research data requirements. These should be considered when preparing your RDMP.
At UTS, researchers can address many funder requirements by creating and maintaining an RDMP in Stash, classifying data according to the UTS Information Security Classification Standard, storing data on UTS-approved and supported systems, and archiving data via Stash at the end of the project. Where appropriate, data can also be made discoverable or available through the UTS Research Data Portal, or a relevant discipline-specific repository.
Grant applications should avoid overly generic wording where possible. Include project specific information about storage, access, sensitive or restricted data, ethical or commercial considerations, de-identification, preservation, reuse, and intellectual property where relevant. See the RDM info for grants and ethics section of the Get Advice page in Stash for more information.
Principles for ethical, reusable data
Good research data management supports data that is well organised, appropriately documented, and managed in ways that enable future use.
The FAIR principles (Findable, Accessible, Interoperable and Reusable) provide a framework for making data easier to organise, understand, share, and reuse over time. In practice, this may include using clear file structures, meaningful metadata, open or widely supported formats, and planned access conditions.
If your research involves Indigenous data, the CARE principles (Collective Benefit, Authority to Control, Responsibility and Ethics) must also be considered. CARE complements FAIR by emphasising Indigenous rights, interests, and sovereignty, and support culturally appropriate, ethical, and community-informed data practices.
For more information and practical guidance, see the Library’s Research Data Management study guide.
Storing your data
Choosing the right storage for your research data starts with understanding its security classification.
All information at UTS, including research data, must be assigned one of four security classifications that determine how data can be stored, accessed, and shared based on risk:
- UTS Public
- UTS Internal
- UTS Sensitive
- UTS Confidential
See the UTS Information Security Classification Standard for definitions and further guidance.
Research data must be stored on UTS-managed or UTS-recommended infrastructure. Once you have your classification, use the Research Data Storage page to identify the storage options that support it and find out how to access them.
Organising your data
Managing your working data throughout a project helps maintain quality, support collaboration, and enable long-term access. This includes how you name files, structure folders, choose file formats, and manage versions. Setting up clear practices early can save time and reduce confusion later. For more guidance see the Research Data Management Study Guide.
Publishing your research data
See Publishing Research Data for information on how and where to publish your research data.
Archiving your research data
Under the UTS Research Data Management Procedure, researchers must archive their data at the end of a project, by creating a data record in Stash and applying the appropriate retention period. A data record describes the research data used to support your findings, including any documentation needed to understand or reproduce the research.
Best-practice varies by discipline, but you may also choose to create data records earlier in the project - for example, after collecting raw data or after a major stage of processing. This can help ensure important data is retained as the project progresses. Creating a data record in Stash helps ensure your data is retained for the minimum required retention period under the State Records Act 1998.
For guidance on retention periods, storage options, and specialist archiving support, see the Research Data Management Study Guide.
Creating a data record in Stash
The video below explains how to create a data record in Stash, and how to create a data publication.
Getting help
Librarians can assist you with these parts of the process:
- Research data management
- Publishing your research data
- Archiving your research data