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Research Data Management: Home

Research Data Management


Research data refers to anything that can be used to validate research findings or used to reproduce the research.

It is valuable and must be managed over the life of your project to ensure you satisfy research integrity requirements, and to ensure you are getting maximum benefit from it.

Research data management (RDM) refers to the practices you will engage in and the decisions made to manage research data. This includes how you will gather, organise, store, and manage access to data. 

This guide will outline recommended practices and where to find further resources from relevant units at UTS.

Your responsibilities


As a researcher at UTS, there are several policies which outline your responsibilities in relation to research data. The relevant policies are linked below, however the policy which has the most impact on your RDM activities is the UTS Research Data Management Procedure, which specifies what your RDM obligations are.

They include:

  • Ensuring you have a Research Data Management Plan (RDMP) in place
  • Storing your research data on UTS systems
  • Nominating an appropriate retention period for your research data
  • Archiving and consideration of publishing your data at the conclusion of your project

At UTS, the system you will use to carry out most of your RDM related activities is called Stash.

The interactive image below details the necessary RDM activities at each stage of your research project, and indicates which module you will use in Stash to complete them. Click the rectangle icon in the top right to enlarge the image.

Movements and initiatives


Open data

Also referred to as providing open access to research data. This is a movement for making research data publicly available to anyone to access and reuse. There are a growing number of publishers and funding bodies requiring or requesting open access to data. 


FAIR Principles

These are international principles that provide guidance on the qualities research data should have to maximise reusability.

They stand for:

  • Findable
    It should be possible to discover the data exists.  It should have a unique identifier (such as a DOI) or be indexed in a searchable source (a repository like GenBank or Harvard's Dataverse)
  • Accessible
    The access conditions governing the data should be clearly articulated. If the data is openly accessibly, ideally there is a standardised, automated way to gain access to the data (like being able to download the dataset directly from the repository rather than having to email the data manager directly for access). If the data is not open or requires some mediated access, the terms are clearly defined. 
  • Interoperable
    The data is stored and provided in standardised and widely available formats (e.g. .mp4 files instead of .mov files which are Mac specific). The data is described using a standardised vocabulary that is widely known (e.g. if the data points are dates, they are provided in YYYY-MM-DD format which is commonly used). 
  • Reusable
    The supplied data comes with provenance (e.g. you know who collected it, what methods they used and how they would like to be attributed). There is a creative commons or other licencing applied to the data which describes how it can be used. Best practices norms for sharing data in your discipline are observed.

Use the ARDC's FAIR Data Self Assessment Tool to discover how FAIR your research dataset is, and get practical tips on how to enhance FAIRness. 


CARE Principles for Indigenous Data Governance

These are international principles which relate to any research involving Indigenous Data. The CARE Principles complement the FAIR Principles and center Indigenous Peoples’ rights to and interests in their data. The AIATSIS Code states that researchers must be aware of these principles.

Further Resources