Study Guides: Research Data Management: Home

Research Data Management

Research data refers to anything collected, generated or created during research that can be used to validate research findings or used to reproduce the research.

Your research data is valuable and must be managed over the life of your project to ensure you meet your responsibilities, satisfy integrity requirements, and so that you achieve 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 key documents which outline your responsibilities in relation to research data. These are linked to below, however the one 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 appropriate UTS systems
Nominating an appropriate retention period for your research data
Creating a data record at the end of the research project
Consideration of publishing your data

Australian Code for the Responsible Conduct of Research, 2018 This code outlines the overarching principles for all researchers conducting research in Australia. It was developed jointly by the National Health and Medical Research Council, the Australian Research Council and Universities Australia. This code is what UTS Policy around RDM is based off.
UTS Research Data Management Procedure Outlines the mutual obligations of UTS and UTS researchers to ensure good data management practices in line with the Australian Code for the Responsible Conduct of Research.
UTS Research Policy Outlines how research at UTS is conducted and managed.

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.

Stash Stash is used to manage all aspects of your research data throughout the lifetime of a project. Using this system to manage your RDM activities helps you fulfil your obligations as a UTS researcher and also allows you to meet any funder requirements.

Movements and initiatives

Open data

Also referred to as '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, and it should be easy to find for both humans and computers. It should have a unique identifier (such as a DOI), have rich machine-readable metadata, and 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 important thing is that the terms are clearly defined. In this way even heavily protected data can be FAIR.
Interoperable
Usually data needs to be integrated with other data, and/or needs to interoperate with applications or workflows for analysis, storage and processing. Data should be 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
This is the ultimate goal of FAIR. The supplied data should come with provenance (e.g. you know who collected it, what methods they used and how they would like to be attributed). Both data and metadata should be well described so that they can be replicated and/or combined in different settings. Creative commons or other licensing should be applied to the data to describe 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 below 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 and apply these principles.

ARDC - Making data FAIR Explains how to make data FAIR and links to the FAIR assessment tool.
ADRC - FAIR Data Self Assessment Tool
CARE Principles for Indigenous Data Governance The CARE Principles for Indigenous Data Governance are people and purpose-oriented, reflecting the crucial role of data in advancing Indigenous innovation and self-determination. These principles complement the existing FAIR principles encouraging open and other data movements to consider both people and purpose in their advocacy and pursuits.
AIATSIS Code of Ethics

Further Resources

ResHub: Introduction to Research Data Management Requires login. Self-paced modules to help build an understanding of the benefits of managing research data.