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Managing Research Data @MQ

Provides an overview of appropriate collection, storage, and dissemination of research data for Macquarie researchers


Research Data is an asset that requires careful management and curation. This guide provides an overview of appropriate collection, storage, and dissemination of research data, and is intended for use by all Macquarie University Researchers. It:

  • supports researchers in meeting their obligations in relation to the Macquarie University Research Data Management Policy;

  • provides information necessary to complete a Data Management Plan using the online DMP Portal in FoRA;

  • outlines practical considerations in Research Data Management;

  • highlights technology, infrastructure, training, and resources available at Macquarie to assist researchers with data management and curation.


The information in these guides outline key data management practices throughout the research data life cycle.

In particular, this guide focuses on three phases of the data lifecycle:

  1. data capture or collection,
  2. ‘active’ or operational data management (including collaboration), and
  3. data archiving and dissemination.


Appropriate collection, storage and dissemination of research data will:

  • enhance the transparency, efficiency, and scalability of research;
  • facilitate reuse of data by other researchers – as well the original researcher in the future;
  • increase research impact;
  • ensure compliance with legal and ethical requirements, including any contractual obligations imposed on researchers or the University by publishers, sponsors, collaborators, partners, or funders.

Compliance and improved practice are the dual purposes behind Macquarie’s research data management approach

Compliance: legal, ethical, and contractual obligations

Research data infrastructure, policy, and support at Macquarie are designed to help researchers meet various obligations. Some obligations are legal in nature, including compliance with NSW privacy law (e.g., the Privacy and Personal Information Protection Act 1998) and NSW record retention requirements (e.g., GA47 Higher & further education and research records).

Ethics compliance imposes further obligations, outlined in the National Statement on Ethical Conduct in Human Research (2007) (updated 2018).

Funders and publishers may impose their own data management requirements. The ARC, for example, requires data management plans from successful applicants. Other funders and publishers go much further, as can be seen by the list of over 5,000 signatories (mostly journals, funding organisations, and professional societies) to the Transparency and Openness Promotion (TOP) guidelines (‘TOP Level II’ requires data to be published in a trusted repository). More and more, researchers will be unable to meet legal and ethical obligations, receive funding, or publish in quality outlets unless they have mature data management plans and can make a comprehensive, well-documented dataset available to support their research.

The Macquarie University Research Data Management Policy incorporates key aspects of compliance; following them should ensure legal and ethical compliance, and meet most funder and publisher requirements regarding data (noting that some funders and publishers have additional requirements around data, code, or other digital research objects).


If you have questions about compliance, please contact Digitally Enabled Research for advice.

Good Practice: The FAIR Data Principles

Beyond compliance, Macquarie research data initiatives seek to improve research practice.  

Since 2016, the FAIR Data Principles have become the international touchstone for good practice in research data management. Implementing the FAIR principles – making data Findable, Accessible, Interoperable, and Reusable – maximises the value of your data.

Familiarising yourself with these principles is an important first step before starting your project or developing a Data Management Plan (DMP): 


Data can be more findable by:

  • properly describing what the data is;
  • putting it in a permanent and easily searchable place;
  • making it easy for humans and computers to search for it.

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;
  • letting others know if the data is no longer available.

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;
  • indicating if it relates to other data and documenting what that relationship is.

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 licences readable to humans and machines;
  • meeting the standards used within the relevant research community.

Where possible and appropriate, data and materials must be collected or generated, stored, and licensed in a way which incorporates the FAIR principles, with (potentially mediated) access allowed to interested parties, thereby enabling data reuse in future research.

We encourage you to complete the 'Online orientation to Research Data Management' via Workday.

This course provides an introduction to the concepts and skills required for effective research data management. It is a good starting point for you to learn about how you can safeguard and preserve your research data, apply good practice to your data management, and comply with the Research Data Management Policy.


Findable, Accessible, Interoperable, and Reusable (FAIR) data