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Macquarie University Research Data Repository


The RDR takes the easy-access, data archiving and publishing capabilities inherent in the Figshare platform and complements them with integrations and automations and additional security features that are all relevant to the research context at Macquarie. Highlights include:

Two-Factor Authentication or 2FA uses a double-step login process and is mandatory when accessing the repository.  This extra level of security helps prevent unauthorized access to your dataset items, even if your primary login credentials become compromised. 2FA is particularly important when working with sensitive and highly sensitive data.

On initial login, Macquarie users will be provided with several 2FA options. Please contact the Macquarie IT Service Desk if you need assistance when setting up 2FA.

How do I upload and publish a dataset?

After logging in to the RDR, refer to the Publishing a dataset RDR how-to guide.

What item types can I upload to the Research Data Repository?

The RDR is predominantly focused on the upload and publishing of research datasets, but will also accept presentations, figures/images, media files, as well as information describing physical objects. 

Can I upload other item types or documents, e.g. software, to the repository?

If you would like to upload other types of research output, please make a request through OneHelp.

Can I upload large files?

Figshare advises that problems with large file uploads in the interface are usually a combination of browser / internet capacity. They recommend that users make use of the ftp uploader or API for large file uploads, or when experiencing slow uploads in the interface: 
If upload issues persist, please raise a ticket with Macquarie OneHelp.

The University has integrated the RDR with the University's Research Management System (Pure RMS). This allows public RMS project information on which you are the principal investigator to be automatically synchronized into your own project area within the RDR.  This integration provides a number of benefits, including:

  • Easy data provenance. You can create your data item within the context of your project and immediately access RMS project contextual information when uploading and describing your data. For more information, see enhanced metadata Title and Description fields.
  • Easily link your data back to your RMS project portal page. If you want to provide an even fuller context for your data, you can use your data item’s metadata to link (permanent URL) to your relevant RMS project. This means that anybody browsing your dataset on the RDR website / DOI landing page can quickly navigate to your public RMS project record and visit your public profile. For more information, see enhanced metadata Research Project Reference field.

The integration between Pure RMS and Figshare RDR is described in the following diagram.

DiagramDescription automatically generated

You can self-assess datasets you upload to the repository for FAIRness using the ARDC Fair Assessment Tool and record your self-assessment as part of the metadata of your dataset before publication. FAIR self-assessment is optional.

You can keep track and publish the quality reviews that have been conducted on your data. Listing the reviews that have been undertaken provides assurance to potential downloaders that your data is high quality. Once you have created the metadata record for your data, you can use the Q/A Review multiple selection field to nominate that one or more reviews have taken place. Supported reviews include:  peer review; departmental review; and institutional review. For more information, see enhanced metadata Q/A Log field.

Published datasets you have uploaded or on which you are nominated as an author or co-author are automatically listed on your Researcher Profile on the Macquarie University research portal, alongside your other research outputs. The listing will include a link to your dataset’s DOI so that anyone viewing your profile will be able to quickly navigate to the repository, access the description of your dataset, and download the data (if it is published openly), or request download. Listing your published datasets enhances your research profile as well as that of your research project.

Metadata enhancements have been made to the RDR above and beyond what is available in the basic institutional Figshare platform. These enhancements make it easy for data uploaders to provide provenance for their datasets and to assess and manage the quality of the data being uploaded. The enhancements will be visible to guest users who view the RDR’s published datasets, allowing them to make informed decisions about the usefulness of the data for further research. For more information, see enhanced metadata

As an extension of the Linked Research Context feature, you can also use the Research Project Reference field to link your data to your Macquarie OSF project,  general OSF project or other external project. This means that anybody browsing your dataset on the RDR website / DOI landing page can quickly navigate to all your projects that relate to the published data, both internal and external to the University.

When you decide to upload your data to an RDR project that has been synchronised from Pure RMS, you will be automatically allocated storage to house and share the data. Should the data’s storage requirements go beyond the initial allocation, you will be able to easily request additional storage within the RDR. The uploaded data will be managed and supported by a team of Macquarie data stewards for its lifespan and according to its sensitivity.

The RDR has been set up to automatically invite your Macquarie-based RMS project partners to collaborate on uploading and publishing your project’s data.  Collaboration with external researchers is also achievable and is just a few clicks away using standard Figshare features. As project owner you will always maintain full control over who you collaborate with and any data you upload to the RDR.

Contact & Support

For help and assistance contact 
MQ Research Data Repository support team

Online: Log a ticket 24x7 using OneHelp - Research Data Repository (requires an MQ OneID login)


Phone: IT Service Desk +61 2 9850-HELP (4357)

Australian Toll-free: 1800-MQHELP (1800 67 4357)