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Submissions can be made by individuals or teams of students. Visualisations must be in a digital format readily accessible by the public. Specifically, they must be either a high-quality stand-alone static image (jpeg, PNG etc), or dashboard-type entry hosted on a public server (). Such formats may include Shiny, Tableau or PowerBI dashboards.Ìý

Entries must be submitted to:Ìýmatilda-centre.dataviz@sydney.edu.auÌýwith the subject ‘data viz entry 2025’.Ìý You must:

1. Attach either an image file, or link to an interactive visualisation AND attach a static graphic screen grab of the visualisation to be used as a cover image.
   
   
2. Include a paragraph (max. 200 words) describing the story you intended to tell and how your visualisation was made.
   
   
3. Tell us your (or your team members’) full name, degree, and year of study. Please ensure there is no mention of your name(s), research institution, or other identifying information on the visualisation itself. This is so we can judge each entry in a non-biased manner.Ìý
   
   
4. Indicate if you are a beginner (i.e., you have no previous coding experience, have never completed any data visualisation as part of a higher education course, and have never entered a data visualisation competition before).Ìý

Ten of the top entries, as voted by an expert advisory committee, will be selected for judging and public voting. These entries will be judged by 3 academic/professional staff members of the Matilda Centre and 1 judge external to the Matilda Centre, according to the following criteria (10 points for each):

• Story told and insights offered about mental health and/or substance useÌý
• Creativity and aesthetic appealÌý
• Effective communication

The ten top entries will also be posted to an online site to facilitate public voting. Links to the survey will be shared via the Matilda Centre, and will be emailed to all entrants. Please encourage your networks to vote!

3 prizes worth $250 each are on offer:

1. Judge’s choice
2. People’s choice (based on public voting results)
3. Best entry by a beginner, determined by the judges

Please provide context and text-based interpretation on the visualisation so a reader can understand what you’re communicating. Think how an infographic communicates all information in a clear and aesthetically pleasing manner.Ìý

There are several tools that automate much of the work of data visualisation. Some of these, such asÌýÌý²¹²Ô»åÌý, are either free or provide free licences for students.ÌýÌý

For making beautiful visualisations, it can help to focus on yourÌý, and choosingÌýÌý²¹²Ô»åÌýÌýthat work well together.

There is plenty of publicly-available data that you could use for this competition – if you know where to look!

You will probably want to make use ofÌýÌýlevel statistics. These are the kind of data you might find published in government reports or journal articles. This could be useful if you want to visualise data on a group-based level e.g., how a particular phenomenon varies across countries or over time. Aggregate data are all that is required for beautiful, insightful visualisations.Ìý

You may wish to take inspiration from Our World in Data’sÌý,Ìý, andÌýÌývizzes. The aggregate level data they use to make these charts are freely available – just click the download button! You can also take inspiration from some research conducted at the Matilda Centre, like the relationship betweenÌý, or whether cannabis is a ''.

Other sources of relevant aggregate data includeÌýÌýand theÌý.

You can search for Australia-specific mental health datasets using the .Ìý°Õ³ó±ðÌýÌýplatform (for which you can make an account using your University details), is also a good place to search for Australia-specific data, whileÌýÌý²¹²Ô»åÌýÌýare broader repositories. Finally, if you are code proficient, there are several packages that function as APIs for accessing data ().

There are also some publicly availableÌýÌýto be found, which allow you to examine how phenomena and relationships vary (or don’t) on a more fine-grained level and to make statistical inferences. Packages for coding languages, e.g., R or Python, often contain individual-level datasets – many of which are relevant to mental health and substance use. For example, we have located a dataset looking atÌý, and one looking atÌý.Ìý

If you do not have access to the above datasets, please view theÌýsubstance use and personality predictors (.csv, 276KB), andÌýalcohol abuse and other demographic/lifestyle factors (.csv, 1.2MB).

Important notes: Only use personal research data if you have permission to share it publicly. Make sure to cite what data you are using somewhere on your viz.

Everything in life has terms and conditions, and this competition is no different.Ìý

Please read theÌýterms and conditions (docx, 41.9KB)Ìýof entry before you begin preparation of or submit your visualisation.