Data visualisation

OxTALENT 2018 winners

In the Data Visualisation category of OxTALENT, the judges were looking for visualisations that tell a story, provide an insight, make the complex simple, or illustrate a beautiful pattern. In this category, we have a runner up and a winner. The co-judges were Maja Zaloznik, Oxford Institute of Population Ageing, and Rowan Wilson, Academic IT.

Winner:

Kasra Hosseini and Maria Tsekhmistrenko (Earth Sciences) for 'SubMachine: Web‐based tools for exploring Seismic Tomography and other models of Earth's interior'

screenshot of a submachine video showing North America

This video, an impressive animation of a 3-D model of our planet, shows the Earth's seismic tomography

Open, interactive, user-friendly tools to explore models of the Earth's interior are limited within the wider solid Earth community. It is often not straightforward to explore these models without specialised knowledge and a customised programming effort. Hence they more often exist as the limited number of 2-D representations seen in original publications rather than as full, 3-D data sets that can be readily accessed.

SubMachine presents a set of web-based tools to facilitate the visualization and analysis of our planet’s interior structure. It focuses on seismic tomography – a technique to image the internal structure of Earth using seismic waves – with over 30 regional and global-scale models available.

four subsurface representations of the earth

SubMachine allows users to compare different models of the earth really easily.

Models can be imaged individually, side-by-side, or through statistical tools. SubMachine holds additional Earth datasets such as plate reconstructions, normal mode observations, crustal structure, shear wave splitting, the geoid, marine gravity, vertical gravity gradients, and topography. 

The OxTALENT judges are convinced that SubMachine is an incredibly comprehensive set of data visualisations, which represents a great resource for users that was not available beforehand.

Runner up:

Anastasia Bow-Bertrand, Marta Favara, Grace Chang and Kristine Briones (International Development) for 'Young Lives: visualizing child poverty data'

The Young Lives dataset represents a wealth of information not only about the Young Lives children’s material and social circumstances, but also their perspectives and aspirations, set against the environmental and social realities of their communities.

two young boys sitting on a stack of hay looking into distance

Young Lives data visualizations are based on an extensive study into children and youth around the world

The Young Lives data visualizations represent a very useful tool for engaging with a non-technical audience as well as with policymakers, both key audiences for disseminating findings on what works in supporting children in poverty through practice and profile. This platform makes content accessible, enabling users to directly engage with data that is interactive and comprehensible (allowing multi-layered variables and determinants to be simultaneously visualized).

a data visualisation on work and study status with a female icon on the left

This data visualisation shows the work and study status by relationship status. Users can change the parameters and directly compare results.

The judges liked the mix of visualisations based on an innovative cross-country comparative data project looking at childhood poverty and found this an interesting dataset to explore.

 

Congratulations to the award winners in this category!

About the category

Software is making it easier to create data visualisations that are interactive and can be shared via the Web. For example, we can create maps with ArcGIS and QGIS, representations of networks with Gephi, interactive models of complex systems with NetLogo, 3D renderings of large datasets with Blender, statistical insights with RStudio and Shiny, and script libraries such as D3.js.

In this category of OxTALENT we're looking for interactive data visualisations that provide an exploratory experience of research data. Static visualisations provide a singular narrative of a dataset, whereas interactive visualisations allows end users to explore and filter datasets - and to understand the breadth, depth and detail of research output. Remember that the judges will have no experience of your research field.

You are eligible to enter even if you've had professional help, but we ask you to let us know on the entry form what assistance you received. Find out how to enter

However, you don't have to provide your users with a 'glossy' experience. For example, you could simply re-imagine the visualisations already built for your print publications for the web; tool-tips and zooming are very engaging for audiences and not too difficult to develop. You might want to consider visiting the Interactive Data Network (IDN) website for advice and support in hosting interactive visualisations on the Web.

What the judges will look for

Your entry will be assessed in terms of:

  • How clearly you have defined the purpose of the visualisation, including the intended audience.
  • How you designed and implemented the visualisation.
  • The tools you used.
  • The amount and quality of interactivity built into the visualisation.
  • How easy it is for people, including non-researchers, to interact with your visualisation.
  • Evidence of impact: whether people have interacted with the visualisation and provided feedback.
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