Data Visualisation Labs – February and March 2018

We held some data visualisation labs in 2018, these were two part sessions. In the first part the datasets were introduced and in the second any resulting visualisations were presented. The labs were to work as follows:

  • One or two datasets, which lend themselves well for visualisation, will be introduced to the working group.
  • Over a time-course of approximately three weeks, members of the working group then create a visualisation using one of the datasets.
  • Visualisations can be made using any tool
  • In a lunch meeting, the resulting visualisations will be presented and discussed in a round table format.
  • There is no prize, the exercise is purely to learn from each other and get inspired. As such, don’t think you have to spend hours on this, just some rough ideas/concepts/prototypes are enough to get the discussion going.

13th February 2018 – Data Visualisation Lab Part 1: Data introduction meeting

The datasets were introduced:

Metabolic reactions of a small human pathogen – Oliver Chalkley

Living cells use enzymes to catalyse reactions to create desired products. Many of these reactions can be connected together to create metabolic pathways. These pathways can be connected together to form a complex network of interactions called a metabolism. The metabolism is central to all life on Earth and can switch between purposes creating a challenging dataanalysis/visualisation problem.

I have included 5 wild-type (e.g. default) simulations of the full metabolism of a small human pathogen, Mycoplasma genitalium. Each simulation contains a time series for all of the 645 reactions. Where the value is the flux through the reaction at time, t. The flux of a reaction is related to the speed of the production/consumption of the molecules in the given reaction i.e. large flux means fast reaction and zero flux means no reaction.

In order to avoid getting bogged down in biological details, I have not included information on the reactions nor the matrix that describes what reactions feed what reactions. This information is available on request but I suggest we see if we can figure out the important relationships from the data.

I have two, related, questions:

  1. Is there a good general visualisation(s) to inspect this metabolic data?
  2. Using the visualisation(s) from 1. (or some other visualisation(s)) can we see phases within the cell cycle i.e. are certain pathways activated at certain times in the cells life?

NOTE: I have created a software suite for whole-cell modelling (where this data is taken from) and I will try to publish it as a “tools” paper in the next 6 months. Should anyone find something worth adding to the software suite then there is potential to be included in the paper.

 

Eye movements when reading news headlines – Bobby Stuiifzand

This dataset contains the eye movement data of 19 participants engaged in an information search experiment involving two tasks. In each task, the participant was presented with a set of 10 news headlines and the participant was instructed to either: find a pre-specified word in the headlines (task 1), or, select the headline which they found most interesting (task 2). Each task occurred 50 times (the tasks occurred in a randomised sequence), with the experiment therefore totalling 100 trials, on 50 unique sets of headlines (the headlines were repeated for each task). The dataset contains 84566 rows. Each row in the dataset contains a single fixation-saccade sequence (i.e. “event”), with information on event timestamps, fixation and saccade location coordinates, saccade velocity, and saccade amplitude available. Further, for each row there are numerical identifiers for the trial, experimental block, type of task, set of news headlines used, and participant (anonymised) available.

Interesting questions that can be asked from this data and lend themselves well for data visualisation focus on the difference between tasks, participants, etc. e.g.:

  • Do eye movement patterns differ between tasks?
  • How do these differences unfold over time?
  • Do different participants display different eye movements patterns?

Anyone that decides to work on the data can do so either on their own or in teams, and with their own tool of choice. As we (Oli and Bobby) are the respective owners of these datasets, we are happy to provide some technical support throughout, or to lead on a team working on this data.

6th March 2018 – Data Visualisation Lab Part 2: Presentations and discussion

In this session the resulting visualisations from the data visualisation lab were presented and discussed.

Meeting 15th January 2018 – Data Realisation

15th January 2018 – Data realisation

13:00 – 14:00 Room G1, 7 Priory Road

Prof Anthony Head from Bath Spa University visited us to discuss approaches to data visualisation, multi sensory representation of data, art and audience. He also explained his recent data vis project for SPHERE, where he created a 3D representation of the house where Uni of Bristol researchers conducted with sensor experiments.

Meeting 28th November 2017

28th November 2017 – Visualisation lab

13.00-14.00  – Seminar Room, Beacon House Study Centre

This session was a visualisation lab. This type of session gives the group space for us to all talk or work on ideas, form groups or chat generally about all things “viz”.
In addition to this we explored if there was any interest to form a group for the JGI data visualisation challenge – http://www.bristol.ac.uk/golding/research/data-competitions/data-visualisation-challenge-2017–2018/.

Meeting 2nd November 2017

2nd November 2017 – The art of the possible in data visualisation

13.00-14.00 – Seminar Room, Beacon House Study Centre

Run by John Kellas

This session included a presentation on cutting-edge visualisation technologies and open source frameworks that can enrich the aesthetic and information communication possibilities. Through horizon scanning, and insight into D3, Cesium, Visual.Tools and Noomap – a range of possibilities in data and information visualisation were explored.
The session had some interactive group work, where we explored what can be done with today’s technologies – in relation to the data that session participants were working with. There is not a one size / solution fits all for visualisation projects, and through consideration of appropriate tools for specific projects, we will advance a collective knowledge base of the art of the possible.
The session was run by guest facilitator and data visualisation expert John Kellas (FRSA, MSc Community Education). John is contracted as an Innovation and Community Engagement Consultant with the Bristol Health Partners and has also been working with Christopher Reay and a team of community volunteers to develop a new open-source visualisation toolkit and data modeling engine Visual.Tools / This Equals over the last 6 years

Meeting 26th September 2017

26th September 2017: Good and bad practices in data visualisation

Led by Bobby Stuijfzand, Harriet Mills and Oli Chalkley

This session was an introduction to the goals of the group. The slides focused on good and bad practices in data visualisation. There was also discussion to identify the future directions and needs of the group.

Slides 26th September 2017.