Meeting 12th December 2018

12th December – “The Clifton Suspension Bridge Dashboard, Data Visualisation from the Sublime to the Ridiculous”

This meeting featured a talk from Sam Gunner: ‘The Clifton Suspension Bridge Dashboard, Data Visualisation from the Sublime to the Ridiculous’.

Talk overview

“During the Clifton Suspension Bridge Dashboard Project, we employed a wide spectrum of different data visualisation techniques.  At one end was the time series database visualisation software Grafana, an open source web interface that provides a very intuitive way of visualising and disseminating time series data.  At the more imaginative end, the JGI commissioned two artists to convert the data into music, and the double robotic harp in the shape of the Clifton Suspension Bridge that they created (although possibly stretching the definition of data visualisation) has proved to be an incredibly successful mechanism for publicising the project and educating the general public, in this case in the field of Infrastructure Health Monitoring.

In my talk I’ll discuss the technical aspects of both these types of data visualisation, and hope to demonstrate their power, especially when used together.” Sam Gunner

Some more information about the Clifton Suspension Bridge Harp can be found in the report on the JGI website.

Meeting 13th September 2018

13th September 2018

In this session we watched a TEDtalk and had some flash presentations.
For those who weren’t able to make it, here’s the link to the TED talk by Beau Lotto that we discussed: Thanks for the really interesting discussion of how visual perception and optical illusions interact with data vis!
Related to this, we highlighted artist Luke Jerram’s current exhibition “The Impossible Garden” in the University Botanic Garden, a set of experimental sculptures exploring visual phenomena, open until 25th November ( Luke will also be delivering this year’s Richard Gregory Memorial Lecture at 6pm on Tuesday 13th November, titled “Exploring the Edges of Perception”. Details here:
Thanks too to our presenters. This year we’re introducing a new show-and-tell format, with three-minute lightning presentations. These can be about anything you like, such as a visualisation you’ve seen and liked, a tool you’ve used, a project you’ve been working on or a problem you’d like help with. The idea is that these are low pressure and should require minimal preparation. We think it worked really well, so do let us know if there’s something you’d like to share at a future meeting. Having said that, there’s no obligation to present, so if you’d like to just come along and watch, that’s fine too.
We also discussed an upcoming opportunity to influence the data visualisation infrastructure available at the University’s new Temple Quarter site. Do let us know if you’ve seen an exciting data visualisation set-up elsewhere that you’d like to bring to Bristol.
Finally, Polly announced a Tableau workshop she’s arranged for 7 November 2018 10.00-12.00 in the Seminar Room, Beacon House. This is one of our most requested training topics, so sign up here if you’d like to learn more:

Atmospheric quality data visualisation labs – May and June 2018

Our second data lab for 2018 used Atmospheric quality data provided by Guy Barkley from Atmotech.

24th May 2018 – Atmospheric quality data visualisation lab (1)

Guy Barkley from Atmotech introduced us to the work they do and the time-series data he kindly provided for the group on atmospheric quality.

Atmotech is an air quality services business. They deploy IoT sensor modules around sites and present the data to clients – ‘building a picture of air quality’ – in addition to professional services to help improve air quality on site and ultimately reduce exposure to our clients’ staff. They are interested in novel and intuitive methods of displaying our data, which readily engage people and facilitate understanding.

We asked the group to use this data to create data visualisations (as teams or individuals), which will then be presented in a follow-up session to skill-share and learn.


7th June 2018 – Atmospheric quality data visualisation (2)

This second session is the follow-up session of the data viz lab. Here, those who had created some visualisations on the atmospheric data presented their work to the group, followed by a discussion of the works and the tools used.

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.