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Making the Invisible Visible: Environmental Data Visualisation for Research and Public Engagement

This project is no longer listed on and may not be available.

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  • Full or part time
    Dr Chris Hassall
  • Application Deadline
    No more applications being accepted
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Note: This project is a self-funded MSc by Research. That means that the candidate would need to pay their own fees and maintenance.

The natural world is experiencing rapid change under influence from human activities. Responses by animals and plants have been recorded and analysed using a variety of different approaches, from radar and satellite data through to analysis of historical texts and indigenous knowledge to find mention of past conditions. Environmental data collection and analysis has advanced and diversified considerably in the past 5-10 years with the advent of new hardware and software that can analyse large quantities of acoustic data but also new approaches to extracting meaning from less empirical methods. There are many opportunities for use of these data across ecology and environmental science, but also as a tool for public engagement and science communication. As part of a newly-funded collaboration, a group of artists and researchers from both the Humanities and the sciences are exploring ways to visualise data in new ways that can create engaging and informative outputs.

This MSc project is a 12-month research project that will work alongside the new collaboration to help develop and evaluate the new formats of data visualisation - "making the invisible visible". There are several possible routes for the project, which could take as their starting point one of the following ideas:
- The creation of an audio-visual art installation derived from bioacoustic data or data on bird migrations collected from the field. This area of work would benefit from some experience in programming Arduino or Raspberry Pi computers and an interest in creative arts.
- Evaluating public perceptions of artwork in terms of the increase in engagement with the research topic or understanding of the research that underpins that work. The candidate might have experience of empirical, survey-based research.
- Analysis of bioacoustic data using machine learning algorithms and other statistical approaches to extract new meaning from the data. Here, a computational background could be useful, or a willingness to learn new computational techniques.
- Collaboration with citizen scientists work on a campus "Living Labs" project on bird and pollinator biodiversity. As part of a new set of projects on campus, the candidate could aid with the visualisation and engagement activities that harness both citizen science data and data from environmental sensors.

The collaboration among academics is broad and we would consider applicants from a wide range of disciplines who have an interest and/or background in areas relevant to the project:
- An undergraduate degree (2:1 and above) in a relevant science (biological sciences, engineering, computer sciences, statistics), or arts (design, art, culture studies, English) subject.
- A stong interest in public engagement and science communication.
- A demonstrated ability to work across disciplines (particularly across arts and sciences)
Desirable (we don’t expect applicants to have all of these!)
- Experience in data handling and analysis.
- Experience working with Arduino, Raspberry Pi, or similar hardware
- Experience of creating artistic installations
- Experience of working in outreach and public engagement

Funding Notes

This project is a self-funded MSc by Research. That means that the candidate would need to pay their own fees and maintenance.

1) Contact the supervisor to register your interest.

2) Apply online
The programme code is ‘MSc by Research in Biological Sciences’. Checklist Item 10 requests information about the research area - you should input the title of the project and the supervisor’s name.

How good is research at University of Leeds in Biological Sciences?

FTE Category A staff submitted: 60.90

Research output data provided by the Research Excellence Framework (REF)

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