This event spans multiple dates:
13 Jun 2024 10:00 - 17:00 Room 327, Raised Faculty Building, Sidgwick SiteThe Old IT Room, 17 Mill Lane, Cambridge CB2 1RX
14 Jun 2024 10:00 - 13:00 Room 327, Raised Faculty Building, Sidgwick Site


An event organised by Cambridge Digital Humanities

Theme: Visualising UK farming’s environmental impact

Applications are open

This year’s theme for the CDH Reactor programme is designed in collaboration with Watershed Investigations and explores remote sensing and AI methods for mapping the impact of farming on water and food systems in the context of climate change. Learn more about Watershed.

Join our Data Lab

We will be running a Data Lab on 13-14 June 2024 to bring together postgraduate students and researchers interested in contributing to the incubation of a data-intensive investigation in collaboration with journalists from Watershed Investigations. A Data Lab is an intensive, practical and participatory workshop led by CDH staff and expert contributors which takes place over a full day. Each Data Lab is different, but participants can expect to be asked to:

  • Work individually and in small groups to discover data sources relevant to the topic of the Lab
  • Take part in cleaning, checking and annotating data
  • Provide context and domain knowledge about the data
  • Assist in analysis and data visualisation
  • Write and edit draft content to accompany dissemination and publication
  • Help shape a plan for future research and public engagement activities based on what the Lab team have discovered

Data Labs are rapid interventions reacting to major events and developing news stories. As such, they will not always produce finalised ‘outcomes’ in the form of datasets, articles or other kinds of media content ready to be published immediately after the Lab has finished. The aim of the Lab is to map out avenues for future research, investigations and journalism and to encourage wider debate about the issues spotlighted.

Project brief: The environmental footprint of intensive farms in the UK

The landscape is overwhelmingly farmed and agriculture is the biggest polluter (nutrients, pharmaceuticals, pesticides, AMR) of waters, according to the Environment Agency. It contributes to air pollution and is well known to be a major source of unchecked greenhouse gas emissions, with meat by far the biggest source in terms of the land required for feed as well as the farm that houses the animals themselves.

Watershed Investigations have mapped intensive pig and chicken farms throughout the UK using data from the Environmental Permitting Regime (EPR). Pig and poultry farms above a certain number of livestock fall under the scope of the regulations and are deemed intensive, but if a farm’s head of livestock falls below the threshold by one animal then it is not and does not appear on the register. Dairy and beef farms do not fall under the scope of EPR and we have been unable to secure a list despite trying numerous avenues. From satellite imagery, it is possible to see that large farms have features in common: the large long sheds, silos and slurry pits. We wish to see if we can use Machine Learning to identify these across the UK – and, if successful, across other geographies – in order to fill in gaps in the data from other sources.

Alongside our work mapping farms from their satellite ‘footprint’, we are also interested in looking at potential correlations between the location of farms and pollution incidents documented elsewhere, including satellite imagery (for algal blooms and methane emissions) and court records of fines (for illegal slurry dumping). In order to generate a richer picture of the factors underlying water pollution, we will also use a variety of methods to analyse data on land and farm ownership.

Minimum time commitment for Data Lab participants:

  • Full day workshop attendance on 13 June
  • Half day workshop attendance on 14 June

There will also be an opportunity for some Data Lab participants to work on projects emerging out of the Data Lab with CDH in the run-up to the Cambridge Social Data School 9 – 13 September 2024, which will showcase some of the work of the Data Lab at a public event. More information on these projects will be available at the workshop.

Principal methods covered:

  • Applications of AI for remote sensing using satellite imagery to fill in gaps in publicly available datasets from other sources
  • Open source triangulation / cross-referencing for data validation and georeferencing
  • Data visualisation for public communication

Outcomes of the Data Lab:

We will be working with Watershed to co-produce datasets and visualisations for publication on the Watershed website and in an open access repository on Github hosted by CDH Learning. Participants will have the opportunity to be credited on these publications.

Required skills and experience:

We are interested in hearing from postgraduate students and researchers who bring expert knowledge relevant to the topic of the call, or can offer skills in data collection, analysis and visualisation or writing, editing and media production.

Is the Lab a public event?

Applications for this Data Lab are now open to the public. Selection will occur via a call for participation which prioritises early career researchers, graduate students and staff at the University of Cambridge, but is open to all. We will offer Lab participants the opportunity for public credit and acknowledgement of their contribution in a blog post documenting the event and at subsequent public events which share the Lab findings with wider audiences, but this is entirely voluntary.

How to apply:

PhD students and early career researchers are encouraged to apply to join the Data Lab using the application form.

Other staff and students at the University of Cambridge, as well as individuals working outside the university, are also welcome to apply, however, applicants from the ECR community will be prioritised if we are oversubscribed.

Applications will be reviewed on a rolling basis until spaces are filled, so participants are strongly encouraged to apply as soon as possible. 


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