University of Edinburgh extends IoT data science to south-east Scottish pupils

First experience of coding

Chapple draws on his own experience in leading the programme. “When I was a kid, I knew nothing about computing. I grew up in a tiny village in Cornwall, and my family didn’t have access to that kind of thing. But I had a friend at school who had a ZX80, and when I saw the coding he did, moving some black and white shapes around on the screen, it was like magic. That’s how I got into computing.”

The university is well known for its heritage in artificial intelligence and informatics. “The community impact side of what we’re doing is a little bit unique in the context of the University of Edinburgh, which is, historically, a research-focused university,” he says. “Here, we have an opportunity to take our educational capability and introduce it at an early age to what will be our future students.”

Chapple points towards the endorsement of that goal by Grace Vickers, chief executive of Midlothian Council, who says the “project not only enhances learning around digital and data, but will, in future years, contribute through the skills gateway to an increased number of data professionals in the workforce” and points to the key objective of the Edinburgh and South East Scotland City Region deal being “to make this area the data capital of Europe”.

Elastic Stack in Auld Reekie

The long-range, low-power networking the project harnesses can send sensor data tens of kilometres, and it does that back to the Edinburgh service, where it is deposited in an Elastic stack instance.

The data can then be analysed using the Elastic data visualisation technology Kibana. “We are creating dashboards in real time, and very quickly and easily,” says Chapple. “Each of the schools has access to their dashboards, where they can see the data coming in. The school pupils love it because they really start to get a handle on the data.

“Data can be quite abstract, quite hard to introduce people to,” he says. “But because the sensors are in their own local environments, they start to develop an interest in what’s actually happening, in terms of weather variables, temperature, humidity, air and water pollution, as well as both human and natural activity that will bring them into biodiversity monitoring, and so to climate change.”

Bat activity

The Edinburgh IoT team has a particular interest in sound with regards to the monitoring of biodiversity, with the detection of bat activity at the fore.

“We have people who have developed machine learning models which can indentify bat species from the signals generated,” says Chapple. “But if you can imagine being able to do that at scale, and with high resolution, you get a very accurate picture of bat biodiversity across the region because of the liveness of the information we’re processing.”

As a related aspect of the school teaching data analytics, he cites the virtual environment the university has built during the pandemic period, known as DataTown. This is being used to teach students about extreme weather events, for instance, by running 24 hours of thunderstorm data in 24 minutes, but without first telling the students it is thunderstorm data.

“I hope the students will be able to experiment with and utilise the features in Kibana itself to do their own data visualisations, so they can start to draw on the creative and art aspects,” says Chapple. “I’m really looking forward to them creating displays of data coming from the sensors.

“It’s about evidence-based reasoning. Our next generations need to be able to understand what’s happening in the world and use that information to be able to make choices to make policy and effect change.”