The Inator Hackathon: Raspberry Pi’s and Remote Sensing for Machine Learning

KEY DETAILS

  • DATE

    18 MARCH, 2026

  • TIME

    09:00 – 17:00

  • COST

    FREE

EVENT LOCATION

  • University of Strathclyde, Curran Building

    101 St James Rd, Glasgow G4 0NS

Whether you’re curious about microcontrollers, want to sharpen your Python skills, or are exploring how data becomes AI-ready, this event gives you the tools, guidance, and space to create something real.

What you’ll do

  • Explore simple remote sensor construction using a Raspberry Pi and commercially available parts
  • Acquire large datasets via remote sensors
  • Explore data cleaning and structuring using Python

Why it matters

Up to 90% of machine learning is data wrangling and good data is all about the 4 V’s: Volume, Velocity, Variety, and Veracity. Add FAIR principles (Findable, Accessible, Interoperable, Reusable), and you have the recipe for trustworthy, powerful big data. Using metadata, which if linked to experimental results is the key to the trickiest of the 4V’s: Veracity.


This Hackathon is about demonstrating how simple it is to link metadata and data, and to clean up datasets so they’re fit for use with machine learning using modern methods, hence encouraging uptake and usage. This Hackathon introduces researchers to using simple microprocessors and environmental sensors through the Raspberry Pi, using it to monitor laboratory and experimental conditions. You will construct a simple remote sensor, use it to acquire large datasets and explore how to clean and structure it, tagging it with useful experimental metadata.

Who should attend

Contact details:

This event is supported by the Royal Society of Chemistry through an RSC Sustainability Grant.

The Organising Committee:

Alchemy HubUniversity of StrathclydeUniversity College London (UCL)
Dr Ben Alston (University of Liverpool)
Caroline Woods (University of Liverpool)
Dr. Chris Mellor (Imperial)
Aysel Sarzosa Llerena (Imperial)
Dr Angie MatusovaDr Mike Parkes
Dr Andy Stewart