Lab-or-a-tol-o-gy: Data’s slime challenge
Outreach means getting down and dirty with spreadsheets, says Matthew Partridge.
There are some outreach topics that come pre-loaded with helpful public engagement features. Outreach about rockets lets you point at the sky. Outreach about dinosaurs lets you bring out a plastic velociraptor and do your best Alan Grant impression. Outreach about explosives lets you remove your eyebrows, to much applause.
The problem with explaining data is it isn’t slimy, smelly, interestingly coloured or explosive. Data comes with a spreadsheet.
This makes developing outreach tricky, which is a shame, as data not only underpins all STEM subjects but is becoming the driving force behind major investments and infrastructure.
It is important that we find ways to explain the value of good data, and the trouble with bad data.
So, not being slimy is a problem for data, which is rarely tangible. It is a set of organised not-things: numbers, labels, units, missing values, file formats and metadata, which is data about data. And don’t get me started on the metadata formetadata. You can print out a giant spreadsheet and explain why HLOOKUP once saved you three hours. But nobody attends a family science day hoping to meet 8,000 rows of solubility data.
The problem is that data is an extra degree of separation from the thing people are engaged by. This data, level one, shows how this material, level two, could solve this problem, level three. The challenge is making data visible without pushing the interesting thing further away
Yet we can do outreach with data, because though complicated, it is not boring. Data is evidence. It’s the difference between “I reckon this will work” and “look, cold fusion”; how we decide if medicines are safe, climate models are improving or sensors reliable.
The problem is that data is an extra degree of separation from the thing people are engaged by. This data, level one, shows how this material, level two, could solve this problem, level three. The challenge is making data visible without pushing the interesting thing further away.
First, it needs to let people do something. Sort objects. Spot patterns. Make a prediction. Compare messy results. Decide what information is missing. Argue about whether two things are really the same. Choose which data they would trust. These actions are much closer to research than showing a finished graph and expecting everyone to applaud the axes.
It also should help make the invisible bits visible. Metadata isn’t “extra admin for people who enjoy forms”. It’s the label on a mystery jar; the difference between “clear liquid” and “clear liquid, do not lick”. Units matter for the same reason. They tell us what has been measured, how it can be compared, and whether two similarlooking numbers are talking about the same thing.
Someone does not need to be told they are learning about reproducibility, responsibility, model training or metadata. They can begin by choosing, testing, spotting a pattern or arguing over whether the result is trustworthy.
Luckily we’ve done all of the above to put together the Future Chemistry Hub! From 16-17 September, the University of Southampton School of Chemistry and Chemical Engineering hosts tons of data-based outreach as part of the British Science Festival 2026. You can get hands-on chemistry, AI, light, sustainability, talks, tours and demonstrations of how data helps turn a good experiment into knowledge we can trust.
There may not be explosions but there will be data and it will be exciting!
- Dr Matthew Partridge is senior enterprise fellow and director of outreach at the School of Chemistry and Chemical Engineering, University of Southampton. He also draws silly cartoons as ErrantScience
