Tom Gaunt created a Python text mining algorithm and Javascript visualisation to identify the quantity of literature which suggested links between exposure and outcome. Tom approached Research IT to help him develop a web version that would enable users to input their own search terms and generate their own results and visualisations.

Tessa Alexander and Kieren Pitts developed the TeMMPO web-tool - a web-based version of Tom's original TeMMPo python code using Django and D3.js to generate visualisations based on users search terms and data.

TeMMPo enables researchers to identify the quantity of literature suggesting specific mechanisms between an exposure and outcome. The tool counts co-occurrence of mechanistic MeSH (Medical Subject Headings) terms between with exposure terms and outcome terms in a set of articles.

TeMMPo is particularly useful when a specific lifestyle or dietary exposure is known to associate with a disease outcome, but little is known about which underlying mechanisms have been most investigated in the scientific literature.