A new app uses data science methods to improve quality assurance and provides insights to environmental monitoring data.


Update: Michael Tso and colleagues work is now published in a journal paper. Additionally, Michael will present his work during a live text-based chat session at the annual EGU event (now moved online). This will take place on the morning of Friday 8th May.


Working with long-term physical, chemical and biological data from the Environmental Change Network, and also the Cumbrian Lakes monitoring scheme, Michael Tso and the team at UKCEH Lancaster working on the UK-SCAPE Data Science Framework have developed a “System State Tagging App”.

The long-term monitoring data from co-located measurements at ECN sites gives us perfect examples to showcase its utility - Michael Tso, UKCEH

The App estimates multivariate ecosystem states, characterised by a user-defined range of ecological drivers and responses, via clustering so that underlying temporal patterns in ecosystem trajectories can be visualised and used as a baseline against which to identify extreme and anomalous observations.

Michael comments: “The App aims to provide insights for users to understand the variability in the data. The long-term monitoring data from co-located measurements at ECN sites gives us perfect examples to showcase its utility“.

Each observed value is associated with a state, such that it can be interpreted based on the conditions characteristic of that particular state. Following substantial development using the ECN data, demonstration versions of the App are available publicly while efforts to extend its applicability are ongoing.

Screenshot of the state tagging app

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