laboratory informatics ➤
l improve the quality and use of data, ensuing we are an evidence-based organisation making maximum use of data collected and supplied to us;
l improve and automate generation of reporting for internal and external audiences. To achieve this, we developed and
employed a highly collaborative, Agile- driven development method that kept us focused on our business needs while ensuring close collaboration between the data scientists in ESIU -- who are data experts and can build these analysis and modelling tools -- and the end-users and
experts, whether they be fellow scientists, business planners, or managers.
Accessibility for all By making these informatics tools available through interactive web pages, we removed the need for specialised soſtware. Tis means that any member of staff, or even members of the public, can access the datasets and take advantage of the interactive tools to perform analyses and research. We configured the vast majority of
tools to open instantly, by preloading the necessary data overnight for each tool. Tis
➤ Some Scottish ecosystems are fragile and unique Case Study 2: Data analysis and visualisation of the environment
SEPA has collected millions of chemistry and ecology samples and supporting data, dating back to the 1960s. Our scientists need a way of being able to analyse and interrogate this sample data, that we invest considerable time and effort in collecting, analysing and storing. The existing software was unable to meet this need, so we developed a Spotfire-driven informatics tool called Data Analysis and Visualisation of the Environment (DAVE). This tool allows anyone to retrieve the data recorded for any site and samples rapidly; then be able to analyse these as time-series charts; perform statistical analysis such as outlier checking, trends analysis, and step change analysis. It also allows the user to perform other tasks, such as testing distributions -- e.g. do the samples follow a normal or log normal distribution -- as well as assessing whether there is seasonality present in the data, and calculating science specific metrics, such as ecological metrics. All these analyses, data, and charts can be
exported as graphs or as data into Excel. Through various filters such as catchment, which team is responsible for that area, river name etc., users are able to search for any location in Scotland where a sample has been taken, or they can browse a map of our sampling network and simply select the sampling locations of interest to analyse. This means users can now analyse samples down an entire river to understand the changing conditions and environmental pressures on that river. This tool has become the most used informatics tool in SEPA. We receive regular feedback on the positive impact of DAVE. For instance, even the simple task of extracting data for data retrieval requests that we receive, now takes a matter of minutes and saves chemists’ and scientists’ time. This tool puts the entirety of Scotland’s sampling at any scientist’s fingertips, allowing scientists to concentrate on research and science, rather than battling with unsuitable and difficult tools previously used to retrieve, manipulate and get data into a usable form.
12 SCIENTIFIC COMPUTING WORLD @scwmagazine l
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