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  • This R application is an implementation of state tagging approach for improved quality assurance of environmental data. The application returns state-dependent prediction intervals on input data. The states are determined based on clustering of auxiliary inputs (such as meteorological data) made on the same day. The method provides contextual information to assess the quality of observational data and is applicable to any point-based, daily time series observational data. To use this application, the user will need to input two separate csv files: one for state variables and the other for observations. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this application can be found at https://doi.org/10.5285/1de712d3-081e-4b44-b880-b6a1ebf9fcd8

  • This model code provides an example to demonstrate a new application of the 'learnr' R package to help authors to make elements of their research analysis more readily reproducible to users. It turns a R Markdown document to guided, editable, isolated, executable, and resettable code sandboxes where users can readily experiment with altering the codes exposed Full details about this application can be found at https://doi.org/10.5285/df57b002-2a42-4a7d-854f-870dd867618c

  • This application is an implementation of a Fuzzy changepoint based approach to evaluate how well numerical models capture local scale temporal shifts in environmental time series. A changepoint in a time series represents a change in the statistical properties of the time series (either mean, variance or mean and variance in this case). These can often represent important local events of interest that numerical models should accurately capture. The application detects the locations of changepoints in two time series (typically one representing observations and one representing a model simulation) and estimates uncertainty on the changepoint locations using a bootstrap approach. The changepoint locations and associated confidence intervals are then converted to fuzzy numbers and fuzzy logic is used to evaluate how well the timing of any changepoints agree between the time series. The app returns individual similarity scores for each changepoint with higher scores representing a better performance of the numerical model at capturing local scale temporal changes seen in the observed record. To use this application, the user will upload a csv file containing the two time series to be compared. This work was supported by Engineering and Physical Sciences Research Council (EPSRC) Data Science for the Natural Environment (DSNE) project (EP/R01860X/1) and the Natural Environment Research Council (NERC) as part the UK-SCAPE programme (NE/R016429/1). Full details about this application can be found at https://doi.org/10.5285/49d04d55-90a7-4106-b8fe-2e75aba228e4