Research software

An R package for assisting calibration and visualising outputs of DYRESM-CAEDYM.

In the dycdtools package, there are two main function categories: calibration assistant and post-processing.

The calibration assistant function (calib.assist) carries out simulations with a large number of possible combinations of parameter values that users regard as potentially suitable for their model calibration, and calculates the values of nominated objective functions (i.e., statistical measures of goodness of fit) for each combination. Based on the calculated objective function values, users can determine the optimal set(s) of parameter values or narrow the ranges of possible parameter values.

Four post-processing functions provide multiple ways to visualise DYRESM-CAEDYM outputs as follows:

Function plot_cont displays a heat map of variable values with depth within the water column and over time. This visualisation is particularly suitable for displaying temporal and depth dynamics of a variable at one lake site. Function plot_prof shows vertical profiles of the simulation and corresponding observations, for all dates where observations are available. Function plot_ts plots simulated values and observations for a specified variable and depth over time. It can be used to compare temporal changes of a variable for simulations and observations at specific depths. Function plot_scatter shows observations against simulated values for corresponding time and depth, with a colour scale representing measured depths. It can be used to demonstrate visually the goodness of fit for a variable across the water column.

Pollination provided by honey bees is crucial for global agricultural production yet bee populations are declining. To investigate potential stressors to honey bee health, the Swarm Sensing Project is developing miniature sensing devices and mounting them on large numbers of bees, in order to provide detailed information on their behaviour. An agent-based computational model has been developed to simulate insect flight behaviours under different environmental conditions. The model is currently generating synthetic data to support the development of analysis and visualisation techniques for the project. Subsequently, it will process field data from the micro-sensors to characterise honey bee flight behaviour and to understand changes in their behaviour once exposed to stressors. This model provides a comprehensive platform for the simulation, representation and analysis of insect flight behaviour. Initial results have been validated using radio-frequency identification tags attached to foraging worker bees.