RIDE is a method for decomposing and reconstructing ERP and analyzing the trial-to-trial variability information of ERP. It was initiated and developed by Guang Ouyang, Changsong Zhou and Werner Sommer in 2011. The main utility of RIDE is to overcome the limitation of conventional stimulus-locked average ERP that the waveform is blurred by trial-to-trial latency variability. This limitation leads to many problems such as distortion of ERP waveform and attenuation of ERP effects, and so on.

The web-based application for the paper, "Validity of Markovian modeling for transient memory-dependent epidemic dynamics", is a tool designed to rectify R0 estimation and epidemic forecasting within the Markovian modeling context. It also provides insights into how the infection, removal, and generation time distributions, along with their average times, are influenced by changes in distribution parameters.