Bayesian Estimation on Microbial Electrochemical Time Profiles obtained by a High-Throughput Bioelectrochemical Device

Bioelectrochemical systems (BESs) are attracting much attention, but the mechanisms are not yet fully clarified. One of the issues for the BESs research is the lack of a high-quality database that is developed through well-controlled experiments with a high-throughput data-collecting system. Here, we developed a new high-throughput potentiostat with 96 well plates with silk-screen-printed electrodes. With this potentiostat, we obtained 576 time-profiles of microbial current production for viewing the complex landscape of the parameters, namely, the redox mediator concentration and the electrode potential. This dataset contains the following materials: Current production profiles for <em>Shewanella </em>(Data with average of four data sets for impact of mediators.zip) Summary of the calculated values (statistics summary.csv) Python code for calculating the maximum slope (Slope EF.py) Python codes for Bayesian estimation (2D Bayesian Optimization mapping.py and 2D Bayesian Optimization_slice.py)