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

<p>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&nbsp;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&nbsp;576 time-profiles of microbial current production for viewing the complex landscape of the parameters, namely,&nbsp;the&nbsp;redox mediator concentration and the electrode potential. This dataset contains the following materials:</p> <ul> <li>Current production profiles for&nbsp;<em>Shewanella </em>(Data with average of four data sets for impact of mediators.zip)</li> <li>Summary of the calculated values (statistics summary.csv)</li> <li>Python code for calculating the maximum slope (Slope EF.py)</li> <li>Python codes for Bayesian estimation (2D Bayesian Optimization mapping.py and 2D Bayesian Optimization_slice.py)</li> </ul>