In catalysis, the accuracy of experimental data is crucial for understanding reaction mechanisms, optimizing conditions, and developing new catalysts. Erroneous data can lead to incorrect conclusions, which can be costly in terms of time and resources. Therefore, data cleaning is an essential step in the data processing pipeline.
