General data management workflow to process tabular data in automated and high-throughput heterogeneous catalysis research

Abstract

Data management and processing are crucial steps to implement streamlined and standardized data workflows for automated and high-throughput laboratories. Electronic laboratory notebooks (ELNs) have proven to be effective to manage data in combination with a laboratory information management system (LIMS) to connect data and inventory. However, streamlined data processing does still pose a challenge on an ELN especially with large data. Herein we present a Python library that allows streamlining and automating data management of tabular data generated within a data-driven, automated high-throughput laboratory with a focus on heterogeneous catalysis R&D. This approach speeds up data processing and avoids errors introduced by manual data processing. Through the Python library, raw data from individual instruments related to a project are downloaded from an ELN, merged in a relational database fashion, processed and re-uploaded back to the ELN. Straightforward data merging is especially important, since information stemming from multiple devices needs to be processed together. By providing a configuration file that contains all the data management information, data merging and processing of individual data sources is executed. Having established streamlined data management workflows allows standardization of data handling and contributes to the implementation and use of open research data following Findable, Accessible, Interoperable and Reusable (FAIR) principles in the field of heterogeneous catalysis.