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 to streamline and automate 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 contain all the data management
information, data merging and processing of individual data sources is executed. Having
established streamlined data management workflows allows to standardize 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.
Supplementary materials
Title
Supporting Information
Description
Additional Information and Figures
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Title
Code Library
Description
Python Code Library for Data Management
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Title
Watcher Script
Description
Python Scripts for Automated Data Upload for Selected Instruments
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Title
Tableau Workbook
Description
Workbook Containing Interactive Visualization Figures
Actions