While UDM and ORD represent important steps towards improving the standardization of reaction data for information sharing and machine learning applications, they pose certain challenges in day-to-day laboratory and data science environments: (i) Complexity: The availability of numerous fields and options for data entry may lead to fewer entries and missing data, as laboratory scientists have limited time for documentation. Focus and simplification, within the constraints of chemistry, are essential for capturing as many data points as possible, including unsuccessful reactions. (ii) IT barrier: Although ORD offers the option of entering and searching reaction data through a web interface in addition to programmed input, this still necessitates multiple manual steps in an external environment. UDM provides programmed input only, requiring IT skills or dedicated specialists, which precludes most chemists from using UDM for their reaction data. (iii) Data sharing between disciplines: Efficient exchange of reaction data within and across research groups, departments, or companies can accelerate research. With UDM and ORD, direct sharing of data between scientists in the same discipline, such as chemist to chemist, or across disciplines, such as chemist to machine learning scientist, may be hindered depending on available IT skills and infrastructure, as these formats are not easily human-readable for untrained individuals. Finally, the nested data structure complicates streaming reactions from these file formats.