The challenges in documentation practices also extend to publications, where comprehensive reaction data, including parameters, reagents, quantities, and roles, should ideally be disclosed. However, this information is often buried within the supplementary materials of publications, presented as unstructured text or, occasionally, substrate scope tables. These tables may also appear in the main manuscript of methodology publications but frequently include footnotes highlighting exceptions, further complicating systematic analysis. Furthermore, both types of documents are typically available in the challenging-to-process Portable Document Format (PDF). Consequently, the barrier to accessing complete reaction data sets in a time-and cost-efficient manner remains high . Additionally, data sourced from scientific literature and patents frequently omit details about unsuccessful reaction outcomes. Yet, these negative results are vital for training machine learning models, as they crucially contribute to generating reliable predictions . These challenges are evident in the state of currently accessible public and commercial databases that encompass chemical reactions. Public resources in this domain are notably limited, with examples including the dataset covering chemical reactions from US patents spanning from 1976 to 2016 . There are commercial offerings like Reaxys and SciFinder , but these, too, face constraints in providing comprehensive and well-structured reaction data. Although these databases do contain a considerable number of reactions from scientific literature and patents, they frequently fall short in terms of providing essential information regarding reaction conditions and outcomes. Moreover, there can be a noticeable bias towards including high-yielding reactions, potentially neglecting the valu-Figure : The missing connection between the laboratory and the data science worlds. Information covering chemical reactions is highly complex as it compromises multiple data points leading to a wide range of documentation methods and, often, to unstructured and not machine-readable data (left). As a consequence, it remains difficult to make use of the experimental information for data science applications. This discipline requires structured data including unsuccessful reaction outcomes (right). Importantly, the data must be machine-readable and interoperable with other file types to allow seamless sharing, analysis and utilization of the reaction data. Bridging the gap between the two disciplines therefore remains a challenging task. able insights that can arise from reactions with lower yields or unsuccessful outcomes . A multitude of different file formats, in which this data is stored, further complicates access to and harmonization of reaction data. Among the most common formats are Reaction Data File (RDFile), ChemDraw Extensible Markup Language (CDXML), Reaction International Chemical Identifier (RInChI), Reaction File (RXN-File), JavaScript Object Notation (JSON), and Chemical Markup Language Reaction(CMLReact) . While these formats can effectively store molecular structures and corresponding chemical reaction diagrams, they tend to lack a controlled vocabulary and detailed reaction conditions, such as equivalents. Their usability is often compromised by the specialized technical knowledge required to work with them, which can hinder accessibility and understanding. Hence, there exists a notable gap in achieving findable, accessible, interoperable, and reusable (FAIR) standards for the reporting, collection, and storage of reaction data. Addressing this gap is imperative to facilitate and advance data-driven research in the field of chemistry. .