NiCOlit was manually extracted from published reaction data cited in a recent review from Diao and co-workers . In this review, the authors focus on the activation of carbon-oxygen bonds of phenol derivatives with nickel catalysts for coupling reactions. In order to reduce the size and the diversity of the dataset we arbitrarily restrained the study to challenging electrophiles towards the oxidative addition: sulfonates , phosphates and in situ activated phenols were left aside. The reactions displayed in both the main articles and their SI were extracted. For each reaction, the Simplified Molecular-Input Line-Entry System (SMILES) chains of substrates, coupling partners, precursors, ligands, bases, additives, solvents, and products were gathered as well as experimental parameters: reaction time, temperature and molar ratios of the different partners (see SI Section 2). This highlighted issues when harmonizing different data sources, such as disparities in yield measurement techniques, or information being reported in prose rather than machine-readable format. The resulting database is unique, it gathers reactions from 45 publications within the chemical space illustrated Fig 1A . The different types of substrates, coupling partners and ligands reported in this dataset are summarized in Fig 1B . The coverage of the substrate-coupling partners combinations is sparse and strongly biased toward a few reactions such as the Kumada coupling (RMgX + ArOCH 3 ), which is the most investigated with 243 reported reactions. This data-driven analysis 21 of nickel-catalyzed couplings shows that published reaction data seems to focus on specific combinations. We were surprised to see that reactions were clustered not by reaction parameters (e.g coupling partner) but almost perfectly by publications (Fig 1C). This minimal overlap between publications may be attributed to the poor reporting of reproduced experiments. Inherent differences in terms of chemical diversity and yield distributions between NiCOlit and HTE data were laid out. This allows further understanding of how performances displayed by ML on HTE could translate to NiCOlit.