with the data needed. In the meantime, there is currently a lack of public unbiased datasets of chemical reactions with detailed experimental conditions. By releasing NiCOlit, we hope to foster the development of impacting data-driven approaches for yield prediction. Our results highlight the specificities of published reaction data. Comparison with HTE data shows a reporting bias towards high yields. Nonetheless, this bias is weaker in NiCOlit than in its counterpart extracted from proprietary database. Our analysis reveals that it is the extraction of optimization tables that allows the presence of negative examples and limits this reporting bias. In the light of recent results , this could lead to a significant improvement of machine learning models trained on published data. We also highlight a clear experiment selection bias, where reactions are explored along the two orthogonal optimization and scope directions. A benchmark of several state-of-the-art machine learning approaches confirms those observations. Indeed, predictive performance on NiCOlit lies between the one obtained on HTE data and on other available data sources of chemical reactions (proprietary databases or USPTO). We further tested the limitations of machine learning models on out-of-sample prediction tasks. While predictive models cannot currently extrapolate to new reactions or coupling partner categories, we showed that the model is able to generalize on reactions with new substrates. Leveraging this observation, we explain how domain-specific chemistry knowledge can help synthetic chemists select relevant data points to build models in low-data regimes. With only a hundred data points, building predictive models is within reach. We hope that these findings and our open-access database will stimulate the adoption of machine learning for yield predictions in the chemistry community, and encourage systematic data sharing in machine readable formats.