While these datasets have enabled rapid progress of ML for yield prediction, there is a need for publicly available datasets more representative of published reaction data or used by chemists in their everyday work. To the best of our knowledge, the most recent works on predicting reaction yields rely on datasets extracted from Reaxys or Sci-Finder n , which are not representative of the whole information contained in published reaction data. The main hurdle to gather a machine readable reaction database is the difficulty to automate data extraction from publications. One of the solution to overcome this issue would be a change toward a numerical data storage in the chemistry community, an option being the use of electronic laboratory notebooks (ELN) interfaced with open-access database . Nevertheless, the implementation of such tools requires significant time and investment. It also requires to convince the chemistry community of the merits of gathering data in a machine-readable format. Thus, we believe that showing the potentiality of a dataset derived from published reaction data to predict reaction yield would encourage chemists to embrace the new technologies available. Such a change would benefit the whole chemistry community.