However, curating such a dataset is non-trivial as old contexts will often outnumber the new and promising ones. It is interesting to analyze if the model benefits at all from historical data, or if such data should be removed from the training. To investigate these hypotheses with our current data set, we created several single-label models to predict contexts for reactions recorded in the last two years (2019-2020). We varied what training set to include reactions recorded in different spans of years. We acknowledge that our exemplified data will present a further unbalanced set of labels (in addition to those outlined in the exploration section above), and that test sets may contain labels underrepresented or absent in training. For the aforementioned single-label model, we re-calculate the top-1 and top-3 accuracies by including only the reactions recorded in 2019 and 2020 (Table ). The accuracy of this reaction subset is on par with the accuracy of entire test set, with top-1 equal to 70% and top-3 equal to 0.89%. We next trained a model on reactions from 2019 to 2020. We observe this model slightly outperforms the model trained on reactions from all years, with top-1 accuracy of 72% and top-3 accuracy of 92%, which reflects that data from the same (short) time periods tend to be more consistent and consequently easier to predict. Next, we trained models without including any reactions from 2019 and 2020 and investigated the predictivity on contexts for those years. Understandably, performance drops when excluding data for those years from modeling, reflecting that historical data are, as expected, not sufficient to predict more recent experiments. Further, a model trained on reactions between 2014 and 2018 had a top-1 accuracy of only 19% and top-3 accuracy of 33%, whilst performance increases for a model trained between 2015-2018, with top-1 accuracy of 32% and top-3 accuracy of 54%. Taken together, this shows that predictions are more accurate when the training is temporally close to the test data.