Despite outstanding performance on many generation tasks, language models are notoriously inclined to make factual errors in tasks requiring arithmetic reasoning. To enable language models to circumvent this deficiency and offload critical computation to a symbolic system, we create a collection of Calc-X datasets that demonstrates the appropriate use of a calculator in reasoning chains. We survey and unify several existing chain-of-thoughts datasets into a proposed novel format, resulting in a standard collection of over 300,000 samples requiring arithmetic reasoning. Finally, we use the new collection to train open-source calculator-assisted language models and show that models trained on Calc-X almost double the accuracy of generating correct results compared to baselines. We make all Calc-X datasets and models publicly available.