While the analysis performed is useful as a means of understanding the ZIF-8 reaction system, a key aim of this study was to systematize the collective synthesis knowledge for the material, thereby connecting synthesis protocols to some key performance indicators either of the synthesis (e.g. yield) or material (e.g. crystal form, surface area). One crucial barrier to this goal was the correlation of material performance data with synthesis protocol information: research papers are inconsistent in reporting of material properties (primarily as different quality metrics are used depending on the motivation of the original research), and the sample naming conventions used within research articles prevent unambiguous linking between the described protocols and materials produced. For example, while a synthesis paragraph might detail the synthesis of "nano-sized ZIF-8," later mentions in the text may be labelled differently e.g. "ZIF-8nano," 79 confounding attempts for automated identification of reaction products using regular expressions. While this issue will undoubtably be resolved by the adoption of transformer-based language models such as BERT and GPT-4, such models became available only recently and the scientific community, including our group, is in the process of probing their extension to scientific data mining. In fact, the current study highlighted a number of issues with the current structure and completeness of reported synthetic protocols, understanding of which will be very helpful in engineering and fine-tuning GPT-based models.