To accelerate development of experimental procedures to make MOFs, however, data-mining approaches must look beyond structure into the synthesis protocols leading to different frameworks. By understanding the relationships between protocol and eventual material, new synthesis methods can be digitally generated, obviating the need for arduous trial-and-error or intuition-based approaches. To this end, large-scale post-hoc analyses of experimental MOF synthesis protocols have recently been developed. These studies apply NLP to the underlying publications in the CSD MOF subset to interpret their synthesis protocols, identifying such details as solvents used, specific reagents, solvents, and reaction parameters. As a result, broad descriptive statistics about the synthesis strategies to produce MOFs have been developed, and even predictive models to suggest synthesis parameters for novel MOF materials when given a hypothetical structure. While these approaches give an excellent overview of the field of MOFs in general, they are vulnerable to bias in the papers submitting to the CSD. As the database focuses of chemical structure rather than synthesis protocols, only 1-2 synthesis examples of each framework are included. Further, the synthesis protocols are generally submitted from initial studies reporting the discovery of a material, rather than exploring the full range of potential approaches to a single target, meaning that only a very vague understanding of any individual MOF can be generated with this approach. For example, while candidate solvents and reaction parameters can be suggested, other salient parameters such as reagent ratios, product isolation methods, and alternative synthesis strategies (e.g. hydrothermal or mechanochemical versus solvent crystallisation) cannot. Deeper insight into individual MOFs and the peculiarities of their synthesis protocols can be gained through targeted meta-analysis of studies focusing on that particular material, enabling regression of product properties like defect density against synthesis details. However, challenges of manually comparing synthesis protocols against one another severely limit the scale of such meta-analyses, preventing their widespread use.