As a result, most new publications and the entire body of prior chemical synthesis reports remains unlabelled, with the potential for far broader data mining and informatics research if these reports could be standardised. Accordingly, with the advent of text mining methods and natural language processing (NLP), software has been developed to interpret chemical details from the plain text within chemistry publications including compound structure, reaction stoichiometry, and performance. Using these tools, large databases of organic and inorganic chemicals and reactions have been developed and used for novel materials discovery. For example, Cole and co-One weakness of these text mining approaches is their reliance on unambiguous identification of the chemical entities in question, using named-entity recognition (NER) and the programmatic naming conventions defined by IUPAC to succeed. In the absence of such well-accepted naming schemes -as is the case for a variety of emerging nanomaterial families like porous silicas, polymers of intrinsic microporosity, and covalent organic framework materials -large scale data mining becomes far less practical. An excellent example of this is metal-organic framework (MOF) materials -infinite condensation polymers of various organic ligands and metal ions or clusters. There are millions of possible MOFs, and hundreds of thousands of frameworks already synthesized, necessitating data-driven approaches to accelerate progress in the field. However, unambiguous naming conventions for MOFs have yet to be fully adopted, frustrating text-mining of the primary publications themselves. Instead, informatics methods have largely been driven by the creation of a subset of the Cambridge Structural Database (CSD) focused on MOF materials, as these resources allow researchers to analyse the full range of experimentally known MOF structures, identifying the best experimentally-realised materials for future research and development.