Data mining from MOF literature. To create a dataset with MOF synthesis parameters and structural information, we took advantage of the fact that well-curated MOF structural databases already exist (e.g. the Computation-Ready Experimental Metal-Organic Framework database CoREMOF and the Cambridge Structural Database CSD), in which MOF structural information and the corresponding publications with successful synthesis protocols are stored. The manual extraction of synthesis procedures from scientific literature is a time-consuming task, requiring the work of many experts. Alternatively, automatic data extraction to convert experimental procedures into a set of the desired synthesis parameters by employing natural language processing (NLP) techniques is a highly efficient and promising approach that we expect to be continuously improved in the upcoming years. In this study, we automatically extracted information on MOF synthesis for all publicly available MOF structures in the CoRE MOF database (SI Section 2.1). The six relevant parameters that were extracted are metal source, linker(s), solvent(s), additive, synthesis time, and temperature (Fig. ). To achieve this, we initially classified literature paragraphs, employing a decision tree with a string search method, to identify the synthesis paragraph related to each MOF structure (SI Section 2.2). After the synthesis paragraphs were determined, we employed the ChemicalTagger software, which focuses on the experimental part of a scientific text, recognizing significant words within the sentences, and annotating phrases inside the paragraph. In an effort to increase the tagging accuracy, we slightly modified the synthesis paragraphs, accounting for MOF-domain specific descriptions (SI Section 2.3). To evaluate the accuracy of the automatically extracted SynMOF-A database, we additionally generated manually corrected versions -the SynMOF-M and SynMOF-ME databases that are discussed in SI Section 2.4.