biostudies-literatureUnknown35(21)Liu ALPolymer-based semiconductors and organic electronics encapsulate a significant research thrust for informatics-driven materials development. However, device measurements are described by a complex array of design and parameter choices, many of which are sparsely reported. For example, the mobility of a polymer-based organic field-effect transistor (OFET) may vary by several orders of magnitude for a given polymer as a plethora of parameters related to solution processing, interface design/surface treatment, thin-film deposition, postprocessing, and measurement settings have a profound effect on the value of the final measurement. Incomplete contextual, experimental details hamper the availability of reusable data applicable for data-driven optimization, modeling (e.g., machine learning), aChemistry of materials : a publication of the American Chemical Society8816-8826https://www.ebi.ac.uk/biostudies/studies/S-EPMC10653076biostudies-literatureConjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors.PMC10653076Reichmanis EBonsu JAMeredith JCVolkovinsky RGrover MALee MVenkatesh RLiu ALfalseConjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors.Polymer-based semiconductors and organic electronics encapsulate a significant research thrust for informatics-driven materials development. However, device measurements are described by a complex array of design and parameter choices, many of which are sparsely reported. For example, the mobility of a polymer-based organic field-effect transistor (OFET) may vary by several orders of magnitude for a given polymer as a plethora of parameters related to solution processing, interface design/surface treatment, thin-film deposition, postprocessing, and measurement settings have a profound effect on the value of the final measurement. Incomplete contextual, experimental details hamper the availability of reusable data applicable for data-driven optimization, modeling (e.g., machine learning), a2023-01-01T00:00:00Z2023 Nov2025-04-04T09:30:30.177Z2025-04-04T09:30:30.177ZS-EPMC106530763802753810.1021/acs.chemmater.3c01842