Experimental database design and management for polymer electronics however is nontrivial, especially when a plurality of the relevant attributes related to the provenance of the sample must be included accurately to form a robust, "reusable" data record. Data ontologies are not standardized in the organic electronics space: fully capturing all relevant experimental information is challenging, and organic device performance is highly sensitive to the many parameters associated with the active layer deposition process. For example, the charge-carrier mobility (μ), a key figure of merit for OFETs, has been shown to vary significantly for poly(3hexylthiophene) (P3HT) (~10 -6 -10 0 cm 2 /V•s) cm 2 /V•s). This performance variation is not only attributed to batch-to-batch characteristics of the polymer, but also a plethora of parameters related to the polymer's process history, starting with the solution state through the thin-film deposition process. Another source of variation is that mobility values are derived from device measurements via model fitting, and employing different methods/parameters (i.e., models, measurement settings, voltage limits) may affect the extracted mobility value. Recording processing and measurement parameters provides indispensable contextual value to organic device data, but nonetheless recording all of them efficiently is not straightforward. Additionally, since the design space is inherently dynamic due to the evolving nature of research, data models must be designed with flexibility in mind without sacrificing consistent vocabulary. Thus, generating a representative data ontology describing the experimental device realm is a challenge that must be addressed to enable reliable database designs. Though process representations are not new problems for the sake of curating materials databases, navigating these challenges for experimental polymer domains has only been explored recently by a minority of materials data researchers. The experimental database effort in MaterialsMine promotes the inclusion of processing terms for polymer nanocomposites, while the Community Resource for Innovation in Polymer Technology (CRIPT) proposes a framework to comprehensively describe polymer data, seeking to unify all aspects of sample provenance from synthesis, processing, characterization, properties, and instrumentation/citation metadata. These active endeavors open the door for a broader adoption of polymer-based data management solutions, but it is up to various communities to enable tailored data models for their specific subdomains. In this work, we use OFETs as a model system to propose an experimental data ontology associated with semiconducting polymer processing. We then produced a data structure that focuses on the deposition of the active semiconducting polymer layer and leveraged it to implement an experimental repository relating the semiconducting polymer process history to device performance. To guide a robust representation of that process history, this work draws upon ISA-88, an international standard for automation in batch process control, to construct generalizable relationships across process transformations within the fabrication procedure to create the semiconducting thin film. Building a data repository that can handle the many nuances of this complex design space is expected to provide a platform to enhance hypothesis design, scientific decision-making, and model development within the traditionally "small data" space of the organic electronics community.