Soft materials with built-in sensing capabilities have become increasingly important for applications in wearable technology, healthcare, and robotics. These materials can detect various stimuli, such as pressure, strain, temperature, and humidity, much like human skin. However, developing effective soft sensors was challenging because their mechanical and sensing properties were closely linked, making simultaneous optimization difficult. Additionally, analyzing and validating these sensors required large datasets, as their responses could be non-linear or subject to drift. Machine learning techniques were often used to enhance data analysis and ensure accuracy. During the project, we contributed hardware and software for generating sensor data in an ORD-friendly data format. These processes and data became available by the end of the project. Specifically, we contributed the following: (C1) Open-source hardware and technical documentation that allowed for readout and was easy and rapid to assemble, even for non-engineering specialists, along with open-source software for low-level hardware control. (C2) Software and scripts for generating datasets using the hardware in a FAIR-compliant data format, complete with necessary identifiers and metadata, which could be uploaded to a suggested ORD platform. (C3) Dissemination of the contributed hardware and software through existing workshops on openly available hardware and software at key conferences and events for the community. Addressing (C2) was particularly important to ensure that the data format was scalable, contained the necessary metadata, and included information relevant to the broader community. The scientific knowledge gap we addressed was enabling a comparison of different sensor types under the same characterization and testing regimes and analyzing how this affected reconstruction performance when coupled with learning or data-driven algorithms. By making this ORD practice widely accessible through the repurposing of widely available printers, we facilitated broader comparisons of different sensor technologies, helping to identify the most promising future directions.