Platform for Autonomous Sensor Characterization and Generation of Provenance-Aware Datasets

Sensor characterization can be laborious, prone to human error, difficult to repeat precisely, and can produce data that are challenging to interpret. To address these challenges, a new platform for digitally designing measurement recipes, automating data acquisition, and analyzing resulting datasets is presented. This flexible platform is capable of managing a large set of diverse instruments, measurement recipes and characterization datasets. By employing several design abstractions, the platform allows users to design, schedule and execute sensor characterization experiments while archiving results along with their measurement recipes and preserving the provenance of the datasets. The platform eliminates manual errors and human omissions, and permits reliable repeatability. An electrochemical sensor experiment was performed to validate the platform‘s capability to design and capture a digital record of the measurement recipe, automate real-time data acquisition, and view/analyze results.