Instrument automation and measurement data curation platform for enhancing research reproducibility and knowledge discovery

"Many applications demand the continued development of sensing systems that employ smart sensors, instrumentation circuits, and signal processing techniques to extract relevant information from real-world environments. In the engineering efforts to develop new sensors, tasks such as instrument automation, measurement process curation, real-time data acquisition, data analysis, and long-term tracking of inter-related datasets generate a significant volume and variety of information that is challenging to organize, record, and analyze. Sensor development and characterization experiments can be laborious, prone to human error, difficult to repeat precisely, and can produce data that are challenging to interpret. Such issues highlight a need for a structured, automated approach to curate measurement processes and data acquisition. This thesis presents the first software platform for i) digitally designing measurement recipes, ii) remotely scheduling and monitoring experiment execution, iii) automatic data acquisition, iv) analyzing and storing results datasets, and v) linking the datasets with their prospective meta-datasets for deeper analysis and inspection. The proposed platform is flexible and capable of managing a large set of diverse instruments, measurement recipes and sensor datasets. By employing several design abstractions, it allows users to remotely design, schedule, monitor and execute measurement-based experiments while archiving results along with their information-rich metadata therefore preserving the provenance of the datasets. The platform enable precise timing control of instruments and stimulus signals along with long-term tracking of datasets eliminating manual errors and human omissions thus enhancing research reproducibility and promoting knowledge discovery methodologies."--Page ii.