Management Solution for Experimental Data in Photosynthetic Research

Abstract

Experimental research in photosynthetic sciences generates large volumes of heterogeneous data from instruments such as Micro Gas Chromatographs (Micro-GC), Membrane Inlet Mass Spectrometry (MIMS) systems, and Photobioreactors (PBR). These data are currently stored across different locations and formats, making systematic reuse, analysis, and traceability difficult. This thesis designs and justifies a structured, scalable data management system tailored for the Photosynthetic Microbes research group at the University of Turku. The study adopts a Design Science Research (DSR) methodology. A structured literature review of existing research data management systems, including OMERO, SEEK, Dataverse, and OpenBIS, reveals persistent limitations in automation, interoperability, and usability for instrument-driven research settings. Building on these findings, a domain-specific Data Management System (DMS) is designed around a layered architecture: an automated Python-based ingestion layer that collects and normalizes instrument outputs; a relational storage layer hosted on institutional cloud infrastructure; and a web-based visualization and access layer for data browsing, upload, and visualization. The system is evaluated against six functional and six non-functional requirements derived from the operational context of the research group. Key design decisions, including a format-agnostic measurement schema, flexible contextual data storage, automated provenance tracking, and hierarchical data organization, are grounded in established data management principles and FAIR guidelines. The evaluation confirms that the proposed system centralizes heterogeneous experimental data, reduces manual effort, and supports long-term data accessibility and reproducibility. The design principles underlying the system are transferable to other instrumentdriven research settings facing comparable data management challenges.