Abstract
Bioprinting is a method to fabricate 3D models that mimic tissue. Future fields of application might be in pharmaceutical or medical context. As the number of applicants might vary between only one patient to manufacturing tissue for high-throughput drug screening, designing a process will necessitate a high degree of flexibility, robustness, as well as comprehensive monitoring. To enable quality by design process optimisation for future application, establishing systematic data storage routines suitable for automated analytical tools is highly desirable as a first step. This manuscript introduces a workflow for process design, documentation within an electronic lab notebook and monitoring to supervise the product quality over time or at different locations. Lab notes, analytical data and corresponding metadata are stored in a systematic hierarchy within the research data infrastructure Kadi4Mat, which allows for continuous, flexible data structuring and access management. To support the experimental and analytical workflow, additional features were implemented to enhance and build upon the functionality provided by Kadi4Mat, including browser-based file previews and a Python tool for the combined filtering and extraction of data. The structured research data management with Kadi4Mat enables retrospective data grouping and usage by process analytical technology tools connecting individual analysis software to machine-readable data exchange formats.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
