The paper is authored by researchers at the Center for Mass Spectrometry and Optical Spectroscopy (CeMOS) at the Mannheim University of Applied Sciences, who identified the need for a new approach to data management after encountering problems at their own institute.
At the CeMOS institute, approximately 80 interdisciplinary scientific staff use a wide variety of hardware and software to collect and process data as part of their work in biotechnology and other fields. According to the paper, the breadth of technologies is “posing a significant challenge for achieving holistic data integration.”
The researchers both develop experimental equipment, including middle infrared scanners for the rapid imaging of biochemical substances in medical tissue sections, and non-customizable equipment such as mass spectrometers, microscopes, and cell imagers. The breadth of technology, and the need to bring all of the outputs together in a reliable research data management system, is at the heart of the challenge.
Ideally, the information will be covered by Findable, Accessible, Interoperable, and Reusable (FAIR) data management approaches that preserve its value to researchers throughout the data lifecycle. Members of the CeMOS team identified digital twins as a way to achieve that goal.
Digital twins are digital representations of real-world physical products and processes. In the context of research data management, the researchers see a digital twin “as a secure data source as it mirrors a physical device, also called physical twin, into the digital world through a bilateral communication stream.”
According to the researchers, digital twins are “a perfectly suitable enabling technology for the central management” of research data and can enable a move away from the existing decentralized and passive approach and its shortcomings.
“By processing research data in the way shown, inconsistencies, accessibility problems, data loss, etc., are no longer issues, also paving the way for more sustainability in research,” wrote the researchers. “Even the reusability of experimental knowledge can dissolve the need of reproducing difficult and energy-consuming experiment setups if still examined and well-labeled data are available.”
However, as the researchers note in the paper, large volumes of data must be collected to fully exploit the data management system, limiting its use to major research institutes. Research sites that generate the required volume of data will need to undertake “a lot of work regarding the integration of measuring devices and experimental setups” to adopt the approach.
2023, 23(1), 468; doi: 10.3390/s23010468
“Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins”
Authors: Joel Lehmann, et al.