Abstract
In engineering research, various sensors and data sources are utilized for data acquisition. Together with subsequent processing and analysis, a large, heterogeneous amount of data and information is generated. To structure data and information within the research project, intelligent tools for research data management (RDM) are required. However, existing tools for RDM focus on data management during a research project and lack of capabilities to describe a technical system over this life cycle in multiple projects. Thus this paper addresses the potential of the Digital Twin (DT) concept for RDM. Based on requirements from RDM we derive specifications for a DT concept in RDM and introduce three criteria to choose suitable technical systems for DT. We present a DT for a research vehicle, implemented within the open-source Industry 4.0 DT tool “AASX Package Explorer”, to show the benefits of using a DT for RDM.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Altun, O., et al.: Integration eines digitalen maschinenparks in ein forschungsdatenmanagementsystem. In: Proceedings of the 32nd Symposium Design for X (2021)
Amorim, R.C., Castro, J.A., Rocha da Silva, J., Ribeiro, C.: A comparison of research data management platforms: architecture, flexible metadata and interoperability. Univ. Access Inf. Soc. 16(4), 851–862 (2016). https://doi.org/10.1007/s10209-016-0475-y
DataCite Metadata Working Group: Datacite metadata schema documentation for the publication and citation of research data and other research outputs v4.4 (2021)
Errandonea, I., Beltrán, S., Arrizabalaga, S.: Digital twin for maintenance: a literature review. Comput. Ind. 123, 103316 (2020)
European Research Council: Guidelines on implementation of open access to scientific publications and research data (2019)
German Research Foundation: Allgemeine Bedingungen für Förderverträge mit der Deutschen Forschungsgemeinschaft e.V. (DFG) (2015)
German Research Foundation: Leitfaden für die Antragstellung 54, 01 (2022)
Glaessgen, E., Stargel, D.: The digital twin paradigm for future NASA and u.s. air force vehicles. In: 53rd Structures, Structural Dynamics and Materials Conference. American Institute of Aeronautics and Astronautics (2012)
Grieves, M.: Digital twin: Manufacturing excellence through virtual factory replication (2014)
Grieves, M., Vickers, J.: Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In: Transdisciplinary Perspectives on Complex Systems, pp. 85–113 (2016)
Jacoby, M., Usländer, T.: Digital twin and internet of things—current standards landscape. Appl. Sci. 10(18), 6519 (2020)
Kannoth, S., Hermann, J., Damm, M., Rübel, P., Rusin, D., Jacobi, M., Mittelsdorf, B., Kuhn, T., Antonino, P.O.: Enabling SMEs to industry 4.0 using the BaSyx middleware: A case study. In: Software Architecture, pp. 277–294 (2021)
Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W.: Digital twin in manufacturing: A categorical literature review and classification. In: 16th IFAC Symposium on Information Control Problems in Manufacturing, vol. 51(11), pp. 1016–1022 (2018)
Mozgova, I., Koepler, O., Kraft, A., Lachmayer, R., Auer, S.: Research data management system for a large collaborative project. In: Proceedings of NordDesign (2020)
Qi, Q., et al.: Enabling technologies and tools for digital twin. J. Manuf. Syst. 58, 3–21 (2021)
Ruohomaki, T., Airaksinen, E., Huuska, P., Kesaniemi, O., Martikka, M., Suomisto, J.: Smart city platform enabling digital twin. In: 2018 International Conference on Intelligent Systems (IS) (2018)
Sangat, P., Indrawan-Santiago, M., Taniar, D.: Sensor data management in the cloud: data storage, data ingestion, and data retrieval. Concurrency Comput. Pract. Exp. 30(1), 1–10 (2017)
Scheidel, W., Mozgova, I., Lachmayer, R.: Structuring information in technical inheritance with pdm systems. In: Proceedings of the 21st International Conference on Engineering Design (ICED 2017), vol. 6, pp. 217–226 (2017)
Schmitt, R.H., Anthofer, V., Auer, S., Başkaya, S., Bischof, C., et al.: Nfdi4ing - the national research data infrastructure for engineering sciences
Schroeder, G.N., Steinmetz, C., Pereira, C.E., Espindola, D.B.: Digital twin data modeling with AutomationML and a communication methodology for data exchange. In: 4th IFAC Symposium on Telematics Applications, vol. 49, pp. 12–17 (2016)
Sheveleva, T., Koepler, O., Mozgova, I., Lachmayer, R., Auer, S.: Development of a domain-specific ontology to support research data management for the tailored forming technology. In: Proceedings of the 5th International Conference on System-Integrated Intelligence, vol. 52, pp. 107–112
Stark, R., Damerau, T.: Digital twin. In: CIRP Encyclopedia of Production Engineering, pp. 1–8 (2019)
Ulrich, T.: Datamanagement for production companies. In: PLM-Jahrbuch 2016 - Der Leitfaden für den PLM Markt, pp. 60–73
Wang, Z., Gupta, R., Han, K., Wang, H., Ganlath, A., Ammar, N., Tiwari, P.: Mobility digital twin: Concept, architecture, case study, and future challenges. IEEE Internet Things J. 1–1 (2022)
Wilkinson, M.D., et al.: The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3(1), 160018 (2016)
Acknowledgement
The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Dierend, H., Altun, O., Mozgova, I., Lachmayer, R. (2023). Management of Research Field Data Within the Concept of Digital Twin. In: Valle, M., et al. Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-16281-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-16281-7_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16280-0
Online ISBN: 978-3-031-16281-7
eBook Packages: EngineeringEngineering (R0)