SPP100+ Research Data Management Guideline (RDM Guideline)
Type of the data | Text | |
Total size of the dataset | 115629 | |
Author | Aqlan Ali, Samar Sameer Abdulla | |
Author | Kang, Chongjie | |
Upload date | 2024-12-03T10:19:55Z | |
Publication date | 2024-12-03T10:19:55Z | |
Publication date | 2024-12-03 | |
Abstract of the dataset | Research data management (RDM) establishes a framework for organizing research data, preventing data loss, and facilitating access to research findings. Furthermore, RDM, along with comprehensive documentation of research outcomes, enhances their durability, scientific reliability, and supports their reuse and reproducibility. In essence, RDM greatly enhances the quality and efficiency of research, fosters collaboration among researchers, and significantly boosts the discoverability and citation frequency of research findings. This document represent the research data management strategy in SPP100+ Program and works as a common agreement between subprojects on how to handle research data. | |
Public reference to this page | https://opara.zih.tu-dresden.de/handle/123456789/1115 | |
Public reference to this page | https://doi.org/10.25532/OPARA-686 | |
Publisher | Technische Universität Dresden | |
Licence | Attribution 4.0 International | en |
URI of the licence text | http://creativecommons.org/licenses/by/4.0/ | |
Specification of the discipline(s) | 4 | |
Title of the dataset | SPP100+ Research Data Management Guideline (RDM Guideline) | |
Project abstract | The approach of the SPP is the fact that the condition of a structure - similar to that of humans - is characterized by an increasingly rapid degradation with advancing age. Early, preventive measures against aging are a basic prerequisite for prolonging the usability of complex structures. The goal must be predictive maintenance. This requires fundamental research into the methods of recording, linking and evaluating a wide range of data, e.g. on geometry, material, stress and aging. Digitization, in particular the concept of the digital twin, is taking on a completely new significance in this context. It enables the combination and real-time evaluation of all data required for operation and maintenance and their implementation in condition and prognosis models. In addition, the digital twin can also help to extend the service life of structures by detecting potential damage or deterioration at an early stage and advising on measures for maintenance or repair. This can help reduce the necessity of new construction and thus also minimise resource consumption and environmental impact. | |
Project title | SPP100+ Extending the service life of complex building constructions using intelligent digitization |
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