Abstract
In the context of research data management (RDM), researchers are confronted with a multitude of new tasks and responsibilities. The totality of all tasks to ensure the re-use of data, long-term archiving, and access to data through data management planning, further data documentation, and provinces of data collection and analysis are described as research data management [1]. Often, the process of RDM is represented with data life cycle models, which include the basic phases of planning, data collection, analysis, archiving, access, and reuse [2].
Reference9 articles.
1. S. Buettner, H.-C. Hobohm, and L. Mueller. "Handbuch Forschungsdatenmanagement, 2011, 978-3-88347-283-6.
2. S. T. Kowalczyk, "Modelling the Research Data Lifecycle." IJDC 12, 2, 2017, pp. 331–361, doi: 10.2218/ijdc.v12i2.429.
3. L. T. M. Blessing and A. Chakrabarti. "Drm, a design research methodology," Springer, Heidelberg, 2014, 978-1-4471-5774-8.
4. D. Iglezakis and B. Schembera, "Anforderungen der Ingenieurwissenschaften an das Forschungsdatenmanagement der Universität Stuttgart - Ergebnisse der Bedarfsanaly-se des Projektes DIPL-ING." o-bib 5, 3, 2018, pp. 46–60, doi: 10.5282/o-bib/2018H3S46-60.
5. M. Kindling, "Qualitätssicherung im Umgang mit digitalen Forschungsdaten / Quality assurance of digital research data / La garantie de la qualité des données numériques de recherche." Information - Wissenschaft & Praxis 64, 2-3, 2013, doi: 10.1515/iwp-2013-0020.