RADAR: Building a FAIR and Community Tailored Research Data Repository
DOI:
https://doi.org/10.52825/cordi.v1i.295Keywords:
RDM, chemistry, culture, infrastructureAbstract
The research data repository RADAR is designed to support the secure management, archiving, publication and dissemination of digital research data from completed scientific studies and projects. Developed as a collaborative project funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (2013-2016), the system is operated by FIZ Karlsruhe - Leibniz Institute for Information Infrastructure - and currently serves as a generic cloud service for about 20 universities and non-university research institutions. Since its launch, RADAR has witnessed significant changes in the landscape of research data repositories and the evolving needs of researchers, research communities and institutions. In our presentation within the “Enabling RDM” Track, we will show how RADAR is responding to these dynamic changes. In order to create a sufficiently large user base for the sustainable operation of the system, we have moved RADAR away from its previous single focus on a discipline-agnostic cloud service and towards a demand-driven functional optimisation. In 2021, we introduced an additional operating model for institutions (RADAR Local), where we operate a separate RADAR instance locally at the institution site exclusively using the institutional IT-infrastructure. In 2022 we opened up RADAR to new target groups with community-specific service offerings, in particular in the context of the National Research Data Infrastructure (NFDI). Beside the expansion of the functional scope, our ongoing development work focuses also on strengthening the system's support for the FAIR principles [1] and the concepts of FAIR Digital Objects (FDO) [2] and Schema.org. Our presentation will outline recent RADAR developments and achievements as well as future plans thus providing solutions and synergy potential for the scientific community and for other service providers.
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Wilkinson M., Dumontier M., Aalbersberg I.,The FAIR Guiding Principles for scientific data management and stewardship (2016). Sci Data 3: 160018. https://doi.org/10.1038/sdata.2016.18
Schultes, E., Wittenburg, P. FAIR Principles and Digital Objects: Accelerating Con-vergence on a Data Infrastructure. In: Manolopoulos, Y and Stupnikov, S (eds.), Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2018. (2018) Communications in Computer and Information Science, 1003. Cham: Springer. DOI: https://doi.org/10.1007/978-3-030-23584-0
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Copyright (c) 2023 Felix Bach, Kerstin Soltau, Sandra Göller, Christian Bonatto Minella, Stefan Hofmann
This work is licensed under a Creative Commons Attribution 4.0 International License.
Accepted 2023-06-29
Published 2023-09-07