25gmds106 10.3205/25gmds106 urn:nbn:de:0183-25gmds1064 Meeting Abstract Ketabi Ketabi Ariane A

Department of Medical Informatics, University Medical Center Göttingen (UMG), Göttingen, Germany German Center for Child and Adolescent Health (DZKJ), Partner Site Göttingen, Göttingen, Germany

author Kossen Kossen Robert R

Department of Medical Informatics, University Medical Center Göttingen (UMG), Göttingen, Germany German Center for Child and Adolescent Health (DZKJ), Partner Site Göttingen, Göttingen, Germany

author Smolorz Smolorz Sabine Andrea SA

Department of Medical Informatics, University Medical Center Göttingen (UMG), Göttingen, Germany

author Kuntz Kuntz Alessandra Simone AS

Department of Medical Informatics, University Medical Center Göttingen (UMG), Göttingen, Germany

author Henke Henke Christian C

Department of Medical Informatics, University Medical Center Göttingen (UMG), Göttingen, Germany

author Schilling Schilling Martin M

Department of Medical Informatics, University Medical Center Göttingen (UMG), Göttingen, Germany Cluster of Excellence “Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells” (MBExC), University of Göttingen, Göttingen, Germany

author Henne Henne Timo T

Göttingen State and University Library, University of Göttingen, Göttingen, Germany

author Veentjer Veentjer Ubbo U

Göttingen State and University Library, University of Göttingen, Göttingen, Germany

author Kusch Kusch Harald H

Department of Medical Informatics, University Medical Center Göttingen (UMG), Göttingen, Germany Cluster of Excellence “Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells” (MBExC), University of Göttingen, Göttingen, Germany Campus-Institute Data Science (CIDAS), Göttingen, Germany

author Krefting Krefting Dagmar D

Department of Medical Informatics, University Medical Center Göttingen (UMG), Göttingen, Germany German Center for Child and Adolescent Health (DZKJ), Partner Site Göttingen, Göttingen, Germany Campus-Institute Data Science (CIDAS), Göttingen, Germany

author German Medical Science GMS Publishing House

Düsseldorf

610 research data management data management plan data policy FAIR data 20251103 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0631 106 Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie 70. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS) PS 2: Interoperability and standards Jena 20250907 20250911 Abstr. 209 Deutsche Forschungsgemeinschaft (DFG) Deutsche Forschungsgemeinschaft (DFG) Deutsche Forschungsgemeinschaft (DFG) Deutsche Forschungsgemeinschaft (DFG) Bundesministerium für Bildung und Forschung (BMBF) TextIntroduction: The development of data management plans (DMPs) and data policies (DPs) is becoming an increasingly prevalent requirement for researchers engaged in biomedical projects , , . However, these concepts are often conflated or inconsistently interpreted, resulting in inefficiencies in data management practices and leading to uncertainty among researchers and institutions. We therefore felt that a clearer understanding of these concepts is needed to move beyond their perceived bureaucratic nature and fully utilize their capacity for ensuring long-term research data quality, accessibility, and reusability.Methods: We conducted a cross-disciplinary review of DMP and DP implementations at Göttingen Campus, analyzing their application and requirements across diverse research contexts. This included the examination of DMPs utilized by PhD students at biomedical Göttingen graduate schools, the data management team of the DZKJ consortium, and clinical trial management teams. We identified commonalities, terminological variations, and specific expectations, enabling us to assess the scope, complexity, and overall effectiveness of DMPs in various settings.Results: Our research highlighted significant differences in terminology and application of DMPs across different domains. For example, PhD students are introduced to DMPs as part of their individual research projects primarily for educational purposes, while the DZKJ data management team develops DMPs at a consortium-wide level. Clinical trial management teams primarily use DMPs to comply with specific regulatory requirements. This range from simple documentation of current workflows to comprehensive best-practice frameworks underscores the need to clearly define the purpose and scope of DMPs for each case. Without proper instruction, DMPs are also at risk to merely reflect the current state of data management without offering pathways for improvement, limiting their utility. As an already existing countermeasure, the Göttingen eResearch Alliance provides first-level-support for DMP/DP development and connects activities at Göttingen campus to broader approaches like the RDMO Community and DMP4NFDI .Discussion: Our findings emphasize the importance of balancing the time and effort required to complete DMPs and DPs with the practical benefits they provide. Therefore, it is crucial to clarify the intended objectives and scope of each document before its development, whether it is focused on individual project needs, institutional guidelines, or compliance with funding or regulatory requirements. Providing well-informed, customized guidance is a key determinant at this step. By clearly defining their goals, DMPs and DPs can serve as practical and efficient tools for improving data management, rather than simply fulfilling bureaucratic obligations. Based on our findings, we recommend to implement a flexible, modular approach to DMP design allowing researchers to select relevant components based on their specific demands and contexts, thereby optimizing both the time investment and the overall impact of DMPs.Conclusion: By adopting a modular design and providing precise guidance, we can effectively use DMPs and DPs as dynamic instruments that improve data management and research outcomes while maintaining a favorable cost-benefit-ratio.Acknowledgements: Human resources were mainly funded by DFG project funding [CRC1565 (Project 469281184, Z), Germany’s Excellence Strategy – EXC 2067/1–390729940, NMDR3 – DFG Project 315072261, NFDI4Health – DFG Project 442326535] and by BMBF funding through the DZKJ (01GL2402C).The authors declare that they have no competing interests.The authors declare that an ethics committee vote is not required. Michener WK Ten simple rules for creating a good data management plan 2015 PLoS Comput Biol e1004525 Michener WK. Ten simple rules for creating a good data management plan. PLoS Comput Biol. 2015 Oct 22;11(10):e1004525. DOI: 10.1371/journal.pcbi.1004525 http://dx.doi.org/10.1371/journal.pcbi.1004525 Miksa T Simms S Mietchen D Jones S Ten principles for machine-actionable data management plans 2019 PLoS Comput Biol e1006750 Miksa T, Simms S, Mietchen D, Jones S. Ten principles for machine-actionable data management plans. PLoS Comput Biol. 2019 Mar 28;15(3):e1006750. DOI: 10.1371/journal.pcbi.1006750 http://dx.doi.org/10.1371/journal.pcbi.1006750 Mittal D Mease R Kuner T Flor H Kuner R Andoh J Data management strategy for a collaborative research center 2022 GigaScience giad049 Mittal D, Mease R, Kuner T, Flor H, Kuner R, Andoh J. Data management strategy for a collaborative research center. GigaScience. 2022 Dec 28;12:giad049. DOI: 10.1093/gigascience/giad049 http://dx.doi.org/10.1093/gigascience/giad049 Anders I Enke H Hausen DA Henzen C Jagusch G Lanza G The Research Data Management Organiser (RDMO) - a strong community behind an established software for DMPs and much more 2024 Data Science Journal Anders I, Enke H, Hausen DA, Henzen C, Jagusch G, Lanza G et al. The Research Data Management Organiser (RDMO) - a strong community behind an established software for DMPs and much more. Data Science Journal. 2024 May 8;23(1). DOI: 10.5334/dsj-2024-028 http://dx.doi.org/10.5334/dsj-2024-028 Diederichs K Förstner K Hausen D Jagusch G Johannsen J Lindstädt B 2024 DMP4NFDI - NFDI Basic Service for Data Management Plans Diederichs K, Förstner K, Hausen D, Jagusch G, Johannsen J, Lindstädt B, et al. DMP4NFDI - NFDI Basic Service for Data Management Plans. Zenodo; 2024 Aug 29. DOI: 10.5281/zenodo.13445355 https://doi.org/10.5281/zenodo.13445355 0 0 0 0