Data management strategy for a collaborative research center

• Verfasst von: Mittal, Deepti [VerfasserIn]
• Verfasst von: Mease, Rebecca A. [VerfasserIn]
• Verfasst von: Kuner, Thomas [VerfasserIn]
• Verfasst von: Flor, Herta [VerfasserIn]
• Verfasst von: Kuner, Rohini [VerfasserIn]
• Verfasst von: Andoh, Jamila [VerfasserIn]
• Titel: Data management strategy for a collaborative research center
• Verf.angabe: Deepti Mittal, Rebecca Mease, Thomas Kuner, Herta Flor, Rohini Kuner and Jamila Andoh
• Jahr: 2023
• Umfang: 25 S.
• Illustrationen: Illustrationen
• Fussnoten: Veröffentlicht: 04. Juli 2023 ; Gesehen am 26.09.2023
• Titel Quelle: Enthalten in: GigaScience
• Ort Quelle: Oxford : Oxford University Press, 2012
• Jahr Quelle: 2023
• Band/Heft Quelle: 12(2023), Artikel-ID giad049, Seite 1-25
• ISSN Quelle: 2047-217X
• DOI: doi:10.1093/gigascience/giad049
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1860302629

Abstract

The importance of effective research data management (RDM) strategies to support the generation of Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience data grows with each advance in data acquisition techniques and research methods. To maximize the impact of diverse research strategies, multidisciplinary, large-scale neuroscience research consortia face a number of unsolved challenges in RDM. While open science principles are largely accepted, it is practically difficult for researchers to prioritize RDM over other pressing demands. The implementation of a coherent, executable RDM plan for consortia spanning animal, human, and clinical studies is becoming increasingly challenging. Here, we present an RDM strategy implemented for the Heidelberg Collaborative Research Consortium. Our consortium combines basic and clinical research in diverse populations (animals and humans) and produces highly heterogeneous and multimodal research data (e.g., neurophysiology, neuroimaging, genetics, behavior). We present a concrete strategy for initiating early-stage RDM and FAIR data generation for large-scale collaborative research consortia, with a focus on sustainable solutions that incentivize incremental RDM while respecting research-specific requirements.