Institute of Pharmacology, Heidelberg University, 69120 Heidelberg, Germany.
Institute of Physiology and Pathophysiology, Heidelberg University, 69120 Heidelberg, Germany.
Gigascience. 2022 Dec 28;12. doi: 10.1093/gigascience/giad049. Epub 2023 Jul 4.
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.
有效的研究数据管理 (RDM) 策略对于支持可发现、可访问、可互操作和可重复使用 (FAIR) 神经科学数据的生成至关重要，随着数据采集技术和研究方法的每一次进步，这种重要性都在不断增加。为了最大限度地发挥各种研究策略的影响，多学科、大规模的神经科学研究联盟在 RDM 方面面临着许多未解决的挑战。虽然开放科学原则在很大程度上被接受，但研究人员实际上很难将 RDM 置于其他紧迫需求之上。对于跨越动物、人类和临床研究的联盟来说，实施一致的、可执行的 RDM 计划变得越来越具有挑战性。在这里，我们展示了为海德堡合作研究联盟实施的 RDM 策略。我们的联盟结合了不同人群（动物和人类）的基础和临床研究，并产生了高度异质和多模态的研究数据（例如，神经生理学、神经影像学、遗传学、行为学）。我们为大型合作研究联盟提出了一种早期 RDM 和 FAIR 数据生成的具体策略，重点是可持续的解决方案，在尊重研究特定要求的同时，激励逐步进行 RDM。
