教学研究数据管理与DataLad:一个多年,多领域的努力.
Michał Szczepanik1, Adina S Wagner2, Stephan Heunis2
1Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany. m.szczepanik@fz-juelich.de.
Neuroinformatics
|May 7, 2024
概括
本研究介绍了在DataLad生态系统中研究数据管理的多模式教学方法. 免费的开源培训材料旨在为早期职业科学家提供基本的数据管理和软件技能.
科学领域:
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 研究数据管理 (RDM) 在现代神经科学中至关重要,但往往不在研究生课程中.
- 社区驱动的举措对于培养早期职业科学家在领域无关的RDM技能方面至关重要.
- 有效的用户文档和社区互动提高了软件开发质量.
研究的目的:
- 详细介绍和评估RDM在DataLad生态系统中的多模式教学方法.
- 为一般的RDM原则和特定软件使用提供可访问的培训材料.
- 通过灵活和开源的教育资源支持多元化的学习者.
主要方法:
- 在DataLad生态系统中开发一个全面的RDM培训计划.
- 创建一个多模式资源,包括在线/印刷手册,模块化课程和知识库.
- 在五年内评估教学方法的有效性.
主要成果:
- 已经成功部署了DataLad生态系统的RDM培训材料.
- 多模式方法满足各种学习偏好和交付格式 (面对面/虚拟).
- 该计划为广泛的利益相关者提供了有价值的RDM和软件培训.
结论:
- 开发的多模式教学方法有效地在DataLad生态系统中传播RDM和软件技能.
- 免费和开源的培训材料对于弥合神经科学家在RDM方面的教育差距至关重要.
- 持续的社区参与和资源开发是推动科学研究中的RDM实践的关键.
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