使用 DataLad 教授研究数据管理:一项多年期、多领域的努力。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2024-10-01 Epub Date: 2024-05-07 DOI:10.1007/s12021-024-09665-7
Michał Szczepanik, Adina S Wagner, Stephan Heunis, Laura K Waite, Simon B Eickhoff, Michael Hanke
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引用次数: 0

摘要

研究数据管理已成为现代神经科学不可或缺的技能。研究人员可以从遵循良好实践和熟练使用特定软件解决方案中获益。但是,由于这些与领域无关的技能通常不包括在特定领域的研究生教育中,因此社区的努力越来越多地为早期职业科学家提供有组织的培训机会和自学材料。在用户文档和与用户群互动方面投入精力,反过来也能帮助开发人员提高软件质量。在这项工作中,我们详细介绍并评估了 DataLad 生态系统中研究数据管理的多模式教学方法,包括一般教学方法和具体的软件使用方法。在过去的五年中,我们的免费开源培训材料集为不同的利益相关者提供了研究数据管理和软件培训,其中包括在线和印刷手册、适合现场和虚拟教学的模块化课程以及知识库中灵活的研究数据管理技巧。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort.

Research data management has become an indispensable skill in modern neuroscience. Researchers can benefit from following good practices as well as from having proficiency in using particular software solutions. But as these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organised training and materials for self-study. Investing effort in user documentation and interacting with the user base can, in turn, help developers improve quality of their software. In this work, we detail and evaluate our multi-modal teaching approach to research data management in the DataLad ecosystem, both in general and with concrete software use. Spanning an online and printed handbook, a modular course suitable for in-person and virtual teaching, and a flexible collection of research data management tips in a knowledge base, our free and open source collection of training material has made research data management and software training available to various different stakeholders over the past five years.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
期刊最新文献
Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort. Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy. Utilizing fMRI to Guide TMS Targets: the Reliability and Sensitivity of fMRI Metrics at 3 T and 1.5 T. Bayesian Tensor Modeling for Image-based Classification of Alzheimer's Disease. A Bayesian Multiplex Graph Classifier of Functional Brain Connectivity Across Diverse Tasks of Cognitive Control.
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