ABCD Course on Reproducible Data Analyses

项目摘要/摘要 目前的提案代表了一种协作伙伴关系，以开发关于可复制数据的课程ABCD 分析。ReproNim：可复制神经成像计算中心，是NIBIB资助的P41生物医学 技术资源中心(BTRC)，其愿景是帮助神经成像研究人员取得更多成就 可重现的数据分析工作流程和结果。ReproNim方法依赖于技术上的 开发易于访问、用户友好的计算工具和服务，这些工具和服务可以方便地集成 目前的研究实践和广泛的教育推广关于神经成像的重复性 社区，包括基础科学和临床领域的开发人员和应用研究人员 纪律。为了实现这一目标，我们提出的目标包括：提高教学技巧的严谨性和 研究方法的重现性；ABCD数据集的全面背景；实施 包容性培训模式；支持跨学科、基于团队的协作；以及传播课程 和项目材料。学生将首先接受为期13周的在线课程的教学指导，其中包括 讲课、阅读和数据练习。完成在线课程后，学生将参加为期5天的- 个人项目周，在那里他们将应用所学到的技能，努力完成数据项目活动， 并学会为开源软件做出贡献。为了完成这个项目的目标，我们已经组建了一个 由讲师和评估员组成的跨学科团队，其中包括JONG研究调查员、ReproNim团队 成员和合作者，以及非ABCD/ReproNim研究人员。成功将导致一代又一代 在支持高效、可重新执行的设计和公平的方式方面接受过良好培训的调查人员队伍 实践，使用ABCD(和其他)数据资源。

PROJECT SUMMARY / ABSTRACT The current proposal represents a collaborative partnership to develop a course on reproducible data ABCD analyses. ReproNim: A Center for Reproducible Neuroimaging Computation, is a NIBIB-funded P41 Biomedical Technology Resource Center (BTRC) whose vision is to help neuroimaging researchers achieve more reproducible data analysis workflows and outcomes. The ReproNim approach relies on both technical development of readily accessible, user-friendly computational tools and services that can be readily integrated into current research practices and broad educational outreach about reproducibility to the neuroimaging community at large, including developers as well as applied researchers across basic sciences and clinical disciplines. To achieve this, we propose aims that include: instruction techniques that enhance the rigor and reproducibility of research methods; a comprehensive background to the ABCD dataset; implementation of an inclusive training model; support of interdisciplinary, team-based collaborations; and dissemination of the course and project materials. Students will first receive didactic instruction across a 13-week online course that includes lectures, readings, and data exercises. At the completion of the online course, students will attend a 5-day in- person Project Week, where they will apply the skills learned, work towards completion of data project activities, and learn to contribute to open source software. To complete the aims of this program, we have assembled an interdisciplinary team of instructors and evaluators that includes ABCD Study Investigators, ReproNim team members and collaborators, and non-ABCD/ReproNim researchers. Success will result in the generation of a cadre of investigators that are well trained in the ways that support efficient, re-executable design and FAIR practices, use of the ABCD (and other) data resources.