Building a data science workforce to improve the reproducibility of rehabilitation research

项目总结 数据科学方法提供了一个令人兴奋的机会来显著提高重复性和 康复研究的可重复性。然而，实现数据科学方法的一个关键障碍是 康复研究缺乏基本的编程知识。特别是，许多临床和基础 科学家们还没有接受过数据科学所需的编程技能的正式培训。对于此目标 观众，开始使用数据科学的最有效方式是从 经验丰富的导师，可以提供实际帮助，并确定哪些技能是最有益的 一个明确的研究目标。然而，许多康复研究人员无法接触到这些类型的导师。 拟议的计划(可再生康复，或ReproRehab)通过提供 针对康复的个性化数据科学技能实践培训，每周提供直接支持 来自经验丰富的导师。ReproRehab计划将实践援助、个性化帮助 指导，以及一个独特的以康复为重点的自我指导学习在线资源的管理，有三个 明确的目标。目标1是建立一支配备了应用数据的全国康复研究人员队伍 科学技能要对自己的康复进行研究。为了达到这一目标，在五年的拨款中， 100名学员将接受为期6个月的个性化课程，其中包括为期2个月的助教指导、动手操作 训练营，将学员分配到具有相似研究需求的小组，然后进行为期4个月的培训 自主学习环节将技能融入到自己的研究中。到节目结束时，学员们 将展示将这些知识应用到他们自己的研究中，包括但不限于 共享开源康复数据集、开源分析代码或方法，以及更严格的 研究产品。目标2是发展数据科学康复研究人员，他们有能力 教育和培训其他人。为了实现这一目标，在赠款的5年时间里，总共40名助教将完善他们的 通过管理训练营和为学员提供实践培训来传授技能。此外，虽然 学生追求自学，助教将在康复过程中发展和举办自己的训练营 社区，从而实现数据科学的指数级增长和传播的培训者培训模式 技能。目标3是通过创建一个在线知识库来广泛传播知识， 康复研究领域组织的康复专用数据科学资源，包括 节目材料。为了实现这一目标，领导团队将开发一个现有的公共网络数据库 在线数据科学资源，包括培训材料、公共数据档案和来自 这项计划是按具体康复研究领域和需求组织的。成功地完成 我们的目标将增加康复研究社区的数据科学能力，并将 提高推动该领域发展所需研究的严谨性、可重复性和可复制性。

PROJECT SUMMARY Data science methods provide an exciting opportunity to significantly improve the reproducibility and replicability of rehabilitation research. However, a key barrier to implementing data science methods in rehabilitation research is a lack of fundamental programming knowledge. In particular, many clinical and basic scientists have not received formal training in programming skills required for data science. For this target audience, the most efficient way to get started in data science is to receive personalized guidance from an experienced mentor who can provide hands-on assistance and identify which skills are the most beneficial given a specific research goal. However, many rehabilitation researchers do not have access to these types of mentors. The proposed program (Reproducible Rehabilitation, or ReproRehab) addresses this need by providing personalized, rehabilitation-specific, hands-on training in data science skills with direct, weekly support from experienced mentors. The ReproRehab program will blend hands-on assistance, personalized mentorship, and a uniquely rehabilitation-focused curation of online resources for self-guided learning, with three specific aims. Aim 1 is to build a national workforce of rehabilitation researchers equipped to apply data science skills to their own rehabilitation research. To accomplish this Aim, over the 5-year grant a total of 100 learners will undergo a personalized, 6-month program consisting of a 2-month TA-guided, hands-on bootcamp in which learners are assigned to small groups with similar research needs, followed by a 4-month self-guided learning segment to integrate the skills into their own research. By the end of the program, learners will demonstrate the implementation of this knowledge into their own research, including but not limited to the sharing of open-source rehabilitation datasets, open-source analysis code or methods, and more rigorous research products. Aim 2 is to develop data science rehabilitation researchers who have the capacity to teach and train others. To accomplish this Aim, over the grant’s 5 years a total of 40 TAs will refine their teaching skills by administering bootcamps and providing hands-on training to learners. In addition, while the learners pursue self-guided learning, TAs will develop and host their own bootcamps in their rehabilitation communities, thereby fulfilling a train-the-trainer model for exponential growth and dissemination of data science skills. Aim 3 is to broadly disseminate knowledge by creating an online repository of curated, rehabilitation-specific data science resources, organized by rehabilitation research area, including program materials. To accomplish this Aim, the leadership team will develop a public web database of existing online data science resources, including training materials, public data archives, and all course materials from this program, organized by specific rehabilitation research areas and needs. Successful accomplishment of our aims will increase capacity in data science across the rehabilitation research community and will improve the rigor, reproducibility and replicability of research needed to move the field forward.