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

项目摘要 数据科学方法提供了一个令人兴奋的机会，可以显着提高再现性， 康复研究的可复制性。然而，在全球范围内实施数据科学方法的一个关键障碍 康复研究缺乏基本的规划知识。特别是许多临床和基础 科学家没有接受过数据科学所需的编程技能的正式培训。为这一目标 作为一名受众，开始学习数据科学的最有效方式是接受 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， 具体的研究目标。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 个性化的，康复特定的，数据科学技能的实践培训，每周提供直接支持 来自经验丰富的导师。该ReproRehab计划将融合动手援助，个性化 辅导，以及一个独特的康复为重点的在线资源的自我指导学习，有三个 明确的目标。目标1是建立一支能够应用数据的全国康复研究人员队伍 科学技能到他们自己的康复研究。为了实现这一目标，在五年的补助金中， 100名学员将接受为期6个月的个性化课程，包括为期2个月的助教指导， 在训练营中，学习者被分配到具有类似研究需求的小组，然后进行为期4个月的 自我引导学习部分，将技能融入自己的研究。在课程结束时，学习者 将展示这些知识的实施到自己的研究，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是培养数据科学康复研究人员，他们有能力 教导和训练他人。为了实现这一目标，在赠款的5年内，共有40名助教将改进他们的 通过管理训练营和为学习者提供实践培训来教授技能。此外，虽然 学习者追求自我指导学习，助教将在康复过程中开发和举办自己的训练营 社区，从而实现数据科学指数增长和传播的培训师模型 skills.目标3是通过建立一个在线知识库， 按康复研究领域组织的康复专用数据科学资源，包括 程序材料。为了实现这一目标，领导小组将开发一个公共网络数据库， 在线数据科学资源，包括培训材料，公共数据档案，以及来自 该计划由特定的康复研究领域和需求组织。成功完成 我们的目标是提高康复研究界的数据科学能力， 提高推动该领域发展所需的研究的严谨性、再现性和可复制性。

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.