Education Pathways for Biomedical Data Science (R25)

生物医学数据科学教育途径 (R25)

• 批准号: 10199482
• 负责人: Jeffrey Wayland Pennington
• 金额: $ 8.57万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10199482
• 关键词: Academic Training; Attitude; Biomedical Research; Certification; Clinical Research; Cloud Computing; Code; Collaborations; Communities; Community of Practice; Computational algorithm; Custom; Data; Data Science; Data Set; Decision Making; Degree program; Development; Discipline; Ecosystem; Education; Educational Curriculum; Effectiveness; Enrollment; Ensure; Feedback; Fellowship; Foundations; Goals; Grouping; Home; Individual; Informatics; Information Sciences; Instruction; Knowledge; Learning; Learning Module; Literature; Machine Learning; Medical Education; Mentorship; Methodology; Methods; Modeling; Motivation; Outcome; Participant; Pathway interactions; Pediatric Hospitals; Peer Review; Philadelphia; Physicians; Plant Roots; Postdoctoral Fellow; Recruitment Activity; Reproducibility; Research; Research Institute; Research Personnel; Resources; Scholarship; Science; Strategic Planning; System; Training; Training Programs; Translating; United States National Institutes of Health; Universities; Work; acronyms; base; biomedical data science; biomedical informatics; career; cohort; college; computer science; education pathway; education resources; effective intervention; evidence base; experience; formative assessment; graduate student; insight; instrument; interdisciplinary collaboration; interest; literacy; novel; online course; outreach program; pedagogy; programs; science education; scientific literacy; skills; statistics; student training; success; user centered design

Project Summary / Abstract This project brings together Drexel University and the Children’s Hospital of Philadelphia (CHOP) to create an adaptive Biomedical Data Science education program to empower researchers to learn and use emerging data science methods. We propose an in-line program to prepare researchers for data-driven work directly in their current field, while also identifying avenues for interdisciplinary collaboration. We will develop novel curricula pathways, leverage existing educational resources, create bridge materials, and provide practicum “lab” activities matching domain-specific projects to participants for hands-on experience. Educational Pathways in Biomedical Data Science will collaborate with the CHOP Office of Academic Training and Outreach Programs to engage a diverse learner audience in novel pedagogical research. Learners will be recruited from active participants in many existing CHOP training initiatives, including a novel data science education program, graduate student training, postdoc mentorship, physician fellowship research, and clinical research staff training. After enrollment, education program managers will cluster participants into collaborative communities of practice where they will receive mentorship and contribute to development of pathways. We acknowledge that we are still learning the most effective interventions for biomedical data science education, both within our specific communities and broadly within medical education (e.g. Federer et al., 2015; Rowhani-Faarid, Allen, and Barnett, 2017). We will develop new evidence of gaps in knowledge, skills, and attitudes among learners. We will develop and implement biomedical data science literacy instruments based on emerging scholarship. We will also gather learner feedback via mixed methods to adapt and evolve our modular resources, ensure robust learner outcomes, and align deliverables with the NIH Strategic Plan for Data Science. Data Science instruction for researchers outside of traditional computer and information sciences is a concrete step toward data-driven scientific literacy for all. We will ensure that participants emerge from this program with computational and algorithmic literacy solidified through hands-on experience. For non-computing researchers, we also will provide the foundational data fluencies necessary for individuals to contribute meaningfully to machine learning research, which will enable data-driven systems, insight-to-decision transformation, decision- making, and data-driven decision management. Finally, for all participants, we will strive to help individuals from a broad spectrum of backgrounds and identities identify new directions in which to develop their careers.

项目摘要/摘要 该项目将德雷塞尔大学和费城儿童医院(CHOP)结合在一起，创建了一个 自适应生物医学数据科学教育计划，使研究人员能够学习和使用新兴数据 科学的方法。我们提出了一个内联计划，让研究人员为数据驱动的工作做好准备 在确定跨学科合作途径的同时，对当前的领域进行了评估。我们将开发新的课程 途径，利用现有的教育资源，创造桥梁材料，并提供实践“实验室” 将特定领域的项目与参与者相匹配的活动，以获得实践经验。 生物医学数据科学的教育途径将与学术培训印章办公室合作 以及外联计划，以吸引不同的学习者受众参与新颖的教学研究。学员将成为 从许多现有CHOP培训计划的积极参与者中招聘，包括一项新的数据科学 教育计划、研究生培训、博士后指导、医生奖学金研究和临床 研究人员培训。注册后，教育项目经理将参与者分组为协作型 在实践社区中，他们将接受指导，并为发展道路做出贡献。 我们承认，我们仍在学习生物医学数据科学最有效的干预措施 教育，既在我们特定的社区内，也在医学教育中(例如，费德勒等人， 2015年；鲁哈尼-法里德、艾伦和巴尼特，2017年)。我们将开发新的证据，证明在知识、技能、 以及学习者的态度。 开发和实施生物医学数据科学素养工具 以新兴学术为基础。我们还将通过混合方法收集学习者的反馈，以适应和发展 我们的模块化资源，确保强大的学员成果，并使交付成果与NIH战略计划保持一致 数据科学。 数据科学对传统计算机和信息科学以外的研究人员的指导是一个具体的 向数据驱动的全民科学素养迈进一步。我们将确保参与者从该计划中走出来 通过实践经验巩固了计算和算法方面的知识。对于非计算研究人员来说， 我们还将提供个人做出有意义贡献所需的基础数据流畅性 机器学习研究，它将使数据驱动系统、洞察力到决策的转换、决策- 和数据驱动的决策管理。最后，对于所有参与者，我们将努力帮助个人 从广泛的背景和身份中确定发展职业生涯的新方向。