Transdisciplinary Big Data Science Training at UVa

 描述(由申请人提供)：我们的目标是培养下一代科学家和工程师，以应对多种类型的生物医学大数据处理、分析和解释方面的巨大挑战。我们提出了一套课程和一套纲领性活动，以创建一个跨学科的培训场地，在那里，学生团队将跨关键学科工作，受益于真正的共同指导和跨学科环境，并发展必要的技术和“软”技能，以成功地成为独立科学家，通过生物医学大数据实现突破性的新发现。这一拟议培训计划的三个主要特点是(1)大数据技术培训的深度，(2)通过协作、团队科学活动进行有形的“软技能”培训，以及(3)近距离的跨学科合作导师。我们提议的项目遵循这样的理念：“由拥有不同专业知识的合作伙伴组成的研究团队的生产率和有效性是毋庸置疑的。”(The National Academy，2004)。我们建议开设课程、研讨会、研讨会和协作活动，以创造一个支持下一代生物医学大数据科学家和工程师发展的培训环境。拟议的课程将必然超出现有的传统课程结构，而且它 将为未来协同生物医学大数据科学在生物医学科学研究中发挥越来越大的作用提供蓝图。该项目由在生物医学大数据科学领域拥有丰富合作和活动历史的教师领导。在任何特定时间将支助8名受训人员，每年有4名新受训人员每人获得两年的支助(在赠款有效期内总共有20名受训人员)。我们建议的培训计划的目标是(1)创建创新和有效的方法来教授跨学科生物医学大数据科学的协作方法；(2)满足弗吉尼亚大学和全国对学生的需求，并最终满足具有数据科学专业知识的科学专业人员的需求，他们能够在跨学科团队中工作，以应对复杂的挑战和问题；(3)为协作大数据科学的教育和培训创建可扩展、可持续和可转移的计划；(4)为来自代表性不足群体的博士生创建新的渠道。认识到多样性和卓越之间的千丝万缕的联系，我们的计划寻求确保生物医学大数据科学和工程的下一代领导者来自不同的背景。凭借出色的基础设施和招收来自代表性不足群体的学生的历史，UVA现有的NIH和其他联邦机构资助的项目，这一拟议的培训计划将在为生物医学大数据科学带来多样性方面蓬勃发展。从历史上看，许多重要科学问题的解决方案深深植根于各种不同的专业知识。我们寻求在我们的生物医学大数据培训计划中灌输这种合作的奉献意识。

 DESCRIPTION (provided by applicant): We aim to prepare the next generation of scientists and engineers to address the monumental challenge of multi-type biomedical big data manipulation, analysis, and interpretation. We propose a curriculum and a set of programmatic activities to create an interdisciplinary training ground wherein teams of students will work across key disciplines, benefit from a true co-mentoring and interdisciplinary environment, and develop the technical and "soft" skills necessary to succeed as independent scientists making groundbreaking new discoveries enabled by biomedical big data. Three key features of this proposed training program are (1) depth in Big Data technical training, (2) tangible "soft skill" training through collaborative, team science activities, and (3) cross-disciplinary co-mentors in close physical proximity. Our proposed program embraces the philosophy that "there can be no question about the productivity and effectiveness of research teams formed of partners with diverse expertise." (The National Academies, 2004). We propose courses, symposia, workshops, and collaborative activities to create a training environment that will support the development of the next generation of biomedical big data scientists and engineers. The proposed program will necessarily lie outside the existing traditional curricular structure, and it will provide the blueprint for the future in which collaborative biomedical big data science will play an ever-increasing role in biomedical science research. The program is led by faculty with a strong history of prior collaboration and activity in biomedical big data science. A total of 8 trainees will be supported at any given time, with 4 new trainees per year each with two years of support (with a total of 20 trainees over the lifetime of the grant). The goals of our proposed training program are to (1) Create innovative and effective approaches to teaching collaborative methods for interdisciplinary biomedical big data science; (2) Address the demand at UVa and nationally for students and ultimately scientific professionals with data science expertise who can work on interdisciplinary teams to address complex challenges and problems; (3) Produce a scalable, sustainable and transferable program for education and training in collaborative big data science; (4) Create new pipelines for Ph.D. students from underrepresented groups. Recognizing the inextricable link between diversity and excellence, our program seeks to ensure that the next generation of leaders in biomedical big data science and engineering emerges from a variety of backgrounds. With an excellent infrastructure and history of recruiting students from underrepresented groups to existing NIH and other federal agency-funded programs at UVa, this proposed training program will flourish in bringing diversity to biomedical big data science. Historically, diverse sets of expertise were deeply embedded in the solution to many important scientific problems. We seek to imbue this sense of dedication to collaboration in our training program on biomedical big data.