Transdisciplinary Big Data Science Training at UVa

弗吉尼亚大学跨学科大数据科学培训

• 批准号: 9248433
• 负责人: Donald E Brown
• 金额: $ 28.91万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9248433
• 关键词: Big Data; Data Science; Training

 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.

 描述（由应用程序提供）：我们旨在准备下一代科学家和工程师，以应对多类生物医学大数据操纵，分析和解释的巨大挑战。我们提出了一项课程和一系列程序化活动，以创建一个跨学科的培训领域，其中的学生团队将跨关键学科工作，受益于真正的辅助和跨学科环境，并开发出成功的技术和“软”技能，以成功地作为独立科学家作为独立科学家创造的独立科学家，从而通过生物学大数据启用了突破性的新发现。该拟议培训计划的三个关键特征是（1）大数据技术培训的深度，（2）通过协作，团队科学活动和（3）跨学科的合作者有形的“软技能”培训，并密切实现了跨学科的培训。我们提出的计划涵盖了这样一种理念：“对具有潜水员专业知识的合作伙伴组成的研究团队的生产力和有效性毫无疑问。” （国家学院，2004年）。我们建议课程，研讨会，研讨会和协作活动，以创建培训环境，以支持下一代生物医学大数据科学家和工程师的发展。拟议的计划必然会在现有的传统课程结构之外，并且 我们提出的培训计划的目标是由教师领导的，具有悠久的生物医学大数据科学领域的合作和活动的历史。在任何给定时间，总共将支持8名学员，每年有4名新学员，并获得了两年的支持（在赠款的一生中共有20名学员）。 （1）为跨学科生物医学大数据科学教学的协作方法创建创新有效的方法； （2）满足UVA的需求以及全国对具有数据科学专业知识的学生以及最终的科学专业人员的需求，他们可以研究跨学科团队以应对复杂的挑战和问题； （3）在合作大数据科学中制定可扩展，可持续和可转让的计划，以进行教育和培训； （4）为博士创建新管道。来自代表性不足的团体的学生。认识到多样性与卓越性之间的不可息联系，我们的计划旨在确保生物医学大数据科学和工程领域的下一代领导者来自各种背景。凭借出色的基础设施和招募学生从代表性不足的群体到UVA现有的NIH和其他由联邦机构资助的计划的学生的历史，该拟议的培训计划将荧光，为生物医学大数据科学带来多样性。从历史上看，潜水员的专业知识集被深深地嵌入了解决许多重要科学问题的解决方案中。我们试图在我们的生物医学大数据培训计划中充满这种致力于协作的感觉。